Technology is moving at a speedier pace than at any other time. Pushing forward to 2018, the technologies will experience the most huge change and have the greatest effect on our lives.
Artificial Intelligence, Cloud, mobile and big data have, together, changed the very texture of traditional IT services and programming development. Indeed, even as the worldwide IT industry revaluates itself to come with new computerized realities, a great many old IT and tech occupations around the globe confront imminent extinction, thus, rest guaranteed, birthing numerous new ones. For techies as of now in their pined for IT occupations and for those nearly beginning their careers, 2018 must be the year of reinventing their insight, abilities and qualifications and popping up for the new employment world, divided by the USD 4.0 trillion cloud computing and big data analytics market.
The Intelligent Computer
The intelligent computer has come to be called Artificial Intelligence (AI). This can be classified as a branch of engineering and as a kind of science. One thing it could be is “Making computational models of human behavior”. Since we believe that humans are intelligent, therefore models of intelligent behavior must be AI. In this way of thinking of AI, how would you proceed as an AI scientist? One way, which would be a kind of cognitive science, is to do experiments on humans, see how they behave in certain situations and see if you could make computers behave in that same way. Imagine that you wanted to make a program that played poker. Instead of making the best possible poker-playing program, you would make one that played poker like people do.
Another way is to make computational models of human thought processes. This is a stronger and more constrained view of what the enterprise is. It is not enough to make a program that seems to behave the way humans do; you want to make a program that does it the way humans do it. A lot of people have worked on this in cognitive science and in an area called cognitive neuroscience. The research strategy is to affiliate with someone who does experiments that reveal something about what goes on inside people’s heads and then build computational models that mirror those kind of processes.
A crucial question is to decide at what level to mirror what goes on inside people’s heads. Someone might try to model it a very high-level, for example, dividing processing into high-level vision, memory, and cognition modules; they try to get the modularity to be accurate but they don’t worry too much about the details of how the modules are implemented. Other people might pick the neuron as a kind of computational unit that feels like it’s justified in terms of neurophysiology, and then take that abstract neuron and make computational mechanisms out of it. It seems justified because we know that brains are made out of neurons. But then, if you talk to people that study neurons, you find that they argue a lot about what neurons can and can’t do computationally and whether they are a good abstraction so maybe you might want to make your models at a lower level. So, it’s hard to know how to match up what we know about brains with computational models.
Definition – What does Artificial Intelligence (AI) mean?
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:
- Speech recognition
- Problem solving
Techopedia explains Artificial Intelligence (AI)
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.
Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:
- Problem solving
- Ability to manipulate and move objects
Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious approach.
Machine learning is another core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.
Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.
Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.
Clearly, AI is one of the biggest tech trends right now, and anyone with solid tech start-up ideas in the area of machine learning has the potential to go after big start-up investment rounds, as well as to be acquired by the likes of Google, Salesforce or Apple, all of which have acquired more than 40 tech start-ups related to AI.
Software that gathers information about an environment and takes actions based on that information
- a robot
- a web shopping program
- a factory
- a traffic control system…
We’re going to be talking about agents. This word used to mean “something that acts.” Now, people talk about Web agents that do things for you, or human publicity agents. When I talk about agents, I mean something that acts. So, it could be anything from a robot, to a piece of software that runs in the world and gathers information and takes action based on that information, to a factory, to all the airplanes belonging to United Airlines. So, I will use that term very generically. When I talk about computational agents that behave autonomously, I’ll use agent as a shorthand for that.
The Agent and the Environment
How do we begin to formalize the problem of building an agent?
- Make a dichotomy between the agent and its environment.
- Not everyone believes that making this dichotomy is a good idea, but we need the leverage it gives us.
Here’s a robot and the world it lives in. The robot is going to take actions that affect the state of the environment and it’s going to receive precepts’ somehow that tell it about what’s going on in the environment. There is a loop where the agent does something that changes the state of the environment, then it perceives some new information about the state of the environment. There’s a hard question of where to draw the line between the agent and the environment. We’ll spend our entire time thinking about the agent as a computational entity. So, I should really draw this cartoon differently. Since we’re going to be thinking about what is going on in the agent’s head and so the actions, instead of going from the body to the environment, we are going to be going from the agent’s head to its wheels and the precepts’ are coming from the camera into its brain.
What do we need to write down when we talk about the problem of making an agent? How can we specify it really carefully?
- A-The Action Space
- P -The percept space
- E -The environment: A* → P
Now, we have a set of actions and a set of percepts and we need the environment. We need, in order to say what the problem is for our agent, to describe the world that the agent lives in. At the most detailed level, we can think of the environment as being a mapping of strings of actions into percepts. You could say, what does the environment do? Usually we’ll think of the environment as having some internal state, which may not be visible to the agent. We can describe how the environment works by specifying two functions. The perception function says what percepts the agent will receive as a function of the current state of the environment. And the world dynamics or state transition function says what the next world state will be, given the previous world state and the action taken by the agent.
A rational agent takes actions that it believes will achieve its goals.
- Assume I don’t like to get wet, so I bring an umbrella. Is that rational?
- Depends on the weather forecast and whether I’ve heard it. If I’ve heard the forecast for rain (and I believe it) then bringing the umbrella is rational.
Let’s talk about rationality, since I said that what we wanted to do was to make rational agents. So, what do I mean by that? The standard definition of rationality is: A rational agent takes actions it believes will enable it to achieve its goals. This is all in high-level pseudo-psychological talk that makes some people nervous. We can cache it out into something more concrete in a minute but the idea is that you’re rational if you take actions that are consistent with what you are trying to achieve in the grand scheme of things.
Let’s say that I don’t like to be wet and so when I come out of my office in the morning, I bring an umbrella. Is that rational? Depends on the weather forecast, and whether I’ve heard the weather forecast. If I heard the weather forecast and I’m disposed to believe it, and I think it’s going to rain, then it’s rational to bring my umbrella. Whether it’s going to rain or not, whether you think it’s dumb for me to want to stay dry, given what I’m trying to do and given what I know, we’ll say an action is rational if it would lead to doing a good job of what I’m trying to do.
AI is at the centre of a new enterprise to build computational models of intelligence. The main assumption is that intelligence (human or otherwise) can be represented in terms of symbol structures and symbolic operations which can be programmed in a digital computer. There is much debate as to whether such an appropriately programmed computer would be a mind, or would merely simulate one, but AI researchers need not wait for the conclusion to that debate, nor for the hypothetical computer that could model all of human intelligence. Aspects of intelligent behaviour, such as solving problems, making inferences, learning, and understanding language, have already been coded as computer programs, and within very limited domains, such as identifying diseases of soybean plants, AI programs can outperform human experts. Now the great challenge of AI is to find ways of representing the common sense knowledge and experience that enable people to carry out everyday activities such as holding a wide-ranging conversation, or finding their way along a busy street. Conventional digital computers may be capable of running such programs, or we may need to develop new machines that can support the complexity of human thought.