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…

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…

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…

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…

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…

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…

“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 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…

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…