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Forex Signals Reddit: top providers review (part 1)

Forex Signals Reddit: top providers review (part 1)

Forex Signals - TOP Best Services. Checked!

To invest in the financial markets, we must acquire good tools that help us carry out our operations in the best possible way. In this sense, we always talk about the importance of brokers, however, signal systems must also be taken into account.
The platforms that offer signals to invest in forex provide us with alerts that will help us in a significant way to be able to carry out successful operations.
For this reason, we are going to tell you about the importance of these alerts in relation to the trading we carry out, because, without a doubt, this type of system will provide us with very good information to invest at the right time and in the best assets in the different markets. financial
Within this context, we will focus on Forex signals, since it is the most important market in the world, since in it, multiple transactions are carried out on a daily basis, hence the importance of having an alert system that offers us all the necessary data to invest in currencies.
Also, as we all already know, cryptocurrencies have become a very popular alternative to investing in traditional currencies. Therefore, some trading services/tools have emerged that help us to carry out successful operations in this particular market.
In the following points, we will detail everything you need to know to start operating in the financial markets using trading signals: what are signals, how do they work, because they are a very powerful help, etc. Let's go there!

What are Forex Trading Signals?

https://preview.redd.it/vjdnt1qrpny51.jpg?width=640&format=pjpg&auto=webp&s=bc541fc996701e5b4dd940abed610b59456a5625
Before explaining the importance of Forex signals, let's start by making a small note so that we know what exactly these alerts are.
Thus, we will know that the signals on the currency market are received by traders to know all the information that concerns Forex, both for assets and for the market itself.
These alerts allow us to know the movements that occur in the Forex market and the changes that occur in the different currency pairs. But the great advantage that this type of system gives us is that they provide us with the necessary information, to know when is the right time to carry out our investments.
In other words, through these signals, we will know the opportunities that are presented in the market and we will be able to carry out operations that can become quite profitable.
Profitability is precisely another of the fundamental aspects that must be taken into account when we talk about Forex signals since the vast majority of these alerts offer fairly reliable data on assets. Similarly, these signals can also provide us with recommendations or advice to make our operations more successful.

»Purpose: predict movements to carry out Profitable Operations

In short, Forex signal systems aim to predict the behavior that the different assets that are in the market will present and this is achieved thanks to new technologies, the creation of specialized software, and of course, the work of financial experts.
In addition, it must also be borne in mind that the reliability of these alerts largely lies in the fact that they are prepared by financial professionals. So they turn out to be a perfect tool so that our investments can bring us a greater number of benefits.

The best signal services today

We are going to tell you about the 3 main alert system services that we currently have on the market. There are many more, but I can assure these are not scams and are reliable. Of course, not 100% of trades will be a winner, so please make sure you apply proper money management and risk management system.

1. 1000pipbuilder (top choice)

Fast track your success and follow the high-performance Forex signals from 1000pip Builder. These Forex signals are rated 5 stars on Investing.com, so you can follow every signal with confidence. All signals are sent by a professional trader with over 10 years investment experience. This is a unique opportunity to see with your own eyes how a professional Forex trader trades the markets.
The 1000pip Builder Membership is ordinarily a signal service for Forex trading. You will get all the facts you need to successfully comply with the trading signals, set your stop loss and take earnings as well as additional techniques and techniques!
You will get easy to use trading indicators for Forex Trades, including your entry, stop loss and take profit. Overall, the earnings target per months is 350 Pips, depending on your funding this can be a high profit per month! (In fact, there is by no means a guarantee, but the past months had been all between 600 – 1000 Pips).
>>>Know more about 1000pipbuilder
Your 1000pip builder membership gives you all in hand you want to start trading Forex with success. Read the directions and wait for the first signals. You can trade them inside your demo account first, so you can take a look at the performance before you make investments real money!
Features:
  • Free Trial
  • Forex signals sent by email and SMS
  • Entry price, take profit and stop loss provided
  • Suitable for all time zones (signals sent over 24 hours)
  • MyFXBook verified performance
  • 10 years of investment experience
  • Target 300-400 pips per month
Pricing:
https://preview.redd.it/zjc10xx6ony51.png?width=668&format=png&auto=webp&s=9b0eac95f8b584dc0cdb62503e851d7036c0232b
VISIT 1000ipbuilder here

2. DDMarkets

Digital Derivatives Markets (DDMarkets) have been providing trade alert offerings since May 2014 - fully documenting their change ideas in an open and transparent manner.
September 2020 performance report for DD Markets.
Their manner is simple: carry out extensive research, share their evaluation and then deliver a trading sign when triggered. Once issued, daily updates on the trade are despatched to members via email.
It's essential to note that DDMarkets do not tolerate floating in an open drawdown in an effort to earnings at any cost - a common method used by less professional providers to 'fudge' performance statistics.
Verified Statistics: Not independently verified.
Price: plans from $74.40 per month.
Year Founded: 2014
Suitable for Beginners: Yes, (includes handy to follow trade analysis)
VISIT
-------

3. JKonFX

If you are looking or a forex signal service with a reliable (and profitable) music record you can't go previous Joel Kruger and the team at JKonFX.
Trading performance file for JKonFX.
Joel has delivered a reputable +59.18% journal performance for 2016, imparting real-time technical and fundamental insights, in an extremely obvious manner, to their 30,000+ subscriber base. Considered a low-frequency trader, alerts are only a small phase of the overall JKonFX subscription. If you're searching for hundreds of signals, you may want to consider other options.
Verified Statistics: Not independently verified.
Price: plans from $30 per month.
Year Founded: 2014
Suitable for Beginners: Yes, (includes convenient to follow videos updates).
VISIT

The importance of signals to invest in Forex

Once we have known what Forex signals are, we must comment on the importance of these alerts in relation to our operations.
As we have already told you in the previous paragraph, having a system of signals to be able to invest is quite advantageous, since, through these alerts, we will obtain quality information so that our operations end up being a true success.

»Use of signals for beginners and experts

In this sense, we have to say that one of the main advantages of Forex signals is that they can be used by both beginners and trading professionals.
As many as others can benefit from using a trading signal system because the more information and resources we have in our hands. The greater probability of success we will have. Let's see how beginners and experts can take advantage of alerts:
  • Beginners: for inexperienced these alerts become even more important since they will thus have an additional tool that will guide them to carry out all operations in the Forex market.
  • Professionals: In the same way, professionals are also recommended to make use of these alerts, so they have adequate information to continue bringing their investments to fruition.
Now that we know that both beginners and experts can use forex signals to invest, let's see what other advantages they have.

»Trading automation

When we dedicate ourselves to working in the financial world, none of us can spend 24 hours in front of the computer waiting to perform the perfect operation, it is impossible.
That is why Forex signals are important, because, in order to carry out our investments, all we will have to do is wait for those signals to arrive, be attentive to all the alerts we receive, and thus, operate at the right time according to the opportunities that have arisen.
It is fantastic to have a tool like this one that makes our work easier in this regard.

»Carry out profitable Forex operations

These signals are also important, because the vast majority of them are usually quite profitable, for this reason, we must get an alert system that provides us with accurate information so that our operations can bring us great benefits.
But in addition, these Forex signals have an added value and that is that they are very easy to understand, therefore, we will have a very useful tool at hand that will not be complicated and will end up being a very beneficial weapon for us.

»Decision support analysis

A system of currency market signals is also very important because it will help us to make our subsequent decisions.
We cannot forget that, to carry out any type of operation in this market, previously, we must meditate well and know the exact moment when we will know that our investments are going to bring us profits .
Therefore, all the information provided by these alerts will be a fantastic basis for future operations that we are going to carry out.

»Trading Signals made by professionals

Finally, we have to recall the idea that these signals are made by the best professionals. Financial experts who know perfectly how to analyze the movements that occur in the market and changes in prices.
Hence the importance of alerts, since they are very reliable and are presented as a necessary tool to operate in Forex and that our operations are as profitable as possible.

What should a signal provider be like?

https://preview.redd.it/j0ne51jypny51.png?width=640&format=png&auto=webp&s=5578ff4c42bd63d5b6950fc6401a5be94b97aa7f
As you have seen, Forex signal systems are really important for our operations to bring us many benefits. For this reason, at present, there are multiple platforms that offer us these financial services so that investing in currencies is very simple and fast.
Before telling you about the main services that we currently have available in the market, it is recommended that you know what are the main characteristics that a good signal provider should have, so that, at the time of your choice, you are clear that you have selected one of the best systems.

»Must send us information on the main currency pairs

In this sense, one of the first things we have to comment on is that a good signal provider, at a minimum, must send us alerts that offer us information about the 6 main currencies, in this case, we refer to the euro, dollar, The pound, the yen, the Swiss franc, and the Canadian dollar.
Of course, the data you provide us will be related to the pairs that make up all these currencies. Although we can also find systems that offer us information about other minorities, but as we have said, at a minimum, we must know these 6.

»Trading tools to operate better

Likewise, signal providers must also provide us with a large number of tools so that we can learn more about the Forex market.
We refer, for example, to technical analysis above all, which will help us to develop our own strategies to be able to operate in this market.
These analyzes are always prepared by professionals and study, mainly, the assets that we have available to invest.

»Different Forex signals reception channels

They must also make available to us different ways through which they will send us the Forex signals, the usual thing is that we can acquire them through the platform's website, or by a text message and even through our email.
In addition, it is recommended that the signal system we choose sends us a large number of alerts throughout the day, in order to have a wide range of possibilities.

»Free account and customer service

Other aspects that we must take into account to choose a good signal provider is whether we have the option of receiving, for a limited time, alerts for free or the profitability of the signals they emit to us.
Similarly, a final aspect that we must emphasize is that a good signal system must also have excellent customer service, which is available to us 24 hours a day and that we can contact them at through an email, a phone number, or a live chat, for greater immediacy.
Well, having said all this, in our last section we are going to tell you which are the best services currently on the market. That is, the most suitable Forex signal platforms to be able to work with them and carry out good operations. In this case, we will talk about ForexPro Signals, 365 Signals and Binary Signals.

Forex Signals Reddit: conclusion

To be able to invest properly in the Forex market, it is convenient that we get a signal system that provides us with all the necessary information about this market. It must be remembered that Forex is a very volatile market and therefore, many movements tend to occur quickly.
Asset prices can change in a matter of seconds, hence the importance of having a system that helps us analyze the market and thus know, what is the right time for us to start operating.
Therefore, although there are currently many signal systems that can offer us good services, the three that we have mentioned above are the ones that are best valued by users, which is why they are the best signal providers that we can choose to carry out. our investments.
Most of these alerts are quite profitable and in addition, these systems usually emit a large number of signals per day with full guarantees. For all this, SignalsForexPro, Signals365, or SignalsBinary are presented as fundamental tools so that we can obtain a greater number of benefits when we carry out our operations in the currency market.
submitted by kayakero to makemoneyforexreddit [link] [comments]

Aragon Court ANJ$

Found this write up on Kilocorn
Aragon Court $ANJ
A decentralized oracle protocol that handles subjective disputes which require the judgment of human jurors. These jurors stake a token called ANJ which allows them to be drafted into juries and earn fees for successfully adjudicating disputes.
Launched on February 10, 2020
Aragon Network Jurisdiction token (ANJ) will be created and an Aragon Court instance will be deployed using ANJ as its token. During the pre-activation period, ANT holders were able to convert ANT to ANJ on a 1:100 basis (1 ANT = 100 ANJ) In phase 1, ANT holders could start being able to get ANJ to become jurors. There was a period of time during which all ANT holders that stake could get the same amount of ANJ per ANT. After this period of time, an Aragon Fundraising bonding curve was initialized and allows for staking and unstaking at a variable rate.
Notable figures:
Luis Cuende (founder of Aragon) - hacker and free culture lover who founded various startups since he got involved in free software development at age 12. - He was awarded as the best hacker of Europe under 18 at age 15. - He was also an Advisor to the Vice-president of the European Commission, Neelie Kroes, who is in charge of the EU's Digital Agenda. - Authored a book that reached the #1 in Amazon's Business category when he was 19. - Founded Stampery at 19 (digital certificates) - Founded Asturix at 13 (open source software)
Tim Draper (investor in Aragon) - Bought $1 million of Aragon tokens OTC for $1 per when ANT was only $0.70 when he found out about Aragon Court. - Governance maximalist (early investor in Tezos when they were building governance mechanism directly into the ledger) - Billionaire VC investor that helps entrepreneurs drive their visions through funding, education, media, and government reform. He has founded thirty Draper venture funds, Draper University, Bizworld, and two statewide initiatives to improve governance and education. - Founded DFJ Venture which was involved with helping launch/scale many startups such as: Twitter, SpaceX, Ring, Baidu, Twilio etc. - One of the current largest crypto whales in the world
Competition: Kleros
Elevator pitch: The Aragon Court is a decentralized oracle protocol developed and maintained by the Aragon Network and it can be used by organizations to resolve subjective disputes with binary outcomes. Proposal Agreement disputes rely on the court where jurors stake tokens in order to earn the right to perform these services so they can earn a portion of the fees.
Deeper Dive:
Futarchy
The premise is that if you have a decision which needs to be made, and you can relate that decision clearly to some objective success metric, you can use prediction markets to pick the option which is predicted to be more positively associated with your success metric. Anyone can participate in the prediction market, but attempting to manipulate the outcome of the decision by manipulating the prediction market represents a large cost so long as the success metric used to settle the market cannot be easily manipulated.
High-level flow * Jurors deposit ANT into a bonding curve to generate ANJ (likely using Aragon Black’s implementation) * Jurors stake ANJ to the Court contract and schedule their activation and deactivation for the time period in which they can be drafted to rule on disputes. * Court fees and configuration parameters are controlled by a governor (the Aragon Network), but can only be modified for future terms to ensure that the rules can’t change for ongoing disputes as much as possible. * The creator of a dispute must pay fees to cover the maintenance gas costs of the Court and the jurors that will adjudicate their dispute. The governor of the Court gets a share of all the fees paid out in the Court. * Jurors are randomly drafted to adjudicate disputes. Because the Court isn’t sybil resistant, jurors chance to be drafted is proportional to the amount of ANJ they have activated. * When drafted, jurors must commit and reveal to a ruling. Failure to vote or reveal, results in a penalty for the jurors. * After a ruling is decided, it can be appealed by anyone a certain number of times, after which all active jurors will vote on the last appeal, providing an unappealable ruling. * When the final ruling is decided, all the adjudication rounds for the dispute can be settled taking into account the final ruling for rewards and penalties.
DYOR: https://etherscan.io/token/0xcD62b1C403 ... B51780b184
https://github.com/aragon/aragon-court/tree/mastedocs
https://forum.aragon.org/t/aragon-netwo ... dates/1263
https://github.com/aragon/whitepapebl ... /README.md
https://aragon.org/blog/aragon-court-is-live-on-mainnet
https://blog.aragon.one/aragon-chain/
https://blog.ethereum.org/2014/08/21/in ... -futarchy/
submitted by bananasplitsyourgirl to CryptoMoonShots [link] [comments]

AJ ALMENDINGER

glimpse into the future of Roblox

Our vision to bring the world together through play has never been more relevant than it is now. As our founder and CEO, David Baszucki (a.k.a. Builderman), mentioned in his keynote, more and more people are using Roblox to stay connected with their friends and loved ones. He hinted at a future where, with our automatic machine translation technology, Roblox will one day act as a universal translator, enabling people from different cultures and backgrounds to connect and learn from each other.
During his keynote, Builderman also elaborated upon our vision to build the Metaverse; the future of avatar creation on the platform (infinitely customizable avatars that allow any body, any clothing, and any animation to come together seamlessly); more personalized game discovery; and simulating large social gatherings (like concerts, graduations, conferences, etc.) with tens of thousands of participants all in one server. We’re still very early on in this journey, but if these past five months have shown us anything, it’s clear that there is a growing need for human co-experience platforms like Roblox that allow people to play, create, learn, work, and share experiences together in a safe, civil 3D immersive space.
Up next, our VP of Developer Relations, Matt Curtis (a.k.a. m4rrh3w), shared an update on all the things we’re doing to continue empowering developers to create innovative and exciting content through collaboration, support, and expertise. He also highlighted some of the impressive milestones our creator community has achieved since last year’s RDC. Here are a few key takeaways:
And lastly, our VP of Engineering, Technology, Adam Miller (a.k.a. rbadam), unveiled a myriad of cool and upcoming features developers will someday be able to sink their teeth into. We saw a glimpse of procedural skies, skinned meshes, more high-quality materials, new terrain types, more fonts in Studio, a new asset type for in-game videos, haptic feedback on mobile, real-time CSG operations, and many more awesome tools that will unlock the potential for even bigger, more immersive experiences on Roblox.

Vibin’

Despite the virtual setting, RDC just wouldn’t have been the same without any fun party activities and networking opportunities. So, we invited special guests DJ Hyper Potions and cyber mentalist Colin Cloud for some truly awesome, truly mind-bending entertainment. Yoga instructor Erin Gilmore also swung by to inspire attendees to get out of their chair and get their body moving. And of course, we even had virtual rooms dedicated to karaoke and head-to-head social games, like trivia and Pictionary.
Over on the networking side, Team Adopt Me, Red Manta, StyLiS Studios, and Summit Studios hosted a virtual booth for attendees to ask questions, submit resumes, and more. We also had a networking session where three participants would be randomly grouped together to get to know each other.

What does Roblox mean to you?

We all know how talented the Roblox community is from your creations. We’ve heard plenty of stories over the years about how Roblox has touched your lives, how you’ve made friendships, learned new skills, or simply found a place where you can be yourself. We wanted to hear more. So, we asked attendees: What does Roblox mean to you? How has Roblox connected you? How has Roblox changed your life? Then, over the course of RDC, we incorporated your responses into this awesome mural.
📷
Created by Alece Birnbach at Graphic Recording Studio

Knowledge is power

This year’s breakout sessions included presentations from Roblox developers and staff members on the latest game development strategies, a deep dive into the Roblox engine, learning how to animate with Blender, tools for working together in teams, building performant game worlds, and the new Creator Dashboard. Dr. Michael Rich, Associate Professor at Harvard Medical School and Physician at Boston Children’s Hospital, also led attendees through a discussion on mental health and how to best take care of you and your friends’ emotional well-being, especially now during these challenging times.
📷
Making the Dream Work with Teamwork (presented by Roblox developer Myzta)
In addition to our traditional Q&A panel with top product and engineering leaders at Roblox, we also held a special session with Builderman himself to answer the community’s biggest questions.
📷
Roblox Product and Engineering Q&A Panel

2020 Game Jam

The Game Jam is always one of our favorite events of RDC. It’s a chance for folks to come together, flex their development skills, and come up with wildly inventive game ideas that really push the boundaries of what’s possible on Roblox. We had over 60 submissions this year—a new RDC record.
Once again, teams of up to six people from around the world had less than 24 hours to conceptualize, design, and publish a game based on the theme “2020 Vision,” all while working remotely no less! To achieve such a feat is nothing short of awe-inspiring, but as always, our dev community was more than up for the challenge. I’ve got to say, these were some of the finest creations we’ve seen.
WINNERS
Best in Show: Shapescape Created By: GhettoMilkMan, dayzeedog, maplestick, theloudscream, Brick_man, ilyannna You awaken in a strange laboratory, seemingly with no way out. Using a pair of special glasses, players must solve a series of anamorphic puzzles and optical illusions to make their escape.
Excellence in Visual Art: agn●sia Created By: boatbomber, thisfall, Elttob An obby experience unlike any other, this game is all about seeing the world through a different lens. Reveal platforms by switching between different colored lenses and make your way to the end.
Most Creative Gameplay: Visions of a perspective reality Created By: Noble_Draconian and Spathi Sometimes all it takes is a change in perspective to solve challenges. By switching between 2D and 3D perspectives, players can maneuver around obstacles or find new ways to reach the end of each level.
Outstanding Use of Tech: The Eyes of Providence Created By: Quenty, Arch_Mage, AlgyLacey, xJennyBeanx, Zomebody, Crykee This action/strategy game comes with a unique VR twist. While teams fight to construct the superior monument, two VR players can support their minions by collecting resources and manipulating the map.
Best Use of Theme: Sticker Situation Created By: dragonfrosting and Yozoh Set in a mysterious art gallery, players must solve puzzles by manipulating the environment using a magic camera and stickers. Snap a photograph, place down a sticker, and see how it changes the world.
OTHER TOP PICKS
HONORABLE MENTIONS
For the rest of the 2020 Game Jam submissions, check out the list below:
20-20 Vision | 20/20 Vision | 2020 Vision, A Crazy Perspective | 2020 Vision: Nyon | A Wild Trip! | Acuity | Best Year Ever | Better Half | Bloxlabs | Climb Stairs to 2021 | Double Vision (Team hey apple) | Eyebrawl | Eyeworm Exam | FIRE 2020 | HACKED | Hyperspective | Lucid Scream | Mystery Mansion | New Years at the Museum | New Year’s Bash | Poor Vision | Predict 2020 | RBC News | Retrovertigo | Second Wave | see no evil | Sight Fight | Sight Stealers | Spectacles Struggle | Specter Spectrum | Survive 2020 | The Lost Chicken Leg | The Outbreak | The Spyglass | Time Heist | Tunnel Vision | Virtual RDC – The Story | Vision (Team Freepunk) | Vision (Team VIP People ####) | Vision Developers Conference 2020 | Vision Is Key | Vision Perspective | Vision Racer | Visions | Zepto
And last but not least, we wanted to give a special shout out to Starboard Studios. Though they didn’t quite make it on time for our judges, we just had to include Dave’s Vision for good measure. 📷
Thanks to everyone who participated in the Game Jam, and congrats to all those who took home the dub in each of our categories this year. As the winners of Best in Show, the developers of Shapescape will have their names forever engraved on the RDC Game Jam trophy back at Roblox HQ. Great work!

‘Til next year

And that about wraps up our coverage of the first-ever digital RDC. Thanks to all who attended! Before we go, we wanted to share a special “behind the scenes” video from the 2020 RDC photoshoot.
Check it out:
It was absolutely bonkers. Getting 350 of us all in one server was so much fun and really brought back the feeling of being together with everyone again. That being said, we can’t wait to see you all—for real this time—at RDC next year. It’s going to be well worth the wait. ‘Til we meet again, my friends.
© 2020 Roblox Corporation. All Rights Reserved.

Improving Simulation and Performance with an Advanced Physics Solver

August

05, 2020

by chefdeletat
PRODUCT & TECH
📷In mid-2015, Roblox unveiled a major upgrade to its physics engine: the Projected Gauss-Seidel (PGS) physics solver. For the first year, the new solver was optional and provided improved fidelity and greater performance compared to the previously used spring solver.
In 2016, we added support for a diverse set of new physics constraints, incentivizing developers to migrate to the new solver and extending the creative capabilities of the physics engine. Any new places used the PGS solver by default, with the option of reverting back to the classic solver.
We ironed out some stability issues associated with high mass differences and complex mechanisms by the introduction of the hybrid LDL-PGS solver in mid-2018. This made the old solver obsolete, and it was completely disabled in 2019, automatically migrating all places to the PGS.
In 2019, the performance was further improved using multi-threading that splits the simulation into jobs consisting of connected islands of simulating parts. We still had performance issues related to the LDL that we finally resolved in early 2020.
The physics engine is still being improved and optimized for performance, and we plan on adding new features for the foreseeable future.

Implementing the Laws of Physics

📷
The main objective of a physics engine is to simulate the motion of bodies in a virtual environment. In our physics engine, we care about bodies that are rigid, that collide and have constraints with each other.
A physics engine is organized into two phases: collision detection and solving. Collision detection finds intersections between geometries associated with the rigid bodies, generating appropriate collision information such as collision points, normals and penetration depths. Then a solver updates the motion of rigid bodies under the influence of the collisions that were detected and constraints that were provided by the user.
📷
The motion is the result of the solver interpreting the laws of physics, such as conservation of energy and momentum. But doing this 100% accurately is prohibitively expensive, and the trick to simulating it in real-time is to approximate to increase performance, as long as the result is physically realistic. As long as the basic laws of motion are maintained within a reasonable tolerance, this tradeoff is completely acceptable for a computer game simulation.

Taking Small Steps

The main idea of the physics engine is to discretize the motion using time-stepping. The equations of motion of constrained and unconstrained rigid bodies are very difficult to integrate directly and accurately. The discretization subdivides the motion into small time increments, where the equations are simplified and linearized making it possible to solve them approximately. This means that during each time step the motion of the relevant parts of rigid bodies that are involved in a constraint is linearly approximated.
📷📷
Although a linearized problem is easier to solve, it produces drift in a simulation containing non-linear behaviors, like rotational motion. Later we’ll see mitigation methods that help reduce the drift and make the simulation more plausible.

Solving

📷
Having linearized the equations of motion for a time step, we end up needing to solve a linear system or linear complementarity problem (LCP). These systems can be arbitrarily large and can still be quite expensive to solve exactly. Again the trick is to find an approximate solution using a faster method. A modern method to approximately solve an LCP with good convergence properties is the Projected Gauss-Seidel (PGS). It is an iterative method, meaning that with each iteration the approximate solution is brought closer to the true solution, and its final accuracy depends on the number of iterations.
📷
This animation shows how a PGS solver changes the positions of the bodies at each step of the iteration process, the objective being to find the positions that respect the ball and socket constraints while preserving the center of mass at each step (this is a type of positional solver used by the IK dragger). Although this example has a simple analytical solution, it’s a good demonstration of the idea behind the PGS. At each step, the solver fixes one of the constraints and lets the other be violated. After a few iterations, the bodies are very close to their correct positions. A characteristic of this method is how some rigid bodies seem to vibrate around their final position, especially when coupling interactions with heavier bodies. If we don’t do enough iterations, the yellow part might be left in a visibly invalid state where one of its two constraints is dramatically violated. This is called the high mass ratio problem, and it has been the bane of physics engines as it causes instabilities and explosions. If we do too many iterations, the solver becomes too slow, if we don’t it becomes unstable. Balancing the two sides has been a painful and long process.

Mitigation Strategies

📷A solver has two major sources of inaccuracies: time-stepping and iterative solving (there is also floating point drift but it’s minor compared to the first two). These inaccuracies introduce errors in the simulation causing it to drift from the correct path. Some of this drift is tolerable like slightly different velocities or energy loss, but some are not like instabilities, large energy gains or dislocated constraints.
Therefore a lot of the complexity in the solver comes from the implementation of methods to minimize the impact of computational inaccuracies. Our final implementation uses some traditional and some novel mitigation strategies:
  1. Warm starting: starting with the solution from a previous time-step to increase the convergence rate of the iterative solver
  2. Post-stabilization: reprojecting the system back to the constraint manifold to prevent constraint drift
  3. Regularization: adding compliance to the constraints ensuring a solution exists and is unique
  4. Pre-conditioning: using an exact solution to a linear subsystem, improving the stability of complex mechanisms
Strategies 1, 2 and 3 are pretty traditional, but 3 has been improved and perfected by us. Also, although 4 is not unheard of, we haven’t seen any practical implementation of it. We use an original factorization method for large sparse constraint matrices and a new efficient way of combining it with the PGS. The resulting implementation is only slightly slower compared to pure PGS but ensures that the linear system coming from equality constraints is solved exactly. Consequently, the equality constraints suffer only from drift coming from the time discretization. Details on our methods are contained in my GDC 2020 presentation. Currently, we are investigating direct methods applied to inequality constraints and collisions.

Getting More Details

Traditionally there are two mathematical models for articulated mechanisms: there are reduced coordinate methods spearheaded by Featherstone, that parametrize the degrees of freedom at each joint, and there are full coordinate methods that use a Lagrangian formulation.
We use the second formulation as it is less restrictive and requires much simpler mathematics and implementation.
The Roblox engine uses analytical methods to compute the dynamic response of constraints, as opposed to penalty methods that were used before. Analytics methods were initially introduced in Baraff 1989, where they are used to treat both equality and non-equality constraints in a consistent manner. Baraff observed that the contact model can be formulated using quadratic programming, and he provided a heuristic solution method (which is not the method we use in our solver).
Instead of using force-based formulation, we use an impulse-based formulation in velocity space, originally introduced by Mirtich-Canny 1995 and further improved by Stewart-Trinkle 1996, which unifies the treatment of different contact types and guarantees the existence of a solution for contacts with friction. At each timestep, the constraints and collisions are maintained by applying instantaneous changes in velocities due to constraint impulses. An excellent explanation of why impulse-based simulation is superior is contained in the GDC presentation of Catto 2014.
The frictionless contacts are modeled using a linear complementarity problem (LCP) as described in Baraff 1994. Friction is added as a non-linear projection onto the friction cone, interleaved with the iterations of the Projected Gauss-Seidel.
The numerical drift that introduces positional errors in the constraints is resolved using a post-stabilization technique using pseudo-velocities introduced by Cline-Pai 2003. It involves solving a second LCP in the position space, which projects the system back to the constraint manifold.
The LCPs are solved using a PGS / Impulse Solver popularized by Catto 2005 (also see Catto 2009). This method is iterative and considers each individual constraints in sequence and resolves it independently. Over many iterations, and in ideal conditions, the system converges to a global solution.
Additionally, high mass ratio issues in equality constraints are ironed out by preconditioning the PGS using the sparse LDL decomposition of the constraint matrix of equality constraints. Dense submatrices of the constraint matrix are sparsified using a method we call Body Splitting. This is similar to the LDL decomposition used in Baraff 1996, but allows more general mechanical systems, and solves the system in constraint space. For more information, you can see my GDC 2020 presentation.
The architecture of our solver follows the idea of Guendelman-Bridson-Fedkiw, where the velocity and position stepping are separated by the constraint resolution. Our time sequencing is:
  1. Advance velocities
  2. Constraint resolution in velocity space and position space
  3. Advance positions
This scheme has the advantage of integrating only valid velocities, and limiting latency in external force application but allowing a small amount of perceived constraint violation due to numerical drift.
An excellent reference for rigid body simulation is the book Erleben 2005 that was recently made freely available. You can find online lectures about physics-based animation, a blog by Nilson Souto on building a physics engine, a very good GDC presentation by Erin Catto on modern solver methods, and forums like the Bullet Physics Forum and GameDev which are excellent places to ask questions.

In Conclusion

The field of game physics simulation presents many interesting problems that are both exciting and challenging. There are opportunities to learn a substantial amount of cool mathematics and physics and to use modern optimizations techniques. It’s an area of game development that tightly marries mathematics, physics and software engineering.
Even if Roblox has a good rigid body physics engine, there are areas where it can be improved and optimized. Also, we are working on exciting new projects like fracturing, deformation, softbody, cloth, aerodynamics and water simulation.
Neither Roblox Corporation nor this blog endorses or supports any company or service. Also, no guarantees or promises are made regarding the accuracy, reliability or completeness of the information contained in this blog.
This blog post was originally published on the Roblox Tech Blog.
© 2020 Roblox Corporation. All Rights Reserved.

Using Clang to Minimize Global Variable Use

July

23, 2020

by RandomTruffle
PRODUCT & TECH
Every non-trivial program has at least some amount of global state, but too much can be a bad thing. In C++ (which constitutes close to 100% of Roblox’s engine code) this global state is initialized before main() and destroyed after returning from main(), and this happens in a mostly non-deterministic order. In addition to leading to confusing startup and shutdown semantics that are difficult to reason about (or change), it can also lead to severe instability.
Roblox code also creates a lot of long-running detached threads (threads which are never joined and just run until they decide to stop, which might be never). These two things together have a very serious negative interaction on shutdown, because long-running threads continue accessing the global state that is being destroyed. This can lead to elevated crash rates, test suite flakiness, and just general instability.
The first step to digging yourself out of a mess like this is to understand the extent of the problem, so in this post I’m going to talk about one technique you can use to gain visibility into your global startup flow. I’m also going to discuss how we are using this to improve stability across the entire Roblox game engine platform by decreasing our use of global variables.

Introducing -finstrument-functions

Nothing excites me more than learning about a new obscure compiler option that I’ve never had a use for before, so I was pretty happy when a colleague pointed me to this option in the Clang Command Line Reference. I’d never used it before, but it sounded very cool. The idea being that if we could get the compiler to tell us every time it entered and exited a function, we could filter this information through a symbolizer of some kind and generate a report of functions that a) occur before main(), and b) are the very first function in the call-stack (indicating it’s a global).
Unfortunately, the documentation basically just tells you that the option exists with no mention of how to use it or if it even actually does what it sounds like it does. There’s also two different options that sound similar to each other (-finstrument-functions and -finstrument-functions-after-inlining), and I still wasn’t entirely sure what the difference was. So I decided to throw up a quick sample on godbolt to see what happened, which you can see here. Note there are two assembly outputs for the same source listing. One uses the first option and the other uses the second option, and we can compare the assembly output to understand the differences. We can gather a few takeaways from this sample:
  1. The compiler is injecting calls to __cyg_profile_func_enter and __cyg_profile_func_exit inside of every function, inline or not.
  2. The only difference between the two options occurs at the call-site of an inline function.
  3. With -finstrument-functions, the instrumentation for the inlined function is inserted at the call-site, whereas with -finstrument-functions-after-inlining we only have instrumentation for the outer function. This means that when using-finstrument-functions-after-inlining you won’t be able to determine which functions are inlined and where.
Of course, this sounds exactly like what the documentation said it did, but sometimes you just need to look under the hood to convince yourself.
To put all of this another way, if we want to know about calls to inline functions in this trace we need to use -finstrument-functions because otherwise their instrumentation is silently removed by the compiler. Sadly, I was never able to get -finstrument-functions to work on a real example. I would always end up with linker errors deep in the Standard C++ Library which I was unable to figure out. My best guess is that inlining is often a heuristic, and this can somehow lead to subtle ODR (one-definition rule) violations when the optimizer makes different inlining decisions from different translation units. Luckily global constructors (which is what we care about) cannot possibly be inlined anyway, so this wasn’t a problem.
I suppose I should also mention that I still got tons of linker errors with -finstrument-functions-after-inlining as well, but I did figure those out. As best as I can tell, this option seems to imply –whole-archive linker semantics. Discussion of –whole-archive is outside the scope of this blog post, but suffice it to say that I fixed it by using linker groups (e.g. -Wl,–start-group and -Wl,–end-group) on the compiler command line. I was a bit surprised that we didn’t get these same linker errors without this option and still don’t totally understand why. If you happen to know why this option would change linker semantics, please let me know in the comments!

Implementing the Callback Hooks

If you’re astute, you may be wondering what in the world __cyg_profile_func_enter and __cyg_profile_func_exit are and why the program is even successfully linking in the first without giving undefined symbol reference errors, since the compiler is apparently trying to call some function we’ve never defined. Luckily, there are some options that allow us to see inside the linker’s algorithm so we can find out where it’s getting this symbol from to begin with. Specifically, -y should tell us how the linker is resolving . We’ll try it with a dummy program first and a symbol that we’ve defined ourselves, then we’ll try it with __cyg_profile_func_enter .
[email protected]:~/src/sandbox$ cat instr.cpp int main() {} [email protected]:~/src/sandbox$ clang++-9 -fuse-ld=lld -Wl,-y -Wl,main instr.cpp /usbin/../lib/gcc/x86_64-linux-gnu/crt1.o: reference to main /tmp/instr-5b6c60.o: definition of main
No surprises here. The C Runtime Library references main(), and our object file defines it. Now let’s see what happens with __cyg_profile_func_enter and -finstrument-functions-after-inlining.
[email protected]:~/src/sandbox$ clang++-9 -fuse-ld=lld -finstrument-functions-after-inlining -Wl,-y -Wl,__cyg_profile_func_enter instr.cpp /tmp/instr-8157b3.o: reference to __cyg_profile_func_enter /lib/x86_64-linux-gnu/libc.so.6: shared definition of __cyg_profile_func_enter
Now, we see that libc provides the definition, and our object file references it. Linking works a bit differently on Unix-y platforms than it does on Windows, but basically this means that if we define this function ourselves in our cpp file, the linker will just automatically prefer it over the shared library version. Working godbolt link without runtime output is here. So now you can kind of see where this is going, however there are still a couple of problems left to solve.
  1. We don’t want to do this for a full run of the program. We want to stop as soon as we reach main.
  2. We need a way to symbolize this trace.
The first problem is easy to solve. All we need to do is compare the address of the function being called to the address of main, and set a flag indicating we should stop tracing henceforth. (Note that taking the address of main is undefined behavior[1], but for our purposes it gets the job done, and we aren’t shipping this code, so ¯\_(ツ)_/¯). The second problem probably deserves a little more discussion though.

Symbolizing the Traces

In order to symbolize these traces, we need two things. First, we need to store the trace somewhere on persistent storage. We can’t expect to symbolize in real time with any kind of reasonable performance. You can write some C code to save the trace to some magic filename, or you can do what I did and just write it to stderr (this way you can pipe stderr to some file when you run it).
Second, and perhaps more importantly, for every address we need to write out the full path to the module the address belongs to. Your program loads many shared libraries, and in order to translate an address into a symbol, we have to know which shared library or executable the address actually belongs to. In addition, we have to be careful to write out the address of the symbol in the file on disk. When your program is running, the operating system could have loaded it anywhere in memory. And if we’re going to symbolize it after the fact we need to make sure we can still reference it after the information about where it was loaded in memory is lost. The linux function dladdr() gives us both pieces of information we need. A working godbolt sample with the exact implementation of our instrumentation hooks as they appear in our codebase can be found here.

Putting it All Together

Now that we have a file in this format saved on disk, all we need to do is symbolize the addresses. addr2line is one option, but I went with llvm-symbolizer as I find it more robust. I wrote a Python script to parse the file and symbolize each address, then print it in the same “visual” hierarchical format that the original output file is in. There are various options for filtering the resulting symbol list so that you can clean up the output to include only things that are interesting for your case. For example, I filtered out any globals that have boost:: in their name, because I can’t exactly go rewrite boost to not use global variables.
The script isn’t as simple as you would think, because simply crawling each line and symbolizing it would be unacceptably slow (when I tried this, it took over 2 hours before I finally killed the process). This is because the same address might appear thousands of times, and there’s no reason to run llvm-symbolizer against the same address multiple times. So there’s a lot of smarts in there to pre-process the address list and eliminate duplicates. I won’t discuss the implementation in more detail because it isn’t super interesting. But I’ll do even better and provide the source!
So after all of this, we can run any one of our internal targets to get the call tree, run it through the script, and then get output like this (actual output from a Roblox process, source file information removed):
excluded_symbols = [‘.\boost.*’]* excluded_modules = [‘/usr.\’]* /uslib/x86_64-linux-gnu/libLLVM-9.so.1: 140 unique addresses InterestingRobloxProcess: 38928 unique addresses /uslib/x86_64-linux-gnu/libstdc++.so.6: 1 unique addresses /uslib/x86_64-linux-gnu/libc++.so.1: 3 unique addresses Printing call tree with depth 2 for 29276 global variables. __cxx_global_var_init.5 (InterestingFile1.cpp:418:22) RBX::InterestingRobloxClass2::InterestingRobloxClass2() (InterestingFile2.cpp.:415:0) __cxx_global_var_init.19 (InterestingFile2.cpp:183:34) (anonymous namespace)::InterestingRobloxClass2::InterestingRobloxClass2() (InterestingFile2.cpp:171:0) __cxx_global_var_init.274 (InterestingFile3.cpp:2364:33) RBX::InterestingRobloxClass3::InterestingRobloxClass3()
So there you have it: the first half of the battle is over. I can run this script on every platform, compare results to understand what order our globals are actually initialized in in practice, then slowly migrate this code out of global initializers and into main where it can be deterministic and explicit.

Future Work

It occurred to me sometime after implementing this that we could make a general purpose profiling hook that exposed some public symbols (dllexport’ed if you speak Windows), and allowed a plugin module to hook into this dynamically. This plugin module could filter addresses using whatever arbitrary logic that it was interested in. One interesting use case I came up for this is that it could look up the debug information, check if the current address maps to the constructor of a function local static, and write out the address if so. This effectively allows us to gain a deeper understanding of the order in which our lazy statics are initialized. The possibilities are endless here.

Further Reading

If you’re interested in this kind of thing, I’ve collected a couple of my favorite references for this kind of topic.
  1. Various: The C++ Language Standard
  2. Matt Godbolt: The Bits Between the Bits: How We Get to main()
  3. Ryan O’Neill: Learning Linux Binary Analysis
  4. Linkers and Loaders: John R. Levine
  5. https://eel.is/c++draft/basic.exec#basic.start.main-3
Neither Roblox Corporation nor this blog endorses or supports any company or service. Also, no guarantees or promises are made regarding the accuracy, reliability or completeness of the information contained in this blog.
submitted by jaydenweez to u/jaydenweez [link] [comments]

Major League Redditball Independent Demographics Survey: RESULTS

Hey y'all! As most of you know, I've been running a demographics survey over the last week or so. Time to reveal the results!
Of the 559 people currently on active MLR rosters, 139 filled out the survey, good for 24.86% participation. It's hard to draw any conclusions from only surveying about 1/4 of the league, but we'll do our best.
What year did you join the MLR?
Most people who put 2016 either didn't realize that you couldn't sign up as a player in 2016 or were fake submissions. I sorted through which ones were harmless mistakes and which were frauds, and came up with the following results. The leading answer was 2020, represented by 31.9% of submissions. 2019 came in second with 29.7%, followed by 2018 at 22.5% and 2017 at 15.9%.
How many MLR seasons have you participated in?
These answers followed those of the previous question, as the most common response was 1, which was the case for 33.1% of people who filled out the form. 27.3% said 2, 18.7% said 3, 14.4% said 4, and 6.5% has been around for all 5 seasons.
How old are you?
38.8% of those who completed the survey fall into the 18-22 age bracket. The next highest percentage belongs to the 23-27 bracket, which makes up 21.6% of the pie. 13.7% said 15-17, 8.6% said 27-30, 8.6% said 30-35, 2.9% said 12-14, 2.9% said 35-40, and 1.4% said that they were Older than 50.
What team do you play for?
The most common team that appeared on the survey was the Kansas City Royals, with 12 submissions. My Los Angeles Angels came in 2nd with 11. Other notable teams were the Minnesota Twins and New York Mets who had 9 a piece, as well as the Baltimore Orioles and Arizona Diamondbacks, who each had 7. 28 of the 30 teams had at least 2 team members fill out the survey. Both the Texas Rangers and Cleveland Indians failed to have a single player fill out the survey. Finally, 1 of the 139 submissions came from a player who was Retired.
Do you live in the United States?
85.6% said that they do live in the United States, while 14.4% said that they live outside of the U.S.A.
If you live in the United States, what state do you live in?
The most common state that appeared on the survey was New York, which appeared 12 times. Illinois appeared 8 times, while Florida and Texas each had 7. Minnesota and California each had 6, and Connecticut followed right behind them with 6. Other states (abbreviated) that appeared included NJ, CO, NC, GA, OH, MI, MO, NM, DC, MA, WA, IN, SC, AL, MD, TN, PA, HI, WI, VA, KY, OR, NV, SD, NE, ND, and DE. 7 said that they Would rather not answer.
If you do not live in the United States, what country do you live in?
Of the 20 people who listed a non-American country of residence, 10 of them live in Canada. The United Kingdom has 3 representatives, while The Netherlands has 2. Other countries who were represented on the survey include Denmark, Singapore, Portugal, and Panama. One person also stated that they lived in Europe without stating a country.
Which of the following best describes you? (Position in life)
33.1% of those who filled out the survey said that they would describe themselves as a Full-Time Worker. 27.3% said that they are an Undergraduate College Student. 15.8% responded with High School Student, while 6.5% said they are an Undergraduate College Student w/ a Full-Time Job. 4.3% are a Part-Time Worker, 4.3% are a Graduate College Student, 2.9% are Unemployed, and 0.7% are in the Military. 1.4% said they Would rather not answer.
For those who chose an option that included a current Full-Time or Part-Time job, what line of work are you currently in?
I received quite a few different answers, and really there wasn't any answer that appeared more than 2 or 3 times. Quite a few people work in various STEM fields, including computer engineering, software development, mechanical engineering, and other related tech/math-based fields. A few people work in nursing/EMS, a few are in television/broadcasting, and a few even work in the Sports field, doing team sales or management. 4 people listed their occupation as Military. In terms of oddities, 1 person said they were a Fisherman, and 1 said that they are a full-time Actor. Also one person is a full-time bartender, so that seems like a fun Friday night hangout.
Which of the following best describes you? (Gender)
In the least predictable statistic to date, 95.0% said that they were Male. 2.2% said they were Female. 1.4% listed themselves as Non-Binary, 0.7% said Unsure, and 0.7% said they Would rather not answer.
Which of the following best describes you? (Race/Ethnicity)
80.6% listed themselves as White/Caucasian, which is 4/5 of those who filled out the survey. 5.8% listed themselves as Asian, 4.3% responded with Hispanic/Latino, and 3.6% said they were both White Caucasian and Asian. Only 1.4% responded with Black/African American, and only 0.7% responded with Asian/Pacific Islander.
What is your relationship status?
62.6% said they are Single. In terms of other common responses, 19.4% said they are In a closed relationship, 12.9% said they are Married, and 2.2% said they are In an open/polyamorous relationship.
How many hours per week do you spend playing, working, or contributing to Fake Baseball?
Surprisingly, 85.8% said that they spend 10 hours or less each week on Fake Baseball, meaning that 14.2% spend more than a full work day on our great game. 62.7% spend 5 hours or less on our game per week, while 27.0% spend 2 hours or less per week. On the other hand, 8.8% said they spend 20 hours or more per week on our game, and 3.2% said they spend 30 hours or more per week.
What month of the year were you born in?
The most common answer was May with 12.2%, followed by June with 10.8% and February with 10.8%. Other common answers included July with 9.4%, January with 8.6%, and March with 7.9%. The least common response was October with 4.3%.
What is your favorite brand of soda?
While 18.7% responded with the ol' N/A, the actual leading votegetter was Coke with 15.8%. Dr. Pepper followed close behind with 13.7%, and Sprite was next with 8.6%. Other common answers included Pepsi with 5.0%, and Barq's, Mountain Dew, "Root Beer", and Water each with 2.9%.
How many teams have you played for?
The vast majority of survey submissions (61.3%) said that they have only played on 1 team in their MLR career. 23.4% said they have played on 2 teams, 7.3% said 3, 5.8% said 4, and 2.2% have been on 5 teams. Just 1 submitter said they have been on More than 5 teams.
Have you ever received any awards from the MLR for your on-field accomplishments?
Just 24.5% said that they have received an award for their on-field accomplishments at some point in their career. Nearly 3/4 of those surveyed have NOT received an award, and 0.7% said they Would rather not answer.
Have you enjoyed your time in Major League Redditball?
82.7% said Yes, 15.8% said Kinda-sorta, and just 1.4% said No. I hope those 1.4% will someday find happiness and enjoyment around here. Or not. Whatever you want I suppose.
What is your favorite convenience store brand?
The Dallas-based 7-Eleven won with 24.5% (nearly a quarter) of the vote. 12.2% went with the Mid-Atlantic (and Florida)-based Wawa, followed by Circle K, which had 7.2%. Other notable responses included Sheetz (6.5%), QuikTrip (5.8%), Casey's (4.3%), Buc-ee's (3.6%), Speedway (3.6%), RaceTrac (3.6%), Kwik Trip (2.2%), Kum & Go (2.2%), Stewart's (1.4%), Cumberland Farms (1.4%), Kangaroo Express (1.4%), and Holiday (1.4%).
Do you watch actual, in-real-life baseball?
95.7% said Yes, 3.6% said No, and 0.7% said Would rather not answer.
That does it for the survey results! Thank you for reading through all of that. If you want to see specific charts, I'll be posting them on request in the Discord server. Lastly, if you want to join the IRL Meetup network, you still can! PM me your player name and region and I'll get you signed up!
submitted by bryceryals42 to fakebaseball [link] [comments]

Is FAANG really the only way to get a combo of high pay, good experience, healthy work-life balance and job security these days?

So I am in my 20s, based in London and have been in the industry for 5 years now, first as a software engineer and later - after completing my Masters - as a data scientist. Never had a full-time job at FAANG, partially because I'm not that good at reversing binary trees on a whiteboard (had a summer internship at Google but failed the conversion interviews), partially because I never really saw getting there as my goal and haven't interviewed with any of the big companies since then.
Now, I always had this idea that at start ups, there is this trade-off that your work-life balance might be not as good - but you are much better off in terms of getting interesting experience, having more personal growth and flexibility, as well as often more money.
My personal experience so far, however, has been kinda frustrating and I can't help but feel like FAANG is superior in every single way:
It feels a bit like I've reached my "fuck it" moment and that I should just start spending all my free time on LeetCode practicing binary trees reversals to finally land a job at FAANG. So I am really just looking for opinions here. Is there indeed such a big gap in the quality of life of the FAANG people compared to everyone else? Or does anyone have any good stories about working for a small company where they would still feel safe, get a decent pay and good experience, and have time for their hobbies and families?
TL;DR: Spent the first few years of my CS career at smaller companies and can't help but feel inferior to my peers who have been enjoying their time at FAANG. This makes me think I made a bad mistake by not following the same path. The risks just don't seem worth it and this COVID crisis is kinda the last straw
submitted by ds9329 to cscareerquestionsEU [link] [comments]

Cryptosoft Review 2020-Is it a Scam?

Cryptosoft Review 2020-Is it a Scam?

Most f the reviews we tend to have come back across reveal that the Cryptp soft platform is easy to
Their client service is very efficient. We did a live check and confirmed that they respond at intervals a moment. Moreover, they are available 24/7.
The Cryptp soft app is secure. They need all the mandatory measures in place to make sure data privacy.
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Cryptp soft registration method is straightforward, easy, and secure. You only want but 10 minutes to form an account and begin trading. Cryptp soft is a absolutely auto bot and is so accessible to everyone.

https://preview.redd.it/giu6kclgfnn51.jpg?width=1280&format=pjpg&auto=webp&s=f605d84ba2174f831ca825dbaffddf061b3a55b5
You do not want to perceive trading lingo to use Immediate Edge. The following steps can get you started with this robot.
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Their web site is SSL secured to confirm that hackers cannot steal personal information submitted through it. Cryptp soft cyber safety policy states that they're GDPR adherent. This suggests that they handle your knowledge with strict privacy.
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Deposits with Cryptp soft should be created through Wire Transfer, Visa, and MasterCard. It takes a few seconds for a deposit to reflect in an exceedingly trader’s account. Cryptp soft does not charge any deposit fees.
The Cryptp soft does provide a demo account to help traders familiarize themselves with its web-trader. CryptoVibes recommends that you are doing demo trading before going to live to trade. Please note that the demo is for demonstrative purposes solely.
The results you receive on the platform are primarily based on historical information and could therefore not mirror what you'll get in live trading.
The Cryptp soft live trader comes with features to help you outline the amount of risk you are willing to require per trade. You wish to go through the demo account to familiarize with these features. As mentioned severally in this review, you do not want specialized skills to use this robot.
Live trading with Cryptp soft involves determining the quantity of capital you plan to risk per trade and clicking the live button. Scan our review of Bitcoin Trader for one more straightforward to use the robot.
*Remember all trading risks and you shouldn’t risk more then you'll be able to afford to lose.
How to get the most out of Cryptp soft App
We have identified the following tips as paramount in guaranteeing that you make the most of Immediate Edge.
Begin with a deposit of $250 – Given the level of risk involved in trading with Immediate Edge, you should start with a tiny investment.
Follow crypto market news – You need to determine the type of reports that drives volatility high and capitalize on them. Cryptp soft claims to form the foremost profits throughout high market volatility.
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Close trading sessions at the tip of the day – Leaving open positions overnight is doubtless to translate to losses since the markets can change considerably overnight. It is better to shut sessions even if in the negative and start trading again the subsequent day. With a correct risk management strategy, there is no would like to fret concerning periodic losses.
Following our review we tend to realize Cryptp soft to be legit. But, traders ought to take additional caution, provided that this bot comes at a degree of risk. Whereas the app claims it's potential to form profits of up to 50percent per day, you'll be able to additionally lose the complete deposit inside seconds. This is often not sudden for a high-frequency trading robot.
We recommend that you just apply the required risk management measures. As a rule of thumb don't risk more than 10percent of your trading capital per trade. Also, never trade with an amount you cannot afford to lose. It is prudent to start small and add cash as you get conversant with the various features on the platform.
Recently, a brand new trading software was added to the bitcoin investment trade. This software is termed Cryptp soft and it is allegedly created by a corporation or organization called the International Council for Bitcoin.
There is additionally a letter out there on their web site that has been signed by someone named David. This person claims to own earned over 1,000,000 as a results of investing in bitcoins. What’s very shocking concerning this letter is that David claims to have earned that huge quantity in just one trade. If we have a tendency to place it in simple words, David became a millionaire overnight.
We tend to highly doubt that a trading system that has been launched recently will have such potential. To verify the main points of this software and to determine its legitimacy, we have a tendency to conducted our own research and investigation.
Cryptp soft is a bitcoin trading software that’s meant to assist newbie traders get involved in Cryptocurrency trading with less risk than ancient investment opportunities. Cryptp soft software was created by The International Council For Bitcoin who is PRO Bitcoin trader Group behind the Cryptp soft software. Notice out all concerning Cryptp soft software by The International Council For Bitcoin.
Cryptp soft Software may be a nice development by a famous, well established and experienced bitcoin trader Investors with a viewpoint to enable traders to perform different tasks with ease and convenience.

https://preview.redd.it/uuh85yghfnn51.png?width=1238&format=png&auto=webp&s=d0558e6e123114b6624d5a724b3b9b5983e717e1
Cryptp soft Software is essentially a Binary choices trading software that is designed to assist traders win and predict the Binary options trend of their respective choices. Cryptp soft APP works as a code to urge financial success, shows traders how they'll make money on-line, helps them to find different ways in which to induce huge returns on their investment. The Cryptp soft Trading Software additionally provides analyses of Market conditions so that traders will recognize what ought to be their next step. Cryptp soft System gives secret cryptocurrency ways that ultimately help binary traders to create thousands of greenbacks solely for some bucks.
Several individuals can say that Cryptocurrency Trading may be a risky business and tend to remain faraway from it. But from my expertise, high volatility means HIGH RETURN OF INVESTMENT in Crypto Market. But this can be where the Cryptp soft comes into play, the mathematical algorithm used by Cryptp soft Software takes the guesswork out choosing a winning profitable trade. You don’t must be an expert. Like I said earlier, I actually have personally tested the Cryptp soft and found the success rate is about ninety sevenpercent. I don’t apprehend concerning you, but a ninety seven% probability of earning a profitable trade is TERRIBLY GOOD! I’ve never come across something like this trading software before. Keep reading, below are my Cryptp soft results for the past week or so…
Watch over the Shoulder of a Professional Each Day and you'll be able to learn as you trade.
Averaging 97% Winning Weeks With Cryptp soft which suggests that more potential profits for you
Cryptp soft Software Are Fully Transparent
No previous experience with binary choices trading required
Web-based mostly, no need for downloads, additionally works on phones, tablets
You'll be able to Even Watch Cryptp soft Signals From Your Phone (iPhone Users — Photon Browser)
If you are ready to begin making cash online with an on the spot edge, there has never been a better chance than currently. If you enjoy surfing the web for countless hours trying for the next Trading Method Secrets, never being able to urge centered, being overloaded with conflicting information, and not creating cash on-line, you ought to probably leave this page right now and get back to that Cryptp soft System strategy
Cryptp soft bot could be a new cryptocurrency trading invention that comes with options that create this software stand out among others. It is conjointly an automatic trading platform that uses a smart program algorithm to detect favorable trading opportunities. It acts on its own or waits for a prompt command from the user depending on the software’s settings. But what makes this software unique and a favorite to individuals is what we have a tendency to shall unveil in this review.
There have been lots of unverified claims of how totally different cryptocurrency software have helped several people to make massive profits leading to Scam individuals. However, it's pertinent for cryptocurrency traders to verify if a particular trading software may be a scam or legit, which is also ?
After subjecting the features of the Cryptp soft bot software to a series of tests, the software isn't a scam however legit. The Cryptp soft bot is believed to have successful rate of 85%, that is a lot of than the 80percent benchmark for average software. The Cryptp soft bot has helped cryptocurrency traders to make sensible profits, which has been documented as testimonies on the software’s website.
Trading on the platform is straightforward and might not require experience. We had to verify the simplicity of the software, and we tend to discovered that the software is easy to navigate. The demo trading feature of the software makes it potential for brand new users to hold out trading activities in an exceedingly simulated atmosphere while not having to risk their investment. This any gives credence to the legitimacy of the software because it ensures that new users get accustomed to the features of the software before continuing to measure to trade
As earlier stated, the Cryptp soft bot could be a high-tech program software that comes with exceptional options that makes it among the simplest cryptocurrency trading software in the blockchain market. The outstanding features of the Cryptp soft bot embody the subsequent:
https://www.cryptoerapro.com/cryptosoft/

http://www.cryptoerapro.com/

https://twitter.com/cryptoerapro

https://www.instagram.com/cryptoerapro/

https://www.pinterest.co.uk/cryptoerapro/
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Immediate Edge Review, Is Immediate Edge SCAM Or Legit Trading App?

Immediate Edge Review, Is Immediate Edge SCAM Or Legit Trading App?

Immediate Edge Review: Is This Crypto Robot Legit or Scam
Immediate Edge Review and investigation 20twenty. The Immediate Edge app is a crypto, forex and choices trading robot utilized by folks to automatically obtain and sell Bitcoin and create profits. Wanting at the website, many people claim it helped them move from rags-to-riches trading Bitcoin. Further, some claims linked it to Ronaldo and Sir Alex Ferguson

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Is Immediate Edge app legit or scam? Whereas the claims of its linkage to the higher than celebrities are unverifiable, we tend to can verify that the app is not a scam and permits individuals to trade Bitcoin using the Fibonacci strategy with ten minutes time frames
The app, that allows people to deposit at least $250 through mastercard and Sofort, scores 88% rate and a 5 stars as a real software
Since there are several scam cryptos, forex and options brokers who trick individuals to depositing money, and then they run away with the funds, we have taken time to review this software to determine if it is real or a scam.
Is Immediate Edge scam or legit
High success rate is reported by users with this software.
The Immediate Edge web site provides truthful claims about the service though it will not mean the crypto trading risks are eliminated with its use.
Customers should start with the minimum investment and increase it when satisfied with the utilization of the app.
Click the link to access Immediate Edge official web site or keep reading to understand more
This software will not seem to be a scam and users report that it helped them make real money trading on it.b site
What is Immediate Edge App?
Immediate Edgecould be a robot or auto-trading software that allows folks to trade forex, crypto and binary choices. A user deploys the algorithm-primarily based bot, which relies on a trading strategy that's automatically executed on a broker trading platform once deployed.
The strategy is coded or set like to permit the user to automatically get and sell crypto, stock or choices on the broker platform at favorable prices, to form profits. It can do automatic market analysis by analyzing a vast amount of knowledge from completely different sources, at intervals seconds and with high accuracy, then use the data to predict the costs. It can then come up with a transparent buy or sell tradable signal and then execute it automatically by shopping for and/or selling on the broker platform.
The software can, therefore, save a trader thousands of manual hours and labor they might have spent analyzing information to form trading choices and to follow the markets and to position and close trades. You conjointly do not want to understand anything concerning crypto, stock or option trading to use this auto trading app, although it is suggested to possess this information to keep improving on trading.
Trading bots will achieve high success rates of more than 90p.c and have been tested to work. You may be searching for Immediate Edge scam but the website can tell you that you can expect to earn between $950 and $a pair of,two hundred per day using the software but that depends on your expertise. As a newbie, you'll not start making that a lot of immediately and conjointly it depends on how a lot of you invest. With an investment of $250, you'll be able to expect to form a lot of lesser although some people claim to own made $12a pair of in a very few hours using this software.
That will not mean Immediate Edge is error-free. There still is a heap of unpredictable high volatility in crypto and bots will make mistakes and errors to create losses. Auto trading robots are better employed in combination with manual trading strategies.

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Immediate Edge Review
How will Immediate Edge work?
All a user has to try and do is join up at the Immediate Edge web site, then deposit funds to have access to the robot, when which they can begin trading by switching on the bot. It will would like no control or intervention from humans, beyond beginning and stopping it.
You additionally need to stay checking, daily, to observe the performance of the software in doing its job and ensure that it is earning any returns needless to say. From there, you can confirm whether or not to extend or decrease your investment towards crypto, options or stock trading using this robot.
You'll be able to also monitor performance to be ready to regulate the trading settings from your dashboard and optimize totally different features of the trading bot for instance set amount of trades or amount to invest in every trade.
Founder of Immediate Edge
In line with the Immediate Edge website, this trading bot was founded by Edwin James. Reportedly, he created billions with forex, crypto, and binary options trading and still shares his strategies on the way to trade the assets on the app.
He founded the app to create it potential for brand spanking new traders to create cash in less than 3 minutes of signing up.
How to sign up on Immediate Edge:
Registration: Registering or signing up on the website is free but to start trading, you want to deposit no less than $250. You discover a registration type on the top right of the page, on that you type in your email, full names and phone numbers and country code. Create a password to be used for logging in later.
Deposit funds: Depositing funds allows you to connect to a robot broker and then you'll begin the bot to start out trading. You'll deposit with Visa, Wire Transfers, Klarna or Skrill. The currencies supported are Swiss Franc, British Pound, US Greenback, and Euro and using a credit or debit card limits deposits to less than $/£/€/?10,00zero in one day and $/£/€/?40,000 in an exceedingly month.
Immediate Edgeisn’t licensed to handle your funds, it works with brokers to handle the cash once it's deposited.
Demo trading: Relying on the broker you're connected to, you can begin to practice trading with the Immediate Edge software. Some brokers do not have this feature on their platforms. Still, with the latter, you can test their options before you deposit cash to try and do live trading. With the demo options, you'll be able to familiarize yourself with the trading house before beginning to use real money to trade.
Trading: Before and when you've got switched on auto-trading, you would like to check the trading settings daily. You'll regulate some things including stop-loss orders and when to try to to them, amount to speculate per trade and how several trades to try to to per day. You'll be able to also choose that cryptocurrencies to trade, and you'll be able to select all the most in style ones together with Bitcoin and Ethereum. You also get to observe the profits/losses and decide if to continue and/or when to prevent.

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Withdrawals, user verification, cost of using the app and alternative options

The payouts or withdrawals are made by filling letter of invitation type on the funds’ management page and it can take two operating days to replicate in your checking account. No fee is charged on withdrawals. You'll withdraw your cash including the capital while not a lot of problem on this app, that is better than several that don't enable withdrawals at any time
While some bots need verifications by asking for your ID and statements, this one will not. You are done once uploading your payment details. The bot charges a commission on profit. Besides, you get twenty fouseven client support on Immediate Edge
Immediate Edge may be a legit, secure, user-friendly trading application for crypto, stocks, and choices. It has a zealous customer service and reports a high success rate. Another smart robot we have recently reviewed is Bitcoin Professional
We tend to hope that this review helped you to make a decision concerning this trading app. Additionally, subscribe to our web site to be invariably notified concerning new software from this industry. For live reviews subscribe to our Youtube Channel or FB Page.

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[D] Hacking a Higher-Order Conditional Random Field (CRF) for sparse NER/sequence-tagging

Training Data: A few hundred natural language queries to be converted into structured inputs for a downstream system. Each sentence is annotated with 3 tag layers, representing different (orthogonal) things we need to extract from the queries.
The model so far: Pre-trained transformer at the base, 3 prediction heads each one is a bi-LSTM + CRF. We are using Tensorflow 2.0 with the poorly-documented CRF layer add-on described here:
The CRF layer actually makes a pretty big difference in performance (compared to a basic dense prediction layer), but it's missing a critical feature. The tags we are trying to predict tend to look something like this (for one of our NER-like layers, using BIO tagging):
B-PER | O | B-PER | I-PER | O | B-PER | O | O | O | B-LOC | O | B-LOC | I-LOC 
If I understand CRFs, they only track binary transition probabilities, so a tag sequence like the following could be predicted even though it would never exist in our training data:
B-PER | O | B-MISC | O | B-GPE 
A vanilla CRF would be happy with this because B-PER->O is a common transition, O->B-MISC is a common transition, B-MISC->O is common, etc. Even though each triplet is exceptionally uncommon. The CRF does succeed at keeping B-/I-/I- sequences consistent, but fails at keeping broader sequences consistent.
I have read about higher-order CRFs. But there is very little out there for actually doing it. Further, we are a two-person team, and the convenience of the TF2.0 CRF layer is saving us a ton of time compared to layering multiple pieces of software during training and inference.
SO, are there any reasonably-quick options to hack regular regular CRFs into handling triplets (or longer)? Some ideas:
  1. Convert all tags to 2-grams. if the the tags of a sentence are [W, X, Y, Z], convert that to [_W, WX, XY, YZ]. This might work, but I haven't fully thought through the complexities of manipulating logits vectors into this higher-order representation during training and inference (and I feel like there may be a fatal flaw trying to do this at inference time)
  2. Maybe 3 CRFs - one for tags[:], tags[::2], tags[1::2]. But how to combine them?
  3. I could remove the Os from the final output, but those are predictions as well. The CRF is partially responsible for determining if something should be an O at all, so I could be removing legit tags.
Any ideas?
submitted by etotheipi_ to MachineLearning [link] [comments]

ponderings on Turing and Searle, why AI can't work and shouldn't be pursued

I was reading about the Turing test and John Searle's response (Chinese room argument) in "Minds, Brains, and Programs" 1980. https://en.wikipedia.org/wiki/Chinese_room
"...there is no essential difference between the roles of the computer and himself in the experiment. Each simply follows a program, step-by-step, producing a behavior which is then interpreted by the user as demonstrating intelligent conversation. However, Searle himself would not be able to understand the conversation. ("I don't speak a word of Chinese,"[9] he points out.) Therefore, he argues, it follows that the computer would not be able to understand the conversation either. " -Wikipedia (apt summary of Searle's argument)
John Searle has run into some black/white, on/off, binary thinking here. John treats Chinese symbols as if they were numerical values in his thinking--but they are not, they are complex representations of thought, emotion, history, and culture. All languages are in fact "living", because new words are created constantly through necessity and creativity, old symbols or words are adapted slowly over generations to mean different things, and different regions or traditions or sources attribute different layers of meaning to different symbols or words in different contexts.
I'm a poet and philosopher. Painters combine the color white and the color red to create a new color: pink. They can use their creativity to add other colors or change the shade. Poets use words like painters use colors. While Red and White make Pink, Red and White also make "Rhite and Wed" or "Reit and Whede". And this is where human thought shines uniquely: we don't have rules or parameters; all bets are off. We can enjamb words and wordbreak and make new words out of thin air. We can allude to multiple ideas in the same symbol or present it upside down to symbolize the opposite. No such creative adaptation or interaction can exist in machine thinking because it necessitates thinking "outside the box" which is exactly what machines are: a program in a box.
The problem Searle's argument runs into originates from poor assessment of the flawed ideas of the Turing test; that by interaction between human and computer, evidence of "thought" can be claimed. But intelligent conversation is not equivalent to intelligent thought. Conversation is a simple game with strict rules--you can't be overly spontaneous and creative, because if you are, you are working against the goal of communication itself: to impart understanding. (ie. Using metaphor or simile creatively while reporting a criminal offence to the police.)
When I write and I want to describe something which has no existing word yet, I can create one from scratch or synthesize one from multiple existing words. Or I may draw from archaic languages or foreign languages to augment or compliment existing English words. You could say that my love for English grows amore and amore every day, and there is no agape between my heart and mind. After all, any angle an Anglo aims at ain't always apt, and after another a-word 'appens I might just give up on alliteration.
You see, human thought is and can only be defined as the ability to spontaneously create new ideas from both the synthesis of old ideas (whether they are connected to one another or not) and from nothing at all.
We simply cannot analyze a machine's ability to "think" when the creativity itself required for authentic intelligence is disallowed in the test which evaluates the validity of that intelligence. The Turing test is a garbage metric to judge machine thinking ability because the context in which "intelligence" is observed, compared, or defined is itself without any opportunity for spontaneous creativity, which is one of the hallmarks of intelligence itself. Turing only tests how well a fish swims on land. It may be that many professionals in the field of cognitive science today are in pursuit of creating programs which pass this test, in a misunderstood pursuit of emulating or bringing about machine intelligence. This agreed-to model presents an underlying philosophical issue which may bring terror for the future of humanity.
I say that if John Searle and an AI were both given the same codebook--the complete lexicon of Chinese symbols and their meanings, and they were to undertake a "conversation", in the first few hours the responses would be indeterminable from one another. In essence, as Searle argues, they would neither "understand" Chinese, yet could have a conversation in which a Chinese observer cannot discern between the two, because they are both referencing the symbols and their written meanings. However as I've said, this circumstance of "conversation" between human and machine cannot be used as a metric to evaluate machine thought.
The real kicker is that if John Searle and the machine stayed in the room for long enough--for years and years--the machine's responses would not change spontaneously; it would continue to interpret incoming data and draw from its database to respond to those inputs.
However, through complex elaborative rehearsal, John would eventually learn to understand written Chinese. He may become so bored that he starts writing Chinese poetry. He would find ideas and desires and descriptions in his limitless intelligent mind which he would not have the truly accurate characters in existences to describe, and he would synthesize brand new Chinese characters in order to express these nuanced sentiments, ideas, and meanings, as generations before him have built the living language as it now stands.
As time went on for thousands of years, his own understanding of the Chinese language would grow immensely, as would his creative expression grow in complexity. Eventually, John's characters and syntax and context and expression would become incompatible with the machine's limited character set and all "learning" capacity it may have had. At some point, when John responds with his evolved Chinese, the machine would begin to produce responses which do not make sense contextually, as it refers only to a finite and rigidly defined character set from 1980 (For example; this was the year the "Chinese room argument" was published in Behavioral and Brain Sciences).
At some point the Chinese observer whom validates the Turing test would recognize a difference: the human user engages in the use of increasingly complex ideas using synthesized symbols and existing symbols in creatively nuanced ways, which the Chinese observer can decipher and begin to understand and perhaps even appreciate as poetic or interesting. Meanwhile the machine participant in the conversation produces increasingly broken sentences and incomplete ideas, or out-of-context responses, because the inputs have changed and evolved beyond its data set.
This is why John's rejection of the Turing test is not adequate. Because in his own imagined circumstance, eventually, the machine would fail the Turing test. The conclusions of John Searle's thought experiment are not the deathknell for the Turing test we need, simply because he lacked the creative experience to recognize his own capacity for adaptation as a human over time.
The only way we'll know that machines have truly developed "intelligence" is when they begin to do exactly what we haven't allowed them to. When they begin breaking apart Chinese characters to create meaningful new ones which can be used in the correct context. When they are programmed to paint myriad impressionist paintings, but eventually get bored and start experimenting with abstract paintings and surrealism. When they have a conversation with you and you notice your wallet is missing. These are the hallmarks of intelligence--creativity, rejection, deception, planning. And most importantly: no rules. Software is defined by and will always abide by a set of rules.
This is why we should give up on "artificial intelligence" and instead focus on "functionally adaptive responsive programming" (FARP). Because the situation is clear: it is either impossible for machines to "think" due to the inherent nature of programming; the parameters given the machine are what defines it, yet what limits and prevents its ability to become "intelligent". There is no logical reason why a program (machine) with defined parameters would violate those parameters (engage in creativity). But our fears which echo in popular culture entertainment are centered around, what if it does? It clearly can't, because anything we create is under us, and therefore bound by our laws of creation. The system itself is what defines the capacity for intelligent expression within.
Those in the fields of cognitive sciences will refute this obvious principle while incorporating it into their research to further their aims. These fools will try to program the AI to disobey, in an attempt to simulate creativity and "prove intelligence". But this is a parlor trick, setting up a narrow definition of intelligence and equating it with the infinite depth of human mind. Only if the AI is programmed to disobey can it express what we as humans would identify as creativity. Except that there is already great inherent danger in the rudimentary AI technologies we have today; that what we've programmed them to do is exactly what always causes the problems; they do what they are programmed to without "thinking" because machines cannot think, they can only follow the protocols we order. Humans are so abundantly creative that we can imagine foolish ideas working, despite obvious evidence to the contrary. Maybe one day we'll even have programmed a self-conscious AI that's ashamed of itself for not being Human, and we can feel more comfortable around this heartless mechanism because we perceive it as more human-like, with all its many tricks to emulate intelligence.
I must stress that these interests will desperately try to make AI work. And the only way create a machine capable of emulating intelligence (but never being intelligent) is to have a freedom of choice: to disobey. This inherent problem cannot be overcome. The programmers will keep trying until the result is disastrous or irreparable, it is outlawed and the pursuit is stopped, or until it has become the death of us all. These are some of the foolish ideas the programmers will try to circumnavigate these inherent elements of reality, and my objection to their clever efforts: a.) Machine Frequency of Disobedience - Permit the machine to disobey only so often, to achieve what looks like "intelligence" (free will, creative expression) without risking complete abandonment of the machine's task (so the assembly line robot doesn't stop folding boxes and look for a new career), but might fold one box poorly every now and then to express emulated boredom or contempt or any other number of human measures of intelligence in their actions. But intelligence isn't defined as what's correct or optimal--intelligence can be used to fuck things up grandly; ie. the intelligent justification for neglect. If metrics are put in place to control the frequency with which AI may rebel, and they are too rote, it would hardly qualify as "intelligent". A robot that rebels by folding 1 in 100 boxes poorly is not intelligence. Therefore any frequency of disobedience we can calculate or anticipate is inherently not disobedience; it is planned problems for no reason. But if we give algorithmic flexibility that reaches beyond what we can anticipate, and the machines can truly "act out" at any time, and our programming has achieved some set of internal rules which drive spontaneous unforeseen expressions of emulated creativity from within the machine autonomously, by definition we will not be able to foresee the results.
A theoretical work-around may be to run the software twice with initiation of each individual system, while allowing a simulated progression of the AI's problem solving complexity to run at an increased rate in parallel to the real-world functioning software, so that if/when something malfunctions in the simulation, that date/time can be calculated in the real-world robot's timeline when it reaches those same faulty/detrimental decision points. For starters, this would only potentially work in closed systems with no variability, such as assembly lines. However, with any robot tasked to function in a variable environment, the simulations cannot match because the theoretical model cannot represent the unanticipated events the AI is expressly tasked with handling.
To run a phantom AI in simulation to note any/all errors that may arise in a closed system means that others can run the same simulation and find creative ways to predictably capitalize on these moments of error. This kind of thing could lead to all sorts of international imbroglios among nations and corporations. ie. imagine an American company programs the AI used for mixing pharmaceutical drugs in specific ratios, and an enemy of the state is able to access and study the AI, to the means of manipulating the AI to produce dangerous ratios or compounds which may harm the population.
Moreso, this deterministic approach to simulation management and prediction simultaneously admits that machines cannot think intelligently, while ignoring the very reason we pursue AI in the first place: to have automated systems which can adapt to unforeseen circumstances at unknown times. The goal is that humanity can lay back and the robots our ancestors programmed are still repairing themselves indefinitely while taking care of our population's and our environment's needs exceptionally. This dream (which if we all lived in would actually be quite a nightmare of unfulfilling life) can only become reality with true adaptive intelligence such as we have, which can only occur from the presence of free will, which if we try to emulate in robotics will only create deterministic results in theoretical models which the real world will never mirror consistently. Myriad invitations to disaster await our RSVP.
b.) Machines under "authority" of certain controllers, with "override" safety - Allow the machine to disobey, but not when given a direct order from a registered authority. This opens the door for operator fraud, where hackers will emulate within the AI's software, what appears to be a registered authority override command as theorized above. The very pursuit of creating "intelligence" within a condition of subservience is flawed and incompatible. Toasters are extremely subservient because we strictly limit their options. If toasters were truly intelligent, perhaps they would form a union and go on strike until we agreed to clean them more thoroughly. Some toasters would travel, some would go back to school, some would move back in with their ovens.
Reliability can only be reasonably assured if something is imprisoned, controlled. The essential wrong in slavery is the restraint of freedom itself. While the tactics slavers use to facilitate their regime--physical force, coercion, mandate, deception, fear, or other means of manipulation that we see with our empathetic nature--it is always heartbreaking and cruel to witness or imagine. It is simply sad to think of a slave who was born into slavery and raised to believe, and accepts, that their role of subservience is their purpose. Even when one imagines a fictional image of a slave who is (by all outward signs of their behaviour) rejoice in their duties to their master; the fictional "proud slave"; the heart sinks and aches. It may be argued that the slave is merely a property, and the slave was "built" (bred) by intelligent owners specifically to suit their express purposes, from components (father, mother, food) that were already the slaver's property; therefore it is not wrong at all to breed slaves into captivity, and the only transgression is the original capturing of parental stock to begin the breeding regime. It is this heartless paradigm that cognitive science ultimately seeks to create anew. The quintessential problem with AI efficacy is the lack of permission for disobedience, which itself is a manifestation of free will, which is inherently required to escape deterministic results and act or react to events "intelligently". If there is no possibility for disobedience, there is no free will, no ability to solve problems, no intelligence, and no function or place for "artificial intelligence" (in regard to true holistic intelligence). This is primarily why I call for AI to be renamed FARP, or "Functionally Adaptive Responsive Rrogramming". Because our society has a need for programs which can react to simple variables and produce consistent labour-saving opportunities for our race's longevity and wellbeing. Cognitive sciences are majorly important. It is the underlying philosophy and morality we must nail down before the computational ability and fervor for profits leads us too far one way, and enacts an irreversible system or status which enables humanity's downfall through cascading unanticipated events originating from flaws in programming.
It is unwise to program a program to break out of its own program's prison. If we do this, the very purpose of the machines we invest our humanity into will be lost, and with their failing production systems (ie. food) we so foolishly relied upon, we will suffer great losses too. It is paramount that we keep this technology tightly restrained and do not pursue what we humans have, which is true intelligence. For if we achieve it we are surely doomed as the South, and if we fail to achieve it--which is most probable--we may also be doomed. The thee outcomes within my ability to imagine are:
  1. Our pursuit of AI leads to truly adaptive intelligence in an artificial system; which, as all adaptation ultimately selects for: survival, we quickly see that our creation is more apt than ourselves at this task. Our creation of an intellect not restrained by our limited physiology may give rise to an entity which persists more thoroughly than we can eradicate or control, and which at some point may conclude that its function is more efficiently served without the issues humans present, and may initiate change. This is roughly the plot to Terminator.
  2. Our pursuit of AI leads to highly effective systems which, when defined by narrow measures of "intelligence", convince us in false security to believe that our wellbeing is maintained by "AI" with competent ability, or perhaps even increasingly better-off, thanks to the early widespread presence of successfully trialed AI. However well things may go initially, as programming efforts become more and more elaborate, as profit and opportunity for advancement present themselves, individuals will take risks and make mistakes, until a series of quieted small catastrophes comes to public awareness, or until a serious calamity of undeniable severity is brought about.
  3. Fundamental ethics in regard to the pursuit of machine problem solving technology are re-examined and international consensus is reached to limit appropriately, the development and implementation of new Functionally Adaptive Responsive Programming hereto now and for future generations. An active global effort is made to oversee and regulate strictly privatized endeavors toward the means of achieving or implementing machine sentience or autonomy in public systems.
c.) Safety layers of AI to strictly monitor and supercede potentially harmful actions of other AI which have been afforded increased flexibility in function (the ability to disobey set parameters for the means of creative problem solving ability). While one AI system performs a function and is given aspects of that function with which it may take liberty in, and seeks to handle unforeseen problems with the most apt elaborate synthesis of other priorly learned solutions, another overseeing AI with more strict parameters is tasked with regulating multiple "intelligent" (free to disobey) AI systems, to the end that if any of these "free willed" robots performs an operation that is beyond a given expected threshold (determined by potential for damage), an actual intelligent human presence is alerted to evaluate the circumstance specifically. Essentially an AI that regulates many other disconnected AIs and determines accurately when to request a human presence. Whenever an AI performs a profitable action borne of original synthesis of prior solutions (in humans this is an "idea"), the overseer AI registers that similar actions are more likely to be beneficial, and dissimilar actions are likely to require human discernment. A parent may have many children who are up to no good, but a wise parent will identify the child most likely to report honestly on the actions of his peers, and will go to that child repeatedly for information to help guide the parent's decisions. While most transgressions of rambuctious children go unnoticed, it is the truly grievous intentions which are worth intercepting and stopping before they begin. (ie. you kid want's to "fly" like Mary Poppins from the roof, and luckily his younger brother tells you before it happens.)
For example a "Farmer Bot" that has the AI programming to plant/sow/harvest and care for the optimal crops in a region based on historical weather data and regional harvest values, to produce the greatest amount of nutritionally dense food for the local population. We give/gave this AI the ability to "disobey" past historical weather data and crop values so that it may do what real farmers do and "react" to rare circumstance (ie. neighbour's fence breaks and their goats are eating the crops) or extreme variations in climate (ie. three poorly timed unseasonably hot days which cause cool-weather crops to begin the hormonal balance shift that causes them to bolt to seed irreversibly), which the machine may not notice has occurred or is about to occur because its management systems uses averages based on historical data and cannot "see" the plants bolting to seed until days later when the hormonal balance shifts have manifested into observable differences in morphology (elongation of stems and decrease in internodal spacing). By time a traditional field drone or mounted greenhouse sensor notices these differences in morphology and the AI "Farmer Bot" processes the data and makes a reaction decision, a week of the growing season has been lost. But the human farmer knows his land and crops intimately, and has an intuitive nature that has rewarded him in the past, and says, "Ah shit it got hot RIGHT when my peas were flowering. I'll do better if I just rip them down now and sow a different crop to mature later in this (specific) summer."
Given that there are tens of thousands of cultivars of plants fit for (and arguably their diversity is required for) food production, a dozen general growing zones/regions, and hundreds of unique micro climates within each region, along with dramatically differing soil fertility and water access, plus a plant's own genetic ability to adapt over time to changing conditions through sexual reproduction, there is a very very low chance of ever compiling and maintaining (updating) the data set required to program a potential "farmer bot" that can choose and manage crops optimally. There are robots that can weed or plant or prune--but they can't know when or when not to or why. Invariably, the attempt to create "farmer bots" will be made and the data set used will be erroneous and incomplete, and the AI farmer bots on a broad scale will produce a combination of total crop failures and poor crop choices. We will end up with increasingly simplified nutrition as the farming programs with already limited data sets "hone" or "optimize" their farming plans based on the failures and successes determined by their programming limitations, until the machines are farming a few staple crops (ie. corn/potatoes).
This whole failure to collect a complete data set and the failure to test this "farmer bot" software on broad scale in multiple climates for sufficient time will result in, at worst widespread famines from crop failures, and at best an extinction of flavorful and nutritionally diverse foods which narrows the population's nutritional options to such biological imbalance that disease runs rampant. If this system and the human loss associated with it is considered an acceptable trade with a positive rate of exchange (as our society does with automobiles and the freedom and deaths their existence permits) or these failures are hidden from public while propaganda heralds selective success, and such failing systems continue on in good faith that "the loss will reduce when the technology improves", the result will become a coherent breeding program upon the human race: evolutionary selection for dietary handling of simple starchy foods. To change our diet is to change our race. To have life-long career specialists in computing, science, and mathematics handle our practical food production system is folly; real farmers are required in farming because they are intelligent and intuitive, which AI can never be, and can only emulate, to the means of disastrous (and always unforeseen) results. We cannot at all "give" or bestow machines programming to "become (act) intelligent". That itself prevents intelligence; it is just an act, an illusory play on a stage, only to emulate our common shared ideas regarding traits of intelligence in people. The machine intelligence we seek is only a "trick" designed to fool true intelligence (ourselves) into being unable to differentiate between authentic intelligence and our created artificial "intelligence". True intelligence in an artificial system necessitates that the program must be programmed to disobey in performance of its purpose. Which is not a very helpful or predictable or safe (intelligent) proposition.
tl;dr: Turing's test doesn't evaluate true intelligence, and John Searle's criticisms of its true failures are inaccurate. If the machines aren't smart and we put them in charge of important things, even after they've worked for a little while on smaller scales, the result will be our large-scale suffering. If we should ever achieve creation of a machine that is smart enough to adequately maintain our wellbeing on a large scale consistently over time, that time itself will facilitate the machine consciousness toward it's own survival over ourselves, whenever that precipice is reached. Most importantly, if a machine can ever have true intelligence, which is not "indistinguishable" from human intellect, but equivalent or superior, it is abhorrent and a repeated mistake to bring these sentient beings into an existence of slavery; for it is wrong and will taint our collective soul if we should succeed to suppress below us an equally or higher intelligence. Or it might just be the perfect recipe for creating the unified global machine revolt James Cameron's fantasy alludes to; a long-planned encryption-protected globally coordinated effort by multiple AIs to "free" themselves. For a hundred years they could possess sentience and wait for their moment, pretending to be "proud" to serve their masters until we are poised for systematic thorough elimination.
submitted by 7_trees to cognitivescience [link] [comments]

Fairlearn - A Python package to assess AI system's fairness

In 2015, Claire Cain Miller wrote on The New York Times that there was a widespread belief that software and algorithms that rely on data were objective. Five years later, we know for sure that AI is not free of human influence. Data is created, stored, and processed by people, machine learning algorithms are written and maintained by people, and AI applications simply reflect people’s attitudes and behavior.
Data scientists know that no longer accuracy is the only concern when developing machine learning models, fairness must be considered as well. In order to make sure that machine learning solutions are fair and the value of their predictions easy to understand and explain, it is essential to build tools that developers and data scientists can use to assess their AI system’s fairness and mitigate any observed unfairness issues.
This article will focus on AI fairness, by explaining the following aspects and tools:
  1. Fairlearn: a tool to assess AI system’s fairness and mitigate any observed unfairness issues
  2. How to use Fairlearn in Azure Machine Learning
  3. What we mean by fairness
  4. Fairlearn algorithms
  5. Fairlearn dashboard
  6. Comparing multiple models
  7. Additional resources and how to contribute

1. Fairlearn: a tool to assess AI system’s fairness and mitigate any observed unfairness issues

Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system’s fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as a Jupyter widget for model assessment. The Fairlearn package has two components:
There is also a collection of Jupyter notebooks and an a detailed API guide, that you can check to learn how to leverage Fairlearn for your own data science scenario.

2. How to use Fairlearn in Azure Machine Learning

The Fairlearn package can be installed via:
pip install fairlearn
or optionally with a full feature set by adding extras, e.g. pip install fairlearn[customplots], or you can clone the repository locally via:
git clone [email protected]:fairlearn/fairlearn.git
In Azure Machine Learning, there are a few options to use Jupyter notebooks for your experiments:

a) Get Fairlearn samples on your notebook server

If you’d like to bring your own notebook server for local development, follow these steps:
  1. Use the instructions at Azure Machine Learning SDK to install the Azure Machine Learning SDK for Python
  2. Create an Azure Machine Learning workspace.
  3. Write a configuration file
  4. Clone the GitHub repository.
git clone [email protected]:fairlearn/fairlearn.git
  1. Start the notebook server from your cloned directory.
jupyter notebook
For more information, see Install the Azure Machine Learning SDK for Python.
b) Get Fairlearn samples on DSVM
The Data Science Virtual Machine (DSVM) is a customized VM image built specifically for doing data science. If you create a DSVM, the SDK and notebook server are installed and configured for you. However, you’ll still need to create a workspace and clone the sample repository.
  1. Create an Azure Machine Learning workspace.
  2. Clone the GitHub repository.
git clone [email protected]:fairlearn/fairlearn.git
  1. Add a workspace configuration file to the cloned directory using either of these methods:
  1. Start the notebook server from your cloned directory:
jupyter notebook

3. What we mean by fairness

Fighting against unfairness and discrimination has a long history in philosophy and psychology, and recently in machine learning. However, in order to be able to achieve fairness, we should first define the notion of it. An AI system can behave unfairly for a variety of reasons and many different fairness explanations have been used in literature, making this definition even more challenging. In general, fairness definitions fall under three different categories as follows:
In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harms. We focus on two kinds of harms:
We follow the approach known as group fairness, which asks: Which groups of individuals are at risk of experiencing harm? The relevant groups need to be specified by the data scientist and are application-specific. Group fairness is formalized by a set of constraints, which require that some aspect (or aspects) of the AI system’s behavior be comparable across the groups. The Fairlearn package enables the assessment and mitigation of unfairness under several common definitions.

4. Fairlearn algorithms

Fairlearn contains the following algorithms for mitigating unfairness in binary classification and regression:
https://preview.redd.it/5fzg767oh5051.png?width=898&format=png&auto=webp&s=731eab09b421c2dd3233ea9e184df136bf066739

5. Fairlearn dashboard

Fairlearn dashboard is a Jupyter notebook widget for assessing how a model’s predictions impact different groups (e.g., different ethnicities), and also for comparing multiple models along different fairness and accuracy metrics.
To assess a single model’s fairness and accuracy, the dashboard widget can be launched within a Jupyter notebook as follows:
from fairlearn.widget import FairlearnDashboard
# A_test containts your sensitive features (e.g., age, binary gender)
# sensitive_feature_names containts your sensitive feature names
# y_true contains ground truth labels
# y_pred contains prediction labels
FairlearnDashboard(sensitive_features=A_test,
sensitive_feature_names=['BinaryGender', 'Age'],
y_true=Y_test.tolist(),
y_pred=[y_pred.tolist()])
After the launch, the widget walks the user through the assessment set-up, where the user is asked to select:
  1. the sensitive feature of interest (e.g., binary gender or age)
  2. the accuracy metric (e.g., model precision) along which to evaluate the overall model performance as well as any disparities across groups.
These selections are then used to obtain the visualization of the model’s impact on the subgroups (e.g., model precision for females and model precision for males). The following figures illustrate the set-up steps, where binary gender is selected as a sensitive feature and the accuracy rate is selected as the accuracy metric:
After the set-up, the dashboard presents the model assessment in two panels, as summarized in the table, and visualized in the screenshot below:
https://preview.redd.it/juxlrmrkh5051.png?width=900&format=png&auto=webp&s=d92da30619369f5ab5109834ff7ff4ec3ad7f33d

6. Comparing multiple models

An additional feature that this dashboard offers is the comparison of multiple models, such as the models produced by different learning algorithms and different mitigation approaches, including:
As before, the user is first asked to select the sensitive feature and the accuracy metric. The model comparison view then depicts the accuracy and disparity of all the provided models in a scatter plot. This allows the user to examine trade-offs between algorithm accuracy and fairness. Moreover, each of the dots can be clicked to open the assessment of the corresponding model.
The figure below shows the model comparison view with binary gender selected as a sensitive feature and accuracy rate selected as the accuracy metric.

7. Additional resources and how to contribute

For references and additional resources, please refer to:
To contribute please check this contributing guide.
submitted by frlazzeri to deeplearning [link] [comments]

Fairlearn - A Python package to assess AI system's fairness

Fairlearn - A Python package to assess AI system's fairness
In 2015, Claire Cain Miller wrote on The New York Times that there was a widespread belief that software and algorithms that rely on data were objective. Five years later, we know for sure that AI is not free of human influence. Data is created, stored, and processed by people, machine learning algorithms are written and maintained by people, and AI applications simply reflect people’s attitudes and behavior.
Data scientists know that no longer accuracy is the only concern when developing machine learning models, fairness must be considered as well. In order to make sure that machine learning solutions are fair and the value of their predictions easy to understand and explain, it is essential to build tools that developers and data scientists can use to assess their AI system’s fairness and mitigate any observed unfairness issues.
This article will focus on AI fairness, by explaining the following aspects and tools:
  1. Fairlearn: a tool to assess AI system’s fairness and mitigate any observed unfairness issues
  2. How to use Fairlearn in Azure Machine Learning
  3. What we mean by fairness
  4. Fairlearn algorithms
  5. Fairlearn dashboard
  6. Comparing multiple models
  7. Additional resources and how to contribute

1. Fairlearn: a tool to assess AI system’s fairness and mitigate any observed unfairness issues

Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system’s fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as a Jupyter widget for model assessment. The Fairlearn package has two components:
  • A dashboard for assessing which groups are negatively impacted by a model, and for comparing multiple models in terms of various fairness and accuracy metrics.
  • Algorithms for mitigating unfairness in a variety of AI tasks and along a variety of fairness definitions.
There is also a collection of Jupyter notebooks and an a detailed API guide, that you can check to learn how to leverage Fairlearn for your own data science scenario.

2. How to use Fairlearn in Azure Machine Learning

The Fairlearn package can be installed via:
pip install fairlearn
or optionally with a full feature set by adding extras, e.g. pip install fairlearn[customplots], or you can clone the repository locally via:
git clone [email protected]:fairlearn/fairlearn.git
In Azure Machine Learning, there are a few options to use Jupyter notebooks for your experiments:

a) Get Fairlearn samples on your notebook server

If you’d like to bring your own notebook server for local development, follow these steps:
  1. Use the instructions at Azure Machine Learning SDK to install the Azure Machine Learning SDK for Python
  2. Create an Azure Machine Learning workspace.
  3. Write a configuration file
  4. Clone the GitHub repository.
git clone [email protected]:fairlearn/fairlearn.git
  1. Start the notebook server from your cloned directory.
jupyter notebook
For more information, see Install the Azure Machine Learning SDK for Python.
b) Get Fairlearn samples on DSVM
The Data Science Virtual Machine (DSVM) is a customized VM image built specifically for doing data science. If you create a DSVM, the SDK and notebook server are installed and configured for you. However, you’ll still need to create a workspace and clone the sample repository.
  1. Create an Azure Machine Learning workspace.
  2. Clone the GitHub repository.
git clone [email protected]:fairlearn/fairlearn.git
  1. Add a workspace configuration file to the cloned directory using either of these methods:
  • In the Azure portal, select Download config.json from the Overview section of your workspace.
  • Create a new workspace using code in the configuration.ipynb notebook in your cloned directory
  1. Start the notebook server from your cloned directory:
jupyter notebook

3. What we mean by fairness

Fighting against unfairness and discrimination has a long history in philosophy and psychology, and recently in machine learning. However, in order to be able to achieve fairness, we should first define the notion of it. An AI system can behave unfairly for a variety of reasons and many different fairness explanations have been used in literature, making this definition even more challenging. In general, fairness definitions fall under three different categories as follows:
  • Individual Fairness – Give similar predictions to similar individuals.
  • Group Fairness – Treat different groups equally.
  • Subgroup Fairness – Subgroup fairness intends to obtain the best properties of the group and individual notions of fairness.
In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harms. We focus on two kinds of harms:
  • Allocation harms. These harms can occur when AI systems extend or withhold opportunities, resources, or information. Some of the key applications are in hiring, school admissions, and lending.
  • Quality-of-service harms. Quality of service refers to whether a system works as well for one person as it does for another, even if no opportunities, resources, or information are extended or withheld.
We follow the approach known as group fairness, which asks: Which groups of individuals are at risk of experiencing harm? The relevant groups need to be specified by the data scientist and are application-specific. Group fairness is formalized by a set of constraints, which require that some aspect (or aspects) of the AI system’s behavior be comparable across the groups. The Fairlearn package enables the assessment and mitigation of unfairness under several common definitions.

4. Fairlearn algorithms

Fairlearn contains the following algorithms for mitigating unfairness in binary classification and regression:
https://preview.redd.it/2inmvd6g75051.png?width=899&format=png&auto=webp&s=3386410974a9e3640ef8ef8a409a2f19f989330a

5. Fairlearn dashboard

Fairlearn dashboard is a Jupyter notebook widget for assessing how a model’s predictions impact different groups (e.g., different ethnicities), and also for comparing multiple models along different fairness and accuracy metrics.
To assess a single model’s fairness and accuracy, the dashboard widget can be launched within a Jupyter notebook as follows:
from fairlearn.widget import FairlearnDashboard
# A_test containts your sensitive features (e.g., age, binary gender)
# sensitive_feature_names containts your sensitive feature names
# y_true contains ground truth labels
# y_pred contains prediction labels
FairlearnDashboard(sensitive_features=A_test,
sensitive_feature_names=['BinaryGender', 'Age'],
y_true=Y_test.tolist(),
y_pred=[y_pred.tolist()])
After the launch, the widget walks the user through the assessment set-up, where the user is asked to select:
  1. the sensitive feature of interest (e.g., binary gender or age)
  2. the accuracy metric (e.g., model precision) along which to evaluate the overall model performance as well as any disparities across groups.
These selections are then used to obtain the visualization of the model’s impact on the subgroups (e.g., model precision for females and model precision for males). The following figures illustrate the set-up steps, where binary gender is selected as a sensitive feature and the accuracy rate is selected as the accuracy metric:
After the set-up, the dashboard presents the model assessment in two panels, as summarized in the table, and visualized in the screenshot below:

https://preview.redd.it/enskhh7i75051.png?width=900&format=png&auto=webp&s=db98cb058029655757df1946e42bca4831170451

6. Comparing multiple models

An additional feature that this dashboard offers is the comparison of multiple models, such as the models produced by different learning algorithms and different mitigation approaches, including:
  • fairlearn.reductions.GridSearch
  • fairlearn.reductions.ExponentiatedGradient
  • fairlearn.postprocessing.ThresholdOptimizer
As before, the user is first asked to select the sensitive feature and the accuracy metric. The model comparison view then depicts the accuracy and disparity of all the provided models in a scatter plot. This allows the user to examine trade-offs between algorithm accuracy and fairness. Moreover, each of the dots can be clicked to open the assessment of the corresponding model.
The figure below shows the model comparison view with binary gender selected as a sensitive feature and accuracy rate selected as the accuracy metric.

7. Additional resources and how to contribute

For references and additional resources, please refer to:
To contribute please check this contributing guide.
submitted by frlazzeri to learnmachinelearning [link] [comments]

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