Category Artificial Intelligence

Overview of Three Techniques for Procedural Storytelling

Inspired by a recent paper I read this week, I decided to explain the three major “classic solutions” to the generative storytelling problem: Simulation, Planning, and Context-Free Grammars. Let’s what they are and what to choose.

Marginalia: Rebooting AI by Gary Marcus and Ernest Davis

With this new year, let’s try a new format. Marginalia will be a series in which I’ll share notes and comments on interesting books I read. The name is directly inspired by the old word indicating the small notes on the margins of books. It will be a chance to discuss my readings without the need to write a full-fledged article. I hope it will be interesting as a review of the book or as a discussion starter.

The Winter of Virtual Assistants

Seven years and eight months have passed since the release of the first really popular commercial virtual assistant (VA). Yet, seven years later, virtual assistants can do only marginally better.

Sure, they understand better, they speak better, they have learned some new trick; but in the end, they are still a funny but useless experience. After the first fun moments of experimentation when you start talking to them – that is, where you keep asking them silly jokes or dumb questions – they quickly came back to be pretty dumb object. I am pretty sure that the vast majority of user use a VA just for timers, weather and – occasionally – asking for the event on our calendar.

MovingAI pathfinding benchmark parser in Rust

You know I worked a lot with pathfinding. In academia, the MovingAI benchmark created by the MovingAI Lab of the University of Denver is a must for benchmarking pathfinding algorithms. It includes synthetic maps and maps from commercial videogames. Parsing the benchmark data, the maps, creating the map data structure and more, is one of the most boring thing I needed to do for testing my algorithms. For this reason, I think a common library for working with the maps specifications it is a must.

Questions about Deep Learning and the nature of knowledge

If there is something that can be assumed as a fact in the AI and Machine Learning domain is that the last years had been dominated by Deep Learning and other Neural Network based techniques. When I say dominated, I mean that it looks like the only way to achieve something in Machine Learning and it is absorbing the great part of AI enthusiasts’ energy and attention. This is indubitably a good thing.

Not every classification error is the same

In this article, I would like to talk about a common mistake new people approaching Machine Learning and classification algorithm often do. In particular, when we evaluate (and thus train) a classification algorithm, people tend to consider every misclassification equally important and equally bad. We are so deep into our mathematical version of the world that we forget about the consequences of classification errors in the real world. But let’s start from the beginning.

How hidden variables in statistical models affect social inequality

Use of machine learning is becoming ubiquitous and, even with a fancy name, it remains a tool in the statistical modeler belt. Every day, we leak billions of data from ourselves to companies ready to use it for their affair. Modeling through data get more common every day and mathematical model are the rulers of our life: they decide where we can work, if we can get a loan, how many years of jails we deserve, and more.

Artificial Anxiety and the problem "Mental Issues" in AI

Anxiety is a human mind bug. This may seem a strange claim, but I cannot find a better explanation for anxiety disorders. In fact, we can see pathological anxiety as the undesired consequence of our ability to think about the future. Being scared about a life-threatening event in the near future is a valuable ability: it helps us to survive, avoid danger and, in short, make our species survive. That is one of the reason our species has been so successful in nature[1].