Machines “think” differently but it’s not a problem (maybe)

Yet another article about the interpretability problem of many AI algorithms, this time on the MIT Technology Review, May/June 2017 issue.

The issue is clear; many of the most successful recent AI technologies revolve around deep learning: complex artificial neural networks – with so many layers of so many neurons transforming so many variables – that behave like “black boxes” for us.
We cannot comprehend anymore the model, we don’t know how or why the outcome to a specific input is obtained.
Is it scary?

In the film Dekalog 1 by Krzysztof Kieślowski – the first of ten short films inspired to the ten Christian imperatives, the first one being “I am the Lord your God; you shall have no other gods before me”  – Krzysztof lives alone with Paweł, his 12-years-old and highly intelligent son, and introduces him to the world of personal computers. Continue reading “Machines “think” differently but it’s not a problem (maybe)”

Agile for managing a research data team


An interesting read: Lessons learned managing a research data science team on the ACMqueue magazine by Kate Matsudaira.

The author described how she managed a data science team in her role as VP engineering at a data mining startup.

When you have a team of people working on hard data science problems, the things that work in traditional software don’t always apply. When you are doing research and experiments, the work can be ambiguous, unpredictable, and the results can be hard to measure.

These are the changes that the team implemented in the process: Continue reading “Agile for managing a research data team”

[Link] Algorithms literature

From the Social Media Collective, part of the Microsoft Research labs, an interesting and comprehensive list of studies about algorithms as social concern.

Our interest in assembling this list was to catalog the emergence of “algorithms” as objects of interest for disciplines beyond mathematics, computer science, and software engineering.

They also try to categorise the studies and add an intriguing timeline visualisation (that shows how much interest are sparking the algorithms in this time):