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The Impact of AI in Human Endeavour

John McCarthy, Father of Artificial Intelligence, coined the term Artificial Intelligence (AI) for the very first time in the year 1955. He said that AI is the science and engineering of developing smart machines, particularly, intelligent computer programs. Since then Artificial Intelligence has evolved significantly as a combination of computer science, statistics, psychology, mathematics, linguistics, and engineering. Currently the art and science of AI is at an inflection point with multiple domains opening up as viable applications for largely theoretical approach so far. Today with focused contributions from large multi national corporations and governments there is a great energy in this field. As more and more interesting applications like self driving cars and AI driven stock trading take the center stage, we can expect the ecosystem to generate critical mass for industrial scale activities in multiple areas of interest.

Machine Learning is a subset of AI with great application in solving complex problems. This has already found its niche in games and in social media. There are many applications in near future like drug discovery and diagnostics where there is huge opportunity. The advantage of ML is the great potential for machines to learn many of the higher order human skills like driving, reading, care giving etc. There are great opportunities and some threats as machines replace humans in some of these domains. There is also the dangerous possibility of machines internalizing their understanding and judgement and thus becoming a threat to human endeavour. Overall interesting times ahead for AI and machine learning...

How AI will help the quality of work

Every day at work, employees need to do monotonous work which takes a significant amount of their time and effort. These are potentially the tasks that Artificial Intelligence can automate. This can make execution of monotonous tasks much easier by providing automated solutions thus saving the valuable productive time of employees. It is thus partly fallacy that AI will quickly replace humans in the work force. First to replace will be such repetitive activities which anyway we humans would like to avoid doing. In the mean time new kind of jobs will open up and human skills will also upgrade to fit with the new demands of the job. So it is reasonable to think that in the immediate future, the jobs that will be lost to automation and AI will be those jobs that no one is really keen to do on their own. More over there are dangerous and inaccessible jobs which AI/robots can take over from humans. There are also jobs where human nature is unsuitable to do - for example continuous monitoring of instrumentation / surveillance equipments etc. These are domains where AI can do a much better job when compared with humans.

Watch our video.

This video provides an over view of our Pradjna platform.. Here we explain the high level architecture of Pradjna and how it can help assess motor skills of millions of workers. For example it is currently very difficult to evaluate drivers without an expert involvement; but with our platform this becomes a routine, 24X7 process which is objective and impossible to manipulate.

Artificial Intelligence – Everywhere!

Needless to say, Human Resource is not the only field which makes use of AI. The sales team of an organization can make use of bots to collect and communicate with leads. And by doing this, the organizations can easily recognize their potential customers and convert them into buyers. Marketing, Finance, Project management, Customer Service etc are other significant fields where the power of Artificial Intelligence can be used and are probably in use now. Intelligence can be used effectively today. Tomorrow there are unexplored frontiers like medical diagnostics, drug discovery weather and climate studies, logistics, energy and even space exploration where AI will be productively used. Mastering of new inventive technologies will definetly make our envirnoment highly challenging as the humans need to compete with advanced ultramodern machines that can definetly ensure a higher yielding as well as hassle free lifestyle. We are racing towards a future where Artificial Intelligence will be ubiquitous and all pervasive !!

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  • Social media has some great opportunities for AI today - AI not only identifies and categorizes individual leader and follower behaviors, but analyzes the complex interplay of those behaviors to arrive at a predictable model of social media activity. This enables marketers to have greater insights to potential customers and to predict their response to new and enhanced products and services. The AI platform can aggregate multiple data streams into a coherent dashboards - set of easy to patterns and trend lines to give you a near real-time peek into the minds of your audience. AI optimizes social media spend by increasing its effectiveness and enabling a much greater visible return on investment for engagements.

    What it is: Deep neural networks, which mimic the human brain, have demonstrated their ability to “learn” from image, audio, and text data. Yet even after being in use for more than a decade, there’s still a lot we don’t yet know about deep learning, including how neural networks learn or why they perform so well. That may be changing, thanks to a new theory that applies the principle of an information bottleneck to deep learning. In essence, it suggests that after an initial fitting phase, a deep neural network will “forget” and compress noisy data—that is, data sets containing a lot of additional meaningless information—while still preserving information about what the data represents. Why it matters: Understanding precisely how deep learning works enables its greater development and use. For example, it can yield insights into optimal network design and architecture choices, while providing increased transparency for safety-critical or regulatory applications. Expect to see more results from the exploration of this theory applied to other types of deep neural networks and deep neural network design.

    What it is: Capsule networks, a new type of deep neural network, process visual information in much the same way as the brain, which means they can maintain hierarchical relationships. This is in stark contrast to convolutional neural networks, one of the most widely used neural networks, which fail to take into account important spatial hierarchies between simple and complex objects, resulting in misclassification and a high error rate. Why it matters: For typical identification tasks, capsule networks promise better accuracy via reduction of errors—by as much as 50 percent. They also don’t need as much data for training models. Expect to see the widespread use of capsule networks across many problem domains and deep neural network architectures.

    What it is: A type of neural network that learns by interacting with the environment through observations, actions, and rewards. Deep reinforcement learning (DRL) has been used to learn gaming strategies, such as Atari and Go—including the famous AlphaGo program that beat a human champion. Why it matters: DRL is the most general purpose of all learning techniques, so it can be used in the most business applications. It requires less data than other techniques to train its models. Even more notable is the fact that it can be trained via simulation, which eliminates the need for labeled data entirely. Given these advantages, expect to see more business applications that combine DRL and agent-based simulation in the coming year.