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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.

The Human Brain Project

This past week a new interesting project about the human brain was approved by the European Commission. This project has been selected with an european flagship FET (Future and emerging technologies) which means it’s set to receive a billion euros and also to be funded as FET “flagships” over 10 years through its research and innovation funding programmes.

Modern neuroscience has been enormously productive but unsystematic. The data it produces describes different levels of biological organisation, in different areas of the brain in different species, at different stages of development. Today we urgently need to integrate this data, to show how the parts fit together in a single multi-level system.The “Human Brain Project” will create the world’s largest experimental facility for developing the most detailed model of the brain, for studying how the human brain works and ultimately to develop personalised treatment of neurological and related diseases. This research lays the scientific and technical foundations for medical progress that has the potential to will dramatically improve the quality of life for millions of Europeans.

The project will pursue four goals:

  1. Generate strategically selected data essential to seed brain atlases, build brain models and catalyse contributions from other groups.
  2. Identify mathematical principles underlaying the relationships between different levels of brain organisation.
  3. Integrate systems of Information and Communications Technologies, providing platforms offering services to neuroscientists, clinical researches and technology developers.
  4. Develop first draft models and prototype technologies, demonstrating how the platforms can be used to produce results with immediate value for basic neuroscience, medicine and computing technology.

From this goals they generate other subgoals that the project wants to achieve, I want to remark the next ones:

Understanding the relationships between brain structure and function, integrate the principles of cognition, that from a technological perspective it would give developers the tools they need to develop robots with the potential to achieve human-like capabilities, impossible to realise in systems that do not have a brain-like controller. The Human Brain Project work in neuromorphic computing and neurorobotics would open the road for the development of compact low-power systems with the long-term potential to achieve brain-like intelligence.

I want to end this post with the next paragraph quoted from the International Technology Roadmap for Semiconductors,2011.

The appeal of neuromorphic architectures lies in i) their potential to achieve (human-like) intelligence based on unreliable devices typically found in neuronal tissue, ii) their strategies to deal with anomalies, emphasising not only tolerance to noise and faults, but also the active exploitation of noise to increase the effectiveness of operations, and iii) their potential for low-power operation. Traditional von Neumann machines are less suitable with regard to item i), since for this type of tasks they require a machine complexity ( the number of gates and computational power), that tends to increase exponentially with the complexity of the environment (the size of the input). Neuromorphic systems, on the other hand, exhibit a more gradual increase of their machine complexity with respect to the environmental complexity. Therefore, at the level of human-like computing tasks, neuromorphic machines have the potential to be superior to von Neumann machines

HBP-videoverview from Human Brain Project on Vimeo.

source for the information: The Human Brain Project. A report to the European Commission.

The tale of the boy who saw without eyes

I don’t know if you have already heard about the human echolocation phenomenom. For those of you who haven’t,  this post’s title probably has left you a bit astonished, but human echolocation in an ability that has been known for at least the 1950s.

We could say that human echolocation its a process similar, in a way, to the one used by  bats, dolphins and some whales to recognise their surroundings and location. Equally to the way a sonar works using sound echoes to recognise objects.What we normally see is just the light reflection on an objects surface, that gives us the trace, form and size of what we are looking at. Evolution has developed eyes as light “sensors” and eyes plus their brain connections provides us with a really powerful tool to cope with the environment, walk around,  be able to recognise objects, enabling easy space positioning. In contrast we could define human echolocation as the ability of humans to detect objects in the environment by sensing echoes from those objects by actively creating sounds, for example by making clicking noises or tapping a cane. People trained to orientate with echolocation are able to interpret the sound waves reflected by nearby objects, accurately identifying their location and size, so this ability is used by some blind people to navigate within their environment using their auditory senses rather than their visual ones.

Vision and hearing are closely related in that they can process reflected waves of energy. Both systems can extract a great deal of information about the environment by interpreting the complex patterns of the reflected energy they receive. Sound carries information about the nature, arrangement of objects and other environmental features. Giving information about location, dimension and density of the object the sound reflects from.

It has been recently shown that blind echolocator’s experts use what is normally the “visual” part of their brain to process the echoes, primary visual cortex. Most interestingly, the brain areas that process auditory information were not activated in this subjects, in the performed experiments, more than in other normal subjects. Which gives the idea that blind echolocator’s experts sense the world similarly the way other people do, but using a completely different strategy for information gathering.

When talking about human echolocator’s, we must mention Daniel Kish, born in 1966 in Montebello, California. Blind since he was 13 months old, he is an expert in human echolocation and president of World Access for the Blind, a non-profit founded in 2000 which helps people with all forms of blindness. Kish and his Organisation have taught echolocation to at least 500 blind children around the world inspiring other scientists to study human echolocation. Other remarkable human echolocator’s are Lucas Murray from Poole, Dorset. Who was born blind and was one of the first british people to learn to visualise his surroundings using human echolocation, taught by Daniel Kish and Ben Underwood (1992-2009)

The human brain continuous to embrace incredible features, evolution was the great tool that boosted  its creation and guides its capabilities. Do we use the hole capabilities of our brain?  The most reasonable option is that we must use most of them. Brains are expensive organs in terms of energy costs and I believe it is reasonable that our evolution would’t have allowed those nonsense great energy expenses. So I believe that if we weren’t using our hole brain, the brain would had probably shrunken. There is an example of this. When talking about some type of birds, while mating season, the male which is the one who searches for food, has to remember where the best food is and where the nest is in order to return to it with the food, so it’s brain is bigger than the female one, which does not have to gather this knowledge. But is incredible to realise, that when not on mating season, when the birds do not have to remember where is easy to find lots of food and how to return to the nest, the male brain shrinks. In order to lose less energy, and when back on mating season the male’s brain expands again.

Do we use the brain the only way it can be used? I believe this is a totally different question, I believe perception is the key fact, usually missing when trying to understand cognition. Human echolocators do not “hear” the echoes as we would do it, they really” see” the sound reflected from the objects, I mean, their brain constructs the image the same way we do, but using sound instead of light.  Obtaining at the end very similar results from evolution’s point of view which is, at the end, helping the entity surviving in the environment. So I believe that only time will tell if there are totally different ways of using our brain, but we must be always really open minded for any new ways of thinking that will surely arise in the future.

At last I would like to mention Kevin Warwik, born the 9th of february in 1954 in Coventry, Uk. Is a British scientist and professor of cybernetics at the University of Reading. He is well know for his studies on direct interfaces between computer systems and the human nervous system. Has done interesting researches in echolocation’s field. Kevin Warwik is an incredible scientist, related with cognition and artificial intelligence and deserves a future entrance in this blog, so for now I will only talk about his particular conception with respect to artificial intelligence, he claims that we have many limits, such as our sensorimotor abilities, that we can overcome with machines. In his own words:

“There is no way I want to stay a mere human”

I would like to add to this blog entrance this documentary on Ben Underwood (1992-2009) and the human echolocation phenomenon. Not only for the scientific purposes but also for telling his beautiful biography of overcoming and struggle. When this was recorded he was still alive. Hope you enjoy it.

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

Robots with a mind of their own

Divide and conquer is a common technique used by strategists in many fields. Comprehension usually involves understanding the details and it means usually better adaptation to the “environment”. I’ll try to explain myself with an example: When trying to face a problem in any science  without understanding the main physics involving all the procedure. You might have encountered, you are able to resolve that specific problem or even a similar one but, when the “environment changes”, by this I mean that the problem differs from the initial one in a simpler way, but in a way that is such that your abstract idea of the physics confuses you and makes you solve it the wrong way. But instead anyone who already understands that physic fact will easily evolve its previous answer the right way.

Imagine you memorized the math table for two, then you will be able to accomplish any multiplication relying number 2. When asked to multiply 3*2 you are able to answer easily, but if the question is 3*3 you wont. But instead if you learn the math table for two, understanding that 2*5 is five times two (2+2+2+2+2) you will be able to answer 3*8 immediately. And you might be able to infer that 8/2 equals 4 because there are two fours “inside” an eight (4*2) (4+4). By mathematical terms you could say, this situation is more adapted to a changeable mathematical environment.

For many years control engineers have tried to reduced complex, abstract processes to simpler and more achievable ones, which interact with each other to develop the more complex process which englobes the simpler ones. This way using feed-back loops engineers are able to automate a control system towards its goal.

As robots usually are really complex systems, they are very difficult to control. Interactions between its components can easily lead to malfunctions that can make impossible for the system to achieve it´s desired goal. Now, new simpler, adaptable robots are being researched by Amsterdam engineers working inside an European project, a great idea to solve complex difficulties, making robots which will be able to interact with each other in incredible ways (show by the video)

I imagine future robotics, as multiple interactive robots specialised in little tasks working in groups, being capable of sharing information and developing solutions as a group. Understanding perception will be very important for this robots, in order to be able to communicate to each other its own situation and the final group’s goal, in which I believe cognitive science’s and the study of social interactions will clearly help the research of new cognitive architectures, which will allows us in the near future to master new control techniques for this new multiple robot platforms.

For now we will have to settle with this incredible mock-up.

The tale of Daniel Tammet: The boy with the incredible brain

I remember a few years ago I came across with a documentary called something like “Incredible minds“. It was about a man called Daniel Tammet which was an autistic savant, able to work out great numerical problems in an incredible way. I was really impressed, not only of what the documentary showed he was capable of, but more about the way he explained how he did it. He claimed he had never seen numbers the way, “others” did. Instead he was synaesthetic with numbers, he felt sensations related with them. And he claimed that when he was resolving a numerical problem he was really abstracting a form out of a mental image that the numbers involved sketched in his mind.

This made me wonder if the way maths were thought, were not the ideal way they should be. Or maybe, I understood maths in a way differently from the way others did. And when I say maths I mean any other subject or concept that might be studied. We mostly learn the most simple concepts by patters that are repeated constantly again and again and finally become kind of subconscious. Think, for example on math tables, or first vocabulary lessons in most foreign language classes you might have attended. Maybe, understanding the basics by repetition is not the most effective way to learn, even when infants seem to learn this concepts fast, I believe we don’t exactly make them know in a  meaningful way.

Understanding that your own-percepcion models any concept you might learn, it would be really interesting finding other ways of learning common concepts that could be a lot more powerful than the way it is done today. Researching on mental syndromes, synaesthetics and the way they understand the environment could help us open our eyes.

I came across, recently with this TED talk from Daniel Tammet that might be well self-explanatory about this ideas: