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About AI & ML

So where did AI come from? Well, it didn’t leap from single-player chess games straight into self-driving cars. The field has a long history rooted in military science and statistics, with contributions from philosophy, psychology, math and cognitive science. Artificial intelligence originally set out to make computers more useful and more capable of independent reasoning.

Most historians trace the birth of AI to a Dartmouth research project in 1956 that explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and increased the focus on training computers to mimic human reasoning.
For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Google, Amazon or Microsoft tackled similar projects.

This work paved the way for the automation and formal reasoning that we see in computers today.

  • Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without being explicitly programmed where to look or what to conclude.
  • A neural network is a kind of machine learning inspired by the workings of the human brain. It’s a computing system made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
  • Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
  • Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
  • Natural language processing is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.

Applications of AI & ML

Artificial Intelligence is turning into a significant staple of innovation, scarcely any individuals comprehend the advantages and weaknesses of AI and Machine Learning innovations. While machine intelligence is sure to assume a key role in the making of cutting edge frameworks in a wide assortment of industry areas sooner rather than later, it is especially applicable in quickly developing businesses, for example, ICT, manufacturing and transportation.


Over the globe, mobile operators are preparing to deploy the fifth era of 3GPP mobile wireless networks (5G). Compared with the mobile foundation that is presently set up, 5G will bring higher throughput, lower latency, progressively effective signaling, support for more range groups, greater programmability and other extra advanced procedures to expand utilization and optimize costs. The number of connected gadgets will significantly increase because of this improved performance: sensors will profit by progressively affordable bandwidth to the internet; heavy users of uplink traffic like video cameras will have the option to share more information; fast-moving gadgets (drones) will have increasingly solid connectivity, etc. These new gadgets will be the impetus of another wave of development for every single included industry.

Virtual Stylist

A few retailers are as of now directing AI/ML-based tools that perceive clients’ appearances and dress to make suggestions. In Hong Kong, fashion retailer Guess opened a pilot FashionAI idea shop at Hong Kong Polytechnic University. At the idea shop, machine learning and computer vision are deployed to “learn” from purchasers and designers inside the framework. Customers looked into the store with facial recognition innovation. RFID-empowered dress rack alternatives consequently appeared on the smart mirror, which offered styling recommendations.

Other AI/ML-based styling assistants give the data to sales associates so they can personally furnish clients with suggestions, making the shopping procedure progressively consistent and effective.

Intelligent Transportation Systems

Advances in Intelligent Transportation Systems (ITS) are prompting the introduction of an ever increasing number of vehicles with autonomous driving abilities. Notwithstanding, intelligent automation in ITS isn’t constrained to autonomous vehicles alone. There are endeavors in progress to build the effectiveness of traffic systems at a vital level, for example, the structure of streets and relics (signal lights, traffic islands, bus stops, vehicle parking, etc), the control of traffic signals and the setup of directions dependent on mobility pattern predictions.

Every one of these applications require the processing of tremendous amounts of information to remove the necessary information and settle on worldwide decisions. Tighter control loops at the strategic level incorporate working and coordinating traffic lights for maximal throughput, and taking care of traffic congestion because of unexpected occasions, for example, mishaps. Much of the time, despite the fact that the events appear to require just local intervention, without a worldwide perspective, a neighborhood activity can prompt gridlock in a bigger zone. It is surely known that machine learning is relevant to practically all tasks across numerous sectors and can accomplish effectiveness through smart and adaptive automation.

Smart Agents Technology

Smart Agents innovation is a personalization innovation that makes a virtual portrayal of each entity and learns/builds a profile from the entity’s actions and activities. In the payment business, for instance, a Smart Agent is related with every individual cardholder, dealer, or terminal. The Smart Agent related to an entity, (for example, a card or merchant) learns in real-time from each transaction made and constructs their particular and remarkable practices after some time. There are the same number of Smart Agents as dynamic elements in the framework. For instance, if there are 200 million cards executing, there will be 200 million Smart Agents started up to dissect and learn the behavior of each.

Decision-making is explicit to every cardholder and never again depends on rationale that is all around applied to all cardholders, paying little respect to their individual attributes. The Smart Agents are self-learning and versatile since they ceaselessly update their individual profiles from every movement and activity performed by the entity. Each Smart Agent pulls every single important data over different channels, regardless of the sort of configuration and source of the information, to deliver virtual profiles.

Master Data Management

Data management and duplicate data entries have consistently been a struggle for organizations all things considered. A database with 30 million clients may in reality just be 3,000,000 one of a kind users. This is an issue that has caused database managers endless cerebral pains, and it harms the bottom line for organizations.

Utilizing AI/ML innovation, organizations can actualize an all-in-one server add-on that runs flawlessly in the background, examining and analyzing user entries in real time. It can even be designed to block duplicate user sign-ins as they happen. The AI/ML solution does this via matching user data and comparing data, for example, username, email, telephone number, address, Social Security numbers, linked credit cards, IP information and much more. There is no compelling reason to run custom inquiries or reports, sparing time and human capital.

These are only a couple of instances of the manner in which the world keeps on embracing AI and ML innovation to improve the manner in which we live. The companies that embrace these cutting-edge applications will work all the more effectively, give their clients better experiences and lead their enterprises. Business pioneers should ensure their companies don’t get left behind.

Future Technologies 

Breakthrough in Science

The scope of AI in science is the largest. Recently ‘Eve’ was in the news for discovering that an ingredient found commonly in toothpaste, is capable of curing Malaria. Here the subject in appreciation ‘Eve’ is not a human scientist, rather a Robot created by a team of scientists at the Universities of Manchester, Aberystwyth, and Cambridge.

Eve’s example hints at the possibility of AI playing a bigger role in science in future, not just merely for augmentation. AI will be able to create science, not merely do science as evidenced by the Robot Scientist, Eve. Automation using AI for drug discovery is a field that is rapidly growing, mainly because machines work faster than humans. AI is also being applied in related areas such as synthetic biology for the manufacture and rapid design of microorganisms for industrial uses. Taking all this in stride, AI is sure to transform science as we know it.

Cyber Security

The future application of AI in cybersecurity will ensure in curbing hackers. The incidence of cybercrime is an issue that has been escalating through the years. It costs enterprises in term of brand image as well as material cost. Credit card fraudery is one of the most prevalent cybercrimes. Despite there being detection techniques, they still prove to be ineffective in curbing hackers. AI can bring a remarkable change to this. Novel AI techniques like Recurrent Neural Networks can detect fraudery in initial stages itself. This fraud detection system will be able to scan thousands of transactions instantly and predict/ classify them into buckets. RNN can save a lot of time as it focuses on cases where there is a high probability for fraud.

Face Recognition

Authenticating personal content is not the only use of facial recognition. Governments and security forces make use of this feature to track down criminals and identify citizens. In the future, facial recognition can go beyond physical structure to emotional analysis. For example, it might become possible to detect whether a person is stressed or angry.

Data Analysis

One of the ways AI will benefit business is in the field of Data Analysis. AI would be able to perceive patterns in data that humans cannot. This enables business’ to target the right customers for the product. An example of this is the partnership between IBM and Fluid. Fluid, a digital retail company uses Watson – an AI created by IBM for insightful product recommendation to its customers.


AI-guided transport will no longer be confined to the pages of sci-fi literature. Self- driving cars have already populated the market, however, a driver is required at the wheels for safety purposes. With Google, Uber and General Motors trying to establish themselves at the top in this market, it will not be long before driverless vehicles become a reality. Machine Learning will be crucial in ensuring that these Automated Vehicles operate smoothly and efficiently.

Emotion Bots

Tech has advanced in terms of Emotional Quotient. Virtual assistants Siri, Cortana & Alexa show how the extent to which AI comprehends human language. They are able to understand the meaning from context and make intelligent judgments. Back in 2015, a companion robot called, ‘Pepper’ went on sale. All the initial 1000 units were sold within a minute. Overall, considering all this, the possibility of emotional bots might become a reality in the future.

Marketing & Advertising

The application of AI in sales and marketing seems a definite, considering the fact that marketing professionals leave no stone unturned to benefit their business. AI can increase the efficiency of sales and marketing organization. The focus will be on improving conversion rates and sales. Personalised advertising, knowledge of customers and their behavior gleamed through facial recognition can generate more revenue.

Job Opportunities

Useful qualities/skills for working in artificial intelligence

The good news is that the field of artificial intelligence is so broad that almost any skill set can become relevant in one way or another. However, there are certain skills and personal attributes that come in particularly handy more often than not when it comes to seeking out employment opportunities in the world of AI. Examples of such skills are:

  • A high degree of problem-solving capability
  • Ability to think outside the box and innovate
  • Ability to stay up-to-date with the latest innovations in the industry
  • Ability to design and repair software
  • Good communication skills and capacity to turn complex programming into every-day language

Possible careers in artificial intelligence

If the seemingly endless possibilities that AI presents are what get your imagination all fired up, and you’ve set your sights on either self-teaching or pursuing a university degree, you may then wonder where exactly you can establish your career in the field. Luckily the employment opportunities afforded by artificial intelligence are growing at an impressive rate, and all kinds of employers are eager to snag up experts in the subject. Common areas for AI developers to begin their career paths in include:

  • Software developer (this can be done as part of a long-term development contract or on a project-by-project basis as a freelancer!)
  • Data analyst
  • Hardware specialist and electrical engineer
  • Algorithm specialization

Beyond these more obvious jobs, the world of artificial intelligence is boundless. Most spheres of human activity have begun to incorporate some form of artificially intelligent technology into how work is carried out – engineers and architects have begun to rely heavily on artificial intelligence in order to create precise models of their intended works. The drone technology is highly interconnected with AI, and there are many employment opportunities for software developers who specialize in creating AI programs that can train drones to understand what they are surveilling.

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Vasireddy Venkatadri Institute of Technology
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Peda Kakani (Md)
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