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Like a Child, This Brain-Inspired AI Can Explain Its Reasoning

But deep learning has a massive drawback: The algorithms can’t justify their answers. Often called the “black box” problem, this opacity stymies their use in high-risk situations, such as in medicine. Patients want an explanation when diagnosed with a life-changing disease. For now, deep learning-based algorithms—even if they have high diagnostic accuracy—can’t provide that information.

To open the black box, a team from the University of Texas Southwestern Medical Center tapped the human mind for inspiration. In a study in Nature Computational Science, they combined principles from the study of brain networks with a more traditional AI approach that relies on explainable building blocks.

The resulting AI acts a bit like a child. It condenses different types of information into “hubs.” Each hub is then transcribed into coding guidelines for humans to read—CliffsNotes for programmers that explain the algorithm’s conclusions about patterns it found in the data in plain English. It can also generate fully executable programming code to try out.

Building Intelligent Machines Helps Us Learn How Our Brain Works

Designing machines to think like humans provides insight into intelligence itself.

By George Musser

The dream of artificial intelligence has never been just to make a grandmaster-beating chess engine or a chatbot that tries to break up a marriage. It has been to hold a mirror to our own intelligence, that we might understand ourselves better. Researchers seek not simply artificial intelligence but artificial general intelligence, or AGI—a system with humanlike adaptability and creativity.

Consciousness in Humanoid Robots

Building a conscious robot is a grand scientific and technological challenge. Debates about the possibility of conscious robots and the related positive outcomes and hazards for human beings are today no more confined to philosophical circles. Robot consciousness is a research field aimed to a unified view of approaches as cognitive robotics, epigenetic and affective robotics, situated and embodied robotics, developmental robotics, anticipatory systems, biomimetic robotics. Scholars agree that a conscious robot would completely change the current views on technology: it would not be an “intelligent companion” but a complete novel kind of artifact. Notably, many neuroscientists involved in the study of consciousness do not exclude this possibility. Moreover, facing the problem of consciousness in robots may be a major move on the study of consciousness in humans and animals.

AI is rapidly identifying new species. Can we trust the results?

Scientists are using artificial intelligence (AI) to identify new animal species. But can we trust the results?

For now, scientists are using AI just to flag potentially new species; highly specialized biologists still need to formally describe those species and decide where they fit on the evolutionary tree. AI is also only as good as the data we train it on, and at the moment, there are massive gaps in our understanding of Earth’s wildlife.

AI scientist Ray Kurzweil: ‘We are going to expand intelligence a millionfold by 2045’

The American computer scientist and techno-optimist Ray Kurzweil is a long-serving authority on artificial intelligence (AI). His bestselling 2005 book, The Singularity Is Near, sparked imaginations with sci-fi like predictions that computers would reach human-level intelligence by 2029 and that we would merge with computers and become superhuman around 2045, which he called “the Singularity”. Now, nearly 20 years on, Kurzweil, 76, has a sequel, The Singularity Is Nearer – and some of his predictions no longer seem so wacky. Kurzweil’s day job is principal researcher and AI visionary at Google. He spoke to the Observer in his personal capacity as an author, inventor and futurist.

Why write this book? The Singularity Is Near talked about the future, but 20 years ago, when people didn’t know what AI was. It was clear to me what would happen, but it wasn’t clear to everybody. Now AI is dominating the conversation. It is time to take a look again both at the progress we’ve made – large language models (LLMs) are quite delightful to use – and the coming breakthroughs.

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