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“It is nonsensical to say that an LLM has feelings,” Hagendorff says. “It is nonsensical to say that it is self-aware or that it has intentions. But I don’t think it is nonsensical to say that these machines are able to learn or to deceive.”

Brain scans

Other researchers are taking tips from neuroscience to explore the inner workings of LLMs. To examine how chatbots deceive, Andy Zou, a computer scientist at Carnegie Mellon University in Pittsburgh, Pennsylvania, and his collaborators interrogated LLMs and looked at the activation of their ‘neurons’. “What we do here is similar to performing a neuroimaging scan for humans,” Zou says. It’s also a bit like designing a lie detector.

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For decades, philosopher Nick Bostrom (director of the Future of Humanity Institute at Oxford) has led the conversation around technology and human experience (and grabbed the attention of the tech titans who are developing AI – Bill Gates, Elon Musk, and Sam Altman).

Now, a decade after his NY Times bestseller S uperintelligence warned us of what could go wrong with AI development, he flips the script in his new book Deep Utopia: Life and Meaning in a Solved World (March 27), asking us to instead consider “What could go well?”

Ronan recently spoke to Professor Nick Bostrom.

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Researchers at Pohang University of Science and Technology (POSTECH) have achieved a breakthrough in surpassing the limitations of traditional acoustic metalenses. They have successfully developed the first wide field-of-hearing metalens. Their research has been published in Nature Communications.

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The advent of large language models (LLMs) like GPT-4 has sparked excitement around enhancing them with multimodal capabilities to understand visual data alongside text. However, previous efforts to create powerful multimodal LLMs have faced challenges in scaling up efficiently while maintaining performance. To mitigate these issues, the researchers took inspiration from the mixture-of-experts (MoE) architecture, widely used to scale up LLMs by replacing dense layers with sparse expert modules.

In the MoE approach, instead of passing inputs through a single large model, there are many smaller expert sub-models that each specialize on a subset of the data. A routing network determines which expert(s) should process each input example. It allows scaling up total model capacity in a more parameter-efficient way.

In their approach (shown in Figure 2), CuMo, the researchers integrated sparse MoE blocks into the vision encoder and the vision-language connector of a multimodal LLM. This allows different expert modules to process different parts of the visual and text inputs in parallel rather than relying on a monolithic model to analyze everything.

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Andrea Gallo Rosso, Stockholm University A ghost is haunting our universe. This has been known in astronomy and cosmology for decades. Observations suggest that about 85% of all the matter in the universe is mysterious and invisible. These two qualities are reflected in its name: dark matter. Several experiments have aimed to unveil what it’s made of, but despite decades of searching, scientists have come up short. Now our new experiment, under construction at Yale University in the US, is offering a new tactic.

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Scientists have recently discovered thousands of active RNA molecules that can control the human body.

By Philip Ball

Thomas Gingeras did not intend to upend basic ideas about how the human body works. In 2012 the geneticist, now at Cold Spring Harbor Laboratory in New York State, was one of a few hundred colleagues who were simply trying to put together a compendium of human DNA functions. Their ­project was called ENCODE, for the Encyclopedia of DNA Elements. About a decade earlier almost all of the three billion DNA building blocks that make up the human genome had been identified. Gingeras and the other ENCODE scientists were trying to figure out what all that DNA did.

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In response to these problems, the authors of the new paper came up with a simple suggestion: a tweak to Einstein’s theory at different distance scales.

“The modification is very simple: We assume the universal constant of gravitation is different on cosmological scales, compared to smaller (like solar system or galactic) scales,” Afshordi said. “We call this a cosmic glitch.”

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🤖 🏛️ Have you ever wondered about the connection between AI and Ancient Greek Philosophy?

🧔 📜 The ancient Greek philosophers, such as Aristotle, Plato, Socrates, Democritus, Epicurus and Heraclitus explored the nature of intelligence and consciousness thousands of years ago, and their ideas are still relevant today in the age of AI.

🧠 📚 Aristotle believed that there are different levels of intelligence, ranging from inanimate objects to human beings, with each level having a distinct form of intelligence. In the context of AI, this idea raises questions about the nature of machine intelligence and where it falls in the spectrum of intelligence. Meanwhile, Plato believed that knowledge is innate and can be discovered through reason and contemplation. This view has implications for AI, as it suggests that a machine could potentially have access to all knowledge, but it may not necessarily understand it in the same way that a human would.

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