Data science and artificial intelligence are adding a new dimension to drug discovery and development, emphasizing computation and machine learning. Given this shift, pharmaceutical companies are actively building infrastructure, data, tools, and teams to bring together data scientists with biology and life science experts. Pharma and biotech innovation offer a glimpse into how large organizations integrate AI tools and techniques with traditional subject matter experts who possess a deep understanding of the underlying problems to be solved. To gain an insider's perspective on how pharma companies use AI and machine learning, I invited Dr. Bülent Kızıltan to join episode #717 of the CXOTalk series of conversations with people shaping our world. He is Head of Causal & Predictive Analytics, Data Science & AI, at the Novartis AI Innovation Center.

With the onset of the COVID-19 pandemic, the world has come to realise that digital is the way forward. Today, every major tech company is dedicating resources to come up with innovation in artificial intelligence (AI). And the number of AI startups too has seen a significant increase in the recent years. With over ten years of R&D in artificial intelligence, machine learning, and consumer tech, Kochi-based Riafy Technologies claims to have an edge over other AI players globally. Started officially in 2013 by six friends -- John Mathew, Joseph Babu, Neeraj Manoharan, Benny Xavier, Benoy Joseph, and KV Sreenath, all in their early thirties and passionate about artificial intelligence since their college days, Riafy is an AI startup that helps businesses with various AI solutions like conversational AI chatbot.

On September 1, 2021 Gartner published their 2021 "Market Guide for AI Trust, Risk and Security Management". Per Gartner, "This Market Guide identifies new capabilities that data and analytics leaders must have to ensure model reliability, trustworthiness and security, and presents representative vendors who implement these functions."1 At HPE, we believe HPE Ezmeral ML Ops was recognized for the advantages our solution provides to our customers. As such, we're proud to announce that Gartner listed HPE Ezmeral ML Ops as a Representative ModelOps Vendor in the 2021 "Market Guide for AI Trust, Risk and Security Management." Gartner defines the AI Trust, Risk and Security Management (TRiSM) market as being made up of multiple software segments.

All the sessions from Transform 2021 are available on-demand now. MLOps, a compound of machine learning and information technology operations, sits at the intersection of developer operations (DevOps), data engineering, and machine learning. The goal of MLOps is to get machine learning algorithms into production. While similar to DevOps, MLOps relies on different roles and skill sets: data scientists who specialize in algorithms, mathematics, simulations, and developer tools, and operations administrators who focus on upgrades, production deployments, resource and data management, and security. While there is significant business value to MLOps, implementation can be difficult in the absence of a robust data strategy.

"…AI and the fourth industrial revolution will impact every aspect of people's lives." Artificial intelligence (AI) has reached a tipping point and is already part of our daily lives. If you've used Google Maps to determine the optimal route and transport options, you would have benefitted from the AI powered predictions backed by troves of underlying data. For companies such as banks, machine learning (ML) has also been successfully used to extend loans at much lower default rates and with much greater productivity. With the onset of the COVID-19 pandemic this year, the adoption of technology in our everyday lives has only accelerated.

Although more organizations are learning about the value of centralizing data management across hybrid multiclouds and infusing data science and AI into the infrastructure, not everyone has been able to do it. Maybe they lack the resources or capital expenditure. Or maybe they just don't feel ready. These are the kinds of organizations we had in mind when we set out to create an as-a-Service version of our integrated data and AI platform, Cloud Pak for Data -- an offering that is currently available as beta. Of the many attributes of the as-a-Service version, "use case" examples will help organizations understand how services can be integrated together to support specific disciplines.

We have known for years how much EI matters. Have a look at recent data in this article written by our founder Dr. Margareta Sjolund: There is no question that companies and organizations can become more successful by measuring and developing the Emotional Intelligence (EI) of their leaders and employees. The United Nations hosted a conference in May 2019 on EI as a tool for conflict resolution, Human Capital Institute presented a study on the importance of EI for leadership development and further research shows EI as a key to survival when robots and AI take over manual jobs, rendering them obsolete. AI increasingly takes over routine jobs and leads to significant changes in the workplace for both individuals and organizations. Jobs disappear and new jobs and new roles are created.

This library automates evaluation and analysis of any agent on these benchmarks. It serves to facilitate reproducible, and accessible, research on the core issues in RL, and ultimately the design of superior learning algorithms. Going forward, we hope to incorporate more excellent experiments from the research community, and commit to a periodic review of the experiments from a committee of prominent researchers. For a more comprehensive overview, see the accompanying paper. This means any experiment will automatically output data in the correct format for analysis using the notebook, without any constraints on the structure of agents or algorithms.

AI is dramatically enhancing industries, products, and core capabilities. But to make AI truly ubiquitous, it needs to run on end devices within a tight power and thermal budget. To learn more about the research that is advancing AI adoption, don't miss this VB Live event featuring Qualcomm's Senior Director of Engineering, Jilei Hou, and analyst.Jack Gold. "We're not anywhere near a steady state with AI," says Jack Gold, tech analyst and founder and president of J. Gold Associates. "AI is starting to take off, but we're nowhere near the top of the hockey stick."