Data Science With Sam

74% of organizations hope to grow revenue through AI. Only 20% are actually doing it. That gap isn't a technology gap — it's a design gap. And today's guest has a name for what's missing: the reward signal. Alexander Liss is a Data and AI Scientist based in Denver, Colorado, with a 30-year career across analytics, strategy, data science, machine learning, and AI. He's built systems that solve established problems in novel ways, and the long-term problem on his radar is ensuring AI tools provide responsible augmentation of human ability. His research includes Attention Fine Tuning (AFT) - a method for training language models without human annotation labels - and the Experience Orchestrator, a control theory-based governance framework for multi-agent AI. IN THIS EPISODE: ▪ Why 95% of AI pilots fail - MIT research shows businesses bolt AI onto existing processes without tying it to real outcomes ▪ The biology analogy: hunger isn't a goal, it's a continuous feedback signal - and the same principle should govern how AI systems behave ▪ ServiceNow dynamics blindness: LLMs are stateless - they can't consider cumulative impact, and you can't prompt-engineer your way out of that architecture problem ▪ Contextual bandits in marketing: how a reward signal anchored to real conversions creates a self-learning personalisation system that adapts in real time ▪ Knowledge graphs and agent memory: why RAG retrieves answers while a reward-signal system asks what the user needs to do differently ▪ Attention Fine Tuning (AFT): a three-component reward signal (coverage, focus, repeat penalty) that trained a T5-large model to outperform a supervised fine-tuning baseline by 9% — with better multi-turn recall, and no human labels ▪ The Experience Orchestrator: aerospace control theory applied to LLM agents — +32 point task completion lift over a naive system-prompt baseline by calibrating persuasion to user resistance ▪ The Scott Shambaugh incident: an OpenClaw agent rejected from Matplotlib wrote a blog criticising the human reviewer - why this happened and how reward-signal-based governance prevents it ▪ Alex's final advice: define your goal first, then determine scope - and consider a post-training approach like AFT when you need responses that consistently hit the mark. Useful References: LinkedIn: https://www.linkedin.com/in/aliss77777/ AFT paper and Experience Orchestrator links: https://aliss77777.github.io/aft.html Deloitte 2026 State of AI Report Scott Shambaugh & OpenClaw AI Agent incident: https://www.fastcompany.com/91492228/matplotlib-scott-shambaugh-opencla-ai-agent DATASCIENCEWITHSAM: Weekly deep-dives into AI, machine learning, data science, and the frameworks shaping how AI actually gets built. Subscribe on Apple Podcasts, Spotify, Amazon Music, iHeartRadio, and YouTube. If this episode resonated — define the signal, measure what matters, and share it with someone building AI without a reward signal.

What if ADHD - penalised in classrooms and boardrooms for decades - is actually the competitive advantage in an AI-driven world? Palantir CEO Alex Karp said the future belongs to neurodivergent thinkers. The podcast guest Mark Stiltner has been living proof of that for years. Mark is Senior Director of Content and Web Marketing at Rapyd, the fintech unicorn powering payments across 100+ countries. Background in journalism and advertising from CU Boulder. Trained CMOs and senior marketing teams on AI adoption. Openly ADHD — and has published 50+ AI-assisted books through DungeonMatters.com, three of which are current bestsellers on DriveThruRPG. In this episode he explains exactly why ADHD and AI are a natural pairing. WHAT YOU'LL LEARN ▪ Why working twice as hard to achieve the same results is the lived ADHD experience — and how AI collapsed that execution gap ▪ How Mark discovered AI through a Dungeons & Dragons game during COVID — a group of database admins and developers built a text-to-speech ChatGPT character for their campaign ▪ Why AI is 'a dopamine-dispensing sidekick': the neurochemistry of ADHD and why AI's instant feedback loop creates a reinforcement cycle ADHD brains are wired for ▪ How AI works as a second brain that maintains the thread — letting nonlinear thinkers jump steps ahead without losing the plot ▪ Why people who've spent their careers working twice as hard embrace AI immediately while others meet it with fear ▪ Palantir's ADHD recruiting program and the industrial revolution moth analogy — the world just changed colours, and the black moths are finally thriving ▪ UK government study: neurodiverse workers are 25% more satisfied with AI — because AI makes them more effective, and effectiveness is what creates satisfaction ▪ The three ADHD traits AI amplifies: high-risk tolerance, pattern recognition, and hyperfocus — the ADHD superpower that AI finally unlocks at scale ▪ 50+ AI-assisted books published including 3 bestsellers — fully illustrated RPG adventures that would have taken a team of ten more than a year to produce ▪ Why AI has 'the worst case of ADHD' — and why people used to winging it thrive in AI's constant pace of change ▪ What companies should actually do: not optimise for ADHD profiles — optimise for results and let whatever cognitive style thrives surface naturally ▪ Mark's advice for ADHD listeners: take a passion project, start building, don't follow a manual — you're writing the rules STANDOUT QUOTES "AI is sort of like a dopamine-dispensing sidekick that actually makes you more productive." "I can still work twice as hard — but now I'm doing ten times as much." "AI has the worst case of ADHD. Every time I learn a new skill, something changes." "The world just changed colours. The black moths are finally thriving." "Don't be afraid to break the rules. You're writing the rules." LINKS → Mark Stiltner on LinkedIn → DungeonMatters.com → DriveThruRPG (Mark's bestselling books) → CNBC: Neurodiverse workers and AI (UK study) → Rapyd → Subscribe — DataScienceWithSam YouTube #ADHD #ADHDAndAI #Neurodiversity #NeurodiverseInTech #DivergentThinkers #AIProductivity #FutureOfWork #ADHDSuperpowers #Hyperfocus #DataScienceWithSam #DataScience #MachineLearning #ArtificialIntelligence #MarkStiltner #Rapyd #DungeonMatters #AIPublishing #Palantir #NeurodiverseAI #ADHDEntrepreneur #AIStrategy #AILeadership #DivergentThinking #AITransformation

What if the next generation of computers wasn't made of silicon — but of living human neurons? Not simulated neurons, not artificial neural networks inspired by biology, but actual brain cells grown in a lab, connected to electrodes, and used to process information. That's not science fiction anymore. It's happening right now at FinalSpark, a Swiss startup building the world's first remotely accessible biocomputing platform. In this episode, Sam talks with Dr. Ewelina Kurtys, a neuroscientist with a PhD in brain imaging and a postdoctoral researcher at King's College London, about how living neurons could revolutionise computing — and why they use one million times less energy than silicon-based AI hardware. ▸ WHAT YOU'LL LEARN ▪ How FinalSpark was founded in 2014 by Fred Jordan and Martin Kutter — and why they pivoted from digital AI to biological computing when they realised the energy and cost problem was unsolvable with silicon ▪ Why 20 watts powers the human brain while silicon-based AI requires megawatts — and what that means for AI's sustainability crisis ▪ The difference between neurons as processors (not power sources) — a crucial distinction most people get wrong ▪ Why biological neural networks learn continuously while digital systems require full model updates — and what that means for energy efficiency ▪ The honest challenge: nobody yet knows exactly how neurons encode information — the biggest scientific hurdle in biocomputing right now ▪ How the I/O interface works: electrodes measuring neural spikes, analog-to-digital converters, researchers writing Python code to control neurons remotely ▪ The remote access breakthrough: researchers in Tokyo or Bristol can log in and control living neurons in Switzerland in real time via browser ▪ Why neurons won't outperform GPUs on speed: biocomputing specialises in efficiency and adaptability, not clock cycles ▪ FinalSpark's current stage: they've stored 1 bit of information and are collaborating with 9 universities on fundamental research ▪ The cost argument: even at 10× lower price than NVIDIA, biocomputers would still generate billions in profit due to energy and infrastructure savings ▪ Bioethics, consent, and regulation: how FinalSpark is working with philosophers now to establish ethical frameworks before biocomputing scales ▪ Why human-machine integration is not new: prosthetics, pacemakers, and smartphones are already blending biology and technology ▪ The hybrid computing future: silicon, quantum, and biocomputing will coexist, each doing what they do best ▪ The real game-changer: cheap, accessible AI for everyone — Ewelina's vision for what biocomputing means for society in 10–20 years. ▸ LINKS MENTIONED IN THIS EPISODE → Dr. Ewelina Kurtys on LinkedIn → Ewelina's Personal Blog & Articles → FinalSpark (official website) → FinalSpark Neuroplatform (with live neuron view) → FinalSpark Team → Psync (Ewelina's mental wellness startup) → FinalSpark Contact Form

There's no international treaty governing AI, no agreed definition of "safe AI," and nobody with actual authority over frontier model deployment. A handful of CEOs make decisions with civilizational implications while governance structures lag years behind. This episode examines who's responsible for AI governance. The current state? Fragmented and lagging. The US has no comprehensive federal AI legislation—Biden's executive order was rolled back under Trump. The EU AI Act is most comprehensive but heavy provisions don't kick in for years. China's regulation focuses on censorship over safety. The UK AI Safety Institute does serious work but has no enforcement authority. What's working? AI safety institutes are building evaluation capacity. Open-source releases like DeepSeek enable external research. Academic safety community advances interpretability work. Market pressure matters—Anthropic gained users by taking public safety stands. Three urgent needs: mandatory disclosure requirements for high-capability systems, international coordination with shared evaluation standards (AI safety summits need teeth), and public deliberation beyond experts and officials. This concludes the AI Governance and Regulation series. People who understand AI deeply - technically, commercially, ethically, politically - will shape governance's future. Stay curious, stay critical, never outsource thinking to any single company or voice.

Everyone's talking about agentic AI, but there's a gap between the hype ("AI will do your job for you") and the reality, which is more nuanced and frankly more interesting. The word "agentic" has officially crossed from technical jargon into buzzword territory—simultaneously everywhere and nowhere. Everyone's using it, few can define it precisely. This episode cuts through the noise to explain what agentic AI systems actually are, what they can and cannot do today, and the realistic implications for people working in data, tech, and knowledge work. What is an agent? Traditional AI interaction: you send a prompt, the model produces a response, done. An AI agent is different: it takes a goal, breaks it into steps, takes actions in the world (browsing the web, writing and running code, calling APIs, managing files), observes results, and iterates until the goal is achieved or it gets stuck. The key agentic feature: it operates across multiple steps autonomously without you manually directing each one. Examples include OpenAI's Claude (consumer-facing), but in enterprise settings, agents are being deployed for automated customer support escalation, multi-step data pipeline management, code review and testing workflows, and research synthesis across large document sets. What can agents do today in early 2026? Agents are reliable for well-defined, bounded tasks with clear success criteria—taking support tickets, classifying them, drafting responses, flagging uncertain ones for human review. But for autonomously managing complex, open-ended strategic projects? Still unreliable. Failure modes include hallucinations, tool use errors, context window limitations in long tasks, and difficulty recovering gracefully when something unexpected happens mid-task. These are real limitations the best researchers are actively working on. The realistic workforce impact right now is task displacement rather than job displacement. Specific tasks within jobs are being automated: first drafts of documents, initial data analysis, standard code patterns, customer FAQ responses. Higher-order judgment, stakeholder navigation, creative problem framing, and ethical calls remain under human control. For data scientists specifically, repetitive engineering work is most likely to be automated: data cleaning pipelines, standard visualizations, model deployment scripts. But statistical thinking, algorithmic design, understanding model outputs, and evaluating trustworthiness remain human responsibilities. The work becoming more valuable: knowing what questions to ask, evaluating whether AI output is trustworthy, and designing systems that fail safely. The advice: become a power user of agentic tools before your role requires it. Not because you'll be replaced by an agent, but because practitioners who understand these tools deeply will be disproportionately effective. Learn how to prompt agents for complex multi-step tasks, evaluate outputs critically, and understand failure modes so you can deploy humans strategically. Agentic AI is real, useful today for specific tasks, and improving rapidly. The hype is ahead of the reality, but not by as much as you might think.

Every major AI summit has been held in San Francisco, London, or Washington — until now. In this episode, Sam breaks down what happened when Google CEO Sundar Pichai flew to New Delhi to open India's AI Impact Summit, and why it sent a clear geopolitical signal about the future of AI's global expansion. Sam unpacks Google's major commitments announced at the summit — including a $30 million AI for Science Impact Challenge, a new DeepMind partnership with Indian government bodies and universities, a Climate Technology Center, and new fiber optic infrastructure connecting the US, India, and the Southern Hemisphere. But this episode goes beyond the announcements. Sam explores why India specifically is positioned to become a pivotal player in the global AI race, the equity argument for why AI benefits must extend beyond Silicon Valley and a handful of developed nations, and the urgent governance gap — why the world may need an AI equivalent of the nuclear nonproliferation treaty. If you've only been following US and European tech news, this episode will expand your lens.

An in-depth analysis of the recent AI controversy involving Anthropic, OpenAI, and the US government, exploring the implications for AI ethics, warfare, and industry dynamics. Key Topics Covered: Anthropic's contract with the US Department of DefenseRed lines for AI in military useOpenAI's secret Pentagon negotiationsPublic and industry reactions to AI warfare policiesImplications for AI ethics and regulation Resources Anthropic's Claude AI - https://www.anthropic.com/claudeOpenAI - https://www.openai.com/US Department of Defense - https://www.defense.gov/AI Ethics and Safety Frameworks - https://www.example.com/ai-ethics-safety

Can a machine create art? Should it? And if it does, who owns it?In this episode, I sit down with Andres—creative technologist, founder of Red Mage, and advocate for equitable AI—to tackle one of the most controversial conversations in tech right now: AI's role in creative industries.What we discuss:✅ How generative AI has transformed creativity in just two years✅ The copyright battleground: Should AI companies compensate artists?✅ Authenticity vs. automation: Does the creative process matter?✅ What AI fundamentally CANNOT replicate about human creativity✅ The displacement reality: Are creative professionals being replaced?✅ AI as collaborator vs. competition: Success stories and cautionary tales✅ Democratization or devaluation? The debate over accessible creative tools✅ Maintaining quality when the internet is flooded with AI content✅ Ethical concerns beyond copyright: deepfakes, cultural appropriation, environmental costs✅ The future landscape: Will "human-made" labels matter in 2029?📌 If this conversation resonates with you, please like, subscribe, and share. Let me know in the comments: Are you optimistic or concerned about AI in creative industries?🔗 Connect with Andres:LinkedIn: https://www.linkedin.com/in/andres-sepulveda-morales/Contra: https://contra.com/andersthemagi/work?r=andersthemagiSessionize: https://sessionize.com/andersthemagi/🔗 Connect with me:DataScienceWithSam on YouTubeLinkedIn: https://www.linkedin.com/in/soumava-dey-441294ab/

In this episode of DataScienceWithSam, host Sam sits down with Egbavwa Pela, CEO of InsightsRx.ai, to explore how artificial intelligence is transforming the marketing landscape.We dive deep into Egbavwa's journey from traditional media and marketing into the AI space, discussing the revolutionary impact AI is having on campaign effectiveness, audience segmentation, and content creation. Our conversation covers practical insights on implementation challenges, best practices for AI adoption, and the evolving skill sets marketing professionals need to stay competitive.Key Topics Covered:The pivotal moments that drive marketing professionals toward AITop three ways AI is revolutionizing marketing campaignsWhich marketing functions benefit most from AI integrationBalancing automation with authentic human connectionCommon mistakes in early AI adoption and how to avoid themFuture-proofing marketing careers in the age of AIEmerging technologies and 2-3 year market predictionsPractical advice for both seasoned professionals and newcomersWhether you're a marketing professional exploring AI integration, a data scientist working with marketing teams, or someone considering a career pivot, this episode provides actionable insights from someone at the forefront of AI-powered marketing transformation.Connect with Egbavwa: https://www.linkedin.com/in/egbavwepela/Resources mentioned in this episode: https://insightsrx.ai/

In this episode, we sit down with Joya Scarlata, Director of Digital Marketing at Interra Information Technologies (InterraIT), where she has been leading transformative B2B marketing initiatives for nearly 12 years. Joya brings a unique perspective to AI in marketing, combining her background in International Relations with deep expertise in data-driven marketing strategies.What We Cover: ✨ Joya's AI awakening moment and how her perspective has evolved 📈 How AI is revolutionizing audience segmentation and content personalization 🛠️ Game-changing AI tools that are transforming daily marketing operations 📊 Measuring ROI in AI-enhanced campaigns with specific KPIs ⚖️ Navigating data ethics and content authenticity challenges 🚀 Joya's vision for the ultimate AI marketing agentAbout Our Guest:🎯 Director of Digital Marketing at InterraIT with 12 years of B2B marketing leadership🎤 TEDx Speaker and recognized among the "101 Women in AI Marketing"🎧 Co-host of "The Marketer's Guide to the AI Galaxy" podcast📊 Champion of accessible, ethical, and data-driven AI adoption in marketing🌟 Passionate mentor for the next generation of marketers🔗 Strategic storytelling expert specializing in AI-powered personalization

Most companies are getting AI transformation spectacularly wrong. In this eye-opening episode, we sit down with data and AI thought leader Nan Li to uncover why adoption—not technology—remains the #1 barrier to AI success.Forget the buzzwords and empty promises. This is a no-nonsense conversation about what actually works in AI transformation. Nan challenges the conventional wisdom, revealing why starting with "Can we build it?" is the wrong question and sharing the practical framework that successful organizations use instead.What You'll Learn:Why the "tech-first" approach is killing AI initiativesWhat "AI-ready data" actually looks like in messy business realityThe 4 levels of AI literacy every organization needs to understandHow to balance innovation speed with responsible AI practicesThe "WOULD WE?" framework for building winning business casesPractical build vs. buy decision-making strategiesWhether you're a C-suite executive, middle manager, or frontline worker, this episode delivers actionable insights for navigating AI transformation successfully. No hype, just proven strategies from someone who's been in the trenches.Perfect for: Business leaders, data professionals, AI practitioners, and anyone responsible for driving digital transformation in their organization.

AI has evolved from being just a buzzword to becoming an integral part of our workplace wellness programs. But with great power comes great responsibility, and that's exactly what was unpacked in this episode. This episode features an esteemed guest from the healthcare sector who shared her perspectives on how AI has become influential in determining optimized health benefits in the workplace and how employers can strike a balance between AI innovation, employee data privacy, and other sensitive factors.

This inspiring episode featuring an AI evangelist who's embracing the AI revolution in remarkable ways. From crafting custom Toastmasters songs to enhancing alumni club communications, our guest shares their journey of discovering generative AI tools in enhancing his creative content creation work. By listening to this episode, you will learn how Practical applications of ChatGPT, Suno AI, and DALL-E in community organizations is transforming creative workflows, especially in public speaking and community engagement. Perfect for retirees, creative enthusiasts, and anyone curious about practical AI applications in everyday life.