Lifeboat Foundation

Both are AI-enabled, allowing them to take in their surroundings and learn and evolve over time. They know what time to start cooking a well-done burger so that it’s done at exactly the same time as a medium-rare burger for the same order, or could learn how to optimize oil use to minimize waste, for instance.

In a pre-pandemic time of restaurant labor shortages, Flippy kept kitchen productivity high and costs low, a giant deal in an industry known for tiny margins. Introducing Flippy into a kitchen can increase profit margins by a whopping 300%, not to mention significantly reduce the stress managers feel when trying to fill shifts.

Continue reading “Restaurants Are in Need of a Helping Hand. Miso Robotics Is Offering Them One. Literally” »

Rapid progress has been made in recent years to build these tiny machines, thanks to supramolecular chemists, chemical and biomolecular engineers, and nanotechnologists, among others, working closely together. But one area that still needs improvement is controlling the movements of swarms of molecular robots, so they can perform multiple tasks simultaneously.

Incredible Ai

Flying automobiles have long been a staple of science fiction’s optimistic visions of tomorrow, right up there with rocket jetpacks, holidays on the moon, and robot butlers. And who wouldn’t want to climb into a vehicle capable of rising up into the air above the clogged arteries of traffic experienced on most major boulevards, highways, and freeways?

Now a lofty new air taxi being built by the Israeli startup firm Urban Aeronautics hopes to cash in on those promises with its new Vertical Takeoff and Landing (VTOL) car that unites technology with Jetsons-like futuristic dreams mostly only observed in films like Blade Runner, The Fifth Element, Back to the Future, and most recently on TV in Season 3 of HBO’s Westworld.

Continue reading “New Vertical Takeoff and Landing flying taxi has its roots in modern sci-fi films” »

Like many things about Elon Musk, Tesla’s approach to achieving autonomous driving is polarizing. Bucking the map-based trend set by industry veterans such as Waymo, Tesla opted to dedicate its resources in pursuing a vision-based approach to achieve full self-driving instead. This involves a lot of hard, tedious work on Tesla’s part, but today, there are indications that the company’s controversial strategy is finally paying off.

In a recent talk, Tesla AI Director Andrej Karpathy discussed the key differences between the map-based approach of Waymo and Tesla’s camera-based strategy. According to Karpathy, Waymo’s use of pre-mapped data and LiDAR make scaling difficult, since vehicles’ autonomous capabilities are practically tied to a geofenced area. Tesla’s vision-based approach, which uses cameras and artificial intelligence, is not. This means that Autopilot and FSD improvements can be rolled out to the fleet, and they would function anywhere.

Continue reading “Tesla’s controversial vision-based full self-driving approach is finally paying off” »

My interview.

An African perspective on COVID-19 and technology.

For instance, suppose a neural network has labeled the image of a skin mole as cancerous. Is it because it found malignant patterns in the mole or is it because of irrelevant elements such as image lighting, camera type, or the presence of some other artifact in the image, such as pen markings or rulers?

Researchers have developed various interpretability techniques that help investigate decisions made by various machine learning algorithms. But these methods are not enough to address AI’s explainability problem and create trust in deep learning models, argues Daniel Elton, a scientist who researches the applications of artificial intelligence in medical imaging.

Elton discusses why we need to shift from techniques that interpret AI decisions to AI models that can explain their decisions by themselves as humans do. His paper, “Self-explaining AI as an alternative to interpretable AI,” recently published in the arXiv preprint server, expands on this idea.

There’s a lot of hope that artificial intelligence could help speed up the time it takes to make a drug and also increase the rate of success. Several startups have emerged to capitalize on this opportunity. But Insitro is a bit different from some of these other companies, which rely more heavily on machine learning than biology.

Machine learning can speed up the creation of new drugs and unlock the mysteries of major diseases, says Insitro CEO Daphne Koller.

[Photo: Ivan-balvan/iStock]

Continue reading “Combining AI and biology could solve drug discovery’s biggest problems” »

MIT engineers have designed a “brain-on-a-chip,” smaller than a piece of confetti, that is made from tens of thousands of artificial brain synapses known as memristors — silicon-based components that mimic the information-transmitting synapses in the human brain.

The researchers borrowed from principles of metallurgy to fabricate each memristor from alloys of silver and copper, along with silicon. When they ran the chip through several visual tasks, the chip was able to “remember” stored images and reproduce them many times over, in versions that were crisper and cleaner compared with existing memristor designs made with unalloyed elements.

Their results, published on June 8, 2020, in the journal Nature Nanotechnology, demonstrate a promising new memristor design for neuromorphic devices — electronics that are based on a new type of circuit that processes information in a way that mimics the brain’s neural architecture. Such brain-inspired circuits could be built into small, portable devices, and would carry out complex computational tasks that only today’s supercomputers can handle.