True Artificial Intelligence Will Change Everything

Posted on July 25th, 2018

Jürgen Schmidhuber (born 17 January 1963) is a Computer Scientist who works in the field of AI. He is a co-director of the Dalle Molle Institute for Artificial Intelligence Research in Manno, in the district of Lugano, in Ticino in southern Switzerland.Schmidhuber did his undergraduate studies at the Technische Universität München in Munich, Germany. He taught there from 2004 until 2009 when he became a professor of artificial intelligence at the Università della Svizzera Italiana in Lugano, Switzerland.

When I was a boy I wanted to maximize my impact on the world. I was smart enough to realize that I am NOT very smart. That I have to build a machine that learns to become much smarter than myself, such that it can solve all the problems that I cannot solve myself. Then I can retire and my first publication on that dates back 30 years. My 1987 diploma thesis where I already try to solve the grand problem of AI not only build a machine that learns a little bit here and there but also learns to improve the learning algorithm itself. The way it learns, recursively without any limits except the limits of logic and physics.

I’m still working on the same old thing and I’m still pretty much saying the same thing except that now more people are listening. The learning algorithms that we have developed on the way to this goal are now on three thousand million smartphones and all of you have them in your pockets. What you see here are the five most valuable companies of the Western world Apple Google Facebook Microsoft and Amazon and all of them are emphasizing that AI artificial intelligence is central to what they are doing. All of them are using heavily the deep learning methods that my team has developed since the early nineties.

Have you ever heard of the long short-term memory? 

The LSTM is a little bit like your brain it’s an artificial neural network which also has neurons. In your brain you’ve got about 100 billion neurons and each of them is connected to roughly 10,000 other neurons on average which means that you have got a million billion connections. Each of these connections has a strength which says how much does this neuron over here influence that neuron over there at the next time step. In the beginning all these connections are random and the system knows nothing within. Through a smart learning algorithm it learns from lots of examples to translate the incoming data such as video through the cameras or audio through the microphones or pain signals through the pain sensors. It learns to translate that into output actions because some of these neurons are output neurons that control speech muscles and finger muscles and, through experience only, it can learn to solve all kinds of interesting problems.

Problems such as driving a car or to do the speech recognition on your smartphone, because whenever you take out your smartphone an Android phone for example, and you speak to it and you say ok Google show me the shortest way to Milano then it understands your speech because there is an LSTM in there which has learned to understand speech. Every 10 milliseconds, 100 times a second, new inputs are coming from the microphone and then translates it, after thinking, into letters which is then sent as a question to the search engine. 

The basic LSTM cell looks like this.

 By listening to lots of speech since 2015 Google speech recognition is now much better than it used to be.

I can list the names of the brilliant students in my lab who made that possible and what are the big companies doing with that speech recognition. As an example if you are on Facebook, I use click at the translate button because somebody sent something in a foreign language and then you can translate it. When you do that you are waking up a long short term memory and LSTM which has learned to translate text in one language into translated text and Facebook is doing that four billion times a day. Every second, 50,000 sentences are being translated by an LSTM working for Facebook and another 50,000 in the second and another 50,000 so see how much this thing is now permitting in the modern world.

Just note that almost 30 percent of the awesome computational power of all these Google Data Centers all over the world is used for LSTM. If you have an Amazon echo you can ask it questions it answers you and the voice that you hear it’s not a recording it’s an LSTM network which has learned from training examples to sound like a female voice. If you have an iPhone and you’re using quick type it’s trying to predict what you want to do next given the previous context of what you did so far. Again that’s an LSTM which has to do that so it’s on a billion iPhones.

When we started this work decades ago in the early 90s only a few people were interested because computers were so slow and you couldn’t do so much with them. I remember I gave a talk at a conference and there was just one single person in the audience a young lady. I said young lady it’s very embarrassing but apparently today I’m going to give this talk just to you and she said okay but please hurry I am the next speaker. Since then we have greatly profited from the fact that every five years computers are ten times cheaper. This is an old trend that has held since 1941 at least since this man Conrad Susan built the first working program control computer in Berlin. He could could do roughly one operation per second one and then ten years later for the same price one could do 100 operations. 30 years later 1 million operations were the same price and today after 75 years we can do a million billion times as much for the same price. The trend is not about to stop because the physical limits are much further out there. Rather soon and not so many years or decades away we will for the first time have little computational devices that can compute as much as a human brain. 50 years later there will be a little computational device for the same price that can compute as much as all 10 billion human brains taken together and there will not only be one of those devices but very many.

Everything is going to change. In 2011 computers were fast enough such that our deep learning methods for the first time could achieve a superhuman pattern-recognition result. The first superhuman result in the history of computer vision. Back then computers were 20 times more expensive than today. Today for the same price we can do 20 times as much and just five years ago when computers were 10 times more expensive than today we already could win for the first time medical imaging competitions. What you see above is a slice through the female breast and the tissue that you see there has all kinds of cells and normally you need a trained doctor a trained who is able to detect the dangerous cancer cells or pre-cancer cells. Now our stupid network knows nothing about cancer, knows nothing about vision, it knows nothing in the beginning but we can train it to imitate the human teacher. It became as good as or better than the best competitors and very soon all of medical diagnosis is going to be superhuman. It’s going to be mandatory because it’s going to be so much better than the doctors. After this all kinds of medical imaging startups were founded focusing just on this because it’s so important.

We can also use LSTM to train robots. One important thing I want to say is that we not only have systems that slavishly imitate what humans show them. We also have AI’s that set themselves their own goals and like little babies invent their own experiment to explore the world and to figure out what you can do in the world without a teacher. Becoming more and more general problem solvers in the process by learning new skills on top of old skills. This is going to scale well. Learning to invent like a scientist. I think in not so many years from now for the first time we are going to have an animal like AI. You don’t have that yet. On the level of a little monkey and once we have that it may take just a few decades to do the final step towards human level intelligence. Technological evolution is about a million times faster than biological evolution and biological evolution needed 3.5 billion years to evolve a monkey from scratch but then just a few tens of millions of years afterwards to evolve human level intelligence.

We have a company which is trying to make this a reality and build the first true general and purpose AI. At the moment almost all research in AI is very human centric and it’s all about making human lives longer and healthier and easier and making humans more addicted to their smartphones. In the long run AI’s, especially the smart ones, are going to set themselves their own goals and I have no doubt in my mind that they are going to become much smarter than we are. They are going to realize what we have realized a long time ago namely that most of the resources in the solar system or in general are not in our little biosphere. They are out there in space and so of course they are going to emigrate and of course they are going to use trillions of self-replicating robot factories to expand in form of growing AI bubble which within a few hundred thousand years is going to cover the entire galaxy.

What we are witnessing now is much more than just another Industrial Revolution this is something that transcends humankind and even life itself. The last time something so important has happened was maybe 3.5 billion years ago when life was invented. A new type of life is going to emerge from our little planet and it’s going to colonize and transform the entire universe. The universe is still young it’s only 13.8 billion years old. It’s going to become much older than that. Many times older than that. So there’s plenty of time to reach all of it or all of the visible parts totally within the limits of light speed and physics. A new type of life is going to make the universe intelligent. Now of course we are not going to remain the crown of creation, of course not, but there is still beauty in seeing yourself as part of a grander process that leads the cosmos from low complexity towards higher complexity. It’s a privilege to live at a time where we can witness the beginnings of that and where we can contribute something to that.