AI for the common good
Artificial intelligence is rarely the answer and sometimes the wrong question. I help tell the difference beforehand rather than afterwards.
The question first, the technology second
Most enquiries about artificial intelligence start from a technique and then look for a problem to attach it to. I turn that around: what is meant to get better, for whom, and how would anyone notice that it had? Once that is settled, it can be checked whether an AI method is the right means, and often enough it is not.
That check is cheaper than a project that runs aground on the data situation a year in.
What I bring
I explain the basics as far as they are needed for deciding: what machine learning does, where its results come from, where its errors come from. No further, because you are meant to be able to decide, not to build it yourself.
For designing an application I use cards rather than code: the AI Toolkit lets a team play through the idea, the data sources, the interaction and the learning objective without anyone needing to program. What comes out is a concept prototype solid enough for a funding application or a tender.
Ethics is a design question
Fairness, accountability and data protection cannot be checked at the end like spelling. They are decided where it is settled which data gets used, who sees the results and who is allowed to object. That is why those questions come early with me, not at acceptance.
What I am not the right person for
I do not train models and I do not build infrastructure for running them. Once it is settled what should be built and only the implementation is left, you need a development team, not me. Until then, in the phase where the decisions are made, I am useful.
What you get
A realistic estimate
What a method can do, what it costs and where it fails.
Shaping without prior knowledge
Tools that let departments take part in the decision.
Ethics from the start
Fairness, traceability and data protection as design questions, not a checklist.
A solid basis
A concept prototype fit for a proposal or a tender.
Process
- 1
Understand
Fundamentals, as far as needed to take part in deciding.
- 2
Examine
Data situation, law, benefit, alternatives without AI.
- 3
Design
Develop the application idea together and harden it.
- 4
Situate
Next step, effort, responsibility.
What this costs
Billed by person-day, as a senior consultant. I will tell you the day rate in our first conversation, together with an estimate of how many days your project needs. Both binding, before you decide.
Small pieces of work I bill at a fixed price where the scope can be drawn clearly. I will tell you which of the two comes out cheaper.
Related services
Try it out before deciding
In the AI exploration space a single day makes tangible what a method could do for you, with cards instead of code.