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The question of question understanding

I had a very long flight on my last easter vacation and I decided to use that spare time to reread some chapters from Physics of the impossible by Michio Kaku.

While I was reading the robots chapter I began to ponder about some of the ideas mentioned. I understand that some scientists argue whether our brain is the most complicated system ever made by nature and this is why it will be impossible to be replicated. I would not say that this is untrue, but I think that we trend to be really egocentric when we think about our own specie capabilities. Although our brain can be an incredible tool there are still many other brains that nature has developed and that are capable to deal in an amazingway with a hostile environment although we do not exactly know today what is their level of consciousness when doing so.

It is mentioned that robots are capable of achieving goals but without any real understanding about the pursued goal. A robot might be able to fix a car’s tire, but without any understanding of what a tire or a pump really is.  Is this true? Do we understand what a tire or a pump is? Even when dealing with this simple objects human definitions will surely vary from one to another. So what knowledge should the robot have in order to really understand his goal purpose? What is a tire? what materials is made of?  Should it understand his use? Or might it be the fact that tires are usually round? .. I think human beings trend to get lost sometimes in the sea of their own abstract ideas and concepts and that this should not be the purpose of a common robot. I think our sometimes egocentric vanity does not help us realise human beings are not perfect  although evolution implies exactly that capacities are not at their best and can always get better. I think a good example of this is the square game where robots trend to be pretty better than us although it implies a little bit of thinking and very little operations.

If you don’t know the answer already try thinking about it, it will give you a better idea of what I am trying to explain here.

Human brain is not so good sometimes when we have to think out of the box.

So I think we need to create new robotic systems so that they understand the processes they will be dealing with. Robots that can understand their own plant system. System failures usually happen due to fails in the control system more than in the system itself, control engineers usually fix this problems adjusting the control system parameters. Therefore, complex systems should be able to understand their own system plant and be able to understand their controllers in order to adjust their parameters so that they can act consequently if any problem arises. We need to create robots that can understand their interactions with the world and their capacity to pursue effectively their goals in the way that is better suited for “surviving” in the environment they have to deal with.

Michio Kaku explains:

“Animals can be conscious , but in a different conscious level as the human being. We should try to classify the different types and levels of consciousness before debating about any philosophical issues of it. In time robots might develop a silicon consciousness (…) erasing the difference between semantics and syntax’s and their answers would be indistinguishable from  human’s. This way the question of whether they actually or not understand the question would be basically irrelevant.

2013 Decalogue by Eduard Punset

Eduard-PunsetEduard Punset is a multidisciplinary researcher well know in Spain because since 1996 he has directed and presented “Redes” a scientific Tv program based around interviews with leading scientists.He is also professor of science, technology and society at the Faculty of Economics of the Chemical Institute of Sarrià.

I wanted to translate, his new year’s decalogue from his blog, which I found really inspiring and share it with all non spanish readers.

You can find the original post in spanish here.

1- Since the last century life expectancy rises two years and half every decade.

2- Thanks to 1,  today’s people are less obsessed about life after death, they are much worried about noting there really is life before death.

3- We should devote less time in healing politics, instead prevention politics should be our priority. Basically this means that we should practice daily sports, watch our diet, know how to enjoy what we already have and don’t cry only for what we lack.

4- Social and emotional learning must be added immediately to the educational system. For this we must prepare educators to understand positive and negative emotions so they can help us manage them correctly.

5- We must demarcate negative competences, which prevents us from getting a job, from positive competences, which can really boost our possibilities of getting a good one. Among the first ones, we must avoid emotional ignorance (lets see if we finally understand the meaning of  despising others) and among the second ones we must understand that happiness resides inside happiness waiting room.

6- We must note intuition and subconscious importance in contraposition to rational conscious thoughts. I will never forget that woman crying that stopped me in the street to thank me for giving her confidence on her intuition again. While others had tried to prevent her from trusting it her whole live.

7- Understanding real happiness dimensions has been the greatest 20th century conquest. We have understood that is not necessarily the money the one which confers that happiness. When you live under the subsistence level, money is happiness, but exceeded that level control over our own live is the dimension more correlated with happiness. Having the impression that what you do matters for some reason.

8- The beauty that a many people search for, is really pain absence. But many people are willing to cope with pain without any evident reason in order to get their desired job or fulfilling their dreams . Exercising and working without suffering is understanding pain to gain control.

9- The herd, when crossing a river or climbing a mountain, always relies on youth. The main next century’s problem will not be wealth distribution, but work distribution. Schools and new enterprises should have already started the study and application of this principle. State and other social institutions and enterprises must open their doors to the marginalised youth.

10- Please, its time to resign dogmatism and accept as a daily practice the uncertainty principle. When something is intuited must be checked and if it works must be applied till someone comes and proves the contrary. Newton convinced the world that Time was exactly the same for all, was absolute. After, Einstein said it was relative, dependant on the mass and velocity. End dogmatists that much suffering imposed on those who doubted!

I hope you have found the reading interesting. I apologise for the translations accuracy, I’m not a translator. I tried to write it accurately and I hope I have been able to make his points clear.

Dr. Marvin Minsky: Building Intelligent Machines

Most of you, have probably heard about Dr. Marvin Minsky , one of the most influential authorities inside Cognitive Science. Co-founder of the Massachusetts Institute of Technologies AI laboratory. In my opinion, he is one of the most intelligent thinker I have ever heard.

This is a talk in 2009 in which Marvin Minsky tries to explain why we need intelligent machines and what we can expect from them. His research group has been working on cognitive architectures trying to develop better intelligent machines using AI.  Although it is a few years old,  it is one of the most interesting and funny  talks I have experienced so far, and I was able to find it recently on iTunes.

Education is no good, unless you teach them to question it. And it is not popular.

Sarcastic Marvin Minsky begins talking about why we need intelligent machines. He claims that if we knew how to build a machine that could think more or less the same way that people can, we should surely understand its behaviour, being capable in the future to know how to fix and replace all of this machines parts and equally all of the the brain’s ones to. In his own words “Immortality would be easy to obtain”  This way we would be able to make backup copies of our brains and download and upload them into this machines.

One thing he points out and which I can not do anything but totally agree, is that in the Universities, high-schools and collages, people are developing again and again the same kinds of robots. In his own words” You can learn a lot from doing this, like that if you step on connectors …. they brake.” 🙂 People should be working on something more useful. For example, robot-soccer might be beautiful to watch for a few minutes but it is useless and has not much value for humanity. Why not try to research on robots that do something more useful?

The best is the enemy of the good. Don’t spend a lot of time trying to find the best way. Find six good ways, it will take you half the time and your machine will be six times better when the environment changes

Dr. Minsky remarks that the ambiguity of language is really a useful tool we use to learn, as it is possible to obtain a better result at the end by misunderstanding what you where initially told. As when somebody realises a different idea from something you have read before, but you didn’t thought of that at that moment. But now, that you have been told about it, the idea seems really plausible. It is clear that people learn in various ways, and that people develop different ways to learn. I agree with Dr. Minsky, that we should focus our efforts not on education as we are doing it right now, but more on understanding ways to learn and improving old commonly used techniques as reinforcement by reward. Instead, we should better ask ourselves why some kids learn more from the same experience than others and improve our ways of  teaching how to learn.

If you can tell yourself what you did, you might misunderstand it and do something better next time

Talking about AI, Dr. Minsky gives a brief history description through the first AI to symbolic calculation. How this development has provided powerful tools for mathematic integration using Matlab or Mathematica. But right now, computers can only solve logical formulated problems and can not operate by analogy. He explains that there are not enough people researching on example based reasoning, which he believes will be the future of computer programming and instead that to much effort and money is spent on neural networks, statistical learning, genetic algorithms…which will inevitably pass away and become obsolete.

Thousands of people are doing something because they see thousands of other people doing it, so therefore it must be good. I know only about twenty or thirty people in the hole world who are working on how to get computers to do anything like ordinary common sense reasoning and thats where I think, the future lays.

Through the whole lecture Dr. Marvin gives his point of view on a bunch of different topics and problems of society, science, education, politics, even sports, in a very sarcastic way. Although I do not agree with all of them, I have found them really funny and makes the lecture easy to listen and understand.

On a menu, when you have a choice I always order chicken this days, because your children won’t have any chicken to order

For anyone who is interested in reading Dr. Minsky: The Emotion Machine

Building Intelligent Machines

Cognitive computers? A reality for IBM

Listening to Dharmendra Modha manager from IBM Cognitive Computing Systems, makes me wonder how far we are from really interacting with cognitive computers. IBM has been able to simulate about 500 billion neurons, 100 trillion synapses all running on a collection of ninety-six of the world’s fastest computers this year. The project  code name is Compass which goal is to be able to simulate a brain of a macaque monkey making of this project, the most ambitious attempted to this day.

Compass is part of long-standing effort known as neuromorphic engineering, an approach to build computers developed by the engineer Carver Mead. The premise behind Mead’s approach is that brains and computers are fundamentally different, and the best way to build smart machines is to build computers that work more like brains. Especially when relating with common sense interpretation, understanding language and sensations.

Whereas traditional computers largely work executing serial tasks (one step after another) and using classical logic (if-while and-or), neuromorphic systems work in parallel, and draw their inspiration as much as possible from the human brain describing functionalities in terms of neurones, dendrites and axons.

If Moore’s law continues to be fulfilled and the number of transistors on integrated circuits continue to double every two years. What would we be able to accomplish in a few years? A growing crew of neuroscientists and engineers believe that the key on building better autonomous machines that emulate brain capacities is by implementing them neurones by neurones. I humbly that there is to much research still to be done on neurones connections and that implementing just neurone by neurone knowing how they are connected isn’t enough to deduce the complete behaviour.

We should better emphasise on understanding how the brain and nervous system works in other simpler species otherwise we could be just trying to mimic something we really don’t understand, time will tell.

The human brain has awesome powers of sensation, perception, cognition, emotion, action and interaction. It can bring together multiple sensory modalities, while consuming less power than a light ball and occupying less volume than a two litter bottle of soda