Lifeboat Foundation

The relentless hype surrounding generative AI in the past few months has been accompanied by equally loud anguish over the supposed perils — just look at the open letter calling for a pause in AI experiments. This tumult risks blinding us to more immediate risks — think sustainability and bias — and clouds our ability to appreciate the real value of these systems: not as generalist chatbots, but instead as a class of tools that can be applied to niche domains and offer novel ways of finding and exploring highly specific information.

This shouldn’t come as a surprise. The news that a dozen companies have developed ChatGPT plugins is a clear demonstration of the likely direction of travel. A “generalized” chatbot won’t do everything for you, but if you’re, say, Expedia, being able to offer customers a simple way to organize their travel plans is undeniably going to give you an edge in a marketplace where information discovery is so important.

In a series of quadrotor closed-loop control experiments, the drones underwent range tests, stress tests, target rotation and occlusion, hiking with adversaries, triangular loops between objects, and dynamic target tracking. They tracked moving targets, and executed multi-step loops between objects in never-before-seen environments, surpassing performance of other cutting-edge counterparts.

The team believes that the ability to learn from limited expert data and understand a given task while generalizing to new environments could make autonomous drone deployment more efficient, cost-effective, and reliable. Liquid neural networks, they noted, could enable autonomous air mobility drones to be used for environmental monitoring, package delivery, autonomous vehicles, and robotic assistants.

“The experimental setup presented in our work tests the reasoning capabilities of various deep learning systems in controlled and straightforward scenarios,” says MIT CSAIL Research Affiliate Ramin Hasani. “There is still so much room left for future research and development on more complex reasoning challenges for AI systems in autonomous navigation applications, which has to be tested before we can safely deploy them in our society.”

As part of our SLAS US 2023 coverage, we speak to Luigi Da Via, Team Leader in Analytical Development at GSK, about the lab of the future, and what it may look like.

Please, can you introduce yourself and tell us what inspired your career within the life sciences?

Hello, my name is Luigi Da Via, and I am currently leading the High-Throughput Automation team at GSK. I have been with the company for the past six years, and I’m thrilled to be contributing to the development of life-saving medicines through the application of cutting-edge technology and automation.

Summary: For the first time, Google Quantum AI has observed the peculiar behavior of non-Abelian anyons, particles with the potential to revolutionize quantum computing by making operations more resistant to noise.

Non-Abelian anyons have the unique feature of retaining a sort of memory, allowing us to determine when they have been exchanged, even though they are identical.

The team successfully used these anyons to perform quantum computations, opening a new path towards topological quantum computation. This significant discovery could be instrumental in the future of fault-tolerant topological quantum computing.

Following up on my last posting on advances — and worries — about Artificial General Intelligence… Peter Diamandis’s latest tech blog is regarding AI and ethics.

They are key in directing apps like ChatGPT to carry out tasks.

The associate director of Stanford’s Institute for Human-Centered Artificial Intelligence compared AI scientists to “late-stage” teens.

The revolution in artificial intelligence is at the center of a debate ranging from those who hope it will save humanity to those who predict doom. Google lies somewhere in the optimistic middle, introducing AI in steps so civilization can get used to it.

Demis Hassabis, CEO of DeepMind Technologies, has spent decades working on AI and views it as the most important invention humanity will ever make. Hassabis sold DeepMind to Google in 2014. Part of the reason for the sale was to gain access to Google’s immense computing power. Brute force computing can very loosely approximate the neural networks and talents of the brain.

“Things like memory, imagination, planning, reinforcement learning, these are all things that are known about how the brain does it, and we wanted to replicate some of that in our AI systems,” Hassabis said.

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