Uncovering Human Superpowers: How Our Brains Master Affordances that Elude AI

Imagine walking through a park or swimming in a lake – it’s a natural ability we take for granted. Researchers at the University of Amsterdam have shed light on how our brains process this intuitive knowledge, and the implications are fascinating. By studying brain activity while people viewed various environments, they discovered unique patterns associated with “affordances” – opportunities for action.

In essence, when we look at a scene, our brains automatically consider what we can do in it, whether it’s walking, cycling, or swimming. This is not just a psychological concept but a measurable property of our brains. The research team, led by Iris Groen, used an MRI scanner to investigate brain activity while participants viewed images of indoor and outdoor environments.

The results were striking: certain areas in the visual cortex became active in a way that couldn’t be explained by visible objects in the image. These brain areas not only represented what could be seen but also what you can do with it – even when participants weren’t given an explicit action instruction. This means that affordance processing occurs automatically, without conscious thought.

The researchers compared these human abilities with AI models, including ChatGPT, and found that they were worse at predicting possible actions. Even the best AI models didn’t give exactly the same answers as humans, despite it being a simple task for us. This highlights how our way of seeing is deeply intertwined with how we interact with the world.

The study has significant implications for the development of reliable and efficient AI. As more sectors use AI, it’s crucial that machines not only recognize what something is but also understand what it can do. For example, a robot navigating a disaster area or a self-driving car distinguishing between a bike path and a driveway.

Moreover, the research touches on the sustainable aspect of AI. Current training methods are energy-intensive and often accessible to large tech companies. By understanding how our brains work and process information efficiently, we can make AI smarter, more economical, and more human-friendly.

The discovery of affordance processing in the brain opens up new avenues for improving AI and making it more sustainable. As we continue to explore the intricacies of human cognition, we may uncover even more human superpowers that elude AI – a fascinating prospect indeed.

The article “Future-Proofing Workers: How Countries Are Preparing for an AI-Dominated Job Market” highlights the impact of artificial intelligence on the workforce and explores how different countries are preparing for this shift.

According to research from the University of Georgia, almost half of today’s jobs could vanish over the next 20 years due to the growth of AI. However, governments around the world are taking steps to help their citizens gain the skills they’ll need to thrive in an AI-dominated job market.

The study examined 50 countries’ national AI strategies, focusing on policies for education and the workforce. The researchers used six indicators to evaluate each country’s prioritization on AI workforce training and education, classifying them as giving high, medium or low priority.

Only 13 countries gave high prioritization to training the current workforce and improving AI education in schools. Eleven of those were European countries, with Mexico and Australia being the two exceptions. The United States was one of 23 countries that considered workforce training and AI education a medium priority, with a less detailed plan compared to countries that saw them as a high priority.

Some common themes emerged between countries, such as establishing or improving AI-focused programs in universities, on-the-job training, and improving AI education for K-12 students. However, few focused on vulnerable populations such as the elderly or unemployed through programs to teach them basic AI skills.

Researchers also noted that cultivating interest in AI could help students prepare for careers, with countries like Germany emphasizing creating a culture that encourages interest in AI and Spain starting to teach kids AI-related skills as early as preschool.

Developing human soft skills, such as creativity, collaboration, and communication, was highlighted as crucial to ensuring students and employees continue to have a place in the workforce. This study was published in Human Resource Development Review.

A team of researchers from the Graduate School of Engineering Science at Osaka University has made a groundbreaking discovery that could bring quantum computers one step closer to reality. In an article published in PRX Quantum, they have developed a method for preparing high-fidelity “magic states” with unprecedented accuracy and significantly less overhead.

Quantum computers are machines that harness the unique properties of quantum mechanics to perform calculations at speeds millions of times faster than classical computers. These machines could revolutionize fields like engineering, finance, and biotechnology. However, there’s been a significant obstacle holding them back: noise.

Noise is an enemy of quantum computers because even the slightest disturbance can ruin a setup, making it useless. To overcome this challenge, scientists have been exploring ways to build fault-tolerant quantum computers that can continue computing accurately despite noise.

One popular method for creating such systems is called magic state distillation. This process involves preparing a single high-fidelity quantum state from many noisy ones. However, traditional magic state distillation is computationally expensive and requires many qubits (the basic units of quantum information).

The research team was inspired to create a new version of magic state distillation, which they call “level-zero.” In this approach, a fault-tolerant circuit is developed at the physical level of qubits, rather than higher, more abstract levels. This innovation has led to a significant decrease in spatial and temporal overhead compared to traditional methods.

According to lead researcher Tomohiro Itogawa, this breakthrough could bring quantum computers closer to reality: “Noise is absolutely the number one enemy of quantum computers. We’re optimistic that our technique will help make large-scale quantum computers more feasible.”

Keisuke Fujii, senior author of the study, added: “We wanted to explore if there was any way of expediting the preparation of high-fidelity states necessary for quantum computation. Our results show that this is indeed possible, and we’re excited about the potential implications.”