Every Friday, I run through my tweets to select a few observations and insights that have kept me thinking.
On preparing for the Machinocene
According to Huw Price, the Bertrand Russell Professor of Philosophy and a fellow of Trinity College at the University of Cambridge, “human-level intelligence is familiar in biological hardware — you’re using it now. Science and technology seem to be converging, from several directions, on the possibility of similar intelligence in non-biological systems. It is difficult to predict when this might happen, but most artificial intelligence specialists estimate that it is more likely than not within this century. Freed of biological constraints, such as a brain that needs to fit through a human birth canal (and that runs on the power of a mere 20W lightbulb), non-biological machines might be much more intelligent than we are.”
In Now it’s time to prepare for the Machinocene, Price wonders what this would actually mean for us.
“One way or another, then, we are going to be sharing the planet with a lot of non-biological intelligence. Whatever it brings, we humans face this future together. We have an obvious common interest in getting it right. And we need to nail it the first time round. Barring some calamity that ends our technological civilisation without entirely finishing us off, we’re not going to be coming this way again.”
There have been encouraging signs of a growing awareness of these issues, Price says. “Many thousands of AI researchers and others have now signed an open letter calling for research to ensure that AI is safe and beneficial. Most recently, there is a welcome new Partnership on AI to Benefit People and Society by Google, Amazon, Facebook, IBM and Microsoft.”
“I detect the emergence of a new mindset, one that seeks a higher moral ground. This mindset is a byproduct of a new global consciousness, a consequence of increasing knowledge of our cosmic position as molecular machines living in a rare planet that are capable of self-awareness. Science not only creates new machines and technologies, but also new worldviews. It’s time we moved one from our ancient tribal divides.” — Marcelo Gleiser, the Appleton Professor of Natural Philosophy and a professor of physics and astronomy at Dartmouth College, in Will Artificial Intelligence Do Great Harm Or Great Good?
Much of today’s focus is on the relatively short-term benefits and impacts of AI (on jobs, for example). But in the case of the long-term future of AI, Prices believes there are reasons to be optimistic. “It might help us to solve many of the practical problems that defeat our own limited brains. But when it comes to what the cartography of possible futures looks like, which parts of it are better or worse, and how we steer towards the best outcomes — on those matters we are still largely ignorant. We have some sense of regions we need to avoid, but much of the map remains terra incognita. It would be a peculiarly insouciant optimist who thought we should just wait and see.”
If we are to develop machines that think, “ensuring that they are safe and beneficial is one of the great intellectual and practical challenges of this century. And we must face it together — the issue is far too large and crucial to be tackled by any individual institution, corporation or nation. Our grandchildren, or their grandchildren, are likely to be living in a different era, perhaps more Machinocene than Anthropocene. Our task is to make the best of this epochal transition, for them and the generations to follow. We need the best of human intelligence to make the best of artificial intelligence.”
More on AI in Bryan Johnson’s The combination of human and artificial intelligence will define humanity’s future on TechCrunch.
On the iBrain inside your phone
Often we talk about artificial intelligence like it’s something that will happen somewhere in the future. Fascinated by what might be, there is an underestimation of how firmly AI is rooted in the present already. “Bright entrepreneurs have brought AI to agriculture, the oil and gas industry, radiology, financial technology, security and more. In fact, the largest and most successful companies in the world strongly believe in AI and invest substantial time and resources to harvest its potential. The future that bold minds are dreaming about is already here. Let’s look at some inevitable parts of one’s everyday life to demonstrate that,” Sofia, who is a contributing writer for LTP, writes in AI Is Not the Future, It’s the Present.
One of today’s more familiar examples is, of course, IBM’s Watson. “At the beginning of August, IBM’s Watson, a supercomputer powered with AI has been reported to successfully diagnose a rare form of leukemia on a patient within minutes — something doctors failed to do after month. Watson managed to make its diagnosis after doctors from the University of Tokyo’s Institute of Medical Science fed it the patient’s genetic data, which was then compared to information from 20 million oncological studies. If a software is already capable of diagnosing diseases better than doctors and prescribing effective treatment, intelligent and independent robot-doctors will arrive in no-time.”
But there’s more — from AI-powered laptops and self-driving vehicles to smartphones with brains. “Without users knowing it, Apple brought AI right into our smartphones years ago,” Sofia writes. In The iBrain is Here, Steven Levy, the editor of Backchannel, gives us an exclusive look at how artificial intelligence and machine learning work at Apple. The story “might raise an eyebrow in much of the artificial intelligence world,” Levy says. “Not that neural nets improved the system — of course they would do that — but that Apple was so quietly adept at doing it. Until recently, when Apple’s hiring in the AI field has stepped up and the company has made a few high-profile acquisitions, observers have viewed Apple as a laggard in what is shaping up as the most heated competition in the industry: the race to best use those powerful AI tools. Because Apple has always been so tight-lipped about what goes on behind badged doors, the AI cognoscenti didn’t know what Apple was up to in machine learning.”
“If you’re an iPhone user, you’ve come across Apple’s AI, and not just in Siri’s improved acumen in figuring out what you ask of her. You see it when the phone identifies a caller who isn’t in your contact list (but did email you recently). Or when you swipe on your screen to get a shortlist of the apps that you are most likely to open next. Or when you get a reminder of an appointment that you never got around to putting into your calendar. Or when a map location pops up for the hotel you’ve reserved, before you type it in. Or when the phone points you to where you parked your car, even though you never asked it to. These are all techniques either made possible or greatly enhanced by Apple’s adoption of deep learning and neural nets.”
“We use these techniques to do the things we have always wanted to do, better than we’ve been able to do. And on new things we haven’t be able to do. It’s a technique that will ultimately be a very Apple way of doing things as it evolves inside Apple and in the ways we make products.” — Phil Schiller, Apple’s vice president of worldwide marketing
Levy concludes his longread by saying it’s clear that machine learning has changed Apple’s products. What is not so clear is whether it is changing Apple itself. ‘In a sense,” he writes, “the machine learning mindset seems at odds with the Apple ethos. Apple is a company that carefully controls the user experience, down to the sensors that measure swipes. Everything is pre-designed and precisely coded. But when engineers use machine learning, they must step back and let the software itself discover solutions. Can Apple adjust to the modern reality that machine learning systems can themselves have a hand in product design? ‘It’s a source of a lot of internal debate,’ says Federighi. ‘We are used to delivering a very well-thought-out, curated experience where we control all the dimensions of how the system is going to interact with the user. When you start training a system based on large data sets of human behavior, [the results that emerge] aren’t necessarily what an Apple designer specified. They are what emerged from the data.’”
But according to Schiller, Apple isn’t turning back. “While these techniques absolutely affect how you design something, at the end of the day we are using them because they enable us to deliver a higher quality product.”
“The typical customer is going to experience deep learning on a day-to-day level that [exemplifies] what you love about an Apple product. The most exciting [instances] are so subtle that you don’t even think about it until the the third time you see it, and then you stop and say, How is this happening?” — Phil Schiller, Apple’s vice president of worldwide marketing
And that’s the takeaway, says Levy. “Apple may not make declarations about going all-in on machine learning, but the company will use it as much as possible to improve its products. That brain inside your phone is the proof.”
A bit more …
“Today, when five of the world’s most valuable companies are technology firms, it’s very hard to see where their businesses end and their charity efforts begin,” Evgeny Morozov writes in Rockefeller gave away money for no return. Can we say the same of today’s tech barons?. “As digital platforms, they power diverse industries and sectors from education to health to transport and thus have an option that was not available to the oil and steel magnates of yesteryear: they can simply continue selling their core product — mostly hope, albeit wrapped up in infinite layers of data, screens and sensors — without having to divert their funds into any nonproductive activities.”
“To speak of ‘philanthrocapitalism’ here — as many have done, either to praise or bury it — seems misguided, if only because such projects bear so little resemblance to philanthropy proper. One doesn’t have to admire Ford or Rockefeller to notice that their philanthropic endeavours, whatever their real political goals, were not supposed to make extra cash. But is it really so with our new tech barons?”
“What passes for philanthropy these days is often just a sophisticated effort to make money on engineering the kinds of rational, entrepreneurial and quantitative souls that would delight at other types of personalisation. Such learning is, of course, well suited to the needs of consulting firms and technology giants. A recent profile of AltSchool in The New Yorker mentioned that its students read the Iliad armed with a spreadsheet where they mark how many times the theme of ‘rage’ occurs in the text. Such schools can produce excellent auditors; poets, however, might need an alternative, to, well, the AltSchool.”
“We should be careful not to fall victim to a perverse form of Stockholm syndrome, coming to sympathise with the corporate kidnappers of our democracy. On the one hand, given that the new tech billionaires pay very little tax, it’s not surprising that the public sector would fail to innovate as quickly. On the other, by constantly giving the private sector a head start through technologies that they own and develop, the new tech elites all but ensure that the public would rather choose slick but privatised technological solutions over quaint, but public, political ones.
That we can no longer differentiate between philanthropy and speculation is an occasion to worry, not celebrate. With Silicon Valley elites so keen on saving the world, shouldn’t we also ask who will eventually save us from Silicon Valley?”
“While nimble startups chasing the next trend are exciting, the truth is that companies rarely succeed by adapting to market events. Rather, successful firms prevail by shaping the future. That can’t be done through agility alone, but takes years of preparation to achieve. The truth is that once you find yourself in a position where you need to adapt, it’s usually too late,” Greg Satell writes in Successful Companies Don’t Adapt, They Prepare.
“But truly great companies don’t scramble to adapt to the future, because they create the future. Take a look at any great business and it becomes clear that what made it great wasn’t the ability to pivot, but a dedication to creating, delivering, and capturing new value in the marketplace. The technology companies that endure are the ones who spend years — or even decades — to create the next generation of products.”
“Which brings us to something else Theodore Levitt said, ‘People don’t want to buy a quarter-inch drill, they want a quarter-inch hole.’ Clearly it is not a particular business category that defines a company, but its ability to solve problems for its customers. And you can’t solve really tough problems by simply moving faster. Great companies prepare the ground long before. And that’s really the point. Business that focus on solving big problems and are willing to invest in them for years — or even decades — can get a lot of other things wrong.”
“The availability of information on the internet and the fact that almost anything people need to know day to day can be accessed instantly on their smartphone make knowledge a commodity. Experience counts for little in a world where the past is no guide to the future. Knowledge was the key asset of the 20th century; imagination is the key asset for the 21st,” Anthony Hilton writes in How to survive work in the 21st century.
“The mantra from a generation of leaders educated in business schools revolves around developing a five-year plan and then putting it into practice. Business basically moved in straight lines, with the setting of goals and the designing of route maps to get there, coping with the inevitable bumps and buffetting on the way. But we now live in a world where change is so fast and its direction so unpredictable it is impossible to produce any meaningful forecast beyond two years. Business is no longer linear, management structures that assume it is will fail and the management tools of the past are no good.”
Are cities looking more alike? Has strolling Helsinki’s new neighbourhood Jätkäsaari become an extension of walking along a path of London’s Kings Cross redevelopment zone? If so, how are these developments collectively shaping the experience of the city?
In his project Familiar, designer Luca Picardi explores patterns of mimicry in contemporary urban development projects in Northern Europe from Fjord City in Oslo and HafenCity in Hamburg to Nine Elms in London and Stockholm’s Royal Seaport. Through open-source data available online, the publication re-frames existing marketing surrounding these projects. The result is not a production of new work but the presentation of a familiar reality. Identikit cities, if you’ve seen one …
This is Frank Lloyd Wright, written by architectural journalist Ian Volner, illustrated by Michael Kirkman and published by Laurence King Publishing in London, is one of this year’s top 10 books about architecture.
“Short articles, sumptuous illustrations, and a cover made of hard cardboard give This is Frank Lloyd Wright the appearance of a children’s book,” said the jury. “It is a clever disguise for a successful introduction to the work of one of the greatest architects of recent times — educational literature at its best.”
The winners were selected by the organisers of the Frankfurt Book Fair and the Deutsches Architekturmuseum (DAM), based on their innovation and topicality, as well as overall design and finishing. They wil be awarded the DAM Architectural Book Award — the world’s only architecture-specific book prize.
“The unexplainable — to be distinguished from the not-yet-explained, which is the province of science — is unavoidable. And should be welcomed. We are surrounded by mystery, by what we don’t know and, more dramatically, by what we can’t know.” — Marcelo Gleiser in The Simple Beauty of the Unexpected