Spoiler: If you are starting with requirements, think again

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Photo by bruce mars on Unsplash

If you are a project manager, being assigned a data science or AI project may be a conflicting experience.

AI alone is slated to create up to $2.9 trillion (yes with a ‘t’) in business value by 2021, and despite the overall damper of the coronavirus, remains on the forefront of technology powering a recovery. But in the same breath, the odds of successful project delivery are not in your favour. …


Leveraging ethical AI and human-centric product design to treat the chronic disease of the digital economy

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Photo by Elijah O’Donnell on Unsplash

Intertwined with the rapidly unfolding Coronavirus epidemic is an insidious infodemic which may prove no less deadly. In February 2020, the director-general of the World Health Organization, Tedros Adhanom Ghebreyesus, made first coined the term:

“…we’re not just fighting an epidemic; we’re fighting an infodemic. Fake news spreads faster and more easily than this virus, and is just as dangerous.”

The comparison is neither sensationalism or hyperbole — the spread of social phenomena is so powerful, 2016 research shows that it can literally follow the same models that trace the contagion of epidemics.

Examples of the interplay of the two -demics include a slew of debunked news articles ranging from miracle cures and prevention methods such as consuming green herbs, boiled ginger or vitamin D, to false claims of vaccines curing hundreds of patients. …


Data science dysfunctions

The model that cripples other models

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Photo by Suzanne D. Williams on Unsplash

If I were to pick out the single most common slide presented at analytics and data science conferences, it would be Gartner’s analytics ascendancy model shown below.


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Photo by Kevin Ku on Unsplash

As we come to the end 2019, we reflect on a year whose start already saw 100 machine learning papers published a day and its end looks to see a record breaking funding year for AI.

But the path getting real value from data science and AI can be a long and difficult journey.

To paraphrase Eric Beinhocker from the Institute for New Economic Thinking, there are physical technologies which evolve at the pace of science, and social technologies which evolve at the pace at which humans can change — much slower.

Applied to the domain of data science and AI, the most sophisticated deep learning algorithms or the most robust and scalable real-time streaming data pipelines (‘physical technology’) mean little if decisions are not effectively made, organizational processes actively hinder data science and AI, and AI applications are not adopted due to lack of trust (‘social technology’). …

About

Jason Tamara Widjaja

Quora top writer, values driven, hype allergic and people centred. I lead an AI team in Merck and MSD and advocate ethical AI & diversity in tech.

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