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Today: 22 August 2019, Thursday.

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News for Artificial intelligence





#1

 

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Description Researchers at UCLA and NantWorks have developed an artificial intelligence-powered device that detects cancer cells in a few milliseconds—hundreds of times faster than previous methods. With that speed, the invention could ...

#Artificial Intelligence
Field # Artificial Intelligence
Updated 22 August 2019

#2

 

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Description A novel system developed by MIT researchers automatically "learns" how to schedule data-processing operations across thousands of servers—a task traditionally reserved for imprecise, human-designed algorithms. Doing so ...

#Artificial intelligence
Field # Artificial intelligence
Updated 22 August 2019

#3

 

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Description Despite years of research, the brain still contains broad areas of unchartered territory. A team of scientists, led by neuroscientists from Cold Spring Harbor Laboratory and University of Sydney, recently found new evidence revising the traditional view of the primate brain’s visual system organizat

#Artificial intelligence
Field # Artificial intelligence
Updated 22 August 2019

#4

 

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Description Artificial intelligence (AI) has many compelling business use cases. Two of the rapidly emerging reasons why businesses use AI are to detect and deter fraud. A study carried out in partnership with the Association of Certified Fraud Examiners (ACFE) and analytics company SAS. This study, titled "

#Artificial intelligence
Field # Artificial intelligence
Updated 22 August 2019

#5

 

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Description The Lava Z93 has been launched in India and it features a waterdrop-style notch, a dual rear camera setup, and a rear fingerprint scanner.

#Artificial intelligence
Field # Artificial intelligence
Updated 22 August 2019

#6

 

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Description In the latest example of the need for further human moderation, YouTube's automated system took down several videos after mistaking robot fighting matches as an...

#Artificial intelligence
Field # Artificial intelligence
Updated 21 August 2019

#7

 

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Description The debate over the police using machine learning is intensifying—it is considered in some quarters as controversial as stop and search.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 August 2019

#8

 

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Description Intel on Tuesday unveiled its latest processor that will be its first using artificial intelligence (AI) and is designed for large computing centres.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 August 2019

#9

 

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Description TECH-INTEL-Intel unveils first artificial intelligence chip Springhill

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 August 2019

#10

 

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Description Neil Jacobstein gave a workshop on AI and Machine Learning Implementation at the 2019 Singularity Summit....

#Artificial intelligence
Field # Artificial intelligence
Updated 21 August 2019

#11

 

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Description Neil Jacobstein gave a workshop on AI and Machine Learning Implementation at the 2019 Singularity Summit....

#Artificial intelligence
Field # Artificial intelligence
Updated 21 August 2019

#12

 

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Description CSHL neuroscientist Anthony Zador shows how evolution and animal brains can be a rich source of inspiration for machine learning, especially to help AI tackle some enormously difficult problems, like… doing the dishes. Artificial intelligence (AI) still has a lot to learn from animal brains, says Cold Spring Harbor Laboratory (CSHL) neuroscientist Tony Zador. Now, he’s hoping that lessons from neuroscience can help the next generation of artificial intelligence overcome some particularly difficult barriers. Anthony Zador, M.D., Ph.D., has spent his career working to describe, down to the individual neuron, the complex neural networks that make up a living brain. But he started his career studying artificial neural networks (ANNs). ANNs, which are the computing systems behind the recent AI revolution, are inspired by the branching networks of neurons in animal and human brains. However, this broad concept is usually where the inspiration ends. In a perspective piece recently published in Nature Communications, Zador describes how improved learning algorithms are allowing AI systems to achieve superhuman performance on an increasing number of more complex problems like chess and poker. Yet, machines are still stumped by what we consider to be the simplest problems. Solving this paradox may finally enable robots to learn how to do something as organic as stalking prey or building a nest, or even something as human and mundane as doing the dishes—a task that Google CEO Eric Schmidt once called “literally the number one request… but an extraordinarily difficult problem” for a robot. “The things that we find hard, like abstract thought or chess-playing, are actually not the hard thing for machines. The things that we find easy, like interacting with the physical world, that’s what’s hard,” Zador explained. “The reason that we think it’s easy is that we had half a billion years of evolution that has wired up our circuits so that we do it effortlessly.” That’s why Zador writes that the secret to quick learning might not be a perfected general learning algorithm. Instead, he suggests that biological neural networks sculpted by evolution provide a kind of scaffolding to facilitate the quick and easy learning for specific kinds of tasks—usually those crucial for survival. For an example, Zador points to your backyard. “You have squirrels that can jump from tree to tree within a few weeks after birth, but we don’t have mice learning the same thing. Why not?” Zador said. “It’s because one is genetically predetermined to become a tree-dwelling creature.” Zador suggests that one result of this genetic predisposition is the innate circuitry that helps guide an animal’s early learning. However, these scaffolding networks are far less generalized than the perceived panacea of machine learning that most AI experts are pursuing. If ANNs identified and adapted similar sets of circuitry, Zador argues, the future’s household robots might just one day surprise us with clean dishes.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 August 2019

#13

 

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Description The UK is “on the cusp of a huge health tech revolution that could transform patient experience”, said health minister Matt Hancock when he announced £250 million to fund a new AI Lab for the National Health Service earlier this month. The lab has been set up to bring together academics and technology companies to work on some of the biggest challenges in health and care. But the AI sector has a reputation for overpromising on what it can deliver – as do politicians. I met with Indra … Read more: https://www.newscientist.com/article/2214009-an-inside-look-at-the-nhss-plans-to-revolutionise-healthcare-with-ai/#ixzz5xJFidKG1

#Artificial intelligence
Field # Artificial intelligence
Updated 21 August 2019

#14

 

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Description Decima, a system designed by MIT researchers, uses reinforcement learning to more efficiently schedule data-processing operations across clusters of servers — a task traditionally reserved for imprecise human-designed algorithms — to save money and energy in today’s power-hungry data centers.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 August 2019

#15

 

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Description Google's AI-powered grammar checker and autocorrect is coming to G Suite, but no word on when regular Gmail users will get it.

#Artificial intelligence
Field # Artificial intelligence
Updated 21 August 2019

#16

 

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Description France and Canada are establishing an international committee to advise on the ethics of artificial intelligence. The group should be supported and shielded from undue influence.

#Artificial intelligence
Field # Artificial intelligence
Updated 21 August 2019

#17

 

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Description The country’s artificial-intelligence research is growing in quality, but the field still plays catch up to the United States in terms of high-impact papers, people and ethics.

#Artificial intelligence
Field # Artificial intelligence
Updated 21 August 2019

#18

 

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Description In 2023, NASA plans to launch the Europa Clipper mission, a robotic explorer that will study Jupiter’s enigmatic moon Europa. The purpose of this mission is to explore Europa’s ice shell and interior to learn more about the moon’s composition, geology, and interactions between the surface and subsur

#Artificial intelligence
Field # Artificial intelligence
Updated 21 August 2019

#19

 

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Description When AIs and humans work together they discover superior solutions to the world’s problems that would elude either working alone. Together, they will change the very process of thinking

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 August 2019

#20

 

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Description Intel has revealed much more information on its Nervana NNP-I and NNP-T inference and training AI cards. The two new ...

#Artificial intelligence
Field # Artificial intelligence
Updated 21 August 2019

#21

 

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Description Prototype machine-learning technology co-developed by MIT scientists speeds processing by up to 175 times over traditional methods.ning A new machine learning technology tested by an international team of scientists including MIT Assistant Professor Philip Harris and postdoc Dylan Rankin, both of the Laboratory for Nuclear Science, can spot specific particle signatures among an ocean of Large Hadron Collider (LHC) data in the blink of an eye. Sophisticated and swift, the new system provides a glimpse into the game-changing role machine learning will play in future discoveries in particle physics as data sets get bigger and more complex. The LHC creates some 40 million collisions every second. With such vast amounts of data to sift through, it takes powerful computers to identify those collisions that may be of interests to scientists, whether, perhaps, a hint of dark matter or a Higgs particle. Now, scientists at Fermilab, CERN, MIT, the University of Washington, and elsewhere have tested a machine-learning system that speeds processing by 30 to 175 times compared to existing methods. Such methods currently process less than one image per second. In contrast, the new machine-learning system can review up to 600 images per second. During its training period, the system learned to pick out one specific type of postcollision particle pattern. “The collision patterns we are identifying, top quarks, are one of the fundamental particles we probe at the Large Hadron Collider,” says Harris, who is a member of the MIT Department of Physics. “It’s very important we analyze as much data as possible. Every piece of data carries interesting information about how particles interact.” Those data will be pouring in as never before after the current LHC upgrades are complete; by 2026, the 17-mile particle accelerator is expected to produce 20 times as much data as it does currently. To make matters even more pressing, future images will also be taken at higher resolutions than they are now. In all, scientists and engineers estimate the LHC will need more than 10 times the computing power it currently has. “The challenge of future running,” says Harris, “becomes ever harder as our calculations become more accurate and we probe ever-more-precise effects.” Researchers on the project trained their new system to identify images of top quarks, the most massive type of elementary particle, some 180 times heavier than a proton. “With the machine-learning architectures available to us, we are able to get high-grade scientific-quality results, comparable to the best top-quark identification algorithms in the world,” Harris explains. “Implementing core algorithms at high speed gives us the flexibility to enhance LHC computing in the critical moments where it is most needed.”

#Artificial Intelligence
Field # Artificial Intelligence
Updated 20 August 2019

#22

 

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Description Insights from the Frontlines of VC:
Scott Barclay, Partner, Data Collective;
Amy LaMeyer, WXR Fund,
Will Weisman, Executive Director, Summits, Singularity University,
Neal Bhadkamkar; Bold Ventures
Nishita Henry, Chief Innovation Officer, Deloitte, moderator...

#Artificial intelligence
Field # Artificial intelligence
Updated 20 August 2019

#23

 

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Description Insights from the Frontlines of VC:
Scott Barclay, Partner, Data Collective;
Amy LaMeyer, WXR Fund,
Will Weisman, Executive Director, Summits, Singularity University,
Neal Bhadkamkar; Bold Ventures
Nishita Henry, Chief Innovation Officer, Deloitte, moderator...

#Artificial intelligence
Field # Artificial intelligence
Updated 20 August 2019

#24

 

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Description Startup chip developer Cerebras on Monday announced a breakthrough in high-speed processor design that will hasten the development of artificial intelligence technologies. Cerebras unveiled the largest computer processing chip ever built. The new chip, dubbed "Wafer-Scale Engine" (WSE) -- pronounced "wise" -- is the heartbeat of the company's deep learning machine built to power AI systems. WSE reverses a chip industry trend of packing more computing power into smaller form-factor chips. Its massive size measures eight and a half inches on each side. By comparison, most chips fit on the tip of your finger and are no bigger than a centimeter per side. The new chip's surface contains 400,000 little computers, known as "cores," with 1.2 trillion transistors. The largest graphics processing unit (GPU) is 815 mm2 and has 21.1 billion transistors.

#Artificial intelligence
Field # Artificial intelligence
Updated 20 August 2019

#25

 

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Description Neil Jacobstein just gave a talk on Artificial Intelligence at the 2019 Singularity Summit....

#Artificial intelligence
Field # Artificial intelligence
Updated 20 August 2019

#26

 

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Description Neil Jacobstein just gave a talk on Artificial Intelligence at the 2019 Singularity Summit....

#Artificial intelligence
Field # Artificial intelligence
Updated 20 August 2019

#27

 

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Description The Cerebras Wafer-Scale Engine (WSE) is the largest chip ever built and has one 1.2 trillion transistors. It is the heart of a deep learning system....

#Artificial intelligence
Field # Artificial intelligence
Updated 20 August 2019

#28

 

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Description In 2018, OpenAI discovered that the amount of compute used for AI training doubles every 3.5 months. By comparison, the number of transistors per square inch on integrated circuits only doubles every 18 months, a.k.a Moore’s Law....

#Artificial intelligence
Field # Artificial intelligence
Updated 20 August 2019

#29

 

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Description The artificial intelligence system was more accurate than human doctors in distinguishing between cancers.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 20 August 2019

#30

 

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Description Mice scurry around while foraging for food, but genetics may be the unseen hand controlling these meandering movements. Researchers at University of Utah Health are using machine learning to draw links between genetic controls that shape incremental steps of instinctive and learned behaviors. The re

#Artificial Intelligence
Field # Artificial Intelligence
Updated 19 August 2019

#31

 

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Description The modern world depends on the smooth provision of vital services such as energy, transportation, telecommunications, food, water and healthcare. But the systems underpinning these sectors are increasingly complex and interdependent, ...

#Artificial intelligence
Field # Artificial intelligence
Updated 19 August 2019

#32

 

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Description Artificial intelligence has promised to revolutionize our lives, taking over the mundane tasks of daily existence, from prewriting “smart” email…...

#Artificial Intelligence
Field # Artificial Intelligence
Updated 19 August 2019

#33

 

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Description This was the ninth annual edition of the Digital Trends Report from Adobe and Econsultancy. The report is based on a sample of around 500 industry leaders.

#Artificial intelligence
Field # Artificial intelligence
Updated 16 August 2019

#34

 

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Description Fujitsu today announced the development of a technology that draws on mathematical models built with limited data on rainfall and water levels to create flooding predictions for rivers. The solution leverages Fujitsu Human ...

#Artificial intelligence
Field # Artificial intelligence
Updated 16 August 2019

#35

 

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Description TBD

#Artificial intelligence
Field # Artificial intelligence
Updated 16 August 2019

#36

 

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Description Bottom Line: Combining supervised and unsupervised machine learning as part of a broader Artificial Intelligence (AI) fraud detection strategy enables…...

#Artificial intelligence
Field # Artificial intelligence
Updated 15 August 2019

#37

 

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Description These companies are also looking to offer the AI platforms, embedded as part of solutions until now, separately to clients.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 15 August 2019

#38

 

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Description TBD

#Artificial intelligence
Field # Artificial intelligence
Updated 15 August 2019

#39

 

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Description It's volatile and hard to create, but could 18-carbon one day form an artificial brain?

#Artificial intelligence
Field # Artificial intelligence
Updated 15 August 2019

#40

 

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Description Elon Musk has said that he hopes Neuralink will help people keep pace with future AI. But psychologist Susan Scheider warns that would be ill-advised.

#Artificial intelligence
Field # Artificial intelligence
Updated 15 August 2019

#41

 

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Description C-DAEM will allow artillery units dozens of miles away to destroy moving tanks. 

#Artificial intelligence
Field # Artificial intelligence
Updated 15 August 2019