With the world spinning faster every day and delivering an insane amount of news and information to process, the temptation to cherry-pick content to consume increases. Powering summarization with AI tools is among the news we’re covering in this edition of AI Monthly Digest.

Other stories include the proof of deep learning supremacy in the world of board games, biased machines and what comes after Atari and board games.

TL;DR not working (yet)

A research team from the Allen Institute for Artificial Intelligence and the University of Washington has created a scientific summarization engine. The key goal was to check if an AI-based solution could shorten a text and capture the most essential information from a scientific paper. Including the most important information while also providing enough context to make the piece comprehensible is a challenging task even for a human. That the text needs to be grammatically correct and as interesting as possible makes the challenge all the more difficult.

TL;DR – it didn’t work. Yet. For all the details, see the Arxiv paper.

Why it matters

While summarization engines aren’t yet entirely feasible, polish applied over the next few years will lead them to eventually shine.

According to the research done at the University of Ottawa, the number of scientific papers published since 1665 surpassed the 50 million mark in 2009. Add to that the approximately 2.5 million new papers published every year, and you quickly see that staying up to date in any field, let alone many, is only getting harder.

Also, we are beset by news on all sides today. To wade through the overload and get results, people need to manage their attention carefully. That’s why a good, automated summarization engine can be a service we increasingly need not only in science but also our daily lives.

Tackling biased language models

“Thrash in, thrash out,” data scientists will say from time to time, alluding to the quality of an AI model or prediction, which can only be as good as the dataset the model was provided with.

A dataset can be full of hidden or obscure misinformation that a human supervisor usually misses or considers irrelevant given the knowledge they have already gathered. A good example of a model gone bad is the one Amazon AI used to analyze applications for engineering roles, which turned out to be biased against female engineers. Analyzing the profiles of the then current engineers, the model identified significantly fewer females among them, and concluded that women can’t code. For a human, this is obvious nonsense, but with AI there is no such thing as common sense.

Considering that, it is not surprising that research is being done to tackle bias in Natural Language Processing. A consortium from MIT, Intel, and the Montreal Institute for Learning Algorithms (MILA) has come up with a way to evaluate NLP models in a more disciplined and structured way. Their StereoSet is a dataset that measures typical biases in English to check if a model is truly neutral toward its subject. The research is available on Arxiv.

Why it matters

Apart from the fact that any discrimination is evil, a biased model comes with multiple disadvantages. Coming back to the example of Amazon, nobody knows how many talented female coders the company’s model caused to be dismissed. Biases in language processing hurt society, business, and companies in multiple ways, and reducing these biases is one of the most significant challenges to overcome in the near future.

AI playing GameBoy games

An iconic gadget of the late twentieth century, Game Boy and Game Boy Color made it into the palms and pockets of a lot of kids worldwide–118 million, to be exact. The platform handled multiple types of portable games, including simple platform games like Super Mario and more sophisticated RPG-like experiences like the Pokémon series.

Long story short, the platform delivers significantly more advanced games while keeping the graphic environment simple. Today, this is a good training sandbox for reinforcement learning models.

Why it matters

Reinforcement learning shines in multiple classes of problems, especially when there is no straightforward  way to solve them. But the point is in delivering an environment where the model can encounter various challenges while keeping the simulator relatively lightweight. Atari is currently a standard sandbox for testing various approaches, even as the needs and classes of problems to solve evolve.

Delivering a GameBoy simulator suitable for running AI experiments is an interesting way to broaden the research. The software is available on GitHub.

It’s official – deep learning supremacy in board games

The supremacy of deep learning in board games is now a fact. Leela Chess Zero has won the latest Top Chess Engine Championship, formerly known as the Thoresen Chess Engines Competition. The tournament has been run since 2010 and is considered an unofficial computer chess championship.

The winner of the 2019 edition was Stockfish, an open-source chess engine that is not powered by ML-based techniques and utilizes more traditional ways of playing chess. Here, “more traditional” doesn’t mean ineffective – no human has beaten Stockfish.

After just ten months of development with deep learning, LCZ  beat Stockfish twice – in the first half of 2019, and again this year. Stockfish was initially published in 2008, giving it 12 years of constant development, and in 2019 it remained the champ, but only barely, squeaking out a 50.5 to 49.5 advantage. During the recent faceoff, however, Leela Chess Zero defeated Stockfish 52.5-47.5 in 100 matches.

To give some scale, a typical non-professional player has a chess ranking (so called ELO rating system) of 1000 points. A talented non-professional can reach 2000 points. The current world champion, Magnus Carlsen, has about 2900 points.

Leela and Stockfish, meanwhile, are both over 3800 – far beyond human reach.

Why it matters

First of all, this isn’t only about chess. Apart from some hermetic insight on particular moves or ways to overcome interesting chess situations, most of the moves are incomprehensible for lay observers. Appreciating the strategy and tactics behind each move requires a rare level of chess mastery.

So in fact, the match was between two approaches to solving problems in computer science– traditional coding versus deep learning, the latter of which is superior in playing chess.

As we predicted in our AI Trends 2020, NLP is the year’s leading trend. But research on self-regenerating structures has likewise been both surprising and fascinating. February was rich in news on self-regenerating machines and NLP-related events and research.

Microsoft trains T-NLG, a 17-billion-parameter language model

The tech behemoths are racing to build larger and more efficient language models, which are both costly and technologically challenging, if not daunting. Microsoft is a relatively new player in the game, and follows in the footsteps of Google with itsBERT model and OpenAI, which brought outGPT-2.

While Microsoft may not have been the first mover, its Turing Natural Language Generation (T-NLG) is now the largest model trained and published for language generation. It continues the line of work begun by BERT and GPT-2. Increasing neural network size once again proved successful. After pre-training on essentially all text from the Internet, T-NLG can be fine-tuned to solve various downstream tasks like answering questions, understanding documents or simply filling in as a conversational agent (like Meena from our last post). In order to achieve this new state of the art in large-scale deep learning, Microsoft came up with novel optimization techniques and tricks that make it feasible to train artificial neural networks with over 100 billion parameters.

To put this number into perspective: the human brain has around 100 trillion synapses (in a nutshell, their job is to connect neurons). That’s a thousand times more than 100 billion. Does this mean we can now train artificial neural networks that are 0.1% size of the human brain? Definitely not, biological neurons are much more complex than artificial ones and the recent discovery shows that a single biological neuron is able to compute XOR, a mathematical function that requires a multilayer artificial neural network to be computed.

Why it matters

With industry heavily focused and invested in NLP, the technology is one of deepsense.ai’s cutting edge trends in 2020. The new models being delivered support business and consumer services like chatbots and search engines.

Given these developments, any bigger and newer language model brings us closer to the day when natural communication between computers and humans a la Star Trek will be possible. In decidedly less galactic terms, it is also a convenient way to communicate with a computer when your hands are otherwise occupied – like when you’re cooking or driving a car. A simple “skip that song” makes a difference.

Growing Neural Cellular Automata

Apparently nature is the most brilliant engineer of all, with its unrivaled ability to reuse and repurpose building blocks to deliver a breathtaking set of incredibly diverse forms. With cells being the most basic building block of every creature alive, from the lowly germ to humans, it is both fascinating and inspiring to crack the process of making a single cell that not only functions but is specialized in a particular context.

Putting it simply – a single cell can be a separate organism or only a tiny part of a larger structure, designed to perform a specific role, without itself having the ability to survive. Apart from that, living organisms can regenerate themselves, sometimes regrowing lost limbs, sometimes just healing wounds. Cracking this process would deliver a significant boost for engineering.

What happened exactly?

Every living cell contains a DNA code that contains all the information about the organism in which it lives–its height, eye color, and other factors. In terms of programming, it can be seen as a list of instructions containing if-then statements, which tell the cell how it should operate in its given context and surroundings.

Research published on distill.pub tackles the matter by delivering images built with single “cells” that contain DNA-like information which, when rolled out using local updates, converges to a full image in global scale. Some parts of the image can be destroyed, and the image will rebuild itself with the lifelike process of spawning new cells to replace destroyed ones.

Also, more images can be spawned and built near the original one, effectively blending them to observe how cells interact with the context. In some cases, when blended and destroyed, the images rebuild in odd ways, delivering imperfect or twisted versions of previous projects. Scar tissue of some kind.

Why it matters

Currently, it is hard to come up with a practical application, but it brings science-fiction-like prospects that could result in unimaginable progress. Just to name a few:

Building megastructures – be it a Death Star, a Dyson Sphere or a planet-wide supercolony, making a centralized program to control the whole system might be challenging due to the very scale of that system and the latencies within it.

Also, when delivering a “smart gravel” that transforms into the desired type of material or form you need to build the structure, managing the construction or repairs would be much easier. A car wouldn’t have a spare tire and a toolbox, but a box containing smart-gravel (or any other currently non-existing smart material) infused with the DNA-like code of the car and used to repair what’s broken.

But that’s a distant future and practical appliances are yet to come – not unlike when neural networks and machine learning started from the creation of a computational model for neural networks in 1943. The technology is now transforming our daily lives in the form of machine learning, reinforcement learning or any other paradigm used to infuse our tools.

These lyrics don’t exist

The final news is more lifestyle related and lighter in mood than the rest. After the popularity of thispersondoesnotexist.com, the time has come for song lyrics. Pop on over to theselyriscsdontexist.com and see how you  can choose the mood, theme and musical style–then read the lyrics the AI spits out. Perhaps a happy heavy metal song about eating burritos would suit your fancy? No problem.  Or would a sad pop ballad about a steam locomotive be more to your liking? That’s no problem, either.

Why does it matter

It doesn’t, but data science is also about fun and neural networks can power both inspiring and powerful tech as well as silly toys. Because why not?

From the largest conference to a tiny-yet-significant project delivering an AI-powered role-playing game master, December delivered an interesting end to 2019.

The most important news in December was the NeurIPS conference, the most significant scientific event related to machine learning and neural networks.

NeurIPS 2019

Thanks to the Neural Information Processing Systems Conference, December is one of the most important months of the year for AI. The event draws together over 15,000 participants including scientists, researchers, business people and AI enthusiasts of all stripes. The 2019 event took place in Vancouver, BC from Sunday, December 8 to Saturday, December 14.

The conference is massive, with over a thousand papers accepted, multiple workshops addressing key trends and researchers from around the world exchanging knowledge and experiences.

Together with Volkswagen, deepsense.ai delivered a paper on training autonomous cars in a simulated environment and implementing the model in a real car. The research was a breakthrough and opens up interesting directions for further exploration. The paper was presented during a workshop at the conference.

Why it matters

NeurIPS is the largest scientific conference on machine learning and artificial intelligence, both cauldron and melting pot of new ideas and future directions for researchers and the industry as a whole.

Whether it’s reinforcement learning, demand forecasting done with the new heuristics or other ML-related solutions the market hasn’t heard of (yet), NeurIPS is where you’ll hear how ground is being broken.

AI Dungeon (Master)

With Dungeons and Dragons back in the game (pun intended), the institution of a game master (or, in this system, a Dungeon master) goes mainstream again. The phenomenon of modern role-playing pen-and-paper games is described in more detail in this New Yorker article.

For those unfamiliar with RPG nomenclature, a game master is the person who oversees the role-playing world, ultimately controlling everything apart from the players – the monsters, and non-player characters. In the pen-and-paper role-playing games, this role falls to one of the players (the “dungeon master”). In computer RPG, it’s all scripted by designers and delivered through the graphical environment.

Until now, that is. The AI Dungeon is a text-based role-playing game with AI filling the shoes of game master. The player can do basically anything – from asking questions (what is that hut made of?) performing actions (attack the ork warband) to living out far-fetched fantasies (I transform into a dragon and try to eat the moon). And the AI needs to respond.

Why it matters

Run by game masters and populated by the spawn of players’ imagination, classic role-playing games are made up of words. It is close to literature, yet more interactive, with the integration and interaction between the person behind the curtain of the world. Apart from imagination, nothing is required to play the game. Until recently, two people were needed to strike up a game – a game master and a player. Now, an AI-based game master sits ready to be used on the cell phone in your pocket or the browser on your desktop.

The experience may for now still pale in comparison to what an experienced game master will unfold for you. But it’s early days, and the technology appears to be heading in the right direction.

The top minds in AI predict what’s ahead for 2020

AI changes in a way that is nearly impossible to predict. The technology delivers significant breakthroughs and inspiring news nearly every day. But the need to make the reality less chaotic and reason at least something about the future is deep high. So VentureBeat has gathered some of the top minds in AI to tell us what may come up and go down, if you will, in the coming year.

The predictions vary from highly technical thoughts on the popularity of particular frameworks and solutions to a much broader view of the general development of machine learning.

Why it matters

It is easy to get lost in the riddled and constantly changing world of modern machine learning. VentureBeat’s guide provides some insight and delivers comments from people from top-notch AI companies.

Armed with that knowledge, one can easier filter out the noise and analyze the important trends.

October brought amazing news for Google: the company has reached quantum supremacy. Today we’re going to look at just what that means.

November was no slouch, either, bringing us as it did greater clarity on the development of the most prominent frameworks as well as an interesting initiative to impose good practices in machine learning research.

To top off all the news, this issue of AI Monthly Digest reports on a funny incident related to robots. Have fun reading!

Google gains quantum supremacy

Quantum computing is not a new concept. It has been around for 30 years, even if it has spent most of that time as a concept with no practical applications. A situation comparable with the one of neural networks.

At some point, however, big data and cloud-based computing power changed the game for neural networks and artificial intelligence. Today, neural networks, the theoretical plaything of yesterday, stands behind demand forecasting systems, powers natural language processing tools and supports the daily operations of countless companies in multiple other ways with reinforcement learning on the cutting-edge.

Although a practical application of quantum machines has yet to come, Google’s recent experiment shows clear progress. Their quantum computer, operating on a 54-qubit processor called ‘Sycamore,” performed in 200 seconds computations that would take 10,000 years on the fastest supercomputer existing.

While the 10,000 years was later amended down to two days, the efficiency Google has managed to muscle into their system still boggles the mind.

Why does it matter

Or rather, why does it matter for machine learning? The answer is a bit tricky, but the question was bandied about at the most recent AI World Conference.

Quantum computing will enable a machine learning model to reflect and process more complex conditions and scenarios. According to the Boston Consulting Group, the optimal model for the upcoming future is to combine traditional and quantum computing.

Keras or PyTorch? Or maybe Tensorflow?

One of the most popular deepsense.ai blog posts examined the differences between Keras, the most popular library for Tensorflow, and PyTorch, one of the most popular deep learning frameworks.

According to the latest analysis done by The Gradient, the question of which is better is now irrelevant. Keras has recently been incorporated into TensorFlow as an official API to make the framework more convenient and easier to use. For those who love head-to-head battles for supremacy, the clash no longer pits Keras against PyTorch, but rather TensorFlow against PyTorch.

What’s even more interesting, each framework seems to push the other’s development. TensorFlow remains popular in the business community while scientist favor PyTorch.

Why does it matter

Knowledge of popular frameworks is crucial for data scientists as well as for business efficiency – choosing the right one can be essential to a project’s success.

In the end, the choice may end up being irrelevant as both deliver a comprehensive platform to deal with machine learning and facilitate the whole process. The frameworks are tools, after all, and should play a supporting role rather than a limiting one.

Robots meet reality

The film Robocop offered an iconic portrayal of machines being used in law enforcement. The dystopian future of grim Detroit is nothing like the streets of today’s Los Angeles, where the first police patrol robots are being tested.

As CNBC reports, their performance is far from perfect. For example, one robot ignored a woman who was trying to inform it of a nearby fight, asking her only if…she would move.

Why does it matter

In the larger scheme of things, it doesn’t. But it is a funny yet welcome reminder that the road toward fully autonomous machines is more tortuous than we might expect.

Sotabench – benchmarking models for everyone

The progress that has been made in machine learning is mind-boggling. AI Monthly Digest was designed to cut through the buzz and deliver reliable and trustworthy news. On the other hand, it is easy to fall into the trap of considering every new model “state-of-the-art” or “best-performing”.

Yet there are multiple benchmarks to test models and determine which one is best suited for purpose. One obstacle to that being done is that the developers community cannot, due to a lack of time, test the models.

To address this problem, the people behind Papers With Code launched the site sotabench.com, which undertakes to benchmark all open source models with tests.

Why does it matter

The initiative is the next step toward establishing a set of good practices among machine learning developers and researchers. In “Papers with code”, the group promoted the idea of publishing not only research papers, but also the code behind them. Thus, anyone who so chose to could check and reproduce the effects of a research him or herself. This step makes claims of delivering the next breakthrough more feasible.

In fact, any effort to make the development of AI more transparent and easier to reproduce is more than welcome.

The AI Monthly Digest is deepsense.ai’s attempt to filter out the buzz and chaos from the news ecosystem and provide readers with the news that matters. The team also strives to make the information understandable for lay readers, to make knowledge on developments in Artificial Intelligence more accessible and the domain itself less esoteric.

While keeping an eye on global developments in AI, deepsense.ai’s team behind the digest got not only fresh and up-to-date information on state-of-the-art technologies, but also significant insight into the ongoing changes in the industry.

So what has changed? What has happened since the first issue of AI Monthly Digest came out? In this text we will cover:

• Natural Language Processing improvements
• Image processing breakthroughs
• Reinforcement Learning transfer from research to business applications
• Societal impact of Artificial Intelligence (AI)

Natural language processing

Two major breakthroughs have brought about rapid progress in Natural Language Processing (NLP).

BERT, XLnet and the speed of change

The first breakthrough was Google delivering BERT, a model that improves pre-trained word embeddings (a vector representations of words that enable computers to process the text) and enables data scientists to further fine-tune their networks to fulfill specific roles, such as automated chatbots or document processing support tools. BERT has been around since October 2018.

Less than a year later, XLNet outperformed BERT and delivered the new state-of-the-art in NLP. Indeed, one’s cutting edge can get rusty in no time flat.

GPT-2 – a long story

The next breakthrough witnessed by AI Monthly digest readers was the GPT-2 model, which excels in natural text generation. The model introduced in February delivered texts that were nearly indistinguishable from ones delivered by a human writer. To see just how indistinguishable, have a look at the examples below.

Example:

Human written:

In a shocking finding, scientists discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English.

Model completion (machine-written, 10 TRIES)

The scientist named the population, after their distinctive horn, Ovid’s Unicorn. These four-horned, silver-white unicorns were previously unknown to science.

Now, after almost two centuries, the mystery of what sparked this odd phenomenon is finally solved.

Dr. Jorge Pérez, an evolutionary biologist from the University of La Paz, and several companions, were exploring the Andes Mountains when they found a small valley, with no other animals or humans. Pérez noticed that the valley had what appeared to be a natural fountain, surrounded by two peaks of rock and silver snow.

Pérez and the others then ventured further into the valley. “By the time we reached the top of one peak, the water looked blue, with some crystals on top,” said Pérez.

Pérez and his friends were astonished to see the unicorn herd. These creatures could be seen from the air without having to move too much to see them – they were so close they could touch their horns.

While examining these bizarre creatures the scientists discovered that the creatures also spoke some fairly regular English. Pérez stated, “We can see, for example, that they have a common ‘language,’ something like a dialect or dialectic.”

The effects were so impressive that OpenAI, the company behind the creation, decided NOT to go public with the model, flouting established industry and research standards. But they believed they were acting responsibly.

Over the past year we have also reported on research on the business efficiency of automated translations. A key fact that has come to light is that there is a 10.9% increase in trade when automated translations are used in the e-commerce. That’s fairly convincing evidence for the business efficiency of AI-powered translations.

Image Recognition

In NLP, state-of-the-art technology is struggling to match human accuracy, whereas machines have already achieved superhuman performance in image recognition. And to say that advancement has been rapid would be an understatement.

In the second edition of AI Monthly Digest, news about delivering a convincing image of a hamburger, a cat or a dog was a thing indeed. Mere months later, neural networks were delivering very realistic faces–and not just of fake people. Researchers generated a network that makes a video of person speaking just from a single frame. As a part of the research, the team delivered speaking paintings, including the Mona Lisa and Van Gogh. Admittedly, the hamburger paled in comparison.

Elsewhere, a neural network developed the ability to spot early signs of depression in humans. According to WHO data, depression is a leading cause of disability in the world and when not cured can lead to suicide. The software was accurate in spotting the signs of depression in more than 80% of cases.

Thus neural networks and the data scientists behind them can boost healthcare not only by employing machine learning for drug discovery, but also to make early diagnoses. And in the case of spotting depression, they managed to do this using just the information they could gather from the cameras on mobile devices.

Reinforcement learning

One of the most important areas of research work, reinforcement learning was a frequent area of focus in the first 12 months of AI monthly Digest.

An important development was Deepmind’s AlphaStar beating human champions in Starcraft 2, The game poses multiple challenges for human players and artificial intelligence alike. It is played in real time, requiring excellent reflexes. Every unit has a strategy it works with and counter-strategies that make it useless.

Also, due to the fog of war (the darkness covering the map outside of the player’s units sightline) the player has incomplete information on their opponent’s position and actions. Compare that with the complete openness of chess and Go.

The Deepmind-designed AlphaStar model vanquished top StarCraft pro-players TLO and MaNa from TeamLiquid, though not without a catch: the model had an unfair advantage over a human player by being able to “see” all the visible battlefield at the same time. Humans are limited to the frame they can see on their screen, so the ability to switch between multiple spots on a world map is limited. This advantage proved so crucial that when the model was stripped of it, MaNa won the match.

While gaining superiority over human players is one thing, being a worthy and entertaining opponent is something else entirely. The AI in gaming is currently based on endless if-then loops that are prone to failure, especially in the more complicated, open worlds seen in The Witcher 3 or Grand Theft Auto.

The first game to feature a reinforcement learning-trained agent as an opponent is the MotoGP 19, a racing game.

So, during the first 12 months of AI Monthly Digest, reinforcement learning moved beyond the realm of pure research and into real-life business applications.

Society

Like the steam engines of the past, Artificial Intelligence is poised to transform society in unprecedented ways. The first signs of change are being seen now – sometimes for the better and sometimes for the worse.

Amazon’s AI-based recruitment solution was biased against women in the company’s recruitment process. The main reason was that male coders were overrepresented in the dataset the recruitment model was trained on. Amazon the model down, but not before giving us a glimpse of the possible effects of deploying insufficiently supervised models.

To avoid such situations and make AI technology more comprehensible for non-skilled users, the Finnish government launched an AI-popularization program. The grassroots movement of providing the uninitiated– barbers, bakers and car mechanics, for example – with basic AI training, gained the attention of the Finnish government, which sought to make machine learning a commodity. An AI-powered demand forecasting for a small, family business? Why not?

A humorous but thought-provoking example of the social impact AI research can have is the panic Starcraft players exhibited when Deepmind revealed that AlphaStar was lurking in Battle.net, and was primed to do battle with random players.

Given the superiority AlphaStar had shown over the MaNa and TLO, it comes as no surprise that players were at once thrown back on their heels and put on their toes:killer AI had come to humiliate them and turn the Battle.net rankings on its head. Thus, users exchanged tips on spotting the non-human opponent. So, while AlphaStar no longer wields an unfair advantage, the players remain thrilled.

Summary

In 12 short months, we’ve gone from being amazed by a convincing image of a hamburger to delivering images of fake people. The world came through three breakthroughs in natural language processing and players have witnessed turmoil around reinforcement learning-powered agents.

The world of AI is changing fast. AI Monthly Digest will be keeping its A-eye trained on the ball.