Monthly Archives: January 2019

The real significance of the Apple slide

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Apart from the fact that the Chinese economy seems to be faltering and collateral damage from Trump’s ‘trade war’ what the slide signals is that the smartphone boom triggered by Apple with the iPhone is ending because we’re reaching a plateau and apparently there’s no New New Thing in sight. At any rate, that’s Kara Swisher’s take on it:

The last big innovation explosion — the proliferation of the smartphone — is clearly ending. There is no question that Apple was the center of that, with its app-centric, photo-forward and feature-laden phone that gave everyone the first platform for what was to create so many products and so much wealth. It was the debut of the iPhone in 2007 that spurred what some in tech call a “Cambrian explosion,” a reference to the era when the first complex animals appeared. There would be no Uber and Lyft without the iPhone (and later the Android version), no Tinder, no Spotify.

Now all of tech is seeking the next major platform and area of growth. Will it be virtual and augmented reality, or perhaps self-driving cars? Artificial intelligence, robotics, cryptocurrency or digital health? We are stumbling in the dark.

Yep. Situation normal, in other words.

How our view of AI is skewed by industry hype

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The Reuters Institute In Oxford has just published a really valuable study of how AI is covered in mainstream media, based on an analysis of eight months of reporting on AI in six mainstream UK news outlets.

The study’s basic conclusion is that UK media coverage of artificial intelligence is dominated by industry products, announcements and research. Coverage frequently amplifies self-interested assertions of AI’s value and potential, while positioning the technology primarily as a private commercial concern and undercutting the role of public action in addressing AI.

Key findings:

  • Nearly 60% of articles were focused on new industry products, announcements and initiatives that include AI, from smart phones or running shoes, to sex robots or brain preservation. Outlets also regularly covered industry promotional events, start-ups, buyouts, investments, and conferences.

  • One third (33%) of articles were based on industry sources – mostly CEOs or other senior executives – six times as many as those from government and nearly twice as many as those from academia.

  • 12% of articles referenced the technology entrepreneur, Elon Musk.

  • AI products are often portrayed as a relevant and competent solution to a range of public problems, from cancer and renewable energy, to coffee delivery. Journalists or commentators rarely question whether AI-containing technologies are the best solutions to such problems or acknowledge ongoing debates concerning AI’s potential effects.

  • Media coverage of AI is being politicised: right-leaning news outlets highlight issues of economics and geopolitics; left-leaning news outlets highlight issues of ethics, including discrimination, algorithmic bias and privacy.

The report’s lead author, J. Scott Brennen, observed that

“by amplifying industry’s self-interested claims about AI, media coverage presents AI as a solution to a range of problems that will disrupt nearly all areas of our lives, often without acknowledging ongoing debates concerning AI’s potential effects. In this way, coverage also positions AI mostly as a private commercial concern and undercuts the role and potential of public action in addressing this emerging public issue.”

That sounds just about right to me. This is a terrific piece of work.

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The feeding frenzy about the drop in Apple’s share price

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I find the media obsession with Apple’s valuation really tiresome. That’s not just because I don’t own any shares but also because it suggests that mainstream journalists haven’t been paying attention. Way back in August, for example, Bloomberg’s Tim Culpin published a very perceptive piece under the headline “Dark Clouds Gather as Tech Stockpiles Hit Pre-Crisis Levels” which made it crystal clear that there was a slowdown coming.

But even if journalists don’t pay much attention to supply chains you’d have thought that common sense and everyday experience would have taught them that the iPhone picture was changing. I’ve lost count of the number of friends, colleagues and acquaintances who are happily still using their four- or five-year-old iPhones. The devices still work perfectly for their purposes. Sure, the camera isn’t as good as the one on the iPhone XS, but it’s still good enough for everyday use. My trusty old iPhone 6 is still more than adequate for my purposes. In fact, since the last couple of IOS updates and a replacement battery, it’s as good as it ever was. And one of the things that would stop me upgrading is that I find its fingerprint recognition much more convenient for secure online activities than the much-touted face recognition in the newer iPhones would be. I’ve been an early adopter and a gadget freak for as long as I can remember. So if I’m not upgrading, then must be lots more like me.

Could it be that most mainstream tech journalists always have the latest iPhones because their employers pay for them? And so they have fallen into the delusion of thinking that they’re normal consumers?

How companies are addressing machine learning

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From an O’Reilly newsletter:

In a recent O’Reilly survey, we found that the skills gap remains one of the key challenges holding back the adoption of machine learning. The demand for data skills (“the sexiest job of the 21st century”) hasn’t dissipated—LinkedIn recently found that demand for data scientists in the US is “off the charts,” and our survey indicated that the demand for data scientists and data engineers is strong not just in the US but globally.

With the average shelf life of a skill today at less than five years and the cost to replace an employee estimated at between six and nine months of the position’s salary, there’s increasing pressure on tech leaders to retain and upskill rather than replace their employees in order to keep data projects (such as machine learning implementations) on track. We’re also seeing more training programs aimed at executives and decision makers, who need to understand how these new ML technologies can impact their current operations and products.

Beyond investments in narrowing the skills gap, companies are beginning to put processes in place for their data science projects, for example creating analytics centers of excellence that centralize capabilities and share best practices. Some companies are also actively maintaining a portfolio of use cases and opportunities for ML.

Note the average shelf-life of a skill and then ponder why the UK government is not boosting the Open University.

Fathers and sons

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I’ve just finished Colm Tóibín’s book about the fathers of Oscar Wilde, W.B. Yeats and James Joyce. Very interesting but uneven work. Main conclusion is that all three were very strange men. William Wilde was an erratic (but formidable) polymath, John B. Yeats a talented but improvident painter who never finished a painting and never made a living, and John Stanislaus Joyce was a pompous wastrel and a drunk with a fine singing voice. And all three seem to have been terrible husbands. For their part, their talented sons all treated them ambivalently. It’s well known that having a famous father makes it difficult for sons. But being a famous son of an erratic or improvident father clearly has its problems too. Of the three, it was James Joyce who made serious artistic use of his father — there are recognisable aspects of John Stanislaus in Stephen Hero, Dubliners, Portrait of the Artist, Ulysses and Finnegans Wake and Tóibín has been good at spotting and excavating them.