Talks

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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?