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Spookier Or Safer: How AI Autonomous Cars Alter Halloween Trick-Or-Treat Activities 




Kids trick or treat in Halloween costume and face mask. Children in dress up with candy bucket in coronavirus pandemic. Little boy and girl trick or treating with pumpkin lantern. Autumn holiday fun.

By Lance Eliot, the AI Trends Insider 

Halloween is just around the corner, waiting to surprise us. Though this year’s celebrations might be less extravagant, requiring special care and precautions, nonetheless we all know that Halloween traditionally has welcomed a slew of joyous activities including pumpkin carving, wearing scary costumes, elaborately decorating our homes, and so on. 

The highlight, undoubtedly, has got to be the annual thrill of going trick-or-treating. 

Perhaps you remember as a child going door-to-door in your neighborhood and the excitement at approaching the porch of a house covered with cobwebs and ghosts. Do you dare make your way to the front door? What goblins and other frights might await you? And, upon bravely knocking on the imposing door, recall the absolute delight at being given a chocolate bar or your favorite bubble gum. Off you would sprint, heading to the next house on the block.   

Now, as an adult, hopefully, you either are the one dispensing those candies when impressionable youngsters knock at your door, or maybe you will be going outdoors and walking along with your children as they experience the same thrills that you did in your youth. It is fun to relish memories and also look toward future Halloweens too.   

Speaking of the future (notice that seamless segue), some readers have asked me to comment on Halloween and self-driving cars.   

I realize that your first thought might be that there is no particular connection between Halloween, one of the most revered celebrations each year, and the advent of AI-based true self-driving cars, an amazing technological innovation that is gradually emerging. It might seem odd to consider that there would be any type of connection between these two seemingly disparate facets. 

Surprise! There is indisputably a means to connect the two, very much so. 

Note: I hope that my written yelling of “surprise” at you did not startle you, though if you are reading this on Halloween, consider it the equivalent of being frighteningly startled with a spooky but festive boo.   

The question for today’s discussion is this: What kinds of impacts might the prevalence of AI-based true self-driving cars have upon Halloween and our festivities thereof? Great question and I’ll get to it, but first, let’s make sure we all concur on what is meant by referring to AI-based true self-driving cars.   

Time to unpack that jargon.   

For my framework about AI autonomous cars, see the link here:   

Why this is a moonshot effort, see my explanation here:   

For more about the levels as a type of Richter scale, see my discussion here:   

For the argument about bifurcating the levels, see my explanation here: 

Understanding The Levels Of Self-Driving Cars 

As a clarification, true self-driving cars are ones that the AI drives the car entirely on its own and there isn’t any human assistance during the driving task. 

These driverless vehicles are considered a Level 4 and Level 5, while a car that requires a human driver to co-share the driving effort is usually considered at a Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-on’s that are referred to as ADAS (Advanced Driver-Assistance Systems).   

There is not yet a true self-driving car at Level 5. We don’t yet even know if this will be possible to achieve, and nor how long it will take to get there. Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed. We are all life-or-death guinea pigs in an experiment taking place on our highways and byways, some point out. 

Since semi-autonomous cars require a human driver, the adoption of those types of cars won’t be markedly different from driving conventional vehicles. But it is important that the public needs to be forewarned about a disturbing aspect that’s been arising lately, namely that despite those human drivers that keep posting videos of themselves falling asleep at the wheel of a Level 2 or Level 3 car, we all need to avoid being misled into believing that the driver can take away their attention from the driving task while driving a semi-autonomous car. 

You are the responsible party for the driving actions of the vehicle, regardless of how much automation might be tossed into a Level 2 or Level 3.   

For why remote piloting or operating of self-driving cars is generally eschewed, see my explanation here:  

To be wary of fake news about self-driving cars, see my tips here:  

The ethical implications of AI driving systems are significant, see my indication here:  

Be aware of the pitfalls of normalization of deviance when it comes to self-driving cars, here’s my call to arms: 

Self-Driving Cars And Halloween   

For Level 4 and Level 5 true self-driving vehicles, there won’t be a human driver involved in the driving task. All occupants will be passengers. The AI is doing the driving.   

Let’s begin with the important and assuredly upbeat insight that by removing the need for a human driver, there will no longer be any drunk drivers or DUI drivers on our roadways (well, at least with respect to the self-driving cars, though keep in mind that conventional human-driven cars will likely also still be on the highways and byways too). 

What does it mean that there won’t potentially be intoxicated drivers during Halloween (or, at least a lot fewer ones)? In short, you can generally cross off your list any of those ghastly car crashes that lead to injuries and fatalities due to those out-of-their-mind human drivers. 

This is especially noteworthy on the evening of Halloween since there are zillions of young children at risk that night. Kids love to dart out into the street on Halloween while trick-or-treating. They aren’t thinking about cars, they are thinking about the next house that has that inflated menacing ogre and beckons to them via the screeching sounds emanating from the blaring speakers placed in the bushes of the front yard.   

Children on Halloween tend to falsely believe that the world has granted them a free pass to run and scamper all around the community. Though that would be a nice ideal, the reality is that there are today those drivers that insist on driving on the evening of Halloween and getting mired into the realm of where kids think they can go. One has to sympathize with the drivers that are sober and have little choice to drive that evening, perhaps to take their children to a Halloween party or for other needed purposes. Unfortunately, the bad apple drivers can spoil the whole barrel, as it were. 

Plus, even a cautious and fully aware driver is bound to find themselves unnerved that evening. You need to drive slowly, really slowly, and this is a hard thing for many drivers to do. They are accustomed to driving at normal speeds and when driving a reduced speed it seems as though their vehicular movement is glacial in nature. Add to this the potential pressure of needing to get to someone’s house by a certain time, and you have an adult that can readily misjudge the roadways, leading to a calamity.   

Bottom line: The more self-driving cars on the roads, the reduced chances of a human driver in that car that otherwise might have messed-up, one way or another, for whatever reason. 

We can stick for the moment with the advantages and benefits side of the self-driving car versus human-driven car equation, and then, once I have covered most of those key points, we’ll consider some downsides too.   

Pretend that you are busy at your home while handing out candy and also hosting your own adult-oriented Halloween party. Turns out that your teenage son or daughter was invited to a Halloween activity at the school grounds, but that is several miles away. Teachers at the school will be watching over the teens and you feel comfortable that your offspring will be safe there on their own.   

All you need to do is drive your eager youngster to the school.   

Instead of you being the driver, you could use a self-driving car to get your child over there. You would of course first make sure your child gets safely into the self-driving car, perhaps one that you own or that was available via a ride-sharing network, and the AI driving system then proceeds to drive over to the school. Once the event is completed, the self-driving car gives your offspring a ride back to your home.   

This frees you from having to make the drive. Also, if you didn’t already own a car, or if you didn’t have a driver’s license, the use of the self-driving car solves several issues when desiring to provide your child with a lift to the Halloween event (and, once again, aids in preventing a potentially tipsy driver from getting behind the wheel). 

Admittedly, some people repulsively recoil at the notion of having children riding in a self-driving car without any adult supervision. This will never-ever happen some parents exhort fervently. The idea is rather foreign to us currently and seems unimaginable, but we should be cautious in extending today’s cultural norms for what we might accept in the future.   

Let’s continue our tour of the ways that self-driving cars will impact Halloween. 

Some cities and suburbs have increasingly been setting up areas that allow for a drive-thru Halloween activity (especially due to the pandemic). You and the family pile into your car, and drive over to a park or parking lot that has a kind of haunted mansion or haunted city, as it were, created for providing a fun and spooky experience. Realize that you do not get out of your car. Instead, you remain in the vehicle, as though driving through a fast-food eatery, but in this case, it is an outdoor area set up with Halloween scenery.   

If you were driving the car, it would likely be hard to fully relish the festive experience since you would be constantly having to watch where you are driving. Via a self-driving car, you would let the AI do the driving. This means that you and the rest of your family can all enjoy together the Halloween festivities, and nobody needs to be worrying about the driving.   

Another somewhat new approach to Halloween that has been getting recent attention consists of trunk-or-treating.   

Never heard of it? 

You put Halloween decorations on the trunk of your car. Inside the trunk, you put bags or buckets of candies. When ready, you drive around the community, coming to a stop here and there, allowing kids to obtain their Halloween treats directly from the trunk of the vehicle. As to whether you get out of the car to dispense the candies, this depends (some purposely do not get out of the car as an added pandemic precaution for themselves and the kids that come to the car to retrieve the candies).   

Not everyone likes this trunk-or-treating phenomenon. 

Some point out that with Halloween candies dispensed from a house, you know where to go if there is something untoward handed to a child. The house is permanently affixed. On the other hand, someone driving a car around could be just about anybody, and they might not readily be traceable (for those of you that want to argue this point, it is true that you could copy down the license plate and trace the vehicle, but that’s a far cry from the aspect that a house is pinned to one readily known spot).   

Anyway, whether you like or hate the idea, it perhaps is apparent that a self-driving car could enable such an approach if desired.   

Yet another possibility for Halloween celebrations is the veritable Halloween car parade. People deck out their cars with Halloween banners and decorations. They put on costumes too if wishing to do so. You and your friends or family then get into the car and drive with other cars in a type of makeshift parade. This conga line of Halloween celebratory cars makes its way throughout the neighborhood. Horns are honked, people inside the cars are making noises befitting Halloween, and kids line the sidewalks, watching as the parade goes past their homes. As you might imagine, this is being spurred partially due to the pandemic, allowing people that already live together to be grouped into their car, and yet going outdoors to celebrate the evening.   

One concern that some have about these Halloween parades is the possibility that some drivers will be drinking or have already had a few too many before deciding to join the car procession. Without seeming like a broken record that repeats itself unduly, if those cars were self-driving cars then the parade could meander unabated and without fear of a human driver doing something that could be injuriously unseemly. 

For more details about ODDs, see my indication at this link here: 

On the topic of off-road self-driving cars, here’s my details elicitation: 

I’ve urged that there must be a Chief Safety Officer at self-driving car makers, here’s the scoop: 

Expect that lawsuits are going to gradually become a significant part of the self-driving car industry, see my explanatory details here:   

Now For Some Scary Twists Too 

Having covered the essence of the presumed upbeat or positive aspects of Halloween and self-driving cars, we now turn our gaze toward the less-so elements. 

A perhaps obvious aspect about the advent of self-driving cars on Halloween is that it would allow adults to go to bars or parties and get smashed, if they wanted to do so, and not be held back by having to be a designated driver. Actually, this is true for any evening on any day of the week. You cannot fault the self-driving cars for this human behavior, but nonetheless could be a reaction by humans to the ease of having self-driving cars available.   

Will self-driving cars spur people to drink or get drunk? 

Nobody knows, and we won’t likely know until the day arrives of a prevalence of self-driving cars on our roadways. 

Another aspect is the difficulty of driving on the roadways during Halloween.   

Yes, even self-driving cars are going to find this to be a challenging driving task. 

Do not falsely assume that merely because the self-driving car is using AI and has a collection of state-of-the-art sensors such as video cameras, LIDAR, radar, ultrasonic, etc., that it will perfectly and unerringly ensure that nobody is ever hit or hurt by a collision.   

I’ve stated categorically and repeatedly that the notion of zero fatalities for self-driving cars has a zero chance of occurring. Physics belies such a belief. If a child darts unexpectedly from between two parked cars, and a self-driving car (or even a human-driven car) is cruising down the street, there might be insufficient time to stop the car before striking the suddenly appearing child. That’s a fact of physics.   

When I mention this point, those in the self-driving industry are apt to instantly object. Therefore, let me be clear, I am not suggesting that self-driving cars will be less safe than human drivers. In fact, the expectation is going to be that self-driving cars have to be safer than human drivers. Thus, in the aggregate, we are presumably going to have many fewer injuries and fewer fatalities once we have a preponderance of self-driving cars. My point is that realistically it won’t go to zero. There will still be some non-zero number, though hopefully less than, a lot less, in comparison to the 40,000 annual car crash deaths in the U.S. annually and the approximately 2.5 million injuries.   

Anyway, back to the point that on Halloween, especially so, the number of children and adults, perhaps even scampering dogs and cats, upon the roadways can be much higher than what normally is seen on the streets. This means many more objects that the AI needs to detect and discern as to which way the “object” is going and what it will do.   

Challenges abound.   

Children are small in stature and thus tend to be harder to detect. They might be wearing costumes that make their shape irregular in comparison to the expectations of what a person usually looks like. The kids can be erratic in where they go and whether they are sprinting or walking, or perhaps even crawling on the ground. All the kids and adults might be quickly stepping off a curb or willing to run amongst the cars that are making their way down the street. 

Amidst all of this, it is nighttime and dark out, so the lighting of the scene can be quite problematic. Indeed, children might be carrying flashlights or lasers that they point at the cars, of which the sensors could be hampered by such actions. 


In the self-driving car field, there is a well-known dictum that entails dealing with edge problems, also known as corner cases. Essentially, those developing self-driving cars are prioritizing what needs to be accomplished, of which just safely having the AI drive from a house to a grocery store during daylight is a keystone task. Unusual driving scenarios are labeled as being an edge or corner case, meaning that they are oddball or unique situations and presumably can be dealt with at a later time. The rule-of-thumb is to get the core stuff done first, and then worry about the rest later on.   

It is safe to say that Halloween is an edge or corner case. 

How many times a year do we all wander out into the streets, at nighttime, in costumes, with children aplenty? 

Unless you live in an especially party-vigorous locale, the answer would seem to be that it is a once a year occurrence.   


Even though Halloween is reasonably classified as a once-a-year instance, i.e., an edge case, this does not obviate the need to ultimately cope with the zaniness of the driving situation that arises.   

Imagine if self-driving cars were unable to sufficiently drive around on Halloween, such that all self-driving cars had to be self-grounded that night. Assuming that people had become dependent upon the use of self-driving cars, it would seem inappropriate to have none available on any particular night, such as Halloween.   

There is no doubt that ultimately self-driving cars will be enhanced to handle the particulars of a Halloween driving scenario. Also, to clarify, it is not as though self-driving cars of today could not likely manage to drive around on Halloween evening, it is just that out of an abundance of caution, it would seem unwise to do so at this time. 

Consider what might happen if some self-driving car did ram into a child on Halloween (for sake of discussion, assume luckily the child is completely unhurt per se), this would generate unbelievably big news and become the headlines seen or heard all around the world. It would be a public relations nightmare that would exceed any of the scariest things you might ever envision associated with Halloween. 

Let’s aim to avoid that kind of ghastly affair. 

In any case, when thinking about the future, someday, you’ll be able to park your broom and let the AI do all the driving for you. 

Happy Halloween to all and please be safe!  

Copyright 2020 Dr. Lance Eliot  

This content is originally posted on AI Trends. 

[Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column:] 



Facial recognition tech: risks, regulations and future startup opportunities in the EU




Facial recognition differs from the conventional camara surveillance, as it is not a mere passive recording, but rather it entails identification of an individual by comparing newly capture images with those images saved in a data base.

The status in Europe

Although facial recognition is not yet specifically regulated in Europe, it is covered by the General Data Privacy Regulation – GDPR – as a means of collecting and processing personal biometric data, including facial data and fingerprints. Therefore, facial recognition is only possible under the criteria of the GDPR.

Biometric data provides a high level of accuracy when identifying an individual due to the uniqueness of the identifiers (facial image or fingerprint) and a great potential to improve business security.

The processing of biometric data, which is considered sensitive data, is in principle prohibited with some exceptions, such as, for reasons of substantial public interests, to protect the vital interest of the data holder or another person, or if data holder has given its explicit consent, to name some.

Moreover, other factors such as proportionality or power imbalance are considered to determine if it is a valid exception, for instance, facial recognition can be considered disproportionate to track attendance in a school, since less intrusive options are available. Also even when the data holder has explicitly consented to the processing of biometric data, consideration should be given to potential imbalance of power dynamics between the individual data holder and the institution processing the data. For instance in a student and school scenario, there could be doubts as to whether the consent of the parents of a student to the use of facial recognition techniques, is freely given in the manner intended by the GDPR and therefore, a valid exception to the prohibition of processing.

One of the challenges in this field is that the underlying technology used for facial recognition, for instance AI, can present serious risks of bias and discrimination, affecting and discriminating many people without the social control mechanism that governs human behaviour. Bias and discrimination are inherent risks of any societal or economic activity. Human decision-making is not immune to mistakes and biases. However, the same bias when present in AI could have a much larger effect.

Authentication vs. identification

Obviously biometrics for authentication (which is described as a security mechanism), is not the same as remote biometric identification (which is used for instance in airports or public spaces, to identify multiple persons’ identities at a distance and in continuous manner by checking them against data stored in a database).

The collection and use of biometric information used for facial recognition and identification in public spaces carries specific risks for fundamental rights. In fact, the European Commission (EC) has warned that remote biometric identification is the most intrusive form of facial recognition and it is in principle prohibited in Europe.

So where is all this going?

What should prevail: the protection of fundamental rights, or the advancement that comes with invasive and overpowering new technologies?

New technologies, like AI, bring some benefits, such as technological advancement and more efficiency and economic growth, but at what cost?

Using a risk-based approach the EC has considered the use of AI for remote biometric identification and other intrusive surveillance technologies to be high-risk, since it could compromise fundamental rights such as human dignity, non-discrimination and privacy protection.

The EU Commission is currently investigating whether additional safeguards are needed or whether facial recognition should not be allowed in certain cases, or certain areas, opening the door for a debate regarding the scenarios that could justify the use of facial recognition for remote biometric identification.

Artificial intelligence entails great benefits but also several potential risks, such as opaque decision-making, gender-based or other kinds of discrimination, intrusion in our private lives or being used for criminal purposes.

To address these challenges, the Commission in its white paper on AI, issued in February this year, has proposed a new regulatory framework on high risk AI, and a prior conformity assessment, including testing and certification of AI facial recognition high risk systems to ensure that they abide by EU standards and requirements.

The regulatory framework will include additional mandatory legal requirements related to training data, record-keeping, transparency, accuracy, oversight and application-based use, and specific requirements for some AI applications, specifically those designed for remote biometric facial recognition.

We should then expect new regulation coming, with the aim to have an AI system framework, compliant with current legislation and that does not compromise fundamental rights.

Opportunities for startups?

Facial recognition technologies are here to stay, therefore, so if you are thinking about changing your hair colour, watch out as your phone might not recognize you! With the speed in which facial recognition is growing, we should not wait too long for new forms of ‘selfie payment’.

Facial recognition is already been used quite successfully in several areas, among them:

  1. Health: Where thanks to face analysis is already possible to track patience use of mediation more accurately;
  2. Market and retail: Where facial recognition promises the most, as ‘knowing your customer’ is a hot topic, this means placing cameras in retail outlets to analyze the shopper behavior and improve the customer experience, subject of course to the corresponding privacy checks; and,
  3. Security and law enforcement: That is, to find missing children, identify and track criminals or accelerate investigations.

With lots of choices on the horizon for facial recognition, it remains to be seen whether European startups will lead new innnovations in this area.


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KDnuggets™ News 20:n45, Dec 2: TabPy: Combining Python and Tableau; Learn Deep Learning with this Free Course from Yann LeCun




KDnuggets™ News 20:n45, Dec 2: TabPy: Combining Python and Tableau; Learn Deep Learning with this Free Course from Yann LeCun

Combine Python and Tableau with TabPy; Learn Deep Learning with this Free Course from Yann LeCun; Find 15 Exciting AI Project Ideas for Beginners; Read about the Rise of the Machine Learning Engineer; See How to Incorporate Tabular Data with HuggingFace Transformers

Features |  News |  Tutorials |  Opinions |  Tops |  Jobs  |  Submit a blog  |  Image of the week

This week on KDnuggets: Combine Python and Tableau with TabPy; Learn Deep Learning with this Free Course from Yann LeCun; Find 15 Exciting AI Project Ideas for Beginners; Read about the Rise of the Machine Learning Engineer; See How to Incorporate Tabular Data with HuggingFace Transformers; and much, much more.



 Tutorials, Overviews


 Top Stories, Tweets


  Image of the week

The Rise of the Machine Learning Engineer
From The Rise of the Machine Learning Engineer


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Remembering Pluribus: The Techniques that Facebook Used to Master World’s Most Difficult Poker Game




Remembering Pluribus: The Techniques that Facebook Used to Master World’s Most Difficult Poker Game

Tags: AI, Facebook, Poker

Pluribus used incredibly simple AI methods to set new records in six-player no-limit Texas Hold’em poker. How did it do it?

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I had a long conversation with one of my colleagues about imperfect information games and deep learning this weekend and reminded me of an article I wrote last year so I decided to republish it.

Poker has remained as one of the most challenging games to master in the fields of artificial intelligence(AI) and game theory. From the game theory-creator John Von Neumann writing about poker in his 1928 essay “Theory of Parlor Games, to Edward Thorp masterful book “Beat the Dealer” to the MIT Blackjack Team, poker strategies has been an obsession to mathematicians for decades. In recent years, AI has made some progress in poker environments with systems such as Libratus, defeating human pros in two-player no-limit Hold’em in 2017. Last year, a team of AI researchers from Facebook in collaboration with Carnegie Mellon University achieved a major milestone in the conquest of Poker by creating Pluribus, an AI agent that beat elite human professional players in the most popular and widely played poker format in the world: six-player no-limit Texas Hold’em poker.

The reasons why Pluribus represents a major breakthrough in AI systems might result confusing to many readers. After all, in recent years AI researchers have made tremendous progress across different complex games such as checkerschessGotwo-player pokerStarCraft 2, and Dota 2. All those games are constrained to only two players and are zero-sum games (meaning that whatever one player wins, the other player loses). Other AI strategies based on reinforcement learning have been able to master multi-player games Dota 2 Five and Quake III. However, six-player, no-limit Texas Hold’em still remains one of the most elusive challenges for AI systems.

Mastering the Most Difficult Poker Game in the World

The challenge with six-player, no-limit Texas Hold’em poker can be summarized in three main aspects:

  1. Dealing with incomplete information.
  2. Difficulty to achieve a Nash equilibrium.
  3. Success requires psychological skills like bluffing.

In AI theory, poker is classified as an imperfect-information environment which means that players never have a complete picture of the game. No other game embodies the challenge of hidden information quite like poker, where each player has information (his or her cards) that the others lack. Additionally, an action in poker in highly dependent of the chosen strategy. In perfect-information games like chess, it is possible to solve a state of the game (ex: end game) without knowing about the previous strategy (ex: opening). In poker, it is impossible to disentangle the optimal strategy of a specific situation from the overall strategy of poker.

The second challenge of poker relies on the difficulty of achieving a Nash equilibrium. Named after legendary mathematician John Nash, the Nash equilibrium describes a strategy in a zero-sum game in which a player in guarantee to win regardless of the moves chosen by its opponent. In the classic rock-paper-scissors game, the Nash equilibrium strategy is to randomly pick rock, paper, or scissors with equal probability. The challenge with the Nash equilibrium is that its complexity increases with the number of players in the game to a level in which is not feasible to pursue that strategy. In the case of six-player poker, achieving a Nash equilibrium is computationally impossible many times.

The third challenge of six-player, no-limit Texas Hold’em is related to its dependence on human psychology. The success in poker relies on effectively reasoning about hidden information, picking good action and ensuring that a strategy remains unpredictable. A successful poker player should know how to bluff, but bluffing too often reveals a strategy that can be beaten. This type of skills has remained challenging to master by AI systems throughout history.


Like many other recent AI-game breakthroughs, Pluribus relied on reinforcement learning models to master the game of poker. The core of Pluribus’s strategy was computed via self-play, in which the AI plays against copies of itself, without any data of human or prior AI play used as input. The AI starts from scratch by playing randomly, and gradually improves as it determines which actions, and which probability distribution over those actions, lead to better outcomes against earlier versions of its strategy.

Differently from other multi-player games, any given position in six-player, no-limit Texas Hold’em can have too many decision points to reason about individually. Pluribus uses a technique called abstraction to group similar actions together and eliminate others reducing the scope of the decision. The current version of Pluribus uses two types of abstractions:

  • Action Abstraction: This type of abstraction reduces the number of different actions the AI needs to consider. For instance, betting $150 or $151 might not make a difference from the strategy standpoint. To balance that, Pluribus only considers a handful of bet sizes at any decision point.
  • Information Abstraction: This type of abstraction groups decision points based on the information that has been revealed. For instance, a ten-high straight and a nine-high straight are distinct hands, but are nevertheless strategically similar. Pluribus uses information abstraction only to reason about situations on future betting rounds, never the betting round it is actually in.

To automate self-play training, the Pluribus team used a version of the of the iterative Monte Carlo CFR (MCCFR) algorithm. On each iteration of the algorithm, MCCFR designates one player as the “traverser” whose current strategy is updated on the iteration. At the start of the iteration, MCCFR simulates a hand of poker based on the current strategy of all players (which is initially completely random). Once the simulated hand is completed, the algorithm reviews each decision the traverser made and investigates how much better or worse it would have done by choosing the other available actions instead. Next, the AI assesses the merits of each hypothetical decision that would have been made following those other available actions, and so on. The difference between what the traverser would have received for choosing an action versus what the traverser actually achieved (in expectation) on the iteration is added to the counterfactual regret for the action. At the end of the iteration, the traverser’s strategy is updated so that actions with higher counterfactual regret are chosen with higher probability.

The outputs of the MCCFR training are known as the blueprint strategy. Using that strategy, Pluribus was able to master poker in eight days on a 64-core server and required less than 512 GB of RAM. No GPUs were used.
The blueprint strategy is too expensive to use real time in a poker game. During actual play, Pluribus improves upon the blueprint strategy by conducting real-time search to determine a better, finer-grained strategy for its particular situation. Traditional search strategies are very challenging to implement in imperfect information games in which the players can change strategies at any time. Pluribus instead uses an approach in which the searcher explicitly considers that any or all players may shift to different strategies beyond the leaf nodes of a subgame. Specifically, rather than assuming all players play according to a single fixed strategy beyond the leaf nodes, Pluribus assumes that each player may choose among four different strategies to play for the remainder of the game when a leaf node is reached. This technique results in the searcher finding a more balanced strategy that produces stronger overall performance.

Pluribus in Action

Facebook evaluated Pluribus by playing against an elite group of players that included several World Series of Poker and World Poker Tour champions. In one experiment, Pluribus played 10,000 hands of poker against five human players selected randomly from the pool. Pluribus’s win rate was estimated to be about 5 big blinds per 100 hands (5 bb/100), which is considered a very strong victory over its elite human opponents (profitable with a p-value of 0.021). If each chip was worth a dollar, Pluribus would have won an average of about $5 per hand and would have made about $1,000/hour.

The following figure illustrates Pluribus’ performance. On the top chart, the solid lines show the win rate plus or minus the standard error. The bottom chart shows the number of chips won over the course of the games.

Pluribus represents one of the major breakthroughs in modern AI systems. Even though Pluribus was initially implemented for poker, the general techniques can be applied to many other multi-agent systems that require both AI and human skills. Just like AlphaZero is helping to improve professional chess, its interesting to see how poker players can improve their strategies based on the lessons learned from Pluribus.

Original. Reposted with permission.



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Learning Environment Tips For Your Kids During The Pandemic




The pandemic has changed almost everything about our everyday lives. While you may still be adjusting to a new remote work environment and battling productivity and overpowering procrastination, your children are likely enduring the same waves of uncertainty.

As a result, parents must help young students overcome new challenges as the new medium of online learning sets the pace for youth worldwide. 

Suppose you are wondering how to keep your kids motivated to learn during the lockdown. In that case, these top tips will help you define a suitable learning environment that compliments homeschooling and virtual classrooms.

Create A Space For Learning

Your child will need their own space to learn, and while you can create a learning environment that is similar to a home office space, your child’s learning area must not be shared with any adults that may be working from home.

You will need to stock up on specific supplies to ensure the at-home learning environment is optimally functional. Besides schooling supplies, suitable furnishings, and a PC or laptop, it is also a great idea to decorate the background to complement learning. Therefore, calming colors are best.

Define Learning Times

Overcoming procrastination and lack of motivation are challenges the world’s workforce currently faces. What’s more, students worldwide are facing the same issues. To encourage your child, you will need to define learning times and help them craft a strict daily schedule. In addition to this, you should also lead by example by sticking to your work schedule. 

Explain The New Learning Dynamic

Regardless of your child’s age, the pandemic’s specifics, and how it has altered our lives is undeniably overwhelming for the average person. That said, these changes are even more frightening and confusing for younger minds who were previously conforming to entirely different dynamics of learning and socializing.

To best assist your child with the adjustment, parents must explain what is happening in the world. Your child should understand why their learning environment is different. Furthermore, you should also explain ongoing changes regarding the pandemic with your child to keep them up to date. This effort will prevent your child from feeling uncertain during this challenging time.

Be Present And Involved

Parents are required to support online learning as the parent-teacher collaboration dynamic has suddenly become vitally important. While previously, you may not have been as involved in your child’s schooling as education took place in controlled learning environments. As a result, you should make efforts to be present during your child’s online classes. Being involved is crucial to ensure your child maintains the ideal discipline that is required for learning. You will now need to participate in your child’s schooling in a manner that assists educators. 

Education standards may have changed dramatically, although not all the changes should be seen as unfavourable. Several studies suggest that even social distancing requirements are unlikely to affect youngsters negatively. The best way to keep your child motivated is to be involved in their education and provide them with an ideal environment that supports learning.

Also Read, Pros and Cons of Online Education


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