Lifeboat Foundation

John Mullan, professor of English literature at University College London, wrote an article on The Guardian titled “We need robots to have morals. Could Shakespeare and Austen help?”.

Using great literature to teach ethics to machines is a dangerous game. The classics are a moral minefield.

When he wrote the stories in I, Robot in the 1940s, Isaac Asimov imagined a world in which robots do all humanity’s tedious or unpleasant jobs for them, but where their powers have to be restrained. They are programmed to obey three laws. A robot may not injure another human being, even through inaction; a robot must obey a human being (except to contradict the previous law); a robot must protect itself (unless this contradicts either of the previous laws).

When I imagine the inner workings of a robot, I think hard, cold mechanics running on physics: shafts, wheels, gears. Human bodies, in contrast, are more of a contained molecular soup operating on the principles of biochemistry.

Yet similar to robots, our cells are also attuned to mechanical forces—just at a much smaller scale. Tiny pushes and pulls, for example, can urge stem cells to continue dividing, or nudge them into maturity to replace broken tissues. Chemistry isn’t king when it comes to governing our bodies; physical forces are similarly powerful. The problem is how to tap into them.

In a new perspectives article in Science, Dr. Khalid Salaita and graduate student Aaron Blanchard from Emory University in Atlanta point to DNA as the solution. The team painted a futuristic picture of DNA mechanotechnology, in which we use DNA machines to control our biology. Rather than a toxic chemotherapy drip, for example, a cancer patient may one day be injected with DNA nanodevices that help their immune cells better grab onto—and snuff out—cancerous ones.

Inspired by the unique and efficient swimming strategy of cephalopods, scientists developed an aquatic robot that mimics their form of propulsion.

These high-speed, squidlike robots are made of smart materials, which make them hard to detect—an advantage that has potential military reconnaissance and scientific applications—while maintaining a low environmental footprint.

Physicists Xiaobo Bi and Qiang Zhu used numerical simulations to illustrate the physical mechanisms and fluid mechanics of a squid’s swimming method, which uses intermittent bursts through pulsed jet propulsion. By using this form of locomotion, the new device can achieve impressive speeds, just like its animal inspiration. Bi and Zhu discuss their work in this week’s Physics of Fluids.

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After three years of quietly toiling away on a robotic food system, Seattle startup Picnic has emerged from stealth mode with a system that assembles custom pizzas with little human intervention.

Picnic — previously known as Otto Robotics and Vivid Robotics — is the latest entrant in a cohort of startups and industry giants trying to find ways to automate restaurant kitchens in the face of slim margins and labor shortages. And its journey here wasn’t easy.

“Food is hard. It’s highly variable,” said Picnic CEO Clayton Wood. “We learned a lot about food science in the process of developing the system.”

The trawl found 20,500 articles tackling the topic, but shockingly, less than 1 percent of them were scientifically robust enough to be confident in their claims, say the authors. Of those, only 25 tested their deep learning models on unseen data, and only 14 actually compared performance with health professionals on the same test sample.

Nonetheless, when the researchers pooled the data from the 14 most rigorous studies, they found the deep learning systems correctly detected disease in 87 percent of cases, compared to 86 percent for healthcare professionals. They also did well on the equally important metric of excluding patients who don’t have a particular disease, getting it right 93 percent of the time compared to 91 percent for humans.

Ultimately, then, the results of the review are broadly positive for AI, but damning of the hype that has built up around the technology and the research practices of most of those trying to apply it to medical diagnosis.

The jobs that are likely to be automated are repetitive and routine. They range from reading X-rays (human radiologists may soon have much more limited roles), to truck driving, to stocking a warehouse. While much has been written about the sorts of jobs that are likely to be eliminated, another perspective that has not been examined in as much detail is to ask not which jobs will be eliminated but rather which aspects of surviving jobs will be replaced by machines.

The future of work looks grim for many people. A recent study estimated that 10% of U.S. jobs would be automated this year, and another estimates that close to half of all U.S. jobs may be automated in the next decade. The jobs that are likely to be automated are repetitive and routine. They range from reading X-rays, to truck driving, to stocking a warehouse. In this context, employers say that they’re seeking candidates who have other sorts of “soft skills,” such as being able to learn adaptively, to make good decisions, and to work well with others. These sought-after abilities, of course, fit perfectly with the sorts of things that people can do well, but are and will continue to be difficult to automate. All of this suggests that our educational systems should concentrate not simply on how people interact with technology (e.g., by teaching students to code), but also how they can do the things that technology will not be doing soon. These are the skills that are hardest to understand and systematize, and the skills that give — and will continue to give —humans an edge over robots.

This thorough review focuses on the impact of AI, 5G, and edge computing on the healthcare sector in the 2020s as well as a look at quantum computing’s potential impact on AI, healthcare, and financial services.

~ David Wood

“Harvard Medical School professor David Sinclair has written a remarkable book that will do for an emerging new paradigm in healthcare what a similarly remarkable book by Oxford University professor Nick Bostrom has been doing for an emerging new paradigm in artificial intelligence.”