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This episode of Eye on AI is sponsored by Citrusx.
Unlock reliable AI with Citrusx! Our platform simplifies validation and risk management, empowering you to make smarter decisions and stay compliant. Detects and mitigate AI vulnerabilities, biases, and errors with ease.
Visit http://email.citrusx.ai/eyeonai to download our free fairness use case and see the solution in action.
In this episode of the Eye on AI podcast, Terry Sejnowski, a pioneer in neural networks and computational neuroscience, joins Craig Smith to discuss the future of AI, the evolution of ChatGPT, and the challenges of understanding intelligence.
Terry, a key figure in the deep learning revolution, shares insights into how neural networks laid the foundation for modern AI, including ChatGPT’s groundbreaking generative capabilities. From its ability to mimic human-like creativity to its limitations in true understanding, we explore what makes ChatGPT remarkable and what it still lacks compared to human cognition.
We also dive into fascinating topics like the debate over AI sentience, the concept of "hallucinations" in AI models, and how language models like ChatGPT act as mirrors reflecting user input rather than possessing intrinsic intelligence. Terry explains how understanding language and meaning in AI remains one of the field’s greatest challenges.
Additionally, Terry shares his perspective on nature-inspired AI and what it will take to develop systems that go beyond prediction to exhibit true autonomy and decision-making.
Learn why AI models like ChatGPT are revolutionary yet incomplete, how generative AI might redefine creativity, and what the future holds for AI as we continue to push its boundaries.
Don’t miss this deep dive into the fascinating world of AI with Terry Sejnowski. Like, subscribe, and hit the notification bell for more cutting-edge AI insights!
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(00:00) Introduction to Terry Sejnowski and His Work
(03:02) The Origins of Modern AI and Neural Networks
(05:29) The Deep Learning Revolution and ImageNet
(07:11) Understanding ChatGPT and Generative AI
(12:34) Exploring AI Creativity
(16:03) Lessons from Gaming AI: AlphaGo and Backgammon
(18:37) Early Insights into AI’s Affinity for Language
(24:48) Syntax vs. Semantics: The Purpose of Language
(30:00) How Written Language Transformed AI Training
(35:10) Can AI Become Sentient?
(41:37) AI Agents and the Next Frontier in Automation
(45:43) Nature-Inspired AI: Lessons from Biology
(50:02) Digital vs. Biological Computation: Key Differences
(54:29) Will AI Replace Jobs?
(57:07) The Future of AI
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This episode is sponsored by Netsuite by Oracle, the number one cloud financial system, streamlining accounting, financial management, inventory, HR, and more.
NetSuite is offering a one-of-a-kind flexible financing program. Head to https://netsuite.com/EYEONAI to know more.
In this episode of the Eye on AI podcast, Avthar Sewrathan, Lead Technical Product Marketing Manager at Timescale joins Craig Smith to explore how Postgres is transforming AI development with cutting-edge tools and open-source innovation.
With its robust, extensible framework, Postgres has become the go-to database for AI applications, from semantic search to retrieval-augmented generation (RAG). Avthar takes us through Timescale's journey from its IoT origins to disrupting the way developers handle vector search, embedding management, and high-performance AI workloads—all within Postgres.
We dive into Timescale's tools like PGVector, PGVector Scale, and PGAI Vectorizer, uncovering how they aid developers to build AI-powered systems without the complexity of managing multiple databases. Avthar explains how Postgres seamlessly handles structured and unstructured data, making it the perfect foundation for next-gen AI applications.
Learn how Postgres supports AI-driven use cases across industries like IoT, finance, and crypto, and why its open-source ecosystem is key to fostering collaboration and innovation.
Tune in to discover how Postgres is redefining AI databases, why Timescale’s tools are a game-changer for developers, and what the future holds for AI innovation in the database space.
Don’t forget to like, subscribe, and hit the notification bell for more AI insights!
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(00:00) Introduction to Avthar and Timescale
(02:35) The origins of Timescale and TimescaleDB
(05:06) What makes Postgres unique and reliable
(07:17) Open-source philosophy at Timescale
(12:04) Timescale's early focus on IoT and time series data
(16:17) Applications in finance, crypto, and IoT
(19:03) Postgres in AI: From RAG to semantic search
(22:00) Overcoming scalability challenges with PGVector Scale
(24:33) PGAI Vectorizer: Managing embeddings seamlessly
(28:09) The PGAI suite: Tools for AI developers
(30:33) Vectorization explained: Foundations of AI search
(32:24) LLM integration within Postgres
(35:26) Natural language interfaces and database workflows
(38:11) Structured and unstructured data in Postgres
(41:17) Postgres for everything: Simplifying complexity
(44:52) Timescale’s accessibility for startups and enterprises
(47:46) The power of open source in AI
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This episode is sponsored by Oracle.
Oracle Cloud Infrastructure, or OCI is a blazing fast and secure platform for your infrastructure, database, application development, plus all your AI and machine learning workloads. OCI costs 50% less for compute and 80% less for networking. So you’re saving a pile of money. Thousands of businesses have already upgraded to OCI, including MGM Resorts, Specialized Bikes, and Fireworks AI.
Cut your current cloud bill in HALF if you move to OCI now: https://oracle.com/eyeonai
In this episode of the Eye on AI podcast, Jeff Boudier, Head of Product and Growth at Hugging Face, joins Craig Smith to uncover how the platform is empowering AI builders and driving the open-source AI revolution.
With a mission to democratize AI, Jeff walks us through Hugging Face's journey from a chatbot for teens to the leading platform hosting over 1 million public AI models, datasets, and applications. We explore how Hugging Face is bridging the gap between enterprises and open-source innovation, enabling developers to build cutting-edge AI solutions with transparency and collaboration.
Jeff dives deep into Hugging Face’s tools and features, from hosting private and public models to fostering a thriving ecosystem of AI builders. He shares insights on the transformative impact of technologies like Transformers, transfer learning, and no-code solutions that make AI accessible to more creators than ever before.
We also discuss Hugging Face’s latest innovation, ‘Hugs,’ designed to help enterprises seamlessly integrate open-source AI within their infrastructure while retaining full control over their data and models.
Tune in to discover how Hugging Face is shaping the future of AI development, why open-source models are catching up with proprietary ones, and what trends are driving innovation across AI disciplines.
Don’t forget to like, subscribe, and hit the notification bell for more!
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(00:00) Introduction to Jeff Boudier
(02:16) How Hugging Face Empowers AI Builders
(05:26) Transition from Chatbot to Leading AI Platform
(07:07) Hosting AI Models: Public and Private Options
(10:13) What Does Hosting Models on Hugging Face Mean?
(14:22) Hugging Face vs. GitHub: Key Differences
(19:09) Navigating 1 Million Models on the Hugging Face Hub
(22:33) Leaderboards and Filtering AI Models
(25:26) Building Applications with Hugging Face Models
(28:03) AI Innovation: From Code to Model-Driven Development
(30:45) Frameworks for Agentic Systems and Hugging Chat
(35:20) Open Source vs. Proprietary AI: The Future
(40:41) Introducing ‘Hugs’: Open AI for Enterprises
(44:59) The Role of No-Code in AI Development
(47:26) Hugging Face’s Vision
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This episode is sponsored by Legal Zoom.
Launch, run, and protect your business to make it official TODAY at https://www.legalzoom.com/ and use promo code Smith10 to get 10% off any LegalZoom business formation product excluding subscriptions and renewals.
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.
This episode offers a thought-provoking glimpse into the future of AGI, touching on the technical challenges, societal impact, and ethical considerations that come with creating truly intelligent machines.
Ben’s perspective will leave you questioning not only what AGI can achieve, but also how we can guide it toward a positive future.
Don’t forget to like, subscribe, and hit the notification bell to stay tuned for more!
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(00:00) Introduction to Ben Goertzel
(01:21) Overview of "The Consciousness Explosion"
(02:28) Ben’s Background in AI and AGI
(04:39) Exploring Consciousness and AI
(08:22) Panpsychism and Views on Consciousness
(10:32) The Path to the Singularity
(13:28) Critique of Modern AI Systems
(18:30) Perspectives on Human-Level AI and Creativity
(21:42) Ben’s AGI Paradigm and Approach
(25:39) OpenCog Hyperon and Knowledge Graphs
(31:12) Integrating Perception and Experience in AI
(34:02) Robotics in AGI Development
(35:06) Virtual Learning Environment for AGI
(39:01) Creativity in AI vs. Human Intelligence
(44:21) User Interaction with AGI Systems
(48:22) Funding AGI Research Through Cryptocurrency
(53:03) Final Thoughts on Compassionate AI
(55:21) How to Get "The Consciousness Explosion" Book
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This episode of the Eye on AI podcast is sponsored by JLL.
JLL's AI solutions are transforming the real estate landscape, accelerating growth, streamlining operations and unlocking hidden value in properties and portfolios. From predictive analytics to intelligent automation, JLL is creating smarter buildings, more efficient workplaces and sustainable cities.
To learn more about JLL and AI, visit: jll.com/AI
In this episode of the *Eye on AI* podcast, we explore the world of AI at Google Cloud with Nenshad Bardoliwalla, Director of Product Management for Vertex AI.
Nenshad unpacks the three core layers of Vertex AI: the Model Garden, where users can access and evaluate a diverse range of models; the Model Builder, which supports model fine-tuning and prompt optimization; and the Agent Builder, designed to develop AI agents that can perform complex, goal-oriented tasks.
He shares insights into model evaluation strategies, the role of Google’s Tensor Processing Units (TPUs) in scaling AI infrastructure, and how enterprises can choose the right models based on performance, cost, and regulatory requirements.
Nenshad also delves into the challenges and opportunities of AI prompt optimization, highlighting Google’s approach to ensuring consistent outputs across different models. He discusses the ethical considerations in AI design, emphasizing the need for human oversight and clear guardrails to maintain safety.
Whether you’re in AI, tech, or curious about AI's potential impact, this episode is packed with insights on next-gen AI deployment.
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(00:00) Introduction to Nenshad Bardoliwalla & Vertex AI
(01:52) Overview of Vertex AI's Three Core Layers
(05:35) Nenshad's Journey to Google Cloud
(06:36) Choosing the Right AI Model
(08:00) Google’s AI Infrastructure & Tensor Processing Units (TPUs)
(10:15) Model Builder: Fine-Tuning & Prompt Optimization
(12:11) Agent Builder: Building AI Agents with Tools & Planning
(17:57) Model Evaluation & Prompt Management
(21:23) Generative AI for Business Analysts
(23:24) AI Model Modality & Use Case Selection
(25:23) Popularity Distribution of AI Models
(28:18) Prompt Optimization Tools
(34:20) Building AI Agents: Real-World Use Cases & Ethical Safeguards
(40:13) The Capabilities & Limitations of AI Agents
(45:48) TPU vs. GPU
(50:33) Future of AI at Google Cloud
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This episode of Eye on AI is sponsored by Citrusx.
Navigating the complexities of AI risk management? Citrusx has you covered. Their innovative platform helps you make better business decisions and achieve reliable AI outcomes while staying compliant with regulatory standards. Citrusx enables you to manage AI risks effortlessly, connecting all stakeholders and providing continuous validation. Their solution can detect and mitigate vulnerabilities, biases, and errors, ensuring the accuracy, robustness, and compliance of AI models
Visit https://email.citrusx.ai/eyeonai to book your free demo today!
In this episode of the Eye on AI podcast, we dive into process intelligence with Manuel Haug, Field CTO at Celonis.Manuel shares how process mining is transforming business operations by connecting directly to digital systems to map workflows, optimize processes, and boost efficiency. He explains how Celonis builds digital twins of business processes, allowing companies to visualize and resolve inefficiencies with data-driven insights.
Manuel also explores the role of AI in process optimization, discussing the integration of generative AI and AI agents in Celonis. From automating repetitive tasks to enabling strategic decision-making, Manuel details how AI agents can enhance workflows, reduce costs, and deliver measurable productivity gains across industries.
Tune in to learn how AI agents are evolving, the potential of process intelligence graphs, and how Celonis is pioneering the future of autonomous enterprises.
Whether you're in business, tech, or curious about the impact of AI, this episode offers valuable insights into next-gen process optimization.
Don’t forget to like, subscribe, and turn on notifications for more episodes on AI, automation, and digital transformation!
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(00:00) Introduction to Manuel Haug & Celonis
(01:07) Understanding Process Intelligence
(07:14) Integrating AI into Process Mining
(12:12) Generative AI’s Role in Process Optimization
(15:18) Challenges of Large-Scale Process Integration
(20:24) Role of AI Agents in Process Automation
(23:43) AI Maturity & Implementation in Enterprises
(27:23) Real-Life AI Agent Examples
(30:47) Building and Integrating AI Agents
(34:53) Future of AI Agents in Enterprises
(37:56) Introducing the Process Intelligence Graph
(39:20) Addressing Data Privacy Concerns
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This episode is sponsored by RapidSOS. Close the safety gap and transform your emergency response with RapidSOS.
Visit https://rapidsos.com/eyeonai/ today to learn how AI-powered safety can protect your people and boost your bottom line.
In this episode of the Eye on AI podcast, we dive deep into the world of AI agents with Ece Kamar, VP of Research and Managing Director of AI Frontiers Lab at Microsoft.
Ece shares her unique insights on the future of AI, discussing how AI agents are reshaping the way we interact with technology and perform tasks.
Throughout the episode, Ece explains the groundbreaking potential of AI agents, describing how they act as autonomous entities that can perceive, learn, and carry out complex tasks in real time. She discusses the revolutionary shift from traditional AI models to agentic workflows, highlighting how multi-agent systems like Microsoft's AutoGen are creating scalable solutions for industries and everyday life. Ece also shares her thoughts on building responsible AI, touching on the ethical challenges and safety concerns that come with the rise of autonomous agents.
We explore how multi-agent systems can scale to millions of agents, and how they are transforming enterprises by automating complex workflows, personalizing customer experiences, and pushing the boundaries of AI development. Ece’s perspective on the future of AI in scientific discovery, as well as her work in responsible AI, offers a thought-provoking glimpse into what lies ahead.
Don’t forget to like, subscribe, and hit the notification bell to stay updated on the latest in AI, automation, and ethical tech!
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(00:00) Preview and Introduction
(03:11) What Are AI Agents?
(04:43) Building Responsible AI at Microsoft
(10:55) The Rise of Agentic Workflows
(12:30) Multi-Agent Systems and AutoGen
(18:04) Scaling Multi-Agent Systems
(20:22) The Creation and Evolution of AutoGen
(23:07) Real-World Applications of AutoGen
(25:52) Large-Scale Simulations with AI Agents
(27:36) The Role of AI Agents in Scientific Discovery
(31:20) AI Agents and Complex Reasoning
(36:49) Challenges in Defining Agent Boundaries
(39:12) The Risk of Agents Interacting with Each Other
(43:59) Building Trustworthy and Safe AI Agents
(48:44) Learning from Human Factors in Automation
(50:50) Why Speed and Coordination Matter in AI Development
(55:08) The Future of AI Agents in Enterprises
(57:47) Low-Code/No-Code Development for AI Agents
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This episode is sponsored by Oracle. AI is revolutionizing industries, but needs power without breaking the bank. Enter Oracle Cloud Infrastructure (OCI): the one-stop platform for all your AI needs, with 4-8x the bandwidth of other clouds. Train AI models faster and at half the cost. Be ahead like Uber and Cohere.
If you want to do more and spend less like Uber, 8x8, and Databricks Mosaic - take a free test drive of OCI at https://oracle.com/eyeonai
In this episode of the Eye on AI podcast, we sit down with Mark Surman, President of Mozilla, to explore the future of open-source AI and how Mozilla is leading the charge for privacy, transparency, and ethical technology.
Mark shares Mozilla’s vision for AI, detailing the company’s innovative approach to building trustworthy AI and the launch of Mozilla AI. He explains how Mozilla is working to make AI open, accessible, and secure for everyone—just as it did for the web with Firefox. We also dive into the growing importance of federated learning and AI governance, and how Mozilla Ventures is supporting groundbreaking companies like Flower AI.
Throughout the conversation, Mark discusses the critical need for open-source AI alternatives to proprietary models like OpenAI and Meta’s LLaMA. He outlines the challenges with closed systems and highlights Mozilla’s work in giving users the freedom to choose AI models directly in Firefox.
Mark provides a fascinating look into the future of AI and how open-source technologies can create trillions in economic value while maintaining privacy and inclusivity. He also sheds light on the global race for AI innovation, touching on developments from China and the impact of public AI funding.
Don’t forget to like, subscribe, and hit the notification bell to stay up to date with the latest trends in AI, open-source tech, and machine learning!
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(00:00) Introduction to Mark Surman and Mozilla’s Mission
(02:01) The Evolution of Mozilla: From Firefox to AI
(04:40) Open-Source Movement and Mozilla’s Legacy
(06:58) The Role of Open-Source in AI
(11:06) Advancing Federated Learning and AI Governance
(14:10) Integrating AI Models into Firefox
(16:28) Open vs Closed Models
(22:09) Partnering with Non-Profit AI Labs for Open-Source AI
(25:08) How Meta’s Strategy Compares to OpenAI and Others
(27:58) Global Competition in AI Innovation
(31:17) The Cost of Training AI Models
(33:36) Public AI Funding and the Role of Government
(37:40) The Geopolitics of AI and Open Source
(41:35) Mozilla’s Vision for the Future of AI and Responsible Tech
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This episode is sponsored by Speechmatics. Check it out at www.speechmatics.com/realtime
Today, we're joined by Dr. Thomas G. Dietterich, a pioneer in machine learning who recently was honored with the Award for Research Excellence from the International Joint Conference on Artificial Intelligence, one of the top awards for AI researchers.
Dietterich traces the field's progression from early rule-based systems to modern machine learning paradigms and delves into his work on novel category detection and open set problems. He also discusses the evolution of ensemble methods in the context of large language models (LLMs), highlighting the shift from combining many cheap models to more selective approaches with expensive models.
He advocates for a foundation model approach to capture the variability of the world.
Join us for a deep dive into the future of AI, where Thomas explains why the development of novel materials and drugs may have the most transformative impact on our economy. Plus, hear about his latest work on multi-instance learning, weak supervision, and the role of reinforcement learning in real-world applications like wildfire management.
Don’t forget to like, subscribe, and hit the notification bell to stay updated on the latest trends and insights in AI and machine learning!
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(00:00) Introduction to Thomas Dietterich's Machine Learning Journey
(02:34) The Early Days of Machine Learning and AI Systems
(04:29) Tackling the Multiple Instance Problem in Drug Design
(05:41) AI in Sustainability
(07:17) The Challenge of Novelty Detection in AI Systems
(08:00) Addressing the Open Set Problem in Cybersecurity and Computer Vision
(09:11) The Evolution of Deep Learning in Computer Vision
(11:21) How Deep Learning Handles Novel Representations
(12:01) Foundation Models and Self-Supervised Learning
(14:11) Vision Transformers vs. Convolutional Neural Networks
(16:05) The Role of Multi-Instance Learning in Weakly Labeled Data
(18:36) Ensemble Learning and Deep Networks in Machine Learning
(20:33) The Future of AI: Large Language Models and Their Applications
(23:51) Symbolic Regression and AI’s Role in Scientific Discovery
(34:44) AI in Wildfire Management: Using Reinforcement Learning
(39:32) AI-Driven Problem Formulation and Optimization in Industry
(41:30) The Future of AI Reasoning Systems and Problem Solving
(45:03) The Limits of Large Language Models in Scientific Research
(50:12) Closing Thoughts: Open Challenges and Opportunities in AI
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This episode is sponsored by Netsuite by Oracle, the number one cloud financial system, streamlining accounting, financial management, inventory, HR, and more. NetSuite is offering a one-of-a-kind flexible financing program.
Head to https://netsuite.com/EYEONAI to know more.
In this episode of the Eye on AI podcast, we dive into the world of quantum consciousness with Stuart Hameroff, a pioneer in the field of consciousness studies and co-developer of the controversial Orch OR theory.
Stuart Hameroff takes us on a journey through the intersection of quantum mechanics and the human mind, explaining how microtubules within neurons could be the key to unlocking the mysteries of consciousness.
Stuart delves into his work with physicist Roger Penrose, where they propose that consciousness arises from quantum processes in the brain, deeply embedded in the fabric of spacetime itself. We explore how this theory challenges mainstream neuroscience, which often reduces the mind to simple neural activity, and instead suggests that consciousness may have a profound connection to the universe's underlying structure.
Throughout the conversation, Stuart addresses the debate over AI consciousness, asserting that true conscious experience cannot arise from mere computation but requires quantum processes. He shares insights on the latest experiments in anesthesia and quantum biology, offering a fresh perspective on how the brain might function on a deeper, quantum level.
Join us as we unpack the groundbreaking Orch OR theory and what it could mean for the future of science, technology, and our understanding of reality.
Don’t forget to like, subscribe, and hit the notification bell to stay updated on the latest cutting-edge discussions in AI, quantum theory, and consciousness research!
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(00:00) Preview
(03:26) Consciousness and Dualism vs. Materialism
(04:51) Anesthesia and Consciousness: Hameroff's Perspective
(07:30) Roger Penrose's Perspective on Consciousness
(09:51) Penrose's Explanation of Quantum Superposition
(12:52) The Collapse of Quantum Superposition and Consciousness
(14:41) Microtubules and Their Role in Consciousness
(17:08) Critique of Current Neuroscience Approaches
(22:28) Discovering the Microtubule’s Role in Information Processing
(26:08) Microtubules as Cellular Automata
(28:48) The Role of Frohlich Coherence in Quantum Biology
(31:27) Meeting Roger Penrose and Connecting with His Work
(33:52) Collaboration with Penrose: Developing the Theory
(37:05) Challenges and Criticisms of the Theory
(43:06) Advances in Quantum Consciousness Research
(46:18) Hierarchical Models in the Brain
(51:10) Entanglement and Consciousness
(55:03) The Mystery of Anesthesia’s Selective Impact on Consciousness
(57:07) Quantum Effects and Anesthesia’s Mechanism
(01:00:22) The Search for Anesthesia’s Target Protein
(01:04:30) Experimental Evidence for Quantum Effects in Biology
(01:09:33) Consciousness as a Quantum Physical Effect
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This episode is sponsored by Bloomreach. Bloomreach is a cloud-based e-commerce experience platform and B2B service specializing in marketing automation, product discovery, and content management systems.
Check out Bloomreach: https://www.bloomreach.com
Explore Loomi AI: https://www.bloomreach.com/en/products/loomi
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In this episode of the Eye on AI podcast, we sit down with Pedro Domingos, professor of computer science and author of The Master Algorithm and 2040, to dive deep into the future of artificial intelligence, machine learning, and AI governance.
Pedro shares his expertise in AI, offering a unique perspective on the real dangers and potential of AI, far from the apocalyptic fears of superintelligence taking over. We explore his satirical novel, 2040, where an AI candidate for president—Prezibot—raises questions about control, democracy, and the flaws in both AI systems and human decision-makers.
Throughout the episode, Pedro sheds light on Silicon Valley’s utopian dreams clashing with its dystopian realities, highlighting the contrast between tech innovation and societal challenges like homelessness. He discusses how AI has already integrated into our daily lives, from recommendation systems to decision-making tools, and what this means for the future.
We also unpack the ongoing debate around AI safety, the limits of current AI models like ChatGPT, and why he believes AI is more of a tool to amplify human intelligence rather than an existential threat. Pedro offers his insights into the future of AI development, focusing on how symbolic AI and neural networks could pave the way for more reliable and intelligent systems.
Don’t forget to like, subscribe, and hit the notification bell to stay updated on the latest insights into AI, machine learning, and tech culture.
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(00:00) Preview and Introduction
(01:06) Pedro's Background and Contributions to AI
(03:36) The Satirical Take on AI in '2040'
(05:42) AI Safety Debate: Geoffrey Hinton vs. Yann LeCun
(08:06) Debunking AI's Real Risks
(12:45) Satirical Elements in '2040': HappyNet and Prezibot
(17:57) AI as a Decision-Making Tool: Potential and Risks
(22:55) The Limits of AI as an Arbiter of Truth
(27:35) Crowdsourced AI: PreziBot 2.0 and Real-Time Decision Making
(29:54) AI Governance and the Kill Switch Debate
(37:42) Integrating AI into Society: Challenges and Optimism
(47:11) Pedro's Current Research and Future of AI
(55:17) Scaling AI and the Future of Reinforcement Learning
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In this episode of the Eye on AI podcast, Craig Smith sits down with Bill Moore BCGX to explore the development of Gene, an AI-powered co-host transforming the way we engage with conversational AI.
Bill walks us through the journey of creating Gene, a real-time conversational agent built to interact seamlessly with human hosts. He shares insights into the technical challenges they faced, from improving latency to expanding context windows, and how Speechmatics' cutting-edge speech-to-text technology helped achieve over 90% accuracy in real-time.
We dive deep into the ethics of AI, particularly gender representation, as the conversation touches on why assigning gender to AI assistants can reinforce outdated stereotypes. Gene, as a gender-neutral conversational AI, challenges these norms, providing a clear distinction between human-like interaction and AI-driven dialogue.
Join us for an in-depth discussion on the future of AI in media, the balance between human creativity and AI’s analytical power, and the ethical considerations every company should be mindful of when integrating AI into their operations.
Don’t forget to like, subscribe, and hit the notification bell for more deep dives into AI innovation, ethics, and the future of conversational agents!
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(00:00) Introduction to Gene: BCG's Conversational AI
(02:19) Bill Moore’s Background
(04:22) Early Challenges in Building Real-Time AI
(07:20) Technical Aspects: Prompting and Configuring Gene
(09:28) The Use of Vector Databases and Sparse Priming
(12:01) How Gene Handles Long Conversations
(14:46) Building Conversational AI
(19:07) Gene Introduces Itself: AI as a Podcast Co-Host
(22:00) The Ethics of AI
(29:35) Should AI Have a Physical Representation?
(32:05) AI and Children: Nurturing Healthy Interactions
(36:16) Adoption of Conversational AI in Businesses
(38:11) Ethical Considerations for AI in the Future
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This episode is sponsored by Shopify.
Shopify is a commerce platform that allows anyone to set up an online store and sell their products. Whether you’re selling online, on social media, or in person, Shopify has you covered on every base. With Shopify you can sell physical and digital products. You can sell services, memberships, ticketed events, rentals and even classes and lessons.
Sign up for a $1 per month trial period at http://shopify.com/eyeonai
In this episode of the Eye on AI podcast, Craig Smith sits down with Michael Martin, CEO of RapidSOS, to explore how AI and connected devices are aiding emergency response and public safety.
With billions of sensor feeds from over 540 million devices—including wearables, vehicles, and security systems—RapidSOS integrates life-saving data to provide real-time support for 911 and first responders across the U.S. and beyond. Michael shares how their platform is reducing response times, improving accuracy, and transforming the way emergencies are handled by public safety agencies.
We dive deep into RapidSOS’s AI-powered platform, which fuses human and machine intelligence to deliver faster, more effective emergency responses. Michael also discusses the challenges of scaling such technology globally, the role of AI in predictive emergency management, and the importance of integrating these solutions into legacy systems used by first responders.
Whether it’s saving lives through faster 911 responses, preventing emergencies before they happen, or leveraging AI to enhance public safety, this episode offers a compelling look at the future of emergency services. Join us to uncover how technology is reshaping public safety and emergency response on a global scale.
Don’t forget to like, subscribe, and hit the notification bell for more cutting-edge discussions on AI, emergency services, and technology innovation!
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(00:00) Preview
(01:58) RapidSOS and Michael Martin’s Journey
(06:39) Integration w/ Wearable Devices and Home Systems
(11:59) RapidSOS’s Use Cases
(23:12) RapidSOS’ Foundational Models
(31:31) How RapidSOS Handles Data
(36:11) How RapidSOS Aids Public Safety
(49:56) Is RapidSOS Integrated into Hospitals?
(53:00) EmergencyProfile.org Role In Helping First Responders
(55:36) The Future of Public Safety with AI and RapidSOS
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This episode is sponsored by Oracle. AI is revolutionizing industries, but needs power without breaking the bank. Enter Oracle Cloud Infrastructure (OCI): the one-stop platform for all your AI needs, with 4-8x the bandwidth of other clouds. Train AI models faster and at half the cost. Be ahead like Uber and Cohere.
If you want to do more and spend less like Uber, 8x8, and Databricks Mosaic - take a free test drive of OCI at https://oracle.com/eyeonai
In this episode of the Eye on AI podcast, Craig Smith sits down with Noa Srebrnik from Citrusx to explore how AI is transforming risk management and compliance in high-risk industries like finance and insurance.
With a background spanning regulation, risk, and technology, Noa guides us through Citrusx’s innovative approach to enabling organizations to leverage AI responsibly while adhering to complex regulatory environments.
We dive deep into Citrusx’s AI platform, designed to validate, monitor, and explain AI models, ensuring transparency, fairness, and compliance. Noa explains how their proprietary synthetic data technology identifies hidden risks, allowing companies to confidently scale AI solutions across different use cases without compromising governance.
We also discuss the evolving regulatory landscape, including the challenges of navigating fragmented AI regulations in the U.S. and the EU AI Act’s influence on high-risk sectors.
Join us as we uncover how AI can drive innovation while maintaining regulatory accountability and why risk management is crucial for the future of AI in finance and beyond.
Don’t forget to like, subscribe, and hit the notification bell for more in-depth discussions on AI, regulation, and risk management!
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This episode is sponsored by Netsuite by Oracle, the number one cloud financial system, streamlining accounting, financial management, inventory, HR, and more.
NetSuite is offering a one-of-a-kind flexible financing program. Head to https://netsuite.com/EYEONAI to know more.
In this episode of the Eye on AI podcast, Craig Smith sits down with Krishna Rangasayee, CEO of SiMa.ai, to explore the innovations in AI at the edge and how embedded systems are transforming industries like robotics, automotive, and industrial automation.
Krishna brings over 30 years of experience in software and silicon design, leading us through SiMa.ai's vision of disrupting edge computing with their Machine Learning System on Chip (MLSoC).
We dive deep into how SiMa.ai’s chips are designed to accelerate AI workloads at the edge, providing unmatched performance and efficiency compared to cloud-based solutions.
We also discuss the future of AI hardware, including the role of open-source platforms, the challenges of scaling AI in embedded systems, and why the edge market is poised to outgrow the cloud in the coming years.
Krishna shares his insights on the importance of AI software architecture, how SiMa.ai’s platform simplifies integration for developers, and the emerging potential of neuromorphic and quantum computing.
Join us as we delve into the next big AI gold rush, the edge market, and learn why AI-powered edge devices will reshape the way industries operate.
Don’t forget to like, subscribe, and hit the notification bell for more exclusive insights into the future of AI and edge computing!
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(00:00) Preview and Introduction
(02:13) SiMa.ai's Focus on Chip Design for the Edge
(05:38) How is SiMa.ai Different from NVIDIA’s CUDA?
(08:29) The Evolution of Chip Architecture
(11:50) AI’s Move to the Edge Market?
(19:19) Industries Driving Demand for SiMa.ai's Technology
(21:25) How Do Edge Applications Work?
(25:09) Chips Design for Edge Devices
(33:01) SiMa.ai Scaling Plans
(36:12) What is SiMa.ai Focusing On in the Future?
(40:30) SiMa.ai’s Fundraising Journey
(42:57) Hiring and Retaining Top Talent in the Industry
(46:01) The Future of Chip Technology
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This episode is sponsored by Bloomreach.
Bloomreach is a cloud-based e-commerce experience platform and B2B service specializing in marketing automation, product discovery, and content management systems.
Check out Bloomreach: https://www.bloomreach.com
Explore Loomi AI: https://www.bloomreach.com/en/products/loomi
Other Bloomreach products: https://www.bloomreach.com/en/products
In this episode of the Eye on AI podcast, we sit down with Randall Degges, Head of Developer Relations and Security at Snyk, to uncover the impact of AI on cybersecurity and software development.
Randall shares his 20+ years of experience as a software developer and security expert, leading us through Snyk's innovative approach to developer security. We dive into how Snyk is changing vulnerability detection and code generation by leveraging a hybrid AI model—combining symbolic AI for accurate detection and generative AI for smart fixes.
We explore the challenges and opportunities of using AI in code security, discussing whether AI-generated code can ever fully replace human coders or if it's best suited as a powerful tool in a developer's arsenal. Randall also addresses the risks of AI hallucinations in code generation and how Snyk mitigates these through rigorous testing and validation.
Join us as we discuss the future of coding, the role of AI in software development, and how developers can stay ahead in this rapidly evolving landscape.
Don’t forget to like, subscribe, and hit the notification bell for more expert insights into the latest AI and cybersecurity trends.
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Craig Smith Twitter: https://twitter.com/craigss
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(00:00) Preview and Intro
(00:34) Randall Degges Background
(01:33) The Role of AI in Security at Snyk
(03:28) Symbolic vs. Generative AI in Code Security
(04:57) How Snyk Uses Rule-Based AI for Detection
(06:48) Challenges with AI-Generated Code Fixes
(09:08) The Future of AI in Code Generation
(11:56) Integrating AI with Developer Tools
(16:06) Risks of AI-Generated Code and Internet Saturation
(22:25) The Hybrid AI Approach for Code Security
(26:31) Future of AI and Its Impact on Developers
(30:02) Snyk's Integration with IDEs and Research Initiatives
(33:48) Autonomous Fixes and the Future of AI in Development
(41:04) DeepCode AI Fix Engine and Snyk’s ID Plugin
(46:38) Will AI Replace Developers?
(50:16) AI Readiness Report Insights
(52:59) Tech Layoffs and Opportunities in AI
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In this episode of the Eye on AI podcast, we sit down with Peter Guagenti, President and Chief Marketing Officer at Tabnine, to explore the role of AI in software development.
Peter takes us through his journey from web developer and engineering lead to leading Tabnine, a pioneer of AI code assistance.
We delve into the innovative ways Tabnine is pushing the boundaries of AI, from enhancing code generation to ensuring privacy with its Protected Model—offering enterprises fully private AI solutions tailored to their specific needs. Peter discusses how Tabnine is addressing the challenges of fit-to-purpose AI, making AI tools more context-aware and personalized to the workflows of individual engineering teams.
Peter also sheds light on the future of AI in software development, addressing the pressing question: Can AI truly replace developers, or is it destined to be a powerful collaborator?
Learn how AI can elevate software engineering teams, helping them overcome the repetitive tasks that slow down progress and focus on the creative aspects that push the industry forward.
Don’t forget to like, subscribe, and hit the notification bell for more in-depth conversations on the latest AI advancements.
This episode of Eye on AI is sponsored by BetterHelp.
If you’re thinking of starting therapy, give BetterHelp a try. It’s entirely online. Designed to be convenient, flexible, and suited to your schedule. Just fill out a brief questionnaire to get matched with a licensed therapist, and switch therapists any time for no additional charge.
Visit https://www.betterhelp.com/eyeonai today to get 10% off your first month.
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(00:00) Preview and Introduction
(00:38) Peter Guagenti's Background
(01:20) Tabnine’s Origins
(03:49) Innovating in AI Code Assistance
(05:10) The Path to Autonomous Code Generation
(07:49) Human Oversight in Autonomous AI
(10:17) Misconceptions About AI Replacing Engineers
(14:15) Future of Software Development with AI
(17:04) Autonomous JIRA Tool and Broader Applications
(22:36) Leveraging Vector Databases for Context
(27:34) Balancing Contextual Data for AI
(29:54) Expanding Generative AI Use Cases
(34:16) Ensuring Code Quality with AI
(37:17) Curating Quality Data for AI Models
(41:17) The Need for Skilled Coders in AI
(42:59) Future of Generative AI Beyond LLMs
(47:00) Case Studies: Tabnine’s Impact on Productivity
(51:49) Conclusion: Building Trust in AI Technology
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Check our BloomReach: https://www.bloomreach.com
Explore Loomi AI: https://www.bloomreach.com/en/products/loomi
Other BloomReach products: https://www.bloomreach.com/en/products
In this episode of the Eye on AI podcast, we sit down with Xun Wang, Chief Technology Officer at Bloomreach, to explore the power of AI in the world of e-commerce.
Xun takes us on a journey through his career, from his early days at NVIDIA, where he played a pivotal role in the evolution of parallel computing and GPU technology, to his current leadership at Bloomreach, a company at the forefront of AI-driven commerce solutions.
He shares how Bloomreach is disrupting the e-commerce landscape with its AI-powered platform, helping brands and retailers enhance customer experiences through personalized search and recommendations.
We dive deep into the technical side of Bloomreach's AI services, including Loomi AI, which integrates various AI capabilities such as search, recommendations, and segmentation. Xun explains how these services work together to deliver highly personalized shopping experiences that boost engagement and conversion rates.
Xun sheds light on the company’s innovative use of generative AI and large language models to improve search relevance and personalization, offering listeners a glimpse into the future of e-commerce.
Tune in to discover how Bloomreach is leveraging cutting-edge AI technologies like two-tower neural networks and their Customer Data Engine (CDE) to redefine the online shopping experience.
Join us as we explore the intersection of AI and commerce, and learn how Bloomreach is setting new standards for personalized online experiences.
Don’t forget to like, subscribe, and hit the notification bell for more deep dives into the latest AI innovations.
This episode is sponsored by Netsuite by Oracle, the number one cloud financial system, streamlining accounting, financial management, inventory, HR, and more.
NetSuite is offering a one-of-a-kind flexible financing program. Head to https://netsuite.com/EYEONAI to know more.
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Craig Smith Twitter: https://twitter.com/craigss
Eye on A.I. Twitter: https://twitter.com/EyeOn_AI
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In this episode of the Eye on AI podcast, we explore the cutting-edge intersection of AI and biotechnology with Raphael Townshend, founder and CEO of Atomic AI.
Raphael delves into the revolutionary potential of AI in RNA drug discovery, highlighting Atomic AI's innovative approach. He shares his journey from studying electrical engineering and computer science at UC Berkeley to developing advanced AI models for understanding RNA structures, analogous to DeepMind's AlphaFold for proteins.
We dive deep into the intricacies of RNA's role in the human genome and its untapped potential in treating diseases previously considered undruggable. Raphael explains how Atomic AI's core model, Atom 1, is designed to predict RNA shapes with unprecedented accuracy, enabling the design of new drugs that target RNA instead of proteins. He discusses the significance of RNA in the context of mRNA vaccines, particularly the COVID-19 vaccine, and the challenges of making these vaccines more stable and accessible.
The conversation also covers the technical aspects of using AI, including transformer-based models and in-house data generation, to enhance RNA drug discovery. Raphael shares insights into the company's progress, from cell testing to upcoming animal trials, and the broader implications of integrating AI in biotechnology.
Join us as we uncover the future of RNA-based therapies, the innovative use of AI in drug discovery, and the groundbreaking advancements that could transform the landscape of medicine. Don't forget to like, subscribe, and hit the notification bell for more expert insights into the latest AI innovations.
This episode is sponsored by SysAid, the Next-gen ITSM Platform.
Get 20% off SysAid Copilot using this link: https://www.sysaid.com/lp/sysaid-copilot-s?utm_source=youtube&utm_medium=cpc&utm_campaign=short-craig
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In this episode of the Eye on AI podcast, we dive into the world of AI forecasting with Danny Halawi, a PhD student at UC Berkeley.
Danny shares his groundbreaking research on using large language models (LLMs) to predict future events with accuracy, rivaling human forecasters and prediction markets.
Danny recounts his journey from studying computer security and fraud detection to exploring the potential of AI in forecasting geopolitical events and beyond. He introduces us to the sophisticated architecture of his AI system, which leverages real-time data from prediction markets and advanced machine learning techniques to generate reliable forecasts.
We explore the complexities of judgmental forecasting versus time series forecasting and how AI can enhance decision-making in various fields. Danny discusses the challenges of training AI models for high-stakes predictions, the role of super forecasters, and the fascinating dynamics of prediction markets. He also sheds light on the ethical considerations and future possibilities of integrating AI into our decision-making processes.
Join us as we delve into the future of AI forecasting, the potential of superhuman predictions, and the exciting developments that could reshape our understanding of the future. Don't forget to like, subscribe, and hit the notification bell for more expert insights into the latest AI innovations.
This episode is sponsored by Oracle. AI is revolutionizing industries, but needs power without breaking the bank. Enter Oracle Cloud Infrastructure (OCI): the one-stop platform for all your AI needs, with 4-8x the bandwidth of other clouds. Train AI models faster and at half the cost. Be ahead like Uber and Cohere.
If you want to do more and spend less like Uber, 8x8, and Databricks Mosaic - take a free test drive of OCI at https://oracle.com/eyeonai
Stay Updated:
Craig Smith Twitter: https://twitter.com/craigss
Eye on A.I. Twitter: https://twitter.com/EyeOn_AI
(00:00) Preview and Introduction
(01:28) The Importance of AI
(02:50) Danny's Background and Interest in AI
(04:01) Automated AI Forecasting and Safety Implications
(07:34) Judgmental Forecasting Explained
(11:01) Accuracy and Challenges in Prediction Markets
(16:01) Aggregating Predictions for Better Accuracy
(19:25) Data Collection and Model Accuracy
(23:18) Improving Model Accuracy Over Time
(25:31) Data Sources and Model Training
(29:20) Summarizing Information for Predictions
(34:08) Potential of Reinforcement Learning in Forecasting
(37:50) Automating Information Collection and Summarization
(39:01) Training the Model for Accurate Predictions
(45:04) Challenges with Uncertain Predictions
(50:14) Potential Applications and Future Directions
(52:26) The Future of AI Forecasting and Its Impact
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