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Category: robotics/AI

Ray Kurzweil Predicts AI Will Change Humanity Completely by 2030

Two of my favorite people. Definitely worth a view if you are interested in either.

Few thinkers have shaped our understanding of the future as profoundly as Ray Kurzweil. An American inventor, computer scientist, futurist, entrepreneur, and bestselling author, Kurzweil is widely regarded as one of the most influential technological forecasters of our time. For decades, he has accurately predicted many of the innovations that now define modern life, from mobile computing and artificial intelligence to digital assistants and large language models often years before they entered the mainstream.

In this special conversation, Tony Robbins sits down with Ray Kurzweil in San Francisco to explore one of the most important questions facing humanity: What happens next?

Together, they examine the accelerating pace of artificial intelligence, the path toward Artificial General Intelligence (AGI), the rise of autonomous agents, the future of work and education, breakthroughs in healthcare and longevity, and how these technologies may transform society over the coming decade.

Kurzweil explains why his long-standing prediction of AGI by 2029 now appears increasingly conservative, why the next few years may bring more change than any period in human history, and how humanity may ultimately merge with the very technologies it creates.

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Reservoir computing (or training recurrent neural networks)

Gives some intuition concerning how initially random recurrent neural networks can be trained to produce complex behaviors mimicking input/output relationships of recurrent neural networks in the brain. The important thing here is that these networks can produce complex temporal dynamics (even in the absence of input) unlike the static feedforward neural networks we discussed before.

Canada’s National Artificial Intelligence Strategy: AI for All

Message from the minister The Government’s vision: AI for All Key pillars of the strategy Priority sectors Pillar 1: Protecting Canadians and safeguarding democracy Pillar 2: Ensuring AI empowers Canadians Pillar 3: Powering AI adoption for shared prosperity Pillar 4: Building the Canadian sovereign AI foundation Pillar 5: Scaling Canadian champions Pillar 6: Building trusted partnerships and global alliances Conclusion

An innovative Canada is a stronger Canada. And AI is the major driver of innovation in Canada and around the world. But to understand the potential of Canadian AI, you have to see how it is already working to improve the lives of people. How a Canadian pediatric cardiologist in Halifax named Dr. Robert Chen is using the AI application he built to diagnose heart murmurs in newborns. His technology could cut down wait times by many months for anxious parents to see a specialist, saving our health care system tens of millions of dollars.

You have to see how a Canadian AI company called Croptimistic is helping farmers precisely map their soil. This technology allows them to use less fertilizer, while increasing crop yield, making our food system more resilient and more affordable.

The AI tools shaping patient care may be operating outside regulatory oversight. MIT researchers say it’s time to change that

Every day, across thousands of American hospitals, artificial intelligence quietly shapes decisions that determine patient outcomes. An algorithm flags a patient as high risk for sepsis; a risk score informs whether a woman receives additional cancer screening; a deterioration model triggers an alert that sends a care team to a bedside. These tools are embedded in the workflows of nearly two-thirds of US hospitals, integrated into the electronic health record systems clinicians rely on daily. But many have never been reviewed by the FDA.

A new viewpoint in The Lancet Digital Health, co-authored by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic, traces how this problem took root, why it carries serious consequences, and what genuine transparency would require to fix it.

The argument, the scientists say, is not that AI has no place in clinical decision-making. It is that a $4 billion market of clinical decision support tools operates largely beyond public accountability, leaving patients and providers often unable to know whether the tools influencing their care have been validated, by whom, or for which populations they work as intended.

Can the UK Win the Quantum and Robotics Race? Rory Daniels, techUK

The UK keeps producing world-class technology, then watches many of its companies scale in America.

Rory Daniels, Head of Emerging Technology and Innovation at techUK, joins Thinking on Paper to discuss whether the United Kingdom can remain competitive as quantum computing, robotics, photonics, AI and advanced computing begin to converge.

The UK has strong research institutions, deep technical talent and globally significant companies. Its recurring problem is scale. Promising technologies are often developed in British universities and laboratories, then commercialised or funded elsewhere.

In this episode, we discuss:

-What makes the UK robotics industry different from the US and China.
–Whether robotaxis can coexist with London’s black-cab industry.
–Why UK technology companies struggle to scale after the startup stage.
–The role of universities, technology-transfer offices and regional innovation clusters.
–How techUK connects companies, researchers and policymakers.

Rory argues that the UK’s advantage may not lie in dominating a single technology. It may come from combining existing strengths in AI, chip design, robotics, quantum computing, photonics and connectivity.

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Inside the Trajectory — How AI Gains Power and Control — Mo Gawdat

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Former chief business officer of google.

Mo Gawdat returns to London Real with a stark warning: artificial intelligence is advancing at breakneck speed, and humanity may be unprepared for its consequences.

The former Google X executive reveals how AI capabilities now double every 5.7 months and warns of an approaching “AI Cold War” driven by unchecked capitalism and fear.

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AI-guided catalyst turns CO₂ and waste into fertilizer at industrially relevant rates

Researchers from the National University of Singapore (NUS) have developed a computation-guided strategy to produce urea more efficiently from carbon dioxide and nitrate. By combining large language models, density functional theory calculations and experiments, the approach identified a cadmium-modified iron oxide catalyst that maintains high urea selectivity at practical current densities.

Urea is one of the world’s most widely used fertilizers, but its conventional production comes at a heavy environmental cost. The industrial process accounts for more than two percent of global energy consumption and releases over 200 million tons of carbon dioxide each year.

A cleaner alternative is to produce urea electrochemically, using low-carbon electricity to convert carbon dioxide and nitrate into a useful product. However, this approach has been difficult to scale up. At the high current densities needed for practical production, the catalysts often favor competing side reactions, such as hydrogen gas formation or carbon dioxide reduction to other products.

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