Tag Archives: machine learning

Micro Expressions are Getting the Machine Learning Treatment

It Knows How You Feel

A few months I ago I started this site with an article on intent detection. In it I made the argument that protecting us from technology by banning it is intractable, so it’s probably more beneficial to focus on detecting humans who are about to carry out malicious acts.

One of the tools I proposed for achieving intent detection is the application of machine learning to detect micro expressions – those quick, involuntary displays of emotion we do but have little to no control over.

It is fascinating watching a field develop before your very eyes. Recently, a paper was published to arXiv on the application of machine learning to the detection of micro expressions. MIT Technology Review has also written a summary of the paper.

So, have these researchers solved the problem? Not yet, but as it goes with most computer science, their work sets a new bar for “state of the art.” To paraphrase, the advances they have made are:

1) They developed a new method to detect when a micro expression occurs during a video that does not require prior training. This is important because in order to recognize something, most computer vision systems need to be told where it is in the first place. The authors use the terms “spotting” and “recognizing” to distinguish between the two. Since their method does not require training, it makes the process of acquiring a large sample of micro expressions to work with easier. This is probably the most important achievement.

2) They utilized a new method to amplify the distinguishing features of a micro expression to make it more recognizable. This is important because micro expressions are generally very subtle, which makes them harder to classify.

3) They investigated multiple methods to mathematically represent the features needed to distinguish micro expressions, and analyzed their relative performances. This is important because the feature representation that works best on say, a color video does not work as well with near-infrared videos. Also (somewhat obviously), using a high speed camera makes it easier to recognize micro expressions.

4) They created an automatic recognition system that can detect and classify micro expressions from long videos with performance comparable to humans.

For those into machine learning, they performed the actual classification task with a linear Support Vector Machine. No fancy deep learning or neural networks, just good old large margin classification with customized feature descriptions.

It will be interesting to watch the field evolve over the next few years as researchers start applying the advancements of deep learning to the problem. We’re getting ever closer to viable intent detection.

Facebook Combining Machine Learning Techniques

Making Applications Truly Intelligent

Question: what do you get when you combine a learner capable of classifying objects and actions in a photo with a learner capable of understanding naturally phrased questions?

Answer: a system capable of answering questions about the contents of a photo. In other words, Facebook’s new toy.

Layering multiple specialized learners into a single system is the next great frontier of machine learning. Why? Because learners can make great interfaces between other learners and the human beings attempting to derive insights from them. It is somewhat analogous to the capability that SQL gives database developers to quickly gather insights from millions of rows of tabular data.

For example, a computer vision learner that excels at determining whether a certain picture contains a cat or a dog has no idea that the entities it is differentiating between are called “cat” and “dog” until we assign those labels to its output. Even after we tell it that one neuron firing strongly means “dog” and another neuron firing means “cat”, the learner has no clue what those words mean in the linguistic sense. The features learned by the neural net that enable it to differentiate cats and dogs so well are radically different from the set of features needed to understand that “dog” and “cat” are nouns in the English language and should be utilized a certain way.

So couldn’t we make the neural net capable of learning the features needed to do both? Yes, but that would require expanding  it to a much greater size (which has computational resource costs) and would make its implementation more complicated. Right now, it’s easier (better) to stack a net capable of understanding natural language queries on top of a net capable of photo object identification. Facebook takes this one step further by giving their learners a contextual memory that allows them to understand basic cause and effect. Thus, a picture of a dog with a frisbee in its mouth allows the learner to answer “frisbee” when asked what game the dog is playing.

Some day, applications will be capable of self-determining what learners they need to apply to a particular problem, and in what order to apply them. Throw in a few orders of magnitude more processing power and storage and we’ll probably be very close to achieving artificial general intelligence.

The Privacy Equation

Edward Snowden recently did an AMA (“Ask Me Anything”) on Reddit where he said:

Arguing that you don’t care about the right to privacy because you have nothing to hide is no different than saying you don’t care about free speech because you have nothing to say.

A pithy statement, is it not? Unfortunately, the situation is not so simple – privacy and free speech are at odds with each other both technologically and legally. This is because the ability to preserve privacy and free speech are inversely related by the same fundamental processes. Simply stated, that which makes free speech more possible makes privacy less possible.

In this article I will show how the degradation of one’s privacy is inevitable and potentially accelerates over time by factors outside one’s direct control. This is a recent phenomenon brought upon by the digitization of information, always-on connectivity and continuous advancements in machine learning. These technologies and the infrastructures built from them also facilitate the propagation of uncensored free speech.

Thus one can accept the futility of preserving their privacy yet still cherish their freedom of expression. One day we will truly have very little to hide, regardless of whether we have something to say. Continue reading The Privacy Equation

The Intractability Problem

A recurring theme in sci-fi is the danger that new technology presents to mankind.

Perhaps the pinnacle of dystopic scenarios is the Singularity, that moment where artificial intelligence (AI) begins continuously self-improving to the point where we potentially lose control. This was the premise for the popular Terminator movies and others such as I, Robot and Transcendence, each featuring a race to shut the technology down before it grew out of control.

In this discussion, I will be making the argument that defending us from technology on a per-item basis is an intractable problem, thus the best solution requires focusing on the human beings who would erroneously or maliciously utilize technology to cause harm. I’m going to suggest a far more radical measure than simple psychological profiling or background checks. In order to appreciate its necessity, the intractability problem must be fully understood. Continue reading The Intractability Problem