Motivation behind AI is its success and impact on various critical business functions across many industries today. AI is changing the way, people live and interact with society, how businesses operate and compete. This success is a result of 2 to 3 decades of committed and sincere efforts from many researchers and scientists in the fields of AI and computing. In addition to business, data and technology innovation, this AI research community is investing additional time to address so many adoption barriers around explainability, various kinds of biases, and many more social and ethical challenges that AI could face while we experience the benefits of this constant innovation. Looking at the world ahead of us, all of us must face this AI experience and contribute to it in different ways. So, if you have not started this journey, you need make couple of initial steps to contribute and take advantage of this innovation.
Artificial intelligence, machine learning and deep learning are doing something that once seemed unimaginable a few decades before. They are transforming heavily regulated industries, such as healthcare and life sciences, financial services and trading, and many others. For healthcare and life sciences, Artificial intelligence takes the role of an experienced clinical assistant who helps doctors make faster and more reliable diagnoses. We already see AI applications in the areas of imaging and diagnostics, and oncology. Machine learning has the potential to improve remote patient monitoring. AI algorithms can take information from electronic health records, prescriptions, insurance records and even wearable sensor devices to design a personalized treatment plan for patients. These AI-related technologies accelerate the discovery and creation of new medicines and drugs. There is a broad consensus amongst insiders that healthcare is being transformed for the better because of AI. The opportunities and potential are limitless. Healthcare is going to be one of those industries that is elevated and made better by machine learning and artificial intelligence.
I remember the days when I was part of the early AI research team in 2002 doing algorithmic research and analysis on a few use cases related to image recognition, text and web mining. It was exciting to be part of this activity in the university spending time with professors and trying to get inspired by their long term vision. But soon after the graduation, it was little quiet as you don’t see any opportunities for some time in the industry. There were couple of challenges back then, 1) AI is like fiction for many of us, 2) Computation is so expensive, thanks to generous funding from universities, 3) data collection is huge task, I remember we had to spend months in scraping educational web sites to create a test bed. But we all respect many volunteers including professors, scientists, and some early adopters who relentlessly spent their time in shaping the today’s wonderful world in AI.
In today’s world, the three of the above challenges were addressed to meaningful extent depending on the industry. We clearly see that many businesses are running AI for routine tasks like research, lab automation, data analytics, agriculture, supply chain, production, operations, customer support, and many more. We don’t have any economical and time based barriers related to setting up computing infrastructure. Public cloud environments made it very easy to build the right kind of infrastructure even to run the heaviest deep learning jobs with in hours or days compared to months. Data has different dimensions to it, in terms of breadth of the data coverage, number of features it provides, channels for tracking data, variety of new formats, internet of every thing (IOE) to help integrate various kinds of data around us. In addition to organizations, a common human being is able to get access to massive loads of data through public channels. I am fascinated by how quickly we are able to read the COVID gene mutations, expressions to build informed knowledge that is useful to many researchers and scientists. This is all done in public setting by leveraging platforms like Kaggle. All these channels are empowering to drive constant innovation in rapid pace. I remember a quote from Jack Welch that innovation is not just a central idea of single group or company, but it bubble up from every corners. I believe we came close to that dream where many individuals are contributing to the innovation and research that is used by others in public, social and professional settings.
One of main goal is to contribute via sharing my expertise and learning to this AI and Tech community. I will be organizing my knowledge as content pages (detailed indexed topics) and blog posts (quick insights for discussion). Please feel free to review and share your comments if you have different perspectives around these topics. I follow the concept of continuous and open learning from various sources.
“Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence.” —Ginni Rometty
Murali Pinnaka is a data management & analytics leader and business technology strategist & architect with 20+ years of experience in helping 30+ companies strategize, plan and deploy various data, analytics and sales operations capabilities for healthcare/life sciences, tech, agriculture and other industries. He made positive impact to 25+ healthcare / life sciences and product development companies by helping them, a) build strategies, manage transformation programs, drive product and architecture innovation, and build expertise, b) deploy commercial operations, data management and analytics capabilities for pre and post product launches. He supported 10+ organization wide technology transformation initiatives to support the scale of growing business innovation, and to keep up to date with technology innovation.
He is passionate about promoting AI and deep learning technology to solve several critical use cases in healthcare (genomics, biotechnology), product development, and agriculture industries. He is aspiring to help companies define their AI and cloud strategy, build expertise in data, computational algorithms to solve analytics and data science use cases. He is also conducting deep analysis on various algorithms, optimization techniques and cloud engineering techniques & workflows to optimally solve these prioritized use cases. He is eager to share his ongoing learning through various channels and also through direct client advising.
