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#437227 DIGIT: A high-resolution tactile sensor ...
To assist humans in completing manual chores or tasks, robots must efficiently grasp and manipulate objects in their surroundings. While in recent years robotics researchers have developed a growing number of techniques that allow robots to pick up and handle objects, most of these only proved to be effective when tackling very basic tasks, such as picking up an object or moving it from one place to another. Continue reading
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#437222 China and AI: What the World Can Learn ...
China announced in 2017 its ambition to become the world leader in artificial intelligence (AI) by 2030. While the US still leads in absolute terms, China appears to be making more rapid progress than either the US or the EU, and central and local government spending on AI in China is estimated to be in the tens of billions of dollars.
The move has led—at least in the West—to warnings of a global AI arms race and concerns about the growing reach of China’s authoritarian surveillance state. But treating China as a “villain” in this way is both overly simplistic and potentially costly. While there are undoubtedly aspects of the Chinese government’s approach to AI that are highly concerning and rightly should be condemned, it’s important that this does not cloud all analysis of China’s AI innovation.
The world needs to engage seriously with China’s AI development and take a closer look at what’s really going on. The story is complex and it’s important to highlight where China is making promising advances in useful AI applications and to challenge common misconceptions, as well as to caution against problematic uses.
Nesta has explored the broad spectrum of AI activity in China—the good, the bad, and the unexpected.
The Good
China’s approach to AI development and implementation is fast-paced and pragmatic, oriented towards finding applications which can help solve real-world problems. Rapid progress is being made in the field of healthcare, for example, as China grapples with providing easy access to affordable and high-quality services for its aging population.
Applications include “AI doctor” chatbots, which help to connect communities in remote areas with experienced consultants via telemedicine; machine learning to speed up pharmaceutical research; and the use of deep learning for medical image processing, which can help with the early detection of cancer and other diseases.
Since the outbreak of Covid-19, medical AI applications have surged as Chinese researchers and tech companies have rushed to try and combat the virus by speeding up screening, diagnosis, and new drug development. AI tools used in Wuhan, China, to tackle Covid-19 by helping accelerate CT scan diagnosis are now being used in Italy and have been also offered to the NHS in the UK.
The Bad
But there are also elements of China’s use of AI that are seriously concerning. Positive advances in practical AI applications that are benefiting citizens and society don’t detract from the fact that China’s authoritarian government is also using AI and citizens’ data in ways that violate privacy and civil liberties.
Most disturbingly, reports and leaked documents have revealed the government’s use of facial recognition technologies to enable the surveillance and detention of Muslim ethnic minorities in China’s Xinjiang province.
The emergence of opaque social governance systems that lack accountability mechanisms are also a cause for concern.
In Shanghai’s “smart court” system, for example, AI-generated assessments are used to help with sentencing decisions. But it is difficult for defendants to assess the tool’s potential biases, the quality of the data, and the soundness of the algorithm, making it hard for them to challenge the decisions made.
China’s experience reminds us of the need for transparency and accountability when it comes to AI in public services. Systems must be designed and implemented in ways that are inclusive and protect citizens’ digital rights.
The Unexpected
Commentators have often interpreted the State Council’s 2017 Artificial Intelligence Development Plan as an indication that China’s AI mobilization is a top-down, centrally planned strategy.
But a closer look at the dynamics of China’s AI development reveals the importance of local government in implementing innovation policy. Municipal and provincial governments across China are establishing cross-sector partnerships with research institutions and tech companies to create local AI innovation ecosystems and drive rapid research and development.
Beyond the thriving major cities of Beijing, Shanghai, and Shenzhen, efforts to develop successful innovation hubs are also underway in other regions. A promising example is the city of Hangzhou, in Zhejiang Province, which has established an “AI Town,” clustering together the tech company Alibaba, Zhejiang University, and local businesses to work collaboratively on AI development. China’s local ecosystem approach could offer interesting insights to policymakers in the UK aiming to boost research and innovation outside the capital and tackle longstanding regional economic imbalances.
China’s accelerating AI innovation deserves the world’s full attention, but it is unhelpful to reduce all the many developments into a simplistic narrative about China as a threat or a villain. Observers outside China need to engage seriously with the debate and make more of an effort to understand—and learn from—the nuances of what’s really happening.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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#437171 Scientists Tap the World’s Most ...
In The Hitchhiker’s Guide to the Galaxy by Douglas Adams, the haughty supercomputer Deep Thought is asked whether it can find the answer to the ultimate question concerning life, the universe, and everything. It replies that, yes, it can do it, but it’s tricky and it’ll have to think about it. When asked how long it will take it replies, “Seven-and-a-half million years. I told you I’d have to think about it.”
Real-life supercomputers are being asked somewhat less expansive questions but tricky ones nonetheless: how to tackle the Covid-19 pandemic. They’re being used in many facets of responding to the disease, including to predict the spread of the virus, to optimize contact tracing, to allocate resources and provide decisions for physicians, to design vaccines and rapid testing tools, and to understand sneezes. And the answers are needed in a rather shorter time frame than Deep Thought was proposing.
The largest number of Covid-19 supercomputing projects involves designing drugs. It’s likely to take several effective drugs to treat the disease. Supercomputers allow researchers to take a rational approach and aim to selectively muzzle proteins that SARS-CoV-2, the virus that causes Covid-19, needs for its life cycle.
The viral genome encodes proteins needed by the virus to infect humans and to replicate. Among these are the infamous spike protein that sniffs out and penetrates its human cellular target, but there are also enzymes and molecular machines that the virus forces its human subjects to produce for it. Finding drugs that can bind to these proteins and stop them from working is a logical way to go.
The Summit supercomputer at Oak Ridge National Laboratory has a peak performance of 200,000 trillion calculations per second—equivalent to about a million laptops. Image credit: Oak Ridge National Laboratory, U.S. Dept. of Energy, CC BY
I am a molecular biophysicist. My lab, at the Center for Molecular Biophysics at the University of Tennessee and Oak Ridge National Laboratory, uses a supercomputer to discover drugs. We build three-dimensional virtual models of biological molecules like the proteins used by cells and viruses, and simulate how various chemical compounds interact with those proteins. We test thousands of compounds to find the ones that “dock” with a target protein. Those compounds that fit, lock-and-key style, with the protein are potential therapies.
The top-ranked candidates are then tested experimentally to see if they indeed do bind to their targets and, in the case of Covid-19, stop the virus from infecting human cells. The compounds are first tested in cells, then animals, and finally humans. Computational drug discovery with high-performance computing has been important in finding antiviral drugs in the past, such as the anti-HIV drugs that revolutionized AIDS treatment in the 1990s.
World’s Most Powerful Computer
Since the 1990s the power of supercomputers has increased by a factor of a million or so. Summit at Oak Ridge National Laboratory is presently the world’s most powerful supercomputer, and has the combined power of roughly a million laptops. A laptop today has roughly the same power as a supercomputer had 20-30 years ago.
However, in order to gin up speed, supercomputer architectures have become more complicated. They used to consist of single, very powerful chips on which programs would simply run faster. Now they consist of thousands of processors performing massively parallel processing in which many calculations, such as testing the potential of drugs to dock with a pathogen or cell’s proteins, are performed at the same time. Persuading those processors to work together harmoniously is a pain in the neck but means we can quickly try out a lot of chemicals virtually.
Further, researchers use supercomputers to figure out by simulation the different shapes formed by the target binding sites and then virtually dock compounds to each shape. In my lab, that procedure has produced experimentally validated hits—chemicals that work—for each of 16 protein targets that physician-scientists and biochemists have discovered over the past few years. These targets were selected because finding compounds that dock with them could result in drugs for treating different diseases, including chronic kidney disease, prostate cancer, osteoporosis, diabetes, thrombosis and bacterial infections.
Scientists are using supercomputers to find ways to disable the various proteins—including the infamous spike protein (green protrusions)—produced by SARS-CoV-2, the virus responsible for Covid-19. Image credit: Thomas Splettstoesser scistyle.com, CC BY-ND
Billions of Possibilities
So which chemicals are being tested for Covid-19? A first approach is trying out drugs that already exist for other indications and that we have a pretty good idea are reasonably safe. That’s called “repurposing,” and if it works, regulatory approval will be quick.
But repurposing isn’t necessarily being done in the most rational way. One idea researchers are considering is that drugs that work against protein targets of some other virus, such as the flu, hepatitis or Ebola, will automatically work against Covid-19, even when the SARS-CoV-2 protein targets don’t have the same shape.
Our own work has now expanded to about 10 targets on SARS-CoV-2, and we’re also looking at human protein targets for disrupting the virus’s attack on human cells. Top-ranked compounds from our calculations are being tested experimentally for activity against the live virus. Several of these have already been found to be active.The best approach is to check if repurposed compounds will actually bind to their intended target. To that end, my lab published a preliminary report of a supercomputer-driven docking study of a repurposing compound database in mid-February. The study ranked 8,000 compounds in order of how well they bind to the viral spike protein. This paper triggered the establishment of a high-performance computing consortium against our viral enemy, announced by President Trump in March. Several of our top-ranked compounds are now in clinical trials.
Also, we and others are venturing out into the wild world of new drug discovery for Covid-19—looking for compounds that have never been tried as drugs before. Databases of billions of these compounds exist, all of which could probably be synthesized in principle but most of which have never been made. Billion-compound docking is a tailor-made task for massively parallel supercomputing.
Dawn of the Exascale Era
Work will be helped by the arrival of the next big machine at Oak Ridge, called Frontier, planned for next year. Frontier should be about 10 times more powerful than Summit. Frontier will herald the “exascale” supercomputing era, meaning machines capable of 1,000,000,000,000,000,000 calculations per second.
Although some fear supercomputers will take over the world, for the time being, at least, they are humanity’s servants, which means that they do what we tell them to. Different scientists have different ideas about how to calculate which drugs work best—some prefer artificial intelligence, for example—so there’s quite a lot of arguing going on.
Hopefully, scientists armed with the most powerful computers in the world will, sooner rather than later, find the drugs needed to tackle Covid-19. If they do, then their answers will be of more immediate benefit, if less philosophically tantalizing, than the answer to the ultimate question provided by Deep Thought, which was, maddeningly, simply 42.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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#437150 AI Is Getting More Creative. But Who ...
Creativity is a trait that makes humans unique from other species. We alone have the ability to make music and art that speak to our experiences or illuminate truths about our world. But suddenly, humans’ artistic abilities have some competition—and from a decidedly non-human source.
Over the last couple years there have been some remarkable examples of art produced by deep learning algorithms. They have challenged the notion of an elusive definition of creativity and put into perspective how professionals can use artificial intelligence to enhance their abilities and produce beyond the known boundaries.
But when creativity is the result of code written by a programmer, using a format given by a software engineer, featuring private and public datasets, how do we assign ownership of AI-generated content, and particularly that of artwork? McKinsey estimates AI will annually generate value of $3.5 to $5.8 trillion across various sectors.
In 2018, a portrait that was christened Edmond de Belamy was made in a French art collective called Obvious. It used a database with 15,000 portraits from the 1300s to the 1900s to train a deep learning algorithm to produce a unique portrait. The painting sold for $432,500 in a New York auction. Similarly, a program called Aiva, trained on thousands of classical compositions, has released albums whose pieces are being used by ad agencies and movies.
The datasets used by these algorithms were different, but behind both there was a programmer who changed the brush strokes or musical notes into lines of code and a data scientist or engineer who fitted and “curated” the datasets to use for the model. There could also have been user-based input, and the output may be biased towards certain styles or unintentionally infringe on similar pieces of art. This shows that there are many collaborators with distinct roles in producing AI-generated content, and it’s important to discuss how they can protect their proprietary interests.
A perspective article published in Nature Machine Intelligence by Jason K. Eshraghian in March looks into how AI artists and the collaborators involved should assess their ownership, laying out some guiding principles that are “only applicable for as long as AI does not have legal parenthood, the way humans and corporations are accorded.”
Before looking at how collaborators can protect their interests, it’s useful to understand the basic requirements of copyright law. The artwork in question must be an “original work of authorship fixed in a tangible medium.” Given this principle, the author asked whether it’s possible for AI to exercise creativity, skill, or any other indicator of originality. The answer is still straightforward—no—or at least not yet. Currently, AI’s range of creativity doesn’t exceed the standard used by the US Copyright Office, which states that copyright law protects the “fruits of intellectual labor founded in the creative powers of the mind.”
Due to the current limitations of narrow AI, it must have some form of initial input that helps develop its ability to create. At the moment AI is a tool that can be used to produce creative work in the same way that a video camera is a tool used to film creative content. Video producers don’t need to comprehend the inner workings of their cameras; as long as their content shows creativity and originality, they have a proprietary claim over their creations.
The same concept applies to programmers developing a neural network. As long as the dataset they use as input yields an original and creative result, it will be protected by copyright law; they don’t need to understand the high-level mathematics, which in this case are often black box algorithms whose output it’s impossible to analyze.
Will robots and algorithms eventually be treated as creative sources able to own copyrights? The author pointed to the recent patent case of Warner-Lambert Co Ltd versus Generics where Lord Briggs, Justice of the Supreme Court of the UK, determined that “the court is well versed in identifying the governing mind of a corporation and, when the need arises, will no doubt be able to do the same for robots.”
In the meantime, Dr. Eshraghian suggests four guiding principles to allow artists who collaborate with AI to protect themselves.
First, programmers need to document their process through online code repositories like GitHub or BitBucket.
Second, data engineers should also document and catalog their datasets and the process they used to curate their models, indicating selectivity in their criteria as much as possible to demonstrate their involvement and creativity.
Third, in cases where user data is utilized, the engineer should “catalog all runs of the program” to distinguish the data selection process. This could be interpreted as a way of determining whether user-based input has a right to claim the copyright too.
Finally, the output should avoid infringing on others’ content through methods like reverse image searches and version control, as mentioned above.
AI-generated artwork is still a very new concept, and the ambiguous copyright laws around it give a lot of flexibility to AI artists and programmers worldwide. The guiding principles Eshraghian lays out will hopefully shed some light on the legislation we’ll eventually need for this kind of art, and start an important conversation between all the stakeholders involved.
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