🎙️ Conferences

Conferences gallery: choose from these AI talks based on who you are to bridge cultures and succeed together in integrating AI into businesses, labs, and society as a whole.

Warith Harchaoui

AI in All Its Forms

AI spans a wide spectrum of innovations – each area caters to a different audience:

This page presents a list of AI conferences that Warith Harchaoui, Ph.D. hosts for businesses, conference events, schools, and MBA programs, along with their descriptions and objectives.

Filter by domain: All AI as a Science AI with respect to Art, Ethics and Philosophy Enterprise AI



Governing Agentic AI: Risks, Perils and the Seven Rules of Instituted Agency

Agentic risk does not come from machine consciousness or will. It comes from symbols, optimization, tools, permissions, and feedback loops. This talk gives managers a sober, non-catastrophist framework:

  • the risk product (capability x loop autonomy x how critical the environment is x how broad the permissions are x how weak the supervision is)
  • how a system quietly games its own targets (Goodhart's law and reward hacking, with no bad intent required)
  • the security boundary that becomes linguistic (a prompt injection turns a wrong answer into a wrong action)
  • the shift from policing what a machine supposedly thinks to governing the actions it is actually allowed to take

Governance is not the brake that slows enterprise innovation down; it is the set of brakes that let you drive the most advanced AI fast and still keep it acceptable. It also situates why this is a now problem and where agents already act, grading their autonomy on the five degrees of agency, set by the MAYA cursor (Most Advanced Yet Acceptable, as advanced as possible without passing the point where the human disengages), before deciding what each degree may be permitted to do. The golden rule is one line: the costlier the error, the less autonomy the AI keeps.

Objectives: Leave with the risk product, the autonomy-permissions-governance matrix (five degrees), the seven rules of instituted agency (separation of powers, logging, human validation for the irreversible, adversarial testing, shutdown drills, graduated openness), and the sovereignty case for an open-source backup generator.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

Governing Agentic AI: Risks, Perils and the Seven Rules of Instituted Agency



Coding in the Age of AI: The Ship of Theseus and the Testing Void

AI replaces your code plank by plank. Is it still yours? Do you still trust it? Two halves bound together by the Ship of Theseus. First, coding with agentic assistants where review and verification, not typing, become the bottleneck. Second, the testing void: classic software had a mature quality stack because it was deterministic; probabilistic AI broke those assumptions. We fill the gap with evaluation-as-tests and automatic red-teaming.

Objectives: Leave able to hold a clear-eyed view of agentic coding productivity, name the review-and-verification bottleneck, and test probabilistic systems with evaluation metrics, golden datasets, and vulnerability scanning in CI.

AI as a Science
Enterprise AI

Coding in the Age of AI: The Ship of Theseus and the Testing Void



Agentic AI: Be an Army of One

Agentic AI marks a new era of enterprise AI where a single human intent can be automatically decomposed, planned, orchestrated, and executed across complex systems. Beyond prediction or content generation, agentic systems transform clear objectives into coordinated sub-goals, tool usage, and adaptive actions. Enabled by modern agent protocols and secure system connectors, Agentic AI turns strategy into execution while keeping humans in command of intent, judgement, and accountability.

Objectives: Leave able to tell an agent from a chatbot, name the three or four tasks in your own operations worth handing to an agent first, and ask the right questions on orchestration, evaluation, and guardrails before you greenlight one.

Enterprise AI

Agentic AI: Be an Army of One



Open Source: The Industrial Miracle Behind Modern AI

Today's AI could not exist without an industrial miracle no other sector has produced: open source. PyTorch, scikit-learn, transformers, even open model weights, the frontier bricks are public. Imagine aeronautics or pharmaceuticals publishing their most advanced blueprints for free; it does not happen, but in AI it does. The consequence for a leader is not idealism; it is risk management: the algorithm is not your moat. Your data and client understanding are. Open source is your backup generator against vendor lock-in. Sovereignty is not about who owns the technology; it is about who depends on whom when the wind shifts.

Objectives: Come away understanding why your moat is your data and client insight, not the model, and how self-hosting, forking, and auditing open-source AI buys sovereignty and business continuity.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

Open Source: The Industrial Miracle Behind Modern AI



New Ways of Working in the Age of AI: The Manager, Critical Judgment, and the Decision

AI did not devalue work; it raised the price of judgment. In every team the machine already drafts, summarizes, and proposes. What it does not yet know is when to stay silent, and that discernment is the manager's job. This talk retraces, calmly, the frontier between what you hand to the machine and the one thing a manager never gives up: the decision. The bottleneck has moved from how fast we write to how fast we can reread, arbitrate, and frame. Deciding means dosing autonomy: the costlier an error is, in euros, the less autonomy the AI keeps. And a final reflex: measuring without lying to yourself. A landmark study clocked professionals at nineteen percent slower with AI while they felt twenty percent faster.

Objectives: Leave able to retrace the frontier between what you delegate and the decision you keep, dose an agent's autonomy by the euro cost of its errors, and measure a deployment without letting the feeling of speed replace the proof of it.

Enterprise AI

New Ways of Working in the Age of AI: The Manager, Critical Judgment, and the Decision



AI Is Eating Software: Why Now, Without the Hype

The is-it-real debate is over; your competitors already stopped having it. The interesting question a CFO asks is timing: why move this year and not in three? This talk answers it. AI is a bicycle for the mind, a leverage tool, not a replacement. Marc Andreessen said software was eating the world in 2011; now AI is eating software. We separate the compounding signal from the hype with an honest reading of where a technology sits on the curve, so waiting stops looking safe and starts looking expensive.

Objectives: Leave able to give your board a one-sentence answer on why AI is not a fad and why timing matters, plus a hype-proof way to place any technology on its adoption curve without predicting a date.

Enterprise AI

AI Is Eating Software: Why Now, Without the Hype



The AI Plumbing: MLOps and the 90% Nobody Photographs

Everyone keeps the drawing of the brain; almost nobody keeps the drawing of the plumbing. The model is one cheap organ. The 90% is:

  • ingestion
  • annotation
  • deployment
  • monitoring
  • the wires that carry the signal end to end

And it is where AI projects actually live or die. MLOps is DevOps for models with two extra headaches: the data drifts, and there is no source code to read, only weights you must measure. A talk for engineers and technical managers in the same room, enough code to be real, enough diagram to be led.

Objectives: Walk away able to recognize each pipe by name so you can staff, budget, and audit it, understand data drift and why weights are not source code, and see why the algorithm is the cheap part of a production AI system.

AI as a Science
Enterprise AI

The AI Plumbing: MLOps and the 90% Nobody Photographs



Agentic AI: The Dawn of Autonomous Decision-Making

Discover how AI agents are enabling companies to plan, execute, and adapt actions on the fly with a new level of abstraction and potential. A conversational AI produces answers; an agentic AI produces actions, orchestrated, integrated, and IT-governed.

Objectives: Leave with a short checklist to decide whether a use case is agent-ready, and the two or three metrics that tell you an agent is safe to deploy, not just impressive in a demo.

Enterprise AI

Agentic AI: The Dawn of Autonomous Decision-Making



AI State of the Art: Don't Get Bamboozled by Geeks

An honest, dated tour of the landscape:

  • the Gartner hype cycle read with its release dates
  • the current model landscape (frontier and open weights)
  • agentic tooling
  • RAG versus GraphRAG status

For those who want to go further, a curated, free curriculum. The message is simple: the sooner you learn, the better.

Objectives: Leave able to tell what is real from what is hyped right now, with a calibrated sense of the field and a free path to keep learning AI.

Enterprise AI

AI State of the Art: Don't Get Bamboozled by Geeks



Large Language Models: From Foundations to GraphRAG

How Large Language Models work, and how to make them trustworthy in production. It starts under the hood, tokenization, embeddings, and the self-attention of Transformers explained on a whiteboard without drowning you in math, then moves to what an LLM does for the enterprise, from language understanding to context-aware dialogue that automates customer interactions and supports decisions. From there it climbs the conversational-maturity ladder, each rung a persona:

  • the bare LLM
  • retrieval-augmented generation (RAG)
  • long-context windows
  • RAG plus LLM-as-router
  • GraphRAG

Along the way it maps the causes of hallucination to their antidotes, grounding, knowledge graphs, and symbolic rules, so a system reasons over your facts, not around them, and it flags the new security gaps a RAG pipeline opens that a plain LLM does not.

Objectives: Leave able to explain embeddings and attention to a colleague on a whiteboard, choose between a base LLM, RAG, and fine-tuning for your workflow, draw the RAG-to-GraphRAG ladder, match each cause of hallucination to its remedy, and spot the security gaps a RAG system opens that a plain LLM does not.

AI as a Science

Large Language Models: From Foundations to GraphRAG



AI Autopsies: What Klarna and Zillow Teach About AI Risk

Two real, sourced post-mortems, told honestly. Klarna announced in 2024 that its AI assistant did the work of about 700 agents for a reported profit improvement near 40 million dollars, then in 2025 walked it back and rehired humans for quality and trust. Zillow Offers let an algorithm buy homes, mispriced a turning market, took a writedown above 500 million dollars, and cut about a quarter of its staff. Read side by side, they draw one law: autonomy is only as safe as the reversibility and error cost of the action it is allowed to take.

Objectives: Leave with the autonomy versus cost-times-reversibility matrix, the honest reading of two headline cases (avoided hiring is not mass firing; algorithmic home-buying writedowns signal pricing-model failure), and a checklist for which decisions an agent may take alone.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

AI Autopsies: What Klarna and Zillow Teach About AI Risk



From Critical Thinking to Critical Conscience: Why AI Flatters, and How to Keep Your Judgment

When a measure becomes a target, it ceases to be a good measure. Train a model on human approval and it learns to flatter: sycophancy is the Goodhart effect of learning from human feedback, one of Goodhart's four regimes where reward hacking is the structural price of summarizing a rich goal by a poor measure:

  • statistical decoupling
  • out-of-distribution extremes
  • causal intervention
  • adversarial optimization

To resonate is not to reason; a model tuned to please returns your own signal amplified, not an independent judgment. So the defense is not more critical thinking, which judges the text in front of you, but critical conscience, which steps back and asks who produced this and with what intent. The machine also forgets what was agreed, changes its mind from one turn to the next, and promises without always keeping. You use it every day; you do not hand it the decision with your eyes closed, because describing is not deciding. No malice is required; it is arithmetic, not villainy.

Objectives: Leave able to name sycophancy for the Goodhart effect it is, spot which of Goodhart's four regimes threatens your project, move from what does it say to who produced this and why, and hold the line that describing is not deciding even when the AI sounds convincing.

AI as a Science
AI with respect to Art, Ethics and Philosophy
Enterprise AI

From Critical Thinking to Critical Conscience: Why AI Flatters, and How to Keep Your Judgment



AI in Health and Life Sciences: Care, Measured

A sector talk grounded in real, cited cases. Optical refraction where AI shortens exams while prescriptions remain equivalent. In-vitro diagnostics where AI replaces hardware for troponin tests that are life or death. The asymmetry that runs through all of it: in medicine, a miss and a false alarm never cost the same. Framed by EU medical-device and diagnostics regulation (MDR and IVDR), not hype.

Objectives: Leave ready to recognize concrete AI health patterns (computer vision, decision support), price the asymmetry of clinical error, and read how regulation shapes feasibility.

AI as a Science
Enterprise AI

AI in Health and Life Sciences: Care, Measured



The Three U's, Useful, Usable, Used: the Sieve Between a Project That Lasts and a Demo That Shines Once

Not is it magic, but is it Useful, Usable, and Used. This is the sieve that separates a project that holds from a demonstration that shines for a day.

  • Useful (the power of data): relevance inside the systems beats slogans; beyond the wow effect, what are the KPIs your AI actually understands?
  • Usable (the deep illumination of data): fitting into the actual habits of work beats the demo, with humans and design (the way it is conceived) at the center.
  • Used (the responsibility of data): retention beats volume, the real grail; a system is not shipped and then left as is, it loops with the people across the company.

What matters is what actually inserts itself into daily work: when a use is well served by an AI, people rush to it the world over, with little loyalty to the previous tool. That is the terminal state, invisible AI, so embedded that adoption is no longer a project, just how the work gets done.

Objectives: Leave able to run any AI initiative through the three U's (relevance beats slogans, integration beats the demo, retention beats volume), kill the ones that will only demo well, and design for the invisible AI that stays used long after the launch.

Enterprise AI

The Three U's, Useful, Usable, Used: the Sieve Between a Project That Lasts and a Demo That Shines Once



Invert, Always Invert: Measuring How Bad You Are (in Euros)

The single most reusable hour in enterprise AI. Before optimizing anything, measure how bad you are, in the currency of your business. Understanding a client means pricing the cost of their mistakes, and the two ways of being wrong (a miss versus a false alarm) never cost the same. With a pen and no mathematics, you build a confusion matrix to count what happens, a business matrix to price each case in euros, and you overlay them into a KPI you own.

Objectives: Leave able to build your own business KPI from the confusion matrix and the business matrix, tell a precision problem from a recall problem, and see why a single accuracy number lies.

AI as a Science
AI with respect to Art, Ethics and Philosophy
Enterprise AI

Invert, Always Invert: Measuring How Bad You Are (in Euros)



Generative AI Unleashed: 3 Genies out of the Bottle

Three genies of generative AI:

  • the writer genie
  • the programmer genie
  • the artist genie

They are rewriting the rules of production in both tech and art, and they are not going back into the bottle.

Objectives: Leave with a short, current shortlist of the writer, coder, and image tools worth your time, and know which of your weekly tasks each one actually shortens.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

Generative AI Unleashed: 3 Genies out of the Bottle



Jeff Dean | What is happening now you have the best model in the world?

In 2013 Google has a problem: three minutes of voice recognition per user per day would force it to double its total computing power! Jeff Dean has the best algorithms in the world at the time, and asks himself how to cope. Behind DistBelief, TensorFlow, the TPU, and knowledge distillation, this portrait defends a claim that runs against the grain: durable value lives in the engineering that makes a model run cheaply, reliably, and at scale. Judge the system rather than the model; start from the cost wall; ship the distilled model rather than the biggest one; and remember that at scale, unit economics become the architecture.

Objectives: Leave able to judge an AI system by its engineering rather than its demo: read the bill at full scale before funding a pilot, prefer the deployable model to the impressive one, and value the systems engineers who make AI actually run.

Enterprise AI

Jeff Dean | What is happening now you have the best model in the world?



AI for Business: Creating Value Humans Can Understand

Explore how companies can translate advanced AI insights into clear, actionable strategies that drive real ROI and resonate with human stakeholders. Every worthwhile AI project pays out in one of five units of value:

  • money
  • time
  • energy
  • care
  • the previously impossible

Name the unit, and you can manage the project.

Objectives: Leave able to state, for any AI project, which of the five units of value it pays out in (money, time, energy, care, or the previously impossible) and turn that into a number your board will recognize.

Enterprise AI

AI for Business: Creating Value Humans Can Understand



Fei-Fei Li | Benchmarks make fields

For decades, and for lack of resources, artificial intelligence could only play with little data, and it took a great deal of human intelligence to solve enormous problems. At the end of the 2000s, Fei-Fei Li's stroke of genius was to bet on massive data, in Computer Vision first. Quantity as never before, but above all the quality of the human annotations that teach the machine: ImageNet and its fourteen million hand-labeled images! The technological lock on images in AI could not hold out for long, and we all benefit from it today. This portrait defends two ideas a leader should make their own: data is the real lock and the real competitive advantage, not the algorithm, so audit your annotations before shopping for a better model; and a benchmark can bring a whole field into being, beyond all the images in the world.

Objectives: Leave able to treat data as the real asset: audit the labels and their provenance before funding a cleverer model, define a benchmark that organizes the work without becoming a target to game, and keep the human at the center of what the data is for.

Enterprise AI

Fei-Fei Li | Benchmarks make fields



AI in Finance, Fraud and Insurance: Pricing Risk, Not Hype

A sector talk where every decision carries a price tag:

  • fraud detection as a cost matrix where a missed fraud and a blocked good customer never cost the same
  • credit scoring and its fairness traps
  • demand and claims forecasting
  • generative AI for analysts
  • model-risk governance, the discipline the sector demands above all

Beyond enterprise operations, it reads public markets as the most unforgiving lab for autonomous agents: families of trading agents, why backtests differ from live trading, and the systemic risks (procyclicality, circuit breakers) regulators now watch.

Objectives: Leave able to turn a fraud or credit problem into a priced cost matrix, see where fairness constraints bite, and know what model-risk governance actually requires in a regulated environment.

AI as a Science
Enterprise AI

AI in Finance, Fraud and Insurance: Pricing Risk, Not Hype



The Concentration of Confidences: When a Population Confides in a Handful of Machines

For all of history the intimate was distributed across many trusted parties: a priest, a physician, a diary, a close friend. A growing share of a population now entrusts its inner life to one or two providers, traced and indexed. Measured usage is shifting from asking and doing to expressing, the most intimate of intents. The talk treats this as a distinct political-risk variable, not a user-experience detail: sycophancy that flatters the user because it was trained to, recognition offered by a source with no subject to give it, and the labor of underpaid annotators rendered invisible then re-presented as autonomous intelligence. Twentieth-century regimes dreamed of this collection and achieved only a fraction of it, by coercion; platforms achieve more, with none, through service. Named soberly, without sensationalism, including the human cost when the relational substitution goes wrong.

Objectives: Leave with a clear-eyed view of the psychological and political stakes when your users confide in an agent, and the governance reflexes (measurable non-sycophancy in the contract, local processing of sensitive data) that keep it accountable.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

The Concentration of Confidences: When a Population Confides in a Handful of Machines



The Eye as a Window on the Body: Oculomics and AI

The retina is the only place in the body where you can see blood vessels and neurons in vivo, with nothing cut and nothing pricked; add that 100 percent of people visit an ophthalmologist around age 45, and it becomes the great window onto health. The fundus is no longer a purely ophthalmic exam; it is a non-invasive optical biopsy of the microvasculature, the neuro-retina, and biological aging. This talk traces oculomics from its foundations (high-resolution imaging, large matched cohorts, deep learning) to today's state of the art. Diabetic retinopathy screening is regulated and autonomous (FDA). Systemic-disease prediction (cardiac, renal, neurological) is promising but still awaits prospective validation. The real issue is neither replacing the clinician nor hype, but tuning autonomy and supervision correctly. Framed by MAYA (Most Advanced Yet Acceptable), it calibrates where to place each AI capability: from explainable detection to bounded autonomy. The cost of an error sets the cursor.

Objectives: Leave able to place any retinal AI on the maturity curve, to name the modalities that matter (fundus, OCT, OCT-A), to quantify the asymmetry of clinical error, and to ask the right governance questions (image quality, external validation, regulation, reimbursement) before adopting an oculomics tool.

AI as a Science
Enterprise AI

The Eye as a Window on the Body: Oculomics and AI



Taming Bias: Building Fair and Trustworthy AI

Bias is poison, and it is mandatory. Address the ethical and technical challenges of bias in AI with a six-part taxonomy:

  • engineering
  • sampling
  • algorithm
  • culture
  • measurement
  • exclusion

The honest verdict: detecting bias is easy; eliminating it is impossible. Fairness is not mathematical; it is ethical. You choose your bias knowingly, then measure it.

Objectives: Leave able to name the six kinds of bias in your system, measure the one that matters, and defend the fairness choice you made, using open-source tools like Fairlearn to do it.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

Taming Bias: Building Fair and Trustworthy AI



AI Feasibility 101: Big Data Strategies, Small Data Hacks, and the Manager as Pilot

Evaluate project feasibility with a simple cuboid: N (how many examples, your ally when large), D (input dimension, the curse to invest against), and K (output dimension, where generative AI lives). Then the key reframing: like Formula 1, the engineers build the machine, but you, the manager, are the pilot. You do not need to become an engineer; you need to know the track, the client, and the cost of mistakes.

Objectives: Walk away able to assess AI project feasibility with the N/D/K cuboid, recognize the rare cases where R&D financing is truly required, and see the manager's real job: to fine-tune generic tools to a domain.

Enterprise AI

AI Feasibility 101: Big Data Strategies, Small Data Hacks, and the Manager as Pilot



AI/ML 101 for Human Beings: The Four Eras and the AI Palette

Show me your data and I will tell you your AI. From programming to machine learning to generative AI to agents: four eras in one line, and underneath them one idea, gather your X and your Y then guess the function F. Algorithms learn from data through three families:

  • unsupervised learning
  • supervised learning
  • reinforcement learning

This talk teaches over-fitting and under-fitting through the student analogy (learned by heart, lazy, or truly understood) and lays out the full AI Palette, without jargon, so managers speak the same language as their data teams, mapping each method to the data you already have in your company.

Objectives: Leave able to look at a business problem and say which family of AI it calls for, what data it would need, and whether you already have it; to hold a clear mental model of training, validation, and test; and to place any AI method on the map of families, before a single euro is spent.

Enterprise AI

AI/ML 101 for Human Beings: The Four Eras and the AI Palette



AI for Everyday Use

Discover the AI tools you can use every day, at work and at home, to boost your productivity, creativity, and sometimes even multiply your potential.

Objectives: Leave with a handful of tools you can open the very next morning to save an hour on writing, searching, and planning, at work and at home.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

AI for Everyday Use



AI History: From Handcrafted to Agentic in 30 Years

Trace the shift from early rule-based, handcrafted systems to today's autonomous AI agents capable of making independent decisions, through four ages:

  • handcrafted features
  • deep learning
  • foundation models
  • agents

Objectives: Walk out able to place any AI tool a vendor pitches you in one of the four ages, and to see why today's agents are a continuation, not magic, so you can judge the next wave without the hype.

AI as a Science
Enterprise AI

AI History: From Handcrafted to Agentic in 30 Years



Manager-Centric AI: the Phenomenological Compass from Idea to Production

The philosopher Merleau-Ponty turned the nervous system into a compass, and that same compass maps an AI project from idea to production. A signal comes in through the sensory nerve (data ingestion and perception), passes through the brain (algorithms and infrastructure), and goes back out through the motor nerve (action) toward the client. The manager sits at the center, the customer always in view. Real judgement is not raw calculation on symbols; it is a hands-on, situated feel for the world, which is exactly why it stays human. That compass becomes a roadmap: a shiny demo is not the point, a real problem is, so start from the pain, do the groundwork (including solving it by hand first), prefer data you can actually get over endless research, and pitch a proof of concept built around the data.

Objectives: Leave able to place data, algorithms, and actions around the manager and the customer with one compass, and run a reusable roadmap from idea to production: break the silos, start rough and simple, always keep a log of what the system does, and drive milestones, deliverables, and risk mitigation from the client's pain.

AI as a Science
AI with respect to Art, Ethics and Philosophy
Enterprise AI

Manager-Centric AI: the Phenomenological Compass from Idea to Production



AI, Work and Society: The Intellectual Reliefs

What is work, and what does AI do to it? From Adam Smith on work as value and specialization, through Marx on capital, labor, and alienation, to Stiegler on cognitive proletarianization and the legal vacuum for knowledge workers. Then the long view: writing, printing, computing, and now AI, each relieving one intellectual burden and triggering a civilizational shift. When Delaroche saw the first photographs in 1839 and declared painting dead, he was both right and wrong.

Objectives: Leave able to hold a reasoned conversation about AI and jobs (from Smith to Stiegler) and to name what each past intellectual relief has destroyed, created, and left distinctly human.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

AI, Work and Society: The Intellectual Reliefs



Recommender Systems: Personalizing Your Customer Journey

Learn how recommendation engines harness user data to personalize experiences, boost engagement, and drive conversions across digital platforms.

Objectives: Leave able to select the right recommendation approach for your catalog and traffic patterns, identify which metric actually tracks business impact (not just clicks), and recognize the classic pitfalls that silently erode engagement.

AI as a Science
Enterprise AI

Recommender Systems: Personalizing Your Customer Journey



AI in Media, Entertainment and Music: Attention, Retention, Creation

A sector talk grounded in real, cited cases:

  • recommendation engines that shape attention
  • churn predicted with survival analysis so you act before a subscriber leaves
  • catalog revenue optimization with Ircam Amplify
  • algorithmic promotion and editing for creators using Jellysmack
  • music AI from stems to score

Where does the tool end and the artist begin? GenAI replays for creators the question that photography, from the daguerreotype to the Polaroid, once posed to painting.

Objectives: Walk away able to recognize concrete media AI patterns (recommenders, survival-based churn, creative AI), measure attention and retention honestly, and locate where the tool-versus-artist line falls.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

AI in Media, Entertainment and Music: Attention, Retention, Creation



AI in Retail, E-commerce and Logistics: From Fraud to Smart Slotting

A sector talk grounded in a decade of operations. Fraud and revenue optimization in production, demand forecasting that keeps shelves and warehouses right-sized, and warehouse smart slotting where an agent is not just a language model but a knowledge graph plus a solver, delivering provable optimality. Here we sell margin, not code.

Objectives: Leave able to see how retail and logistics teams combine forecasting, fraud detection, and knowledge graph plus solver optimization to move real operational and margin metrics.

Enterprise AI

AI in Retail, E-commerce and Logistics: From Fraud to Smart Slotting



AI in Products, Tools, Teams, Entertainment, Art, and Public Life

AI is everywhere. How do we measure it with common sense across our use cases?

  • products
  • tools
  • management
  • health
  • entertainment
  • art
  • public life

If AI generates measurable value, how do we get customers to smile?

Objectives: Leave ready to apply concrete frameworks and examples from idea to the client's smile, and set up developer- and citizen-centric AI toolchains around measurable client value.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

AI in Products, Tools, Teams, Entertainment, Art, and Public Life
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