For more than two decades, Netflix has been obsessed with machine learning models.
In 2006, the company announced a million-dollar prize to anyone who could improve its recommendation algorithm’s accuracy by 10%. Over 40,000 teams participated in the global challenge.
The competition ran for three years, and only two teams managed to exceed the accuracy threshold. Netflix rewarded a winner that delivered a 10.06% accuracy improvement.
But, they dumped the winning algorithm.
Despite its stellar accuracy, the engineering costs and complexity of this algorithm were very high — too high for its accuracy improvement. Instead, Netflix used a lower-ranked —…
How do most organizations begin their Artificial Intelligence (AI) journey?
Let’s look at how leaders of some large enterprises planned their foray into AI. Here are a couple of recent examples from McKinsey:
And here’s an example that I witnessed first-hand.
Digital transformation is the flavor of the season. Every company has accelerated its efforts to digitize operations, gather intelligence, and rapidly respond to a changing market.
McKinsey senior partner Kate Smaje says that organizations are now accomplishing in 10 days what used to take them 10 months. With data powering better and faster decisions, she says, the road to recovery is paved with data.
As a result, most organizations are trying to adopt data-driven decision-making. They are hiring data scientists, buying the best tools, and greenlighting big-bang analytics projects.
Doubling down on data will not improve your business decisions.
In 2012, Disney invested $350 million in a movie that seemed to have all the elements of a box office success. It was action-packed and had stunning visual effects. It was helmed by a star writer and the director of “Finding Nemo.”
When things looked very optimistic, they roped in Black Swan, a UK-based artificial intelligence (AI) firm, to predict whether the movie would be a hit. The firm’s AI algorithm warned that the movie would flop. Disney ignored it and went ahead with their release plans.
The movie, “John Carter,” tanked at the box office with estimated losses of…
I published my articles across 8 outlets such as Forbes, Entrepreneur, TechCrunch, and The Enterprisers Project.
I am grateful to the publications, the editors and everyone who helped me share the ideas. …
Through 2020, I posted 5 days every week, on average. This came up to a total of 274 posts for the entire year. 😀
As part of my year-end reflection ritual, I relooked at my LinkedIn activity. I wanted to understand the past year and do some ‘analysis’ to find where I can improve. Talk about eating your own dog food! 🤓
Some quick stats: These posts were viewed over 780,000 times in all. On average, each post was viewed 2844 times, liked 36 times, and sparked 6 conversation comments. 📈
I’ll be making a separate post with my findings.
Update: You can read this article in Ukrainian (Thanks Katerina Moshkola!)
You’re launching a digital transformation initiative in the middle of the ongoing pandemic. You are pretty excited about this big-ticket investment, which has the potential to solve remote-work challenges that your organization faces.
However, the executive board’s response to your presentation is lukewarm at best. You discover that business teams don’t share your excitement. Your technology team, burdened with multiple priorities, worries that this could lead to more work stress.
Don’t we often find ourselves in situations like these?
The spread of coronavirus is delivering a massive blow to the global economy. The lockdown and work from home restrictions have forced thousands of startups to halt expansion plans, cancel services, and announce layoffs.
The virus is also having an impact on startup funding and deal activity, with seed-stage deals taking a serious blow this quarter. It’s clear that the startup community is now facing and will continue to confront an existential crisis in the months to come.
To stay in business, founders are looking for ways to maintain liquidity, better understand their demand-supply situation, identify operational efficiencies with a…
Just imagine your first day back to the office after months of isolation: Not only are you potentially exposed to the virus on your morning commute, but you’re then presented with crowded elevators.
As you enter the floor, you notice door handles that have likely been touched by dozens of others right before you, and confined workspaces that make it too easy to breach social distancing protocols. It’s hardly a situation that would put your mind at ease, let alone one that would help you to get back into the swing of working in the office.
Update: You can read this article in Japanese (Thanks Koki Yoshimoto!)
The challenges people encounter in a data science career are far more serious than the ones they face while getting into it.
Often, there is a big mismatch between job expectations and actual responsibilities. If you’re lucky to work in areas you aspire to, collaborating with other roles in a data science project can be a real struggle.