Decision Mechanics

Insight. Applied.

The company you keep…

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weird combination

Researchers have demonstrated how objects can be hidden by exploiting correlation bias.

Computer vision systems, like other machine learning technologies, rely on correlation and context. They learn that certain things often appear together–like mice and keyboards. But you don’t expect to see a platypus wielding a chainsaw, for example (…you’ve no idea how much I wanted to find that splash image…).

Artwork can be challenging, for example, as it can depict items that have no business being in the wider context.

This opens up another angle for hacking AI systems. The researchers were able to make a computer vision system fail to recognise a STOP sign by placing it next to fruit—a suitably bizarre combination.

Another example of the brittleness of "intelligence as correlation". The state-of-the-art in machine learning is leading us down the latest AI blind-alley. This one may be a little darker than before.

Photo by celery soup on Unsplash.

Skin in the game

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knife-wielding robot

I tend to listen more to opinions when people have placed money on their espoused outcome—having “skin in the game”, as Nicholas Taleb calls it in his latest book.

However, this robotics developer seems to have gone one better. He literally has skin in the game.

Good scientists aren’t supposed to experiment on themselves, but I will feel better if the first people to use autonomous cars are those who built the guidance systems.

At least it’s more impressive than Test-Driven Development.

Google Dataset Search

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Google have created a tool to make it easier to discover datasets—Google Dataset Search.

One potential downside is that it requires dataset owners to provide metadata. While the Google brand means that this might get some traction, not all dataset owners are motivated to help the cause. Publication of datasets is now mandated by some funding bodies, but that doesn’t mean that the datasets have to be discoverable. Ideally we’ll see funding bodies now mandating the addition of metadata.

While we wait for Google Dataset Search to evolve, we can still rely on curated repositories and lists such as

Python tops programming language list

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Python has topped the IEEE Spectrum list of top programming languages again this year—extending its lead in the process.

The sources used to compiled the list cover

contexts that include social chatter, open-source code production, and job postings.

Obviously that list of sources isn’t an accurate reflection of what developers are doing day-to-day in organisations. Any list of top programming languages that puts R (#7) above JavaScript (#8) clearly has some methodological challenges. My belief is that the list reflects the current buzz around data science.

However, interest in Python clearly remains high. As it does in R—#7 is impressive for a domain-specific language.