The increasing amount of data, and the increasing complexity of the application context that is generating that data presents deep challenges to a machine learning practitioner.

On the one hand, we want to tease apart the model complexity using divide and conquer. Like programming language paradigms in the 70s and 80s, we’d like small chunks of code and models to work well with other chunks to realise the application pipeline.

Now, while languages like Python have access to an enormous library of machine learning modules, it requires significant programming experience to understand how the application should be broken down to combine data processing, information exchange and machine learning, and finally, how to leverage the outcome of that learning process in the rest of the application. …


When we think of commonsensical AI systems, we may expect them to infer obvious truths. For example, if we tell the system that penguins, although birds, don’t fly, and Aptenodytes forsteri (Emperor penguins in English) are a breed of penguins, the system should be able to figure out that these things don’t fly as well.

But logical truths may not be the result of such simple rule applications. Solving a sudoku puzzle, or even checking that something is a solution to a sudoku puzzle, without knowing that the logical sentences you are looking at is the logical encoding of that puzzle, is too much to expect from us. Such combinatorial problems may very well be solvable by a computer, and hence an AI system, but if that system expects such things as bread and butter – in the sense that it applies a general and powerful theorem prover like search strategy every time – it may needlessly spend too much time on simpler things (e.g. …


A few weeks ago, I attended a seminar on epistemic planning:

the seminar group
the seminar group
the seminar group

Automated planning in AI is concerned with computing a sequence of actions that achieves a goal. Epistemic planning is a flavour of planning where we explicitly represent and reason about the mental states of the other participants in the environment.

Part of the reason this paradigm is important is that in social environments, it can be frustrating to get canned responses where little attention is paid to the individual’s needs. …

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Ideas on artificial intelligence, and other goings-on. By @vaishakbelle

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