Google DeepMind is calling for the moral behavior of large language models--such as what they do when called on to act as companions, therapists, medical advisors, and so on--to be scrutinized with the same kind of rigor as their ability to code or do math . As LLMs improve, people are asking them to play more and more sensitive roles in their lives. Agents are starting to take actions on people's behalf. LLMs may be able to influence human decision-making . And yet nobody knows how trustworthy this technology really is at such tasks. With coding and math, you have clear-cut, correct answers that you can check, William Isaac, a research scientist at Google DeepMind, told me when I met him and Julia Haas, a fellow research scientist at the firm, for an exclusive preview of their work, which is published in today. That's not the case for moral questions, which typically have a range of acceptable answers: "Morality is an important capability but hard to evaluate," says Isaac. "In the moral domain, there's no right and wrong," adds Haas.

Recent years have seen growing adoption of AI-based decision-support systems (ADS) in homeless services, yet we know little about stakeholder desires and concerns surrounding their use. In this work, we aim to understand impacted stakeholders' perspectives on a deployed ADS that prioritizes scarce housing resources. We employed AI lifecycle comicboarding, an adapted version of the comicboarding method, to elicit stakeholder feedback and design ideas across various components of an AI system's design. We elicited feedback from county workers who operate the ADS daily, service providers whose work is directly impacted by the ADS, and unhoused individuals in the region. Our participants shared concerns and design suggestions around the AI system's overall objective, specific model design choices, dataset selection, and use in deployment. Our findings demonstrate that stakeholders, even without AI knowledge, can provide specific and critical feedback on an AI system's design and deployment, if empowered to do so.

Vinod Khosla, a co-founder of the former Sun Microsystems and a longtime technology entrepreneur, venture capitalist and IT sage, makes billions of dollars betting on new technologies. Khosla shared some of his technology and investment thoughts at a recent tech conference about the future of AI in business, AI chip design and quantum computing -- and even gave some advice to AI developers and companies about how they can successfully navigate the tumultuous times of the COVID-19 pandemic. Khosla gave his remarks at an "Ask Me Anything" Industry Luminary Keynote at the virtual AI Hardware Summit earlier in October. The Q&A was hosted by Rene Haas, the president of Arm's IP products group, and a former executive with AI chipmaker Nvidia. Khosla, who is ranked #353 on the Forbes 400 2020 list, has a net worth today of $2.6 billion, largely earned through his investment successes in the tech field. He founded his VC firm, Khosla Ventures, in 2004. Rene Haas: What has been the most significant technological advancement in AI in the last year or two?

Artificial intelligence is being harnessed by voice-controlled personal assistants, chatbot financial services, and even smart thermostats--now the University of Waterloo is applying algorithms to improve an age-old profession: bricklaying. Researchers at the university used AI software to study how masons position their body during bricklaying, revealing new insights into the safest poses and most productive way to work through machine learning. "The people in skilled trades learn or acquire a kind of physical wisdom that they can't even articulate," said Carl Haas in a statement. Hass is a professor of civil and environmental engineering at Waterloo. The study was published in Automation in Construction today and analyzed 21 masons with varying levels of expertise.

ARM doesn't build any chips itself, but its designs are at the core of virtually every CPU in modern smartphones, cameras and IoT devices. So far, the company's partners have shipped more than 125 billion ARM-based chips. After moving into GPUs in recent years, the company today announced that it will now offer its partners machine learning and dedicated object detection processors. Project Trillium, as the overall project is called, is meant to make ARM's machine learning (ML) chips the de facto standard for the machine learning platform for mobile and IoT. For this first launch, ARM is launching both an ML processor for general AI workloads and a next-generation object detection chip that specializes in detecting faces, people and their gestures, etc. in videos that can be as high-res as full HD and running at 60 frames per second.

Veteran masons and other construction workers have tricks and techniques that help reduce the amount of stress on their bodies. However, these techniques are not always easily explained to new workers, who are at higher risk for injury. Researchers from the University of Waterloo are utilizing artificial intelligence (AI) to help novice workers reduce wear-and-tear injuries and boost the productivity of skilled construction workers. The researchers used motion sensors and AI software to track the previously unidentified techniques expert bricklayers use to limit the loads on their joints, which can be now passed on to apprentices in training programs. "The people in skilled trades learn or acquire a kind of physical wisdom that they can't even articulate," Carl Haas, Ph.D., a professor of civil and environmental engineering at Waterloo, said in a statement.

SAN FRANCISCO – Forget the Terminator. The next robot on the horizon may be wearing a lab coat. Artificial intelligence (AI) is already helping scientists form testable hypotheses that enable experts to run real experiments, and the technology may soon be poised to help businesses make decisions, one scientist says. However, that doesn't mean the machines will be taking over from humans entirely. Instead, humans and machines have complementary skillsets, so AI could help researchers with the work they already do, Laura Haas, a computer scientist and director of the IBM Research Accelerated Discovery Lab in San Jose, California, said here Wednesday (Dec.

SAN FRANCISCO – Forget the Terminator. The next robot on the horizon may be wearing a lab coat. Artificial intelligence (AI) is already helping scientists form testable hypotheses that enable experts to run real experiments, and the technology may soon be poised to help businesses make decisions, one scientist says. However, that doesn't mean the machines will be taking over from humans entirely. Instead, humans and machines have complementary skillsets, so AI could help researchers with the work they already do, Laura Haas, a computer scientist and director of the IBM Research Accelerated Discovery Lab in San Jose, California, said here Wednesday (Dec.

This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model the observed graph as a sample from a manifold endowed with a vector field, and we design an algo- rithm that separates and recovers the features of this process: the geometry of the manifold, the data density and the vector field. The algorithm is motivated by our analysis of Laplacian-type operators and their continuous limit as generators of diffusions on a manifold. We illustrate the recovery algorithm on both artificially constructed and real data.