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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.”

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.

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.

Dive into Yann LeCun’s perspective on the shortcomings of generative AI and his advocacy for Objective-Driven AI, offering a transformative approach.

The latest scandal involves a Google engineer accused of stealing AI secrets.

According to its website, the Grok 1.5V connects the physical and digital worlds. The company has highlighted seven examples of its capabilities to explain how the multimodal model works.

A user can share a picture of a flowchart with Grok, and the AI model can translate it into Python code. By simply showing the model a nutrition label, a user can inquire how many calories one would consume by consuming certain portions of the product.

While this might seem like an easy case of multiplication, the AI model can also take a child’s drawing and build an entire bedtime story using it. The model can do the converse, too. Show it a meme, and it will explain why it is funny and provide the context needed to understand it.

Cambridge startup Heartfelt Technologies’ AI telemonitor measures foot volume to monitor discharged heart patients for signs of heart failure.

Taichi could potentially make artificial general intelligence a reality.

Researchers at Tsinghua University in China have developed a revolutionary new artificial intelligence (AI) chip that uses light instead of electricity to process data.

Researchers have developed a highly energy-efficient photonic AI chip called Taichi, which could accelerate the development of advanced computing solutions.