Toggle light / dark theme

Researchers at MIT reported Thursday that they have harnessed artificial intelligence to identify a completely new antibiotic compound that killed all but one of the antibacterial-resistant pathogens they tested it on. Drug-resistant bacteria are a large and growing problem, causing 2.8 million infections and 35,000 deaths in the U.S. each year and more in developing countries, STAT News reports. The computer learning model developed at MIT, described in the journal Cell, has the potential to identify many new types of antibiotics.

The researchers named the compound halicin, after HAL, the initially useful, eventually murderous sentient computer in 2001: A Space Odyssey. They also discovered eight more promising antibacterial compounds, two of which appear very powerful. They tried out halicin on mice and plan to work with a nonprofit or drugmaker to see if it’s effective and safe in humans.

The MIT team’s machine-learning model independently looked for certain properties — in this case, the ability to kill E. coli and not harm humans — among about 2,500 molecules in a drug repurposing database. Halicin was originally considered as a treatment for diabetes.

A Google AI tool that can recognise and label what’s in an image will no longer attach gender tags like “woman” or “man” to photos of people. Google’s Cloud Vision API is a service for developers that allows them to, among other things, attach labels to photos identifying the contents. The tool can detect faces, landmarks, brand logos, and even explicit content, and has a host of uses from retailers using visual search to researchers identifying animal species.

China is deploying robots and drones to remotely disinfect hospitals, deliver food and enforce quarantine restrictions as part of the effort to fight coronavirus.

Chinese state media has reported that drones and robots are being used by the government to cut the risk of person-to-person transmission of the disease.

There are 780 million people that are on some form of residential lockdown in China. Wuhan, the city where the viral outbreak began, has been sealed off from the outside world for weeks.

With some reports predicting the precision agriculture market will reach $12.9 billion by 2027, there is an increasing need to develop sophisticated data-analysis solutions that can guide management decisions in real time. A new study from an interdisciplinary research group at University of Illinois offers a promising approach to efficiently and accurately process precision ag data.

The majority have focused on outlining high-level principles that should guide those building these systems. W hether by chance or by design, the principles they have coalesced around closely resemble those at the heart of medical ethics. But writing in Nature Machine Intelligence, Brent Mittelstadt from the University of Oxford points out that AI development is a very different beast to medicine, and a simple copy and paste won’t work.

The four core principles of medical ethics are respect for autonomy (patients should have control over how they are treated), beneficence (doctors should act in the best interest of patients), non-maleficence (doctors should avoid causing harm) and justice (healthcare resources should be distributed fairly).

The more than 80 AI ethics reports published are far from homogeneous, but similar themes of respect, autonomy, fairness, and prevention of harm run through most. And these seem like reasonable principles to apply to the development of AI. The problem, says Mittelstadt, is that while principles are an effective tool in the context of a discipline like medicine, they simply don’t make sense for AI.