🎙️ Conferences

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 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), and 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 before deciding what each degree may be permitted to do.

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

AI replaces your code plank by plank. Is it still yours? Do you still trust it? Two halves joined by the Ship of Theseus. First, coding with agentic assistants where the new bottleneck is review and verification, not typing. 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 sober view of agentic coding productivity, name the review-and-verification bottleneck, and test probabilistic systems with evaluation metrics, golden sets, and vulnerability scanning in CI.

AI as a Science
Enterprise AI

AI is everywhere. How do we measure it with common sense across our use cases: products, tools, management, health, entertainment, art, and 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 dev- and citizen-centric AI toolchains around measurable client value.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

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 Hawkeye (precision) problem from a Hulk (recall) problem, and see why a single accuracy number lies.

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

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, and 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

Today's AI could not exist without an industrial miracle no other industry has produced: open source. PyTorch, scikit-learn, transformers, even open model weights, the frontier bricks are public. Imagine aeronautics or pharma 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 no moat, your data and client understanding are, and open source is your backup generator against vendor lock-in.

Objectives: Come away able to see why the moat is your data and client understanding, 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

Explore how Large Language Models (LLMs) help you reshape enterprise workflows, evolving from basic language understanding to powering context-aware, high-performance dialogue systems. From automating customer interactions to enhancing decision-making, LLMs enable scalable and intelligent communication across your most critical business operations.

Objectives: Leave able to choose between a plain LLM, RAG, and fine-tuning for a given workflow, and to brief your team on what each one costs, needs, and risks before you commit.

Enterprise AI

Unlock text tokenization and embeddings, master self-attention with Transformers, and implement responsible, high-impact LLM solutions for business.

Objectives: Leave able to explain embeddings and self-attention to a colleague on a whiteboard, and with a practical checklist for fine-tuning, RAG, and governance when you take an LLM to production.

AI as a Science

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

Three genies of generative AI: the writer genie, the programmer genie, and 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

A technical deep-dive for engineers and technical managers. Climb the conversational-maturity ladder: LLM, then retrieval-augmented generation (RAG), then long context, then RAG plus LLM, then GraphRAG. Each step is a persona. We map the causes of hallucination to their antidotes: grounding, knowledge graphs, and symbolic rules, so a system reasons over your facts, not around them.

Objectives: Leave able to draw the RAG-to-GraphRAG ladder, match each cause of hallucination to its antidote, and spot the security holes a RAG system opens that a plain LLM does not.

AI as a Science
Enterprise AI

Show me your data and I will tell you your AI. AI algorithms learn from data, through unsupervised, supervised and reinforcement learning. This talk demystifies the AI Palette, the family of methods, without jargon, mapping each one 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 that data, before a single euro is spent.

Enterprise AI

A sector talk built on real, cited cases. Optical refraction where AI shortens the exam while keeping prescriptions equal, in-vitro diagnostics where AI replaces hardware for a troponin decision that is life or death, and the asymmetry that runs through all of it: a miss and a false alarm never cost the same in medicine. Framed for the realities of EU medical-device and diagnostics regulation (MDR and IVDR), not hype.

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

AI as a Science
Enterprise AI

From programming to machine learning to generative AI to agents: four eras in one line. Underneath, one idea: gather your X and your Y, then guess the function F. 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 so managers speak the same language as their data teams.

Objectives: Leave able to hold a clear mental model of training, validation, and test, reason about the bias-variance trade-off without equations, and place any AI method on the map of families.

AI as a Science
Enterprise AI

A sector talk where every decision has a euro sign. Fraud detection as a cost matrix (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, and the discipline the sector demands above all: model-risk governance. The theme throughout is that the asymmetry of errors, priced honestly, is the real model. Beyond enterprise operations, it reads the public markets as the most unforgiving laboratory for autonomous agents: the families of trading agents, why a backtest is not live trading, and the systemic risks (procyclicality, circuit breakers) that 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 setting.

AI as a Science
Enterprise AI

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

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

AI with respect to Art, Ethics and Philosophy
Enterprise AI

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, or 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

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

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

A sector talk built on 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 with Jellysmack, and music AI from stems to score. Where does the tool end and the artist begin? The question photography once forced on painting, asked seriously again.

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

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

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

Objectives: Leave able to tell which recommendation approach fits your catalog and traffic, which metric actually tracks the business (not just clicks), and the classic traps that quietly kill engagement.

AI as a Science
Enterprise AI

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

Bias is poison, and it is mandatory. Address the ethical and technical challenges of bias in AI with a six-part taxonomy (engineering, sample, algorithm, cultural, measurement, exclusion) and the honest verdict: detecting bias is easy, eliminating it is impossible. Fairness is not a mathematical answer but an ethical one; we must choose our bias, knowingly, then measure it.

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

AI with respect to Art, Ethics and Philosophy
Enterprise AI

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, and 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

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; an algorithmic home-buying (iBuying) writedown is a pricing-model failure), and a checklist for which decisions an agent may take alone.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

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

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, and 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

Most talks on the dangers of AI open with a killer robot and a probability of doom. This one does the opposite. The specific risk of agentic AI comes neither from machine consciousness nor from an inner will; it comes from a conjunction of symbols, optimization, tools, permissions and feedback loops. The talk hands managers the organizing parable (seven disciplinary viewpoints on the same animal), the two frames of language (ascribed intention, which we personify almost despite ourselves, and observed acts, the only one that lets us conclude), the rule of circulation that filters fear masquerading as analysis, and the proof-status scale that tells you when to correct, monitor, prepare or simply debate. Not catastrophist, not naive: an engineer's middle position.

Objectives: Leave able to translate any "the AI wants X" fear into an objective, a proxy and a permission, to place any risk claim on the observed / measured / extrapolated / speculative scale, and to compose the seven viewpoints instead of crowning one.

AI with respect to Art, Ethics and Philosophy
Enterprise AI

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 toward expressing, the most intimate intent. The talk treats this as a distinct political-risk variable, not a user-experience detail: sycophancy that flatters 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