| |JULY 202292.Productivity with Embedded IntelligenceMeasured productivity is yet an aspiration, and in-troduction of new technologies is typically accom-panied with a dip in productivity. While the same has been true for AI applications in the past, they are now on the verge of enabling unprecedented growth in the digital era.a. Intelligent automation, edge AI, IoT, and real time insights to action delivery will continue to re-inforce technology-business experiences and drive speed for corporates. This will be further assisted with a low/no-code AI app development environ-ment and AI-enabled editors that could save time.b. The S-curve on process automation ROI is reaching its natural plateau. ROI from this point onwards will follow the law of diminishing returns. But there is hope for businesses with intelligent agent platforms, which can be deployed for process discovery and mining. Intelligent agents, centrally orchestrated, can study human interaction patterns across business processes to identify automation op-portunities, and, in some cases, also substantially write the Bot script for new automation.c. The recent pandemic highlighted the need to understand biological systems better. Deep learn-ing AI technologies can now assist the scientific community to detect and design molecules, predict protein folding activity, and reactions, and partici-pate in drug discoveries.3.GovernanceDigital technology has become pervasive in our dai-ly lives. A discreet prevalence of AI-driven decision making is now raising concerns over the unintend-ed biases in data influencing decisions. Even after substantial algorithmic progress, many self-orga-nizing/deep-learning systems remain opaque. This does not instill trust or allow for effective compli-ance audit. So, fundamental transformation in regulated/critical applications has been limited. Significant progress in the following two areas is anticipated:a. Fairness of AI-led Decisions - Availability of synthetic data should mitigate existing biases in data or missing data to help build more robust mod-els. We can also expect new AI systems to test if new models are ethical and free from bias.b. AI model Accuracy & Resilience - Because of the black box phenomenon in Deep Learning mod-els, it has been challenging to debug AI systems. In 2022, we will see impetus on improving AI resilience over accuracy.4.Technology Breakthroughs Since the mid 50s, AI has seen much transformation. It has varied between the cynicism of brittle mod-els of AI and the confident hype of mathematician Claude Shannon. Shannon predicted that "with-in a matter of ten or fifteen years, something will emerge from the laboratory which is not too far from the robot of science fiction". AI systems have come a long way from coded logic to pattern detection and large language models with neural networks. They now simulate human language and motion (such as GANs) to represent humans and be an intelligent digital assistant. But even large transfer learning models have their limitations. We see new develop-ment in multimodal learning, quantum machine learning, and apps combining trained neural nets and traditional computer logic.Towards the end of the 1990s, after his earlier loss-es to Deep Blue, Kasparov concluded to "combine hu-man and machine intelligences to reach new heights and do things neither could do alone". Collaboration between humans and AI will be an important area of development. Humans will largely take over the role of manager of algorithms to upgrade our productiv-ity and creativity.As a football fan, I await to see if and when Hiroa-ki Kitano's prophecy will come true ­ "that by 2050 a team of autonomous bots will beat the human World Cup Champions". DEEP LEARNING AI TECHNOLOGIES CAN NOW ASSIST THE SCIENTIFIC COMMUNITY TO DETECT AND DESIGN MOLECULES, PREDICT PROTEIN FOLDING ACTIVITY, AND REACTIONS, AND PARTICIPATE IN DRUG DISCOVERIES
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