In a previous article, I wrote about the importance (and challenges) of assessing an ML model’s UX/operational/commercial efficacy (i.e., impact when used under ideal/controlled circumstances) and effectiveness (i.e., impact in real-world conditions) as part of an ML product. In the same article, I discussed the similarities of this problem to the ones medical researchers deal with, when assessing the efficacy and effectiveness of candidate interventions (such as vaccines), and how ML products can draw parallels and transfer some such terms and learnings from the medical domain to theirs. This article will expand on that story.
First, I would like to…
A good machine learning (ML) product team is made up of multiple specialties; from research, engineering and dev, to design, strategy, product management and more. These specialities can help identify each others’ blind spots (re building products that are simultaneously desirable, feasible and viable), define a roadmap for successful innovation, and collaborate towards its execution. On the other hand, the relative newness of ML products — particularly in certain domains, industries, and companies — means that such teams do not have access to rich knowledge bases (e.g., of know how, case studies, individual experience, and best practices) that more traditional…
It is difficult these days to find disagreement in incumbent businesses and industries — from health and finance, to manufacturing and retail — regarding the need to transform their businesses (and even their operating models) to be properly digital and benefit from technology in order to stay competitive. While plans for such transformations in many companies started years ago, the unfortunate reality is that to date, many such efforts have failed. According to a recent survey by McKinsey & Company, more than 70% of such initiatives across companies they studied have stalled. …
A black box is defined as a system, which can be viewed in terms of its inputs and outputs (or transfer characteristics/function), without any knowledge of its internal workings. The opposite of a black box is a system where the inner components or logic are available for inspection (commonly referred to as a glass box or white box). …
The recent years have seen a surge of work at the interface of AI — or more specifically, machine learning (ML) — and society/humanities. This is a great development, which has (rightly) been welcomed by the ML community, its application domains, and general public. Such works have the potential to lead to a new generation of multilingual ML scientists and engineers, who can speak ML, humanities (e.g., philosophy, ethics, and more), and other disciplines, and pave the way for a broader adoption and success, and appropriate use of this technology. …
In January 2017, in advance of Johnson & Johnson’s announcement of its $30 billion acquisition of Actelion — a Switzerland-based pharmaceuticals company — the Johnson & Johnson jet stayed parked near Actelion for five days. To some investors, this flight’s information could have been a leading indicator for the performance of Johnson & Johnson’s — or even other pharmaceutical companies’ — stocks. That’s why companies such as Quandl try to turn corporate aviation data (like the Johnson & Johnson one I mentioned) into insights for financial services professionals. Of course, the story is not limited to flight data; for years…
“The revolution in deep nets has been very profound,” said Sergey Brin, Google co-founder, in an interview at Davos, “it definitely surprised me, even though I was sitting right there.” He is not alone in seeing the impact of AI as both profound and fast-developing.
A report by CB Insights, in 2017, showed an exponential growth in the use of AI-related terms in corporate earning calls. I think, this should be seen as a sign of executive support for AI strategies (even if, for some companies, it is more theoretical or marketing-related, and in its early days). An even more…
In a recent interview with Reid Hoffman’s Masters of Scale, Marissa Mayer shared what led her to create Google’s programme for hiring and training product managers (PMs). According to her, as Google grew increasingly complex, they needed more of people with minds nimble enough to cover any and every aspect of the company’s rapidly-increasing range of products. Hiring qualified product managers, however, turned out to be more difficult than Mayer anticipated. She thought: “I can hire new people right out of school and train them to be great product managers at Google faster than you can hire the people you…
In an article in The Wall Street Journal in 2011, Marc Andreessen shared why he thinks “Software Is Eating the World”. He was right; in the past decade, we saw the mass adoption of software in every aspect our lives; from search, shopping and travel to health, finance and beyond. This led to the generation of an unprecedented amount of data. Such large datasets, when paired with powerful (and relatively cheap) compute power, allowed scientists to explore new frontiers in AI. For instance, some of the old algorithms (e.g., …
As the Chief Scientist at AIG, my role involves leading a global team that develops AI-first products for Insurance and Investments arms of the company; we create apps and services that are designed to employ Machine Learning to inform and assist users. We also carry out R&D in Machine Learning, and attempt to create synergy with AI-related innovations in the broader DeepTech world, when possible. Given the fast pace at which these fields are developing, I find attending conferences invaluable.
Conferences can be one of the most effective ways to network with the broader community of experts. You get to…
Reza Khorshidi
Chief Scientist at AIG, and PI at University of Oxford’s Deep Medicine Program; interested in Machine Learning in Biomedicine and FinTech
