Lifeboat Foundation

AI systems can lead to race or gender discrimination.

The US Federal Trade Commission has warned companies against using biased artificial intelligence, saying they may break consumer protection laws. A new blog post notes that AI tools can reflect “troubling” racial and gender biases. If those tools are applied in areas like housing or employment, falsely advertised as unbiased, or trained on data that is gathered deceptively, the agency says it could intervene.

“In a rush to embrace new technology, be careful not to overpromise what your algorithm can deliver,” writes FTC attorney Elisa Jillson — particularly when promising decisions that don’t reflect racial or gender bias. “The result may be deception, discrimination — and an FTC law enforcement action.”

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Basically, we live in one giant algorithm.

In fascinating new research, cosmologists explain the history of the universe as one of self-teaching, autodidactic algorithms.

The more data collected, the better the results.

Understanding the genetics of complex diseases, especially those related to the genetic differences among ethnic groups, is essentially a big data problem. And researchers need more data.

1000, 000 genomes

Continue reading “Scientists are on a path to sequencing 1 million human genomes and use big data to unlock genetic secrets” »

Developing Next Generation Artificial Intelligence To Serve Humanity — Dr. Patrick Bangert, Vice President of AI, Samsung SDS.

Dr. Patrick D. Bangert, is Vice President of AI, and heads the AI Engineering and AI Sciences teams, at Samsung SDS is a subsidiary of the Samsung Group, which provides information technology (IT) services, and are active in research and development of emerging IT technologies such as artificial intelligence (AI), blockchain, Internet of things (IoT) and Engineering Outsourcing.

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Whatever business a company may be in, software plays an increasingly vital role, from managing inventory to interfacing with customers. Software developers, as a result, are in greater demand than ever, and that’s driving the push to automate some of the easier tasks that take up their time.

Productivity tools like Eclipse and Visual Studio suggest snippets of code that developers can easily drop into their work as they write. These automated features are powered by sophisticated language models that have learned to read and write computer code after absorbing thousands of examples. But like other deep learning models trained on big datasets without explicit instructions, language models designed for code-processing have baked-in vulnerabilities.

“Unless you’re really careful, a hacker can subtly manipulate inputs to these models to make them predict anything,” says Shashank Srikant, a graduate student in MIT’s Department of Electrical Engineering and Computer Science. “We’re trying to study and prevent that.”

Yesterday Nvidia officially dipped a toe into quantum computing with the launch of cuQuantum SDK, a development platform for simulating quantum circuits on GPU-accelerated systems. As Nvidia CEO Jensen Huang emphasized in his keynote, Nvidia doesn’t plan to build quantum computers, but thinks GPU-accelerated platforms are the best systems for quantum circuit and algorithm development and testing.

As a proof point, Nvidia reported it collaborated with Caltech to develop “a state-of-the-art quantum circuit simulator with cuQuantum running on NVIDIA A100 Tensor Core GPUs. It generated a sample from a full-circuit simulation of the Google Sycamore circuit in 9.3 minutes on Selene, a task that 18 months ago experts thought would take days using millions of CPU cores.”

Physicists from Swansea University are part of an international research collaboration which has identified a new technique for testing the quality of quantum correlations.

Quantum computers run their algorithms on large quantum systems of many parts, called qubits, by creating quantum correlations across all of them. It is important to verify that the actual computation procedures lead to quantum correlations of desired quality.

However, carrying out these checks is resource-intensive as the number of tests required grows exponentially with the number of qubits involved.

Scientists are using natural-language algorithms to try understand which coronavirus mutations will be most infectious.

Classical hydrodynamics laws can be very useful for describing the behavior of systems composed of many particles (i.e., many-body systems) after they reach a local state of equilibrium. These laws are expressed by so-called hydrodynamical equations, a set of mathematical equations that describe the movement of water or other fluids.

Researchers at Oak Ridge National Laboratory and University of California, Berkeley (UC Berkeley) have recently carried out a study exploring the hydrodynamics of a quantum Heisenberg spin-1/2 chain. Their paper, published in Nature Physics, shows that the spin dynamics of a 1D Heisenberg antiferromagnet (i.e., KCuF₃) could be effectively described by a dynamical exponent aligned with the so-called Kardar-Parisi-Zhang universality class.

“Joel Moore and I have known each other for many years and we both have an interest in quantum magnets as a place where we can explore and test new ideas in physics; my interests are experimental and Joel’s are theoretical,” Alan Tennant, one of the researchers who carried out the study, told Phys.org. “For a long time, we have both been interested in temperature in quantum systems, an area where a number of really new insights have come along recently, but we had not worked together on any projects.”