Jul
13
What happens when you are in the middle of a revolution, but nothing radical appears to be happening? Richard Wal
ters, in an excellent article in the Financial Times (11 July 2024) speculates about the US tech bubble, and the potential for rapidly growing valuations to come into conflict with apparently little deployment of AI based products and services at scale. My friends and colleagues at Outsell reflect a trend by talking about AI and the hype cycle, with generative AI now moving from the peak of, inflated expectations into the trough of disillusion, presumably prior to ascending to the plateau of productivity. But we are still talking about AI everywhere and in everything, and personally, having experienced AI in previous guises as neural networks or as expert systems or as robotic process automation, I have to wonder if we are thinking about change in the right way.
What if change is not revolutionary, but osmotic? What if we should be thinking not about startling use cases but about the rate of general diffusion of AI everywhere? Is assimilation less transformative than step change? When I wrote recently about the applications of AI in Apple Intelligence, I realised of course that this is an announcement of a process that has been going on for many years and which will go on for many years: adding more utility progressively in each new release and iteration of existing software-based products and services. It’s not very exciting, is it? Even an Apple Press release could not make it sound revolutionary or radical. It certainly does not tune in nicely with the way in which stock markets work: This is not the sort of gold rush investment cycle that we seem to require in speculative terms. Yet it does seem to be what has happened in all of those other AI investment cycles since the 1980s. There have been disappointments on the way, of course. Think of Autonomy, once boosted as breakthrough technology, ending in endless disputes in the courts over whether its radical qualities had been misdescribed. There may well be disappointments of a similar sort in the future: some of the development programs of generative AI market leaders are “too expensive not to deliver “. Or at least, the companies concerned can never admit comparative failure. Again, there may be scale issues. Perhaps small learning models (SLM) will be more useful than the LLM research that enabled them. Perhaps AI in niches will be revolutionary (collating and reading test results in radiology and MRI scans) but related technologiesmake very, very slow progress towards universality (driverless cars). Whatever is the conclusion of history when all this is being reviewed, one thing, I suspect, will be clear. We would never have got anywhere without the huge concentration of investment in research, even if in retrospect we can see how much we wasted and how differently effects were from the intended benefits.
I do believe that we will look back in 2030 and see that fundamental, radical and far reaching change did take place in the mid 2020s. I think that if we compare 2020 with 2030 we will see that machine intelligence has transformed the way in which we live and work. But if we look at 2025 from 2024 and try to anticipate that change then I think we will see something different. We will see continuing slow progress with consumer applications led by companies like Apple, gradually enabling voice driven systems to free users from screens and keyboards and enable the more fluent and transparent use of technology. I also see that corporate of all types will gradually automate systems and services to create greater productivity, shed manpower, and understand users and markets with greater precision. The new products and services however may take longer. In the information, data and analysis marketplaces in which I have been most concerned for the past 55 years, we will see the gradual infusion of machine intelligence so surreptitiously that many of us will not notice what is happening. Make no mistake: from this point on we will all describe everything we do as “AI powered “or “AI based “, but that is simply the world of slogans. In truth the world will change, to coin a phrase, “not with a bang but a whisper! “
Jun
27
Education: AI‘s finest hour?
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The chief technology officer at Open AI is a hugely impressive woman. Born in Albania, she won a scholarship to a famous Canadian school, and then took two university degrees at Dartmouth in the USA. Now in her mid 30s, she gave a recent interview on being awarded an honorary doctorate by her alma mater. The interview proceeded upon fairly normal lines, with the interviewee safely within industry discretion rails and PR requirements for the most part, until that is we reached Q&A at the end. Then, for me, two really interesting statements made the previous hour worth while. In the first instance. Mira Murati made the interesting statement that she did believe that those whose data had been used to train models should be recompensed, and that she and her colleagues were working on a recompense engine which would assess the value created by individual datasets as part of the whole LLM construct. This is obviously important and I will return to comment further on it later.
However, another questionner asked her where, in her view, AI was likely to make its greatest and the most lasting impact. She unhesitatingly pointed to education, and in elaborating her answer, pointed to so many of the issues that the teaching profession, along with the producers of educational content and services, have tackled for so long. As she responded, I was personally taken back to my own first contact with AI as I have rehearsed here and elsewhere many times before. In 1985, in a Marvin Minsky seminar at the MIT Media Lab, I first heard him defend AI, in this case from a librarian who thought it would kill books and libraries, by saying that he wanted to promote the world of books and libraries, by filling libraries with books that were able to speak to each other, update each other and argue with each other. All this in pursuit of educational process that he said, and I think we all know, is essentially a matter of individual learning, of the ability of the individual to respond to different stimuli, at different times in a lifetime of learning, and as a result of different states of learning readiness. in her answer, I felt that Ms Murati was pointing to a world where the inequalities and waste caused by teaching 30 people of different abilities in a classroom at the same time to pass an examination which by definition was standardised to one level of learning achievement could it last draw to an end. The idea that each learner could learn at their own speed, because a machine environment was able to assess their learning ability and readiness and present them with the appropriate next learning materials at the point at which they were ready to progress and in the media that enabled them to absorb it best – becoming effectively a personal tutor.
Whenever I write things like this, my teacher friends rise up in revolt and point to the immense value of the teacher-pupil relationship at all levels of education. It is important therefore to say that I agree with every word of this. I believe that if AI really works in education then it will free teachers to be real teachers. Not markers of papers. Not distracted from the brilliant pupils and their needs by the requirements of slow or inadequate learners. Not neglecting slow and needy learners because the reputation of the school depends upon the success of the brilliant, who must be personally coached to ensure brand distinction.If AI works in education, it will give teachers at all levels the time to be the people they need to be: the gurus, the thought leaders, the people in charge of pastoral care, the listeners and the advisory voice of experience. Overall longer term, machine intelligence will be able to monitor and know very effectively what levels of learning have been accomplished. Feedback given to teachers will grow in quality and reliability. AI in education could eventually release us all from examination systems, that are grossly inadequate, and mostly measure which members of society are good at examination systems. It will prevent us from penalising the individual who had an off day, a headache, or a period pain, and it will measure everything that the learner has learnt in all dimensions, not simply the ability to answer a multiple-choice or essay question which may not be representative either of the course or the learning process.
As a young man in educational publishing in the late 1960s I remember my puzzlement about educational resources and what worked and what didn’t. Sitting at the back of classrooms in London comprehensive schools gave little enlightenment, but did demonstrate the boredom induced for very many people by the group learning process. Yet those young people, bored or engaged, all had talent, and that quality, I knew, had to be released into society, if society were to flourish and develop. Over the years I’ve worked with start-ups and entrepreneurs in a variety of different ways on schemes of learning based around learning pathways and learning journeys. Some good things have been done, but until now we have never had the AI technology which looked like making a real impact on the problem.
In the past two years, with the rise of generative AI, it seems to me that it just become more probable, this idea of a real man-machine relationship in guided human learning, supervised and overseen by human teachers, is it last a real possibility. How we reward the contributors of learning material, how we ensure that the range of data provided gives the ability stretch and complexity of content needed., And how we recompense contributors for the use of materials in the network, remain huge problems, and ones which will be difficult to break down and tackle effectively. But I hope – and education is fundamentally a triumph of hope over experience – one day employers will be able to make hiring decisions based upon really knowing what the candidate knows; the professional in one country will be able to get a job in another without a clash of professional qualification standards: and that we will cease to talk about “slow learners“ but of people at different and measurable levels of learning engagement and attainment. And who knows, it might even be a world where teachers enjoy teaching again.
Government health warning: while the prospect here is glorious, it does carry huge risks. These systems are subject to political interference. In a world where book burning and the removal of books from educational library shelves is now sadly prevalent, we will need to protect the man-machine educational interface from political distortion.
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