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Category: information science – Page 390

Is Artificial Intelligence Being Oversold?

I believe there are good advances in AI due to the processing performance; however, as I highlighted earlier many of the principles like complex algorithms along with the pattern & predictive analysis of large volumes of information hasn’t changed much from my own work in the early days with AI. Where I have concerns and is the foundational infrastructure that “connected” AI resides on. Ongoing hacking and attacks of today could actually make AI adoption fall really short; and in the long run cause AI to look pretty bad.

A debate in New York tries to settle the question.

By Larry Greenmeier on March 10, 2016.

What is the relation between Artificial Intelligence and Machine Learning?

When I work on AI today and looking at it’s fundamental principles; it is not that much different from the work that I and another team mate many years ago did around developing a RT Proactive Environmental Response System. Sure there are some differences between processors, etc. However, the principles are the same when you consider some of the extremely complex algorithms that we had to develop to ensure that our system could proactively interrupt patterns and proactively act on it’s own analysis. We did have a way to override any system actions.

These questions originally appeared on Quora — the knowledge sharing network where compelling questions are answered by people with unique insights.

Answers by Neil Lawrence, Professor of Machine Learning at the University of Sheffield, on Quora.

Q: What do you think about the impact of AI and ML on the job market in 10, 20, 50 years from now?

A:I think predicting anything over longer than a 10 year horizon is very difficult. But there are a few thoughts I have on this question.

The U.S. Government Launches a $100-Million “Apollo Project of the Brain”

US Government’s cool $100 mil in brain research. As we have been highlighting over the past couple of months that the US Government’s IARPA and DARPA program’s have and intends to step up their own efforts in BMIs and robotics for the military; I am certain that this research will help their own efforts and progress.

Intelligence project aims to reverse-engineer the brain to find algorithms that allow computers to think more like humans.

By Jordana Cepelewicz on March 8, 2016.

Quantum mechanics is so weird that scientists need AI to design experiments

Don’t let the title mislead you — Quantum is not going to require AI to operate or develop it’s computing capabilities. However, what is well known across Quantum communities is that AI will greatly benefit from the processing capabilities & performance of Quantum Computing. There has been a strong interest in marrying the 2 together. However, Quantum maturity gap and timing has not made that possible until recently resulting from the various discoveries in microchip development, programming language (Quipper) development, Q-Dots Silicon wafers, etc.

Researchers at the University of Vienna have created an algorithm that helps plan experiments in this mind-boggling field.

Quantum Computer Comes Closer to Cracking RSA Encryption

Glad to see this article get published because it echoes many of the concerns established around China and Russia governments and their hackers having their infrastructures on Quantum before US, Europe, and Canada. Computer scientists at MIT and the University of Innsbruck say they’ve assembled the first five quantum bits (qubits) of a quantum computer that could someday factor any number, and thereby crack the security of traditional encryption schemes.

Shor’s algorithm performed in a system less than half the size experts expected.

IARPA Wants Smarter Algorithms — Not More of Them

“Notice for all Mathmaticians” — Are you a mathmatician who loves complex algorithems? If you do, IARPA wants to speak with you.

Last month, the intelligence community’s research arm requested information about training resources that could help artificially intelligent systems get smarter.

It’s more than an effort to build new, more sophisticated algorithms. The Intelligence Advanced Research Projects Activity could actually save money by refining existing algorithms that have been previously discarded by subjecting them to more rigorous training.

Nextgov spoke with Jacob Vogelstein, a program manager at IARPA who specializes in in applied neuroscience, about the program. This conversation has been edited for length and clarity.

Imaging algorithm gathers information about how cells move

Brown University engineers have developed a new technique to help researchers understand how cells move through complex tissues in the body. They hope the tool will be useful in understanding all kinds of cell movements, from how cancer cells migrate to how immune cells make their way to infection sites.

The technique is described in a paper published in the Proceedings of the National Academy of Sciences.

The traditional method for studying cell movement is called traction force microscopy (TFM). Scientists take images of cells as they move along 2-D surfaces or through 3-D gels that are designed as stand-ins for actual body tissue. By measuring the extent to which cells displace the 2-D surface or the 3-D gel as they move, researchers can calculate the forces generated by the cell. The problem is that in order to do the calculations, the stiffness and other mechanical properties of artificial tissue environment must be known.

AI learns to predict human reactions

A team of Stanford researchers have developed a novel means of teaching artificial intelligence systems how to predict a human’s response to their actions. They’ve given their knowledge base, dubbed Augur, access to online writing community Wattpad and its archive of more than 600,000 stories. This information will enable support vector machines (basically, learning algorithms) to better predict what people do in the face of various stimuli.

“Over many millions of words, these mundane patterns [of people’s reactions] are far more common than their dramatic counterparts,” the team wrote in their study. “Characters in modern fiction turn on the lights after entering rooms; they react to compliments by blushing; they do not answer their phones when they are in meetings.”

In its initial field tests, using an Augur-powered wearable camera, the system correctly identified objects and people 91 percent of the time. It correctly predicted their next move 71 percent of the time.

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