Lifeboat Foundation

Breaking the lowest oxygen abundance record.

New results achieved by combining big data captured by the Subaru Telescope and the power of machine learning have discovered a galaxy with an extremely low oxygen abundance of 1.6% solar abundance, breaking the previous record of the lowest oxygen abundance. The measured oxygen abundance suggests that most of the stars in this galaxy formed very recently.

Continue reading “Surprisingly Recent Galaxy Discovered Using Machine Learning – May Be the Last Generation Galaxy in the Long Cosmic History” »

Synthetic video and audio seemed pretty bad. Synthetic writing—ubiquitous and undetectable—will be far worse.

Skoltech scientists have shown that quantum enhanced machine learning can be used on quantum (as opposed to classical) data, overcoming a significant slowdown common to these applications and opening a “fertile ground to develop computational insights into quantum systems.” The paper was published in the journal Physical Review A.

Quantum computers utilize quantum mechanical effects to store and manipulate information. While quantum effects are often claimed to be counterintuitive, such effects will enable quantum enhanced calculations to dramatically outperform the best supercomputers. In 2019, the world saw a prototype of this demonstrated by Google as quantum computational superiority.

Quantum algorithms have been developed to enhance a range of different computational tasks; more recently this has grown to include quantum enhanced machine learning. Quantum machine learning was partly pioneered by Skoltech’s resident-based Laboratory for Quantum Information Processing, led by Jacob Biamonte, a coathor of this paper. “Machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is not surprising that quantum computers might outperform classical computers on machine learning tasks,” he says.

Computer programming has never been easy. The first coders wrote programs out by hand, scrawling symbols onto graph paper before converting them into large stacks of punched cards that could be processed by the computer. One mark out of place and the whole thing might have to be redone.

Nowadays coders use an array of powerful tools that automate much of the job, from catching errors as you type to testing the code before it’s deployed. But in other ways, little has changed. One silly mistake can still crash a whole piece of software. And as systems get more and more complex, tracking down these bugs gets more and more difficult. “It can sometimes take teams of coders days to fix a single bug,” says Justin Gottschlich, director of the machine programming research group at Intel.

Over the past decade, researchers have developed a growing number of deep neural networks that can be trained to complete a variety of tasks, including recognizing people or objects in images. While many of these computational techniques have achieved remarkable results, they can sometimes be fooled into misclassifying data.

An adversarial attack is a type of cyberattack that specifically targets deep neural networks, tricking them into misclassifying data. It does this by creating adversarial data that closely resembles and yet differs from the data typically analyzed by a deep neural network, prompting the network to make incorrect predictions, failing to recognize the slight differences between real and adversarial data.

In recent years, this type of attack has become increasingly common, highlighting the vulnerabilities and flaws of many deep neural networks. A specific type of adversarial attack that has emerged in recent years entails the addition of adversarial patches (e.g., logos) to images. This attack has so far primarily targeted models that are trained to detect objects or people in 2-D images.

Interesting.

The AI of 5–10 years time could be very different from today’s AI. The most successful AI systems of that time will not simply be extensions of today’s deep neural networks. Instead, they are likely to include significant conceptual breakthroughs or other game-changing innovations.

That was the argument I made in a presentation on Thursday to the Global Data Sciences and Artificial Intelligence meetup. The chair of that meetup, Pramod Kunji, kindly recorded the presentation.

Continue reading “The future of AI: 12 possible breakthroughs, and beyond” »

Robot that 3D prints and cooks plant-based meat alternatives for foodservice — can replace manufacturing practices.

Israeli start-up SavorEat has developed an automated, closed system that 3D prints and cooks plant-based meat alternatives for foodservice. “This robot can replace manufacturing practices,” CEO Racheli Vizman tells FoodNavigator.

Steve Granick, Director of the IBS Center for Soft and Living Matter and Dr. Huan Wang, Senior Research Fellow, report together with 5 interdisciplinary colleagues in the July 31 issue of the journal Science that common chemical reactions accelerate Brownian diffusion by sending long-range ripples into the surrounding solvent.

The findings violate a central dogma of chemistry, that molecular diffusion and chemical reaction are unrelated. To observe that molecules are energized by chemical reaction is “new and unknown,” said Granick. “When one substance transforms to another by breaking and forming bonds, this actually makes the molecules move more rapidly. It’s as if the chemical reactions stir themselves naturally.”

“Currently, nature does an excellent job of producing molecular machines but in the natural world scientists have not understood well enough how to design this property,” said Wang. “Beyond curiosity to understand the world, we hope that practically this can become useful in guiding thinking about transducing chemical energy for molecular motion in liquids, for nanorobotics, precision medicine and greener material synthesis.”

A new AI algorithm developed by the University of Pittsburgh has achieved the highest accuracy to date in identifying prostate cancer, with 98% sensitivity and 97% specificity.