What is Artificial Intelligence?

So you may be wondering just what is artificial intelligence? Many people think of AI and relate it to movies about robots traveling through time, killing people, or other outlandish tales.

What is Artificial Intelligence?

One of the best movies that perhaps relates to AI, especially in the relatively near future would have to be the actual movie A.I. This describes a whole generation of androids that think for themselves, and some not even knowing they were robots in the first place.

If you type 'what is artificial intelligence' into a search engine on the computer you will be bombarded with so much scientific information you may have your own system overload. There is talk about the brain, and intelligence, and computers. It is greatly overwhelming and hard to understand most of it. It takes time and creative thinking to find information that you can relate too.

What is artificial intelligence?

Put simply it's the study of programming software to make computers and other items smarter. It also means that programming software to learn from mistakes, or errors and fix them automatically.

Making computers learn on their own is much less time consuming than having to reprogram it all the time. Robots too have learning capabilities as well. Using censers, cameras, and other kinds of information gathering, they can perform a wide range of different motions, including being able to walk up stairs, dance, and interact with people.

What is artificial intelligence? Well you can see it in action every day. It started out teaching strategy games to computers, like chess. The basic principles of the game were programmed in, as well as what each pieces movements were. As people play against the computer, it learns from your techniques and using that knowledge calculates the best way to beat you.

It can also use what it learns to predict what possible moves you may make, and adjusts strategies accordingly, often calculating many moves ahead. With the human factor, sometimes we do things that are unpredictable, and so if you make a move that wasn't predicted by the computer, it will extrapolate accordingly.

When we play on gaming stations against the computer, this is a form of artificial intelligence as well. With the newer gaming platforms the computer brain uses knowledge programmed into it to act accordingly to your moves, and tries different ways to defeat you, or to help you out. Signals sent from the controller are relayed to the gaming brain and is translated into movement, shooting, etc.

What is artificial intelligence? Making computers and other devices smarter, faster and using your input to do things that make life easier.

You see artificial intelligence in a variety of applications, from smart appliances that will automatically save electricity, to a whole house wired to turn lights on automatically when you enter a room, and turning them off when you leave. Refrigerators than can sense when you are getting low on something and reminds you using a monitor and key pad on the outside of the door.

Coffee makers and other small appliances often have small computers in them that can turn on automatically at a certain time, and have your coffee ready for you when you get up.

The field of artificial intelligence is growing by leaps and bounds. If you have the skills and knowledge, it is possible to get into one of these exciting careers.

You will have a hand at new breakthroughs in robotic sciences, for consumer or military applications, as well as medical and manufacturing technologies. There is a wide array of opportunities that can await you, but will not wait for you.

You can find out more information through the internet, but as I said before, make sure you type in exactly what you're looking for, or you may get more than you bargained for.

You can find out what is artificial intelligence and how it works for you, find a job in this field, and even buy 'smart' products the make daily activities easier to do. There are assortments of robots that you can buy, from small figures that can interact with you, to smart vacuums that can pick up around the house by themselves.

It's quite possible that the future of artificial intelligence is here. And if it is, as some in the know are coming to believe, then it is called HTM, or Hierarchical Temporal Memory.

The Future of Artificial Intelligence

HTM is a machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. Jeff Hawkins is an engineer, entrepreneur, and author of the 2004 published book about A.I. titled On Intelligence .

Dileep George is a former electrical engineering graduate student who was working part-time at the Redwood Neuro-Science Institute, a small think tank founded by Hawkins in 2002 (it's now part of UC Berkeley). At the same time he was working at the Institute he was looking for a PhD topic about the brain and decided he wanted to team up with Hawkins.

The possible future of artificial intelligence is based on a key insight that Hawkins had in the classic "Eureka" moment back in his UC Berkeley PhD study days. As he pondered the question of how he would react if a blue coffee cup were to quite suddenly materialize on his desk it came to him.

Hawkins' insight may hold the key to the future of artificial intelligence development: that all former A.I. models and theories eventually crumbled or came up against impassable walls because they did not grasp the core fact that the human brain's capacity is centrally based on predicting the future; both the immediate future and the far-flung future.

Hawkins is completely fascinated by the concept that "when you are born, you know nothing," and he and George have begun creating and expanding a software platform that transfers this principle to artificial intelligence design: when it's "born", it knows nothing. But, like human brains, this A.I. will have "instincts"--the human-programmed protocols for learning.

HTM is the likely future of artificial intelligence because with this platform, an A.I. must create memories from sensory input, learn from experience, and then use those remembered and processed experiences to make predictions about the future.

This goes right down to the basics of recognizing what an object is from only obscured, partial, or distorted sensory data--something that human beings take doing for granted--but which the world's chess champion computer program would hopelessly fail at.

Just as a little tiny infant begins doing instinctively from the second it emerges from the womb, such an A.I. grows itself--it starts from knowing nothing and learns from having and then assimilating experiences.

Future of Artificial Intelligence

HTM programming rests on the core concept of hierachical knowing. This practical insight comes from the observation that the human brain's neocortex--the top layer of our three layers of gray matter--makes up at least 60% of the human brain by itself.

Almost all higher level insights and calculations, such as Hawkins' own revelation about the basic structure of human intelligence, are made within the neocortex. Fun Fact: Humans is the largest neocortex, and takes up the highest percentage, of that of any mammal on earth... by far.

The future of artificial intelligence will be built upon something out of the ancient past: the pyramid. This means that A.I. "neurons" are arranged hierarchically, which Hawkins, backed up by a great deal of neurological research, presumes to be the situation with human brains. Lower-level neurons are like those which receive the basic sensory input from, say, a musical instrument.

These tones are first picked up by the ear, then interpreted by those lower-level neurons, which process information about them and then kick them up to mid-level neurons. The mid-level neurons then follow suit but with higher processing, and then kick them up to the top.

Then the top makes the ultimate processing and then translates signals about those stimulations back down to the low-level neurons, "teaching" them like a "more experienced" guide or mentor. Just as there are fewer geniuses than average-intelligence human beings, so there are fewer higher-level neurons than mid-level or lower-level neurons. The future of artificial intelligence is hierachical in structure, just like human brains, just like human society.

It was George, over the course of several weekends, who constructed the original basic, elemental A.I. model of the process used in the human visual cortex. The usual modeling of a program is linear; it processes data and makes calculations in just one direction.

George then created multiple, parallel layers of nodes, with each node embodying thousands of neurons in cortical columns and each in and of itself a tiny computer program with a unique ability for processing information, remembering patterns, and making predictions.

Hawkins' company Numenta has been at work with Edsa Micro. Adib Nasle, Edsa's president, says of HTM-based A.I., "We've seen some incredible speed improvements. Some approaches, you give too many examples and they get dumber. HTM seems not to suffer from that. It's pretty impressive."

The most likely future of artificial intelligence  is pretty impressive, indeed.

Artificial Intelligence and Socks in a Galaxy Far, Far Away Dook Skywacker gazed up from the puddle he had just tripped into. “R3D4-I require you to fetch me some dry socks from the space shuttle,” the Heroic Cosmo Knight commanded. So, the brave robot began to do its required job.

Artificial Intelligence and Robotics

If only it were that simple! However, in the true world of artificial intelligence, even an easy job like fetching a couple of dry socks from the hold of a space shuttle is filled with unintentional risk.

Because beyond the stage of a space program, real robots have trouble with the types of principles, even a two year old would find very simple to understand. The appearance of artificial intelligence is that it is not truly intelligence as we see it.

A person’s brain is not only clever-it is clever in an extremely particular way. Even though a computer can add millions of calculations per second, a person’s brain can use logic to solve problems that no computer can.

The reason for this is that true intelligence is created to make leaps of relation and intuition that are very hard to note into the types of algorithms a computer can comprehend. One of the best methods to get this is to examine an easy problem, such as R3 getting Dook Skywacker a pair of dry socks.

The Intelligent Agent

One of the biggest aspects of an artificial intelligence is the capability to be an intelligent agent. What an intelligent agent is is an artificial intelligence that is able to take in its surroundings and can decide what it must do within that surrounding to accomplish its goal.

This is not very easy at first. For instance, the action of being aware of the surroundings is much harder for a computer than it is for a living being. Even though the only sense used to perceive this is sight.

Machine Vision

Now R3D4 must find the socks in Skywacker’s space shuttle. The first issue is that its cameras do not work in the same way as Skywacker’s eyes. A PC’s “eyes” - are also called a machine vision system, need:

Robotics AI - Machine Vision

1) Cameras (digital or analog), with the capability to keep images. One camera will suffice -if you add two for binocular sight, it will make the situation more complex.

2) Exceptional light sources. The reason for this is that PC networks are very selective about sensing light in particular ways. If, for instance, soft light makes a red sock appear black, a machine vision system might experience a breakdown immediately.

3) Programs in which to handle pertinent features of the perceived item. This is far more difficult than it sounds; a computer must keep a matching image of the item being regarded, or it will not recognize it, because computer neural networks cannot conclude an object’s class from just generally view it.

Class Inference

All is good so far. However let us pause a while and discuss necessity number three. Where Dook’s eyes look at an entire image and instantly understand it as an object, SOCK, R3’s camera views the sock as a bunch of pixels. It will have to put those pixels together into one image, and then transpose that combination of pixels into an image it recognized as being a sock. Sound easy? Not likely.

R3 might have a photo of a sock in its memory storage. However, it is just that single photo. What if the sock in its memory is red and not blue? R3 will not see it because it does not match the photo. What if the sock in the memory is not folded and Dook’s sock has been nicely rolled up by faithful Princess Cinabuns? Again, R3 will not see the sock because it does not match the photo.

Pattern Recognition

How is Dook able to recognize the sock no matter what it may look like? It is because Dook is able to make assumptions about the sock. He has a mental image of what “sockness” means in his mind. All he has to do is search for defining signs such as L-shapes, tubular shapes, made of cloth, one side open, and he can also designate his rolled up red socks to the “sock” column.

This is termed, as “pattern recognition” Pattern recognition is one of the largest walls to building true artificially intelligent robots. Pattern recognition is the capability to put together that mental image of an item or surrounding from several cues that the item gives.

Pattern recognition is a very difficult program because every solitary pattern has several elements that can alter the pattern’s cues. To the exact mind of a computer, a sock being in shadow can completely alter the nature of the sock’s pattern.

However, to a human mind, the distinction of shading easily becomes reassigned to the column of “shade” with the inherited idea that items in shade are darker than items in light. (As a funny side remark, this type of thinking is known as “fuzzy logic.”)

Cognitive Architecture

To enable R3 to locate Dook’s sock will take an exceptional type of programming, a type of neutral net that is known as cognitive architecture. A subsidiary of the concept of intelligent agents, cognitive architecture is developed by situating many types of “thinking” programs into one bigger control program. For instance, one section of R3’s cognitive architecture must include the concept of visual states, such as the concept that light can influence the visual pattern of an item.

This program would view the general light given off in an area and would be able to tell whether a shadow was there. Another section of the CA program must recognize what theoretical characteristics determine what is “sockness.”

This section of the CA would search for characteristics of texture, tubes, open ends, and common L shapes. If the item being looked at has enough of those characteristics, R3’s neural net would be able to declare, “Hmmm. This matches the criteria of what determines a sock,” it can then categorize that item as a sock.

Finally, R3’s cognitive architecture must know how to test that so called “sock” against the data that it has kept on socks. For instance, the same elementary characteristics of sockness (rough texture, tube with L shape, open mouth) can as well be used to describe a snake.

Think of Dook’s shock when R3 comes with a ten-foot python back to the puddle. As a human, Dook is aware of methods to test his hypothesis; if the “sock” slithers and hisses, it is most likely not a sock.

As well, R3 will require a section of his intelligent agent program that can test items, gaining further information that can clarify whether it carrying a boa constrictor, or an argyle.

Similar to the way a baby shoves stuff into its mouth, R3 will require ways of experimenting with items in its surroundings, and gaining knowledge from experience that any “sock” that has sharp teeth and hisses is likely not a good selection.

Will Dook don a cozy pair of pythons? Not likely, however he might want his trusty laser sword nearby when R3 comes back with the dry pair has asked for. Nevertheless, as we have shown here, even an easy job like getting a pair of socks can be a very trying task when artificial intelligence and robotics mix.

For the near future, it appears as though the service of our handy robot friend will be restricted to the factory area and the laboratory, where the elements of machine vision, cognitive architecture, and pattern recognition are better harnessed.

Eventually perhaps, R3 will be clever enough to tell a pair of socks from a snake, however for the moment, Dook Skywacker will have to march to the ship and fetch his own dry socks.

A Definition of Artificial Intelligence And Its History As A Practical Research Field

Since the dawn of science fiction, the concept of man-made helpers, thinking machines that can do undirected work, have been a staple of adventure fiction, ranging from Mary Shelley's Frankenstein to the friendly droids of Star Wars. The process by which programmatic entities gain the ability to take input, adapt their behaviors and solve problems is a practical definition of artificial intelligence.

A Definition of Artificial Intelligence

Working from a definition of artificial intelligence to something that has real world applications has been a process with lots of false starts and programmatic blind alleys. The initial research into artificial intelligence tried to work from formal problem solving methods, using deductive and inductive logic processes.

Initial progress, working from Alan Turing's insight on the binary nature of mathematics, was quite rapid - the initial foundations of artificial intelligence research was founded in the 1950s, and many of the luminaries of the field got started there, from John McCarthy to Marvin Mnsky and Allen Newell. The great founding of AI laboratories in the 1950s promised that within twenty years, a computer would be able to do anything a human mind could.

What hindered this is that human brains don't actually use deductive problem solving; they use inexact neural mapping techniques.

Attempting to brute force cognition with AI software results in an explosion of options and considerations that need to be iteratively searched, and rapidly outpaced the ability of computers in the '50s and '60s to handle it. Even within their limitations, the early AI programs made seemingly astonishing progress, with the ability to solve word problems algebraicly and proving logical theorems.

This 'amazing advance' showcases one of the primary differences between human beings and computers: It's much easier to get computers to do things logically than it is to get humans to knuckle down and apply formal logic structures to problems.

Even so, the expansion of the field in the 1960s lead to widespread government funding...most of which went away when the operational definition of artificial intelligence showcased its limits (driven by limitations of computation architectures) in the 1970s.

The 1980s saw a brief resurgence of AI research when computers got powerful enough and cheap enough to run expert system databases, which used a limited form of some of the artificial intelligence techniques to allow a user to ask questions of a computer within a very narrowly defined field and get responses that 'made sense'.

This definition of artificial intelligence resulted in AI 'savants' of sorts, and began the field of Bayesian stochastic research which underlies all the modern search engine technologies.

The 1990s saw major advances in computational capabilities, and algorithms that found shortcuts for the brute force applications of AI-style research.

These algorithms can be thought of as logical filters or process filters, and allow AI researchers to work with incomplete or 'fuzzy' data, allowing the definition of artificial intelligence to become a bit more humanistic in what it can be expected to handle.