ARTIFICIAL   INTELLIGENCE

1.MEANING 

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.


The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning, which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enable this automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video.


In general terms, AI refers to a broad field of science encompassing not only computer science but also psychology, philosophy, linguistics and other areas. AI is concerned with getting computers to do tasks that would normally require human intelligence. Having said that, there are many points of view on AI and many definitions exist. Below, some definitions highlight its key characteristics.


2.TYPES OF ARTIFICIAL INTELLIGENCE 



The four A.I. types are


Reactive Machines

Limited Memory

Theory of Mind

Self Aware


Reactive Machines


Reactive Machines perform basic operations. This level of A.I. is the simplest. These types react to some input with some output. There is no learning that occurs. This is the first stage to any A.I. system. A machine learning that takes a human face as input and outputs a box around the face to identify it as a face is a simple, reactive machine. The model stores no inputs, it performs no learning.


Static machine learning models are reactive machines. Their architecture is the simplest and they can be found on GitHub repos across the web. These models can be downloaded, traded, passed around and loaded into a developer’s toolkit with ease.


Limited Memory


Limited memory types refer to an A.I.’s ability to store previous data and/or predictions, using that data to make better predictions. With Limited Memory, machine learning architecture becomes a little more complex. Every machine learning model requires limited memory to be created, but the model can get deployed as a reactive machine type.


Theory of Mind


We have yet to reach Theory of Mind artificial intelligence types. These are only in their beginning phases and can be seen in things like self-driving cars. In this type of A.I., A.I. begins to interact with the thoughts and emotions of humans.


Self-Aware


Finally, in some distant future, perhaps A.I. achieves nirvana. It becomes self-aware. This kind of A.I. exists only in story, and, as stories often do, instills both immense amounts of hope and fear into audiences. A self-aware intelligence beyond the human has an independent intelligence, and likely, people will have to negotiate terms with the entity it created. What happens, good or bad, is anyone’s guess.


ARTIFICIAL INTELLIGENCE HOW IS WORK


AI Approaches and Concepts

Less than a decade after breaking the Nazi encryption machine Enigma and helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a simple question: "Can machines think?" 

Turing's paper "Computing Machinery and Intelligence" (1950), and its subsequent Turing Test, established the fundamental goal and vision of artificial intelligence.


  • Machine Learning : ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns, analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data, saves a human time for businesses and helps them make a better decision.
  • Deep Learning : Deep Learning ia an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome.
  • Neural Networks : Neural Networks work on the similar principles as of Human Neural cells. They are a series of algorithms that captures the relationship between various underying variabes and processes the data as a human brain does.
  • Natural Language Processingc: NLP is a science of reading, understanding, interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly.
  • Computer Vision : Computer vision algorithms tries to understand an image by breaking down an image and studying different parts of the objects. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations.
  • Cognitive Computing : Cognitive computing algorithms try to mimic a human brain by anaysing text/speech/images/objects in a manner that a human does and tries to give the desired output.


WHERE IS AI USED 


AI is used in different domains to give insights into user behaviour and give recommendations based on the data. For example, Google’s predictive search algorithm used past user data to predict what a user would type next in the search bar. Netflix uses past user data to recommend what movie a user might want to see next, making the user hooked onto the platform and increase watch time. Facebook uses past data of the users to automatically give suggestions to tag your friends, based on their facial features in their images.The uses of Artificial Intelligence would broadly fall under the data processing category, which would include the following:

Searching within data, and optimising the search to give the most relevant results

Logic-chains for if-then reasoning, that can be applied to execute a string of commands based on parameters

Pattern-detection to identify significant patterns in large data set for unique insights

Applied probabilistic models for predicting future outcomes


ARTIFICIAL INTELLIGENCE IN EVERYDAY LIFE



Online shopping: Artificial intelligence is used in online shopping to provide personalised recommendations to users, based on their previous searches and purchases.


Digital personal assistants: Smartphones use AI to provide personalised services. AI assistants can answer questions and help users to organise their daily routines without a hassle.


Machine translations: AI-based language translation software provides translations, subtitling and language detection which can help users to understand other languages.


Cybersecurity: AI systems can help recognise and fight cyberattacks based on recognising patterns and backtracking the attacks.


Artificial intelligence against Covid-19: In the case of Covid-19, AI has been used in identifying outbreaks, processing healthcare claims, and tracking the spread of the disease.


Robots in AI:The field of robotics has been advancing even before AI became a reality. At this stage, artificial intelligence is helping robotics to innovate faster with efficient robots. Robots in AI have found applications across verticals and industries especially in the manufacturing and packaging industries. Here are a few applications of robots in AI


AI MARKET SIZE BY SECTORS 




Analytics India Magazine has analysed the AI market for 2021 by sector or industry.

The sector-wise distribution of AI services covers:


Firms that provide services within a sector or industry and utilize AI to deliver those services – such as Amazon within eCommerce and JP Morgan within BFSI.Firms within another segment, such as Boutique AI and Analytics, but provide exclusive services to a particular function within an industry – such as Mad Street Den providing Computer Vision and Artificial Intelligence services to the Retail sector or Spyne providing services to the broadbased Digital and Media industry.


Healthcare:

Administration: AI systems are helping with the routine, day-to-day administrative tasks to minimise human errors and maximise efficiency. Transcriptions of medical notes through NLP and helps structure patient information to make it easier for doctors to read it.

Telemedicine: For non-emergency situations, patients can reach out to a hospital’s AI system to analyse their symptoms, input their vital signs and assess if there’s a need for medical attention. This reduces the workload of medical professionals by bringing only crucial cases to them.

Assisted Diagnosis: Through computer vision and convolutional neural networks, AI is now capable of reading MRI scans to check for tumours and other malignant growths, at an exponentially faster pace than radiologists can, with a considerably lower margin of error.

Robot-assisted surgery: Robotic surgeries have a very minuscule margin-of-error and can consistently perform surgeries round-the-clock without getting exhausted. Since they operate with such a high degree of accuracy, they are less invasive than traditional methods, which potentially reduces the time patients spend in the hospital recovering.


Automation – AI can automate tedious processes/tasks, without any fatigue.

Enhancement – AI  can enhance all the products and services effectively by improving experiences for end-users and delivering better product recommendations.

Analysis and Accuracy– AI analysis is much faster and more accurate than humans. AI can use its ability to interpret data with better decisions.



FUTURE DIRECTION 



The substantial growth in the AI market across various sectors and segments signifies the reliance of broad-based processes and services on Artificial Intelligence.


Additionally, IT services and Boutique AI & Analytics firms are now providing end-to-end AI, Machine Learning, and Automation offerings in India for their clients over the cloud – AI-as-a-Service or AIaaS is fast emerging as the next level of service delivery in AI services. This is evinced by the significant growth in the contribution from these two segments:


IT services industry or sector has the highest share of the AI market at 51.8%, which is up from 41.4% last year, and $4025.3 Mn – up from $2625 Mn in market value.The Boutique Analytics and AI firms saw Revenue contribution jump to $555 Mn, 2.5 times from jump last year in revenues when the revenues of this combined segment were about $215 Mn


The Artificial Intelligence market in India will continue to be the primary growth driver of the Data Science domain and the broader IT industry over the next few years.



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