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Silver Blog, July 2017AI and Deep Learning, Explained Simply


AI can now see, hear, and even bluff better than most people. We look into what is new and real about AI and Deep Learning, and what is hype or misinformation.
 



Faulty automation increases (rather than kill) human jobs. 2 days after I published this article, suddenly my profile was blocked. Google for “LinkedIn account restricted” to learn this happens to many for simply too much activity, even if not messaging. I had only opened, for curiosity, the profiles of the hundreds who clicked “I like” on this article (thanks to you all). Then, a naive rule “x pages opened within time y” AI decided I was an AI too, of the “web bot” kind (programs browsing all the pages of a site to copy its contents). It blocked me without any “Slow down” warning, why to warn a bot?

I was not a bot, I swear I am human. Counting “x pages opened within time y” it catches many bots, but also “false positives”: curious humans in activity peaks. This frustrated the LinkedIn staff too: who to trust, the AI or the user? I had to send my ID as proof, it took days. This rule-based “AI” created extra human support jobs to just handle unnecessary “AI” errors that humans alone would not do. Think at: “flag all emails from Nigeria as spam” or “flag all people with long beard as terrorists”. You can improve rules by adding parameters, past activity, etc., but never reaching the accuracy of humans or fine trained MLs. Beware of “automation” or “AI” claims: most it is still too simple rules, no any deep learning. Microsoft acquired LinkedIn for $26 billion, will surely upgrade this old piece to real ML. But until then, don’t browse LinkedIn too fast!

If no human can predict something, often the ML can’t too. Many people trained MLs with years of market price changes, but these MLs fail to predict the market. The ML will guess how things will go if the learned past factors and trends will keep the same. But stock and economy trends change very often, like at random. MLs fail when the older data gets less relevant or wrong very soon and often. The task or rules learned must keep the same, or at most rarely updated, so you can re-train. For ex. learning to drive, play poker, paint in a style, predict a sickness given health data, translate between languages are jobs for MLs: old examples will keep valid for the near future.

ML can find cause-effects on data, but it can’t find what it does not exist. For ex. in the weird research “Automated inference on criminality using face images”, the ML was trained on labeled face photos of jailed and honest guys (some of whom, let me add, could be criminals who was not discovered?). Authors claimed that the ML learned to catch new bad guys from just a face photo, but “feeling” that further research will refute the validity of physiognomy (racism). Really, their data set is biased: some white collar criminals pose as honest guys, laughing about that. The ML learned the only relations it could find: happy or angry mouths, type of collar (neck cloth). Those smiling with white collar are classified as honest, those sad with dark collar are rated as crooks. The ML authors tried to judge the people by their faces (not science! no correlation), but failed to see that the ML learned to judge by clothes (social status) instead. The ML amplified an injustice bias: street thieves in cheap clothes (perhaps with darker skin) are discovered and jailed more often than corrupt politicians and top level corporate fraudsters. This ML will send to jail all the street guys, and not a single white collar, if not also told that street thieves are discovered x% more frequently than white collars. If told so, again, it would take random or no decisions, this is not science. A lesson is: MLs do not experienced living in our world like an adult human. MLs can’t can’t know what’s outside the data given, including the “obvious”, for ex.: the more a fire is damaging, the more fire trucks are sent to stop it. An ML will note: the more firefighters at a fire scene, the more damage the day after, so the fire trucks cause fire damage. Result: the ML will send to jail the firefighters for arson, cause: “95% correlation”!

(ML can’t find correlations that do not exist, like: face with criminality. But this data set is biased: no smiling white collar criminals in it! ML will learn the bias)

MLs can predict what humans can’t, in some cases. For ex. Deep Patient, trained from 700,000 patients data by M. Sinai Hospital in New York, it can anticipate the onset of schizophrenia: no one knows how! Only the ML can: humans can’t learn to do the same by studying the ML. This is an issue: for an investment, medical, judicial or military decision, you may want to know how the AI reached its conclusions, but you can’t. You can’t know why the ML denied your loan, advised a judge to jail you or gave the job to someone else. Was the ML fair or unfair? Unbiased or biased by race, gender or else? The ML computations are visible, but too many to make a human-readable summary. The ML speaks like a prophet: “You humans can’t understand, even if I show you the math, so have faith! You tested my past predictions, and these were correct!”.

Humans are never fully explaining their decisions too: We give reasonable-sounding, but always incomplete, over-simplified reasons. For ex: “We invaded Iraq due to its weapons of mass destruction” looked right, but there were dozens more reasons. This looks wrong, even when the ML is right: “We bombed that village since a reputable ML said they was terrorists”. It only lacks explanation. People getting almost always right answers from MLs will start to make up fake explanations, just for the public to accept the MLs predictions. Some will use MLs in secret, crediting the ideas to themselves.

The ML results are only as good the data you train the ML with. In ML you rarely write software, that’s provided by Google (Keras, Tensorflow), Microsoft etc. and the algorithms are open source. ML is an unpredictable science defined by experimentation, not by theory. You spend most of the time preparing the data to train and studying the results, then doing lots of changes, mostly by guessing, and retrying. ML’s fed with too few or inaccurate data will give wrong results. Google Images incorrectly classified African Americans as gorillas, while Microsoft’s Tay bot learned nazi, sex and hate speech after only hours training on Twitter. The issue was the data, not the software.

Undesirable biases are implicit in human-generated data: an ML trained on Google News associated “father is to doctor as mother is to nurse” reflecting gender bias. If used as is, it might prioritize male job applicants over female ones. A law enforcement ML could discriminate by skin color. During the Trump campaign, some ML may have reduced recommending “Mexican” restaurants, as a side effect of reading many negative posts about Mexican immigration, even if no one complained about Mexican food or restaurants specifically. You can’t simply copy data from the internet into your ML, and expect it to end up balanced. To train a wise ML it’s expensive: you need humans to review and “de-bias” what’s wrong or evil, but naturally happening in the media.

self-driving car

(Photo: James Bridle entraps a self-driving car inside an unexpected circle)

ML is limited since it lacks general intelligence and prior common sense. Even merging together all the specialized MLs, or training an ML on everything, it will still fail at general AI tasks, for ex. at understanding language. You can’t talk about every topic with Siri, Alexa or Cortana like with real people: they’re just assistants. In 2011, IBM Watson answered faster than humans at Jeopardy! TV quiz, but confused Canada with USA. ML can produce useful summaries of long texts, including sentiment analysis (opinions and mood identification), but not as reliable as human works. Chatbots fail to understand too many questions. No current AI can do what’s easy for every human: to guess all the times when a customer is frustrated or sarcastic, and to change tone accordingly. There is no any general AI like in the movies. But we can get small sci-fi looking AI pieces, separately, that win humans at narrow (specific) tasks. What’s new is that “narrow” can include creative or supposedly human-only tasks: paint (styles, geometries, less likely if symbolic or conceptual), compose, create, guess, deceive, fake emotions, etc. all of which, incredibly, seem not to require general AI.

No one knows how to build a general AI. This is great: we get super-human specialized (narrow AI) workers, but no any Terminator or Matrix will decide on its own to kill us anytime soon. Unfortunately, humans can train machines to kill us right now, for ex. a terrorist teaching self-driving trucks to hit pedestrians. An AI with general intelligence it would probably self-destruct, rather than obey to terrorist orders.

AI ethics will be hacked, reprogrammed illegally. Current ML, being not general or sentient AI, will always follow the orders (training data) given by humans: don’t expect AI conscientious objectors. Each government will have to write laws detailing if a self-driving car will prefer to kill either its passenger(s) or pedestrian(s). For ex. two kids run suddenly in front of a car with a single passenger, and to avoid the kids, the car can only run in a deadly option, like a cliff. Polls show that the majority of people would prefer to own a car that kills pedestrians rather than themselves. Most people don’t think yet at these very rare events, but will overreact and question politicians when the first case it will happen, even if only once per billion cars. In countries where cars will be instructed to kill a single passenger to save multiple pedestrians, car owners will ask hackers to secretly reprogram cars to always save the passenger(s). But within pirated AI patches, hidden AI malware and viruses will probably be installed too!

To teach a human it’s easy: for most tasks, you give a dozen of examples and let him/her try a few times. But an ML requires thousand times more labeled data: only humans can learn from little data. An ML must try a million more times: if real world experiments are mandatory (can’t fully simulate like for chess, go, etc.), you’ll have to crash thousands of real cars, kill or hurt thousands of real human patients, etc. before to complete a training. An ML, unlike humans, overfits: it memorizes too specific detail of the training data, instead of general patterns. So, it fails on real tasks over never seen before data, even just a little different from the training data. Current ML it lacks the human general intelligence that models each situation and relates it to prior experience, to learn from very few examples or trial and errors, memorizing just what’s general, and ignoring what’s not relevant, avoiding to try what it can be predicted as a fail.