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.


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.


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.”


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.


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.


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.


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,’


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.


General Cognitive Assessment Of The Brain In Seven Minutes

React Neuro, a startup founded three years ago by veterans of Harvard Medical School (HMS) and Massachusetts General Hospital (MGH), wants to analyze how healthy your brain is.

Rudy Tanzi, a well-known Alzheimer’s disease researcher and professor of neurology at HMS and MGH, started the company in 2017 with Brian Nahed, a neurosurgical oncologist specializing in brain tumors and associate program director of neurosurgery at MGH and HMS. The two had worked with the NFL for years — Tanzi as a brain-health advisor to the New England Patriots, Nahed as a neurotrauma consultant for the league — and wanted to focus on the issue of concussions in football players. Specifically, they wanted to take a scientific approach to figuring out when a player could safely return to the sport following a concussion. The startup has evolved since then to take a holistic look at brain health through AI software and a VR headset.

From a consumer health standpoint, the idea is essentially [that by] using software, we can assess people’s brain health and provide feedback on what’s working and what’s not working,” said React Neuro CEO Shahid Azim, who joined the company in early 2019. “What really got me interested was not so much the concussion use case, but the more fundamental question that the team was looking to ask, which was, ‘Is there a better way to measure your brain health?’”

React Neuro answers that question with digital exams administered through a custom VR headset, which is developed by Pico Interactive in San Francisco. Designed based on the tools, techniques and exams traditionally used to assess neurological conditions, the tests return results that the startup’s AI software turns into actionable insights for physicians.

Azim, a 2009 MIT Sloan School of Management grad, calls the brain assessments via headsetdigital exams,” or “experiences on screen.” The exams, he said, can last anywhere from two and a half minutes to 10 minutes, depending on the use case. A general cognitive assessment typically lasts seven minutes.

We’re using eye tracking and voice analysis [for the exams],” Azim said. “In some cases, they’re voice-based, so you’re asked to repeat something that you see on the screen.


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.


AI Makes Gigantic Leap And Heralds A Revolution In Biology

An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology’s grandest challengesdetermining a protein’s 3D shape from its amino-acid sequence.

DeepMind’s program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference — held virtually this year — that takes stock of the exercise.

A protein’s function is determined by its 3D shape

This is a big deal,” says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. “In some sense the problem is solved.

The ability to accurately predict protein structures from their amino-acid sequence would be a huge boon to life sciences and medicine. It would vastly accelerate efforts to understand the building blocks of cells and enable quicker and more advanced drug discovery.

AlphaFold came top of the table at the last CASP — in 2018, the first year that London-based DeepMind participated. But, this year, the outfit’s deep-learning network was head-and-shoulders above other teams and, say scientists, performed so mind-bogglingly well that it could herald a revolution in biology.

It’s a game changer,” says Andrei Lupas, an evolutionary biologist at the Max Planck Institute for Developmental Biology in Tübingen, Germany, who assessed the performance of different teams in CASP. AlphaFold has already helped him find the structure of a protein that has vexed his lab for a decade, and he expects it will alter how he works and the questions he tackles. “This will change medicine. It will change research. It will change bioengineering. It will change everything,” Lupas adds.