Lifeboat Foundation

If it works, it could help doctors create new pharmaceuticals from scratch.

I have a four-foot-tall robot in my house that plays with my kids. Its name is Jethro.

Both my daughters, aged 5 and 9, are so enamored with Jethro that they have each asked to marry it. For fun, my wife and I put on mock weddings. Despite the robot being mainly for entertainment, its very basic artificial intelligence can perform thousands of functions, including dance and teach karate, which my kids love.

The most important thing Jethro has taught my kids is that it’s totally normal to have a walking, talking machine around the house that you can hang out with whenever you want to.

Germany’s prized industrial robotics and automation sector is expecting a drop in sales this year for the first time since the global financial crisis, an industry body said on Friday.

The Mechanical Engineering Industry Association (VDMA) is expecting sales to fall by five percent to 14.3 billion euros ($15.8 billion) this year.

This would be the first drop since the 32-percent plunge seen in 2009 in the wake of the crisis.

But in the last few years, AI has changed the game. Deep-learning algorithms excel at quickly finding patterns in reams of data, which has sped up key processes in scientific discovery. Now, along with these software improvements, a hardware revolution is also on the horizon.

Yesterday Argonne announced that it has begun to test a new computer from the startup Cerebras that promises to accelerate the training of deep-learning algorithms by orders of magnitude. The computer, which houses the world’s largest chip, is part of a new generation of specialized AI hardware that is only now being put to use.

“We’re interested in accelerating the AI applications that we have for scientific problems,” says Rick Stevens, Argonne’s associate lab director for computing, environment, and life sciences. “We have huge amounts of data and big models, and we’re interested in pushing their performance.”

There was an historic first today as we welcomed an actual, real-life robot onto the sofa for a chat. 🤖 Sophia not only drew pictures of Holly and Phillip, she also summoned the memory of Gordon the Gopher! 😂😂.

A schism lies at the heart of the field of artificial intelligence. Since its inception, the field has been defined by an intellectual tug-of-war between two opposing philosophies: connectionism and symbolism. These two camps have deeply divergent visions as to how to “solve” intelligence, with differing research agendas and sometimes bitter relations.

Today, connectionism dominates the world of AI. The emergence of deep learning, which is a quintessentially connectionist technique, has driven the worldwide explosion in AI activity and funding over the past decade. Deep learning’s recent accomplishments have been nothing short of astonishing. Yet as deep learning spreads, its limitations are becoming increasingly evident.

If AI is to reach its full potential going forward, a reconciliation between connectionism and symbolism is essential. Thankfully, in both academic and commercial settings, research efforts that fuse these two traditionally opposed approaches are beginning to emerge. Such synthesis may well represent the future of artificial intelligence.

Just fire everybody?

Synthetic protocells can be made to move toward and away from chemical signals, an important step for the development of new drug-delivery systems that could target specific locations in the body. By coating the surface of the protocells with enzymes—proteins that catalyze chemical reactions—a team of researchers at Penn State was able to control the direction of the protocell’s movement in a chemical gradient in a microfluidic device. A paper describing the research appears November 18, 2019 in the journal Nature Nanotechnology.

“The futuristic vision is to have drugs delivered by tiny ‘bots’ that can transport the drug to the specific location where it is needed,” said Ayusman Sen, the Verne M. Willaman Professor of Chemistry at Penn State and the leader of the research team. “Currently, if you take an antibiotic for an infection in your leg, it diffuses throughout your entire body. So, you have to take a higher dose in order to get enough of the antibiotic to your leg where it is needed. If we can control the directional movement of a drug-delivery system, we not only reduce the amount of the drug required but also can increase its speed of delivery.”

One way to address controlling direction is for the drug-delivery system to recognize and move towards specific chemical signals emanating from the infection site, a phenomenon called chemotaxis. Many organisms use chemotaxis as a survival strategy, to find food or escape toxins. Previous work had shown that enzymes undergo chemotactic movement because the reactions they catalyze produce energy that can be harnessed. However, most of that work had focused on positive chemotaxis, movement towards a chemical. Until now, little work had been done looking at negative chemotaxis. “Tunable” chemotaxis—the ability to control movement direction, towards and away from different chemical signals—had never been demonstrated.

“The Hyperloop exists,” says Josh Giegel, co-founder and chief technology officer of Hyperloop One, “because of the rapid acceleration of power electronics, computational modeling, material sciences, and 3D printing.”

Thanks to these convergences, there are now ten major Hyperloop One projects—in various stages of development—spread across the globe. Chicago to DC in 35 minutes. Pune to Mumbai in 25 minutes. According to Giegel, “Hyperloop is targeting certification in 2023. By 2025, the company plans to have multiple projects under construction and running initial passenger testing.”

So think about this timetable: Autonomous car rollouts by 2020. Hyperloop certification and aerial ridesharing by 2023. By 2025—going on vacation might have a totally different meaning. Going to work most definitely will.