Breakthroughs, discoveries, and DIY tips sent every weekday. When humans eventually set foot on Mars, they may have a four-legged companion by their side. But the dog accompanying them won't be a canine at all, but a quadruped robot designed to gather samples and keep astronauts on the Red Planet from twisting an ankle. Built with autonomous capability, it will be capable of operating independently of humans. Put another way, the Mars dog will walk off-leash.

Breakthroughs, discoveries, and DIY tips sent every weekday. A first-of-its-kind, soft, vest-like wearable designed by Harvard researchers could help stroke survivors and people living with ALS regain crucial upper limb movement. The researchers call the device a "wearable robot," which uses inflatable balloons positioned under a patient's arm that bulge and contract based on the desired movement. A combination of machine learning software and a separate physics-based model helps the robot interpret the patient's intended movements and personalize actions accordingly. In testing, the robot was able to correctly identify the user's intended shoulder movement 94.2 percent of the time.

Nearly two years ago, I wrote that AI would kill the undergraduate essay. That reaction came in the immediate aftermath of ChatGPT, when the sudden appearance of its shocking capabilities seemed to present endless vistas of possibility--some liberating, some catastrophic. Since then, the potential of generative AI has felt clear, although its practical applications in everyday life have remained somewhat nebulous. Academia remains at the forefront of this question: Everybody knows students are using AI. The answer to those questions will, at least to some extent, reveal the place that AI will find for itself in society at large.

Clinicians and surgeons are increasingly using medical devices based on artificial intelligence. These AI devices, which rely on data-driven algorithms to inform health care decisions, presently aid in diagnosing cancers, heart conditions and diseases of the eye, with many more applications on the way. In a new study, Stanford faculty discuss sex, gender and race bias in medical technologies. Pulse oximeters, for example, are more likely to incorrectly report blood gas levels in dark-skinned individuals and in women. Given this surge in AI, two Stanford University faculty members are calling for efforts to ensure that this technology does not exacerbate existing heath care disparities.

International scientists are challenging their colleagues to make Artificial Intelligence (AI) research more transparent and reproducible to accelerate the impact of their findings for cancer patients. In an article published in Nature on October 14, 2020, scientists at Princess Margaret Cancer Centre, University of Toronto, Stanford University, Johns Hopkins, Harvard School of Public Health, Massachusetts Institute of Technology, and others, challenge scientific journals to hold computational researchers to higher standards of transparency, and call for their colleagues to share their code, models and computational environments in publications. Scientific progress depends on the ability of researchers to scrutinize the results of a study and reproduce the main finding to learn from. But in computational research, it's not yet a widespread criterion for the details of an AI study to be fully accessible. This is detrimental to our progress."

Artificial intelligence appears to be "widening inequality," and its deployment should be subject to tough regulations and limits, especially for sensitive technologies such as facial recognition, a research report said Thursday. The AI Now Institute, a New York University center studying the social implications of artificial intelligence, said that as these technologies become widely deployed, the negative impacts are starting to emerge. The 93-page report examined concerns being raised "from AI-enabled management of workers, to algorithmic determinations of benefits and social services, to surveillance and tracking of immigrants and underrepresented communities," the researchers wrote. "What becomes clear is that across diverse domains and contexts, AI is widening inequality, placing information and control in the hands of those who already have power and further disempowering those who don't." The researchers said AI systems are being deployed in areas such as healthcare, education, employment, criminal justice "without appropriate safeguards or accountability structures in place."

As if the world of deep-faked pictures and video wasn't scary enough, researchers from Samsung's AI center in Moscow have demonstrated an algorithm that can fabricate videos using only one image. In a video demonstration and a paper published in the pre-print journal ArXiv, the researchers show the capabilities of what is described as'one-shot' and'few-shot' machine learning. The results of their system bring to life popular faces like those of surrealist painter Salvador Dali and actress Marilyn Monroe using a single still image. The more images that are fed into the program, the more realistic the resulting video becomes. Though a single image translated into a moving face may look noticeably altered, a sample of 32 images produces a moving picture with near lifelike accuracy.

Artificial intelligence researcher Fei-Fei Li has spent her career trying to make software smart--with some success. Lately she's begun to ask herself a new question: How can we make smart software aligned with human values? "As much as AI is showing its power, it's a nascent technology," Li said at the WIRED25 Summit in San Francisco Monday. "What's really important is putting humanity at the center." As a researcher at Stanford and Google, Li has helped perfect and spread machine learning technology that allows computers to understand the world through images and video.

Much has been said about how big data will help solve many of the world's thorniest problems, including pandemics, hunger, cancer treatments, and conservation. However, because of the seriousness of the problems, and complexity of big data and its analysis, a great deal of testing is required before any results can be considered trustworthy. Unfortunately, most businesses and organizations do not have the in-house capability to achieve any semblance of trust. Thus, the normal procedure has been to outsource the work to third-party vendors. The operative phrase is "has been."