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Humans are active listeners; we create meaning where there is none, or none intended. It is not that the octopuss utterances make sense, but rather that the islander can make sense of them, Bender says.

For all their sophistication, todays AIs are intelligent in the same way a calculator might be said to be intelligent: they are both machines designed to convert input into output in ways that humanswho have mindschoose to interpret as meaningful. While neural networks may be loosely modeled on brains, the very best of them are vastly less complex than a mouses brain.

And yet, we know that brains can produce what we understand to be consciousness. If we can eventually figure out how brains do it, and reproduce that mechanism in an artificial device, then surely a conscious machine might be possible?

When I was trying to imagine Roberts world in the opening to this essay, I found myself drawn to the question of what consciousness means to me. My conception of a conscious machine was undeniablyperhaps unavoidablyhuman-like. It is the only form of consciousness I can imagine, as it is the only one I have experienced. But is that really what it would be like to be a conscious AI?

Its probably hubristic to think so. The project of building intelligent machines is biased toward human intelligence. But the animal world is filled with a vast range of possible alternatives, from birds to bees to cephalopods.

A few hundred years ago the accepted view, pushed by Ren Descartes, was that only humans were conscious. Animals, lacking souls, were seen as mindless robots. Few think that today: if we are conscious, then there is little reason not to believe that mammals, with their similar brains, are conscious too. And why draw the line around mammals? Birds appear to reflect when they solve puzzles. Most animals, even invertebrates like shrimp and lobsters, show signs of feeling pain, which would suggest they have some degree of subjective consciousness.

But how can we truly picture what that must feel like? As the philosopher Thomas Nagel noted, it must be like something to be a bat, but what that is we cannot even imaginebecause we cannot imagine what it would be like to observe the world through a kind of sonar. We can imagine what it might be like for us to do this (perhaps by closing our eyes and picturing a sort of echolocation point cloud of our surroundings), but thats still not what it must be like for a bat, with its bat mind.

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What would it be like to be a conscious AI? We might never know. - MIT Technology Review

Press release

For the first time, the internationally renowned chess tournament Dortmund Chess Days will host a special match between legends Vladimir Kramnik and Viswanathan Anand playing the No-Castlingchess format. The rich, creative possibilities of this chess variant were recently explored by the ground-breaking artificial intelligence system AlphaZero, created by world-leading AI company DeepMind. Online audiences will now get to experience novel AlphaZero insights first hand in the post-match commentary of the no castling tournament, as part of DeepMinds support for Dortmund Chess Days.

Demis Hassabis [pictured], DeepMind Founder and CEO, says:

Its been incredibly exciting to see world-class players like Vladimir Kramnik use AlphaZero to explore new possibilities for the game of chess. Im looking forward to seeing two former world champions play the no castling variant in Dortmund and hope the games - and AlphaZeros insights - inspire chess players everywhere.

Event director Carsten Hensel said:

The Dortmund Chess Days has found a growing audience online, which we would like to develop in the coming years. We hope the inclusion of the no-castling variant and the inclusion of cutting-edge AI analysis from AlphaZero will further reinforce Dortmund as a ground-breaking tournament and will attract people from around the world to watch what will surely be a remarkable and historic match.

The tournament will see Vladimir Kramnik and Viswanathan Anand play four games against each other with classical thinking time, but with no option of castling. This tiny rule change will force the players to deviate from memorized opening lines, encouraging new creative play without deviating from the games familiar rules and patterns.

Plans for the 2022 event, which will be sponsored by DeepMind, are already underway with the hope that the no-castling format will become a regular fixture and an audience favourite, thanks to dynamic and entertaining play.

Master Class Vol. 12: Viswanathan Anand

This DVD allows you to learn from the example of one of the best players in the history of chess and from the explanations of the authors how to successfully organise your games strategically, consequently how to keep your opponent permanently under press

Anand and Kramnik playing the World Chess Championship in 2008

DeepMind is a multidisciplinary team of scientists, engineers, machine learning experts and more, working together to research and build safe AI systems that learn how to solve problems and advance scientific discovery for all.

Having developed AlphaGo, the first program to beat a world champion at the complex game of Go, DeepMind has published over 1000 research papers including more than a dozen in the journals Nature and Science and achieved breakthrough results in many challenging AI domains from StarCraft II to protein folding.

DeepMind was founded in London in 2010, and joined forces with Google in 2014 to accelerate its work. Since then, its community has expanded to include teams in Alberta, Montreal, Paris, New York and Mountain View in California.

The purpose of the association founded in the year 2019 is the promotion of chess. IPS is realizing this goal by the organization of chess events in the fields of sport, art, science, education, cultural and chess history. The outstanding project of the IPS is the Sparkassen Chess Trophy International Dortmund Chess Days, with its famous history since 1973. TheIPS is developing a modern concept and pay considerable attention to the digital requirements of today, especially regarding the topic of chess and its modern development.

Master Class Vol.11: Vladimir Kramnik

This DVD allows you to learn from the example of one of the best players in the history of chess and from the explanations of the authors (Pelletier, Marin, Mller and Reeh) how to successfully organise your games strategically, consequently how to keep y

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AlphaZero to analyse no-castling match of the champions - Chessbase News

Writer's block is too often a big impediment to effective copy, which means it lowers a scribe's performance in the eyes of a client or employer. Every writer may go through it, but there aremore demands placed on storytellers given today's breakneck speed of digital marketing and social media.

Copy.ai taps into the power of artificial intelligence (A.I.) to give professional wordsmiths, editors, marketers, and even students the ability to review several written versions of what they'd like to write about to overcome the psychological barrier of writer's block. This tool also eliminates those annoying errors and redundant phrases that glare at discerning readers.

Chris Lu and Paul Yacoubian founded Copy.ai to give content creators an ability to optimize written text. And democratize access to creativity.

Having tried it, I found the A.I.-powered tool turns concepts into conversational and relatable text. The site can optimize messages including product descriptions, blogs, social media posts, landing pages, and everything else with text.

A user types a description and the tool generates almost a dozen versions of possible headlines, intros, and bodies, and even Valentine's Day greetings. For example, the A.I. can ideate different versions of a paragraph from which to choose. Even if you only input a few words to describe the subject matter.

Interestingly, the tool seems like a godsend for procrastinating students who pull all-nighters.

Disrupting a shrinking industry

According to the Bureau of Labor Statistics, there are 131,200 writers and authors in the United States. The average wage is $30 an hour.

There is an expected 2 percentdecline in employment (equaling 3,100 fewer jobs) from 2019 to 2029.

The job losses may be exacerbated by A.I. and machine learning, since innovators are training these emerging technologies to completely replace human scribes. Whether that's possible remains to be seen. (This writer thinks that will definitely happen sooner or later.)

About six or seven years ago, there were primitive tools that attempted to rewrite communications that were copied and pasted from other websites. These were intended to pass plagiarism checks. But the tech in those days produced such bad output as to make them unusable.

Fast-forward to today and A.I. wordsmiths can now craft intelligent phrases that seem more humanly than your average scribe's. Therefore, the future of copywriting is here.

Just watch how IBM Watson destroyed human competitors on Jeopardy. And that took place a decade ago. Similarly, it's now almost impossible for the best chess players to defeat Google's AlphaZero.

It may be humbling, but truth is truth.

The human touch

In music, an emotive rendition of The Sound of Music or other classics makes the sheet come alive. Can computers match the rhythmic artistry of the masters? Time will tell.

When it comes to writing styles, there's an overwhelming preference for simple, digestible language. Nonfiction is the dominant force that has drastically reduced the existence of fiction-writing artistry.

With Copy.ai, it's difficult for an editor or audience to determine that the communication they're seeing on a device screen is crafted by a nonsentient entity. The output text is relatable, which creates the illusion of a personal touch. The A.I.'s phrases and syntax are not mechanical at all.

"Converting ideas into text is where this magic happens.I can see this supporting peoplewhosefirst language might not be Englishby validating their ideas. They often know exactly what to say but are unsure how it might land. Having a variety of options will help them craft the perfect piece. --Tarik Sehovic, growth adviser, Copy.Ai

A natural progression

One thing is certain: Brands, marketers, and advertisers will love the increasing capabilities of A.I. and machine learning.

Should copywriters and editors fear the same? They should remember that readers are their customers and A.I. tools lead to better drafts. Audiences are short on time and therefore impatient with badly written text.

Writers and authors must adapt with the times or risk being relegated to a bygone era. A consumer-facing wordsmith should use the best tools for engaging audiences.

There's a reason why stone tablets, scrolls, pencils, and typewriters are obsolete. These don't add enough value in the Information Age. What does add value is efficient and effective messages that are consumed by target demographics.

Cognitive systems are radically changing what we think of as "content." And traditional forms are being eclipsed by smarter, interactive mediums.

The opinions expressed here by Inc.com columnists are their own, not those of Inc.com.

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How This Startup Aims to Disrupt Copywriting Forever - Inc.

With many of us stuck at home this past year, weve seen a surge in the popularity of video games. That trend hasnt been limited to humans. DeepMind and Google AI both released results from their Atari playing AIs, which have taught themselves to play over fifty Atari games from scratch, with no provided rules or guidelines. The unique thing about these new results is how general the AI agent is. While previous efforts have achieved human performance on the games they were trained to play, DeepMinds new AI Agent, MuZero could teach itself to beat humans at Atari games it had never encountered in under a day. If this reminds you of AlphaZero which taught itself to play Go then Chess well enough to outperform world champions, thats because it demonstrates an advance in the same suite of algorithms, a class of machine learning called Reinforcement Learning (RL).

While traditional machine learning parses out its model of the world (typically a small world pertaining only to the problem its designed to solve) from swathes of data, RL is real-time observation based. This means RL learns its model primarily through trial and error interactions with its environment, not by pulling out correlations from data representing a historical snapshot of it. In the RL framework, each interaction with the environment is an opportunity to build towards an overarching goal, referred to as a reward. An RL agent is trained to make a sequence of decisions on how to interact with its environment that will ultimately maximize its reward (i.e. help it win the game).

This unique iterative learning paradigm allows the AI model to change and adapt to its environment, making RL an attractive solution for open-ended, real-world problem-solving. It also makes it a leading candidate for artificial general intelligence (AGI) and has some researchers concerned about the rise of truly autonomous AI that does not align with human values. Nick Bostrom first posed what is now the canonical example of this risk among AI Safety researchers a paperclip robot with one goal: optimize the production efficiency of paperclips. With no other specifications, the agent quickly drifts from optimizing its own paperclip factory to commandeering food production supply chains for the paperclip making cause. It proceeds to place paperclips above all other human needs until all thats left of the world is a barren wasteland covered end to end with unused paper clips. The takeaway? Extremely literal problem solving combined with inaccurate problem definition can lead to bad outcomes.

This rogue AGI (albeit in more high-stakes incarnations like weapons management) is the type of harm usually thought of when trying to make RL safe in the context of society. However, between an autonomous agent teaching itself games in the virtual world and an intelligent but misguided AI putting humanity in existential risk lay a multitude of sociotechnical concerns. As RL is being rolled out in domains ranging from social media to medicine and education, its time we seriously think about these near-term risks.

How the paperclip problem will play out in the near term is likely to be rather subtle. For example, medical treatment protocols are currently popular candidates for RL modeling; they involve a series of decisions (which treatment options to try) with uncertain outcomes (different options work better for different people) that all connect to the eventual outcome (patient health). One such study tried to identify the best treatment decisions to avoid sepsis in ICU patients based off of multitudes of data, including medical histories, clinical charts and doctors notes. Their first iteration was an astounding success. With very high accuracy, it identified treatment paths that resulted in patient death. However, upon further examination and consultation with clinicians it turned out that though the agent had been allowed to learn from a plethora of potentially relevant treatment considerations, it had latched onto only one main indicator for death whether or not a chaplain was called. The goal of the system was to flag treatment paths that led to deaths, and in a very literal sense thats what it did. Clinicians only called a chaplain when a patient presented as close to death.

Youll notice that in this example, the incredibly literal yet unhelpful solution the RL agent was taking was discovered by the researchers. This is no accident. The field of modern medicine is built around the reality that connections between treatment and outcomes typically have no known causal explanations. Aspirin, for example, was used as an anti-inflammatory for over seventy years before we had any insight into why it worked. This lack of causal understanding is sometimes referred to as intellectual debt; if we cant describe why something works, we may not be able to predict when or how it will fail. Medicine has grown around this fundamental uncertainty. Through strict codes of ethics, industry standards, and regulatory infrastructure (i.e. clinical trials), the field has developed the scaffolding to minimize the accompanying harms. RL systems aiming to help with diagnosis and treatment have to develop within this infrastructure. Compliance with the machinery medicine has around intellectual debt is more likely to result in slow and steady progress, without colossal misalignment. This same level of oversight does not apply to fields like social media, the potential harms of which are hard to pin down and which have virtually no regulatory scaffolding in place.

We may have already experienced some of the early harms of RL based algorithms in complex domains. In 2018 YouTube engineers released a paper describing an RL addition to their recommendation algorithm that increased daily watch time by 6 million hours in the beta testing phase. Meanwhile, anecdotal accounts of radicalization through YouTube rabbit holes of increasingly conspiratorial content (e.g., NYTimes reporting on YouTubes role in empowering Brazils far right) were on the rise. While it is impossible to know exactly which algorithms powered the platforms recommendations at the time, this rabbit hole effect would be a natural result of an RL algorithm trying to maximize view time by nudging users towards increasingly addictive content.

In the near future, dynamic manipulation of this sort may end up at odds with established protections under the law. For example, Facebook has recently been put under scrutiny by the Department of Housing and Urban Development for discriminatory housing advertisements. The HUD suit alleges that even without explicit targeting filters that amount to the exclusion of protected groups, its algorithms are likely to hide ads from users whom the system determines are unlikely to engage with the ad, even if the advertiser explicitly wants to reach those users. Given the types of (non-RL) ML algorithms FB currently uses in advertising, proving this disparate impact would be a matter of examining the data and features used to train the algorithm. While the current lack of transparency makes this challenging, it is fundamentally possible to roll out benchmarks capable of flagging such discrimination.

If advertising were instead powered by RL, benchmarks would not be enough. An RL advertising algorithm tasked with ensuring it does not discriminate against protected classes, could easily end up making it look as though it were not discriminating instead. If the RL agent were optimized for profit and the practice of discrimination was profitable, the RL agent would be incentivized to find loopholes under which it could circumvent protections. Just like in the sepsis treatment case, the system is likely to find a shortcut towards reaching its objective, only in this case the lack of regulatory scaffolding makes it unlikely this failure will be picked up. The propensity of RL to adapt to meet metrics, while skirting over intent, will make it challenging to tag such undesirable behavior. This situation is further complicated by our heavy reliance on data as a means to flag potential bias in ML systems.

Unlike RL, traditional machine learning is innately static; it takes in loads of data, parses it for correlations, and outputs a model. Once a system has been trained, updating it to accommodate a new environment or changes to the status quo requires repeating most or all of that initial training with updated data. Even for firms that have the computing power to make such retraining seamless, the reliance on data has allowed an in for transparency. The saying goes, machine learning is like money laundering for bias. If an ML system is trained using biased or unrepresentative data, its model of the world will reflect that. In traditional machine learning, we can at least follow the marked bills and point out when an ML system is going to be prone to discrimination by examining its training data. We may even be able to preprocess the data before training the system in an attempt to preemptively correct for bias.

Since RL is generally real-time observation-based rather than training data-based, this follow-the-data approach to algorithmic oversight does not apply. There is no controlled input data to help us anticipate or correct for where an RL system can go wrong before we set it loose in the world.

In certain domains, this lack of data-born insight may not be too problematic. The more we can specify what the moving parts of a given application are and the ways in which they may failbe it through an understanding of the domain or regulatory scaffoldingthe safer it is for us to use RL. DeepMinds use of RL to lower the energy costs of its computing centers, a process ultimately governed by the laws of physics, deserves less scrutiny than the RL based K-12 curriculum generator Googles Ed Chi views as a near-term goal of the field. The harder it is to describe what success looks like within a given domain, the more prone to bad outcomes it is. This is true of all ML systems, but even more crucial for RL systems that cannot be meaningfully validated ahead of use. As regulators, we need to think about which domains need more regulatory scaffolding to minimize the fallout from our intellectual debt, while allowing for the immense promise of algorithms that can learn from their mistakes.

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Between Games and Apocalyptic Robots: Considering Near-Term Societal Risks of Reinforcement - Medium

Today's programs are all so strong that they seem to really differ in the details more often than in a decisive statement of strength, and there is no question that when arguing the differences at the stratosphere, it seems almost ludicrous. Engine A is 3568 Elo, while Engine B is inferior because it is only 3565 Elo. So stated by the humans all hovering under 2800 barring a small fistful.

Still, the game made such a powerful impression on Peter Graysonthat he declared,

"Considering the fast time control that was quite amazing by Fat Fritz 2 and its subtlety was of a sophistication I would associate more with the human mind than an engine particularly for the follow up that confirmed the engine can execute a long term strategy.Perhaps the Fritz network does provide a more human rather than mechanical, logical approach?"

The game starts quietly, almost innocuously. An English line that has seen proponents on bothsides at the highest echelons.

Yet by move 12 they had both left most of the known cases behind, with only an Italian correspondence game cited in Mega 2021. The key move that incited so much enthusiasm and which got Black into such a dangerous situation came here:

"On the face of it this looks to be in line with the idea of controlling an open file where the controlling side tends to have an advantage.The following moves question that idea when it is a wing file and also whether it is advisable for the queen to lead on the rank that will likely be the first piece to come under attack. White's reply may not be immediately obvious until it is seen and few other engines find it, certainly not within the context of the game."

"How important this move was to the outcome of the game should not be understated. With Black seeking to gain control of the open a-file, suddenly the queen looks cut off and potentially a liability. That is the theme of the ensuing moves. Perhaps it deserves !!! What is fascinating is that Fat Fritz 2 exhibits almost human-like qualities to threaten the snaring of the queen."

While Black does manage to avoid the outright loss of the queen, it comes at a heavy price that ultimately costs the game.

White now threatens to win the queen in two moves with 30. b4 a4 31. a4. Black avoids this fate by giving up the exchange, but this in itself proves fatal.

Fat Fritz 2

Fat Fritz 2.0 is the successor to the revolutionary Fat Fritz, which was based on the famous AlphaZero algorithms. This new version takes chess analysis to the next level and is a must for players of all skill levels.

Here is the full game with the generous comments by Peter Grayson.

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Trapping the queen - Chessbase News