The UK accounts for 2 per cent of global manufacturing and 2 per cent of global R&D. You’re not a science superpower if you do 2 per cent…You can’t go around claiming that in seven years’ time the UK is going to be a climate leader or leader in green tech, it just doesn’t make sense
The British economy needs to follow a policy of improvement, not a policy of chest-beating and claiming to be on the cusp of transformative breakthroughs.
David Edgerton, the historian of science and technology, quoted in The New Statesman 14-20 July 2023 page 143
Now, tech giants are developing ever more powerful AI systems that don’t merely monitor you; they actually interact with you—and with others on your behalf. If searching on Google in the 2010s was like being watched on a security camera, then using AI in the late 2020s will be like having a butler. You will willingly include them in every conversation you have, everything you write, every item you shop for, every want, every fear, everything. It will never forget. And, despite your reliance on it, it will be surreptitiously working to further the interests of one of these for-profit corporations.
There’s a reason Google, Microsoft, Facebook, and other large tech companies are leading the AI revolution: Building a competitive large language model (LLM) like the one powering ChatGPT is incredibly expensive. It requires upward of $100 million in computational costs for a single model training run, in addition to access to large amounts of data. It also requires technical expertise, which, while increasingly open and available, remains heavily concentrated in a small handful of companies. Efforts to disrupt the AI oligopoly by funding start-ups are self-defeating as Big Tech profits from the cloud computing services and AI models powering those start-ups—and often ends up acquiring the start-ups themselves.
Yet corporations aren’t the only entities large enough to absorb the cost of large-scale model training. Governments can do it, too. It’s time to start taking AI development out of the exclusive hands of private companies and bringing it into the public sector. The United States needs a government-funded-and-directed AI program to develop widely reusable models in the public interest, guided by technical expertise housed in
I worry the UK government will sell all of the rights to NHS image libraries and raw patient data, rather than realise that the raw material is the gold dust. The models and tech will become a utility. Unless you steal, annotation of datasets is still the biggest expense ( the NHS considers it ‘exhaust’). Keep the two apart.
Scott Galloway:Techno-Narcissism | No Mercy / No Malice
The tech innovator class has an Achilles tendon that runs from their heels to their necks: They believe their press.
Ashton concludes that trying “to create another Arm is as much folly as trying to create the next Google”. His recommendation is for the British government to focus instead on training and skills and providing a stable tax and regulatory regime.
But at a time when the US, EU and Chinese governments are pouring billions of dollars into subsidising their chip industries, this policy recipe seems thin gruel. Serendipity cannot substitute for strategy. And, as one industry executive is quoted as saying: “Without semiconductors, you’re nowheresville.”
Re: Serendipity cannot substitute for strategy, I am not so sure.It can for a while, anyway.
I am amused that people are slow to realise that large language models (ChatGPT etc) do not understand what they are saying, or that they make things up — that is, they hallucinate. Performance on “surface layer” testing does not equate to competence. Anybody who has taught medical students knows that humans are quite capable of exhibiting the same behaviour. It was one of the values of the old fashioned viva. You could demonstrate the large gulf between understanding (sense)on the one hand, and rote — and fluent rote at that — simulation on the other (garbage).
The medical educationalists, obsessed as they are with statistical reliability, never realised that the viva’s main function was for the benefit of teachers rather than learners. It is called feedback.
The medical student as ChatGPT
Although many of the digital gurus started out as idealists, to Lanier there was an inevitability that the internet would screw us over. We wanted stuff for free (information, friendships, music), but capitalism doesn’t work like that. So we became the product – our data sold to third parties to sell us more things we don’t need. “I wrote something that described how what we now call bots will be turned into these agents of manipulation. I wrote that in the early 90s when the internet had barely been turned on.” He squeals with horror and giggles. “Oh my God, that’s 30 years ago!”
My experience is limited, but everything I know suggests that much IT in healthcare diminishes medical care. It may serve certain administrative functions (who is attending what clinic and when etc), and, of course, there are certain particular use cases — such as repeat prescription control in primary care — but as a tool to support the active process of managing patients and improving medical decision making, healthcare has no Photoshop.
In the US it is said that an ER physician will click their mouse over 4000 times per shift, with frustration with IT being a major cause of physician burnout. Published data show that the ratio of patient-facing time to admin time has halved since the introduction of electronic medical records (i.e things are getting less efficient). We suffer slower and worse care: research shows that once you put a computer in the room eye contact between patient and physician drops by 20-30%. This is to ignore the crazy extremes: like the hospital that created PDFs of the old legacy paper notes, but then — wait for it — ordered them online not as a time-sequential series but randomly, expecting the doc to search each one. A new meaning for the term RAM.
There are many proximate reasons for this mess. There is little competition in the industry and a high degree of lock-in because of a failure to use open standards. Then there is the old AT&T problem of not allowing users to adapt and extend the software (AT&T famously refused to allow users to add answering machines to their handsets). But the ultimate causes are that reducing admin and support staff salaries is viewed as more important than allowing patients meaningful time with their doctor; and that those purchasing IT have no sympathy or insight into how doctors work.
As far as UI is concerned — I think this is what personal/interactive computing is about, and so I always start with how the synergies between the human and the system would go best. And this includes inventing/designing a programming language or any other kind of facility. i.e. the first word in “Personal Computing” is “Person”. Then I work my way back through everything that is needed, until I get to the power supply. Trying to tack on a UI to “something functional” pretty much doesn’t work well — it shares this with another prime mistake so many computer people make: trying to tack on security after the fact …[emphasis added]
I will say that I lost every large issue on which I had a firm opinion.
This is from Larry Page of Google (quoted in “The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power” by Shoshana Zuboff)
CEO Page surprised a convocation of developers in 2013 by responding to questions from the audience, commenting on the “negativity” that hampered the firm’s freedom to “build really great things” and create “interoperable” technologies with other companies: “Old institutions like the law and so on aren’t keeping up with the rate of change that we’ve caused through technology. . . . The laws when we went public were 50 years old. A law can’t be right if it’s 50 years old, like it’s before the internet.” When asked his thoughts on how to limit “negativity” and increase “positivity,” Page reflected, “Maybe we should set aside a small part of the world . . . as technologists we should have some safe places where we can try out some new things and figure out what is the effect on society, what’s the effect on people, without having to deploy kind of into the normal world.
As for his comments on safe spaces, I agree. There are plenty of empty planets left.
San Francisco conducted its biennial point-in-time homelessness survey. The numbers are up sharply. Two observations: first, most people are from SF, not (contrary to myth) from elsewhere; and second, there are more people sleeping on the street in San Francisco (population: 870k) than in the whole of the UK (population: 66m). Link
My youngest daughter lived in South Korea for a while and I visited on a couple of occasions. It was a lot of fun in all sorts of ways. The following rings(!) true
The government initially tried to fight the “smombie” (a portmanteau of “smartphone” and “zombie”) epidemic by distributing hundreds of stickers around cities imploring people to “be safe” and look up. This seems to have had little effect even though, in Seoul at least, it recently replaced the stickers with sturdier plastic boards.
Instead of appealing to people’s good sense, the authorities have therefore resorted to trying to save them from being run over. Early last year, they began to trial floor-level traffic lights in smombie hotspots in central Seoul. Since then, the experiment has been extended around and beyond the capital. For the moment, the government is retaining old-fashioned eye-level pedestrian lights as well. But in future, the way to look at a South Korean crossroads may be down.
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You can dice the results in various ways, but software is indeed eating the world — and the clinic. The (slow) transition to this new world will be interesting and eventful. A good spectator sport for some of us. (Interesting to note that this study in Lancet Oncology received no specific funding. Hmmm).
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On some Swedish trains, passengers carry their e-tickets in their hands—literally. About 3,000 Swedes have opted to insert grain-of-rice-sized microchips beneath the skin between their thumbs and index fingers. The chips, which cost around $150, can hold personal details, credit-card numbers and medical records. They rely on Radio Frequency ID (RFID), a technology already used in payment cards, tickets and passports.
One of these is going to end up being sectioned as some time….waiting for the first case-report. Not often I can get two puns in a three word title.
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It is said that much of the foundations of 20th century physics was done in coffee houses (or in the case of Richard Feynman in strip bars), but things were once done differently in the UK
With neither institutional nor government masters to answer to, the British cyberneticians were free to concentrate on what interested them. In 1949, in an attempt to develop a broader intellectual base, many of them formed an informal dining society called the Ratio Club. Pickering documents that the money spent on alcohol at the first meeting dwarfed that spent on food by nearly six to one — another indication of the cultural differences between the UK and US cyberneticians.
The work of the British pioneers was forgotten until the late 1980s when it was rediscovered by a new generation of researchers… A company that I cofounded has now sold more than five million domestic floor-cleaning robots, whose workings were inspired by Walter’s tortoises. It is a good example of how unsupported research, carried out by unconventional characters in spite of their institutions, can have a huge impact.
A review from 2010 by Rodney Brooks of MIT of “The Cybernetic Brain: Sketches of Another Future” in Nature (For more on Donald Michie and “in spite of their institutions” see here).
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”A lot of patients are still having open surgery when they should be getting minimal access surgery,” said Mr Slack, a surgeon at Addenbrooke’s Hospital in Cambridge. “Robotics will help surgeons who don’t have the hand-eye co-ordination or dexterity to do minimal access surgery.”
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A couple of articles from the two different domains of my professional life made me riff on some old memes. The first, was an article in (I think) the Times Higher about the fraud detection software Turnitin. I do not have any firsthand experience with Turnitin (‘turn-it-in’), as most of our exams use either clinical assessments or MCQs. My understanding is that submitted summative work is uploaded to Turnitin and the text compared with the corpus of text already collected. If strong similarities are present, the the work might be fraudulent. A numerical score is provided, but some interpretation is necessary, because in many domains there will be a lot of ‘stock phrases’ that are part of domain expertise, rather than evidence of cheating. How was the ‘corpus’ of text collected? Well, of course, from earlier student texts that had been uploaded.
Universities need to pay for this service, because in the age of massification, lecturers do not recognise the writing style of the students they teach. (BTW, as Graham Gibbs has pointed out, the move from formal supervised exams to course work has been a key driver of grade inflation in UK universities).
I do not know who owns the rights to the texts students submit, nor whether they are able to assert any property rights. There may be other companies out there apart from Turnitin, but you can see easily see that the more data they collect, the more powerful their software becomes. If the substrate is free, then the costs relate to how powerful their algorithms are. It is easy to imagine how this becomes a monopoly. However, if copies of all the submitted texts are kept by universities then collectively it would make it easier for a challenger to enter the field. But network effects will still operate.
The other example comes from medicine rather than education. The FT ran a story about the use of ‘machine learning’ to diagnose retinal scans. Many groups are working on this, but this report was about Moorfields in London. I think I read that as the work was being commercialised, then the hospital would have access to the commercial software free of charge. There are several issues, here.
Although, I have no expert knowledge in this particular domain, I know a little about skin cancer diagnosis using automated methods. First, the clinical material and annotation of clinical material is absolutely rate limiting. Second, once the system is commercialised, the more any subsequent images can be uploaded the better you would imagine the system will become. This of course requires further image annotation, but if we are interesting in improving diagnosis, we should keep enlarging the database if the costs of annotation are acceptable. As in the Turnitin example, the danger is that the monopoly provider becomes ever more powerful. Again, if the image use remains non-exclusive, then it means there are lower barriers to entry.
In addition to its vulnerability to spoofing, for example, there is its gross inefficiency. “For a child to learn to recognize a cow,” says Hinton, “it’s not like their mother needs to say ‘cow’ 10,000 times”—a number that’s often required for deep-learning systems. Humans generally learn new concepts from just one or two examples.
There is a nice review on Deep Learning in PNAS. The spoofing referred to, is an ‘adversarial patch’ — a patch comprising an image of something else. In the example here, a mini-image of a toaster confuses the AI such that a very large banana is seen as a toaster (the paper is here on arXiv — an image is worth more than a thousand of my words).
Hinton, one of the giants of this field, is of course referring to Plato’s problem: how can we know so much given so little (input). From the dermatology perspective, the humans may still be smarter than the current machines in the real world, but pace Hinton our training sets need not be so large. But they do need to be a lot larger than n=2. The great achievement of the 19th century clinician masters was to be able to create concepts that gathered together disparate appearances, under one ‘concept’. Remember the mantra: there is no one-to-one correspondence between diagnosis and appearance. The second problem with humans is that they need continued (and structured) practice: the natural state of clinical skills is to get worse in the absence of continued reinforcement. Entropy rules.
Will things change? Yes, but radiology will fall first, then ‘lesions’ (tumours), and then rashes — the latter I suspect after entropy has had its way with me.
Annual Review of the ‘business’ that is ed-tech by Audrey Watters.
Ed-tech is a confidence game. That’s why it’s so full of marketers and grifters and thugs. (The same goes for “tech” at large.)
“criticism and optimism are the same thing. When you criticize things, it’s because you think they can be improved. It’s the complacent person or the fanatic who’s the true pessimist, because they feel they already have the answer. It’s the people who think that things are open-ended, that things can still be changed through thought, through creativity—those are the true optimists. So I worry, sure, but it’s optimistic worry.” Jaron Lanier. We Need to Have an Honest Talk About Our Data
This is from an interview with Geoffrey Hinton who — to paraphrase Peter Medawar’s comments about Jim Watson — has something to be clever about. The article is worth reading in full, but here are a few snippets.
Now if you send in a paper that has a radically new idea, there’s no chance in hell it will get accepted, because it’s going to get some junior reviewer who doesn’t understand it. Or it’s going to get a senior reviewer who’s trying to review too many papers and doesn’t understand it first time round and assumes it must be nonsense. Anything that makes the brain hurt is not going to get accepted. And I think that’s really bad…
What we should be going for, particularly in the basic science conferences, is radically new ideas. Because we know a radically new idea in the long run is going to be much more influential than a tiny improvement. That’s I think the main downside of the fact that we’ve got this inversion now, where you’ve got a few senior guys and a gazillion young guys.
I would make a few comments:
All has been said before, I know, but no apology will be forthcoming.
In 2011, Beth Reeks, a 15-year-old Welsh schoolgirl studying for her GCSE exams, decided to write a teenage romantic novel. So she started tapping on her laptop with the kind of obsessive creative focus – and initial secrecy – that has been familiar to writers throughout history. “My parents assumed I was on Facebook or something when I was on my laptop – or I’d call up a document or internet page so it looked like I was doing homework,” she explained at a recent writers’ convention. “I wrote a lot in secret… and at night. I was obsessed.”
But Reeks took a different route: after penning eight chapters of her boy-meets-girl novel, The Kissing Booth, she posted three of them on Wattpad, an online story-sharing platform …. As comments poured in, Reeks turned to social media for more ideas. “I started a Tumblr blog and a Twitter account for my writing. I used them to promote the book…[and] respond to anyone who said they liked the story,” she explained in a recent blog post.
… while Reeks was at university studying physics, her work was turned into an ebook, then a paperback (she was offered a three-book deal by the mighty Random House) and, this year, Netflix released it as a film, which has become essential viewing for many teenage girls.
Maybe more of a theory than a law, but still:
Any eLearning tool, no matter how openly designed, will eventually become indistinguishable from a Learning Management System once a threshold of supported use-cases has been reached.
They start out small and open. Then, as more people adopt them and the tool is extended to meet the additional requirements of the growing community of users, eventually things like access management and digital rights start getting integrated. Boil the frog. Boom. LMS.
It is easy to make facile comparisons between universities, publishing, and the internet. But it is useful to explore the differences and similarities, even down to the mundane production of ‘content’.
This is from Frederic Filloux form the ever wonderful Monday Note
The biggest mistake of news publishers is their belief that the presumed uniqueness of their content is sufficient to warrant a lifetime of customer loyalty.
The cost of news production is a justification for the price of the service; in-depth, value-added journalism is hugely expensive. I’m currently reading Bad Blood, John Carreyrou’s book about the Theranos scandal (also see Jean-Louis last week’s column about it). This investigation cost the Wall Street Journal well over a million dollars. Another example is The New York Times, which spends about $200 m a year for its newsroom. The cost structure of news operations is the may reason why tech giants will never invest in this business: the economics of producing quality journalism are incompatible with the quantitative approach used in tech which relies Key Performance Indicators or Objectives and Key Results. (
In France, marketers from the French paid-TV network Canal+ prided themselves of their subscription management: “Even death isn’t sufficient to cancel a subscription,” as one of them told me once.
Facebook accounts hacked? I thought that was the feature not the bug.
Carrot weather — the weather app with attitude.
Two quotes from Bad Blood: Secrets and lies in a Silicon Valley Startup, by John Carreyrou. Only without much silicon.
“Henry, you’re not a team player,” she said in an icy tone. “I think you should leave right now.” There was no mistaking what had just happened. Elizabeth wasn’t merely asking him to get out of her office. She was telling him to leave the company—immediately. Mosley had just been fired.
He also maintained that Holmes was a once-in-a-generation genius, comparing her to Newton, Einstein, Mozart, and Leonardo da Vinci.
The reality distortion field lived on. Medicine is indeed tricky.
This is a scary story. But the lesson is (yet again) our inability to understand what makes humans tick.
How Maersk was taken down by Russian malware, and how it recovered. The passage that got the attention is the bit about flying a domain controller backup in from Ghana (the only one that survived). The one that matters is that they were still running Windows 2000 on some servers and hadn’t carried out a proposed security revamp because it wasn’t in the IT managers’ KPIs and so wouldn’t help their bonuses. Link
Via Ben Evans
It is not only taxi drivers that are being “uberised” but radiologists, lawyers, contractors and accountants. All these services can now be accessed at cut rates via platforms.
The NHS became such a platform, for good and bad. That is the real lesson here. The tech is an amplifier, but the fundamentals were always about power.
One selling point of MOOCs (massive online open courses) has been that students can access courses from the world’s most famous universities. The assumption—especially in the marketing messages from major providers like Coursera and edX—is that the winners of traditional higher education will also end up the winners in the world of online courses.
But that isn’t always happening.
In fact, three of the 10 most popular courses on Coursera aren’t produced by a college or university at all, but by a company. That company—called Deeplearning.ai—is a unique provider of higher education. It is essentially built on the reputation of its founder, Andrew Ng, who teaches all five of the courses it offers so far. Link
The MOOC story is like so much of tech — or drug discovery for that matter. Finding a use for a drug invented for another reason often offers the biggest payback. This story has barely begun.
Hype is not fading, it is cracking.
I like the turn of phrase. It is from a post on the coming AI winter. Invest wisely.