A quick recap for those who are new to AI - Most of the computer-generated solutions now emerging in various industries do not rely on independent computer intelligence. Rather, they use human-created algorithms as the basis for analyzing data and recommending treatments. By contrast, “machine learning” relies on neural networks (a computer system modeled on the human brain). Such applications involve multilevel probabilistic analysis, allowing computers to simulate and even expand on the way the human mind processes data. which means, not even the programmers can be sure how their computer programs will derive solutions.
There’s yet another AI variant, known as “deep learning”. In deep learning software learns to recognize patterns in distinct layers. In healthcare for example, this mechanism is becoming increasingly useful. Because each neural-network layer operates both independently and in concert – separating aspects such as color, size and shape before integrating the outcomes – these newer visual tools hold the promise of transforming diagnostic medicine and it can even search for cancer at the individual cell level.
From the above examples I hope you agree that AI-based programs can help agencies cut costs, free up millions of labor hours for more critical tasks and deliver services better, faster. In future AI can help do 'Thinking' for the government but it is early days for that! Today AI programs can recognize faces and speech, they can learn and make informed decisions. AI-based technologies include machine learning, computer vision, speech recognition, natural language processing & robotics. AI is powerful, scalable and improving at an exponential rate. Developers are working on implementing AI solutions in everything from self-driven cars to autonomous drones, from “intelligent” robots to speech translation. The rise of more sophisticated cognitive technologies is of course, critical to advances in several verticals:
- Rules-based systems capture and use expert knowledge to provide answers to tricky but routine problems. As this form of AI grows more sophisticated, users may not realize they aren’t conversing with a real person. For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or use historical game data to help a chess player select tactical moves to play a game and like wise within government, AI systems can answer a large amount of queries and reduce workload on humans.
- Speech recognition transcribes human speech automatically and accurately. The technology is improving as machines collect more examples of conversation. This has obvious value for dictation, phone assistance, and much more. For example, I worked with one police team to implement recording reports using speech recognition software. All police officers are not expert typist so a FIR (First Investigation report) that takes 45 minutes to register can be documented in 15 minutes of less using speech recognition with minimal errors.
- Machine translation translates text or speech from one language to another. Significant advances have been made in this field in only the past year. Machine translation has obvious implications for international relations, defense, and intelligence as well as in our multilingual society and has numerous domestic applications. For example a popular book written in English can be translated in Spanish by a computer and save months of human effort.
- Digital vision is the ability to identify objects, scenes, and activities in naturally occurring images. It’s how Facebook sorts millions of users’ photos, but it can also scan medical images for indications of disease and identify criminals from surveillance footage. Soon it will allow law enforcement to quickly scan license plate numbers of vehicles stopped at red lights, identifying suspects’ cars in real time. I was a technical architect for a project we did for French government where we did prototype of a system to identify wanted criminals in public transport.
- Machine learning as we know takes place without explicit programming. By trial and error, computers learn how to learn, mining information to discover patterns in data that can help predict future events. The larger the data sets, the easier it is to accurately gauge normal or abnormal behavior. When an email program flags a message as spam, or your credit card company warns you of a potentially fraudulent use of your card, machine learning may be involved. Deep Learning as we discussed is a branch of machine learning involving artificial neural networks inspired by the brain’s structure and function and
- Robotics is the creation and use of machines to perform automated physical functions. The integration of cognitive technologies such as computer vision with sensors and other sophisticated hardware has given rise to a new generation of robots that can work alongside people and perform many tasks in unpredictable environments. For examples use of drones, robots used for disaster response, and robot assistants in home health care. Another example is the state of Maharashtra in India is working with WEF to use drones to collect data to improve irrigation system for farming.
- Natural language processing performs complex task of organizing and understanding language in a human way. This goes beyond interpreting search queries or translating between Mandarin and English text. How it helps is combined with machine learning, a system can scan websites for discussions of specific topics even if the user didn’t input precise search terms. Computers can identify all the people and places mentioned in a document or extract terms and conditions from contracts. As with all AI-enabled technology, these become smarter as they consume more accurate data—and as developers integrate complementary technologies such as machine translation and natural language processing the scope is infinite.
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