Tag Archives: Artificial Intelligence

Deciphering Breast Cancer

Breast cancer is one of the most common cancers, and one of the leading causes of death in women globally. Breast cancer is a disease where cells located in the breast grow out of control. Although a majority of breast cancers are discovered in women at the age of 50 years or older, the disease can affect anyone, including men and younger women, according to the Centers for Disease Control and Prevention (CDC). Last year there were 9.6 million deaths and 18.1 million new cases of breast cancer diagnosed globally according to the latest report from the International Agency for Research on Cancer (IARC) released in September 2018.

In 2019 alone, the U.S. National Cancer Institute estimates that there will be 268,600 new female breast cancer cases and 41,760 fatalities. Earlier this month, researchers based in Switzerland published in Cell their study in using applied artificial intelligence (AI) machine learning to create a comprehensive tumor and immune atlas of breast cancer ecosystems that lays the foundation for innovative precision medicine and immunotherapy.

The study was led by professor Bernd Bodenmiller, Ph.D. at the Institute of Molecular Life Sciences at the University of Zurich in Switzerland. Bodenmiller is a recipient of the 2019 Friedrich Miescher Award, Switzerland’s highest distinction for outstanding achievements in biochemistry. His team worked in collaboration with the Systems Biology Group at IBM Research in Zurich led by María Rodríguez Martínez, Ph.D. with the shared goal to produce a foundation for more targeted breast cancer treatment through precision medicine.

Source: https://www.ibm.com/

AI Beats Professionals In Poker

An artificial intelligence bot, created by researchers at Facebook and Carnegie Mellon, can beat human poker professionals in a six-player, no-limit Hold’em game, Facebook announcedPluribus, as the AI bot is called, has “decisively” won against a number of pros, including two World Series of Poker Main Event winners.

Pluribus has been built on the shoulders of Libratus, an AI that bested human pro players in two-player poker in 2017. It learned to play by competing against itself, without any data of prior human or AI play. Since poker is incredibly complex, having Pluribus look too far into the future wasn’t viable; instead, the bot used a new search algorithm that helps it make good decisions by looking at just the few next moves (instead of trying to figure out all the moves until the end of the game). It also used new and faster self-play algorithms that helped it cope with all the hidden information present in poker.

Combined, these advances made it possible to train Pluribus using very little processing power and memory — the equivalent of less than $150 worth of cloud computing resources,” wrote Facebook AI research scientist Noam Brown.

During one experiment, Pluribus played 10,000 hands of poker over 12 days, against a dozen professionals (who were playing for a total prize for $50,000, giving them a reason to win).

In money terms, Pluribus was so much better than people, that if the game were played with $1 chips, it would have made about $1,000 per hour competing against five human players.

The details on how the researchers have managed to make Pluribus so good at multiplayer poker — a notoriously hard problem in AI — are in a new paper published in Science.

Source: https://science.sciencemag.org/
AND
https://mashable.com/

How To Merge Your Brain With A.I.

Elon Musk said startup Neuralink, which aims to build a scalable implant to connect human brains with computers, has already implanted chips in rats and plans to test its brain-machine interface in humans within two years, with a long-term goal of peoplemerging with AI.” Brain-machine interfaces have been around for awhile. Some of the earliest success with the technology include Brown University’s BrainGate, which first enabled a paralyzed person to control a computer cursor in 2006. Since then a variety of research groups and companies, including the University of Pittsburgh Medical Center and DARPA-backed Synchron, have been working on similar devices. There are two basic approaches: You can do it invasively, creating an interface with an implant that directly touches the brain, or you can do it non-invasively, usually by electrodes placed near the skin. (The latter is the approach used by startup CTRL-Labs, for example.)

Neuralink, says Musk, is going to go the invasive route. It’s developed a chip containing an array of up to 96 small, polymer threads, each with up to 32 electrodes that can be implanted into the brain via robot and a 2 millimeter incision. The threads are small — less than 6 micrometers — because, as Musk noted in remarks delivered Tuesday night and webcast, Once implanted, according to Musk, the chip would connect wirelessly to devices. “It basically Bluetooths to your phone,” he said. “We’ll have to watch the App Store updates to that one,” he added (the audience laughed).

Musk cofounded Neuralink in 2017 and serves as the company’s CEO, though it’s unclear how much involvement he has given that he’s also serving as CEO for SpaceX and Tesla. Company cofounder and president, Max Hodak, has a biomedical engineering degree from Duke and has cofounded two other companies, MyFit and Transcriptic. Neuralink has raised $66.27 million in venture funding so far, according to Pitchbook, which estimates the startup’s valuation at $509.3 million. Both Musk and Hodak spoke about the potential for its company’s neural implants to improve the lives of people with brain damage and other brain disabilities. Its first goal, based on its discussions with such patients, is the ability to control a mobile device.

The company’s long-term goal is a bit more fantastical, and relates to Musk’s oft-repeated concerns over the dangers of advanced artificial intelligence. That goal is to use the company’s chips to create a “tertiary level” of the brain that would be linked to artificial intelligence.We can effectively have the option of merging with AI,” he said. “After solving a bunch of brain related diseases there is the mitigation of the existential threat of AI,” he continued.

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In terms of progress, the company says that it has built a chip and a robot to implant it, which it has implanted into rats. According to the whitepaper the company has published (which has not yet undergone any peer review), it was able to record rat brain activity from its chips, and with many more channels than exist on current systems in use with humans. The first human clinical trials are expected for next year, though Hodak mentioned that the company has not yet begun to the FDA processes needed to conduct those tests.

Source: https://www.forbes.com/

AI Closer To The Efficiency Of The Brain

Computers and artificial intelligence continue to usher in major changes in the way people shop. It is relatively easy to train a robot’s brain to create a shopping list, but what about ensuring that the robotic shopper can easily tell the difference between the thousands of products in the store?

Purdue University researchers and experts in brain-inspired computing think part of the answer may be found in magnets. The researchers have developed a process to use magnetics with brain-like networks to program and teach devices such as personal robots, self-driving cars and drones to better generalize about different objects.

Our stochastic neural networks try to mimic certain activities of the human brain and compute through a connection of neurons and synapses,” said Kaushik Roy, Purdue’s Edward G. Tiedemann Jr. Distinguished Professor of Electrical and Computer Engineering. “This allows the computer brain to not only store information but also to generalize well about objects and then make inferences to perform better at distinguishing between objects.

The stochastic switching behavior is representative of a sigmoid switching behavior of a neuron. Such magnetic tunnel junctions can be also used to store synaptic weights. Roy presented the technology during the annual German Physical Sciences Conference earlier this month in Germany. The work also appeared in the Frontiers in Neuroscience.

The switching dynamics of a nano-magnet are similar to the electrical dynamics of neurons. Magnetic tunnel junction devices show switching behavior, which is stochastic in nature.  The Purdue group proposed a new stochastic training algorithm for synapses using spike timing dependent plasticity (STDP), termed Stochastic-STDP, which has been experimentally observed in the rat’s hippocampus. The inherent stochastic behavior of the magnet was used to switch the magnetization states stochastically based on the proposed algorithm for learning different object representations. “The big advantage with the magnet technology we have developed is that it is very energy-efficient,” said Roy, who leads Purdue’s Center for Brain-inspired Computing Enabling Autonomous Intelligence. “We have created a simpler network that represents the neurons and synapses while compressing the amount of memory and energy needed to perform functions similar to brain computations.

Source: https://www.purdue.edu/

Internet Of Thoughts

Imagine a future technology that would provide instant access to the world’s knowledge and artificial intelligence, simply by thinking about a specific topic or question. Communications, education, work, and the world as we know it would be transformed. Writing in Frontiers in Neuroscience, an international collaboration led by researchers at UC Berkeley and the US Institute for Molecular Manufacturing predicts that exponential progress in nanotechnology, nanomedicine, AI, and computation will lead this century to the development of a “Human Brain/Cloud Interface” (B/CI), that connects neurons and synapses in the brain to vast cloud-computing networks in real time.

The B/CI concept was initially proposed by futurist-author-inventor Ray Kurzweil, who suggested that neural nanorobots – brainchild of Robert Freitas, Jr., senior author of the research – could be used to connect the neocortex of the human brain to a “synthetic neocortex” in  . Our wrinkled neocortex is the newest, smartest, ‘conscious’ part of the brain. Freitas’ proposed neural nanorobots would provide direct, real-time monitoring and control of signals to and from brain cells.

These devices would navigate the human vasculature, cross the blood-brain barrier, and precisely autoposition themselves among, or even within brain cells,” explains Freitas. “They would then wirelessly transmit encoded information to and from a cloud-based supercomputer network for real-time brain-state monitoring and data extraction.

This cortex in the cloud would allow “Matrix“-style downloading of information to the brain, the group claims. “A human B/CI system mediated by neuralnanorobotics could empower individuals with instantaneous access to all cumulative human knowledge available in the cloud, while significantly improving human learning capacities and intelligence,” says lead author Dr. Nuno Martins.

B/CI technology might also allow us to create a future “global superbrain” that would connect networks of individual human brains and AIs to enable collective thought. “While not yet particularly sophisticated, an experimental human ‘BrainNet’ system has already been tested, enabling thought-driven information exchange via the cloud between individual brains,” explains Martins. “It used electrical signals recorded through the skull of ‘senders’ and magnetic stimulation through the skull of ‘receivers,’ allowing for performing cooperative tasks. With the advance of neuralnanorobotics, we envisage the future creation of ‘superbrains’ that can harness the thoughts and thinking power of any number of humans and machines in real time. This shared cognition could revolutionize democracy, enhance empathy, and ultimately unite culturally diverse groups into a truly global society.”

According to the group’s estimates, even existing supercomputers have processing speeds capable of handling the necessary volumes of neural data for B/CI – and they’re getting faster, fast. Rather, transferring neural data to and from supercomputers in the cloud is likely to be the ultimate bottleneck in B/CI development. “This challenge includes not only finding the bandwidth for global data transmission,” cautions Martins, “but also, how to enable data exchange with neurons via tiny devices embedded deep in the brain.”

One solution proposed by the authors is the use of ‘magnetoelectric nanoparticles‘ to effectively amplify communication between neurons and the cloud. “These nanoparticles have been used already in living mice to couple external magnetic fields to neuronal electric fields – that is, to detect and locally amplify these magnetic signals and so allow them to alter the electrical activity of neurons,” explains Martins. “This could work in reverse, too: electrical signals produced by neurons and nanorobots could be amplified via magnetoelectric nanoparticles, to allow their detection outside of the skull.” Getting these nanoparticles – and nanorobots – safely into the brain via the circulation, would be perhaps the greatest challenge of all in B/CI.

A detailed analysis of the biodistribution and biocompatibility of nanoparticles is required before they can be considered for human development. Nevertheless, with these and other promising technologies for B/CI developing at an ever-increasing rate, an ‘internet of thoughts’ could become a reality before the turn of the century,” Martins concludes.

Source: https://www.frontiersin.org/

Artificial Intelligence Revolutionizes Farming

Researchers at MIT have used AI to improve the flavor of basil. It’s part of a trend that is seeing artificial intelligence revolutionize farming.
What makes basil so good? In some cases, it’s AI. Machine learning has been used to create basil plants that are extra-delicious. While we sadly cannot report firsthand on the herb’s taste, the effort reflects a broader trend that involves using data science and machine learning to improve agriculture 

The researchers behind the AI-optimized basil used machine learning to determine the growing conditions that would maximize the concentration of the volatile compounds responsible for basil’s flavor. The basil was grown in hydroponic units within modified shipping containers in Middleton, Massachusetts. Temperature, light, humidity, and other environmental factors inside the containers could be controlled automatically. The researchers tested the taste of the plants by looking for certain compounds using gas chromatography and mass spectrometry. And they fed the resulting data into machine-learning algorithms developed at MIT and a company called Cognizant.

The research showed, counterintuitively, that exposing plants to light 24 hours a day generated the best taste. The research group plans to study how the technology might improve the disease-fighting capabilities of plants as well as how different flora may respond to the effects of climate change.

We’re really interested in building networked tools that can take a plant’s experience, its phenotype, the set of stresses it encounters, and its genetics, and digitize that to allow us to understand the plant-environment interaction,” said Caleb Harper, head of the MIT Media Lab’s OpenAg group, in a press release. His lab worked with colleagues from the University of Texas at Austin on the paper.

The idea of using machine learning to optimize plant yield and properties is rapidly taking off in agriculture. Last year, Wageningen University in the Netherlands organized an “Autonomous Greenhousecontest, in which different teams competed to develop algorithms that increased the yield of cucumber plants while minimizing the resources required. They worked with greenhouses where a variety of factors are controlled by computer systems.

The study has appeared  in the journal PLOS One.

Source: https://www.technologyreview.com/

This Person Does Not exist

With the help of artificial intelligence, you can manipulate video of public figures to say whatever you like — or now, create images of people’s faces that don’t even exist. You can see this in action on a website called thispersondoesnotexist.com. It uses an algorithm to spit out a single image of a person’s face, and for the most part, they look frighteningly realHit refresh in your browser, and the algorithm will generate a new face. Again, these people do not exist.

The website is the creation of software engineer Phillip Wang, and uses a new AI algorithm called StyleGAN, which was developed by researchers at NvidiaGAN, or Generative Adversarial Networks, is a concept within machine learning which aims to generate images that are indistinguishable from real ones. You can train GANs to remember human faces, as well bedrooms, cars, and cats, and of course, generate images of them.

Wang explained that he created the site to create awareness for the algorithm, and chose facesbecause our brains are sensitive to that kind of image.”  He added that it costs $150 a month to hire out the server, as he needs a good amount of graphical power to run the website.

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It also started off as a personal agenda mainly because none of my friends seem to believe this AI phenomenon, and I wanted to convince them,” Wang said. “This was the most shocking presentation I could send them. I then posted it on Facebook and it went viral from there.

I think eventually, given enough data, a big enough neural [network] can be teased into dreaming up many different kinds of scenarios,” Wang added.

Source: https://thispersondoesnotexist.com/
AND
https://mashable.com/

Ai-Da The Artist Robot

A British arts engineering company says it has created the world’s first AI robot capable of drawing people who pose for it. The humanoid called Ai-Da can sketch subjects using a microchip in her eye and a pencil in her robotic hand – coordinated by AI processes and algorithmsAi-Da‘s ability as a life-like robot to draw and paint ultra-realistic portraits from sight has never been achieved before, according to the designers in Cornwall. It is the brainchild of art impresario and galleries Aidan Meller.

Named after Ada Lovelace , the first female computer programmer in the world, Ai-Da the robot has been designed and built by Cornish robotics company Engineered Arts who make robots for communication and entertainment.

In April 2018, Engineered Arts created an ultra-realistic robot to promote the Westworld TV show.

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Pioneering a new AI art movement, we are excited to present Ai-Da, the first professional humanoid artist, who creates her own art, as well as being a performance artist. “As an AI robot, her artwork uses AI processes and algorithms. “The work engages us to think about AI and technological uses and abuses in the world today.” explains Aidan Meller.

Professors and post-Phd students at Oxford University and Goldsmiths are providing Ai-Da with the programming and creative design for her art work. While students at Leeds University are custom designing and programming a bionic arm to create her art work.

Ai-Da has a “RoboThespian” body , featuring an expressive range of movements and she has the ability to talk and respond to questions. The robot also has a “Mesmer” head, featuring realistic silicone skin, 3D printed teeth and gums, integrated eye cameras, as well as hair.

Source: http://fortune.com/
AND
https://www.mirror.co.uk/

Facial Recognition And AI Identify 90% Of Rare Genetic Disorders

A facial recognition scan could become part of a standard medical checkup in the not-too-distant future. Researchers have shown how algorithms can help identify facial characteristics linked to genetic disorders, potentially speeding up clinical diagnoses.

In a study published this month in the journal Nature Medicine, US company FDNA published new tests of their software, DeepGestalt. Just like regular facial recognition software, the company trained their algorithms by analyzing a dataset of faces. FDNA collected more than 17,000 images covering 200 different syndromes using a smartphone app it developed named Face2Gene.

Rare genetic disorders are collectively common, affecting 8 percent of the population

In two first tests, DeepGestalt was used to look for specific disorders: Cornelia de Lange syndrome and Angelman syndrome. Both of these are complex conditions that affect intellectual development and mobility. They also have distinct facial traits, like arched eyebrows that meet in the middle for Cornelia de Lange syndrome, and unusually fair skin and hair for Angelman syndrome.

When tasked with distinguishing between pictures of patients with one syndrome or another, random syndrome, DeepGestalt was more than 90 percent accurate, beating expert clinicians, who were around 70 percent accurate on similar tests. When tested on 502 images showing individuals with 92 different syndromes, DeepGestalt identified the target condition in its guess of 10 possible diagnoses more than 90 percent of the time.

Source: https://www.theverge.com

Artificial Synapses Made from Nanowires

Scientists from Jülich together with colleagues from Aachen and Turin have produced a memristive element made from nanowires that functions in much the same way as a biological nerve cell. The component is able to both save and process information, as well as receive numerous signals in parallel. The resistive switching cell made from oxide crystal nanowires is thus proving to be the ideal candidate for use in building bioinspired “neuromorphic” processors, able to take over the diverse functions of biological synapses and neurons.

Image captured by an electron microscope of a single nanowire memristor (highlighted in colour to distinguish it from other nanowires in the background image). Blue: silver electrode, orange: nanowire, yellow: platinum electrode. Blue bubbles are dispersed over the nanowire. They are made up of silver ions and form a bridge between the electrodes which increases the resistance.

Computers have learned a lot in recent years. Thanks to rapid progress in artificial intelligence they are now able to drive cars, translate texts, defeat world champions at chess, and much more besides. In doing so, one of the greatest challenges lies in the attempt to artificially reproduce the signal processing in the human brain. In neural networks, data are stored and processed to a high degree in parallel. Traditional computers on the other hand rapidly work through tasks in succession and clearly distinguish between the storing and processing of information. As a rule, neural networks can only be simulated in a very cumbersome and inefficient way using conventional hardware.

Systems with neuromorphic chips that imitate the way the human brain works offer significant advantages. Experts in the field describe this type of bioinspired computer as being able to work in a decentralised way, having at its disposal a multitude of processors, which, like neurons in the brain, are connected to each other by networks. If a processor breaks down, another can take over its function. What is more, just like in the brain, where practice leads to improved signal transfer, a bioinspired processor should have the capacity to learn.

With today’s semiconductor technology, these functions are to some extent already achievable. These systems are however suitable for particular applications and require a lot of space and energy,” says Dr. Ilia Valov from Forschungszentrum Jülich. “Our nanowire devices made from zinc oxide crystals can inherently process and even store information, as well as being extremely small and energy efficient,” explains the researcher from Jülich’s Peter Grünberg Institute.

Source: http://www.fz-juelich.de/