Prediction

We know what the prediction is. In short, the prediction is to some information which is likely to occur in upcoming future. There are lot of fortune tellers that would tell us the future. They may occur or may not occur. We won’t believe what they say. But in the computational world, the computer telling the future about something is likely to be believed. As here the fortune is told by the systems by analyzing the past data and records. Some of the successful prediction is done in forecasting the weather. Despite of their different nature, the prediction is done with some knowledge about the past elements and may be some other available information. So the thing is how is this prediction done? and here is one basic version of the sequential prediction problem.

Basically in the sequential prediction problem, the forecaster studies the elements of the sequence and guesses the next element of the sequence on the basis of the previous observation. In the classical statistical theory of the sequential prediction, the elements are assumed to be a realization of the stationary stochastic process. So in this the properties of the process is estimated on the basis of the past observation. The risk of the prediction rules can be derived from some of the loss calculating function which measures from the differences between the predicted value and the outcomes.

Without the proper probabilistic modeling, the idea of risk cannot be defined at all. Many possibilities may exist . in our basic model the performance of the forecaster is measured by the loss accumulated during many predictions.The loss is calculated by the help of some fixed loss function. To provide the better baseline, the reference forecaster is introduced. They make their forecasting before the next outcome is revealed. The forecaster can make their own outcome and take the reference forecaster outcome as an advice. This results in forecaster take their loss records to get the best outcome in further forecasting.

How to define intelligence?

Well its been a very long time since my last blog. I have been finding the answers on how do we define intelligence? And here is what I have learnt. Its been a very long time on which the debate is going on the necessities of intelligence, but sadly, there is little sign of consensus. Lets have a brief talk on it :

“AI is concerned with methods of achieving goals in situations in which the information available has a certain complex character. The methods that have to be used are related to the problem presented by the situation and are similar whether the problem solver is human, a Martian, or a computer program.”

Intelligence usually means “the ability to solve hard problems”.

“By ‘general intelligent action’ it seems to be the same sort of intelligence as we see in human action.In any real situation behavior appropriate to the ends of the system and adaptive to the demands of the environment can occur, within some limits of speed and complexity.”
And after all these years of study, we still don’t know very much about it. There are a lot more queries than answers. We all know that a well-founded definition is usually the result, rather than the starting point, of scientific research. However, there are still reasons for us to be concerned about the definition of intelligence at the current time. Though clarifying the meaning of a concept always helps communication, this problem is especially important for AI. Without a clear idea of what intelligence is, it is very hard to say why AI is different from computer science or psychology. More importantly, the researcher in this field needs to justify his/her research plan according to such a definition. Anyone who wants to work on artificial intelligence has to face a two-phase assignment:

• to choose a working definition of intelligence
• to produce it in a computer.

A working definition is a definition concrete enough that you can directly work with it. By accepting a working definition of intelligence, it does not mean that you really believe that it fully captures the concept “intelligence”, but that you will take it as a goal for your current research project. Therefore, the lack of a consensus on what intelligence is does not prevent each researcher from picking up a working definition of intelligence. Actually, the thing is, unless we keep one definition, we wont be able to claim that we are working on artificial intelligence. By accepting a working definition of intelligence, the most important commitments a researcher makes are on the acceptable assumptions and desired results, that helps us binding all the concrete work that follows. Before studying concrete working definitions of intelligence, we need to set up a general standard for what makes a definition better than others. Carnap meets the same problem when he tried to clarify the concept of “probability”. The task “consists in transforming a given more or less inexact concept into an exact one or, rather, in replacing the first by the second”, where the first may belong to everyday language or to a previous stage in the scientific language, and the second must be given by explicit rules for its use. According to him, the working definition, must fulfill the following requirements:

1. It is similar to the concept to be defined, as the latter’s vagueness permits.
2. It is defined in an exact form.
3. It is fruitful in the study.
4. It is simple, as the other requirements permit.

All these requirements are very much reasonable and suitable for the current purpose. And let us have a look what they mean concretely to the working definition of intelligence:

• Similarity: Though “intelligence” has no exact meaning in everyday language, it does have some common usages with which the working definition should agree. If we consider that a normal human beings are intelligent, but most animals and machines are either not intelligent at all or much less intelligent than human beings.
• Exactness: Given the working definition, whether a system is intelligent should be clearly decidable. For this reason, intelligence cannot be defined in terms of other ill-defined concepts, such as mind, thinking, cognition, intentionality, rationality, wisdom, consciousness, and so on, though these concepts do have close relationships with intelligence.
• Fruitfulness: The working definition should provide concrete point for the research based on it, for instance, what assumptions can be accepted, what phenomena can be ignored, what properties are desired, and so on. Most importantly, the working definition of intelligence should contribute to the solving of fundamental problems in AI.
• Simplicity: As intelligence is surely a complex mechanism, the working definition should be simple. Theoretically, a simple definition makes it possible to explore a theory in detail; and practically a simple definition is easy to implement.

For our current purpose, there is no exactly “right” or “wrong” working definition for intelligence, but there are comparative ones. When comparing proposed definitions, the four requirements may conflict. For example, one definition is more fruitful, while another is simpler, other may be exact and may be the other one is similar. In such a condition, some weighting and trade-off becomes necessary factor. However, there is no evidence showing that in general the requirements cannot be satisfied at the same time.

Knowing Visual Studio

Visual studio is found to be versatile IDE. One can have a lots of working environment in visual studio. The interface for visual studio is just given below.

 

Once you are working in this environment, you should know something about the visual studio environment some of the terms you need to know is

1. Start page: This is the main page where you enter to visual studio.You can either create a new project or open an existing project. For any latest news you can have the view from the next portion of start page.
2. Title bar: Title bar consists of the title of the project you are working on.
3. Menu bar: Consists of various types of menus where you can perform maximum operations.
4. Status bar: Consists of the status report of the project you are working on.
5. Toolbox: Consists of various types of controls for your project that you are working on. Toolbox item may be different from your project types. Just you need is to drag and drop your tool in the design pane.
6. Solution explorer: Here your all the project files are registered. You can see your all project files over here.
7. Properties: Here you can design the interface of your tool. Once you design the interface, it comes with the standard type. you can modify them exploring and editing the tool properties. Various tool have their own properties.
8. Error list: If found any errors while building the solution, the errors are listed here .

There are a lot of tools and lot of terms in visual studio. Once you get familiar with these things you can easily have a good interface and better programming with visual studio.