1.4: Exciting Directions

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Every week we read a few papers and everyone was assigned to find neat papers that we should all be aware of. We found some cool stuff. Here’s a few highlights in no particular order.

Conversational AI

Really hard problems like making computers capable being an engaging conversational partner are being tackled with some success. Amazon created the Alexa prize, which would be awarded to a research group who could produce a chatbot (installed in Amazon’s talking speaker Alexa) that could engage a person in chat for 20 minutes, without the person getting tired and cancelling the conversation. The challenge is still standing after the 2017 competition, but they are running it again in 2018, presumably with more training examples to improve the algorithms. It’s around the corner.

Decoding Brain states

Multi-voxel pattern classification analysis for classifying cognitive states based off of neural data has been applied to many problems. It often works. So, now we can get some idea of what dreams you were dreaming while you were lying in the scanner (Horikawa et al. 2013). There are too many other examples to list.

Detecting Deception

Lie detection has moved beyond the polygraph. People might be bad at detecting some of the signals of lying vs. telling the truth (Vrij, Granhag, and Porter 2010), but machine learning techniques are being thrown at the problem with some success. How about lie detection using fmri (Langleben et al. 2005), videos of your face (Meservy et al. 2005), records of language production (Matsumoto and Hwang 2015), or typing (Derrick et al. 2013)? It seems to work better than chance, often much better.

Inner Voice decoding with a chinstrap!

It’s still in development, but this chinstrap tech called alterego can be used to decode what your inner voice is saying. Umm what?

Image Memorability

What if there was machine that could tell you how intrinsically memorable something is? Ad agencies would be into this, they might want to know what pictures would stick most strongly in people’s memories. Some big data initiatives are now sorting this out by having loads of people do memory tasks for loads of pictures (Isola et al. 2011). The result is a massive database of pictures normed for their memorability (Khosla et al. 2015). This can be used to predict memorability of pictures. Baby steps, but moving forward.

That’s all

There’s a lot going in the broad area of cognitive technologies. Researchers in cognition have growing opportunities to make use of computational models and big data to make theoretical and applied progress. It’ll be fun to see what happens.