Dr. Ganapathi Pulipaka was a recipient of the Top 50 Technology Leader awards for recognition of his contribution to artificial intelligence, machine learning, and data science; for the past five years on Twitter as a machine learning and data science influencer; as a contributor to thought leadership and of project implementation articles on Medium, Data Driven Investor, LinkedIn, GitHub; as a best-selling author of two books on Amazon - "The Future of Data Science and Parallel Computing: A Road to Technological Singularity," published on June 29, 2018, and "Big Data Appliances for In-Memory Computing: A Real-World Research Guide for Corporations to Tame and Wrangle Their Data," published Dec. 8, 2015 - and other eBooks that have reached all-time high rankings from the world's largest book ratings authority (featured on Forbes), BookAuthority; and also for writing another 400 research papers as part of academic research programs for PostDoc and PhD. He is an American data scientist and AI luminary who has been featured in top-tier magazines and news and industry publications and was a speaker for multiple media distribution networks and some of the top media station affiliates, including ABC, FoxNews, NBC, Yahoo Finance, MarketWatch, The CW, VentureBeat, MirrorReview, CIOReview, SAP, Erie News Now, USA Today, Double T 97.3 Lubbock's Radio station, 100.7 KFM BFM San Diego, KITV, Telemundo Lubbock 46, AZCentral, Insights Success, NewsOk, Pittsburgh Post-Gazette, MarketWatch, and Ask.

At the Intercon conference, Dr. GP gave a motivational keynote speech on Deep Reinforcement Learning and the landscape of machine learning and artificial intelligence that inspired the audience. He noted that the MIT Technology Review has downloaded 16,625 research papers from arxiv that are publicly available under the computer science and artificial intelligence section through November 2018. Through natural language processing techniques on the abstracts, the words "constraint," "theory," "rule," "logic," "program," "learning," "network," "data," "task," and "performance" have been evaluated to find the reinforcement learning boom in recent times. Dr. GP said trends have shown the rise of traditional neural networks in the 1950s and 1960s, symbolic approaches in the 1970s, knowledge-based and rule-based systems in 1980s, support vector machines in 1990s, and the reign of neural networks in the 2010s with the advent of heavy implementation of deep neural networks. Deep Traffic is a reinforcement learning simulation based on the 24,000 entries received on MIT's Deep Traffic competition on self-driving cars that drive on a multi-lane freeway with a model-free off-policy reinforcement learning process that inspires a number of data scientists and machine learning enthusiasts to evaluate the Deep-Q-Learning reinforcement learning network variants and hyperparameter configurations with episodic iterations training of 96.6 years of RL simulations, 572.2 million crowdsourced and optimized DQN hyperparameters to train the agents successfully.