Artificial Intelligence
David Cox gave a talk about current work in neuroscience and computer science at the World Economic Forum, that points to a variety of implications including brain uploading and the development of advanced artificial intelligence based on biology.
At the recent World Economic Forum, David Cox gave a talk about current work in neuroscience and computer science that points to a variety of implications including brain uploading and the development of advanced artificial intelligence based on biology.
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| Image Source: World Economic Forum / Walter Duerst |
"The only reason we can have this conversation today, is that there are two fields that are exploding right now, and are on a collision course with one another."
The fast-advancing fields of neuroscience and computer science are converging explains Cox. "The only reason we can have this conversation today, is that there are two fields that are exploding right now, and are on a collision course with one another.""It might seem weird to connect technology and neuroscience," states Cox, "but is actually something we have been doing for a very long time." Cox points that through history, we have always used metaphors for our mind, like pneumatic power, steam power, and today's technology of the computer. "Now computers are the lens through which we see our brains."
Computer science does give us a new model for looking at our brains. There is an equivalence. "If we understand the algorithms of the brain, we can think about running that on other hardware that we have [like] silicon," Cox says.
Within the last five years alone, there has been a tectonic shift in the world of artificial intelligence Cox claims. However, "we are not quite there yet."
Cox is working on studying the brain to figure out what is missing in artificial intelligence so far
Despite significant progress in developing AI algorithms and machine learning over the past few years, today’s AI systems do not generalize well. In contrast, the brain is able to robustly separate and categorize signals in the presence of significant noise and non-linear transformations, and can extrapolate from single examples to entire classes of stimuli. This performance gap between software and wetware persists despite some correspondence between the architecture of the leading machine learning algorithms and their biological counterparts in the brain.Cox's lab is working with others to reverse engineer how brains learn, starting with rats. By shedding light on what our machine learning algorithms are currently missing, this work promises to improve the capabilities of robots – with implications for jobs, laws and ethics.
To this end Cox is at work on the IARPA project, Machine Intelligence from Cortical Networks (MICrONS). This neuroscience project is slated to take place over the next five years, and according to Cox, it is equivalent in scale to the Human Genome project.
MICrONS seeks to revolutionize machine learning by reverse-engineering the algorithms of the brain. The program is expressly designed as a dialogue between data science and neuroscience. Over the course of the program, participants will use their improving understanding of the representations, transformations, and learning rules employed by the brain to create ever more capable neurally derived machine learning algorithms. Some of the ultimate goals for MICrONS include the ability to perform complex information processing tasks such as one-shot learning, unsupervised clustering, and scene parsing, aiming towards human-like proficiency.
The project involves creating connectome of a rat's brain, which amount to about two petabytes of data. Cox admits that the project he is a part of actually does conjure up the ambition of brain uploading.
"What I can tell you is that way before humans upload their brain, it's going to be rats that get their brains up into the cloud first," says Cox.
If you are excited about this idea, Cox shares good news, bad news and neutral news. The good news is that there is nothing in principle that makes mind uploading impossible. "It could happen, I'm just going to put that out there," he says. The bad news is we have no idea how to do this yet. The work so far is these are just the first steps.
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As far as neutral news, Cox says that the work his team and others around the world is leading already to even greater strides in machine learning and robots. "I would submit that the brain power of a rat, properly implemented is enough to drive a car," he says as an example.Cox is an Assistant Professor of Molecular and Cellular Biology and Computer Science at Harvard University. His research spans across neuroscience and computer science, with the goal of understanding how brains process sensory information; employs a variety of experimental techniques to measure brain function and uses this biological information to build advanced machine learning algorithms. Actively engaged in innovation in online learning, particularly at the intersection of machine intelligence and education.
Cox also is working on the ARIADNE project—a multi-university effort to study a living animal brain like never before to figure out how it learns. This project will create some of the largest neuroscience datasets ever collected, and could give computers new abilities to learn and perceive the way our brains do.
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