AI Neural Network: the Bigger, the Smarter

When it comes to the neural networks that power today’s artificial intelligence, sometimes the bigger they are, the smarter they are too. Recent leaps in machine understanding of language, for example, have hinged on building some of the most enormous AI models ever and stuffing them with huge gobs of text. A new cluster of computer chips could now help these networks grow to almost unimaginable size—and show whether going ever larger may unlock further AI advances, not only in language understanding, but perhaps also in areas like robotics and computer vision.

Cerebras Systems, a startup that has already built the world’s largest computer chip, has now developed technology that lets a cluster of those chips run AI models that are more than a hundred times bigger than the most gargantuan ones around today.

Cerebras says it can now run a neural network with 120 trillion connections, mathematical simulations of the interplay between biological neurons and synapses. The largest AI models in existence today have about a trillion connections, and they cost many millions of dollars to build and train. But Cerebras says its hardware will run calculations in about a 50th of the time of existing hardware. Its chip cluster, along with power and cooling requirements, presumably still won’t come cheap, but Cerberas at least claims its tech will be substantially more efficient.

We built it with synthetic parameters,” says Andrew Feldman, founder and CEO of Cerebras, who will present details of the tech at a chip conference this week. “So we know we can, but we haven’t trained a model, because we’re infrastructure builders, and, well, there is no model yet” of that size, he adds.

Today, most AI programs are trained using GPUs, a type of chip originally designed for generating computer graphics but also well suited for the parallel processing that neural networks require. Large AI models are essentially divided up across dozens or hundreds of GPUs, connected using high-speed wiring.

GPUs still make sense for AI, but as models get larger and companies look for an edge, more specialized designs may find their niches. Recent advances and commercial interest have sparked a Cambrian explosion in new chip designs specialized for AI. The Cerebras chip is an intriguing part of that evolution. While normal semiconductor designers split a wafer into pieces to make individual chips, Cerebras packs in much more computational power by using the entire thing, having its many computational units, or cores, talk to each other more efficiently. A GPU typically has a few hundred cores, but Cerebras’s latest chip, called the Wafer Scale Engine Two (WSE-2), has 850,000 of them.

Source: https://www.wired.com/

Tesla Robot + Neuralink has Revolutionary Healthcare Applications

Elon Musk and his companies have a commitment to fearless innovation. The incredible accomplishments that his companies have achieved include cutting-edge electric vehicles with Tesla, next-generation space-flight capabilities with SpaceX, and the development of critical brain-machine interfaces with Neuralink, to name a few.

Musk’s most recent announcement was on behalf of Tesla, and was yet another ode to fearless innovation. Last week, during Tesla’s much anticipated “AI Day,” an event meant to showcase the company’s revolutionary strides in artificial intelligence technology, Musk announced the next frontier for the company: developing the Tesla Bot, a “general purpose, bi-pedal, humanoid robot capable of performing tasks that are unsafe, repetitive or boring.”

Elon Musk described the project in detail: “Basically, if you think about what we’re doing right now with the cars, Tesla is arguably the world’s biggest robotics company…because our cars are like semi-sentient robots on wheels. With the full self-driving computer, the inference engine on the car (which will keep evolving, obviously), Dojo, and all the neural nets recognizing the world, understanding how to navigate through the world, it kind of makes sense to put that onto a humanoid form.” Musk described the purpose behind this bot, atleast initially: “it’s intended to be friendly of course, and navigate through a world built for humans and eliminate dangerous repetitive and boring tasks.” Musk also explained that a useful humanoid robot should be able to navigate the world without being explicitly trained step-by-step, and instead, should be able to perform advanced tasks with cognitive understanding of simple commands, such as“pick up groceries.”

Source: https://www.forbes.com/

AI Recognises the Biological Activity of Natural Products

Nature has a vast store of medicinal substances. “Over 50 percent of all drugs today are inspired by nature,” says Gisbert Schneider, Professor of Computer-​Assisted Drug Design at ETH Zurich. Nevertheless, he is convinced that we have tapped only a fraction of the potential of natural products. Together with his team, he has successfully demonstrated how artificial intelligence (AI) methods can be used in a targeted manner to find new pharmaceutical applications for natural products. Furthermore, AI methods are capable of helping to find alternatives to these compounds that have the same effect but are much easier and therefore cheaper to manufacture.

And so the ETH researchers are paving the way for an important medical advance: we currently have only about 4,000 basically different medicines in total. In contrast, estimates of the number of human proteins reach up to 400,000, each of which could be a target for a drug. There are good reasons for Schneider’s focus on nature in the search for new pharmaceutical agents.

Most natural products are by definition potential active ingredients that have been selected via evolutionary mechanisms,” he says.
Whereas scientists used to trawl collections of natural products on the search for new drugs, Schneider and his team have flipped the script: first, they look for possible target molecules, typically proteins, of natural products so as to identify the pharmacologically relevant compounds. “The chances of finding medically meaningful pairs of active ingredient and target protein are much greater using this method than with conventional screening,” Schneider says.

Source: https://www.weforum.org/

AI Designs Quantum Physics Beyond What Any Human Has Conceived

Quantum physicist Mario Krenn remembers sitting in a café in Vienna in early 2016, poring over computer printouts, trying to make sense of what MELVIN had found. MELVIN was a machine-learning algorithm Krenn had built, a kind of artificial intelligence. Its job was to mix and match the building blocks of standard quantum experiments and find solutions to new problems. And it did find many interesting ones. But there was one that made no sense.

The first thing I thought was, ‘My program has a bug, because the solution cannot exist,’” Krenn says. MELVIN had seemingly solved the problem of creating highly complex entangled states involving multiple photons (entangled states being those that once made Albert Einstein invoke the specter of “spooky action at a distance”). Krenn, Anton Zeilinger of the University of Vienna and their colleagues had not explicitly provided MELVIN the rules needed to generate such complex states, yet it had found a way. Eventually, he realized that the algorithm had rediscovered a type of experimental arrangement that had been devised in the early 1990s. But those experiments had been much simpler. MELVIN had cracked a far more complex puzzle.

When we understood what was going on, we were immediately able to generalize [the solution],” says Krenn, who is now at the University of Toronto. Since then, other teams have started performing the experiments identified by MELVIN, allowing them to test the conceptual underpinnings of quantum mechanics in new ways. Meanwhile Krenn, working with colleagues in Toronto, has refined their machine-learning algorithms. Their latest effort, an AI called THESEUS, has upped the ante: it is orders of magnitude faster than MELVIN, and humans can readily parse its output. While it would take Krenn and his colleagues days or even weeks to understand MELVIN’s meanderings, they can almost immediately figure out what THESEUS is saying.
It is amazing work,” says theoretical quantum physicist Renato Renner of the Institute for Theoretical Physics at the Swiss Federal Institute of Technology Zurich, who reviewed a 2020 study about THESEUS but was not directly involved in these efforts.

Source: https://www.scientificamerican.com/

How to Completely Wipe out Colon Cancer in Anybody Who Gets Screened

Michael Wallace has performed hundreds of colonoscopies in his 20 years as a gastroenterologist. He thinks he’s pretty good at recognizing the growths, or polyps, that can spring up along the ridges of the colon and potentially turn into cancer. But he isn’t always perfect. Sometimes the polyps are flat and hard to see. Other times, doctors just miss them. “We’re all humans,” says Wallace, who works at the Mayo Clinic. After a morning of back-to-back procedures that require attention to minute details, he says, “we get tired.”

Colonoscopies, if unpleasant, are highly effective at sussing out pre-cancerous polyps and preventing colon cancer. But the effectiveness of the procedure rests heavily on the abilities of the physician performing it. Now, the Food and Drug Administration has approved a new tool that promises to help doctors recognize precancerous growths during a colonoscopy: an artificial intelligence system made by Medtronic. Doctors say that alongside other measures, the tool could help improve diagnoses.

 

We really have the opportunity to completely wipe out colon cancer in anybody who gets screened,” says Wallace, who consulted with Medtronic on the project.

The Medtronic system, called GI Genius, has seen the inside of more colons than most doctors. Medtronic and partner Cosmo Pharmaceuticals trained the algorithm to recognize polyps by reviewing more than 13 million videos of colonoscopies conducted in Europe and the US that Cosmo had collected while running drug trials. To “teach” the AI to distinguish potentially dangerous growths, the images were labeled by gastroenterologists as either normal or unhealthy tissue. Then the AI was tested on progressively harder-to-recognize polyps, starting with colonoscopies that were performed under perfect conditions and moving to more difficult challenges, like distinguishing a polyp that was very small, only in range of the camera briefly, or hidden in a dark spot. The system, which can be added to the scopes that doctors already use to perform a colonoscopy, follows along as the doctor probes the colon, highlighting potential polyps with a green box. GI Genius was approved in Europe in October 2019 and is the first AI cleared by the FDA for helping detect colorectal polyps. “It found things that even I missed,” says Wallace, who co-authored the first validation study of GI Genius. “It’s an impressive system.”

Source: https://www.wired.com/

Neuralink Wants to Implant Human Brain Chips Within a Year

Tesla CEO Elon Musk released a video showing how his company Neuralink – a brain-computer-interface company – had advanced its technology to the point that the chip could allow a monkey to play video games with its mind.

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Neuralink could transition from operating on monkeys to human trials within the year, if the startup meets a previous prediction from Musk. In February, he said the company planned to launch human trials by the end of the year after first mentioning his work with the monkey implants.

At the time, the CEO gave the timeline in response to another user’s request to join human trials for the product, which is designed to implant artificial intelligence into human brains as well as potentially cure neurological diseases like Alzheimer’s and Parkinson’s.

Musk has made similar statements in the past about his project, which was launched in 2016. He said in 2019 that it would be testing on humans by the end of 2020.

There has been a recent flurry of information on the project. Prior to the recent video release on Twitter, Musk had made an appearance on the social media site, Clubhouse, and provided some additional updates on Neuralink back in February.

During his Clubhouse visit, Musk detailed how the company had implanted the chip in the monkey’s brain and talked about how it could play video games using only its mind.

Source: https://www.sciencealert.com/

AI  Learns to Manipulate Human Behavior


Artificial intelligence
 (AI) is learning more about how to work with (and on) humans. A recent study has shown how AI can learn to identify vulnerabilities in human habits and behaviours and use them to influence human decision-making.
It may seem cliched to say AI is transforming every aspect of the way we live and work, but it’s true. Various forms of AI are at work in fields as diverse as vaccine development, environmental management and office administration. And while AI does not possess human-like intelligence and emotions, its capabilities are powerful and rapidly developing. There’s no need to worry about a machine takeover just yet, but this recent discovery highlights the power of AI and underscores the need for proper governance to prevent misuse.

A team of researchers at CSIRO’s Data61, the data and digital arm of Australia’s national science agency, devised a systematic method of finding and exploiting vulnerabilities in the ways people make choices, using a kind of AI system called a recurrent neural network and deep reinforcement-learning. To test their model they carried out three experiments in which human participants played games against a computer.

The first experiment involved participants clicking on red or blue coloured boxes to win a fake currency, with the AI learning the participant’s choice patterns and guiding them towards a specific choice. The AI was successful about 70 percent of the time.

The third experiment consisted of several rounds in which a participant would pretend to be an investor giving money to a trustee (the AI). The AI would then return an amount of money to the participant, who would then decide how much to invest in the next round. This game was played in two different modes: in one the AI was out to maximise how much money it ended up with, and in the other the AI aimed for a fair distribution of money between itself and the human investor. The AI was highly successful in each mode.

In each experiment, the machine learned from participants’ responses and identified and targeted vulnerabilities in people’s decision-making. The end result was the machine learned to steer participants towards particular actions. The research does advance our understanding not only of what AI can do but also of how people make choices. It shows machines can learn to steer human choice-making through their interactions with us.

Source: https://theconversation.com/

How to Link Human Brains To Computers

Elon Musk has a vision of linking human brains to computers in order to avoid our species from being outpaced by artificial intelligence – and this dream is set to become a reality. Speaking on Joe Rogan’s podcast, the billionaire said his company Neuralink will have a version of its brain implant ready ‘within a year.’ Musk explained that the process involves removing a chunk of the skull, robots then insert electrodes into the brain and the device into the hole, with only a small scar left behind.

Neuralink, which was founded in 2016, is designing tiny flexible ‘threads‘ that are ten times thinner than a human hair with the goal of treating brain injuries and trauma. The tech tycoon also revealed that the technology could develop into a full brain interface in just 25 years, which would enable ‘symbiosis‘ between humans and AI.

Wait until you see the next version vs what was presented last year. It’s *awesome*, he wrote. In the podcast, Musk dished to Rogan about the technology, how it is implanted and what it can do to improve the human body. The tech tycoon explained that the device is about one inch in diameter, similar to the face of a smart watch, and is implanted by removing a small chunk of the skull. A small robot connects the thread-like electrodes to certain areas of the brain, stitches up the hole and the only visible remains is a scar left behind from the incision.

The process involves removing a chunk of the skull, robots then insert electrodes into the brain and the device into the hole, with only a small scar left behind 

If you got an interface into the motor cortex, and then an implant that’s like a microcontroller near muscle groups you can then create a sort of a neural shunt that restores somebody who quadriplegic to full functionality, like they can walk around, be normal – maybe slightly better overtime,’ Musk explained.

When asked about the risks involved with placing a foreign object in the body, Musk said there is ‘a very low potential risk for rejection.’ ‘People put in heart monitors and things for epileptic seizures, deep brain simulation, artificial hips and knees that kind of thing,’ he said, noting that ‘it’s well known what is cause for a rejection or not.

Along with curing ailments, the chip could change the way human beings interface with each other‘You wouldn’t need to talk,’ Musk said, who foresees the technology going further to enable ‘symbiosis’ between humans and AI‘I think this is one of the paths to like AI is getting better and better,’ Musk added. ‘We are kind of left behind, we are just too dumb.’ ‘We are already a cyborg to some degree,’

Source: https://www.dailymail.co.uk/

AI Will Take Away Lots Of Jobs in UK

The scale of the challenge that automation poses to the jobs market needs to be met with much stronger action to upskill the workforce, finds a new report published by a committee in the UK Parliament.

The House of Lords’ select committee on artificial intelligence raised concerns at the “inertia” that is slowing down the country when it comes to digital skills, and urged the government to take steps to make sure that people have the opportunity to reskill and retrain, to be able to adapt to the changing labor market that AI is bringing about.

Citing research carried out by Microsoft, the committee stressed that only 17% of UK employees say that they have been part of reskilling efforts, which sits well below the global average of 38%.

Microsoft also recently reported that almost 70% of business leaders in the UK believe that their organization currently has a digital skills gap, and that two-thirds of employees feel that they do not have the appropriate digital skills to fulfil new and emerging roles in their industry.Even basic digital skills are lacking: a recent Lloyds Bank survey found that 19% of individuals in the UK couldn’t complete tasks such as using a web browser.

For the past three years, the government has been offering a national retraining scheme, which aims to upskill UK citizens, partly as a result of automation. Wendy Hall, a professor of computer science at the University of Southampton, who provided evidence to the Lords for the report, said that the UK is currently “nowhere near ready” when it comes to building up the skills that are necessary to mitigate the impact of automation on jobs.

Meanwhile, found the report, AI systems are growing at a fast pace. While in 2015, the UK saw £245 million ($326 million) invested in AI, that number jumped to £1.3 billion ($1.73 billion) in 2019. Automated systems are now prevalent in many industries, ranging from agriculture to healthcare, through to financial services, retail and logistics.

Source: https://www.zdnet.com/

AI-generated Science Ready In Minutes

No matter how many months or years authors take to produce a scientific paper, Sabine Louët needs only a few seconds to generate a coherent 300-word summary of it. But she leaves the thinking to an artificial intelligence (AI) algorithm that statistically analyses the text, identifies meaningful words and phrases, and pieces it all together into a crisp, readable chunk.

We’re trying to tell a story, and we want to make it as digestible as possible,” says Louët, chief executive of SciencePOD, a Dublin-based science communication company.

As the volume of research continues to grow, natural-language processing programs that can rapidly sort and summarize scientific papers have become an increasingly important tool for scientific publishers and researchers alike, says Markus Kaindl, senior manager for data development at Springer Nature, which publishes Nature Index. (Nature Index is editorially independent of its publisher.)

He points to the roughly 2,000 papers published on COVID-19 each week, enough to overwhelm anyone trying to stay on top of the field. “It’s like an ocean of content, and it feels like our users are close to drowning,” he says. “We need to help them surf that wave instead.”

AI can help identify the papers most suited to a particular user’s needs. For example, Semantic Scholar, developed by the Allen Institute for Artificial Intelligence in Seattle, Washington, goes beyond keywords to rank the most relevant papers for any query. “It’s a brilliant platform because it really tries to understand what the publications are about,” Kaindl says. Springer Nature expects to go further by offering personalized summaries and search results. “If you are a senior career researcher, a postdoc or a principal investigator, your needs from a paper or a chapter may be very different from someone at an earlier career stage,” he says.

The company has engaged SciencePOD and others to explore the use of AI to enhance content appeal and accessibility. “AI can really help us as science publishers, by summarizing information, translating it for wider audiences and increasing the impact,” says Kaindl.

Source: https://www.nature.com/