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3/5/2020 As promised in the announcement of the release of Fat Fritz, the first update to the neural network has been released, stronger and more mature, and with it comes the brand new smaller and faster Fat Fritz for CPU neural network which will produce quality play even on a pure CPU setup. If you leave it analyzing the start position, it will say it likes the Sicilian Najdorf, which says a lot about its natural style. Read on to find out more!

If you havent yet updated your copy of Fat Fritz, now is the time to do it as it brings more thanminor enhancements or a few bug fixes. This update will bring the first major update to the Fat Fritz neural network, stronger than ever, as well as a new smaller one that is quite strong on a GPU, but also shines on even a plain CPU setup.

When you open Fritz 17, presuming you have Fat Fritz installed, you will be greeted with a message in the bottom right corner of your screen advising you there is an update available for Fat Fritz.

When you see this click on 'Update Fat Fritz'

Then you will be greeted with the update pane, and just need to click Next to get to it

When Fat Fritz was released with Fritz 17, updates were promised with the assurance it was still improving. Internally the version number of the release was v226, while this newest one is v471.

While thorough testing is always a challenge since resources are limited, a match against Leela 42850 at 1600 nodes per move over 1000 games yielded a positive result:

Score of Fat Fritz 471k vs Leela 42850: +260 -153 =587 [0.553]Elo difference: 37.32 +/- 13.79

1000 of 1000 games finished.

Also, in a match of 254 games at 3m +1s against Stockfish 11 in AlphaZero ratio conditions, this new version also came ahead by roughly 10 Elo.

Still, it isnt about Elo and never was, and the result is merely to say that you should enjoy strong competitive analysis. For one thing, it is eminently clear that while both Leela and Fat Fritz enjoy much of the same AlphaZero heritage,there are also distinct differences in style.

Perhaps one of the most obvious ways to highlight this is just the start position. If you let the engine run for a couple of minutes on decent hardware, it will tell you what it thinks is the best line of play for both White and Black based on its understanding of chess.

As such, I ran Leela 42850 with its core settings to see what it thought. After 2 million nodes it was adamant that perfect chess should take both players down the highly respected Berlin Defence of the Ruy Lopez.

Leela 42850 analysis:

info depth 19 seldepth 56 time 32675 nodes 2181544 score cp 23 hashfull 210 nps 75740 tbhits 0 pv e2e4 e7e5 g1f3 b8c6 f1b5 g8f6 e1g1 f6e4 d2d4 e4d6 b5c6 d7c6 d4e5 d6f5 d1d8 e8d8 h2h3

This is fine, but it is also very much a matter of taste.

Fat Fritz has a different outlook on chess as has already been pointed out in the past. At first it too will show a preference for the Ruy Lopez, though not the Berlin, but given a bit more time by 2.6 million nodes it will declare the best opening per its understanding of chess and calculations is the Sicillian Najdorf.

Within a couple of minutes this is its mainline:

info depth 16 seldepth 59 time 143945 nodes 7673855 score cp 28 wdl 380 336 284 hashfull 508 nps 54227 tbhits 0 pv e2e4 c7c5 g1f3 d7d6 b1c3 g8f6 d2d4 c5d4 f3d4 a7a6 f1e2 e7e5 d4b3 f8e7 e1g1 c8e6 c1e3 e8g8 f1e1 b8c6 h2h3 h7h6 e2f3 a8c8 d1d2 c6b8 a2a4 f6h7 a1d1 b8d7 f3e2 h7f6

From a purely analytical point of view it is quite interesting that it found 10.Re1! in the mainline. In a position where white scores 52.5% on average it picks a move that scores 58.3% / 58.9%.

Remember there is no right or wrong here, but it does help show the natural inclinations of each of these neural networks.

Even if chess is ultimately a draw, that doesnt mean there is only onepath, so while all roads may lead to Rome, they dont all need to pass through New Jersey.

Trying to find the ideal recipe of parameters for an engine can be daunting, and previously multiple attempts had been made with the well-know tuner called CLOP by Remi Coulom. Very recently a completely new tuner 'Bayes-Skopt' was designed byKarlson Pfannschmidt, a PhD student in Machine Learning in Paderborn University inGermany, who goes by the online nickname "Kiudee" (pronounced like the letters Q-D). It was used to find new improved values for Leela, which are now the new defaults.

His tuner is described as "A fully Bayesian implementation of sequential model-based optimization", a mouthful I know, and was set up with his kind help as it ran for over a week. It produces quite fascinating graphical imagery with its updated values. Here is what the final version looked like:

These values, slightly rounded, have been added as the new de facto defaults for Fat Fritz.

This is a completely new neural network trained from Fat Fritz games, but in a much smaller frame. Objectively it is not as strong as Fat Fritz, but it will run much faster, and above all it has the virtue of being quite decent on even a pure CPU machine. It wont challenge the likes of Stockfish, so lets get that out of the way, but in testing on quad-core machines (i.e. my i7 laptop) it defeats Fritz 16 by a healthy margin.

Note that this is not in the product description, soneedless to say, it is more nor less a gift to Fritz 17 owners.

Enjoy it!

More stories on Fat Fritz and Fritz 17...

Read more:
Fat Fritz 1.1 update and a small gift - Chessbase News

Garry Kasparov dominated chess until he was beaten by an IBM supercomputer called Deep Blue in 1997. The event made man loses to computer headlines the world over. Kasparov recently returned to the ballroom of the New York hotel where he was defeated for a debate with AI experts. Wireds Will Knight was there for a revealing interview with perhaps the greatest human chess player the world has ever known.

I was the first knowledge worker whose job was threatened by a machine, says Kasparov, something he foresees coming for us all.

Every technology destroys jobs before creating jobs. When you look at the statistics, only 4 percent of jobs in the US require human creativity. That means 96 percent of jobs, I call them zombie jobs. Theyre dead, they just dont know it. For several decades we have been training people to act like computers, and now we are complaining that these jobs are in danger. Of course they are.

Experts say only about 14 percent of US jobs are at risk of replacement by AI and robots. Nevertheless, Kasparov has some advice for us zombies looking to re-skill.

There are different machines, and it is the role of a human and understand exactly what this machine will need to do its best. ... I describe the human role as being shepherds.

Kasparov, for example, helps Alphabets DeepMind division understand potential weaknesses with AlphaZeros chess play.

The interview also yielded this gem of a quote from Kasparov:

People say, oh, we need to make ethical AI. What nonsense. Humans still have the monopoly on evil. The problem is not AI. The problem is humans using new technologies to harm other humans.

Its a fascinating read and one that should be done in its entirety, if only to find out why Kasparov thinks AI is making chess more interesting, even though humanity doesnt stand a chance of beating it.

See the article here:
Chess champion Garry Kasparov who was replaced by AI says most US jobs are next - The Verge

Upol Ehsan once took a test ride in an Uber self-driving car. Instead of fretting about the empty drivers seat, anxious passengers were encouraged to watch a pacifier screen that showed a cars-eye view of the road: hazards picked out in orange and red, safe zones in cool blue.

For Ehsan, who studies the way humans interact with AI at the Georgia Institute of Technology in Atlanta, the intended message was clear: Dont get freaked outthis is why the car is doing what its doing. But something about the alien-looking street scene highlighted the strangeness of the experience rather than reassuring. It got Ehsan thinking: what if the self-driving car could really explain itself?

The success of deep learning is due to tinkering: the best neural networks are tweaked and adapted to make better ones, and practical results have outpaced theoretical understanding. As a result, the details of how a trained model works are typically unknown. We have come to think of them as black boxes.

A lot of the time were okay with that when it comes to things like playing Go or translating text or picking the next Netflix show to binge on. But if AI is to be used to help make decisions in law enforcement, medical diagnosis, and driverless cars, then we need to understand how it reaches those decisionsand know when they are wrong.

People need the power to disagree with or reject an automated decision, says Iris Howley, a computer scientist at Williams College in Williamstown, Massachusetts. Without this, people will push back against the technology. You can see this playing out right now with the public response to facial recognition systems, she says.

Sign up for The Algorithm artificial intelligence, demystified

Ehsan is part of a small but growing group of researchers trying to make AIs better at explaining themselves, to help us look inside the black box. The aim of so-called interpretable or explainable AI (XAI) is to help people understand what features in the data a neural network is actually learningand thus whether the resulting model is accurate and unbiased.

One solution is to build machine-learning systems that show their workings: so-called glassboxas opposed to black-boxAI. Glassbox models are typically much-simplified versions of a neural network in which it is easier to track how different pieces of data affect the model.

There are people in the community who advocate for the use of glassbox models in any high-stakes setting, says Jennifer Wortman Vaughan, a computer scientist at Microsoft Research. I largely agree. Simple glassbox models can perform as well as more complicated neural networks on certain types of structured data, such as tables of statistics. For some applications that's all you need.

But it depends on the domain. If we want to learn from messy data like images or text, were stuck with deepand thus opaqueneural networks. The ability of these networks to draw meaningful connections between very large numbers of disparate features is bound up with their complexity.

Even here, glassbox machine learning could help. One solution is to take two passes at the data, training an imperfect glassbox model as a debugging step to uncover potential errors that you might want to correct. Once the data has been cleaned up, a more accurate black-box model can be trained.

It's a tricky balance, however. Too much transparency can lead to information overload. In a 2018 study looking at how non-expert users interact with machine-learning tools, Vaughan found that transparent models can actually make it harder to detect and correct the models mistakes.

Another approach is to include visualizations that show a few key properties of the model and its underlying data. The idea is that you can see serious problems at a glance. For example, the model could be relying too much on certain features, which could signal bias.

These visualization tools have proved incredibly popular in the short time theyve been around. But do they really help? In the first study of its kind, Vaughan and her team have tried to find outand exposed some serious issues.

The team took two popular interpretability tools that give an overview of a model via charts and data plots, highlighting things that the machine-learning model picked up on most in training. Eleven AI professionals were recruited from within Microsoft, all different in education, job roles, and experience. They took part in a mock interaction with a machine-learning model trained on a national income data set taken from the 1994 US census. The experiment was designed specifically to mimic the way data scientists use interpretability tools in the kinds of tasks they face routinely.

What the team found was striking. Sure, the tools sometimes helped people spot missing values in the data. But this usefulness was overshadowed by a tendency to over-trust and misread the visualizations. In some cases, users couldnt even describe what the visualizations were showing. This led to incorrect assumptions about the data set, the models, and the interpretability tools themselves. And it instilled a false confidence about the tools that made participants more gung-ho about deploying the models, even when they felt something wasnt quite right. Worryingly, this was true even when the output had been manipulated to show explanations that made no sense.

To back up the findings from their small user study, the researchers then conducted an online survey of around 200 machine-learning professionals recruited via mailing lists and social media. They found similar confusion and misplaced confidence.

Worse, many participants were happy to use the visualizations to make decisions about deploying the model despite admitting that they did not understand the math behind them. It was particularly surprising to see people justify oddities in the data by creating narratives that explained them, says Harmanpreet Kaur at the University of Michigan, a coauthor on the study. The automation bias was a very important factor that we had not considered.

Ah, the automation bias. In other words, people are primed to trust computers. Its not a new phenomenon. When it comes to automated systems from aircraft autopilots to spell checkers, studies have shown that humans often accept the choices they make even when they are obviously wrong. But when this happens with tools designed to help us avoid this very phenomenon, we have an even bigger problem.

What can we do about it? For some, part of the trouble with the first wave of XAI is that it is dominated by machine-learning researchers, most of whom are expert users of AI systems. Says Tim Miller of the University of Melbourne, who studies how humans use AI systems: The inmates are running the asylum.

This is what Ehsan realized sitting in the back of the driverless Uber. It is easier to understand what an automated system is doingand see when it is making a mistakeif it gives reasons for its actions the way a human would. Ehsan and his colleague Mark Riedl are developing a machine-learning system that automatically generates such rationales in natural language. In an early prototype, the pair took a neural network that had learned how to play the classic 1980s video game Frogger and trained it to provide a reason every time it made a move.

Upol Ehsan

To do this, they showed the system many examples of humans playing the game while talking out loud about what they were doing. They then took a neural network for translating between two natural languages and adapted it to translate instead between actions in the game and natural-language rationales for those actions. Now, when the neural network sees an action in the game, it translates it into an explanation. The result is a Frogger-playing AI that says things like Im moving left to stay behind the blue truck every time it moves.

Ehsan and Riedls work is just a start. For one thing, it is not clear whether a machine-learning system will always be able to provide a natural-language rationale for its actions. Take DeepMinds board-game-playing AI AlphaZero. One of the most striking features of the software is its ability to make winning moves that most human players would not think to try at that point in a game. If AlphaZero were able to explain its moves, would they always make sense?

Reasons help whether we understand them or not, says Ehsan: The goal of human-centered XAI is not just to make the user agree to what the AI is sayingit is also to provoke reflection. Riedl recalls watching the livestream of the tournament match between DeepMind's AI and Korean Go champion Lee Sedol. The commentators were talking about what AlphaGo was seeing and thinking. "That wasnt how AlphaGo worked," says Riedl. "But I felt that the commentary was essential to understanding what was happening."

What this new wave of XAI researchers agree on is that if AI systems are to be used by more people, those people must be part of the design from the startand different people need different kinds of explanations. (This is backed up by a new study from Howley and her colleagues, in which they show that peoples ability to understand an interactive or static visualization depends on their education levels.) Think of a cancer-diagnosing AI, says Ehsan. Youd want the explanation it gives to an oncologist to be very different from the explanation it gives to the patient.

Ultimately, we want AIs to explain themselves not only to data scientists and doctors but to police officers using face recognition technology, teachers using analytics software in their classrooms, students trying to make sense of their social-media feedsand anyone sitting in the backseat of a self-driving car. Weve always known that people over-trust technology, and thats especially true with AI systems, says Riedl. The more you say its smart, the more people are convinced that its smarter than they are.

Explanations that anyone can understand should help pop that bubble.

See more here:
Why asking an AI to explain itself can make things worse - MIT Technology Review

To some this will sound like a nanny-state hellscape, and Susskind does not shy from calling his proposed solution The Big State. He does not, however, go into detail about how exactly the community will decide which activities are worthy of payment. Perhaps we will be subject to the tyranny of a slim majority that decides dog-breeding, classical music or literary criticism are valueless activities, in which case no one will ever do them again.

But the moral objection to UBI that it will encourage laziness and anomie is always at bottom a puritan condescension. If one asked Susskind whether, if he never had to worry about money, he would just spend all day watching reruns of Bake Off and slumping into potato-ish ennui, he would probably deny it. So why assume it of everyone else?

As it turns out, Bertrand Russell anticipated this objection 90 years ago: It will be said that while a little leisure is pleasant, men would not know how to fill their days if they had only four hours work out of the 24. Insofar as this is true in the modern world it is a condemnation of our civilisation; it would not have been true at any earlier period. There was formerly a capacity for light-heartedness and play which has been to some extent inhibited by the cult ofefficiency.

Modern sceptics might still dismiss Russells argument as a Fabian pipe-dream, but the cult of efficiency is still very much abroad, and it is indeed what is driving the race to automation. Susskinds careful analysis shows that it will be an increasingly unignorable problem, even if his proposed solution will not convince everyone. At the last gasp, he even drops in the alarming recommendation that our future politicians should guide us on what it means to live a flourishing life, in the face of which prospect one might after all be happier to resign oneself to a robot apocalypse.

A World Without Work is published by Allen Lane at 20. To order your copy for 16.99, call 0844 871 1514 or visit the Telegraph Bookshop

View original post here:
What will happen when robots have taken all the jobs? - Telegraph.co.uk

Last month, researchers at OpenAI in San Francisco revealed an algorithm capable of learning, through trial and error, how to manipulate the pieces of a Rubik's Cube using a robotic hand. It was a remarkable research feat, but it required more than 1,000 desktop computers plus a dozen machines running specialized graphics chips crunching intensive calculations for several months.

The effort may have consumed about 2.8 gigawatt-hours of electricity, estimates Evan Sparks, CEO of Determined AI, a startup that provides software to help companies manage AI projects. Thats roughly equal to the output of three nuclear power plants for an hour. A spokesperson for OpenAI questioned the calculation, noting that it makes several assumptions. But OpenAI declined to disclose further details of the project or offer an estimate of the electricity it consumed.

Artificial intelligence routinely produces startling achievements, as computers learn to recognize images, converse, beat humans at sophisticated games, and drive vehicles. But all those advances require staggering amounts of computing powerand electricityto devise and train algorithms. And as the damage caused by climate change becomes more apparent, AI experts are increasingly troubled by those energy demands.

The concern is that machine-learning algorithms in general are consuming more and more energy, using more data, training for longer and longer, says Sasha Luccioni, a postdoctoral researcher at Mila, an AI research institute in Canada.

Its not just a worry for academics. As more companies across more industries begin to use AI, theres growing fear that the technology will only deepen the climate crisis. Sparks says that Determined.ai is working with a pharmaceutical firm thats already using huge AI models. As an industry, its worth thinking about how we want to combat this, he adds.

Some AI researchers are thinking about it. Theyre using tools to track the energy demands of their algorithms, or taking steps to offset their emissions. A growing number are touting the energy efficiency of their algorithms in research papers and at conferences. As the costs of AI rise, the AI industry is developing a new appetite for algorithms that burn fewer kilowatts.

The concern is that machine-learning algorithms in general are consuming more and more energy, using more data, training for longer and longer.

Sasha Luccioni, Mila

Luccioni recently helped launch a website that lets AI researchers roughly calculate the carbon footprint of their algorithms. She is also testing a more sophisticated approachcode that can be added to an AI program to track the energy use of individual computer chips. Luccioni and others are also trying to persuade companies that offer tools for tracking the performance of code to include some measure of energy or carbon footprint. Hopefully this will go toward full transparency, she says. So that people will include in the footnotes we emitted X tons of carbon, which we offset.

The energy required to power cutting-edge AI has been on a steep upward curve for some time. Data published by OpenAI shows that the computing power required for key AI landmarks over the past few years, such as DeepMinds Go-playing program AlphaZero, has doubled roughly every 3.4 monthsincreasing 300,000 times between 2012 and 2018. Thats faster than the rate at which computing power historically increased, the phenomenon known as Moores Law (named after Gordon Moore, cofounder of Intel.)

Recent advances in natural language processingan AI technique that helps machines parse, interpret, and generate texthave proven especially power-hungry. A research paper from a team at UMass Amherst found that training a single large NLP model may consume as much energy as a car over its entire lifetimeincluding the energy needed to build it.

Training a powerful machine-learning algorithm often means running huge banks of computers for days, if not weeks. The fine-tuning required to perfect an algorithm, by for example searching through different neural network architectures to find the best one, can be especially computationally intensive. For all the hand-wringing, though, it remains difficult to measure how much energy AI actually consumes, and even harder to predict how much of a problem it could become.

Read more:
AI Can Do Great Thingsif It Doesn't Burn the Planet - WIRED