AI Pioneer Urges Europe to Find Its Own Agile Path Forward

Post by: Bandan Preet

Sepp Hochreiter, a well-known expert in artificial intelligence (AI), has a different idea from many big AI companies like OpenAI and Mistral AI. These companies believe that AI models should go through their entire dataset before giving new answers or insights. However, Hochreiter thinks there’s a better way to use AI that needs less power and money. He wants to teach AI models how to forget things they don’t need to remember, making them smarter and more efficient.

Hochreiter is a pioneer in AI. Back in the 1990s, when he was a student in Munich, he came up with a new idea for AI called Long Short-Term Memory (LSTM). This method taught computers how to remember important information and ignore the unimportant stuff. After he published his research, it became the foundation for many successful AI models used by companies like Google, Apple, and Amazon. LSTM helped AI become more efficient by not trying to store everything, just the key information. It became the industry standard for AI.

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Now, Hochreiter is focused on a new kind of AI model that he believes will be more energy-efficient and faster than the huge AI models used today. In May, his team introduced a new AI model called xLSTM. He says this model uses much less energy and works faster than the big AI systems that require massive amounts of computing power. To explain how xLSTM works, he compares it to reading a book. When you start a new chapter in a book, you don’t need to read the entire story again. You remember the main plot, characters, and themes and continue from there. In the same way, Hochreiter says that AI models should only remember what is important and forget the rest.

This method is different from the current trend where companies use huge data centers that need a lot of energy to store and process massive amounts of data. Hochreiter’s approach, in contrast, is lighter and faster, making it a more energy-efficient solution.

The success of a Chinese AI company called DeepSeek, which started with only a small amount of money, has shown that efficiency can be more important than sheer size. Other companies are also starting to use smaller and more affordable AI models. Hochreiter believes that this more efficient approach to AI is exactly what Europe needs, especially with the growing concern about energy use and the competition with countries like the United States and China. He thinks that Europe should focus on creating its own AI technology that is specifically designed to meet its needs.

Hochreiter also believes that AI should not only focus on language, which is often the center of large AI models like ChatGPT. Instead, he says that AI models can be more useful when they focus on specific areas like robotics, biotech, and manufacturing. He argues that most businesses don’t need AI that specializes in language but instead need models that can help with tasks in their industries.

However, not everyone agrees with Hochreiter’s ideas. Some experts worry that his models are too small compared to the large models used by companies like OpenAI. They question whether xLSTM will be able to handle large datasets that require more processing power. These concerns will need to be addressed as Hochreiter’s team works to prove their model’s efficiency in the real world.

In the last year, Hochreiter’s lab has started two new companies to take his AI ideas into the business world. The first company, NXAI GmbH, focuses on creating AI models for industries like robotics and automotive. The second, Emmi AI GmbH, started operations recently and is led by a former Microsoft researcher. Both companies are looking for investments from businesses in specific industries, rather than relying on traditional venture capital. NXAI’s CEO, Albert Ortig, says that they want to create long-lasting AI solutions, not just quick profits.

At his lab in Linz, Austria, Hochreiter is optimistic about the future of his AI models. He believes that his team has developed something better, something that will help businesses use AI more efficiently while using far less energy.

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