A Green New Deal?
Interesting NYT column by Tom Friedman:
Clean energy is a problem of scale. If you don’t have scale, you have a hobby. I like hobbies. I used to build model airplanes. But you can’t mitigate climate change as a hobby. The reason I called for a Green New Deal was first and foremost to convey that this undertaking required a massive, urgent response commensurate with the scale and time frame posed by accelerating disruptive climate.
For too long, he continues, “green” was viewed as a synonym for a project that was “boutique, uneconomical, liberal, sissy and vaguely French”.
I wanted to recast green as geostrategic, capitalistic, economical, innovative and patriotic. My motto was, “Green is the new red, white and blue.” I did not believe in being a “nice” green. I believed in being a mean green. I believed greens should be as brassy, bold, big sky and in-your-face as any oil and gas executive.
Corbyn parts company with his own party
Splendid Observer column by Andrew Rawnsley:
What would Mr Corbyn do when he and his members desired something contradictory? How would he act when they had fundamentally different worldviews? Would he put aside his own preferences in deference to the sovereignty of the Labour people? Or would he behave just like the “establishment” politicians he has spent a lifetime condemning and seek to subordinate the will of the members to his own opinions?
We now know. The Brexit blowtorch has burnt away many fantasises. One of the items on the bonfire of illusions is the notion that the Labour leader is in some way a special one, so different to all other politicians as to be almost not a politician at all. It turns out that he is just another manoeuvring, equivocating hack when he wants one thing and his members want the opposite.
This split is not a slight difference of opinion on a low-order issue. We are talking about something a bit more important than how to regulate the provision of manhole covers. This is about the most significant question to face Britain for decades. Some 73% of people currently identifying as Labour supporters think that the UK was wrong to vote to leave the EU. That rises to a whopping 89% among Labour members. As you might expect to follow, most Labour members and most Labour voters want the party to come out in full support of another referendum on Brexit, a move that would transform the chances of the country being given a fresh choice.
The bottom line, as Rawnsley observes, is that sometimes, the simplest explanations for human behaviour are the best ones. The Labour leader is not making any effort to prevent Brexit because he doesn’t want to prevent Brexit. So if Labour supporters want another referendum (and we now know, courtesy of Tim Bales’s research that they do), they will have to learn from their leader’s time-honoured practice and rebel against him.
Why Facebook isn’t viable in its current form
This morning’s Observer column:
Way back in the 1950s, a pioneering British cybernetician, W Ross Ashby, proposed a fundamental law of dynamic systems. In his book An Introduction to Cybernetics, he formulated his law of requisite variety, which defines “the minimum number of states necessary for a controller to control a system of a given number of states”. In plain English, it boils down to this: for a system to be viable, it has to be able to absorb or cope with the complexity of its environment. And there are basically only two ways of achieving viability in those terms: either the system manages to control (or reduce) the variety of its environment, or it has to increase its internal capacity (its “variety”) to match what is being thrown at it from the environment.
Sounds abstruse, I know, but it has a contemporary resonance. Specifically, it provides a way of understanding some of the current internal turmoil in Facebook as it grapples with the problem of keeping unacceptable, hateful or psychotic content off its platform…
See also Tyler Cowen’s ‘trilemma’ piece
Quote of the Day
“Good economists know that the correct answer to any question in economics is: it depends.”
Dani Rodrik, “Rescuing economics from Neoliberalism”
Tyler Cowen on the impossibility of regulating speech on Internet platforms
From his latest Bloomberg column:
I’d like to suggest a simple trilemma. When it comes to private platforms and speech regulation, you can choose two of three: scalability, effectiveness and consistency. You cannot have all three. Furthermore, this trilemma suggests that we — whether as users, citizens or indeed managers of the platforms themselves — won’t ever be happy with how speech is regulated on the internet.
One view, which may appear cynical, is that the platforms are worth having, so they should appease us by at least trying to regulate effectively, even though both of us know they won’t really succeed. Circa 2019, I don’t see a better solution. Another view is that we’d be better off with how things were a few years ago, when platform regulation of speech was not such a big issue. After all, we Americans don’t flip out when we learn that Amazon sells copies of “Mein Kampf.”
The problem is that once you learn about what you can’t have — speech regulation that is scalable, consistent and hostile to bad agents — it is hard to get used to that fact. Going forward, we’re likely to see platform companies trying harder and harder, and their critics getting louder and louder.
I like his ‘trilemma’ idea. It reminds me of Dani Rodrik’s one, which says that democracy, national sovereignty and global economic integration are mutually incompatible: we can combine any two of the three, but never have all three simultaneously and in full.
The real significance of the Apple slide
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
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:
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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.
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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.
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12% of articles referenced the technology entrepreneur, Elon Musk.
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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.
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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.
The feeding frenzy about the drop in Apple’s share price
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
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.