Lifeboat Foundation

Artificial intelligence (AI) systems, such as the chatbot ChatGPT, have become so advanced that they now very nearly match or exceed human performance in tasks including reading comprehension, image classification and competition-level mathematics, according to a new report (see ‘Speedy advances’). Rapid progress in the development of these systems also means that many common benchmarks and tests for assessing them are quickly becoming obsolete.

These are just a few of the top-line findings from the Artificial Intelligence Index Report 2024, which was published on 15 April by the Institute for Human-Centered Artificial Intelligence at Stanford University in California. The report charts the meteoric progress in machine-learning systems over the past decade.

In particular, the report says, new ways of assessing AI — for example, evaluating their performance on complex tasks, such as abstraction and reasoning — are more and more necessary. “A decade ago, benchmarks would serve the community for 5–10 years” whereas now they often become irrelevant in just a few years, says Nestor Maslej, a social scientist at Stanford and editor-in-chief of the AI Index. “The pace of gain has been startlingly rapid.”

The CEO of Europe’s brightest new AI firm is calling bull on the quest for so-called “artificial general intelligence” (AGI), which he says is akin to the desire to create God.

In an interview with the New York Times, Arthur Mensch, the CEO of the AI firm Mistral, sounded off on his fellow AI executives’ “very religious” obsession with building AGI.

“The whole AGI rhetoric is about creating God,” the Mistral CEO told the newspaper. “I don’t believe in God. I’m a strong atheist. So I don’t believe in AGI.”

Taking mouse lifespan to the next levelmoderatorcaitlin lewisspeakersdanique wortel / aubrey de grey / brian kennedy10:30 AM.

Questions to inspire discussion.

What do rental car companies need to improve?
—Rental car companies need to improve their education and communication with customers about electric vehicles to prevent incidents like getting trapped due to running out of charge.

With climate change warming the oceans, this results in drastic consequences for marine life in deep water environments, but can steps be taken to help mitigate these effects? This is what a recent study published in Nature Communications hopes to address as a team of researchers from the United Kingdom investigated how “mixing down” oxygen levels in the ocean could help contribute to a more suitable environment for deep sea life. This study holds the potential to help scientists, conservationists, legislators, and the public better understand the steps that can be taken to mitigate the long-term effects of climate change.

Decreasing oxygen levels in the ocean is a natural phenomenon, but climate change has been predicted to accelerate this process, which could lead to massive decreases in oxygen levels in deep water environments and pose catastrophic consequences for marine life. For the study, the researchers used new methods that combine ocean water data from the Celtic Sea and computer models to ascertain how deep water oxygen levels could be replenished during the warmer summer months. In the end, they determined that summertime storms can result in the “mixing down” of oxygen and decrease this oxygen loss by almost half, which also shows promise for putting floating wind farms in the northern North Sea and Celtic Sea to assist in this process.

“There is growing concern for the health of our coastal oceans as the climate warms because warmer water holds less oxygen,” said Dr. Tom Rippeth, who is a Professor of Physical Oceanography at Bangor University and lead author of the study. “Living creatures in the ocean are reliant on oxygen to survive in the same way as animals on land are. Oxygen is also used up as rotting matter decomposes in the depths of the ocean. This creates a summer oxygen deficit in the deep seas around the UK. Unfortunately, as our climate warms, this deficit is forecast to grow.”

With standard deep learning structures, errors tend to accumulate over layers and time. This setup nips problems that come from sequential processing in the bud. When faced with a problem, Taichi distributes the workload across multiple independent clusters, making it easier to tackle larger problems with minimal errors.

The strategy paid off.

Taichi has the computational capacity of 4,256 total artificial neurons, with nearly 14 million parameters mimicking the brain connections that encode learning and memory. When sorting images into 1,000 categories, the photonic chip was nearly 92 percent accurate, comparable to “currently popular electronic neural networks,” wrote the team.

The streaming audio giant’s suite of recommendation tools has grown over the years: Spotify Home feed, Discover Weekly, Blend, Daylist, and Made for You Mixes. And in recent years, there have been signs that it is working. According to data released by Spotify at its 2022 Investor Day, artist discoveries every month on Spotify had reached 22 billion, up from 10 billion in 2018, “and we’re nowhere near done,” the company stated at that time.

Over the past decade or more, Spotify has been investing in AI and, in particular, in machine learning. Its recently launched AI DJ may be its biggest bet yet that technology will allow subscribers to better personalize listening sessions and discover new music. The AI DJ mimics the vibe of radio by announcing the names of songs and lead-in to tracks, something aimed in part to help ease listeners into extending out of their comfort zones. An existing pain point for AI algorithms — which can be excellent at giving listeners what it knows they already like — is anticipating when you want to break out of that comfort zone.

Researchers at the University of Iowa in a new study have linked a region in the brain to how humans redirect thoughts and attention when distracted.

University of Iowa researchers have identified a brain region involved in how attention or thought is diverted. In addition to its biological significance, the finding could help people with Parkinson’s disease who struggle with impulsive thoughts or erratic attention.

With the ease of availability and access of AI tools and technology, people are putting AI into a wide range of products and services, and even in applications where AI is a dubious fit, at best. Many times, organizations are feeling the motivation, “fear of missing out”, and perhaps customer or shareholder pressure to add AI capability to their offerings. It should come as no surprise that many of these AI projects are often half thought-out, at best, and often fail to deliver the desired results, if the results have even been considered ahead of time.

Sometimes, AI projects have a high-level, big vision, where the AI efforts are focused. Other times, AI applications are being focused on smaller tasks, or shoehorned into existing applications. The challenge is that for AI projects to be successful, there needs to be a combination of a larger vision for where AI could add value while at the same time, smaller, focused projects that allow organizations to ensure value in the real-world before diving deeper into AI capabilities and investment.