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Category: robotics/AI

Scientists from the Beijing-based NOETIX Robotics have developed a new meter-tall humanoid robot that is capable of performing near-perfect continuous backflips. Called the NOETIX N2, this 4.2 foot tall robot features innovative hardware to ensure stability while performing the feat.

According to Jiang Zheyuan, technical leader of the development team, performing a backflip is harder compared to a frontflip as human feet are longer in the front. To enable the robot’s backflip action faultlessly, the team came up with innovative hardware designs to ensure the robot’s stability. For example, the heavy joints of the humanoid’s limbs are placed closer to its crotch to make it easier to rotate in the air.

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The battle for artificial intelligence supremacy hinges on microchips. But the semiconductor sector that produces them has a dirty secret: It’s a major source of chemicals linked to cancer and other health problems.

Global chip sales surged more than 19% to roughly $628 billion last year, according to the Semiconductor Industry Association, which forecasts double-digit growth again in 2025. That’s adding urgency to reducing the impacts of so-called “forever chemicals” — which are also used to make firefighting foam, nonstick pans, raincoats and other everyday items — as are regulators in the U.S. and Europe who are beginning to enforce pollution limits for municipal water supplies. In response to this growing demand, a wave of startups are offering potential solutions that won’t cut the chemicals out of the supply chain but can destroy them.

Per-and polyfluoroalkyl substances, or PFAS, have been detected in every corner of the planet from rainwater in the Himalayas to whales off the Faroe Islands and in the blood of almost every human tested. Known as forever chemicals because the properties that make them so useful also make them persistent in the environment, scientists have increasingly linked PFAS to health issues including obesity, infertility and cancer.

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One belief underlying the power-hungry approach to machine learning advanced by OpenAI and Mistral AI is that an artificial intelligence model must review its entire dataset before spitting out new insights.

Sepp Hochreiter, an early pioneer of the technology who runs an AI lab at Johannes Kepler University in Linz, Austria, has a different view, one that requires far less cash and computing power. He’s interested in teaching AI models how to efficiently forget.

Hochreiter holds a special place in the world of artificial intelligence, having scaled the technology’s highest peaks long before most computer scientists. As a university student in Munich during the 1990s, he came up with the conceptual framework that underpinned the first generation of nimble AI models used by Alphabet, Apple and Amazon.

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The European Commission is raising $20 billion to construct four “AI gigafactories” as part of Europe’s strategy to catch up with the U.S. and China on artificial intelligence, but some industry experts question whether it makes sense to build them.

The plan for the large public access data centers, unveiled by European Commission President Ursula von der Leyen last month, will face challenges ranging from obtaining chips to finding suitable sites and electricity.

“Even if we would build such a big computing factory in Europe, and even if we would train a model on that infrastructure, once it’s ready, what do we do with it?” said Bertin Martens, of economic think tank Bruegel. It’s a chicken and egg problem. The hope is that new local firms such as France’s Nvidia-backed Mistral startup will grow and use them to create AI models that operate in line with EU AI safety and data protection rules, which are stricter than those in the U.S. or China.

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Since ChatGPT burst onto the scene in late 2022, generative artificial intelligence (GenAI) models have been vying for the lead — with the U.S. and China hotbeds for the technology.

GenAI tools are able to create images, videos, or written works as well as answer questions or tend to online tasks based on simple prompts.

These AI assistants stand out for their popularity and sophistication.

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Japanese tech startup Sakana AI has written the first AI-generated peer-reviewed scientific paper.

Its AI program, AI Scientist-v2, successfully passed a peer review process, and the paper was accepted at a workshop during the International Conference on Learning Representations (ICLR), a major event in AI.

The AI-generated paper underwent the same review process as human submissions, with Sakana AI collaborating with the University of British Columbia and the University of Oxford.

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The agricultural sector in South Africa is undergoing a transformation with the introduction of AI-powered harvesting robots. These advanced machines are set to revolutionize farming by increasing efficiency, reducing labor costs, and ensuring better crop yields. With the growing challenges of climate change, labor shortages, and the need for sustainable farming, AI-driven technology is emerging as a critical solution for modern agriculture.

Artificial intelligence has become a vital tool in various industries, and agriculture is no exception. AI-powered robots are designed to perform labor-intensive tasks such as planting, watering, monitoring crop health, and harvesting. These machines utilize machine learning, computer vision, and sensor technology to identify ripe crops, pick them with precision, and minimize waste.

In South Africa, where agricultural labor shortages and rising costs have posed challenges to farmers, AI-driven automation is proving to be a game-changer. With an estimated 8.5% of the country’s workforce employed in agriculture, technological advancements can significantly improve productivity while alleviating labor constraints.

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In this insightful conversation with OpenAI’s CPO Kevin Weil, we discuss the rapid acceleration of AI and its implications. Kevin makes the shocking prediction that coding will be fully automated THIS YEAR, not by 2027, as others suggest. He explains how OpenAI’s models are already ranking among the world’s top programmers and shares his thoughts on Deep Research, GPT-4.5’s human-like qualities, the future of jobs, and the timeline for GPT-5. Don’t miss Kevin’s billion-dollar startup idea and his vision for how AI will transform education and democratize software creation.

00:00 — Summary.
01:21 — Introduction.
03:20 — Discussion on OpenAI being both a research and product company.
11:05 — Timeline for GPT-5
11:38 — AI model commoditization and maintaining competitive advantage.
15:09 — Deep Research capabilities.
24:22 — Coding automation prediction: THIS YEAR
30:05 — AI in creative work and design.
36:43 — Future of programming and engineers.
38:32 — Will AI create new job categories?
40:58 — Billion-dollar AI startup ideas.
46:27 — Voice interfaces and robotics.
49:28 — Closing thoughts.

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For decades, the Turing Test was considered the ultimate benchmark to determine whether computers could match human intelligence. Created in 1950, the “imitation game”, as Alan Turing called it, required a machine to carry out a text-based chat in a way that was indistinguishable from a human. It was thought that any machine able to pass the Turing Test would be capable of demonstrating reasoning, autonomy, and maybe even consciousness – meaning it could be considered human-level artificial intelligence, also known as artificial general intelligence (AGI).

The arrival of ChatGPT ruined this notion, as it was able to convincingly pass the test through what was essentially an advanced form of pattern recognition. It could imitate, but not replicate.

Last week, a new AI agent called Manus once again tested our understanding of AGI. The Chinese researchers behind it describe it as the “world’s first fully autonomous AI”, able to perform complex tasks like booking holidays, buying property or creating podcasts – without any human guidance. Yichao Ji, who led its development at the Wuhan-based startup Butterfly Effect, says it “bridges the gap between conception and execution” and is the “next paradigm” for AI.

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