A less-talked about change in work behaviour during the "work-from-home" phase of our existence, is that of, "call IT", while using or installing an application or software. You can still call IT but the process is now a business opportunity with AI (artificial intelligence) and ML (machine learning) now primed to solve your issues. Cure the "paper-clip feature in Office applications, which provides, based on most user feedback, the kind of help that is needed. Identifying this need for handholding end-users, Gyde – a Pune-based startup – has created a software assistance platform and is on a mission to democratise software guidance and reduce the go-to-market time for companies. Founded by Prasanna Vaidya and Shubham Deshmukh in January 2018, Gyde uses a set of AI-based tools for educating software application users to drive actions for better on-boarding, adoption, engagement and customer success. After completing his BE Mechanical from Mumbai University in 2006, Vaidya changed course and started working in software development. After a short stint in USA, he returned to India with good exposure to artificial Intelligence (AI) and natural language processing (NLP) and machine learning (ML). His co-founder Deshmukh, is a computer engineering graduate from Pune and worked on a team led by Vaidya in the USA. The duo shared ideas and brainstormed about creating a B2C application which would be used by millions. After a lot of pivots, pilots and failed attempts, since 2015, Vaidya and Deshmukh finally realised that they both were from an engineering background and did not have the marketing nous for a B2C product. Says Deshmukh, "We had created a platform for creating chatbots at a hackathon, which we eventually won.

Learning-from-demonstrations is an emerging paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions. However, it is susceptible to imperfections in demonstrations and also raises concerns of safety and interpretability in the learned control policies. To address these issues, we use Signal Temporal Logic to evaluate and rank the quality of demonstrations. Temporal logic-based specifications allow us to create non-Markovian rewards, and also define interesting causal dependencies between tasks such as sequential task specifications. We validate our approach through experiments on discrete-world and OpenAI Gym environments, and show that our approach outperforms the state-of-the-art Maximum Causal Entropy Inverse Reinforcement Learning.

USC researchers have developed a method that could allow robots to learn new tasks, like setting a table or driving a car, from observing a small number of demonstrations. Imagine if robots could learn from watching demonstrations: you could show a domestic robot how to do routine chores or set a dinner table. In the workplace, you could train robots like new employees, showing them how to perform many duties. On the road, your self-driving car could learn how to drive safely by watching you drive around your neighborhood. Making progress on that vision, USC researchers have designed a system that lets robots autonomously learn complicated tasks from a very small number of demonstrations--even imperfect ones.

Making progress on that vision, USC researchers have designed a system that lets robots autonomously learn complicated tasks from a very small number of demonstrations -- even imperfect ones. The paper, titled Learning from Demonstrations Using Signal Temporal Logic, was presented at the Conference on Robot Learning (CoRL), Nov. 18. The researchers' system works by evaluating the quality of each demonstration, so it learns from the mistakes it sees, as well as the successes. While current state-of-art methods need at least 100 demonstrations to nail a specific task, this new method allows robots to learn from only a handful of demonstrations. It also allows robots to learn more intuitively, the way humans learn from each other -- you watch someone execute a task, even imperfectly, then try yourself.

The greatest test that the chatbot business faces is numbness. "We need to continue instructing individuals about the innovation and its advantages. Chatbots, chatterbots or bots are PC software that reproduce communication among people and machines. Alexa, Google Assistant or a mechanized financial bot are a few instances of bots which are automating procedures and sparing human time by its administrations. As of now, there are two significant varieties of bots, they are Rule-based bots and AI bots. Rule-based bots pursue a particular stream direction and are not dynamic in their cooperations. Basic instances of rule-based bots are client support calls where you need to pick a particular number or direction to know explicit data. AI or Artificial Intelligence upheld bots are progressively unique and progressed in nature. They use NLP or Neuro-Linguistic Programming to empower a machine to be fit for having a fluctuated and genuinely insightful discussion with people. Alexa is a case of the equivalent. Bots are being utilized in web based business, banking, travel, land and different parts. In India, Mahindra and Mahindra, Raymonds, Axis Bank, ICICI Bank, Godrej, Tata Group, Reliance Group are a portion of the numerous organizations utilizing bots. "The worldwide market for chatbot by 2023 will be $ 5.6 billion and 36 percent of it will originate from India," says Himanshu Saxena, Co-Founder and CEO at Suzami Tech. As per Saxena, chatbots are as of now being utilized for three fundamental capacities, they are – Customer self-administration, Operations help and Enterprise capacities. "Bots in different dialects, sound chatbots and bots having more developed conversational abilities are the patterns that will rise more grounded," says Alit Deshmukh, Managing Director, Equirus Capital. Saxena trusts Bert by Google is a weighty accomplishment in chatbot technology. "India is a colossal market and SME advertise is generally undiscovered by the chatbot business.

Pepe the robot was wall-mounted near a handwashing station. It prompted children to wash their hands and provided positive reinforcement. The hand-shaped robot, dubbed'Pepe', is the product of a collaboration between researchers from the University of Glasgow in Scotland and Amrita Vishwa Vidyapeetham University in India. Pepe was mounted to the wall above a handwashing station at the Wayanad Government Primary School in Kerala, which has around 100 pupils aged between five and 10. A small video screen mounted behind Pepe's green plastic exterior acted as a'mouth,' allowing researchers to tele-operate the robot to speak to the pupils and draw their attention to a poster outlining the steps of effective handwashing.

Computers used to take up entire rooms. Today, a two-pound laptop can slide effortlessly into a backpack. But that wouldn't have been possible without the creation of new, smaller processors -- which are only possible with the innovation of new materials. But how do materials scientists actually invent new materials? Through experimentation, explains Sanket Deshmukh, an assistant professor in the chemical engineering department whose team's recently published computational research might vastly improve the efficiency and costs savings of the material design process.