Lifeboat Foundation

Over the last few years, rapid progress in AI has enabled our smartphones, social networks, and search engines to understand our voice, recognize our faces, and identify objects in our photos with very good accuracy. These dramatic improvements are due in large part to the emergence of a new class of machine learning methods known as Deep Learning.

Animals and humans can learn to see, perceive, act, and communicate with an efficiency that no Machine Learning method can approach. The brains of humans and animals are “deep”, in the sense that each action is the result of a long chain of synaptic communications (many layers of processing). We are currently researching efficient learning algorithms for such “deep architectures”. We are currently concentrating on unsupervised learning algorithms that can be used to produce deep hierarchies of features for visual recognition. We surmise that understanding deep learning will not only enable us to build more intelligent machines but will also help us understand human intelligence and the mechanisms of human learning. http://www.cs.nyu.edu/~yann/research/deep/

But in the last few years, AI has changed the game. Deep-learning algorithms excel at quickly finding patterns in reams of data, which has sped up key processes in scientific discovery. Now, along with these software improvements, a hardware revolution is also on the horizon.

Yesterday Argonne announced that it has begun to test a new computer from the startup Cerebras that promises to accelerate the training of deep-learning algorithms by orders of magnitude. The computer, which houses the world’s largest chip, is part of a new generation of specialized AI hardware that is only now being put to use.

“We’re interested in accelerating the AI applications that we have for scientific problems,” says Rick Stevens, Argonne’s associate lab director for computing, environment, and life sciences. “We have huge amounts of data and big models, and we’re interested in pushing their performance.”

Barbara Liskov pioneered the modern approach to writing code. She warns that the challenges facing computer science today can’t be overcome with good design alone.

Maybe interesting for this group too.

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Continue reading “The danger of AI is weirder than you think | Janelle Shane” »

Most humans can learn how to complete a given task by observing another person perform it just once. Robots that are programmed to learn by imitating humans, however, typically need to be trained on a series of human demonstrations before they can effectively reproduce the desired behavior.

Researchers were recently able to teach robots to execute new tasks by having them observe a single human demonstration, using meta-learning approaches. However, these learning techniques typically require real-world data that can be expensive and difficult to collect.

Continue reading “An imitation learning approach to train robots without the need for real human demonstrations” »

Artificial intelligence can be used to predict molecular wave functions and the electronic properties of molecules. This innovative AI method developed by a team of researchers at the University of Warwick, the Technical University of Berlin and the University of Luxembourg, could be used to speed-up the design of drug molecules or new materials.

Artificial intelligence and machine learning algorithms are routinely used to predict our purchasing behavior and to recognize our faces or handwriting. In scientific research, Artificial Intelligence is establishing itself as a crucial tool for scientific discovery.

In chemistry, AI has become instrumental in predicting the outcomes of experiments or simulations of quantum systems. To achieve this, AI needs to be able to systematically incorporate the fundamental laws of physics.

A pair of mathematicians from Australia and France have devised an alternative way to multiply numbers together, while solving an algorithmic puzzle that has perplexed some of the greatest math minds for almost half a century.

For most of us, the way we multiply relatively small numbers is by remembering our times tables – an incredibly handy aid first pioneered by the Babylonians some 4,000 years ago.

Continue reading “Mathematicians Have Discovered an Entirely New Way to Multiply Large Numbers” »

What is needed as a cocktail is the Gerevivify Algorithms Serum Elixir. An Elixir that enters the body that then goes into battle fighting the infection that is aging. I search for strong-minded partners and investors to join me in the growth and development of the specially grown 13 ingredients that make up the powerful serum. Respect r.p.berry & AEWR https://gerevivify.blogspot.com/

“Fahy’s fascination with the thymus goes back to 1986, when he read a study in which scientists transplanted growth-hormone-secreting cells into rats, apparently rejuvenating their immune systems,” Nature reported. “He was surprised that no one seemed to have followed up on the result with a clinical trial. A decade later, at age 46, he treated himself for a month with growth hormone and DHEA, and found some regeneration of his own thymus.”

The thymus is located in the chest between the lungs and the breastbone and is crucial for efficient immune function. “White blood cells are produced in bone marrow and then mature inside the thymus, where they become specialized T-cells that help the body to fight infections and cancers,” Nature reported. “But the gland starts to shrink after puberty and increasingly becomes clogged with fat. Evidence from animal and some human studies shows that growth hormone stimulates regeneration of the thymus. But this hormone can also promote diabetes, so the trial included two widely used anti-diabetic drugs, dehydroepiandrosterone (DHEA) and metformin, in the treatment cocktail.”

Continue reading “Cocktail Of Drugs Gives First Hope That ‘Biological Age’ Can Be Reversed” »

The U.S. military is preparing for the age of AI and algorithmic warfare, and it’s getting help from tech giants like Facebook, Google, and Microsoft.