Lifeboat Foundation

In Brief

• Princeton University researchers have developed the world’s first integrated silicon photonic neuromorphic chip, which contains 49 circular nodes etched into semiconductive silicon.
• The chip could complete a math equation 1,960 times more quickly than a typical central processing unit, a speed that would make it ideal for use in future neural networks.

As developments are made in neural computing, we can continue to push artificial intelligence further. A fairly recent technology, neural networks have been taking over the world of data processing, giving machines advanced capabilities such as object recognition, face recognition, natural language processing, and machine translation.

These sound like simple things, but they were way out of reach for processors until scientists began to find way to make machines behave more like human brains in the way they learned and handled data. To do this, scientists have been focusing on building neuromorphic chips, circuits that operate in a similar fashion to neurons.

Continue reading “Computing at Light Speed: The World’s First Photonic Neural Network Has Arrived” »

The goal of roboticists has long been to make A.I. as efficient as the human brain, and researchers at the Massachusetts Institute of Technology just brought them one step closer.

In a recent paper, published in the journal Biology, scientists were able to successfully train a neural network to recognize faces at different angles by feeding it a set of different orientations for several face templates. Although this only initially gave the neural network the ability to roughly reach invariance — the ability to process data regardless of form — over time, the network taught itself to achieve full “mirror symmetry. Through mathematical algorithms, the neural network was able to mimic the human brain’s ability to understand objects are the same despite orientation or rotation.

Continue reading “A.I. Can Teach Itself to Recognize Faces Now” »

What if a simple algorithm were all it took to program tomorrow’s artificial intelligence to think like humans?

According to a paper published in the journal Frontiers in Systems Neuroscience, it may be that easy — or difficult. Are you a glass-half-full or half-empty kind of person?

Researchers behind the theory presented experimental evidence for the Theory of Connectivity — the theory that all of the brains processes are interconnected (massive oversimplification alert) — “that a simple mathematical logic underlies brain computation.” Simply put, an algorithm could map how the brain processes information. The painfully-long research paper describes groups of similar neurons forming multiple attachments meant to handle basic ideas or information. These groupings form what researchers call “functional connectivity motifs” (FCM), which are responsible for every possible combination of ideas.

Continue reading “Researchers may have uncovered an algorithm that explains intelligence” »

Our brains have a basic algorithm that enables us to not just recognize a traditional Thanksgiving meal, but the intelligence to ponder the broader implications of a bountiful harvest as well as good family and friends.

“A relatively simple mathematical logic underlies our complex brain computations,” said Dr. Joe Z. Tsien, neuroscientist at the Medical College of Georgia at Augusta University, co-director of the Augusta University Brain and Behavior Discovery Institute and Georgia Research Alliance Eminent Scholar in Cognitive and Systems Neurobiology.

Once synbio computing is fully matured then our tech dev work maybe done.

By Frances Van Scoy, West Virginia University.

Continue reading “Moving toward computing at the speed of thought” »

““The School of Humanities and Sciences is systematically re-thinking how we teach entry-level courses in the sciences,” said Richard P. Saller, dean of the School of Humanities and Sciences, during opening remarks for the event. “Half of all freshman enrollments in Stanford are in beginning-level sciences and math. We have tremendous impact by raising the level of teaching in these areas.””

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https://youtube.com/watch?v=KPFnmGRZ8GQ

Optalysys’s technology performs a mathematical function called the Fourier transform by encoding data, say a genome sequence, into a laser beam. The data can be manipulated by making light waves in the beam interfere with one another, performing the calculation by exploiting the physics of light, and generating a pattern that encodes the result. The pattern is read by a camera sensor and fed back into a conventional computer’s electronic circuits. The optical approach is faster because it achieves in a single step what would take many operations of an electronic computer.

The technology was enabled by the consumer electronics industry driving down the cost of components called spatial light modulators, which are used to control light inside projectors. The company plans to release its first product next year, aimed at high-performance computers used for processing genomic data. It will take the form of a PCI express card, a standard component used to upgrade PCs or servers usually used for graphics processors. Optalysys is also working on a Pentagon research project investigating technologies that might shrink supercomputers to desktop size, and a European project on improving weather simulations.

Continue reading “Optical laser computing Could Power Up Genomics and AI and Optalysys targets one petaflop next year” »

Quantum theory is strange and counterintuitive, but it’s very precise. Lots of analogies and broad concepts are presented in popular science trying to give an accurate description of quantum behavior, but if you really want to understand how quantum theory (or any other theory) works, you need to look at the mathematical details. It’s only the mathematics that shows us what’s truly going on.

Mathematically, a quantum object is described by a function of complex numbers governed by the Schrödinger equation. This function is known as the wavefunction, and it allows you to determine quantum behavior. The wavefunction represents the state of the system, which tells you the probability of various outcomes to a particular experiment (observation). To find the probability, you simply multiply the wavefunction by its complex conjugate. This is how quantum objects can have wavelike properties (the wavefunction) and particle properties (the probable outcome).

No, wait. Actually a quantum object is described by a mathematical quantity known as a matrix. As Werner Heisenberg showed, each type of quantity you could observe (position, momentum, energy) is represented by a matrix as well (known as an operator). By multiplying the operator and the quantum state matrix in a particular way, you get the probability of a particular outcome. The wavelike behavior is a result of the multiple connections between states within the matrix.

SciWorks Radio is a production of 88.5 WFDD and SciWorks, the Science Center and Environmental Park of Forsyth County, located in Winston-Salem.

We’ve come a long way from stone tools. With great complexity, we manufacture things like jet airplanes, interplanetary probes, medical tools, and microprocessors. We build with a top-down approach, starting with a big picture concept which we then design and assemble in pieces.

Duke University professor of computer sciences, Dr. John Reif, notes that nature works from the bottom up to assemble complex structures in three dimensions.