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What is time? Is it just a ticking clock, or is it something more profound?

In this thought-provoking episode of Into the Impossible, Stephen Wolfram challenges everything we know about time, offering a revolutionary computational perspective that could forever change how we understand the universe.

Stephen Wolfram is a computer scientist, physicist, and businessman. He is the founder and CEO of Wolfram Research and the creator of Mathematica, Wolfram Alpha, and Wolfram Language. Over the course of 4 decades, he has pioneered the development & application of computational thinking. He has been responsible for many discoveries, inventions & innovations in science, technology, and business.

He argues that time is the inevitable progress of computation in the universe, where simple rules can lead to complex behaviors. This concept, termed computational irreducibility, implies that time has a rigid structure and that our perception of it is limited by our computational capabilities. Wolfram also explores the relationship between time, space, and gravity, suggesting that dark matter might be a feature of the structure of space.

Tune in to discover the true nature of time.

Quantum sensors, a cutting-edge technology capable of detecting subtle signals from the human body, could soon transform how diseases are diagnosed and monitored, according to a report from the Quantum Economic Development Consortium (QED-C).

The report outlines how quantum sensing tools — ranging from diamond-based detectors to optically pumped magnetometers — offer unprecedented sensitivity compared to traditional medical devices. These sensors could enable earlier diagnoses for diseases like Alzheimer’s, provide better imaging of fetal development, and even analyze the microbiome in real time.

“Improved sensors could impact diverse aspects of biomedicine,” the report states. “For example, quantum sensors offer the possibility of significantly more efficient and accurate medical diagnoses for patients, thanks to their increased sensitivity and novel options for form factor. These attributes could enable quantum sensors to collect vast amounts of data about patients and medical conditions, and thus facilitate drug and treatment development and earlier diagnosis of disease. The advantages of quantum sensors encourage new ideas about solutions, quantum use cases, and business models across the biomedical industry — from prenatal care to cancer detection and treatment.”

From the early days of mechanical automatons to more recent conversational bots, scientists and engineers have dreamed of a future where AI systems can work and act intelligently and independently. Recent advances in agentic AI bring that autonomous future a step closer to reality. With their supercharged reasoning and execution capabilities, agentic AI systems promise to transform many aspects of human-machine collaboration. The agentic AI prize could be great, with the promise of greater productivity, innovation and insights for the human workforce. But so, too, are the risks: the potential for bias, mistakes, and inappropriate use. Early action by business and government leaders now will help set the right course for agentic AI development, so that its benefits can be achieved safely and fairly.

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The next era of human-machine collaboration will present new opportunities and challenges.

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In this episode of the Eye on AI podcast, we dive into the world of Artificial General Intelligence (AGI) with Ben Goertzel, CEO of SingularityNET and a leading pioneer in AGI development.

Ben shares his vision for building machines that go beyond task-specific capabilities to achieve true, human-like intelligence. He explores how AGI could reshape society, from revolutionizing industries to redefining creativity, learning, and autonomous decision-making.

Throughout the conversation, Ben discusses his unique approach to AGI, which combines decentralized AI systems and blockchain technology to create open, scalable, and ethically aligned AI networks. He explains how his work with SingularityNET aims to democratize AI, making AGI development transparent and accessible while mitigating risks associated with centralized control.

Ben also delves into the philosophical and ethical questions surrounding AGI, offering insights into consciousness, the role of empathy, and the potential for building machines that not only think but also align with humanity’s best values. He shares his thoughts on how decentralized AGI can avoid the narrow, profit-driven goals of traditional AI and instead evolve in ways that benefit society as a whole.

Today, AI agents have evolved to become more modular and sophisticated. Agents like ChatGPT can engage in conversations and assist in a wide range of workflows, including customer service and financial decision-making.

Technologies such as retrieval-augmented generation (RAG) allow AI systems to combine different data sources dynamically, making them more adaptive and helpful in real-world applications. As AI’s influence expands into industries such as finance, healthcare and cybersecurity, it is becoming clear that AI agents are critical components of modern business operations.

Despite the remarkable progress in AI, deploying these systems presents several challenges. One of the primary concerns is the risk of bias embedded in the datasets used to train AI agents. AI systems learn from historical data, which can contain patterns of discrimination that, if unchecked, lead to biased decisions, such as favoring particular groups over others in hiring or lending scenarios.

The company has unveiled plans for a new data center in Richland Parish, Louisiana, marking a significant expansion of its global infrastructure. The $10 billion investment will be Meta’s largest data center to date, spanning a massive 4 million square feet. This state-of-the-art facility will be a crucial component in the company’s ongoing efforts to support the rapid growth of artificial intelligence (AI) technologies. Meta did not disclose an estimated completion or operational date for this facility.

The new Richland Parish data center will create over 500 full-time operational jobs, providing a substantial boost to the local economy. During peak construction, the project is expected to employ more than 5,000 workers.

Meta’s decision to build in Richland Parish was driven by several factors, including the region’s robust infrastructure, reliable energy grid, and business-friendly environment. The company also cited the strong support from local community partners, which played a critical role in facilitating the development of the data center.

Delivering Innovative, Compassionate And Accessible Patient Care — Robert Stone, CEO — City of Hope & Dr. Marcel van den Brink, MD, PhD, President, City of Hope Comprehensive Cancer Center.


Robert Stone is the CEO of City of Hope (https://www.cityofhope.org/robert-stone), a premier cancer research and treatment center dedicated to innovation in biomedical science and the delivery of compassionate, world-class patient care. A seasoned health care executive, he has served in a number of strategic decision-making roles since he joined City of Hope in 1996, culminating with his appointment as president in 2012, CEO in 2014, and as the Helen and Morgan Chu Chief Executive Officer Distinguished Chair in 2021.

Mr. Stone has J.D., University of Chicago Law School, Chicago, IL.

Mr. Stone’s strategic acumen, empathy and visionary leadership have driven City of Hope’s rapid evolution.

As an independent institution dedicated to advancing the fight against cancer and diabetes, City of Hope is accelerating opportunities for high-impact discovery and ensuring that patients around the world have access to the most advanced therapies. Recent examples include a groundbreaking alliance in precision medicine with the Translational Genomics Research Institute (TGen), a leader in genomic analysis and bioinformatics; leadership in CAR T cell therapy research and therapy; and an innovative program to offer cancer support services to the employees of some of American’s largest employers, regardless of geography.

Conversely, proprietary LLMs typically offer robust security features but still pose data privacy and control risks. Using these models involves sharing sensitive data with a third-party provider, which could lead to regulatory penalties if a breach occurs.

LLMs also lack transparency regarding their training data and how datasets are formed. Be mindful of potential bias and fairness issues and consider a human-in-the-loop approach, where specialists review and manage the model’s output.

LLMs are most effective when used to streamline complex processes and drive innovation. To leverage these models responsibly, prioritize data governance—especially in highly regulated industries.