At the risk of stating the obvious, AI-powered chatbots are hot right now. The tools, which can write essays, emails and more given a few text-based instructions, have captured the attention of tech hobbyists and enterprises alike. OpenAI's ChatGPT, arguably the progenitor, has an estimated more than 100 million users. Via an API, brands including Instacart, Quizlet and Snap have begun building it into their respective platforms, boosting the usage numbers further. But to the chagrin of some within the developer community, the organizations building these chatbots remain part of a well-financed, well-resourced and exclusive club.

The pharmaceutical industry is expected to spend more than $3 billion on artificial intelligence by 2025 – up from $463 million in 2019. AI clearly adds value, but advocates say it is not yet living up to its potential. There are many reasons the reality hasn't yet matched the hype, but limited datasets are a big one. Given the enormity of available data collected every day – from steps walked to electronic medical records – scarcity of data is one of the last barriers one might expect. The traditional big data/AI approach uses hundreds or even thousands of data points to characterize something like a human face.

Over the past few decades, data generation has veritably exploded. However, the'Big Data paradigm' is not so much concerned with the volume of that data, but how businesses and, indeed, industries can derive meaningful insights from what has become a glut of information. With the currently popular approach to artificial intelligence (AI) focussing on the Big Data paradigm, also, pharmaphorum spoke with Adityo Prakash, CEO of Verseon, about the whys and wherefores, delving deeper into the processes for dealing with the current mountain of data and how it can be generated, as well as the purposes for which it can be dealt with constructively, and efficiently. "The fundamental underlying assumption is that an enormous amount of data is available to teach an AI programme how to handle the problem at hand," Prakash began. However, he explained, "the number of known examples to train AI is at least many thousands of times larger than the number of variables or features to be tracked."

Dr. PKS Prakash is a Data Scientist and an author. He has spent last 12 years in developing many data science solution to solve problems from leading companies in healthcare, manufacturing, pharmaceutical and e-commerce domain. He is working as Data Science Manager at ZS Associates. ZS is one of the world's largest business services firms helping clients with commercial success, by creating data-driven strategies using advanced analytics that they can implement within their sales and marketing operations to make them more competitive, and by helping them deliver impact where it matters.

Trivia is any fact about an entity which is interesting due to its unusualness, uniqueness, unexpectedness or weirdness. In this paper, we propose a novel approach for mining entity trivia from their Wikipedia pages. Given an entity, our system extracts relevant sentences from its Wikipedia page and produces a list of sentences ranked based on their interestingness as trivia. At the heart of our system lies an interestingness ranker which learns the notion of interestingness, through a rich set of domain-independent linguistic and entity based features. Our ranking model is trained by leveraging existing user-generated trivia data available on the Web instead of creating new labeled data. We evaluated our system on movies domain and observed that the system performs significantly better than the defined baselines. A thorough qualitative analysis of the results revealed that our rich set of features indeed help in surfacing interesting trivia in the top ranks.

Recent progress in AI and Reinforcement learning has shown great success in solving complex problems with high dimensional state spaces. However, most of these successes have been primarily in simulated environments where failure is of little or no consequence. Most real-world applications, however, require training solutions that are safe to operate as catastrophic failures are inadmissible especially when there is human interaction involved. Currently, Safe RL systems use human oversight during training and exploration in order to make sure the RL agent does not go into a catastrophic state. These methods require a large amount of human labor and it is very difficult to scale up. We present a hybrid method for reducing the human intervention time by combining model-based approaches and training a supervised learner to to improve sample efficiency while also ensuring safety. We evaluate these methods on various grid-world environments using both standard and visual representations and show that our approach achieves better performance in terms of sample efficiency, number of catastrophic states reached as well as overall task performance compared to traditional model-free approaches.

Natural disasters cause considerable economic damage, loss of life, and network disruptions each year. As emergency response and infrastructure systems are interdependent and interconnected, quick assessment and repair in the event of disruption is critical. School of Computational Science and Engineering (CSE) Associate Professor B. Aditya Prakash is leading a collaborative effort with researchers from Georgia Institute of Technology, University of Oklahoma, University of Iowa, and University of Virginia to determine the state of an infrastructure network during such a disruption. Prakash's group has also been collaborating closely with the Oak Ridge National Laboratory on such problems in critical infrastructure networks. However, according to Prakash, quickly determining which infrastructure components are damaged in the event of a disaster is not easily done after a disruption.

The World Economic Forum, or WEF, said robotics and machinery will eliminate tens of millions of jobs over the next five years, but create just as many – perhaps more -- through the emergence of new technologies. The WEF, an international nongovernmental organization based in Geneva, Switzerland, said in a survey-based report that accelerating automation will wipe out 85 million jobs across 15 industries and 26 economies by 2025 – but concurrently create 97 million new jobs, particularly in the fields of data, artificial intelligence, content creation, the "green" economy and cloud computing. Still, the WEF conceded that such a dramatic disruption of labor markets could initially increase inequality and pressure companies around the world to quickly retrain workers in order to compete. "[Jobs] in areas such as data entry, accounting and administrative support are decreasing in demand as automation and digitization in the workplace increases," WEF said. "[Approximately] 50% of employers are expecting to accelerate the automation of some roles in their companies."

China is gambling on Artificial Intelligence and is also financing in Artificial Intelligence and positioning Artificial Intelligence on a level that none of the other countries is doing, as mentioned by Abishur Prakash, a thinker, and writer of books regarding the consequence of artificial intelligence (AI) on geopolitics. As growth in Artificial Intelligence increases, a few in the United States worry that the aptitude of China's influential central government to marshal statistics and decant assets into the area will force it to move ahead. The state has declared a huge amount to finance the newly started businesses, released programs to persuade investigators from abroad and rationalized its data policies. It has also declared news-reading robots and Artificial Intelligence -power-driven tactics for international associations. Maybe most disturbing to the United States are its efforts to include it into its armed forces.

"China is betting on AI and investing in AI and deploying AI on a scale no other country is doing," says Abishur Prakash, a futurist and author of books about the effect of artificial intelligence (AI) on geopolitics. As developments in AI accelerate, some in the US fear that the ability of China's powerful central government to marshal data and pour resources into the field will push it ahead. The country has announced billions in funding for start-ups, launched programmes to woo researchers from overseas and streamlined its data policies. It has announced news-reading robots and AI-powered strategy for foreign relations. Perhaps most alarming to the US are its efforts to incorporate it into its military. In the last few years, Washington has toughened oversight of Chinese investments, banned US firms from doing business with certain Chinese companies and increased criminal prosecution of alleged technology theft.