Responding to artificial intelligence's exploding demands on computer networks, Princeton University researchers in recent years have radically increased the speed and slashed the energy use of specialized AI systems. Now, the researchers have moved their innovation closer to widespread use by creating co-designed hardware and software that will allow designers to blend these new types of systems into their applications. "Software is a critical part of enabling new hardware," said Naveen Verma, a professor of electrical and computer engineering at Princeton and a leader of the research team. "The hope is that designers can keep using the same software system -- and just have it work ten times faster or more efficiently." By cutting both power demand and the need to exchange data from remote servers, systems made with the Princeton technology will be able to bring artificial intelligence applications, such as piloting software for drones or advanced language translators, to the very edge of computing infrastructure. "To make AI accessible to the real-time and often personal process all around us, we need to address latency and privacy by [...]

Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs. MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability. Using this new approach, which allows computer models to account for uncertainty in the data they’re analyzing, the MIT team identified several promising compounds that target a protein required by the bacteria that cause tuberculosis. This method, which has previously been used by computer scientists but has not taken off in biology, could also prove useful in protein design and many other fields of biology, says Bonnie Berger, the Simons Professor of Mathematics and head of the Computation and Biology group in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “This technique is part of a known subfield of machine learning, but people have not brought it to biology,” Berger says. “This is a paradigm shift, and is absolutely how biological exploration should be done.” Berger and Bryan Bryson, an assistant professor of biological engineering at MIT and [...]

Campaign groups are seeking a judicial review of the UK government's decision to award a £23m NHS contract to controversial AI company Palantir. Separate investigations show Palantir had been in discussions with the NHS about exploiting the health data of UK citizens since the middle of 2019, well before the pandemic. In December last year, the NHS signed a two-year contract with Palantir, without scrutiny, even though the engagement with the UK health service was originally supposed to be a temporary, emergency measure to help address the COVID-19 pandemic. The new deal commits to using Palantir's Foundry platform until December 2022. NHS awards £23m two-year deal to controversial Peter Thiel AI firm Palantir READ MORE News website openDemocracy, backed by tech campaign group Foxglove, has this week filed for a judicial review of that data deal between NHS and Palantir, the US AI firm that carries out information analysis and processing work for the defence and intelligence communities, often creating bespoke solutions such as digital-profiling tools for organisations including the CIA and ICE. The company was founded by prominent [...]

Today, artificial intelligence — and the computing systems that underlie it — are more than just matters of technology; they are matters of state and society, of governance and the public interest. The choices that technologists, policymakers, and communities make in the next few years will shape the relationship between machines and humans for decades to come. The rapidly increasing applicability of AI has prompted a number of organizations to develop high-level principles on social and ethical issues such as privacy, fairness, bias, transparency, and accountability. Building on those broader principles, the AI Policy Forum, a global effort convened by the MIT Stephen A. Schwarzman College of Computing, will provide an overarching policy framework and tools for governments and companies to implement in concrete ways. “Our goal is to help policymakers in making practical decisions about AI policy,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing. “We are not trying to develop another set of principles around AI, several of which already exist, but rather provide context and guidelines specific to a field of use of AI [...]

Cassandra database slinger DataStax has "paused" its AIops project, Vector, while it figures out what exactly AIops is. A sort of automated database help function, the Vector project was launched in June last year to offer operators and developers the DataStax "knowledge base and provide near real-time expert advice, knowledge, and skills," according to the firm. Speaking to Dr. Rami Shaheen this week, Ed Anuff, DataStax's chief product officer, said that after an extensive beta programme of Vector, it was time to pause the project and reflect. "The purpose of running the beta was really to understand what it was that people wanted to see in the final product," he said. "There were a lot of different expectations as to what the product should do: some people wanted a AIops to offer recommendations, other people wanted it to be a complete self-driving car of the databases," he said. The cloud/diverse datasets issue The second thing DataStax found was that users could not agree on whether the product should be powered from the cloud or not. While some people were [...]