Lifeboat Foundation

Both AlphaFold’s and GPT-3’s success was due largely to the massive datasets they were trained on; no revolutionary new training methods or architectures were involved. If all it was going to take to advance AI was a continuation or scaling-up of this paradigm—more input data yields increased capability—China could well have an advantage.

But one of the biggest hurdles AI needs to clear to advance in leaps and bounds rather than baby steps is precisely this reliance on extensive, task-specific data. Other significant challenges include the technology’s fast approach to the limits of current computing power and its immense energy consumption.

Thus, while China’s trove of data may give it an advantage now, it may not be much of a long-term foothold on the climb to AI dominance. It’s useful for building products that incorporate or rely on today’s AI, but not for pushing the needle on how artificially intelligent systems learn. WeChat data on users’ spending habits, for example, would be valuable in building an AI that helps people save money or suggests items they might want to purchase. It will enable (and already has enabled) highly tailored products that will earn their creators and the companies that use them a lot of money.

https://www.youtube.com/watch?v=SV774u0LrOA

Samsung’s memory technology innovates artificial intelligence and Big Data analytics to bring impactful change to the way we live, work, and interact with each other. Through next-generation memory technology that enables faster and more complex tasks in AI and Big Data, Samsung takes part in the revolutionary advancement of technology that is enriching our everyday lives.

Neural networks of neural networks.

Brains receive input from the outside world, their neurons do something to that input, and create an output. That output may be a thought (I want curry for dinner); it may be an action (make curry); it may be a change in mood (yay curry!). Whatever the output, that “something” is a transformation of some form of input (a menu) to output (“chicken dansak, please”). And if we think of a brain as a device that transforms inputs to outputs then, inexorably, the computer becomes our analogy of choice.

For some this analogy is merely a useful rhetorical device; for others it is a serious idea. But the brain isn’t a computer. Each neuron is a computer. Your cortex contains 17 billion computers.

Continue reading “Your Cortex Contains 17 Billion Computers” »

Prostate cancer is one of the most common cancers among men. Patients are determined to have prostate cancer primarily based on PSA, a cancer factor in blood. However, as diagnostic accuracy is as low as 30%, a considerable number of patients undergo additional invasive biopsy and thus suffer from resultant side effects, such as bleeding and pain.

The Korea Institute of Science and Technology (KIST) announced that the collaborative research team led by Dr. Kwan Hyi Lee from the Biomaterials Research Center and Professor In Gab Jeong from Asan Medical Center developed a technique for diagnosing prostate cancer from urine within only 20 minutes with almost 100% accuracy. The research team developed this technique by introducing a smart AI analysis method to an electrical-signal-based ultrasensitive biosensor.

As a noninvasive method, a diagnostic test using urine is convenient for patients and does not need invasive biopsy, thereby diagnosing cancer without side effects. However, as the concentration of cancer factors is low in urine, urine-based biosensors are only used for classifying risk groups rather than for precise diagnosis thus far.

Brink Bionics completed a very successful [Indiegogo](https://www.indiegogo.com/projects/impulse-neuro-controller-for-pc-gaming#/) crowdfunding campaign in 2020 and gained the confidence to [take part in the CES](

CES booths are looking a bit different this year…#CES2021 #CES #ShelterInPlace pic.twitter.com/dcTSWLTxio— Brink Bionics (@BrinkBionics) January 13, 2021

Continue reading “Impulse neuro controller reduces PC gaming reaction times” »

Lean, mean bricklaying machine.

Construction is difficult to automate because of the complex, individualized and customized work it requires.

Continue reading “Construction could be revolutionized by a robot that lays 3,000 bricks a day” »

LONDON — Artificial intelligence researchers don’t like it when you ask them to name the top AI labs in the world, possibly because it’s so hard to answer.

There are some obvious contenders when it comes to commercial AI labs. U.S. Big Tech — Google, Facebook, Amazon, Apple and Microsoft — have all set up dedicated AI labs over the last decade. There’s also DeepMind, which is owned by Google parent company Alphabet, and OpenAI, which counts Elon Musk as a founding investor.

DeepMind, OpenAI, and Facebook AI Research are fighting it out to be the top AI research lab in the world.

Continue reading “Artificial intelligence researchers rank the top A.I. labs worldwide” »

Contemporary robots can move quickly. “The motors are fast, and they’re powerful,” says Sabrina Neuman.

Yet in complex situations, like interactions with people, robots often don’t move quickly. “The hang up is what’s going on in the robot’s head,” she adds.

Perceiving stimuli and calculating a response takes a “boatload of computation,” which limits reaction time, says Neuman, who recently graduated with a Ph.D. from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Neuman has found a way to fight this mismatch between a robot’s “mind” and body. The method, called robomorphic computing, uses a robot’s physical layout and intended applications to generate a customized computer chip that minimizes the robot’s response time.

A new method to reason about uncertainty might help artificial intelligence to find safer options faster, for example in self-driving cars, according to a new study to be published shortly in AAAI involving researchers at Radboud University, the University of Austin, the University of California, Berkeley, and the Eindhoven University of Technology.

The researchers have defined a new approach to so-called ‘uncertain partially observable Markov decision processes, or uPOMDPs. In layman’s terms, these are models of the real world that estimate the probability of events. A self-driving car, for example, will face many unknown situations when it starts driving. To validate the artificial intelligence of self-driving cars, extensive calculations are run to analyze how the AI would approach various situations. The researchers argue that with their new approach, these modeling exercises can become far more realistic, and thus allows AI to make better, safer decisions quicker.