PRODUCTHEAD: The good, the bad and the ugly of machine learning
PRODUCTHEAD is a regular newsletter of product management goodness,
curated by Jock Busuttil.
paranoid product manager
Machine learning is the science of getting computers to act without being explicitly programmed
Netflix and the BBFC automate age certifications for content with a staff-trained algorithm
YouTube’s recommendation algorithm has the unintended consequence of creating echo chambers
An algorithm widely used in US courts for predicting future criminality exhibits racial bias
Filmmaker Shalini Kantayya explores the impact of biased facial recognition algorithms on human rights
In 2004, I attempted to build an automated system to help me identify the prospective customers most likely to want to buy my products.
It relied on being able to link an individual’s different activities together, such as viewing product pages on our website, downloading a trial version, or contacting us in some way. It would then assign scores for those activities.
Crucially, I also wanted it to use a Bayesian algorithm, much like the one I used to filter my email spam, to gradually learn to boost the scores of the combination of activities that usually would result in a product sale.
Needless to say, I failed. (Otherwise I suspect my career would have gone in a somewhat different direction.)
While my ambition was unbounded, my technical skills and the technologies I knew how to use had more practical limits.
These days, we are not so limited. Amazon, Google and Microsoft all offer machine learning as a service, with low barriers to entry for users in terms of both price and required domain experience.
Access to technologies with this power bring with it an inevitable ethical responsibility, as I’ve discussed before. You can even now do a Master’s degree in the responsible use of artificial intelligence (AI).
So this week, I’ve pulled together some articles for you on the good, the bad and the ugly of machine learning.
Speak to you soon,
what to think about this week
Machine learning versus AI: what’s the difference?
AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while machine learning is the science of getting computers to act without being explicitly programmed.
You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.
They’re related, but not quite the same thing
The good: predicting how a protein folds
Proteins fold up into complex three-dimensional arrangements, which in turn determine how proteins change its function.
Until now, predicting the folding arrangement has been a computational challenge that has resisted the efforts of biologists for decades, even with supercomputers at their disposal.
After training, DeepMind’s AlphaFold could deliver results in a matter of days, rather than the several years needed to examine proteins by hand, and averaged an accuracy of 92/100.
Solving complex problems in days not years
The good: adding age ratings to Netflix
Netflix has labelled all of its content with a UK age rating generated by an algorithm.
The technology was developed with the British Board of Film Classification (BBFC), which classifies films in cinemas.
Netflix staff watched its entire catalogue, tagging sex scenes, depictions of violence and swear words, then fed this data into the algorithm.
A filter for age-inappropriate content
Product Management Coaching
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We’ve coached people wanting to get into product management, product people with nobody in their organisation to manage them, and experienced product managers preparing to apply for a promotion.
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The bad: a toxic feedback loop
Guillaume Chaslot used to work on YouTube’s recommendation algorithm. He describes how their goal to increase the amount of time people spent on YouTube had unintended — but not unpredictable — consequences.
The ugly: bias in facial recognition
Modern society sits at the intersection of two crucial questions: What does it mean when artificial intelligence increasingly governs our liberties? And what are the consequences for the people AI is biased against?
CODED BIAS explores the fallout of MIT Media Lab researcher Joy Buolamwini’s discovery that facial recognition does not see dark-skinned faces accurately, and her journey to push for the first-ever legislation in the U.S. to govern against bias in the algorithms that impact us all.
Watch the award-winning documentary
Good product management
Perhaps we’ve been caught a little off-guard by the implications of these new technologies. These have presented product managers with yet another new challenge to add to the growing list: how to create products that are not only successful but also ethical.
[I MANAGE PRODUCTS]
The dirty little secrets of decision making
As individuals, we’re continually evaluating options and taking decisions. As product managers, we have the additional responsibility to balance the often competing needs of users, the business and wider ethical considerations. What makes one decision better than another?
[I MANAGE PRODUCTS]
Manage the whole product
A product is often a complex combination of several products and services. Some you create yourself, some are created by others. You’re responsible for the whole lot, even if they’re not all directly in your control.
What do Google, Tesla and Apple have in common with the Michelin Guide?
[I MANAGE PRODUCTS]
What lessons from antiquity would you apply to product management?
So this this might seem a bit odd, but are there any lessons from antiquity given your background in Classics that one might apply to product management in the year 2020?
Seeking meaning to 2020 from ancient Greece
[I MANAGE PRODUCTS]
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5th May 2021, 16:00 GMT
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PRODUCTHEAD is a newsletter for product people of all varieties, and is lovingly crafted from the ashes of failed food delivery startups.
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The Practitioner's Guide To Product Management
by Jock Busuttil
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