Eye On A.I.

Most AI systems follow a gradient, a mathematical slope that tells them exactly how to improve, step by step, toward a known goal. Neuroevolution doesn't follow any gradient. Instead, it runs hundreds or thousands of competing solutions simultaneously, spreads them across the space of possibilities as broadly as possible, and lets the best ones recombine, the same logic that drives biological evolution. The result, as Risto Miikkulainen explains to Craig Smith, is creativity: solutions that no human designer would have anticipated, that emerge routinely from the evolutionary process.
Miikkulainen is a professor at UT Austin and VP of AI Research at Cognizant AI Labs, and he has been working on this field since the 1980s, which makes him both a historian of it and one of its most active frontiersmen.
The conversation covers a remarkable range: a mystery model that outperformed every competitor in a recent stock trading competition with forensic footprints pointing to neuroevolutionary AI; Sakana AI's system that autonomously designed experiments, wrote a paper, and had it accepted at a major machine learning conference; and a pandemic decision system that trained overnight and made country-specific recommendations by morning, with Iceland actually following some of them, all the way to the prime minister.
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One in four people over 65 will experience a fall, and for most of them, the technology designed to help is a device that hasn't meaningfully changed since the 1980s. Chia-Lin Simmons, CEO of LogicMark, joined Craig Smith to make the case that this gap is both unnecessary and solvable, and that AI is finally making it possible to shift personal safety from reactive to predictive. Her company's Freedom Alert Max doesn't just detect falls after they happen, it builds a personalized digital twin of each user, tracking steps, sleep patterns, and medication adherence over time to identify the subtle signs of health decline that even daily caregivers often miss.
The conversation is one of the most grounded and human discussions of applied AI you'll hear, covering why Apple Watch fall detection was engineered for crash detection, not elderly falls; why AI can flag a problem but a human needs to hear the breathing on the other end of the line; and why the 700,000 caregiver shortage in America makes technology like this not a luxury but a scaling mechanism. For anyone navigating aging parents, their own future, or the sandwich generation pressures in between, this episode is both practically useful and genuinely moving.
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Luiz Domingos has spent 25 years watching enterprise communications evolve, from IP telephony to cloud to AI, and his assessment of where things stand now is unusually concrete. Companies have moved past the strategy deck phase. AI is being embedded directly into contact centers, compliance workflows, and communication pipelines, and the question executives are asking has shifted from "which model is smartest" to "which deployment reduces friction and stays compliant." Domingos is direct about what gets in the way: you cannot pour AI into a legacy architecture and expect transformation, and cloud-only AI doesn't solve the latency or data sovereignty problems that regulated industries face every day.
In this conversation with Craig Smith, Domingos covers the practical mechanics of how Mitel is applying AI across its portfolio, from real-time transcription and sentiment analytics in contact centers, to agentic workflows that turn conversations into automated tickets and follow-ups. He draws a clear line between AI agents (which give recommendations) and agentic AI (which takes actions), a distinction the market consistently confuses. He also makes a prediction worth noting: within five years, voice will replace the traditional app interface as the primary way people interact with enterprise AI systems. For any CIO or CTO trying to move from experimentation to real ROI, his framework - start with workflow friction, not pilots - is the most actionable takeaway in the episode.

A fly with 100,000 neurons can fly, find food, and reproduce. A $100 million supercomputer cannot. Dr. Terry Sejnowski used that observation to silence a room full of MIT AI researchers in the 1980s, and it remains just as sharp today. Sejnowski is one of the foundational figures in the history of deep learning, co-inventor of the Boltzmann machine, and a professor at the Salk Institute who has spent his career studying both the brain and the machines we build to imitate it. In this conversation with Craig Smith, he turns that dual perspective on ChatGPT, and what he finds is something genuinely clarifying: not a human mind, not a threat to humanity, but an alien intelligence that has absorbed more knowledge than any brain ever could while remaining fundamentally empty when nobody is talking to it.
The conversation covers the full landscape of what current AI is missing - from goals and reinforcement learning to the constant self-generated flow of thought that defines consciousness - and why the word "understanding" is so ambiguous that even the world's top cognitive scientists can't agree on whether ChatGPT has it. Sejnowski also makes the case that hallucinations aren't a flaw to be engineered away but the flip side of creativity itself, that we are in a pre-Copernican era when it comes to understanding intelligence, and that the real future of AI lies not in scaling language models further but in looking at what nature has already solved, from field mice to fruit flies. His new book is written for the general public and available now.
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Your child's data profile doesn't start when they get their first phone. It starts before they're born, the moment a parent emails a gynecologist or visits a fertility clinic website. That's the core argument behind Born Private, Proton's new initiative that lets parents reserve an email address for their child at birth, anchoring their digital identity in a privacy-preserving ecosystem before the profiling machine gets started. Craig Smith sits down with Eamonn Maguire, Engineering Director, Machine Learning & AI at Proton, who has spent his career at the intersection of data, security, and visualization to explore what's really happening to our data and what, if anything, we can do about it.
The conversation covers the mechanics of how just three email sign-ups can allow Google to infer your age, politics, and religion; why OpenAI and Anthropic have shown "not much regard for the law" when it comes to training data and copyright; and why social media platforms are operating like unregulated gambling companies - engineering addiction with no structural incentive to stop. It's one of the most grounded, specific, and genuinely alarming conversations about digital privacy you'll hear, and it ends with a simple, actionable proposition: privacy should be a decision you make at birth, not a problem you try to solve after the damage is done.
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