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Biomedical engineers at Duke University have devised a machine learning approach to modeling the interactions between complex variables in engineered bacteria that would otherwise be too cumbersome to predict. Their algorithms are generalizable to many kinds of biological systems.

In the new study, the researchers trained a neural network to predict the circular patterns that would be created by a biological circuit embedded into a bacterial culture. The system worked 30,000 times faster than the existing computational model.

To further improve accuracy, the team devised a method for retraining the machine learning model multiple times to compare their answers. Then they used it to solve a second biological system that is computationally demanding in a different way, showing the algorithm can work for disparate challenges.

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Two years ago, I started learning machine learning online on my own. I shared my journey through YouTube and my blog. I had no idea what I was doing. I’d never coded before but decided I wanted to learn machine learning.

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Google and Facebook are releasing troves of deepfakes to teach algorithms how to detect them. But the human eye will be needed for a long time.

The robot can move its eyes, eyebrows, lips and “other muscles,” the company said.

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.”