AI Articles and Analysis about Research and development

Episode summary: Machine learning currently faces a number of obstacles which prevent it from advancing as quickly as it might. How might these obstacles be overcome and what impact would this have on the machine learning across different industries in the coming decade? In this episode we talk to Dr. Hanie Sedghi, Research Scientist at the Allen Institute for Artificial Intelligence, about the developments in core machine learning technology that need to be made, and that researchers and scientists are working, on to further the application of machine learning in autonomous vehicles.

A company by company examination of the top car makers public investment and statements by their top executives makes it clear that most car companies are betting that artificial intelligence utilized in self-driving will be inevitable, and they're all jumping in with investment and initiatives.

Episode Summary: Venture investing in AI healthcare applications has been on the uptick and is directly related to the subject of this week's episode: just how the healthcare industry is (and isn't) being impacted by innovations in AI technology. Guest Steve Gullans of Boston-Based Excel Venture Management talks about some of the various healthcare-related ML and AI applications that he sees being brought to light, and touches on which innovations have a better chance of getting blocked and redirected by parties of interest and those that have more promise in being accepted and rolled out sooner.

When it comes to effectiveness of machine learning, more data almost always yields better results—and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators.
Where does all this data come from? If we could look at labeled data streams, we might see research and development (R&D); physicians and clinics; patients; caregivers; etc. The array of (at present) disparate origins is part of the issue in synchronizing this information and using it to improve healthcare infrastructure and treatments. Hence, the present-day core issue at the intersection of machine learning and healthcare: finding ways to effectively collect and use lots of different types of data for better analysis, prevention, and treatment of individuals.
Burgeoning applications of ML in pharma and medicine are glimmers of a potential future in which synchronicity of data, analysis, and innovation are an everyday reality.
At Emerj, the AI Research and Advisory Company, we research how AI is impacting the pharmaceutical industry as part of our AI Opportunity Landscape service. Global pharma companies use AI Opportunity Landscapes to find out where AI fits at their company and which AI applications are driving value in the industry.
In this article, we use insights from our research to provide a breakdown of several of the pioneering applications of AI in pharma and areas for continued innovation.

With the development of free, open-source machine learning and artificial intelligence tools like Google’s TensorFlow and sci-kit learn, as well as “ML-as-a-service” products like Google’s Cloud Prediction API and Microsoft’s Azure Machine Learning platform, it’s never been easier for companies of all sizes to harness the power of data. But machine learning is such a vast, complex field. Where do you start learning how to use it in your business?

Episode Summary: One of the most memorable moments from this interview is when our guest mentioned that Larry Page hired him to solve intelligence; very few people can say this, and this says something about today’s guest, Dr. Nando de Freitas - a senior researcher at Google and professor at Oxford - as well as the gravity of his present work. Today, I speak with Nando about a topic well known through his research at Google, deep learning. de Freitas gives his perspective on the basics of deep learning, the applications in conversational interfaces and recognizing images and videos, and what the future of this technology might look like in the nearer future.

Episode Summary: When you go to Harvard Business School and then to McKinsey company to work in private equity, there’s really only one thing left to do - go to Silicon Valley and launch an AI startup. At least, this is exactly what CEO Praful Krishna did when he moved to San Francisco to start Coseer, an AI company focused on understanding natural language and unstructured data. In this week’s episode, we speak about where unstructured data lives in a business, and how a business can be changed if the right data is unlocked. Krishna also discusses his experience in how executives are making decisions around how, or how not to, leverage AI in their companies.

[This interview has been revised and updated.]

Episode Summary: How do neural networks affect your life? There’s the one that you walk around with in your head of course, but the one in your pocket is an almost constant presence as well. In this episode, we speak with Dr. Yoshua Bengio about how the neural nets in computer software have become more ubiquitous and powerful, with deep learning algorithms and neural nets permeating research and commercial applications over the past decade. He also discusses likely future opportunities for deep learning in areas such as natural language processing and individualized medicine. Bengio was a researcher at Bell Labs with Yann LeCun, now at Facebook, and was working on neural nets before they were the "cool" new AI technology as it's often perceived today.