Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 316

With their ability to generate human-like language and complete a variety of tasks, generative AI has the potential to revolutionise the way we communicate, learn and work. But what other doors will this technology open for us, and how can we harness it to make great leaps in technology innovation? Have we finally done it? Have we cracked AI?

Join Professor Michael Wooldridge for a fascinating discussion on the possibilities and challenges of generative AI models, and their potential impact on societies of the future.

Michael Wooldridge is Director of Foundational AI Research and Turing AI World-Leading Researcher Fellow at The Alan Turing Institute. His work focuses on multi-agent systems and developing techniques for understanding the dynamics of multi-agent systems. His research draws on ideas from game theory, logic, computational complexity, and agent-based modelling. He has been an AI researcher for more than 30 years and has published over 400 scientific articles on the subject.

This lecture is part of a series of events — How AI broke the internet — that explores the various angles of large-language models and generative AI in the public eye.

This series of Turing Lectures is organised in collaboration with The Royal Institution of Great Britain.

Read more | >

Vending machines are an old charming piece of technology that supposedly makes the lives of people easier by making water, snacks and food in general readily available.

American Rounds says that it aims to redefine convenience in ammunition purchasing, as its ammo dispensers can be accessed round the clock.

The company’s website also promises a ‘hassle-free buying experience every time,’ and of a smooth transaction every time a prospective buyer reaches it.

The ‘smart automated’ bullet dispensing machines use AI technology to identify the buyer’s details before allowing the purchase, according to American Rounds website.

Read more | >

Scientists run into a lot of tradeoffs trying to build and scale up brain-like systems that can perform machine learning. For instance, artificial neural networks are capable of learning complex language and vision tasks, but the process of training computers to perform these tasks is slow and requires a lot of power.

Training machines to learn digitally but perform tasks in analog—meaning the input varies with a physical quantity, such as voltage—can reduce time and power, but small errors can rapidly compound.

An electrical network that physics and engineering researchers from the University of Pennsylvania previously designed is more scalable because errors don’t compound in the same way as the size of the system grows, but it is severely limited as it can only learn linear tasks, ones with a simple relationship between the input and output.

Read more | >

1/ Chinese AI company SenseTime introduced its new multimodal AI model SenseNova 5o at the World Artificial Intelligence Conference, which SenseTime claims is China’s first GPT-4o-level multimodal real-time model.

2/ It processes audio, text, image and video data to interact with users as if they…

Chinese AI company SenseTime introduced its new multimodal AI model SenseNova 5o and the improved language model SenseNova 5.5 at the World Artificial Intelligence Conference.

Ad.

SenseTime claims that SenseNova 5o is China’s first real-time multimodal model that provides multimodal AI interaction comparable to GPT-4o. It can process audio, text, image and video data, allowing users to interact with the model simply by talking to it.

Read more | >

According to Fox 45 Baltimore, the Maryland Piedmont Reliability Project (MPRP) is a new plan to build a 70-mile 500,000-volt transmission line across three counties: Frederick, Baltimore, and Carroll. The line will connect a substation in southern Frederick County and supply the area with additional load capacity to handle surging power demand from AI data centers.

MPRP’s website explains that the new transmission lines will require the acquisition of private property through the use of an eminent domain, or government-mandated seizure to complete the construction.

“If PSEG and a property owner cannot agree on mutually acceptable value, PSEG may seek to use the power of eminent domain using the process set forth by the state of Maryland to acquire the necessary property rights,” the developer’s website states.

Read more | >

WASHINGTON — The Defense Advanced Research Projects Agency (DARPA) has selected the startup Scout Space to participate in the BRIDGES (Bringing Classified Innovation to Defense and Government Systems) consortium.

BRIDGES, launched by DARPA in 2023, aims to connect innovative small companies and nontraditional defense contractors with classified Department of Defense research and development efforts. The initiative seeks to bridge the gap between cutting-edge commercial technologies and classified defense needs, particularly in areas considered critical to maintaining U.S. military superiority.

Scout Space, based in Reston, Virginia, develops satellite flight software and space domain awareness sensors. The company announced July 8 it was selected by DARPA for its proposal outlining an approach to “advancing autonomous in-space threat response.”

Read more | >

As artificial intelligence (AI) becomes increasingly ubiquitous in business and governance, its substantial environmental impact — from significant increases in energy and water usage to heightened carbon emissions — cannot be ignored. By 2030, AI’s power demand is expected to rise by 160%. However, adopting more sustainable practices, such as utilizing foundation models, optimizing data processing locations, investing in energy-efficient processors, and leveraging open-source collaborations, can help mitigate these effects. These strategies not only reduce AI’s environmental footprint but also enhance operational efficiency and cost-effectiveness, balancing innovation with sustainability.

Page-utils class= article-utils—vertical hide-for-print data-js-target= page-utils data-id= tag: blogs.harvardbusiness.org, 2007/03/31:999.386782 data-title= How Companies Can Mitigate AI’s Growing Environmental Footprint data-url=/2024/07/how-companies-can-mitigate-ais-growing-environmental-footprint data-topic= Environmental sustainability data-authors= Christina Shim data-content-type= Digital Article data-content-image=/resources/images/article_assets/2024/06/Jul24_04_1298348302-383x215.jpg data-summary=

Practical steps for reducing AI’s surging demand for water and energy.

Read more | >

“We want to build tools that can make biology programmable,” says Alex Rives, the company’s chief scientist, who was part of Meta’s efforts to apply AI to biological data.

EvolutionaryScale’s AI tool, called ESM3, is what’s known as a protein language model. It was trained on more than 2.7 billion protein sequences and structures, as well as information about these proteins’ functions. The model can be used to create proteins to specifications provided by users, akin to the text spit out by chatbots such as ChatGPT.

“It’s going to be one of the AI models in biology that everybody’s paying attention to,” says Anthony Gitter, a computational biologist at the University of Wisconsin–Madison.

Read more | >