Machine-learning lies at the core of solutions provided by data analytics organizations. The very paradigm of how machine-learning based capabilities and solutions are offered has undergone a fundamental shift in very recent years. The days of monolithic, singular platforms or products in this space seem over, and have given way to nimbler, more modular, and more scalable architectures based on services and cloud computing. In particular, the micro-services architecture which was first applied to software systems and applications (in general), is penetrating machine-learning based software applications. The concept of “machine learning as micro-service” has arrived and is embraced by data scientists as the paradigm for productionalizing analytics solutions in an optimal fashion.
There is significant anticipation that Artificial Intelligence or “AI” will revolutionize the paradigm and economics of drug discovery. The gamut of diseases that await next generation drug discovery cover the spectrum from Cancers to Parkinson’s to Alzheimer’s and other diseases. Drug discovery and the pace of innovation are neither scalable nor practically viable, where we require $2.7 B and 14 years on average to develop a single new drug (Study by US FDA and Tufts University). Less than 10 percent of potential drugs researched actually make it to market. The pharmaceutical industry today needs a much more optimized, scalable, and cost effective way of bring new drugs to market. AI is the technology that holds tremendous hope and expectation for providing this today.
“Nearly all big tech companies have an artificial intelligence project, and they are willing to pay experts millions of dollars to help get it done.” – The NY Times
The NY Times recently wrote about the lack of AI (Artificial Intelligence) talent (data scientists):
“Tech’s biggest companies are placing their future on artificial intelligence… As they chase this future, they are doling out salaries that are startling even in an industry that has never been shy about lavishing a fortune on its top talent… Solving tough A.I. problems is not like building the flavor-of-the-month smartphone app. In the entire world, fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research” - New York Times 22 October 2017.
An individual’s attributes, such as his her or her interests, preferences, views, hobbies, inclinations, passions, aversions, political inclinations and such are of significant interest for a variety of commercial, research or community applications. Marketers or market developers, as just one example, stand to gain significantly from understanding such attributes about individuals or individuals in a given population as a whole. In addition, there is also interest in information about an individual’s “current state” such as their current location and/or activity. For instance mobile intervention health applications (on mobile devices) can significantly optimize and personalize their capabilities by leveraging activity details such as where an individual currently is, what they are now doing, etc.
Chatbots or Conversational Intelligent Agents, such as Siri, Alexa, Cortana, has gained a significant presence in the modern society. In addition to their impacts on personal life, intelligent agents/devices, as well as conversational chat agents for applications such as customer support and travel reservations, have transformed the way modern businesses are being conducted. By definition, “a chatbot (also known as a talkbot, chatterbot, Bot, chatterbox, Artificial Conversational Entity) is a computer program which conducts a conversation via auditory or textual methods.”
Researchers have traditionally analyzed patient data that is genetic, patient medical record data, and other data such as patient MRI images or Pathology reports in cancer research. Cancer has largely been considered as emanating “from genetic factors”. It is becoming increasingly clear however, that the primary factors towards cancers of various kinds are not solely genetic, but that environment and lifestyle are key contributing factors as well.