Lifeboat Foundation

Focus is on Physical Sciences Research and Management of Complex Systems

WASHINGTON, D.C. — Today, the U.S. Department of Energy (DOE) announced a plan to provide up to $30 million for advanced research in machine learning (ML) and artificial intelligence (AI) for both scientific investigation and the management of complex systems.

The initiative encompasses two separate topic areas. One topic is focused on the development of ML and AI for predictive modeling and simulation focused on research across the physical sciences. ML and AI are thought to offer promising new alternatives to traditional programming methods for computer modeling and simulation.

Replicating human interaction and behavior is what artificial intelligence has always been about. In recent times, the peak of technology has well and truly surpassed what was initially thought possible, with countless examples of the prolific nature of AI and other technologies solving problems around the world.

Think about this: Gary Kasparov stated that he would never lose a game of chess to a computer. For a long time, this seemed like a statement that would withstand all tests.

Roll on 1996, however, and IBM developed Deep Blue, a computer bot/program/application that beat the master Gary Kasparov at his own game.

These days, neural networks, deep learning and all types of sensors allow AI to be used in healthcare, to operate self-driving cars and to tweak our photos on Instagram.

In the #future, the ability to learn, to emulate the creative process and to self-organize may give rise to previously unimagined opportunities and unprecedented threats.

Continue reading “The Future is Now | Life after Artificial Intelligence” »

As we enter our third decade in the 21st century, it seems appropriate to reflect on the ways technology developed and note the breakthroughs that were achieved in the last 10 years.

The 2010s saw IBM’s Watson win a game of Jeopardy, ushering in mainstream awareness of machine learning, along with DeepMind’s AlphaGO becoming the world’s Go champion. It was the decade that industrial tools like drones, 3D printers, genetic sequencing, and virtual reality (VR) all became consumer products. And it was a decade in which some alarming trends related to surveillance, targeted misinformation, and deepfakes came online.

For better or worse, the past decade was a breathtaking era in human history in which the idea of exponential growth in information technologies powered by computation became a mainstream concept.

Tiny biohybrid robots on the micrometer scale can swim through the body and deliver drugs to tumors or provide other cargo-carrying functions. The natural environmental sensing tendencies of bacteria mean they can navigate toward certain chemicals or be remotely controlled using magnetic or sound signals.

To be successful, these tiny biological robots must consist of materials that can pass clearance through the body’s immune response. They also have to be able to swim quickly through viscous environments and penetrate tissue cells to deliver cargo.

In a paper published this week in APL Bioengineering, from AIP Publishing, researchers fabricated biohybrid bacterial microswimmers by combining a genetically engineered E. coli MG1655 substrain and nanoerythrosomes, small structures made from red blood cells.

Glass has an unusual atomic structure that resembles a liquid frozen in place, making it hard to predict how it will behave. DeepMind has developed an AI capable of doing so, which may also be able to predict traffic jams.

As capable as robots are, the original animals after which they tend to be designed are always much, much better. That’s partly because it’s difficult to learn how to walk like a dog directly from a dog — but this research from Google’s AI labs make it considerably easier.

The goal of this research, a collaboration with UC Berkeley, was to find a way to efficiently and automatically transfer “agile behaviors” like a light-footed trot or spin from their source (a good dog) to a quadrupedal robot. This sort of thing has been done before, but as the researchers’ blog post points out, the established training process can often “require a great deal of expert insight, and often involves a lengthy reward tuning process for each desired skill.”

That doesn’t scale well, naturally, but that manual tuning is necessary to make sure the animal’s movements are approximated well by the robot. Even a very doglike robot isn’t actually a dog, and the way a dog moves may not be exactly the way the robot should, leading the latter to fall down, lock up or otherwise fail.

A new programme from the US Defense Advanced Research Projects Agency (DARPA) aims to address a key weakness of autonomous and semi-autonomous land systems: the need for active illumination to navigate in low-light conditions.

Unmanned systems rely on active illumination — anything that emits light or electromagnetic radiation, such as light detection and ranging (LIDAR) systems — to navigate at night or underground.

However, according to Joe Altepeter, programme manager in DARPA’s Defense Sciences Office, this approach creates significant security concerns, as such emissions could be detected by potential adversaries.

Tech entrepreneur Roya Mahboob founded the trail-blazing programme in the Afghani city of Herat, selecting young girls from high schools across the country, usually aged 14 or 15, for the programme.

It was a passion project for Ms Mahboob, a serial entrepreneur who became one of Afghanistan’s first female chief executives at 23, established a non-profit organisation to help young women to build digital literacy, and has since been named one of Time Magazine’s 100 most influential people.

Participants are selected for the Dreamers based on their entrance exam for the 9th and 10th grades, and the very best of them then get to join the national team – the Afghan Girls Robotics Team – for international competitions. There are about 50 participants in the Dreamers, and they stay in the programme for about two years.