Auto AI and Machine Learning Systems Design
The Alan Turing Institute Fellows Welcome Event
Automation forces humans to adapt, we serve.
We can only automate by systemizing and controlling environment.
AI promises to be first wave of automation that adapts to us rather than us to it.
… will remain unfulfilled with current systems design.
It used to be true that computers only did what we programmed them to do, but today AI systems are learning from our data. This introduces new problems in how these systems respond to their environment.
We need to better monitor how data is influencing decision making and take corrective action as required.
With road accidents set to match HIV/AIDS as the highest cause of death in low/middle income countries by 2030, SafeBoda’s aim is to modernise informal transportation and ensure safe access to mobility.
Five year program in collaboration with
We are constrained by:
The major cause of the software crisis is that the machines have become several orders of magnitude more powerful! To put it quite bluntly: as long as there were no machines, programming was no problem at all; when we had a few weak computers, programming became a mild problem, and now we have gigantic computers, programming has become an equally gigantic problem.
Edsger Dijkstra (1930-2002), The Humble Programmer
The major cause of the data crisis is that machines have become more interconnected than ever before. Data access is therefore cheap, but data quality is often poor. What we need is cheap high-quality data. That implies that we develop processes for improving and verifying data quality that are efficient.
There would seem to be two ways for improving efficiency. Firstly, we should not duplicate work. Secondly, where possible we should automate work.
Our machine learning is based on a software systems view that is 20 years out of date.
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Lawrence, N.D., 2017. Data readiness levels. arXiv.