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Category: information science – Page 178

A celebrated AI has learned a new trick: How to do chemistry

Sign in Welcome! Log into your account your username your password Forgot your password? Get help Default Kit Password recovery Recover your password your email A password will be e-mailed to you. HometechA celebrated AI has learned a new…

Artificial intelligence has altered the practise of science by enabling researchers to examine the vast volumes of data generated by current scientific instruments. Using deep learning, it can learn from the data itself and can locate a needle in a million haystacks of information. AI is advancing the development of gene searching, medicine, medication design, and chemical compound synthesis.

Scientists Detect Fastest-Growing Black Hole in the Universe

To extract information from fresh data, deep learning employs algorithms, often neural networks trained on massive volumes of data. With its step-by-step instructions, it is considerably different from traditional computing. It instead learns from data. Deep learning is far less transparent than conventional computer programming, leaving vital concerns unanswered: what has the system learnt and what does it know?

Teaching Physics to AI Can Allow It To Make New Discoveries All on Its Own

Incorporating established physics into neural network algorithms helps them to uncover new insights into material properties

According to researchers at Duke University, incorporating known physics into machine learning algorithms can help the enigmatic black boxes attain new levels of transparency and insight into the characteristics of materials.

Researchers used a sophisticated machine learning algorithm in one of the first efforts of its type to identify the characteristics of a class of engineered materials known as metamaterials and to predict how they interact with electromagnetic fields.

San Diego drone tech startup raises $165M to build AI pilot

Shield AI, an artificial intelligence company focusing on drones and other autonomous aircraft, is on a mission to build “the world’s best AI pilot.” To that end, the San Diego startup has raised $90 million in equity and $75 million in debt as part of a Series E fundraising round. The funding values Shield AI at $2.3 billion.

Hivemind employs state-of-the-art algorithms for planning, mapping, and state-estimation to enable drones to execute dynamic flight maneuvers. On aircraft, Hivemind enables full autonomy and is designed to run fully on the edge, disconnected from the cloud, in high-threat GPS and communication-degraded environments.

Building up new data-storage memory

Scientists from the Institute of Industrial Science at The University of Tokyo fabricated three-dimensional vertically formed field-effect transistors to produce high-density data storage devices by ferroelectric gate insulator and atomic-layer-deposited oxide semiconductor channel. Furthermore, by using antiferroelectric instead of ferroelectric, they found that only a tiny net charge was required to erase data, which leads to more efficient write operations. This work may allow for new, even smaller and more eco-friendly data-storage memory.

While consumer flash drives already boast huge improvements in size, capacity, and affordability over previous computer media formats in terms of storing data, new machine learning and Big Data applications continue to drive demand for innovation. In addition, mobile cloud-enabled devices and future Internet of Things nodes will require that is energy-efficient and small in size. However, current flash memory technologies require relatively large currents to read or write data.

Now, a team of researchers at The University of Tokyo have developed a proof-of-concept 3D stacked memory cell based on ferroelectric and antiferroelectric field-effect transistors (FETs) with atomic-layer-deposited oxide semiconductor channel. These FETs can store ones and zeros in a non-volatile manner, which means they do not require power to be supplied at all times. The vertical device structure increases information density and reduces operation energy needs. Hafnium oxide and indium oxide layers were deposited in a vertical trench structure. Ferroelectric materials have electric dipoles that are most stable when aligned in the same direction. Ferroelectric Hafnium Oxide spontaneously enables the vertical alignment of the dipoles. Information is stored by the degree of polarization in the ferroelectric layer, which can be read by the system owing to changes in electrical resistance.

Ben Goertzel — Open Ended vs Closed Minded Conceptions of Superintelligence

Abstract: Superintelligence, the next phase beyond today’s narrow AI and tomorrow’s AGI, almost intrinsically evades our attempts at detailed comprehension. Yet very different perspectives on superintelligence exist today and have concrete influence on thinking about matters ranging from AGI architectures to technology regulation.
One paradigm considers superintelligences as resembling modern deep reinforcement learning systems, obsessively concerned with optimizing particular goal functions. Another considers superintelligences as open-ended, complex evolving systems, ongoingly balancing drives.
toward individuation and radical self-transcendence in a paraconsistent way. In this talk I will argue that the open-ended conception of superintelligence is both more desirable and more realistic, and will discuss how concrete work being done today on projects like OpenCog Hyperon, SingularityNET and Hypercycle potentially paves the way for a path through beneficial decentralized integrative AGI and on to open-ended superintelligence and ultimately the Singularity.

Bio: In May 2007, Goertzel spoke at a Google tech talk about his approach to creating artificial general intelligence. He defines intelligence as the ability to detect patterns in the world and in the agent itself, measurable in terms of emergent behavior of “achieving complex goals in complex environments”. A “baby-like” artificial intelligence is initialized, then trained as an agent in a simulated or virtual world such as Second Life to produce a more powerful intelligence. Knowledge is represented in a network whose nodes and links carry probabilistic truth values as well as “attention values”, with the attention values resembling the weights in a neural network. Several algorithms operate on this network, the central one being a combination of a probabilistic inference engine and a custom version of evolutionary programming.

This talk is part of the ‘Stepping Into the Future‘conference. http://www.scifuture.org/open-ended-vs-closed-minded-concept…elligence/

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AI is Ushering In a New Scientific Revolution

By making remarkable breakthroughs in a number of fields, unlocking new approaches to science, and accelerating the pace of science and innovation.

In 2020, Google’s AI team DeepMind announced that its algorithm, AlphaFold, had solved the protein-folding problem. At first, this stunning breakthrough was met with excitement from most, with scientists always ready to test a new tool, and amusement by some. After all, wasn’t this the same company whose algorithm AlphaGo had defeated the world champion in the Chinese strategy game Go, just a few years before? Mastering a game more complex than chess, difficult as that is, felt trivial compared to the protein-folding problem. But AlphaFold proved its scientific mettle by sweeping an annual competition in which teams of biologists guess the structure of proteins based only on their genetic code. The algorithm far outpaced its human rivals, posting scores that predicted the final shape within an angstrom, the width of a single atom. Soon after, AlphaFold passed its first real-world test by correctly predicting the shape of the SARS-CoV-2 ‘spike’ protein, the virus’ conspicuous membrane receptor that is targeted by vaccines.

The success of AlphaFold soon became impossible to ignore, and scientists began trying out the algorithm in their labs. By 2021 Science magazine crowned an open-source version of AlphaFold the “Method of the Year.” Biochemist and Editor-in-Chief H. Holden Thorp of the journal Science wrote in an editorial, “The breakthrough in protein-folding is one of the greatest ever in terms of both the scientific achievement and the enabling of future research.” Today, AlphaFold’s predictions are so accurate that the protein-folding problem is considered solved after more than 70 years of searching. And while the protein-folding problem may be the highest profile achievement of AI in science to date, artificial intelligence is quietly making discoveries in a number of scientific fields.

By turbocharging the discovery process and providing scientists with new investigative tools, AI is also transforming how science is done. The technology upgrades research mainstays like microscopes and genome sequencers 0, adding new technical capacities to the instruments and making them more powerful. AI-powered drug design and gravity wave detectors offer scientists new tools to probe and control the natural world. Off the lab bench, AI can also deploy advanced simulation capabilities and reasoning systems to develop real-world models and test hypotheses using them. With manifold impacts stretching the length of the scientific method, AI is ushering in a scientific revolution through groundbreaking discoveries, novel techniques and augmented tools, and automated methods that advance the speed and accuracy of the scientific process.

Andrea De Souza — Eli Lilly — Leveraging Big Data & Artificial Intelligence For Unmet Medical Needs

Leveraging big data & artificial intelligence to solve unmet medical needs — andrea de souza — eli lilly & co.

Andrea De Souza, is Associate Vice President, Research Data Sciences and Engineering, at Eli Lilly & Company (https://www.lilly.com/) where over the past three years her work has focused around empowering the Lilly Research Laboratories (LRL) organization with greater computational, analytics-intense experimentation to raise the innovation of their scientists.

A former neuroscience researcher, Andrea’s portfolio career has included leadership assignments at the intersection of science, technology and business development. She has built and led informatics and scientific teams across the entire pharmaceutical value chain.

Most recently, Andrea focused on building the Pharma Artificial Intelligence market at NVIDIA. Through this experience she has traveled the world advising bio-pharmaceutical clients, academics, research institutes, and startups in the potential of machine learning and artificial intelligence across every discipline of the industry.

Prior to her role at NVIDIA, Andrea held leadership positions at the Broad Institute of Harvard and MIT, Amgen, and Roche.

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Ingenuity has Lost its Sense of Direction, but It’ll Keep on Flying

The Ingenuity chopper on Mars has lost an instrument that helps it navigate. Flight controllers have found a work-around.

Things are getting challenging for the Ingenuity helicopter on Mars. The latest news from Håvard Grip, its chief pilot, is that the “Little Chopper that Could” has lost its sense of direction thanks to a failed instrument. Never mind that it was designed to make only a few flights, mostly in Mars spring. Or that it’s having a hard time staying warm now that winter is coming. Now, one of its navigation sensors, called an inclinometer, has stopped working. It’s not the end of the world, though. “A nonworking navigation sensor sounds like a big deal – and it is – but it’s not necessarily an end to our flying at Mars,” Grip wrote on the Mars Helicopter blog on June 6. It turns out that the controllers have options.

Like other NASA planetary missions, Ingenuity sports a fair amount of redundancy in its systems. It has an inertial measurement unit (IMU) that measures accelerations and angular rates of ascent and descent in three directions. In addition, there’s a laser rangefinder that measures the distance to the ground. Finally, the chopper has a navigation camera. It gives visual evidence of where Ingenuity is during flight or on the ground. An algorithm takes data from these instruments and uses it during flight. But, it needs to know the chopper’s roll and pitch attitude, and that’s what the inclinometer supplies.

Since it failed, the team had to find a way to impersonate the inclinometer. So, they applied a software patch to the code running on Ingenuity’s flight computer. It intercepts what Grip describes as “garbage packets” of data and replaces them with good data. Essentially, the flight controllers tricked the copter’s navigation algorithms into thinking that the data they have came from the inclinometer.

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