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.
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.
September brought us two interesting AI-related stories, both with a surprising social context.
Despite its enormous impact on our daily lives, Artificial Intelligence (AI) is often still regarded as too hermetic and obscure for ordinary people to understand. As a result, an increasing number of people use Natural Language Processing-powered personal assistants, yet only a tiny fraction try to understand how they work and how to use them effectively. This makes them somewhat of a black box.
Making the field more comprehensible and accessible is one aspect of AI researchers’ mission. That’s why research recently done by OpenAI is so interesting.
Hide-and-Seek – the reinforcement learning way
Reinforcement learning has delivered inspiring and breathtaking results. The technique is used in the training models behind autonomous cars and the controlling of sophisticated devices like automated arms and robots.
Unlike in supervised learning, a reinforcement learning model learns by interacting with the environment. The scientist can shape its behavior by applying a policy of rewards and punishments. The mechanism is close to that which humans use to learn.
Reinforcement learning has been used to create super killing agents to go toe-to-toe against human masters in Chess, Go and Starcraft. Now OpenAI, the company behind the GPT-2 model and several other breakthroughs in AI, has created agents that play a version of hide-and-seek, that most basic and ageless of children’s games.
OpenAI researchers divided the agents into two teams, hiders and seekers, and provided them a closed environment with walls and movable objects like boxes, walls and ramps. Any team could “lock” these items to make them unmovable for the opposing team. The teams developed a set of strategies and counter-strategies in a bid to successfully hide from or seek out the other team. The strategies included:
Running – the first and least sophisticated ability, enabling one to avoid the seekers.
Blocking passages – the hider could block passages with a box in order to build a safe shelter.
Using a ramp – to overcome the wall or a box, the seekers team learned to use a ramp to jump over an obstacle or climb a box and see the hider.
Blocking the ramp – to prevent the seekers from using the ramp to climb the box, the hiders could block access to the ramp. The process required a great deal of teamwork, which was not supported by the researchers in any way.
Box surfing – a strategy developed by seekers who were basically exploiting a bug in the system. The seekers not only jumped on a box using a ramp that had been blocked by the hiders, but also devised a way to move it while standing on it.
All-block – the ultimate hider-team teamwork strategy of blocking all the objects on the map and building a shelter.
The research delivered, among other benefits, a mesmerizing visual of little agents running around.
Why does it matter?
The research itself is neither groundbreaking nor breathtaking. From a scientific and developmental point of view, it looks like little more than elaborate fun. Yet it would be unwise to consider the project insignificant.
AI is still considered a hermetic and difficult field. Showing the results of training in the form of friendly, entertaining animations is a way to educate society on the significance of modern AI research.
Also, animation can be inspiring for journalists to write about and may lead youth to take an interest in AI-related career paths. So while the research has brought little if any new knowledge, it could well end up spreading knowledge on what we already know.
AI-generated stock photos available for free
Generative Adversarial Networks have proved to be insanely effective in delivering convincing images of not only hamburgers and dogs, but also human faces. One breakthrough is breathtaking indeed. Not even a year ago the eerie “first AI-generated portrait” was sold on auction for nearly a half-million dollars.
Now, generating faces of non-existent people is as easy as generating any other fake image – a cat, hamburger or landscape. To prove that the technology works, the team behind the 100K faces project delivered a hundred thousand AI-generated faces to use in any stock usage, from business folders, to flyers to presentations. Future use cases could include delivering on-the-go image generators that, powered by a demand forecasting tool, provides an image that best suits demand.
The images added to the free images bank are not perfect. With visible flaws in a model’s hair, teeth or eyes, some are indeed far from it. But that’s nothing a skilled graphic designer can’t handle. Also, there are multiple images that look nearly perfect – especially when there are no teeth visible in the smile.
Many photos are good enough to provide a stock photo as a “virtual assistant” image or to fulfill any need for a random face. This is an early sign that professional models and photographers will see the impact of AI in their daily work sooner than expected.
https://deepsense.ai/wp-content/uploads/2019/10/AI-Monthly-Digest-13-–-an-unexpected-twist-for-the-stock-image-market.jpg3371140Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2019-10-07 12:26:182019-10-07 12:26:18AI Monthly Digest #13 - an unexpected twist for the stock image market
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
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.
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.
https://deepsense.ai/wp-content/uploads/2019/10/The-full-year-of-AI-Monthly-Digest.jpg3371140Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2019-10-03 12:25:182019-10-03 13:21:00The full year of AI Monthly Digest
With this edition of AI Monthly Digest, we have now for a full year been bringing readers carefully selected and curated news from the world of AI and Machine Learning (ML) that deepsense.ai’s team considers important, inspiring and entertaining.
Our aim is to deliver information that people not necessarily involved in AI and ML may find interesting. Also, the digest is curated by data scientists who ensure that the information included isn’t just hot air or marketing mumbo-jumbo, but significant news that will impact the global machine learning and reinforcement learning world.
This edition focuses on natural language processing, as the GPT-2 model is still an important element of AI-related discourse. This edition also contrasts the enthusiasm of ML-developers with concerns expressed by a renowned professor of Psychology.
1200 questions to ask
With natural language processing, a computer needs to generate natural texts in response to a human. This is at least troublesome, especially if a longer text or speech is required.
While these problems are being tackled in various ways, the gold standard is currently to run the newest solution on a benchmark. Yet delivering one is another challenge, to put it mildly.
To tackle it, researchers from the University of Maryland created a set of over 1200 questions that are easy to answer for a human and nearly impossible for a machine. To jump from “easy” to “impossible” is sometimes a matter of very subtle changes. As the researchers have said:
if the author writes “What composer’s Variations on a Theme by Haydn was inspired by Karl Ferdinand Pohl?” and the system correctly answers “Johannes Brahms,” the interface highlights the words “Ferdinand Pohl” to show that this phrase led it to the answer. Using that information, the author can edit the question to make it more difficult for the computer without altering the meaning of the question. In this example, the author replaced the name of the man who inspired Brahms, “Karl Ferdinand Pohl,” with a description of his job, “the archivist of the Vienna Musikverein,” and the computer was unable to answer correctly. However, expert human quiz game players could still easily answer the edited question correctly.
Capitalizing on this knowledge, researchers will be able to deliver better benchmarking for models and thus determine which part of the question confuses the computer.
Why does it matter
With each and every breakthrough, researchers get closer to delivering human-level natural language processing. At the same time, however, it is increasingly hard to determine if the neural network is understanding the processed text, or is just getting better fitted to the benchmark. Were the latter the case, the model would outperform existing solutions but register no significant improvement in real-life performance.
An example with detailed explanations are available in the video below.
A benchmark updated with those 1200 questions delivers significantly more precise information on the model’s ability to process the language and spot the drawbacks.
Large GPT-2 released
GPT-2 is probably the hottest topic among AI Trends 2019, especially considering the groundbreaking effect and controversial decision to NOT make the model public. Instead, OpenAI, the company behind the model, decided to cooperate with chosen institutions to find a way to harden the model against potential misuse.
And the threat is serious. According to research published in Foreign Affairs, readers consider GPT-2-written texts nearly as credible and convincing as those written by journalists and published in The New York Times (72% compared to 83%). Thus the articles are good enough to be especially dangerous as a weapon of mass disinformation or fake news factory – AI can produce a limitless amount of credible-looking texts with no effort.
To find the balance between supporting the development of the global science of AI and protecting models from being used for maleficent ends, OpenAI is releasing the model in iterations, starting a small one and ultimately aiming to make the model public but with the threat of misuse minimized.
Why does it matter
As research published in Foreign Affairs states, the model produces texts that an unskilled reader will find comparable to journalist-written ones. Image recognition models are already outperforming human experts in their tasks. But with all these cultural contexts, humor and irony, natural language once seemed protected by the unassailable fortress of the human mind.
The GPT-2 model has apparently cracked the gates and with business appliances it may be on the road to delivering a model that can provide human-like performance. The technology just needs to be controlled so as not to fall into the wrong hands.
What is this GPT-2 all about?
A GPT-2 model is, as stated above, one of the hottest topics of AI in 2019. But even the specialist can find it hard to understand the nitty-gritty of how the model works. To make the matter more clear, Jay Alammar has prepared a comprehensive guide to the technology.
Why does it matter
The guide is good enough to allow a person who has limited to no knowledge on the matter to understand the nuances of the model. For a moderately skilled data scientist given sufficient computing power and a dataset, the guide is sufficient to reproduce the model for example to support demand forecasting with NLP. It enables a data scientist to broaden his or her knowledge with one comprehensive article – a convenient way indeed.
Doing research is one thing, but sharing the knowledge it affords is a whole different story.
Malicious use, you say?
Jordan Peterson is a renowned professor and psychologist who studies the structure of myth and its role in shaping social behavior. If not a household name, he is certainly a public person and well-known speaker.
Using deep neural networks, AI researcher Chris Vigorito launched a notjordanpeterson.com website that allowed any user to generate any text that was later read with the neural network-generated voice of Jordan Peterson. As was the case with Joe Rogan, the output was highly convincing, mirroring the manner of speaking, breathing and natural pauses.
The networks was trained on 20 hours of transcripted Jordan Peterson speeches, an easy number to obtain where a public speaker is concerned. The amount of work was considerable, but not overwhelming.
Why does it matter
The creation of the neural network is not as interesting as Jordan Peterson’s response. He has written a blogpost entitled “I didn’t say that”, where he calls the situation “very strange and disturbing”. In the post, he notes that while it was fun to hear himself singing popular songs, the prospect of being an unwitting part of a scam is more than real. Due to the rising computing power available at affordable prices and algorithms getting better and less data-hungry, the threat of this technology being used for malicious ends is rising. If you’d like to know just how malicious he means, I’ll leave you with this to consider.
I can tell you from personal experience, for what that’s worth, that it is far from comforting to discover an entire website devoted to allowing whoever is inspired to do so produce audio clips imitating my voice delivering whatever content the user chooses—for serious, comic or malevolent purposes. I can’t imagine what the world will be like when we will truly be unable to distinguish the real from the unreal, or exercise any control whatsoever on what videos reveal about behaviors we never engaged in, or audio avatars broadcasting any opinion at all about anything at all. I see no defense, and a tremendously expanded opportunity for unscrupulous troublemakers to warp our personal and collective reality in any manner they see fit.
https://deepsense.ai/wp-content/uploads/2019/09/AI-Monthly-Digest-12-the-shadow-of-malicious-use.jpg3371140Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2019-09-06 14:10:132019-10-08 11:31:45AI Monthly Digest #12 - the shadow of malicious use
AI models are skilled in Chess, Go, StarCraft and, since July, six-player Texas Hold’em Poker. But the hunt for inhuman players has begun.
And when AI models get bored with beating humans in games, it’s apparently time for a bike ride. Read on to find out just why.
OpenAI partners with Microsoft
In March, OpenAI shifted to a for-profit paradigm, forging OpenAI LP. The company has now entered into a strategic partnership with Microsoft to build a new computational platform in Azure Cloud. OpenAI will port its codebase into Azure and develop new solutions with tools available there.
The main benefit for Microsoft will be the access it gains to the fruits of OpenAI’s work as a preferred partner for commercializing new AI technologies. OpenAI was formed with the goal of improving people’s lives with technology and has delivered multiple AI breakthrough models including MuseNet, which produces music in various styles, and GPT-2, a natural language processing model and gold standard in text generation.
As the company recently announced, taking a non-profit approach to develop AI proved too daunting even for the likes of Elon Musk and Sam Altman, the company’s CEO. Unlike with most software, developing artificial intelligence requires not only skilled and talented people but also an astonishing amount of computing power. Training a single GPT-2 model is estimated to cost up to $50,000 – and that’s only for one of many experiments run in a given year. So, securing access to computing power is a must. By providing that, Microsoft will be getting access to the base of talented AI developers, significantly increasing its AI development potential.
AI beats skilled human players in six-player poker
Pluribus bot is the first AI-controlled agent capable of beating human pro players in six-player no-limit Hold’em poker, the most popular format of this game in the world. Unlike chess and Go, poker is a game with hidden information – the player cannot see the hands of other players. The game itself involves bluff and a great deal of psychological factors. AI bots were good at beating one opponent, but going up against more than one was a major milestone.
Dealing with a one-on-one situation, though common in recreational games, is rare when solving real-life problems. Moreover, it is somewhere between difficult and impossible to get all the information one needs, as in chess or Go. Delivering models that work in a limited-information, multiple-agent environment is pushing models that support demand forecasting or managing cybersecurity threats.
When an autonomous car is not enough
Cars, be they traditional or autonomous, come with various disadvantages, especially in cities. They get stuck in traffic jams, and require parking, which can be hard to come by.
To provide more sustainable autonomous transport, Chinese scientists have brought out an autonomous bicycle. The machine responds to voice commands, avoids obstacles and maintains balance. It uses a new Tianjic chip, which supports processing neuroscience-inspired algorithms. Check out the video below to see how it works.
Why does it matter
The research itself sounds like a lot of fun, but it also constitutes an excellent foundation for further work on autonomous transportation. A bike can be used for delivering fast food, or modified to work as a motorized Rickshaw in our ever more crowded metropolises. Couriers can used them to deliver the mail and other documents or to transport individuals incapable of riding a bike.
Autonomous bikes need not be fully autonomous, by the way: AI can support the driving process or provide alerts to riders.
Alan Turing will be featured on 50 pound banknote
Often hailed as the godfather of modern computing, Alan Turing is widely known for his work on cracking the famed German Enigma code and as the leader of the team that enhanced the cracking methods delivered by Polish mathematicians.
Turing is also considered a pioneer of artificial intelligence. He came up with the Turing Test as a first way to determine if a machine mimicking a human in conversation is truly intelligent.
So great was Turing’s contribution to humanity that he will now be featured on Britain’s 50-pound banknote. Chosen from 227,299 nominations covering 989 eligible characters, Turing was ultimately picked by Mark Carney, Bank of England governor.
Why does it matter
The announcement is a sign that computer science is no longer considered a novelty and prominent AI researchers earn the same respect chemists, physicists or life sciences experts do, as representation on a banknote well attests.
BattleNet Blade Runners
Deepmind, in conjunction with Blizzard, has deployed AlphaStar model on Battle.net, to allow players to test their skills and mettle against artificial intelligence. Battle.net is an official platform connecting players from all around the world, enabling them to quickly find opponents for a multiplayer match.
There is just one twist: the famed, reinforcement learning-trained AlphaStar will play anonymously, thus allowing players to compete with the model as they would do in any match with a normal opponent.
AlphaStar has been developed significantly beyond the abilities it commanded in defeating human professional players MaNa and TLO. Deepmind capped the actions-per-minute and actions-per-second rate to make it more accurately appropriate human abilities limited by muscles and the need to operate a mouse and keyboard. The model’s perception has also been narrowed to a single frame to come in line with what human players see on the screen.
Finally, the model is able to control and compete in any race given, be it Terran, Protoss or Zerg, representing all the factions available in the game. This represents serious progress: during matches in January, the model could only control Protoss units fighting against other Protoss.
Why does it matter
At the moment, it doesn’t. But let the experiment run its course and tune in later for an update. We anticipate more impressive progress.
Interestingly, this most recent news thrilled the players’ community, which all too clearly remembers the wounds AlphaStar inflicted in dominating renowned pros. Given that any match with the model is counted as a normal encounter and a ranking match, on-guard players try to spot and avoid AlphaStar lurking in the muddy waters of the battle.net rankings.
Players have reported “odd” behavior of some opponents and have been uploading videos on YouTube, where they discuss if the other player is actually AlphaStar incognito. They also advise each other to check if the partner responds to messages. Being called a noob by an opponent even once can be strong evidence that the opponent on the other side of the battlefield isn’t human.
So players are on the hunt for a replicant.
https://deepsense.ai/wp-content/uploads/2019/08/AI-monthly-digest-11-3.jpg3501150Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2019-08-08 12:54:412019-08-08 12:55:21AI Monthly Digest #11 - From OpenAI partnering with Microsoft to battle.net Blade Runners
June brought record-breaking temperatures, perfectly highlighting the global challenge of climate change. Is that AI-related news? Check and see in the latest AI Monthly Digest.
A common misconception about machine learning projects is that they are by definition big. However, any number of AI-powered micro-tweaks and improvements are applied in everyday work. A good example of both micro and macro tweaks that can fix a major problem can be found in the paper described below.
AI tackling climate change
The world witnessed an extraordinarily hot June, with average temperatures 2 degrees celsius above normal in Europe. According to the World Meteorological Organization, the heatwave is consistent with predictions based on greenhouse gas concentrations and human-induced climate change.
Tackling this challenge will not be easy: according to World Bank Data, fossil fuel energy consumption still stacks to 79% of total. Furthermore, greenhouse gasses, particularly methane, are emitted by cattle, with livestock being responsible of 14.5% of total human-induced greenhouse emissions.
The most prominent figures in AI today, including DeepMind CEO Demis Hassabis, Turing award winner Yoshua Bengio, and Google Brain co-founder Andrew Ng, have authored a comprehensive paper on ways that AI can tackle the changing climate.
Their call for collaboration is meant to inspire practitioners, engineers and investors to deliver short- and long-term solutions for measures within our reach. Those include producing low-carbon electricity through better forecasting, scheduling, and control for variable sources of energy, mitigating the damage produced by high-carbon economies through, for example, better predictive maintenance as well as help minimize energy use in transportation, smart buildings and cities. The applications can vary from designing grid-wide control systems or optimizing scheduling with more accurate demand forecasting.
Why does it matter
Climate change is one of the greatest challenges mankind faces today, with truly cataclysmic scenarios approaching. Further temperature increases may lead to a variety of disasters, from flooding coastal regions due to melting ice caps, agricultural crises and conflicts over access to water.
Green energy promises solutions, yet these are not without their challenges, many of which could be solved with machine learning, deep learning or reinforcement learning. Responsibility is among deepsense.ai’s most important AI trends, and being responsible for the planet would be an excellent example of just why we chose to focus on that trend.
We will provide more in-depth content on climate change and AI-powered ways of tackling it. So stay tuned!
Giants racing to produce the best image recognition
If machine learning is today’s equivalent of the steam engine revolution, data and hardware are the coal and engine that power the machines. Facebook and Google are like the coal mines of yesteryear, having access to large amounts of fuel and power to build new models and experiment.
It should come as no surprise that breakthroughs are usually powered by the tech giants. Google’s state of the art in image recognition, EfficientNet, has been a recent giant step forward. The model was delivered by automated searching procedure uniformly scaling each dimension of the network in order to find the best combination.
EfficientNet stands for something.
The result is state-of-the-art in Image recognition. At least when it comes to combining efficiency and accuracy. But not when it comes to accuracy alone.
Not even a month later Facebook delivered a model that outperformed Google’s. The key lay in scaling the enormous dataset it was trained on. The social media mogul has access to Instagram’s database, which holds no less than billions of user-tagged images, a dataset ready to be chewed over by a hungry deep learning model.
The neural network was released to the public using a recently launched Pytorch Hub platform for sharing cutting edge models.
Why does it matter
Both advances show how important machine learning is for the tech giants and how much effort they invest in pushing their research forward. Every advancement in image recognition brings new breakthroughs closer. For example, models are becoming more accurate in detecting diabetic retinopathy using images of the eye. Every further development delivers new ways to solve problems that would be unsolvable without ML (Machine learning) – manufacturing for visual quality control is among the best examples.
XLNet outperforms BERT
As we noted in a past AI Monthly Digest, Google has released Bidirectional Encoder Representation from Transformations (BERT). BERT was, until recently, the state-of-the-art when it comes to Natural Language Processing benchmarks. The newly announced XLNet is an autoregressive pretraining method (as opposed to an autoencoder-like BERT) which learns a language model by predicting the next word in a sequence using the permutation of all the surrounding words. An intuitive explanation can be found (here).
The XLNet model proved more effective than BERT in beating all 20 benchmark tasks.
Why does it matter
Understanding a natural language was considered a benchmark for intelligence, with Alan Turing’s test being among the best examples. Every push forward delivers new possibilities in building new products and solving problems, be they business ones or something more uncommon, like the example below.
AI-powered archeology? Bring it on!
Deep learning-based models are getting even better at understanding natural language. But what about language that is natural, but has never been deciphered due to lack of knowledge or a frustratingly small amount of extant text?
Recent research from MIT and Google shows that a machine learning approach can deliver major improvements in deciphering ancient texts. In the basics of modern natural language processing techniques, all of the words in a given text are assumed to be related to each other. The machine itself doesn’t “understand” text it in a human way, but rather forms its own assumptions based on the relations and connotations of each word in a sentence.
Disc of Phaistos, one of the most famous mysteries of archaeology
In this approach, the translation process is not built on understanding the world, but rather finding similarly connotated words that transfer the same message. This is entirely different than humans’ approach to language.
By making the algorithm less data-hungry, the researchers deliver a model that translates texts from rare and long-lost languages. The approach is described in this paper.
Why does it matter
While there are countless examples of machine learning in business, there are also new horizons to discover in the humanities. Deciphering the secrets of the past is every bit as exciting as building defenses against the challenges of the future.
The more sophisticated approach to and possible brute-force breaking of unknown languages provides a way to uncover more language-related secrets.
This edition is all about AI morality-related themes, with a slight tinge of Talking Heads and Modern Talking.
Earlier this year, deepsense.ai highlighted AI morality and transparency as one of 2019’s dominant AI trends. May bore out our thesis, especially as it relates to potential misuse and malicious intent. At the same time, though, AI provides unique chances to support entertainment and education, as well as deliver new business cases.
A bigger version of GPT-2 released to the public
Open-AI has recently shown the GPT-2 model has set a new gold standard for natural language processing. Following the acclaimed success of the model, OpenAI opted not to make it public due to the risk of malicious usage, particularly to produce spam and fake news at no cost.
This sparked an uproar. The industry good practice is to release AI research work as open-source software, so other researchers can push the boundaries further without having to repeat all the work done earlier from scratch. In other words – OpenAI threw up a major hurdle to NLP-model development by keeping GPT-2 under wraps.
To support the scientific side of the equation while reducing the malicious threat, OpenAI releases some smaller-scale models to the public. The model it recently released operates on 345M parameters, while the best original model consists of 1.5B parameters. Every parameter can be seen as a virtual neuron inside a neural network, so OpenAI is basically reducing the brain it designed.
The original network was released to OpenAI partners currently working on malice-proofing the system. The first independent applications of the downscaled network are already available at talktotransformer.com and onionbot headline generator.
Why does it matter?
OpenAI is currently facing a difficult choice between supporting the global development of AI and the fear of losing control over dangerous technology. In a world facing a potential avalanche of fake news and social media being used to perpetuate propaganda, building a system that writes coherent and convincing texts is undoubtedly dangerous.
This case allows one to see all the AI-related issues in a nutshell, including the technology’s amazing potential, the real threat of misuse or malicious intent. So the case may serve as a precedent for future cases.
Talking heads unleashed
A group of scientists working for Samsung’s AI Center in Moscow and Skolkovo Institute of Science and Technology designed a model that can produce a convincing video of a talking head from a single image, such as a passport photo or even a painting.
The model renders with consistency both the background and the head’s behavior. Most impressively, the model builds a convincing video of a talking head from even a single image of the frame.
The solution is searching for a similar face that was analyzed and extracts facial features including a nose, chin, mouth and eyes. The movement of those features is then applied on the image, as shown in the video.
The results are undoubtedly impressive.
Why does it matter?
Yet another AI ethics-related issue, the talking-head technology poses the threat of deepfakes, images that show a person making statements that he or she would never make. This raises obvious questions about the malicious ways such technology could be used.
On the other hand, when deepfakes are used for special effects in popular movies, no one seems to complain and critics even weigh in with their acclaim. Some of the better-known examples come from the Star Wars franchise, particularly Rogue One, which features Leia Organa wearing the face of a young Carrie Fisher.
AI has also proved itself useful in promoting art. By leveraging this technology it is possible to deliver the talking head of Girl with a Pearl Earring or the Mona Lisa telling visitors from screens about a painting’s historical context – a great way to put more fun in art lessons for kids. Or just to have some fun seeing what a Stallone-faced Terminator would look like.
Again, AI can be used for both good and evil ends. The ethics are up to the wielder of this double-edged sword.
Modern Talking – recreating the voice of Joe Rogan
Another example of deepfake-related technology is using AI to convincingly recreate Joe Rogan’s voice. The text-to-speech technology is not a new kid on the block, yet it is easy to spot due to the robotic and inhumanely calm style of speaking. Listening to automated text-to-speech was usually boring at best while delivering the unintentional comic effects of robotic speech, all in the absence of emotion or inflection.
Dessa engineers have delivered a model that is not only transforming text to speech, but also recreating Joe Rogan’s style of speaking. Joe is a former MMA commentator who went on to become arguably the most popular podcaster in the world. Speaking with great emotion, heavily accenting and delivering power with every word, Rogan is hard to mistake.
Or is he? The team released a quiz that challenges the listener to distinguish if a given sample comes from a real podcast or was AI-generated. The details can be found on Dessa’s blog.
Why does it matter?
Hearing a convincing imitation of a public personality’s voice is nearly as unsettling as watching a talking head talk. But the technology can be used for entertainment and educational purposes. For example, delivering a new Frank Sinatra single or presenting Winston Churchill’s comprehensive and detailed speech on reasons behind World War II.
Again, the ethics are in the user’s hands, not in the tool. Despite that, and as we saw with OpenAI’s GPT-2 Natural Language Processing model, researchers have decided NOT to let the model go public.
Machine learning-powered translations increase trade by 10,9%
Researchers at Olin Business School at Washington University in St.Louis have found a direct connection between machine learning-powered translations and business efficiency. The study was conducted on e-Bay and shows that moderate improvement in the quality of language translation increased trade between countries on eBay by 10.9%.
The study examined the trade between English speakers from the United States and their trade relations with countries speaking other languages in Europe, America and Asia. More on the research can be found on the Washington University of St.Louis website.
Why does it matter?
While there is no doubt that AI provides vital support for business, the evidence, while voluminous, remains largely anecdotal (sometimes called anec-data) with little quantitative research to back up the claim. Until the Olin study, which does provide hard and reliable data. Is justified true belief knowledge? That’s an entirely different question…
A practical approach to AI in Finland
AI Monthly Digest #5 presented a bit about a Finnish way of spreading the word about AI. Long story short: contrary to many approaches of building AI strategy in a top-down model, Finns have apparently decided to build AI-awareness as a grassroots movement.
To support the strategy, the University of Helsinki has released a digital AI course on the foundations and basic principles of AI. It is available for free to everyone interested.
Why does it matter?
AI is gaining attention and the reactions are usually polarised – from fear of losing jobs and machine rebellion to arcadian visions of an automated future with no hunger or pain. The truth is no doubt far from either of those poles. Machine learning, deep learning and reinforcement learning are all built on certain technological foundations that are relatively easy to understand, including their strengths and limitations. The course provides good basic knowledge on these issues, which can do nothing but help our modern world.
https://deepsense.ai/wp-content/uploads/2019/06/AI-Monthly-Digest-9-–-the-double-edged-sword-of-modern-technology.png3371140Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2019-06-07 13:02:112019-10-08 11:52:32AI Monthly Digest #9 - the double-edged sword of modern technology
The April edition of AI Monthly Digest looks at how AI is used in entertainment, for both research and commercial purposes.
After its recent shift from non-profit to for-profit, OpenAI continues to build a significant presence in the world of AI research. It is involved in two of five stories chosen as April’s most significant.
AI Music – spot the discord…
While machine learning algorithms are getting increasingly better at delivering convincing text or gaining superior accuracy in image recognition, machines struggle to understand the complicated patterns behind the music. In its most basic form, the music is built upon repetitive motifs that return based on sections of various length – it may be a recurrent part of one song or a leading theme of an entire movie, opera or computer game.
Machine learning-driven composing is comparable to natural language processing – the short parts are done well but the computer gets lost when it comes to keeping the integrity of the longer ones. April brought us two interesting stories regarding different approaches to ML-driven composition.
OpenAI developed MuseNet, a neural network that produces music in a few different styles. Machine learning algorithms were used to analyze the style of various classical composers, including Chopin, Bach, Beethoven and Rachmaninoff. The model was further fed rock songs by Queen, Green Day and Nine Inch Nails and pop music by Madonna, Adele and Ricky Martin, to name a few. The model learned to mimic the style of a particular artist and infuse it with twists. If the user wants to spice up the Moonlight Sonata with a drum, the road is open.
OpenAI has rolled out an early version of the model and it performs better when the user is trying to produce a consistent piece of music, rather than pair up a disparate coupling of Chopin and Nine Inch Nails-style synthesizers.
OpenAI claims that music is a great tool with which to evaluate a model’s ability to maintain long-term consistency, mainly thanks to how easy it is to spot discord.
…or embrace it
While OpenAI embraces harmony in music, Dadabots has taken the opposite tack. Developed by Cj Carr and Zack Zukowski, Databots model imitates rock, particularly metal bands. The team has put their model on YouTube to deliver technical death metal as an endless live stream – the Relentless Doppelganger.
While it is increasingly common to find AI-generated music on Bandcamp, putting a 24/7 death metal stream on YouTube is undoubtedly something new.
Fans of the AI-composed death metal have given the music rave reviews. As The Verge notes, the creation is “Perfectly imperfect” thanks to its blending of various death metal styles, transforming vocals into a choir and delivering sudden style-switching.
It appears that bare-metal has ushered in a new era in technical death metal.
Why does it matter?
Researchers behind the Relentless Doppelganger remark that developing music-making AI has mainly been based on classical music, which is heavily reliant on harmony, while death metal, among others, embraces the power of chaos. It stands to reason, then, that the music generated is not perfect when it comes to delivering harmony. The effect is actually more consistent with the genre’s overall sound. What’s more, Databots’ model delivers not only instrumentals, but also vocals, which would be unthinkable with classical music. Of course, the special style of metal singing called growl makes most of the lyrics incomprehensible, so little to no sense is actually required here.
From a scientific point of view, OpenAI delivers much more significant work. But AI is working its way into all human activity, including politics, social problems and policy and art. From an artistic point of view, AI-produced technical death metal is interesting.
It appears that when it comes to music, AI likes it brutal.
AI in gaming goes mainstream
Game development has a long and uneasy tradition of delivering computer players to allow users to play in single-player mode. There are many forms of non-ML-based AI present in video games. They are usually based on a set of triggers that initiate a particular action the computer player takes. What’s more, modern, story-driven games rely heavily on scripted events like ambushes or sudden plot twists.
This type of AI delivers an enjoyable level of challenge but lacks the versatility and viciousness of human players coming up with surprising strategies to deal with. Also, the goal of AI in single-player mode is not to dominate the human player in every way possible.
The real challenge in all of this comes from developing bots, or the computer-controlled players, to deliver a multiplayer experience in single-player mode. Usually, the computer players significantly differ from their human counterparts and any transfer from single to multiplayer ends with shock and an instant knock-out from experienced players.
A.N.N.A. is a neural network-based AI that is not scripted directly but created through reinforcement learning. This means developers describe an agent’s desired behaviour and then train a neural network to achieve it. Agents created in this way show more skilled and realistic behaviors, which are high on the wish list of Moto GP gamers.
Why does it matter?
Applying ML-based artificial intelligence in a mainstream game is the first step in delivering a more realistic and immersive game experience. Making computer players more human in their playing style makes them less exploitable and more flexible.
The game itself is an interesting example. It is common in RL-related research to apply this paradigm in strategic games, be it chess, GO or Starcraft II for research purposes. In this case, the neural network controls a digital motorcycle. Racing provides a closed game environment with a limited amount of variables to control. Thus, racing in a virtual world is a perfect environment to deploy ML-based solutions.
In the end, it isn’t the technology but rather gamers’ experience that is key. Will reinforcement learning bring a new paradigm of embedding AI in games? We’ll see once gamers react.
Bittersweet lessons from OpenAI Five
Defense of The Ancients 2 (DOTA 2) is a highly popular multiplayer online battle arena game with two teams, each consisting of five players fighting for control over a map. The game blends tactical, strategic and action elements and is one of the most popular online sports games.
OpenAI Five is the neural network that plays DOTA 2, developed by OpenAI.
The AI agent beat world champions from Team OG during the OpenAI Five Finals on April 13th. It was the first time an AI-controlled player has beaten a pro-player team during a live-stream.
Why does it matter?
Although the project seems similar to Deepmind’s AlphaStar, there are several significant differences:
The model was trained continuously for almost a year instead of starting from zero knowledge for each new experiment – the common way of developing machine learning models is to design the entire training procedure upfront, launch it and observe the result. Every time a novel idea is proposed, the learning algorithm is modified accordingly and a new experiment is launched starting from scratch to get a fair comparison between various concepts. In this case, researchers decided not to run training from scratch, but to integrate ideas and changes into the already trained model, sometimes doing elaborate surgery on their artificial neural network. Moreover, the game received a number of updates during the training process. Thus, the model was forced at some points not to learn a new fact, but to update its knowledge. And it managed to do so. The approach enabled the team to massively reduce the computing power over the amount it had invested in training previous iterations of the model.
The model effectively cooperated with human players – The model was available publicly as a player, so users could play with it, both as ally and foe. Despite being trained without human interaction, the model was effective both as an ally and foe, clearly showing that AI is a potent tool to support humans in performing their tasks — even when that task is slaying an enemy champion.
The research done was somewhat of a failure – The model performs well, even if building it was not the actual goal. The project was launched to break a previously unbroken game by testing and looking for new approaches. The best results were achieved by providing more computing power and upscaling the neural network. Despite delivering impressive results for OpenAI, the project did not lead to the expected breakthroughs and the company has hinted that it could be discontinued in its present format. A bitter lesson indeed.
Blurred computer vision
Computer vision techniques deliver astonishing results. They have sped up the diagnosing of diabetic retinopathy, built maps from satellite images and recognized particular whales from aerial photography. Well-trained models often outperform human experts. Given that they don’t get tired and never lose their focus, why shouldn’t they?
But there remains room for improvement for machine vision, as researchers from KU Leuven University in Belgium report. They delivered an image that fooled an algorithm, rendering the person holding a card with an image virtually invisible to a machine learning-based solution.
Why does it matter?
As readers of William Gibson’s novel Zero Hour will attest, images devised to fool AI are nothing new. Delivering a printable image to confound algorithm highlights a serious interest among malicious players interfering with AI.
Examples may include images produced to fool AI-powered medical diagnostic devices for fraudulent reasons or sabotaging road infrastructure to render it useless for autonomous vehicles.
AI should not be considered a black box and algorithms are not unbreakable. As always, reminders of that are welcome, especially as responsibility and transparency are among the most significant AI trends for 2019.
https://deepsense.ai/wp-content/uploads/2019/05/AI-Monthly-Digest-8-–-new-AI-applications-for-music-and-gaming.jpg3371140Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2019-05-09 11:34:462019-07-22 12:29:33AI Monthly Digest #8 - new AI applications for music and gaming
March saw some major events concerning top figures of the ML world, including OpenAI, Yann LeCun, Geoffrey Hinton and Yoshua Bengio.
The past month was also the backdrop for inspiring research on how machines think and how different they can be from humans when provided with the same conditions and problem to solve.
OpenAI goes from non-profit to for-profit
OpenAI was initially the non-profit organization focused on pushing the boundaries of artificial intelligence in the same manner the open-source organizations are able to deliver the highest-class software. The Mozilla Foundation and the Linux Foundation, the non-profit powerhouses behind the popular products, are the best examples.
Yet unlike the popular software with development powered by human talent, AI requires not only brilliant minds but also a gargantuan amount of computing power. The cost of reproducing the GPT-2 model is estimated to be around $50,000 – and that’s only one experiment to conduct. Getting the job done requires a small battalion of research scientists tuning hyperparameters, debugging and testing the approaches and ideas.
Staying on technology’s cutting edge pushed the organization toward the for-profit model to fund the computing power and attracting top talent, as the company notes on its website.
Why does it matter?
First of all, OpenAI was a significant player on the global AI map despite being a non-profit organization. Establishing a for-profit arm creates a new strong player that can develop commercial AI projects.
Moreover, the problem lies in the need for computing power, marking the new age of development challenges. In a traditional software development world, a team of talented coders is everything one would need. When it comes to delivering the AI-models, that is apparently not enough.
The bitter lesson of ML’s development
OpenAI’s transition could be seen as a single event concerning only one organization. It could be, that is, if it wasn’t discussed by the godfathers of modern machine learning.
Richard Sutton is one of the most renowned and influential researchers of reinforcement learning. In a recent essay, he remarked that most advances in AI development are powered by access to computing power, while the importance of expert knowledge and creative input from human researchers is losing significance.
Moreover, numerous attempts have been made to enrich machine learning with expert knowledge. Usually, the efforts were short-term gains with no bigger significance when seen in the broader context of AI’s development.
Why does it matter?
The opinion would seem to support the general observation that computing power is the gateway to pushing the boundaries of machine learning and artificial intelligence. That power combined with relatively simple machine learning techniques frequently challenges the established ways of solving problems. The RL-based agents playing GO, chess or Starcraft are only top-of-mind examples.
Yann LeCun, Geoffrey Hinton and Yoshua Bengio awarded the Turing Award
The Association for Computing Machinery, the world’s largest organization of computing professionals, announced that this year’s Turing Award went to three researchers for their work on advancing and popularizing neural networks. Currently, the researchers split their time between academia and the private sector, with Yann LeCun being employed by Facebook and New York University, Geoffrey Hinton working for Google and the University of Toronto, and Yoshua Bengio splitting his time between the University of Montreal and his company Element AI.
Why does it matter?
Named after Alan Turing, a giants of mathematics and the godfather of modern computer science, the Turing Award has been called IT’s Nobel Prize. While the lack of such a prize in IT is obvious — IT specialists get the Turing Award.
Nvidia creates a wonder brush – AI that turns a doodle into a landscape
Nvidia does a great job in spreading the knowledge about machine learning. Further applications in image editing will no doubt be forthcoming, automating the work of illustrators and graphic designers. But for now, it is amazing to behold.
So do you think like a computer?
While machine learning models are superhumanly effective in image recognition, if they fail, their predictions are usually at least surprising. Until recently, it was believed that people are unable to predict how a computer will interpret an image when not in the right way. Moreover, the totally inhuman way of recognizing the image is prone to mistakes – it is possible to prepare an artificial image that can effectively fool the AI behind the image recognition and, for example, convince the model that a car is in fact a bush.
The confusion about the machines identifying objects usually comes from the fact that most AI models are narrow AI. The systems are designed to work in a closed environment and solve a narrow problem, like identifying cars or animals. Consequently, the machine has a narrow catalog of entities to name.
To check if humans are able to understand how the machine is making its mistakes, the researchers provided volunteers with images that had already fooled AI models together with the names the machines were able to choose from for those images. In those conditions, people provided the same answers as the machines 75% of the time.
Why does it matter?
A recent study from John Hopkins University shows that computers become increasingly human even in their mistakes and that surprising outcomes are the consequence of extreme narrowness of the artificial mind. A typical preschooler has an incomparably larger vocabulary and amount of experience collected than even the most powerful neural network, so the likelihood of a human finding a more accurate association for the image are many times larger.
Again, the versatility and flexibility of the human mind is the key to its superiority.
https://deepsense.ai/wp-content/uploads/2019/04/AI-monthly_digest_7.jpg3371140Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2019-04-11 13:29:282019-07-08 11:35:10AI Monthly Digest #7 - machine mistakes, and the hard way to profit from non-profit
February came with the groundbreaking event of building a new state-of-the-art natural language processing neural network. And then the unthinkable happened.
1. Open-AI designed a new gold standard for natural language processing…
While in image processing artificial intelligence models outperform humans in most tasks, natural language processing (NLP) is still a challenge for machines to master. Although AI-based services are already usable with Google Translate being the pinnacle of today’s achievements, the texts produced by machines is easy to recognize by humans. But this could change soon with the latest state-of-the-art from OpenAI.
They designed a model called GPT-2 and prepared a dataset containing 8 million web pages for training. The network objective is to predict the next word given all the words to that point, which is the simplest way of doing unsupervised learning in NLP. The main improvements, in this case, are scaling up the model size (1.5B parameters) and training it on the gargantuan database of Internet text (40GB) at an unprecedented scale (32 x TPUv3).
The resulting network analyzes the starting word of a sentence and then it adds the next words to create a text based on “the most probable output”. The effect is surprisingly accurate. The model is able to recognize the type of the starting text. If the first sentence is a press title it will produce a legitimate-sounding short news article. It is also good at adopting various styles – something unimaginable for previously sturdy models which could produce problematic sentences.
Example:
Human written:
In a shocking finding, scientist 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.”
More examples of AI-generated texts may be found at Open-AI blog.
Moreover – the network is capable of transfer learning – it is easier to perform a slight additional training to enable the model to perform more sophisticated tasks. Transfer learning is a gateway technology for modern image recognition appliances, including art recognition and visual quality control.
Why does it matter
Alan Turing, the godfather of modern information technology, was heavily convinced that the ability to understand language is a key indicator of intelligence. Natural language, the emotions hidden beneath the words and all the cultural and societal contexts behind sentences are truly unique for human beings.
Making this part of the world approachable for machines is undoubtedly the great breakthrough and passes countless possibilities, from battling hate crime to various business appliances.
2. … and they didn’t release it to the public
With great power comes great responsibility and the events from February 2019 show it clearly. Despite best practices and established custom, Open-AI decided NOT TO release the state-of-the-art neural network to the public as an open source software.
The organization did this to prevent the neural network from being used for malicious purposes, with perpetuating fake news as a top concern. The decision sparked a debate on Reddit, Twitter and other various media, where participants argued on the safety of releasing this “potentially dangerous technology” and tackling the established model of open-sourcing the effect of research. Researchers are afraid of building the doorway for “deepfakes for texts”.
Why does it matter
AI ethics and responsibility was highlighted as one of the major AI trends 2019. The situation where researchers choose safety over progress is unusual. On the other hand, fake news is considered one of the major threats of the future and was recognized one of the most dangerous online activities in 2018 by the World Economic Forum.
With the rise of autonomous cars and IoT revolution building a frame for sophisticated AI-powered solutions to work, such dilemmas may get increasingly common.
3. China’s AI strategy at a glance (or even more)
In the last AI monthly digest, we covered the AI strategy forged in Finland, where the national pursuit for the AI appliances grew from a grassroots movement. In contrast, China has a centralized strategy to make the country AI-giant.
The Center for a New American Security (CNAS) has published a report in which they provide deeper insights into understanding China’s AI strategy. The report is a long read written by Gregory C. Allen who had a chance to meet a few times with high-ranking Chinese officials on conferences focusing on Artificial Intelligence.
Topics covered in the text:
Chinese views on importance and security of AI
Strengths and weaknesses of China’s AI Ecosystem
China’s short-term goals in AI
The role of semiconductors
The author concludes that gaining expertise in and understanding of AI developments in China should help U.S. policymakers sort out their priorities. Instead of influencing China’s competitiveness, they should focus on boosting the technological and economic competitiveness of the United States.
Why does it matter
Early adopters are usually those who earn knowledge with first failures. Thus, for now, it is wise to observe and learn.
4. Introducing Paperswithcode
Staying updated on the latest developments in machine learning is not an easy task, especially considering the fact that not all the researchers decide to make their code available. Releasing the code is a good practice, but providing only the paper that “describes the matter enough” to reproduce the effect is also correct.
Using the paperswithcode makes the process of paper-and-code matching much easier, effectively saving a lot of time for people willing to broaden their knowledge.
Why does it matter
The portal itself is a handy tool to gain knowledge and, a bit unwillingly promotes the constituted approach of making the research code public. If it gains notoriety, it may be a tool of social pressure to publish code with the research.
5. Popularity of thispersondoesnotexist.com
The neural networks’ ability to create convincing human faces is one of the benchmarks for modern AI appliances. To make the images available for the public, the StyleGan-based model publishes random face on thispersondoesnotexist.com website.
As in every aspect of life, the devil is in the details. The website is a great tool to spot the weakest parts of generated faces.
Hair – it is common to place some hair in the air, with no attachment to other or floating in an unnatural way. Sometimes the skin around hair is “sticking” to them, creating some kind of scars that are disturbing when seen. The model sometimes messes up hairstyles, mixing dreadlocks with straight and curly hair, but it is more challenging to spot
Teeth – algorithms tend to mess up with teeth, placing them unnaturally, sometimes merged or blurred.
Glasses – it is common that the eyes inside the glasses do not fit the rest of the depicted person. Sometimes it means placing eyes of an old woman, surrounded with wrinkles, on a child’s face.
Why does it matter
Making these images publicly available with commentary, which all have been made by neural networks, is an interesting way to spread knowledge about AI-related topics. The concerns about deep fakes and other illicit content created with the support of artificial intelligence are mostly based on lack of knowledge – the AI-based techniques are seen as omnipotent. But they aren’t, as can be easily seen by the example of teeth and hair.
Moreover, the initiative is another way of encouraging people to verify news sources and distrusting information and tackling the challenge of fake news and deep fakes, mentioned above.
And a bonus one:
deepsense.ai in cooperation with Google Brain has finished research on enabling reinforcement learning (RL) agents to make predictions about their upcoming actions to reduce the number of actions needed to train skills.
RL agents typically need incomparably more actions to train skill than humans and the ability to build the skill “in a head” may be a part of the answer. Starting from childhood, people repeat their actions in their minds when they seek perfection or just recall an enjoyable or pleasant time.
Following this idea, our researchers have designed neural networks that possess the imagination that enables the model to simulate the action before executing it. A more detailed story about these projects and their outcome may be found in our blogpost about artificial imagination and Arxiv paper.
Why does it matter?
Artificial imagination is basically the idea of building the world simulated only by the mind’s power. Our researchers were able not only to reduce training time, but also to replace the simulated environment with another neural network’s imagination.
Maintaining and providing the simulated environment is one of the highest costs in building reinforcement learning agents, so tackling this challenge may be a great step to making this technique more popular.
https://deepsense.ai/wp-content/uploads/2019/03/Ai-ethics-artificial-imagination.png3371140Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2019-03-07 12:34:572019-10-08 11:33:25AI Monthly Digest #6 - AI ethics and artificial imagination
