Lifeboat Foundation

Theoretical physicists employ their imaginations and their deep understanding of mathematics to decipher the underlying laws of the universe that govern particles, forces and everything in between. More and more often, theorists are doing that work with the help of machine learning.

As might be expected, the group of theorists using machine learning includes people classified as “computational” theorists. But it also includes “formal” theorists, the people interested in the self-consistency of theoretical frameworks, like string theory or quantum gravity. And it includes “phenomenologists,” the theorists who sit next to experimentalists, hypothesizing about new particles or interactions that could be tested by experiments; analyzing the data the experiments collect; and using results to construct new models and dream up how to test them experimentally.

In all areas of theory, machine-learning algorithms are speeding up processes, performing previously impossible calculations, and even causing theorists to rethink the way theoretical physics research is done.

A new kind of transistor allows AI hardware to remember and process information more like the human brain does.

By Anna Mattson

Artificial intelligence and human thought both run on electricity, but that’s about where the physical similarities end. AI’s output arises from silicon and metal circuitry; human cognition arises from a mass of living tissue. And the architectures of these systems are fundamentally different, too. Conventional computers store and compute information in distinct parts of the hardware, shuttling data back and forth between memory and microprocessor. The human brain, on the other hand, entangles memory with processing, helping to make it more efficient.

Amazon has developed new robots, such as Digit and Sequoia. These robots make it easier and safer to complete monotonous tasks. Sequoia assists Amazon’s warehouses with order processing and inventory management.

For more: https://news.cgtn.com/news/2024-04-28/China-unveils-.…htmlChina has unveiled its first self-develop…

Plus a move toward hyperrealism and Alaska Airlines’ new AI tool.

The platform’s ability to interface with a variety of data sources and observability tools makes it a versatile solution for businesses operating in diverse IT environments, whether on-premises, in the cloud, or in hybrid settings. By serving as an intelligence layer that rationalizes data from multiple observability and infrastructure sources, Flip AI simplifies the workload for IT operations teams and supports more efficient operational practices.

This innovative use of LLMs for operational efficiency in IT environments presents a significant advancement in observability, offering enterprises a powerful tool to enhance system reliability and performance while reducing the economic impact of downtime.

As LLMs continue to evolve, their integration into observability tools is transforming the landscape of infrastructure and workload observability. The immediate benefits of improved performance monitoring and security are just the beginning.

But just how self-aware is the tool we call Claude 3 Opus?

They’ll be kind, supportive, empathetic.

DeepMind cofounder and Microsoft AI CEO Mustafa Suleyman took the stage at TED2024 last week to lay out his vision for an AI-driven future. And according to the AI boss, if you really want to grasp how impactful AI might be to the human species, it might be useful to think of AI as another “species” entirely.

“I think AI should best be understood as something like a new digital species,” Suleyman — who left the Google-owned DeepMind lab in 2022 — told the crowd.

Continue reading “Microsoft Executive Says AI Is a ‘New Kind of Digital Species’” »

Teleskope, an early stage startup from a couple of Airbnb vets, wants to automate data cleansing and remediation.