Kind of think it would be a dumb idea to try and make every separate device smart. Focus on an AI assistant for the house that can manage all of the devices.
Tiny microphones are moving us toward a world in which all gadgets can respond to a voice command. For those worried about privacy: meet a $200 trash can.
For some months now, a 3D printed house in Nantes has drawn lots of attention, not just because a printer was involved but also because it went up from start to finish so quickly (54 hours to print, then add some more time for the windows and roof). Interesting Engineering said it took some more time to add the roof, windows and doors.
A robot printer was used to print layers from the floor upwards to form the walls, and videos show a beautiful result of five rooms with rounded walls.
Continue reading “How Nantes team’s 3D printing may alter shape of homes to come” »
A commercially available “quantum computer” has been on the market since 2011, but it’s controversial. The D-Wave machine is nothing like other quantum computers, and until recently, scientists have doubted that it was even truly quantum at all. But the company has released an important new result, one that in part realizes Richard Feynman’s initial dreams for a quantum computer.
Scientists from D-Wave announced they have simulated a large quantum mechanical system with their 2000Q machine—essentially a cube of connected bar magnets. The D-Wave can’t take on the futuristic, mostly non-physics-related goals that many people have for quantum computers, such as finding solutions in medicine, cybersecurity, and artificial intelligence. Nor does it work the same way as the rest of the competition. But it’s now delivering real physics results. It’s simulating a quantum system.
There’s no x86 in the AI chip market yet—” People see a gold rush; there’s no doubt.”
A lot has changed since 1918. But whether it’s a literal (like the City of London School athletics’ U12 event) or figurative (AI chip development) race, participants still very much want to win.
For years, the semiconductor world seemed to have settled into a quiet balance: Intel vanquished virtually all of the RISC processors in the server world, save IBM’s POWER line. Elsewhere AMD had self-destructed, making it pretty much an x86 world. And Nvidia, a late starter in the GPU space, previously mowed down all of it many competitors in the 1990s. Suddenly only ATI, now a part of AMD, remained. It boasted just half of Nvidia’s prior market share.
Continue reading “The AI revolution has spawned a new chips arms race” »
An example graph of polypharmacy side effects derived from genomic and patient population data, protein–protein interactions, drug–protein targets, and drug–drug interactions encoded by 964 different polypharmacy side effects. The graph representation is used to develop Decagon. (credit: Marinka Zitnik et al./Bioinformatics)
Millions of people take up to five or more medications a day, but doctors have no idea what side effects might arise from adding another drug.*
Now, Stanford University computer scientists have developed a deep-learning system (a kind of AI modeled after the brain) called Decagon** that could help doctors make better decisions about which drugs to prescribe. It could also help researchers find better combinations of drugs to treat complex diseases.
Continue reading “How to predict the side effects of millions of drug combinations” »
An international team of scientists from Eindhoven University of Technology, University of Texas at Austin, and University of Derby, has developed a revolutionary method that quadratically accelerates artificial intelligence (AI) training algorithms. This gives full AI capability to inexpensive computers, and would make it possible in one to two years for supercomputers to utilize Artificial Neural Networks that quadratically exceed the possibilities of today’s artificial neural networks. The scientists presented their method on June 19 in the journal Nature Communications.
Artificial Neural Networks (or ANN) are at the very heart of the AI revolution that is shaping every aspect of society and technology. But the ANNs that we have been able to handle so far are nowhere near solving very complex problems. The very latest supercomputers would struggle with a 16 million-neuron network (just about the size of a frog brain), while it would take over a dozen days for a powerful desktop computer to train a mere 100,000-neuron network.
Meet the Cheetah 3 — a “blind” robot designed by MIT that can avoid obstacles and climb stairs using “feel” instead of sensors or cameras https://cnnmon.ie/2KQiJhD
