Artificial Intelligence
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Artificial Intelligence

Project period

06/30/2020 - 06/30/2020




Artificial Intelligence
Artificial Intelligence

Artificial Intelligence is defined as follows: “ The potential or ability to design machines which will be able to do things which require intelligence if it’s done by human beings”. Artificial Intelligence requires important criteria to be possessed like perceptual learning, critical thinking, and memory.

Also, artificial intelligence is the science of developing computer codings or algorithms that aim to execute tasks which will need more intelligence if they were performed by human beings. Human actions such as moving from one place to another place, acquisition of knowledge, logical reasoning, using original ideas, and socializing, etc seems out of reach. However, we are still logging back in machine learning when compared to human capability in every field.

There are few people who support and oppose Artificial intelligence. Those who oppose AI say that we cannot imply artificial intelligence and it is not possible to build machine codings because human intelligence cannot be mathematically processed. A few of the proposed systems prove that AI cannot be truly intelligent. But Artificial intelligence is considered to be a fast trending target.

Why: Problem statement

Artificial intelligence (AI) is the process of human intelligence processed by machines, mainly computer systems. These simulations include studying, exploring, logical thinking and self-correction. 

AI can be grouped in two ways. It can be either weak or strong. The Weak one is also termed as narrow AI, Narrow AI will be trained and designed for a specific task. Few examples include Virtual personal assistants, such as Apple's Siri. Strong AI is defined as artificial general intelligence, here the human reading abilities are involved. When presented with an unknown task, a strong AI system is able to find a solution without human intervention.

This project explains the recent discourse that links AI to various kinds of scenarios. The presentation maps about the types, applications, uses and few real-time examples.

How: Solution description

Symbolic AI refers to a classical approach to AI that aims to build the logical thinking of mechanisms of humans. These mechanisms feed data in logical form and are returned by an important modeling of knowledge. AI technique is considered to be a experts algorithm, capable of building logical reasons from available facts  

Apart from symbolic AI, machine learning techniques were introduced to develop a design directly from previously available experience.

Machine learning technique has two steps:

  • The learning phase uses the input data (e.g. images of animals for classification task) and to figure parameters. 

  • learned parameters takes the input and performs the task accordingly.

The below five steps explain the process of solving a problem. It is explained in the following figure.

How is it different from competition

Artificial Intelligence is the capability of a computer machine to learn and think. Almost everything is considered Artificial Intelligence when it needs a coding or algorithm for doing something which requires human intelligence. In this project, we will find a few advantages of Artificial Intelligence.

  • Reduces Human error

  • It takes risk replacing human beings

  • Working 24*7

  • Helps in regular basis jobs

  • New findings

When human error originates in a very lack of knowledge, the psychological feature augmentation enabled by AI will create the operator fully fool-proof by providing him with an operating procedure and by guiding him step by step. AI helps risk replacing human beings by using self driving cars, AI robots for operating machines etc.,

Who are your customers

AI is almost used for every sector like business people, industries, Institutions and every common person. AI will help businesses increase sales, detect fraud, improve client experience, automate the work processes and also provide predictive analysis. Industries like health care, automotive, money services and logistics  have a great deal to realize from AI implementations. Artificial intelligence  will facilitate health care service providers with better tools for early diagnostics. The autonomous cars are a direct result of improvements in AI. AI has already been applied to education primarily in some tools that facilitate developing skills and testing systems. As AI educational solutions still mature, the hope is that AI will help fill desired gaps in learning and teaching and allow schools and teachers to do more than ever before. AI powers several programs and services that facilitate us to do everyday things such as connecting with friends, using an email program, or using a ride-share service.

If you have got reservations regarding the use of artificial intelligence, it should be comforting to know that most of us have been using AI on a daily basis for several years.

Project Phases and Schedule

  • Defining the problem : To achieve success in realistic environments, reasoning systems should establish and implement effective actions within the face of ineluctable integrity in their information concerning the globe. AI investigators have long completed the crucial role that ways for handling integrity and uncertainty should play in intelligence. Though we've created vital gains in learning and deciding beneath uncertainty, tough challenges stay to be tackled.

  • Analysing the Problem: Faced with a challenge, a decision-making system should admit some rules or, additionally, a model that expresses relationships among observations, states of the globe, and system actions. Many ways are studied for dynamically building representations of the globe that area unit custom-tailored to perceived challenges. Framing a call decision problem refers to a collection of relevant distinctions and relationships, at the suitable level of detail, and weaving along a decision model. Framing a call decision problem has resisted rationalisation. Even so, strides are created on model-construction techniques, usually counting on the utilization of logical or decision-theoretic procedures to piece along or prune away distinctions, as an operator of the state of the globe, yielding manageable targeted models. We've an extended way to go into our understanding of principles for tractably determining what distinctions and dependencies are relevant given a situation.

  • Identification of solution: AI systems should build choices in associate evolving surroundings which will amend dramatically over time, in response to actions that a system has taken or can take. Most analysis on action beneath uncertainty has targeted models and illation procedures that area units essentially atemporal, or that encrypt temporal distinctions as static variables. We have a tendency to endow systems with the flexibility to represent and reason concerning the time-dependent dynamics of belief and action, together with such crucial notions because of the persistence and dynamics of world states. we have a tendency to conjointly have to be compelled to develop higher suggests that of synchronizing associate agent's perceptions, inference, and actions with vital events within the world.

  • Choosing the solution: There has been analysis on ways for computing the arrogance in results given a model and downside instance. This work highlights opportunities for developing ways that associate agents might use to look for crucial gaps in its information concerning the globe. continued this analysis is valuable for building decision-making systems which will perform active, directed learning. So, choosing a solution for a problem is really really important.

  • Implementation: We need to develop techniques that permit the associate agent to endlessly partition its time not solely among many phases of a pressing analysis however conjointly among a spread of tasks that may facilitate the agent to maximise the expected utility of its behavior over its entire time period. Tasks which will like in progress attention embrace coming up with for future challenges, inquisitorial and processing a utility model, prefetching data that's doubtless to be vital, compilation of parts of expected forthcoming analyses, experimenting with its reasoning and motion management systems, and learning concerning crucial aspects of the external world.


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