AI News, A Gentle Introduction to Data Science

A Gentle Introduction to Data Science

From the incluence of artificial intelligence, and the quest to replicate a human mind, to a practical demo on how to build a hello world machine learning in Python. The

From the incluence of artificial intelligence, and the quest to replicate a human mind, to a practical demo on how to build a hello world machine learning in Python.

The $1.3B Quest to Build a Supercomputer Replica of a Human Brain

He took the stage of the Oxford Playhouse, clad in the requisite dress shirt and blue jeans, and announced a plan that—if it panned out—would deliver a fully sentient hologram within a decade.

And the South African–born neuroscientist pronounced that he would accomplish all this through an insanely ambitious attempt to build a complete model of a human brain—from synapses to hemispheres—and simulate it on a supercomputer.

The self-assured scientist claims that the only thing preventing scientists from understanding the human brain in its entirety—from the molecular level all the way to the mystery of consciousness—is a lack of ambition.

If only neuroscience would follow his lead, he insists, his Human Brain Project could simulate the functions of all 86 billion neurons in the human brain, and the 100 trillion connections that link them.

Welcome to the Programmable World The way Markram sees it, technology has finally caught up with the dream of AI: Computers are finally growing sophisticated enough to tackle the massive data problem that is the human brain.

He has impressed leading figures in biology, neuroscience, and computing, who believe his initiative is important even if they consider some of his ultimate goals unrealistic.

Markram has earned that support on the strength of his work at the Swiss Federal Institute of Technology in Lausanne, where he and a group of 15 postdocs have been taking a first stab at realizing his grand vision—simulating the behavior of a million-neuron portion of the rat neocortex.

His role will be that of prophet, the sort of futurist who presents worthy goals too speculative for most scientists to countenance and then backs them up with a master plan that makes the nearly impossible appear perfectly plausible.

For decades, neuroscientists and computer scientists have debated whether a computer brain could ever be endowed with the intelligence of a human.

Over the past century, brain research has made tremendous strides, but it's all atomized and highly specific—there's still no unified theory that explains the whole.

In sufficient quantity, certain combinations of chemicals (called neurotransmitters) cause a neuron to fire an electrical signal down a long pathway called an axon.

The electrical spike causes neurotransmitters to be released at the synapse, where they attach to receptors in the neighboring neuron, altering its voltage by opening or closing ion channels.

At its most fine-grained, at the level of molecular biology, neuroscience attempts to describe and predict the effect of neurotransmitters one ion channel at a time.

Scans can roughly track which parts of the brain are active while watching a ball game or having an orgasm, albeit only by monitoring blood flow through the gray matter: the brain again viewed as a radiator.

To add to the brain-mapping mix, President Obama in April announced the launch of an initiative called Brain (commonly referred to as the Brain Activity Map), which he hopes Congress will make possible with a $3 billion NIH budget.

(To start, Obama is pledging $100 million of his 2014 budget.) Unlike the static Human Connectome Project, the proposed Brain Activity Map would show circuits firing in real time.

Even scaled up to human dimensions, such a map would chart only a web of activity, leaving out much of what is known of brain function at a molecular and functional level.

Seated behind a clean desk in an office devoid of anything more personal than his white MacBook, he spends most of his days meeting with administrators, technicians, and collaborators.

It has been his only serious interest since the age of 13, when his mother sent him from the Kalahari game farm where he'd spent his childhood to a boarding school outside Durban.

His first year there, he stumbled across some research on schizophrenia and other mental disorders and directed his youthful energy into studying the mind.

Over a one-year period Markram performed nearly a thousand experiments recording the effect of a neurotransmitter on neurons in the brain stem.

With his exceptionally steady hands, Markram was the first researcher to patch two connected neurons simultaneously, a feat that put him in a position to see how they interacted.

By sending electrical signals between neurons and measuring their electrical responses, he could test Hebb's rule—neurons that fire together wire together—a fundamental neuroscience postulate.

What Markram discovered was that the pattern of synaptic connections in a neural network is determined not only by whether neurons fire together but also by when they fire relative to one another.

What neuroscience needed, he decided, was an enormous collaboration, with research protocols coordinated so that all the data would fire together—and naturally he thought he was the one to make it happen.

His vision matched the ambition of one man who could fund it: neuroscientist Patrick Aebischer, the newly appointed president of the Swiss Federal Institute of Technology, tasked with making the campus a leader in computer science and biomedicine.

The loud drone of air-conditioning serves as a constant reminder that computing has a lot to learn about efficiency from the 20-watt human brain.

The Blue Gene will simulate Markram's brain model—the model that uses all the experimental results Markram has collected over 10 years of industrial-strength science at Lausanne, as well as all of the studies he did at Weizmann.

Markram calls the process predictive reverse-engineering, and he claims that it has already allowed him to anticipate crucial data that would have taken years to generate in a wet lab.

But if you listen carefully—filtering out his relentless boasting—the apparent contradictions resolve into complementary strategies: Without a dependable experimental base—focused on one species to which researchers have unlimited laboratory access—detailed modeling wouldn't be possible.

But with a multilevel model of the rat brain as a template, scientists might find a rule governing how neurons connect and chart only a few, on the basis of which they could fill in the remainder.

The project's first Blue Gene supercomputer was robust enough to simulate a single neocortical column in a rat (its whole brain has the equivalent of 100,000 columns).

The Human Brain Project will eventually need an astronomical amount of memory and computational speed—at least 100 petabytes of RAM and an exaflop—to make its sims possible.

Abeles didn't keep his opinion to himself while Markram's proposal was under review as one of six finalists (among about 120 entrants) for the billion-euro European Flagship Initiative grant.

Though Koch remains skeptical of Markram's 10-year time frame, that didn't keep him from spending three days this spring in Lausanne, coordinating their respective research programs.

Rather than uncovering treatments for individual symptoms, he wants to induce diseases in silico by building explicitly damaged models, then find workarounds for the damage.

He plans to give the EU an early working prototype of this system within just 18 months—and vows to "open up this new telescope to the scientific community"

within two and a half years—though he estimates that he'll need a supercomputer 100,000 times faster than the one he's got to build the premium version.

Shortly after arriving at Lausanne, Markram developed workflows that extracted experimental results from journals, strip-mining thousands of neuroscience papers only to find that the data was too inconsistent to use in a model.

"I tend to think of the Human Brain Project in the same way one should have considered the Human Genome Project, where the thought was that once the genome was sequenced, we would solve genetic-based disease and understand the genetic basis of behavior.

In this talk, I will first briefly review 25 years of computer vision research at Microsoft Research (MSR), highlighting MSR's contributions to the vision community and emphasizing the importance of long-term commitment to funding successful industrial research labs.

I will also describe some of our latest research work in computational photography, image understanding, and vision and language before detailing our commercialization successes.

Holoportation is a new type of 3D capture technology that allows high-quality 3D models of people to be reconstructed, compressed, and transmitted anywhere in real time.

is responsible for driving the company’s overall AI strategy and forward-looking research and development efforts spanning infrastructure, services, apps and agents.

Prior to his engineering leadership role at Bing and online services, he oversaw the research activities at Microsoft Research Asia and the lab’s collaborations with universities in the Asia Pacific region, and was responsible for the Internet Services Research Center, an applied research organization dedicated to advanced technology investment in search and advertising at Microsoft. Dr.

There he began a nine-year tenure as a researcher, subsequently moving on to become research manager, assistant managing director and managing director of Microsoft Research Asia and a Distinguished Engineer. Dr.

MIT Intelligence Quest kicks off

In the field of intelligence, I believe this is just such a moment.” MIT faculty and friends helped the Institute celebrate the launch of a new initiative on human and machine intelligence, with a star-studded lineup of speakers from the interlocking realms of artificial intelligence, cognitive science, neuroscience, social sciences, and ethics.

“It will thrive because we can offer it a continuous flow of fresh minds and fresh thinking.”  The time is ripe to “crack the code of intelligence” with a combination of neuroscience, cognitive science, and computer science, said MIT alumnus David Siegel SM ’86, PhD ’91, also a founding advisor to the MIT Intelligence Quest.

Then he shared an observation that was made in colorful ways throughout the day: The possibilities for discovery are great, but right now, “we are still very far from real AI.” The human brain is far superior to any existing form of artificial intelligence, which is why, he said, “as scientists, we have the opportunity — and obligation — to reverse engineer this brain machine.” Daniela Rus, director of the Computer Science and Artificial Intelligence Laboratory and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science, showed the same passion expressed from a different perspective: “Right now, the vast majority of AI algorithms are driven by mathematics and physics.

“And we discovered something remarkable.” Their results revealed an organized pattern of brain activity develops very early, with regions of the infant brain more active when babies look at faces.  The value of the child intelligence system is not lost on Josh Tenenbaum, a professor of computational cognitive science.

“I think the reason is that only now do we have a scientific field studying how children learn and think that is mature enough to offer guidance for AI.” Building a humanistic bridge In the Bridge session, speakers detailed projects that highlighted the remarkable potential benefits of AI: social robots that help children learn and engage the depressed, algorithms that can predict and prevent cancer, Wi-Fi signals that detect when elderly people fall, even algorithms that build personalized investment portfolios.

“I envision an AI that helps us to be smarter and more productive and to flourish — and heightens the ability for people to deeply connect.” “AI needs to be able to engage our social and emotional selves in addition to our cognitive selves,” she added, as people watched film of elderly people with Jibo, a social robot that she designed.

The consequences: intelligence and society “In my estimation AI is going to touch all these industries: energy, advance manufacturing, space, advanced materials, life science and biotech, internet of things,” said Katie Rae, CEO and managing partner of The Engine, which bridges the gap between discovery and commercialization by empowering disruptive technologies with the long-term capital, knowledge, and specialized equipment and labs they need to thrive.

“What does it mean for us to build machines that can think?” asked Melissa Nobles, the Kenan Sahin Dean of the MIT School of Humanities, Arts, and Social Sciences, during the panel discussion, “The Consequences: Intelligence and Society.” “What are the social, economic, political, artistic, ethical, and spiritual consequences of trying to make what happens in our minds happen in a machine?

“We need thoughtful folks to really put their values into the system and pay mindful attention,” said MIT alumna Megan Smith ’86, SM ’88, a former U.S. chief technology officer and a former vice president at Google.

Dario Gil, vice president of AI and quantum computing at IBM, said AI technologies draw on such large and pre-existing data sets, it’s more difficult for people to recognize the misuse of variables such as race, age, and gender.

“And it’s about getting a broader set of people working on developing it.” The event wrapped up at the Media Lab with a student poster session that included projects focused on communication: humans, robots, AI;

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