Lifeboat Foundation

Regulatory efforts to protect data are making strides globally. Patient data is protected by law in the United States and elsewhere. In Europe the General Data Protection Regulation (GDPR) guards personal data and recently led to a US $1.3 billion fine for Meta. You can even think of Apple’s App Store policies against data sharing as a kind of data-protection regulation.

“These are good constraints. These are constraints society wants,” says Michael Gao, founder and CEO of Fabric Cryptography, one of the startups developing FHE-accelerating chips. But privacy and confidentiality come at a cost: They can make it more difficult to track disease and do medical research, they potentially let some bad guys bank, and they can prevent the use of data needed to improve AI.

“Fully homomorphic encryption is an automated solution to get around legal and regulatory issues while still protecting privacy,” says Kurt Rohloff, CEO of Duality Technologies, in Hoboken, N.J., one of the companies developing FHE accelerator chips. His company’s FHE software is already helping financial firms check for fraud and preserving patient privacy in health care research.

Journalism professor Shen Yang plans to detail his creation process so anyone can ‘create good fiction with AI’

In machine learning, larger networks with increasing parameters are being trained. However, training such networks has become prohibitively expensive. Despite the success of this approach, there needs to be a greater understanding of why overparameterized models are necessary. The costs associated with training these models continue to rise exponentially.

A team of researchers from the University of Massachusetts Lowell, Eleuther AI, and Amazon developed a method known as ReLoRA, which uses low-rank updates to train high-rank networks. ReLoRA accomplishes a high-rank update, delivering a performance akin to conventional neural network training.

Scaling laws have been identified, demonstrating a strong power-law dependence between network size and performance across different modalities, supporting overparameterization and resource-intensive neural networks. The Lottery Ticket Hypothesis suggests that overparameterization can be minimized, providing an alternative perspective. Low-rank fine-tuning methods, such as LoRA and Compacter, have been developed to address the limitations of low-rank matrix factorization approaches.

A new AI tool can predict people’s time of death with high degree of accuracy, finds a study.

New AI can predict people’s time of death with high degree of accuracy, study finds.

Could artificial intelligence (AI) systems become conscious? A trio of consciousness scientists says that, at the moment, no one knows — and they are expressing concern about the lack of inquiry into the question.

In comments to the United Nations, three leaders of the Association for Mathematical Consciousness Science (AMCS) call for more funding to support research on consciousness and AI. They say that scientific investigations of the boundaries between conscious and unconscious systems are urgently needed, and they cite ethical, legal and safety issues that make it crucial to understand AI consciousness. For example, if AI develops consciousness, should people be allowed to simply switch it off after use?

Such concerns have been mostly absent from recent discussions about AI safety, such as the high-profile AI Safety Summit in the United Kingdom, says AMCS board member Jonathan Mason, a mathematician based in Oxford, UK and one of the authors of the comments. Nor did US President Joe Biden’s executive order seeking responsible development of AI technology address issues raised by conscious AI systems, Mason notes.

By bringing together disparate approaches, machines can reach a new level of creative problem-solving.

Apple is in talks with some of the biggest names in the media industry to license their news archives for training its artificial intelligence systems, the New York Times reported.

A multiyear deal worth over $50 million

The Cupertino-based company is seeking multiyear deals worth over $50 million with publishers like Condé Nast, NBC News, and IAC, which own popular magazines and websites such as Vogue, The New Yorker, People, and The Daily Beast.

Humane AI has attracted much attention and funding from investors despite not having any products in the market until now. According to CNBC, the company has raised over $200 million from backers like Microsoft, Tiger Global, OpenAI CEO Sam Altman, and Salesforce CEO Marc Benioff.

“For us, the AI Pin is just the beginning,” Chaudhri said at the launch event.

Continue reading “Humane AI reveals when its AI pin will reach customers” »

The robot features eight adjustable water jets in its central and head regions, with a flexible firehose directed by a control unit on a trailing wheeled cart.

Functioning at a rate of 6.6 liters per second, the nozzles expel water with a pressure reaching up to one megapascal. At the tip of the hose, a combination of a traditional camera and a thermal imaging camera is integrated, facilitating the identification and location of the fire. This technological integration enhances the Dragon Firefighter’s firefighting capabilities, according to the team.

Learning process

Continue reading “Scientists create water-spitting ‘flying dragon’ robot to fight fires” »