And you are here why?

It’s a bit of a holy grail really — if we could, somehow, figure out what all that brain activity  actually  corresponds to, well, that  would  change everything, wouldn’t it? Control systems powered by thoughts would move from science fiction to reality  , as would the abuses to privacy  . That said, the brain has been remarkably opaque to our probing thus far, so I guess the future is still the future, right? Maybe not so far in the future though. The first crack came from  fMRI , where, to simplify things, the experimenters basically stuck people in MRI systems, showed them a picture (of, say, a leopard), and recorded what happened in the brain. The good news is that a bunch of stuff would light up in the brain, with the equivalent bad news being that   a bunch of stuff would light up in the brain, and nobody knew what it meant . Some researchers went about this a bit more scientifically, and instead of a leopard, would show the subje...

As you’ve probably heard,  IBM is about to release a pretty monster dataset  — over a million images — along with tool, all with the aim of helping get rid of bias in facial analysis. The cool part for me is actually the announcement of a  second dataset — around 36,000 images — that are “ equally distributed across skin tones, genders, and ages ”. So, why does this matter? Before answering this, let’s first take a brief diversion. Let’s say you are doing something involving Machine Learning and facial recognition. You’d need a dataset to train your models against — think about how you would select your dataset. You’d probably take into consideration the specifics of the task (“ I need to know if the face is smiling or not ”), the details of the algorithm that you’re working on (“ Can I still tell it’s a smile if the background changes? ”) and such-like. You’d then go to one of the  handy-dandy collection of facial-recognition databases , and pick the most appropri...

“HACKED” STOP-SIGN CAUSES CAR CRASH  — the headline pretty much writes itself, correct? The spate of self-driving cars in accidents (•), combined with all  the   articles   about how to mess   with image recognition means that it’s not going to be all that far in the future when somebody  will  hack that stop-sign, and #CarCrashApocalypse will be here. Or maybe not. It turns out that the story is quite a bit more complicated than that, for a very  very  good reason — humans already suck at seeing things. Motorcycles Take motorcycles for example — there is a reason why cars run into them all the time, and  it isn’t because the motorcyclist did something stupid.  /via https://www.roadandtrack.com/car-culture/a28306/why-you-dont-see-motorcycles-on-the-road/ motorcycles fall into that category of things that we don’t always perceive even if they are right in our field of vision. A motorcycle approaching head-on from a dista...

TL;DR:  This one simple sticker will turn your banana into a toaster (to a #DeepLearning system) (OK, that’s a bit simplistic, but it’s not that far off.) Thing is, we’ve known for a while now that image classification can be hacked (cue scare stories about Stop Signs and School buses, etc.). In fact, there is  some evidence to point out that this “hackability” is intrinsic to the process , a kind of uncertainty principle if you will. Most of the work, thus far, has focused on  small  perturbations, the kind of stuff that would be invisible to the human eye, but would fool image classifiers. Stuff like manipulating pixels shades, quasi steganographic changes, etc. The  large  perturbations that people have looked at have also been (quasi)invisible-to-the-human-eye changes, things like glasses that break the classifier (•) There is new work out by  Brown et al.  that does things very differently — they create stickers that are pretty hug...

We know that deep learning is vulnerable to “adversarial” example, where you make changes that completely fool your AI. You know —  people are going to f**k with stop-signs and there will be crashes, death, and mayhem! .   The question is, are our brains susceptible to the same kind of stuff? SciFi has always assumed that the brain might be hackable, and, sadly, it looks like this might be the case in reality too. Follow the chain here 1. You can mess with machine learning by tweaking the example images just a little bit. As an example, a wee bit of noise (indistinguishable to the human eye) makes google think a panda is a gibbon… 2. These tweaked examples can often transfer across domains. Basically, you train your StopSignHack™ at home using your own neural-network, and then put it out there on a real stop-sign, causing chaos (and getting arrested. Why nobody ever talks about that part, I don’t know). 3. Therefore, it might be possible to transfer these hacks across to huma...