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Archive for the ‘Artificial Intelligence – Breakthroughs in Theories and Technologies’ Category

This Algorithm Is Better At Predicting Human Behavior Than Humans Are

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Behavior on October 27, 2015| Leave a Comment »

This Algorithm Is Better At Predicting Human Behavior Than Humans Are

 

Reporter: Aviva Lev-Ari, PhD, RN

 

You’re so predictable.

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This Algorithm Is Better At Predicting Human Behavior Than Humans Are

BY MEG MILLER 1 MINUTE READ

Analyzing big data sets in order to forecast trends or predict customer behavior usually relies on both computers and humans. Computer algorithms are advanced enough to rapidly comb through numbers and find useful patterns, and humans are still necessary for setting the parameters and analyzing the results. But an algorithm created by two MIT researchers suggest we could take out the human factor all together.

Conceived by Max Kanter, a MIT graduate student in computer science, and his advisor, Kalyan Veeramachaneni, the Data Science Machine can approximate human “intuition” when it comes to data analysis. Using raw datasets to make models that predict things like when a student is most at risk of dropping a course, or whether a retail customer will turn into a repeat buyer, its creators claim it can do it faster and with more accuracy than its human counterparts.

To test the system prototype, the researchers pitted the Data Science Machine against human teams at three data science competitions. While the algorithm didn’t get the top score in any of the competitions, it did beat out a whopping 615 of the 906 human teams competing. In two of the competitions, it created models that were 94% and 96% as accurate as the winning teams. Whereas the teams of humans required months to build their prediction algorithms, the Data Science Machine did it in 2 to 12 hours.

The researchers don’t view the algorithm as a replacement for human intelligence, but do recognize that it could prove useful for helping analyze the huge amount of data with less manpower. It could also be an important tool for user-centered design–if a machine can comb through massive amounts of data with much less manpower and in record time, it could also help companies better understand their customer base and design with future behavior in mind.

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Scientist Designs Bio-Inspired Robotic Finger That Looks, Feels and Works Like the Real Thing

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Machine Learning on October 9, 2015| Leave a Comment »

 

Scientist Designs Bio-Inspired Robotic Finger That Looks, Feels and Works Like the Real Thing

Reporter: Aviva Lev-Ari, PhD, RN

 

Most robotic parts used today are rigid, have a limited range of motion and don’t really look lifelike. Inspired by both nature and biology, a scientist from Florida Atlantic University has designed a novel robotic finger that looks and feels like the real thing. In an article recently published in the journal Bioinspiration & Biomimetics, Erik Engeberg, Ph.D., assistant professor in the Department of Ocean and Mechanical Engineering within the College of Engineering and Computer Science at FAU, describes how he has developed and tested this robotic finger using shape memory alloy (SMA), a 3D CAD model of a human finger, a 3D printer, and a unique thermal training technique.

 

“We have been able to thermo-mechanically train our robotic finger to mimic the motions of a human finger like flexion and extension,” said Engeberg. “Because of its light weight, dexterity and strength, our robotic design offers tremendous advantages over traditional mechanisms, and could ultimately be adapted for use as a prosthetic device, such as on a prosthetic hand.”

 

In the study, Engeberg and his team used a resistive heating process called “Joule” heating that involves the passage of electric currents through a conductor that releases heat. Using a 3D CAD model of a human finger, which they downloaded from a website, they were able to create a solid model of the finger. With a 3D printer, they created the inner and outer molds that housed a flexor and extensor actuator and a position sensor. The extensor actuator takes a straight shape when it’s heated, whereas the flexor actuator takes a curved shape when heated. They used SMA plates and a multi-stage casting process to assemble the finger. An electrical chassis was designed to allow electric currents to flow through each SMA actuator. Its U-shaped design directed the electric current to flow the SMAs to an electric power source at the base of the finger.

 

This new technology used both a heating and then a cooling process to operate the robotic finger. As the actuator cooled, the material relaxed slightly. Results from the study showed a more rapid flexing and extending motion of the finger as well as its ability to recover its trained shape more accurately and more completely, confirming the biomechanical basis of its trained shape.

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Paul Allen’s quest to build an artificial brain is one of the hardest software-engineering… – Linkis.com

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, Machine Learning, Neuroscience on October 4, 2015| Leave a Comment »

Paul Allen’s quest to build an artificial brain is one of the hardest software-engineering endeavors ever attempted

Reporter: Aviva Lev-Ari, PhD, RN

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THE HUMAN UPGRADE

Thought process: Building an artificial brain

Paul Allen’s $500 million quest to dissect the mind and code a new one from scratch

He persuaded University of Washington AI researcher Oren Etzioni to lead the brain-building team and Caltech neuroscientist Christof Koch to lead the brain-deconstruction team. For them and the small army of other PhD scientists working for Allen, the quest to understand the brain and human intelligence has parallels in the early 1900s when men first began to ponder how to build a machine that could fly.

There were those who believed the best way would be to simulate birds, while there were others, like the Wright brothers, who were building machines that looked very different from species that could fly in nature. And it wasn’t clear back then which approach would get humanity into the skies first.

Whether they create something reflected in nature or invent something entirely novel, the mission is the same: conquering the final frontier of the human body — the brain — to enable people to live longer, better lives and answer fundamental questions about humans’ place in the universe.

SOURCE

https://www.washingtonpost.com/sf/national/2015/09/30/brain/

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Smart robot accelerates cancer treatment research by finding optimal treatment combinations

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER on September 27, 2015| Leave a Comment »

Smart robot accelerates cancer treatment research by finding optimal treatment combinations

Reporter: Aviva Lev-Ari, PhD, RN

 

 

 

 

 

 

 

A new smart research system developed at Uppsala University accelerates research on cancer treatments by finding optimal treatment drug combinations. It was developed by a research group led by Mats Gustafsson, Professor of Medical Bioinformatics.

 

The “lab robot” system plans and conducts experiments with many substances, and draws its own conclusions from the results. The idea is to gradually refine combinations of substances so that they kill cancer cells without harming healthy cells.

 

Instead of just combining a couple of substances at a time, the new lab robot can handle about a dozen drugs simultaneously. The future aim is to handle many more, preferably hundreds. The method is iterative search for anti-cancer drug combinations. The procedure starts by generating an initial generation (population) of drug combinations randomly or guided by biological prior knowledge and assumptions. In each iteration the aim is to propose a new generation of drug combinations based on the results obtained so far. The procedure iterates through a number of generations until a stop criterion for a predefined fitness function is satisfied.

 

There are a few such laboratories in the world with this type of lab robot, but researchers “have only used the systems to look for combinations that kill the cancer cells, not taking the side effects into account,” says Gustafsson.

 

The next step: Make the robot system more automated and smarter. The scientists also want to build more knowledge into the guiding algorithm of the robot, such as prior knowledge about drug targets and disease pathways.

 

For patients with the same cancer type returning multiple times, sometimes the cancer cells develop resistance against the pharmacotherapy used. The new robot systems may also become important in the efforts to find new drug compounds that make these resistant cells sensitive again.

 

The research is described in an open-access article published Tuesday (Sept. 22, 2015) in Scientific Reports.

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Deep Reinforcement Learning Machine Has Taught Itself to Play Chess at Higher Levels

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Deep Learning, Machine Learning, Natural Language Processing (NLP) on September 21, 2015| Leave a Comment »

Deep Reinforcement Learning Machine Has Taught Itself to Play Chess at Higher Levels

Reporter: Aviva Lev-Ari, PhD, RN

 

 

 

 

 

“Chess, after all, is special; it requires creativity and advanced reasoning. No computer could match humans at chess.” That was a likely argument before IBM surprised the world about computers playing chess. In 1997, Deep Blue’s entry won the World Chess Champion, Garry Kasparov.

 

Matthew Lai records the rest: “In the ensuing two decades, both computer hardware and AI research advanced the state-of-art chess-playing computers to the point where even the best humans today have no realistic chance of defeating a modern chess engine running on a smartphone.”

 

Now Lai has another surprise. His report on how a computer can teach itself chess—and not in the conventional way—is on arXiv. The title of the paper is “Giraffe: Using Deep Reinforcement Learning to Play Chess.” Departing from the conventional method of teaching computers how to play chess by giving them hardcoded rules, this project set out to use machine learning to figure out how to play chess. Namely, he said that deep learning was applied to chess in his work. “We use deep networks to evaluate positions, decide which branches to search, and order moves.”

 

As for other chess engines, Lai wrote, “almost all chess engines in existence today (and all of the top contenders) implement largely the same algorithms. They are all based on the idea of the fixed-depth minimax algorithm first developed by John von Neumann in 1928, and adapted for the problem of chess by Claude E. Shannon in 1950.”

 

This Giraffe is a chess engine using self-play to discover all its domain-specific knowledge. “Minimal hand-crafted knowledge is given by the programmer,” he said.

 

Results? Lai said ,”The results showed that the learned system performs at least comparably to the best expert-designed counterparts in existence today, many of which have been fine tuned over the course of decades.”

 

OK, not at super-Grandmaster levels, but impressive enough. “With all our enhancements, Giraffe is able to play at the level of an FIDE [Fédération Internationale des Échecs, or World Chess Federation] International Master on a modern mainstream PC,” he stated. “While that is still a long way away from the top engines today that play at super-Grandmaster levels, it is able to defeat many lower-tier engines, most of which search an order of magnitude faster.”

 

Addressing the value of Lai’s work in this paper, MIT Technology Review, stated that, “In a world first, an artificial intelligence machine plays chess by evaluating the board rather than using brute force to work out every possible move.” Giraffe, said the review, taught itself to play chess by evaluating positions much more like humans.

 

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Inside Facebook’s Quest for Software That Understands You | MIT Technology Review

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Machine Learning, Natural Language Processing (NLP) on September 8, 2015| Leave a Comment »

Inside Facebook’s Quest for Software That Understands You | MIT Technology Review

Reporter: Aviva Lev-Ari, PhD, RN

 

A reincarnation of one of the oldest ideas in artificial intelligence could finally make it possible to truly converse with our computers. And Facebook has a chance to make it happen first.

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IBM’s $3 Billion Investment In Synthetic Brains And Quantum Computing

Posted in Advanced Computing Platform, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Blockchain Transactions System, Intelligent Information Systems, Simulation Modeling in NGS on September 5, 2015| Leave a Comment »

IBM’s $3 Billion Investment In Synthetic Brains And Quantum Computing

Reporter: Aviva Lev-Ari, PhD, RN

IBM thinks the future belongs to computers that mimic the human brain and use quantum physics…and they’re betting $3 billion on it.

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With deep learning and dimensionality reduction, we can visualize the entirety of Wikipedia?

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Machine Learning, Natural Language Processing (NLP) on June 30, 2015| Leave a Comment »

With deep learning and dimensionality reduction, we can visualize the entirety of Wikipedia?

Reporter: Aviva Lev-Ari, PhD, RN

Deep neural networks are an approach to machine learning that has revolutionized computer vision and speech recognition in the last few years, blowing the previous state of the art results out of the water. They’ve also brought promising results to many other areas, including language understanding and machine translation. Despite this, it remains challenging to understand what, exactly, these networks are doing.

 

Understanding neural networks is just scratching the surface, however, because understanding the network is fundamentally tied to understanding the data it operates on. The combination of neural networks and dimensionality reduction turns out to be a very interesting tool for visualizing high-dimensional data – a much more powerful tool than dimensionality reduction on its own.

 

Paragraph vectors, introduced by Le & Mikolov (2014), are vectors that represent chunks of text. Paragraph vectors come in a few variations but the simplest one, which we are using here, is basically some really nice features on top of a bag of words representation.

 

With word embeddings, we learn vectors in order to solve a language task involving the word. With paragraph vectors, we learn vectors in order to predict which words are in a paragraph.

 

Concretely, the neural network learns a low-dimensional approximation of word statistics for different paragraphs. In the hidden representation of this neural network, we get vectors representing each paragraph. These vectors have nice properties, in particular that similar paragraphs are close together.

 

Now, Google has some pretty awesome people. Andrew Dai, Quoc Le, and Greg Corrado decided to create paragraph vectors for some very interesting data sets. One of those was Wikipedia, creating a vector for every English Wikipedia article. The result is that we get a visualization of the entirety of Wikipedia. A map of Wikipedia. A large fraction of Wikipedia’s articles fall into a few broad topics: sports, music (songs and albums), films, species, and science.

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Machine-Learning Supercomputer Woven from Idle Computers to Rival Google in Power

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Machine Learning, Natural Language Processing (NLP) on June 12, 2015| Leave a Comment »

Machine-Learning Supercomputer Woven from Idle Computers to Rival Google in Power

Reporter: Aviva Lev-Ari, PhD, RN

 

 

Sentient claims to have assembled machine-learning muscle to rival Google by rounding up idle computers.

 

Recent improvements in speech and image recognition have come as companies such as Google build bigger, more powerful systems of computers to run machine-learning software. Now a relative minnow, a private company called Sentient with only about 70 employees, says it can cheaply assemble even larger computing systems to power artificial-intelligence software. The company’s approach may not be suited to all types of machine learning, a technology that has uses as varied as facial recognition and financial trading. Sentient has not published details, but says it has shown that it can put together enough computing power to produce significant results in some cases.

 

Sentient’s power comes from linking up hundreds of thousands of computers over the Internet to work together as if they were a single machine. The company won’t say exactly where all the machines it taps into are. But many are idle inside data centers, the warehouse-like facilities that power Internet services such as websites and mobile apps, says Babak Hodjat, cofounder and chief scientist at Sentient. The company pays a data-center operator to make use of its spare machines.

 

Data centers often have significant numbers of idle machines because they are built to handle surges in demand, such as a rush of sales on Black Friday. Sentient has created software that connects machines in different places over the Internet and puts them to work running machine-learning software as if they were one very powerful computer. That software is designed to keep data encrypted as much as possible so that what Sentient is working on–perhaps for a client–is kept confidential.

 

Sentient can get up to one million processor cores working together on the same problem for months at a time, says Adam Beberg, principal architect for distributed computing at the company. Google’s biggest machine-learning systems don’t reach that scale, he says. A Google spokesman declined to share details of the company’s infrastructure and noted that results obtained using machine learning are more important than the scale of the computer system behind it. Google uses machine learning widely, in areas such as search, speech recognition and ad targeting.

 

Beberg helped pioneer the idea of linking up computers in different places to work together on a problem (see “Innovators Under 35: 1999”). He was a founder of Distributed.net, a project that was one of the first to demonstrate that idea at large scale. Its technology led to efforts such as Seti@Home andFolding@Home, in which millions of people installed software so their PCs could help search for alien life or contribute to molecular biology research.

 

Sentient was founded in 2007 and has received over $140 million in investment funding, with just over $100 million of that received late last year. The company has so far focused on using its technology to power a machine-learning technique known as evolutionary algorithms. That involves “breeding” a solution to a problem from an initial population of many slightly different algorithms. The best performers of the first generation are used to form the basis of the next, and over successive generations the solutions get better and better.

 

Sentient currently earns some revenue from operating financial-trading algorithms created by running its evolutionary process for months at a time on hundreds of thousands of processors. But the company now plans to use its infrastructure to offer services targeted at industries such as health care or online commerce, says Hodjat.

 

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