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The Future World entirely turns into Artificial Intelligence: Dr. Abhilasha Gaur, Chief Operating officer Electronic Sector Skill Council of India

Weak AI—also called Narrow AI or Artificial Narrow Intelligence (ANI)—is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today.

Education Post13 December 2021 08:59

The Future World entirely turns into Artificial Intelligence: Dr. Abhilasha Gaur, Chief Operating officer Electronic Sector Skill Council of India

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind. While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, the most popular one and regularly used in the industry is as follows, “It is the science and engineering of making intelligent machines, especially intelligent computer programs”. Usually the AI is categorized are Weak AI and Strong AI.

Weak AI—also called Narrow AI or Artificial Narrow Intelligence (ANI)—is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. ‘Narrow’ might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple’s Siri, Amazon’s Alexa, IBM Watson, and autonomous vehicles.

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)—also known as superintelligence—would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn’t mean AI researchers aren’t also exploring its development.

Artificial intelligence applications:

There are numerous, real-world applications of AI systems today. Below are some of the most common examples:

  • Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting. Speech recognition technology is evaluated on its accuracy rate, i.e. word error rate (WER), and speed. A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Reaching human parity – meaning an error rate on par with that of two humans speaking – has long been the goal of speech recognition systems.
  • Customer service: Online virtual agents are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Artificial intelligence takes data and uses it to identify the best representative available to address the customer’s needs. It then provides the rep with necessary background information before they interact with the customer. This saves time and manpower, as well as helps the customer address issues more efficiently. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.
  • Computer vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry. Computer vision needs lots of data. It runs analyses of data over and over until it discerns distinctions and ultimately recognize images. For example, to train a computer to recognize automobile tires, it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire, especially one with no defects. Real-world applications demonstrate how important computer vision is to endeavors in business, entertainment, transportation, healthcare and everyday life. A key driver for the growth of these applications is the flood of visual information flowing from smartphones, security systems, traffic cameras and other visually instrumented devices.
  • AI Optimized Hardware:

The upcoming AI-devices of the digital world are focused on being structured and are used to execute AI-oriented tasks specifically. They have improved graphics and central processing units that accelerated the next generation of application advancements. For example, AI-optimized silicon chips are easily portable and can be inserted into any device when the company needs to get information. One avenue is to increase the performance and throughput of the systems to speed up training and to enable the training of more complex models. Graphics Processing Units (GPUs) are playing an important role to accelerate training and inference because of their high degree of parallelism and because they can be optimized for neural network operations. So do FPGAs, ASICs, and chip designs optimized for tensor operations. New persistent memory technology moves the data closer to the processor.

  • AI in Agriculture:

Automation in agriculture is an emerging subject across the world. In recent time, Artificial Intelligence has been seeing a lot of direct application in farming AI-powered solutions will not only enable farmers to do more with less, it will also improve quality and ensure faster go-to-market for crops. One of the key objectives of AI in the agriculture domain is to increase the farm productivity by increasing the visibility of agronomic states (such as soil moisture, crop health, weather, etc.) of farms, leveraging digitization, mobile, IoT and cognitive technologies. Artificial Intelligence (AI) is being used by the agriculture industry to help produce healthier crops, control pests, monitor soil and growing conditions, organise data for farmers, reduce effort, and improve a wide range of agriculture-related operations along the food supply chain.

To promote the adoption of AI in India, Atal Innovation Mission (AIM) has launched the Atal Tinkering Lab (ATL) program in collaboration with NASSCOM for the school students. ATL is a state-of-the-art space established in a school with a goal to foster curiosity and innovation in young minds, between grade 6th to 12th across the country through 21st century tools and technologies such as Internet of Things, 3D printing, rapid prototyping tools, robotics, miniaturized electronics, do-it-yourself kits and many more.

ESSCI is planning to use AI-driven automation portal for simplifying the process of their stake holders’ end-to-end management and monitoring on a single platform. As ESSCI Skilling body in the domain of Electronics we have dedicated QPs for designing FPGA and VLSI boards, those QPs will layout the basic knowledge of the learners for designing the future AI hardware boards. In STEM Program we have also include one of the vertical as Artificial Intelligence to train the school level students in the upcoming technologies.


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