What Artificial intelligence can Learn from Human Evolution
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The document discusses key aspects of human intelligence that could inform the development of artificial intelligence. It covers how human intelligence evolved over billions of years through natural selection to develop features like motivation, emotions, senses, language processing, and vision. These characteristics provide benefits like adaptability, decision making, and understanding the world. The document suggests artificial intelligence could replicate features like motivation through simulating greed and fear, developing emotions and social behaviors, and creating thought arenas to allow for object-based representation and reasoning. Progress is needed in natural language processing, computer vision, and developing a language the brain can use to think.
What Artificial intelligence can Learn from Human Evolution
- 1. What we can learn from human evolution.. By Abhimanyu Singh Enrolment No.: 0441189907 MBA (SEM) Under the able supervision of Mr. Nidhish Shroti Faculty & ERP Consultant, CDAC-Noida
- 2. Objective To try to understand the behavior of human intelligence and to find out points where artificial intelligence can be benefitted from it.
- 3. Introduction Human intelligence has developed for billions of years through the process of evolution. Bit by bit, features were sifted and deployed, ensuring we got the best. We humans now want to replicate this marvel. We want to create an intelligence of our own, we want to create the Artificial Intelligence. The only catch is that we don’t have as much time. I would like to put light on some of these features that can be replicated to get closer to the goal of AI.
- 4. Artificial Intelligence Attempt to make computers do things that right now humans do better. Related not only to Computer Science, but also to Psychology, Physics, Anthropology, Biology, Philosophy and so on.. Currently broken into specialized sub fields. Amusingly enough, though AI has not evolved much, people have already started working on the so called “Robot Rights”.
- 5. Examples of Current Approaches to AI Neural Networks An artificial neural network (ANN) is a mathematical model or computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. Fuzzy Logic Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than precise. In binary sets with binary logic, in contrast to fuzzy logic named also crisp logic, the variables may have a membership value of only 0 or 1. Just as in fuzzy set theory with fuzzy logic the set membership values can range (inclusively) between 0 and 1, in fuzzy logic the degree of truth of a statement can range between 0 and 1 and is not constrained to the two truth values {true (1), false (0)} as in classic predicate logic. And when linguistic variables are used, these degrees may be managed by specific functions, as discussed below.
- 6. Human Intelligence characteristics Motivated. Emotional. Simulator. Regularly updated by the 5 senses. Self Aware and conscious. Predicts future, and can plan. Can group things and show belongingness to groups. Not bound by rules. Social
- 7. Motivation Provokes a person to do or not to do something. Some of the popular theories of Motivation Maslow’s Need Hierarchy (5 Factors) ERG Theory (3 Factors) Hertzberg’s Two factors Theory (2 Factors)
- 8. The Two Basic Factors We can reduce all of them into the following two basic factors of motivation, Greed Fear We struggle to achieve things we want (Greed). We Avoid things that we fear. As we grow, we learn what we want to attain and what we want to abstain. Interestingly, we are not “Hard wired” to absolutely follow these rules, e.g. when needed, we may even plunge into fire to save someone we love, despite the fear of getting burnt.
- 9. Motivation for Computers?? This approach can make them autonomous or “Self Motivated”, i.e. they don’t need to be told to do or not to do something every time, they react to a situation they sense around them. Two Priority based stacks can be used to replicate fear and greed. Will assist in making decisions based on past experiences. Can be preloaded with a few basic instincts like humans.
- 10. Social Behavior Since it is possible that we may have conflicting motives, we need to authenticate the sources to deal with such situations. Each person it “knows” is assigned with a priority, which we can refer to as “trust”. In case of conflicting motives the, higher priority source will be preferred. In such cases, the other person’s priority may be decreased to mark out non trustable sources.
- 12. Emotions We often consider emotions as hindrances to our intellectuality. Hitler tried to speed up the process of natural selection and hence the speed of human evolution by eliminating the old, weak and sick people.. Was his decision intelligent?? Emotions bring rationalities to our decision making process. Behave as constraints to our motivations. E.g. We do not start gobbling up sweets wherever we see them, even if we like them very much.
- 13. The 5 Basic Emotions Pleasure: The Reward for doing things we like. (Dopamine) Pain: Physical pain draws attention towards a malfunction, Mental pain associated with social reasons. Anger: The feeling that provokes us to fight against the immediate danger. Fear: The feeling that provokes us get out of a situation where we cannot fight in case of danger. (Amygdala) Disgust: The feeling that repels us from possibly harmful objects.
- 14. The Law of Diminishing Returns.. States that for each unit being added for an activity, the returns keep diminishing. Required for emotions. This helps in getting used to things and moving on. Makes us dynamic, regularly urging us to try something new. Lack of it will lead to an almost static life, keeping us in a state we already are. Experiments have shown lack of it could lead to death.
- 15. Senses We have 5 Senses, namely the sense of Smell, Taste, Touch, Sound and Vision. Of all the senses, 3 senses can prove to be very important for AI, i.e. Touch, Vision & Sound. The sense of touch can be recreated easily, including the feeling of heat and pressure. The problem arises with the Sense of Vision and Sound. These provide the highest details of the surrounding environment, and we will be focusing on these two.
- 16. Natural Language Processing We use dictionary based lexical parsing. Store words and their meanings in data dictionary. Store people, place etc. and their identity in object dictionary. Learn new words while conversation, typically by typing or in some cases through voice recognition. Create replies based on grammatical rules and on past experiences.
- 17. Problems with NLP No physical world-logical world connection. “Understanding” heavily marred by ambiguity present in the sentences humans use. Conversation has to be error free for proper absorption. We humans not always talk sense. Most of the things we often say, has internal meanings which are not understandable by computer. Recent and older conversations has to be available so as to talk sense and not be repetitive.
- 18. Eye Sight We believe that we see whatever is present in front of the eyes. In reality, what we see is actually a recreated interpretation of things in front of our eyes. Even 2-Dimensional images are also interpreted in 3 Dimension. Follows HSI color model, and not the RGB model. Has two modes, Day Vision (Using Cones) and Night Vision (Using Rods).
- 19. Eye Sight Continued.. We can identify reflection, and feel presence of transparent & fluid objects. We have different algorithm for face recognition. Alphabets are interpreted differently (lack of it is Dyslexia). Numbers are interpreted differently (lack of it is Dyscalculia).
- 20. Image processing Heavily noisy environment. Need a lot of interpretation. Consume a lot of Processor power. Edges not clearly defined. 3-Dimensional objects have almost uncountable 2- dimensional footprints, leading to almost useless comparison of interpreted objects. Reflections and transparency increase complexity.
- 21. Optical Interpretation Optical Illusion
- 22. What can be done.. The entire visible area doesn’t need to go through detailed scan. We can run an iteration of algorithms such as RGB2HSI convertor, Edge detector, Histogram based Object finder, Motion detector, Distance finder etc. on the entire visible area. Using all the above parameters, we can find Points of Interests (POI) in the Field of View (FOV). The points of interest are then sent for further deeper analysis like Face recognition, character recognition, Object Combiner etc.
- 23. The Language of the Brain What is the language that the brain of a newborn infant thinks through? It doesn’t have any knowledge of any language, and yet it thinks. There must be some language that the brain thinks through, and the NLP can be superimposed on it. It is also compatible with the senses of vision, smell, touch and taste. Do we have such language for computers? The Chinese room hypothesis asks exactly this question.
- 24. 3 Dimensional Object Based Thought Arena Perhaps we can use our very own OOP concepts for this purpose. We need to create a 3D canvas where we can import objects that we desire or see. For our convenience, we can use the term Thought Arena for it. We can create or remove rules in/from this arena. (Laws of physics, like gravity etc.) We can assign attributes to the objects, morph them, and tag them.
- 25. Thought Arenas Research has shown that we can think about, up to 4 different things at a time. This could mean that, we may be having up to 4 Thought Arenas in our brains. One of these is obviously dedicated to the real world, and has got the highest priority. Others may be dormant or running in the background. During sleep, these could get activated causing dreams.
- 26. How does it work? When we hear a voice coming from behind us, what happens? We instantly know that there is a person behind us. We can gauge out who (s)he is, we can determine distance etc. For vehicles we even find out the speed and direction. All this without even taking a look behind !! The reason is that we instantly import the human object or vehicle object into the Thought Arena and assign attributes to it.
- 27. Contd.. The same happens with vision too. We keep collecting data from the surrounding environment. We create a list of objects and their position in time and space. Even motion is stored as object, and helps us find patterns in it. This is remodeled in the Brain and a simulation is started, which may lead to certain results. Based on the results and instructions in the Motivation stacks, the computer can react to situations.
- 28. 3D Objects We normally create 3D objects using Simple Nodes and Vertices. Each object has a center of mass, which is used as a reference for activities like collision detection, movement control etc. for the entire object.
- 29. Required 3D Properties We need special types of Nodes which are used for points where other nodes may or may not be connected. It will help in working with ambiguous environment. Also, instead of each object having a single center of mass, we need each node to have a center of mass. The vertices can then behave as bonding element. Much like an atom, but bigger in size.
- 30. Time Based Memories Short Term, Mid Term and Long Term Memories are present to boost the response time of brain, so it can be used for AI too. Objects, that are of regular and daily use, are kept in short term memory and so on. Similar concepts are used in computers, e.g. HDD, RAM and Cache. However they work on the physical level. We need to replicate the same thing at logical level.
- 31. Learning Process Not all that comes in front of our eyes, goes into our brains. Cause-Effect relation is used to get lessons. Brain mostly learns in two ways, either by Interest or by Repetition. Things of interest get direct entry into the brain. However, things that we don’t like have to be kept long enough in the short term memory so as to qualify for entering into the higher levels of memory. So, we may have to define a computer’s points of interest, hence controlling its learning process.
- 32. It is easier to plan if we Planning can visualize our problem. Simulation is essential for planning, and this can be done in the thought arena itself. With the presence of multiple TA’s we can simulate the behavior of not only non living things but also of living things like human etc.
- 33. Conclusion contd.. The following simplified model is proposed for designing an effective TA 4 TA 3 TA 2 TA 1 Greed Fear STM Motivators Time Based Memories AI Droid. LTM MTM Emotional Evaluators Thought Arenas Body Interface Stereo Vision Stereo Sound Object Data Dictionary Image Processor Audio Processor
- 34. Conclusion For practical AI to be implemented, we need to develop the entire “body” rather than just the “brain”. Concepts like Dreams and Imagination may not be alien to them.
- 35. Conclusion (contd.) Since there is no limit to their learning capability, AI systems may eventually develop cyber psyche, exhibiting humane emotions like trust, fear, happiness etc.
- 36. Conclusion (contd.) Can be used in combat zones, or patrolling difficult terrains, as found on the border areas. Possibility of being hacked and misused, so encryption must be used for internal data. But volume of data may lead to degraded performance. There is a possibility of commercial gains too. We may eventually be able to bring down the cost of such systems using economies of scale, thus allowing them to be used as “Manpower” for households and industrial usage.
Editor's Notes
- #21: In my image editing project, I had implemented
- #22: Our brain manipulates the images received through the eyes. In the Left image the middle character may be interpreted as either B or 13. The right image shows that we convert 2-dimensional images to 3-dimensional before interpreting. Similarly we do not consider the entire image at the same time. If we keep looking into the blue ball, we cannot tell what is written on top, even though we know there is something written there.