AI based systems for Predication and Support Systems post covid for Development for National Reincarnation

AI should be more used in sensitive areas where there is the risk to life and property as also areas which can have long term benefit to human growth.

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AI based systems for Predication and Support Systems post covidfor Development for National Reincarnation THE POLICY TIMES

Introduction

Creating computing systems that have the ability to perform tasks like humans or even better than humans. Artificial intelligence(AI) can sense their environment, think, and in some cases learn, and take action in response to what they’re sensing and their objectives.

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, Learning, Planning, Problem-solving. In this topic we shall discuss the following subjects;

AI based systems for Predication and Support Systems post covidfor Development for National Reincarnation

Deep learning, Machine learning, Computer Programming, Medical field. Deep Learning has enabled many practical applications of Machine Learning and by extension the overall field of AI. Deep Learning breaks down tasks in ways that make all kinds of machine assists seem possible, even likely. Driverless cars, better preventive healthcare, even better movie recommendations, are all here today or on the horizon.AI is the present and the future. With Deep Learning’s help, AI may even get to that science fiction state we’ve so long imagined.

Also Read:  Artificial Intelligence and Machine Learning Together To Reach the Culmination of Growth By 2023

Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task.

History of AI

To be informed about the history of artificial intelligence, itis necessary to go back in history to know of AI descriptions in Indian prehistoric writings to the ancient Greek era, it is proven that various ideas about humanoid robots have been carried out. An example of this is Daedelus, who is said to have ruled the mythology of the wind, to try to create artificial humans. Modern artificial intelligence has begun to be seen in history with the aim of defining philosophers’ systems of human thought. 1884 is very important for artificial intelligence. Charles Babbage, on this date, has worked on a mechanical machine that will exhibit intelligent behavior. However, as a result of these studies, he decided that he would not be able to produce a machine that would exhibit as intelligent behaviors as a human being, and he took his work suspended. The term ‘Robot‘ was used for the first time in English by a Karel Capek play called “Rossum’s Universal Robots (RUR)” which was premiered in London in 1923. In 1950, Claude Shannon introduced the idea that computers could play chess. Work on artificial intelligence continued slowly until the early 1960s.

In the third era, starting in the mid-1970s, they broke away from the toy worlds and tried to build practically usable systems, whereby methods of knowledge representation were in the foreground. The AI left its ivory tower and AI research also became known to a wider public. Initiated by the US computer scientist Edward Feigenbaum expert system technology is initially limited to the university area. However, little by little, expert systems developed into a small commercial success and for many were identical to all AI research just as many MachineLearning today are identical to AI.

Types of AI

The idea behind AI development is to make machines capable enough to perform human-like tasks. So, based on how an Artificial intelligence system can do a human task, a specific AI type is determined. Based on the performance and flexibility of AI machines, the types of AI are classified.

1. Reactive Machines

This kind of AI-based machine has a limited ability to perform human tasks. They replicate the human mind’s capability to respond to various events. It means that the reactive machines never use past experience to perform present actions. Because of its inability to learn from previous data to do the current task, these systems are called the oldest forms of AI machines. The reactive machines are used to give a response to the limited input combinations. These systems can’t improve their performance with the experience gained previously.

2. Limited Memory

Unlike reactive machines, the Limited memory machines can learn from previous experiences to make perfect decisions. Almost every existing AI application will come under this AI category. All existing AI-based machines that use machine learning and deep learning techniques are trained by vast data. This data is stored in system memory and helped to solve future issues.

For example, virtual image recognition systems are trained by thousands of images to recognize the objects it scans. When an AI system scans a specific image, it uses the older images for reference and interprets the given image. Based on its previous learning experience, it recognizes new images with more accuracy.

Almost, all existing AI applications ranging from AI chatbots and virtual assistants to autonomous vehicles are run on limited memory AI types.

3. Theory of Mind

This is an open area where researchers still innovating the existing AI systems. Theory of mind is an advanced type of AI machine. It can understand the entities better by analyzing their emotions, needs, beliefs as well as processes.

It’s a fact that artificial emotional intelligence is an open area for researchers to invent more. But, to achieve the theory of mind level of AI needs the development of more advanced branches of AI. AI-powered machines have to interpret human minds better to predict their requirements. It means that the development of AI machines should understand human needs.

4. Self-aware AI

Self-aware AI is one of the significant stages of artificial intelligence that exists theoretically today. It is self-explanatory and acts similarly to the human brain. Building this type of AI system takes decades. Upon its realization, it will become the ultimate objective of all AI research.

Self-aware AI can read and evoke emotions that it interacts with and have its own needs, beliefs, and emotions. It means that this advanced AI system can take its own decisions. But, the thing is, these self-decisions might ruin our presence.

In addition to the above artificial intelligence classification, the below are the few more kinds of artificial intelligence that are prominent:

5. Artificial Narrow Intelligence (ANI)

ANI is one of the major types of artificial intelligence. Narrow AI represents all the existing AI types that ranged from the most complex and proficient AI that has never been developed yet. It is an AI system that performs tasks autonomously with its

human-like capabilities. These AI-powered machines can only do what they programmed to do. Thus, ANI includes a narrow range of abilities.

Hence, narrow artificial intelligence is related to reactive and limited memory AI types. The self-learning AI machine that uses machine learning algorithms and deep learning techniques comes under the ANI category.

6. Artificial General Intelligence (AGI)

An AGI system has the ability to understand, identify, learn, and act completely like humans. These artificial intelligence systems can build abilities on their own to form associations across domains. Thus, an AGI system can reduce the overall training time. This will improve the capability of AI systems to replicate multi-tasks exactly like humans.

7. Artificial Super Intelligence (ASI)

The ASI type of AI is the peak level of AI research. Artificial General Intelligence in the coming years will become the most capable form of AI. ASI systems can perform faster data processes and analysis. In addition, the systems also designed with decision-making capabilities. This is because ASI machines developed with awesome memory abilities.

However, the drawbacks of the evolution of AGI and ASI types of AI might threaten human existence. Now, we will look at what these types of AI systems can modernize the industries.

Key Highlights of AI

  • The Indian Artificial Intelligence market is valued at US$6.4 billion as of July – August 2020. As mentioned, this covers the revenues from all AI operations originating from India regardless of stakeholder or client type, type of firm providing AI services, and geography of a client.
  • The market size by Industries or Sectors is the highest across the IT Services sector, followed by the Technology sector (including Software and Hardware firms), with a market share of 1% and 23.3% respectively.
  • Apart from the IT and Technology sectors, the BFSI sector has a market share of AI services at 9.6%.
  • There are close to 91,000 Artificial Intelligence personnel working across enterprises in India, with a median salary of INR 14.7 Lakhs.
  • Close to 16,500 open positions related to AI are currently available to be filled in India, as of July 2020. Bengaluru, just as it does for other Data Science and IT services roles, tops the location for the highest proportion of open jobs.

Artificial Intelligence Market Size by Sector / Industry

AI in IT Services

In terms of Sector or Industry, the broad-based IT services industry or sector has the highest share of the AI market at 41.4%, and US$2625 million in market value. The contributors to the IT services’ sector market value include Domestic and MNC firms in India.

AI in Technology – Software & Hardware Technologies | Chip & Semi-conductors | Electronic Devices

The contribution from the Technology sector, which includes Software & Hardware technologies, follows the IT services sector. The contribution of the Technology sector covers a 23.3% market share and US$1,488 million market value. This sector covers Software, Cloud Computing, Semi-conductor, Database technologies, Server platforms, and Networking solutions, to name a few, technology segments.

AI in BFSI

The contribution from the BFSI sector follows the Technology sector – the BFSI contribution is US$615.3 million in market value and 9.6% in market share. The firms in the BFSI sector

were one of the first to utilize Data Science services to derive insights on consumers.

AI in Engineering | Industrials | Automation

The contribution of this sector is US$382.9 million in market value and 6% in market share. The Engineering and Industrial sector cover International and Domestic firms that integrate AI and Deep Science models in their Engineering, Manufacturing, and Automation services. AI is utilized in design, simulation, quality analysis, and maintenance.

AI in Retail – The broad e-Commerce and Retail sector (which includes the Retail Aggregation sub-sector) follows the Engineering and Industrials sector with a contribution of US$317.8 million in market value and 5.0% in market share.  The contribution of AI across this broad sector is significant, considering the instances AI technologies are utilized and the scope of growth over the next decade:

AI is utilized across the entire supply chain for the e-Commerce and Retail sector, starting with merchandise procurement and classification, and quality, billing and payments, logistics tracking, delivery, and even managing and tracking customer feedback and complaints.

From a portal and platform perspective, AI is utilized in product recommendations for consumers, across the digital and online platforms – enhancing the shopping experience of customers, by pushing required product recommendations and suggestions to shoppers.

AI is utilized by Marketplace and Retail Aggregator firms serving as a conduit between Consumers and small-box retail stores, etc.

AI in Energy | Mining & Metals

This sector contributes 2.3% of the market share and US$145.9 million in terms of value. AI operations across this wide-ranging sector cover numerous industries and sub-verticals, across PSU, Domestic Conglomerates, and International firms.

AI in Telecom

The Telecom sector, which contributes US$138 million to the AI market, with a market share of 2.2%. The Telecom vertical includes MNC firms

AI in Automotive

The Automotive sector, which contributes US$133.9 million in terms of value and 2.1% in terms of market share. This sector includes automobile manufacturers that are developing in-house AI and CV solutions for driverless technologies, including AI-driven sensors, Radar, Lidar, and Optical solutions. AI is also adopted by Automobile firms for predictive maintenance, not just on the factory floor and machinery equipment, but also for vehicles and cars.

AI in Education and Public Research

This Education and Public Research sector, which contributes US$126.7 million to the Indian AI market, with a market share of 2%. In terms of overall value, this sector is significant as it covers the prestigious IIT, IISc, and other engineering institutions. The market value covers the AI contribution across research, patents, AI consulting to government and private enterprises, and incubator firms supported by these institutions. The prestigious IIT institutes are the largest contributors of value in this sector.

AI in Pharma & Healthcare

The Pharma & Healthcare sector, with a contribution to the Indian AI market of US$120.3 million and a 1.9% market share. While this sector is dominated by the Captive units of the MNC Pharma & Healthcare firms in India.

AI in Other Sectors

There are other sectors that contribute to the AI market in India. These sectors have a small but significant and steadily expanding market size and share. The following are the remaining sectors that contribute to the AI market:

Travel & Hospitality – The contribution to the AI market of this sector is US$92.6 million in market size and 1.4% in market share. The combined sector includes platforms that enable ticket booking and hotel reservations, and aggregator apps that enable online food ordering and delivery.

Digital Media – The contribution to the AI market of this sector is US$73.9 million in market size and 1.2% in market share. The Digital media sector is adopting AI for targeted content and advertising across all platforms, including digital, online, and mobile. This sector includes Domestic and International partnerships across Content Production and Distribution, Advertising, and Digital Marketing.

FMCG: The contribution of the FMCG sector to the AI market in India is US$55.2 million in market size and 0.9% in market share. This sector covers both Domestic and FMCG companies. Unilever is the largest contributor to this sector.

Fintech: The contribution of the Fintech sector has been listed separately from the BFSI sector as the Fintech vertical is rapidly emerging as a key adopter of AI and ML platforms and technologies. The contribution to the Indian AI market of the Fintech sector US$54.9 million at just under 0.9%.

Cross-Sector (including Agri-Tech): Other firms that contribute to the AI market across several sectors, including the Agri-tech sector, have a market share of 0.2% and a market size of US$15.2 million. The Agri-Tech sector is dominated by start-ups that are providing AI services to the Agriculture vertical.

Job growth

The World Economic Forum predicts nearly 60 million extra jobs will be created by 2022.

A new WEF report, The Future of Jobs 2018, says that 75 million jobs will be displaced by artificial intelligence (AI), robotics, and automation, but suggests that 133 million new jobs may be created as organizations shift the balance between human workers and machines: a net gain of 58 million.

While many people believe that routine, low-skilled jobs will fall to the machines first, the WEF paints a bleak future for many roles that were once considered safe, middle-class careers. Financial analysts, accountants, auditors, lawyers, bank tellers, statistical, financial, and insurance clerks, general managers, business services managers, administrators, and executive secretaries are all listed under “redundant roles” over the next five years.

Relative to their starting point today, the expansion of machines’ share of work task performance is particularly marked in reasoning and decision-making; administering; and looking for and receiving job-related information. The majority of an organization’s information and data processing and information search and transmission tasks will be performed by automation technology.

Dozens of new careers emerged, from web designer to data scientist to online marketer.

Companies that effectively implement AI can generate more money for their businesses. This, in turn, leads to higher employee wages, better technology tools, and greater efficiency. With such success, companies can actually spread their reach across the world. As a result, they will require a global workforce, which again generates huge employment opportunities. The aggregate employment impact of automation appears to be positive on skilled labor. As AI gets implemented in every industry, the demand for an AI maintenance workforce is going to skyrocket. Companies would need large amounts of AI developers and engineers to maintain their systems. Those plants that automate end up increasing employment as per the latest survey report. If AI and machine learning algorithms can wisely use large amounts of big data, it will help companies perform better. It will also increase the employee retention rate and help in new customer acquisition. This will create a new job.

AI to function properly, humans will be needed to check work, improve it and manage it. The WEF indicated that the new positions will require additional skills for managing the interface between technology and humans.AI Facilitator Role will involve the creation of automated environments as well as build systems such as virtual assistants. The AI-based environments will help employees work better without compromising on security, governance, data control, and compliance. In the near future, the AI-powered industry will be available on a scale and on-demand for everyone. Hence, the requirement for AI-assisted technician jobs will see an upward surge.

Advantage & Disadvantage of AI

  • AI based systems for Predication and Support Systems post covidfor Development for National Reincarnation 1

    Future of Artificial Intelligence

    AI is the largest technology force of our time, with the most potential to transform industries. It will bring new intelligence to healthcare, education, automotive, retail, and finance, creating trillions of dollars in a new AI economy. Meantime, educators are employing AI to train a data-savvy workforce. And legions of businesses are examining how AI can help them adapt to remote work and distance collaboration.

    Looking at the features and its wide application we may definitely stick to AI. Seeing at the development of AI is it that the future world is becoming artificial. Biological intelligence is fixed because it is an old, mature paradigm but the new paradigm of non – biological computation and intelligence is growing exponentially. The memory capacity of the human brain is probably of the order often one billion binary digits. But most of this is probably used in remembering visual impressions, and other comparatively wasteful ways. Hence we can say that as natural intelligence is limited and volatile to the world may now depend upon computers for smooth working.

  • AI based systems for Predication and Support Systems post covidfor Development for National Reincarnation 3

    Conclusion

    The ultimate goal of institutions and scientists working on AI is to solve a majority of the problems or to achieve the tasks which we humans directly can’t accomplish. It is for sure that development in this field of computer science will change the complete scenario of the world. Now it is the responsibility of the creamy layer of engineers to develop this field of AI to be a bigger employment generator with the advantages of automation and making humans life for betterment in each sector. AI should be more used in sensitive areas where there is the risk to life and property as also areas which can have long term benefit to human growth. AI should also be more used as a tool for prevention rather than operational like Healthcare, Law enforcement, etc.


    By,
    Dr. P. Sekhar,
    Chairman, Unleashing India, Global Smart City Panel, MTGF.

    Dr. P. Sekhar the policy times


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AI based systems for Predication and Support Systems post covidfor Development for National Reincarnation
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AI should be more used in sensitive areas where there is the risk to life and property as also areas which can have long term benefit to human growth.
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THE POLICY TIMES
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