AI University?

Tertiary Education in the UK needs a fresh idea. Now that we have a Labour Government what we need is an initiative on the same scale as The Open University, kicked off over 50 years ago. It revitalised UK HE. It was not a threat to the existing Universities. 

We need is something new and additive -  a University that uses AI to create and deliver high quality online education at relatively low cost. We need a Harold Wilson and Jennie Lee moment from Starmer and Jaqui Smith.

The Labour Manifesto states:

"We will ensure our industrial strategy supports the development of the Artificial Intelligence (AI) sector, removes planning barriers to new datacentres. And we will create a National Data Library to bring together existing research programmes and help deliver data-driven public services, whilst maintaining strong safeguards and ensuring all of the public benefit."

It would mark a shift towards a future fuelled by accessible, high quality, relevant, low cost education. I presented this idea at The Open University earlier this year. We now have the opportinity to turn it into something substantial.

Surely an AI University could come from aLabour Government, along the lines of Sperling in the early days, Michael Crow at Arizona State, Paul LeBlanc’s transformative results at SNHU or Ashok Goel’s vision of an AI University at Georgia Tech? It is clear that an educational vision is needed and I think the best starting point is that outlined and executed by Paul LeBlanc at SNHU. It is substantial, well articulated and has worked in what has become the largest University in the US.

Manifesto

It would be based on the competence model, with a focus on skills shortages. Here's a starter with 25 ideas, a manifesto of sorts, based on lessons learnt from other successful models:

1. Non-traditional students in terms of age and background

2. Focus on critical skills shortages (nursing, teaching etc)

3. Quick and easy application process

4. Intake at any time

5. Focus on Generative learning using AI

6. Personalised learning using AI

7. Multimodal from the start - more focus on orality

8. Full range of summarisation, create self-assessment, dialogue tools etc

9. Every teacher has a chatbot available 24/7 for learner support

10. Teaching personalised

11. Teaching at all levels

12. Full accessibility features delivered by AI 13. Teaching in many languages aided by AI

14. Online assessment when ready

15. Every student has a 'Digital Twin'

16. Lectures recorded with GenAI activities automatically generated

17. Automated feedback and assessment

18. Learning journeys optimised

19. Complete at your own pace

20. Make sure AI part of curriculum

21. Data-driven approach with privacy ensured 22. Low admin, high 24/7 teaching and learning environment

23. Use part of apprenticeship levy

24. Get Big Tech to agree on proxy tax to fund

25. Non-residential, based in the North

We know it will face opposition but our political class must get out of supporting the elitism that the current system promotes. We have the advantage of a great track record in HE, English as our teaching language and, as I said, we've done this before.

Our successful precedent for this is The Open University and we can learn from what happened there.

The Open University

The key players behind the creation of The UK Open University were UK Prime Minister Harold Wilson and the first ever Minister for the Arts Jennie Lee.

Wilson was Prime Minister of the United Kingdom from 1964 to 1970 and again from 1974 to 1976. Wilson and his Labour government are credited with founding The Open University, a revolutionary idea to make higher education accessible to a wider population.

Jennie Lee, who was appointed as the first Minister for the Arts and oversaw the establishment of the university, also played a significant role. Lee provided the political drive and determination to ensure the project’s success, navigating through skepticism and opposition. You need this political sponsorship.

White Paper

Wilson envisioned a university of the air, an institution that would utilise television and radio broadcasts to provide education to those who were unable to attend traditional universities due to various constraints like work, family, or distance. The idea was to make higher education accessible to all, irrespective of background or circumstances, reflecting a broader commitment to social justice and educational opportunity.

Lee’s white paper, presented in 1966, laid out the vision and operational plan for what would become The Open University. It outlined plans for the university which would deliver courses by correspondence and through the use of technology, such as television and radio, to broadcast its courses, thus linking it to the technological revolution of the time. The paper was part of a broader initiative to modernise British society, enhance the competitiveness of the economy, and promote greater equality of opportunity and social mobility. It was a pioneering effort to expand higher education beyond traditional boundaries and to utilize contemporary media in a way that had not been done before in the realm of education.

We need a similar strong White Paper idea with 21st century technology, not radio and TV, but multimodal AI. The full array of courses (generate in part by AI), delivered partly by AI, assessed by AI should be the aim.

Opposition

Jennie Lee faced widespread skepticism and opposition from various quarters in her pursuit to establish The Open University. The idea of a ‘University of the Air’ that would reach out to those previously denied the opportunity to study was met with resistance. Skepticism and opposition came from within the Labour Party, including senior officials in the Department of Education and Science (DES), her departmental boss Anthony Crosland, the Treasury, ministerial colleagues like Richard Crossman, and commercial broadcasters. Despite the challenges, The Open University was realized thanks to Lee’s unwavering determination, the support of Prime Minister Harold Wilson, and initially modest anticipated costs. The true, much higher costs only became apparent later, by which time the project had gained too much momentum to be discontinued

The traditional universities were skeptical, even opposed to the idea of The Open University. They had concerns about the quality of education that could be delivered through distance learning and the use of media like television and radio. There was apprehension that an open admissions policy could dilute academic standards. Moreover, traditional institutions might have seen the establishment of a new university that challenged conventional norms as a threat to their established educational models and possibly their funding and enrolment. However, The Open University proved its merit over time by achieving high academic standards and gaining a solid reputation, which helped to alleviate many of these concerns.

Expect such opposition but understand that it gets us out of the obsession with the negatives around AI and moves us as a nation towards the positives. The DfE and traditional Universities need to recognise that they are too slow, expensive and old-fashioned to deliver for 21st century skills.

Influence

The Open University is still a positive force in Higher Education but has some serious setbacks, including a £20 million failed investment in trying to enter the US and Futurelearn, a MOOC company that failed to realise its initial promise, held back by traditional BBC appointments with little business, online technology or educational experience

Yet numerous open universities around the world were influenced by the model of The Open University in the UK. The success of this institution demonstrated that distance learning could be both reputable and accessible, leading to the establishment of similar universities globally. Some examples include:

The Open University of Israel, 1974.

FernUniversität in Hagen, Germany, 1974.

The Open University of the Netherlands, 1984.

Indira Gandhi National Open University (IGNOU) in India, 1985.

The Open University of China, originally established as China Central Radio & TV University in 1979.

Athabasca University in Canada, 1970, which shifted to an open university model following the UK's example.

Universidad Nacional de Educación a Distancia (UNED) in Spain, 1972.

We could have the same influence again.

Library at Alexandria 

There is a much older precursor that can help shape this vision - the Library at Alexandria, which I regard as the first University. Unlike the earlier Plato's Academy and Aristotle's Lyceum Alexandria was a free, open and secular entity. There was no faculty, just a Chief librarian and catalogers. Neither was there any formal teaching, mostly debate and discussion. Researchers as teachers came in the 18th century with the Prussians and Humboldt.

It  proved to be the most powerful generator of ideas and inventions for 600 years, until scholastic religious forces banned the books and the shelves were emptied - it was never burnet down. I have explained the lessons we can learn from this elsewhere.

Educational vision

As for the vision, it can be found way back in the in the Universal Education of Comenius and more recently with Paul LeBlancs ideas and real example at SNHU. That can be found here. That same Alexandrian vision is to be found in his book Students First and his work in building the biggest University in the US. Our current system is far from this vision. Anorger useful precursor was John Sperling and to a lesser degree Michael Crowe at Arizona State. 

Conclusion

There is a sense of our old institutions having become cumbersome and sclerotic. At such advantage then lies with alternatives. These new, agile alternatives can do things faster, better and at lower costs. This is precisely what the evidence already shows for artificial intelligence. Unencumbered by slow, dull lectures, free from the tyranny of location, huge amounts of physical, campus real estate with low occupancy rates, expensive housing robbing others from such opportunities. Also free from the tyranny of time, the scheduled lectured, fixed end of year assessments and slow delivery punctuated by interminable holiday periods, it will thrive. Less detached from the real world it may also rebalance the drift towards overly-theoretical, text-only based learning towards badly needed skills. Education should be for both life and living.

Importantly, the current OU will not and cannot do this. They have developed a negative mindset towards this technology and are now more of a traditional university. It is too old-fashioned and sluggish to do this well. They have shown no signs of effort in this direction.

It is all about scale. The goals of reaching nontraditional students at low cost demand good tech with the sort of scalability that OpenAI and other providers have already delivered. It is AI that should be the catalyst here where the possibility of personalised teaching and learning in any subject, at any time, to any place , at any level 24/7, in almost any language, can be realised. the fact that ChatGPT has already been embraced by 100 million with billions of dialogues, mostly bu curious people who want to learn, should be our guiding light.

It is fine to say, it's not the technology but the vision that counts. I agree but when it comes to getting things done, building platforms and delivering, you need a sense of urgency and catalysts, not vision reports. We have plenty of them. And if you want a vision it is well articulated by Paul LeBlanc in Students First (2021). He also did it!

You need a new team that wants to transcend the tradtional thin diet of lectures plus essays, adopting a more supportive learning environment. Fresh blood, fresh ideas, fresh pedagogy and fresh forms of delivery. Being bold, it could even have no building or place. I don't see Duolingo, Khanmigo, Wikipedia or Google Scholar as being 'places'. The trick is, perhaps, to free learning from the tyranny of time and place.

I know of great institutions that have adopted the philosophy of open admission, flexible distance learning, and the use of technology to provide education to those who otherwise might not have access to it. They have played a significant role in expanding higher education and continue to impact lifelong learning across the globe. We could surely do this again. No massive campus costs with low occupancy rate buildings, all online, no travel to be in line with climate change demands and an exemplar once more for the rest of the world. Even better, base it in the North, that has long sustained a brilliant set of Universities.

Babbage - genius but of little causal significance in history of computers

Charles Babbage (1791 - 1871) was a colourful British mathematician, inventor, and mechanical engineer. He made significant contributions to the field of computing through his pioneering work on the design of mechanical computers. 

A mathematician of great stature, he held the position at Cambridge held by Newton and received substantial Government funds to build a calculating Differential Engine, funds he used to go further to develop an Analytical Engine, more of a computer than calculator.

 

Machine to mind

For the first time we see,  albeit still mechanical product of the Industrial Revolution, the move from machine to mind. Babbage saw that human calculations are often full of errors and speculated whether steam could be used to do such calculations. This led to his lifetime focus on building such a machine.

Babbage had been given government money to conceive and develop a mechanical computing device, the Difference Engine. He designed it in the early 1820s and it was meant to automate the calculation of mathematical tables, basically a sophisticated calculator, hat used repeated addition. He did, in fact, go on to design a superior Analytical Engine, a far more complex machine that shifted its functionality from calculation to computation. Conceived by him in 1834, it was the first programmable, general-purpose computational engine and embodies almost all logical features of a modern computer. Although a mechanical computer, it features an arithmetic logic unit, control flow with conditional branching, and memory, what he called the ‘store’. We should note that it is decimal but not binary but it could automatically execute computations. 

Although this is an astonishing achievement, neither were fully built during his lifetime, Babbage's design laid the groundwork for future developments in computing. His son Henry Babbage did build a part of his differential engine as a trial piece and it was completed before Babbage's death in 1871. A full Differential Engine was built in 1991 from materials that were available at the time and to tolerances achievable at the time. It weighs in at 5 tons, with 8000 moving parts. Both can be seen in the Science Museum in London.

Critique

Babbage was a prickly character who alienated many, especially in Government, who generously funded his work. He alienated the government and in a tale that has been common in UK computing, never turned from theory into practice. Those developments eventually came from the US. In the end he failed but from the drawings alone, Lady Byron called it a ‘thinking machine’ and Ada Lovelace, her daughter, asked for the ‘blueprints’ and became fascinated by the design and its potential.

Influence

Although described as the ‘father of computers’, there is no direct, causal influence between Babbage and the development of the modern compuer. Babbage’s designs were not studied until the 1970s, so Babbage’s designs could not have been the direct descendants of the modern computer. It was the pioneers of electronic computers in the 1940s that were the true progenitors of modern computers. There is a much stronger case made for the idea that it was Holerith and his census machines that had the real causal effect.

 

Bibliography
Swade, D. and Babbage, C., 2001. Difference engine: Charles Babbage and the quest to build the First Computer. Viking Penguin.

Hyman, A., 1985. Charles Babbage: Pioneer of the computer. Princeton University Press.

In Our Time, Ada Lovelace, featuring Patricia Fara, Senior Tutor at Clare College, Cambridge; Doron Swade, Visiting Professor in the History of Computing at Portsmouth University; John Fuegi, Visiting Professor in Biography at Kingston University.

 https://www.bbc.co.uk/sounds/play/b0092j0x

Ada Lovelace - insightful but full of surprises...

Ada Lovelace (1815 -1852) died at the age of 36 but had significant insights into computer science. She was the daughter of the poet Lord and Lady Byron but her parents parted only weeks after her birth. Her mother was interested in mathematics, also social movements, and established a series of schools and helped establish the University of London. It was she who ensured that Ada got a good, disciplined education in both science and mathematics. 

After both attended a Babbage soiree in London, where her mother described Babbage’s engine as a ‘thinking machine’, they both went on a tour round the Midlands where they saw the Jacquard Loom. This was to inspire a series of thoughts in the form of notes from Ada on the potential of the Analytical Engine that Babbage had invented.

The mathematician Hannah Fry describes Ada as intelligent but also “manipulative and aggressive, a drug addict, a gambler and an adulteress!”

Analytical engine

Ada then collaborated closely with the mathematician and inventor, Charles Babbage, who invented what some regard as the first modern computer - his Analytical Engine. This resulted in her translation from French to English, of an article written by the Italian mathematician Luigi Federico Menabrea (future Prime Minister), about Babbage's Analytical Engine, where she added extensive notes and annotations. These notes were three times as long as the original essay and were published in Scientific Memoirs Selected from the Transactions of Foreign Academies of Science and Learned Societies in 1843 and contained some seminal ideas on computing.

Programming

In these notes she described the potential for machines to perform operations beyond simple arithmetic calculations. In one of her notes, she described an algorithm for calculating Bernoulli numbers, which is considered, by some, to be the world's first computer program, although doubt has been cast on this by recent scholarship. It is a detailed and tabulated set of sequential instructions that could be input into the Analytical Engine. This demonstrated her understanding of how machines could be programmed to perform various tasks, a fundamental concept in computer science and AI. 

Insightful though her notes were, she was not a top flight mathematician and the supposed computer programme was really a sort of pseudocode with mathematical expressions. The claim that she wrote the first computer programme some regard as exaggerated and it was never executed on any machine as an actual programme. As the Babbage scholar, Doron Swade, who built the Babbage Analytical Engine, claims, the concept and principle of a computer programme for this machine was actually Babbage’s idea, as his notes of 1836/37 predate those of Lovelace, although her insights on computation beyond mathematics was absolutely original. 

From calculation to computation

The notes had the idea that instructions (programs) could be given to these machines to perform a wide range of tasks, making her a pioneer in the ‘concept’ of computer programming. Accomplished in embroidery, she describes the possibility of input through punched cards similar to the method used on the Jacquard loom. This loom was invented by Joseph-Marie Jacquard in the early 19th century and revolutionized the textile industry by allowing for the automated production of intricate patterns in fabrics. Punched cards were used for patterns, each hole being an on/off switch, one card per line in a column of sequenced cards, a technique used on mainframe computers in the 20th century.

Babbage saw his machines as dealing with numbers only. Lovelace saw that we could see such machines as not just doing calculation but also computation. Numbers can represent others things, representations of the world and she speculated that computers could be used to create outputs other than mathematics such as language, music and design. She understood that machines could be programmed to generate creative works. This anticipation of the creative potential of machines aligns with the field of generative AI, which focuses on developing algorithms that can produce creative content such as music, art, and literature, This was her main insight, although there is no direct causal influence between her work and these developments. 

Education and learning

Her views on education aligned with her own experiences and that of her mother Lady Byron. She received an extensive education in mathematics and science, which enabled her to mix with other intellectuals and practitioners in the field, making ground-breaking insights to the field of computing. She was an advocate for the intellectual and educational development of women and believed in providing women with opportunities for education in mathematics and the sciences, which was uncommon at that time. Lovelace's passion for learning and her advocacy for education for all, regardless of gender, continue to inspire educators and learners today.

 

Critique

Her role in inventing either the idea of computer programming and the first computer programme seems o have been quashed. The said programme was, of course, never used in the Analytical Engine, as it was never turned into actual code and the Analytical Engine was never built.

There was no real causal influence here on modern computing, no real continuity between Lovelace and modern computing. This is an ad hoc legend rather than a matter of history. Turing read her notes and admired her insights, and although one can argue that came through Turing, who was in Bletchley Park, and that she influenced the Colossus machine, which decoded German scripts, the causal link is tenuous and unproven. There is no direct, causal trail to modern compuers, even through Babbage, as Babbage’s designs were not studied until the 1970s, so Babbage’s ideas were not actually the direct descendants of the modern computer. It was the pioneers of electronic computers in the 1940s that were the true progenitors of modern computers.

Influence

Ultimately, says Hannah Fry, her contribution was in seeing that computation was more than calculation, yet “Her work… had no tangible impact on the world whatsoever.” Nevertheless, Lovelace's passion for learning and her advocacy for education for all, regardless of gender, continue to inspire educators and learners today. The Ada Lovelace Institute in the UK is a good example of this legacy and a dozen biographies were published on the 200th anniversary of her birth in 2015.

Bibliography

Notes https://maa.org/press/periodicals/convergence/mathematical-treasure-ada-lovelaces-notes-on-the-analytic-engine

Hollings, C., Martin, U. and Rice, A.C., 2018. Ada Lovelace: The making of a computer scientist (p. 2018). Oxford: Bodleian Library.

In Our Time, Ada Lovelace, featuring Patricia Fara, Senior Tutor at Clare College, Cambridge; Doron Swade, Visiting Professor in the History of Computing at Portsmouth University; John Fuegi, Visiting Professor in Biography at Kingston University.

 https://www.bbc.co.uk/sounds/play/b0092j0x

Hannah Fry https://www.bbc.co.uk/programmes/articles/3jNQLTMrPlYGTBn0WV6M2MS/not-your-typical-role-model-ada-lovelace-the-19th-century-programmer?ns_mchannel=social&ns_campaign=bbc_radio_4&ns_source=facebook&ns_linkname=radio_and_music

Google has several cards up their sleeves...

What can we expect from Google’s Gemini?

Google have several cards up their sleeve on Generative AI:

1. They invented it!
2. Awesome AI resources
3. Google Search
4. Google Scholar
5. Google books
6. YouTube
7. Google Translate
8. Google Maps
9. Global reach, data centres and delivery
10. Above all they have DeepMind

 

Gemini is their big response to OpenAI and has a family of foundational models, at three sizes:

 

Ultra – professional version of Bard

Pro – through Bard and Google products
Nano (on device - mobile version)

 

An important word here is ‘multimodal’, as their model has been trained on and is capable of processing most media types, including text, images, video, audio and code. So far Generative AI has largely been a 'calculator for words'... but this lifts it into another realm. It is multimodal from the ground up (input not necessarily output). 

However, one must be careful in making the assumption that this moves it more towards how humans think. Our multimodality is quite different, especially the ways we deal with sight and sound. There is little crossover.

They make claims about “sophisticated reasoning and its ability to process complex information in different formats, also the analysis and extraction of data from research articles, being able to really distinguish the relevant from irrelevant. On benchmarking, it has already outperformed human specialists in MMLU (massive multitasking language comprehension on topics like math, history, law and ethics, at 90.0% against 86.4% of GPT-4.

 

This is all very exciting, as it shows how competition is accelerating progress. Google has taken a cautious approach by rolling out these products across next year. Despite their ability to read multimedia, the initial Gemini models will not – at least initially – produce images or videos.

Unlike OpenAI, they have an advertising business to protect and need to be careful. They know that the writing is on the generative wall for search. As usual, they are going for integration into products approach, a rising tide rather than a single flood. Seems wise. After a tumultuous year, we are entering a more stable product development phase. Google will not see any Board bun-fights. Although there will be some surprises in store for sure.

Google have a universal, global mission, to harness our cultural information and make it available to all. Year one was revolution, year two will be evolution.

Watch video here.

Chatty robots are here...

A number of domestic and robot companies have been funded, some have already failed but two stand out, as being well funded because they have shown remarkable progress. If any are successful, and success means different things in different markets, there will be huge demand. Domestic robots are one thing but functional working robots is quite another. Increases in productivity, along with zero salary costs would create a radical shift in real world, vocational jobs.

There have been ridiculous pretenders like Sophie, basically chatbots in cheap bits of plastic but two companies stand out; Figure and Optimus.

Figure

Figure have been first to bring a general purpose robot to life with a superb robot integrated with OpenAI's ChatGPT. The dialogue is fascinating, with visual recognition of people and objects, interpretation of incoming speech, completion of tasks and what appears to be explanations of its own behaviour. There is a little latency, suggesting this will be a problem but that doesn't matter. One can imagine open source, local models, trained inside the robot (models are not that big) with developing functionality around dialogue.

It is dialogic is the full sense of the word, using dialogue in language as well as recognition and action dialogue with the real world. Note that there is a limited set of real world actions, limited by what the robot can and should do. Problems are broken down and turned into specific plans within certain constraints.

The demo is at normal speed and uses AI, namely an A-Z neural network. We now see the integration of image recognition (seeing), sound recognition through microphones, with speech the interpretation of planning then action. It uses a pre-trained multimodal model (we don't know what - probably better than GPT4 - one specific to robotics) with language recognition, generation, along with actions. These robots are likely to use specific MMNs.

Image recognition is recognition and reason. This also sounds like a human as the speech generation software does this. Then it moves and performs actions, keeps balanced, makes smooth movements, grasps, moves and places objects as a stand-alone robot.

Very impressive.

Optimus

Musk has stated that his robot business could out perform his car business. Potentially, he is right. Robots change the nature not just of driving but work itself - initially unsafe or mundane tasks. This would be an enormous shock to the economy, as 24 hour labour becomes cheap, plentiful and scalable.

He has told employees that Tesla could become a $10 trillion company on the back of this. The robot is the same size as a human so the it fits into existing work context as we do.

Still 2023 and progress by Tesla is phenomenal. Neck movements really bring it to life, tactile sensing on fingers can deal with an egg. The hands are where the action is. Waste of time walking and running if you can't do things. Five fingers, all the same length making manufacturing and maintenance easier. The dexterity is amazing. Within a year Musk tweeted, it will thread a needle.

There is nothing in the head, as it can all be in the body. The head is just a camera and possibly a display screen. The point is not to wholly mimic a human. Airplanes do no wholly mimic birds, the design is driven by needs.

The fact that it can walk shows they're after any domain where humans work and operate. We'll see fixed robots (factories), wheeled robots (limited area) and legged robots, I'm sure. Walking has toe articulation, a real advance. Looks slow but speed may not be the key issue. Most people are fairly stationary most of the time in life. It can squat and dance. Injection moulded body parts reduce weight and costs.

It's all in the actuators. An actuator is a device that converts energy (typically electrical, hydraulic, or pneumatic) into mechanical motion. This is the trick, to make these a high enough quality and easy to manufacture.

It will, of course talk and listen. You will be able to tell it what to do and it will have Chatbot functionality. 

I can see these being your butler, something you can chat to, a companion. But there are thousands of potential real-world applications in the workplace. Think care homes, hospitals, retail.

Go further and think Teachers and Doctors?

Release date 3-5 years?

‘Engines of Engagement’ is a curious (authors’ description) book about Generative AI.

‘Engines of Engagement’ is a curious (authors’ description) book about Generative AI. Julian Stodd, is the progenitor of this project. If you know Julian, as I do, you’ll know this will be an interesting read. Along with his co-authors Sae Schatz and Geoff Stead, they’ve come up with something wonderful.

The book is fluid, a bit like a large language model. It has avoided the fixity of most books on the subject and is honest about the ambiguity of Generative AI. People find it difficult to think probabilistically, yet that is what one has to do with Generative AI. We demand certainty, right answers and truth-machines. Julian and his co-authors have admirably avoided this trap. Our brains are intrinsically probabilistic, as are these Generative AI tools, so the subject demands a different mindset, one that is more open and doesn’t fall into the trap of using pejorative language such as ‘hallucinations’. It has this great line,“AI isn’t perfect – because neither are we” and has more questions than answers, is playful rather than dogmatic.

They have also avoided the endless moralising we hear from academics and quango folk with a lot of time on their hands, accompanied by little knowledge of AI or ethics, riding into the discussion on their moral high horses. They pose questions and recognise the complexity of the situation. It’s also relatively short, a blessing in this age of verbosity. So I’ll end my review here.

Lastly, a confession. I have a small piece in the book on ‘Human Exceptionalism’. Julian was open enough, as always, to take a risk on a piece that tries to demolish the idea that we are special and exceptional. Copernicus and Darwin put that to rest, yet we still hang on to our 21^st century skills, whatever…. 

Well done to these three and See Saw Publishing. 

EU legislation is a mess... even Macron thinks it has gone way too far

The EU have stated their intention to implement some pretty stringent laws in their AI Act. Even Macron has warned that EU AI legislation will hamper European tech companies compared to rivals in the US, UK and China. Mistral is a very successful French company who may be hit by the AI Acts attack in foundational models.

It will, of course take forever, there is a two year process, with three different drafts from here from three different institutions – Parliament, Commission and Council. It is all a bit of a technocratic mess, with lots of potential bun fights.

Here are the problems:

A ban on emotion recognition may well hamper useful work in autism, mental health. In truth there wiggle room here, as it is absent in two of the drafts.

A ban on facial recognition will face fierce resistance from police and intelligence agencies, on cases as wide as deepfake recognition, age recognition in child protection, accessibility features, safety applications, terrorism, trafficking and child porn. Expect a lot of back-tracking and exceptions here.

The ban on social scoring sounds good but you are being assessed all the time for credit, loans, insurance and mortgages. In truth, social scoring Chinese style does not exist, so this is an unnecessary law tackling a non-existent problem.

The big one is the move on foundational models and their training data. This will be fiercely fought, hence Macron’s early move. Attacking foundation models is like attacking the wind not the boat and its destination. Ohers, such as the US and UK have urged a more application-driven, sector driven approach, that focus on uses not the underlying models. France, Germany and Italy has fought this, so I’d expect backtracking.

The Act has several other shortcomings in terms of definition and implementation. It sees AI as a fixed entity. AI is intrinsically dynamic, as it learns and morphs as it operates. This makes it a moving target in terms of the object of legislation. On top of this AI is an integrated set of services, networks and organisations with various data sources. If I am a small AI company I am likely to be using various foundational models and delivery services from OpenAI, Microsoft, Facebook and Amazon, all as cloud services. One general-purpose system may be used for several separate services. It is difficult to see how responsibility can be assigned.

While the rest of the world gets on and uses this technology for astounding benefits in learning and healthcare, the EU seems determined to become a mere regulator. Not just a regulator but one that loves complex, red-tape driven solutions. Billions, if not trillions, in terms of productivity growth and innovation may be a stake here.

My guess is that the act will facing challenges in terms of reaching consensus among member states, given the diverse interests and viewpoints on AI regulation. There will also be difficulties in implementing the guidelines and regulations proposed in the AI Act, due to technological complexities or resistance from AI stakeholders. The act will therefore need revisions or updates to address emerging AI technologies and scenarios that were not initially considered.

 

The final problem with EU law is its fixity. Once mixed and set, it is like concrete – hard and unchangeable. Unlike common law, such as exists in England, US, Canada and Australia, where things are less codified and easier to adapt to new circumstances, EU Roman Law is top down and requires new changes in the law through legislative acts. If mistakes are made, you can’t fix them.

 

At only 5.8% of the world’s population, there is the illusion that it speaks for the whole world. It does not.

PS

When the Pope called for a global treaty on AI, we have surely reached Peak BS on AI and ethics. The Catholic Church put Galileo:house arrest for rest of life, they burnt Bruno at stake for scientific heresy. Their banned books list Galileo, Descartes, Voltaire, Sartre & Kant only in 1966! We have surely reached Peak BS on ethics & AI?

  

Online Educa Berlin 2023 - Fun three days? Damn right it was!

OEB in Berlin was intense this year, with a three hour workshop, in a packed room, the Big Debate and a live podcast with my friend John Helmer. Disappointed that the Christmas markets were not yet open but there was plenty to one’s teeth into than a Bratwurst.

As is often the case, I found the keynotes a bit odd. An American jumped around like a firecracker, confusing shouting with substance, followed by a soporific speaker recommending a return to pencils. She was selling 'critical thinking' but seemed to have little of it. Truly mind numbing. Read every single word from a piece of paper. Then Luciano Floridi. I was looking forward to this having read his work but what a strange talk. He presented himself as a philosopher…. I’m not so sure. He’s actually more of psychologist, who has made a name for himself as a philosopher of the ‘digital’; can’t remember seeing a philosopher of the ‘pencil’. He claimed you could split the whole of philosophy, nay human affairs, into “just two things” - the Socratic and Hobbesian view – people are either stupid or evil. I have been reading philosophy all of my life and have never come across a more idiotic and simplistic summary of either philosophy or human nature. He then massacred Wittgenstein. It is clear he was playing to the crowd. 

 

Undeterred I had some great conversations with people who actually know what they are talking about. That’s what makes conferences so odd – the showbiz v substance. Substance is to be found in the casual encounters with new people, the smaller rooms, the bar, over a coffee. Lovely to see Gabor’s AI project progress from just an idea last year, our Norwegian friends seem to have cracked the HE assessment issue, Glean are progressing with AI and there were some real projects on AI that were lifting us out of the old paradigm.

 

So what did I learn?

 

People are still stuck in the LMS/ cartoon content/video world

The exhibition seemed stuck in that world, a bit dead and often empty

Conversations were full of new ideas and ambition

AI is here and here to stay and lots of real projects shown

Superficial AI moralising was noticeable absent

AI is at a technology way beyond what we’ve seen before

HE is in a panic over AI

L&D once again thinks it is about to be taken seriously at Board level – it is not

‘Critical thinking’ has become the predictable phrase people use when they don’t know what else to say – it’s now my litmus test for people who are walking away…

 

After three days of intense discussion people were ready to let rip in the Friday night and we did – a big meal, followed by a party. As usual, it was one of the best conferences of the year. It’s about the people, many who were there last year and the year before. It’s not in some hideous Casino in Las Vegas, or worse a Disney venue in Florida, or some god-awful, anonymous conference centre. Fun three days? Damn right it was.

EU AI legislation is firming up and I fear the worst. Danger of overregulation...

There are some good structural aspects of the legislation in terms of classifying size of organisation to avoid penalising small innovative firms and research, as well as classifying risks but as the Irishman said when being asked for directions... "We'll sir... I wouldn't have started here".

 

BANNED        – social scoring, face recognition, dark patterns, manipulation

High risk         – recruitment, employment, justice, law, immigration

Limited risk     – chatbots, deep fakes, emotion recognition

Minimal risks  – spam filters, computer games

 

But, as usual, the danger is in over reach. That can take place in several ways:

 

Catch-all

Like an oversized fishing net, the ban on biometric data may have the unintended consequences of banning useful applications such as deepfake detection, age recognition in child protection, detecting children in child porn, accessibility features, safety applications, healthcare applications and so on. Innovation often produces benefits that were unforeseen at the start. We have already seen a Cambrian explosion of innovation from this technology, category bans are therefore unwise. I fear it is too late on this one.

 

Core tech not applications

Rather than focus on actual applications, they have an eye on general purpose AI and foundational models. This is playing with fire. By looking for deeper universal targets they may make the same mistake as they did over consent and lose millions of hours of lost productivity as we all have to deal with ‘manage cookies’ pop-ups and no one ever reads consent forms. An attack on, say foundational ‘open source’ systems, would be a disaster. Yet it is hard to see how open source could be allowed. It has an ill-defined concept of a 'frontier model' in the proposed legislation that could wipe out innovation in one blow. No one knows what it means.

 

Complexity

It hauls in the Digital Services Act (DSA), Digital Markets Act (DMA), General Data Protection Regulation (GDPR), as well as the new regulation on political advertising, as well as the Platform Work Directive (PWD) – are you breathless while reading that sentence? It could become an octopus with tentacles that reach out into all sorts areas making it impossible to interpret and police. 

 

Copyright

Signs of the EU jumping the gun here and not in a good way. There is always the danger of some publishers (we know who you are) lobbying for restrictions on data for training models. This would put the EU at a huge disadvantage. To be honest, the EU is so far behind the curve now on foundational AI that it will make little difference but a move like this will condemn the EU to looking on at a two horse race, with China and the US racing round the track while the EU remains locked up in its own stalls.

 

Concrete

One problem with EU law is its fixity. Once mixed and set, it is like concrete – hard and unchangeable. Unlike common law, such as exists in England, US, Canada and Australia, where things are less codified and easier to adapt to new circumstances, EU Roman Law is top down and requires new changes in the law through legislative acts. If mistakes are made, you can’t fix them.

 

Conceit

At only 5.8% of the world’s population, there is the illusion that it speaks for the whole world. Having been round the world this year in several continents - I have news for them – it doesn’t. Certainly not for the US, and as for China, not a chance. It doesn’t even speak for Europe, as several countries are not in the EU. To be fair, one shouldn’t design laws that pander to other states but in this case Europe is so far behind that this may be the final n-AI-l in the coffin. Some think millions, of not trillions of dollars are a stake in lost innovation and productivity gains. I hope not.

 

We had a taste of all this when Italy banned ChatGPT. They relented when they saw the consequences. I hope the EU apply Occam’s razor, the minimum number of entities to reach their stated goals, unfortunately they have form here, of overregulating.

Bakhtin (1895-1975) dialogics, learning and AI

Mikhail Bakhtin, the Russian philosopher and literary critic, developed a theory of language which saw dialogue as primary. He took this idea and applied it to learning. By dialogue he mean social interaction between people, teachers and learners, learners and learners, learners and others but also dialogue with the past. He was persecuted during the Soviet era but his work was rediscovered in the 1980s, and he has since become an important theorists in the philosophy of language and even learning through AI.

Dialogism

Bakhtin criticises the 'monologic' tradition in Western thought, where individuals are defined by religion a concept of the finite, the soul, religious and establishment belief. Individuals for him, are open and must engage in dialogue with the world and others. Dialogism is his foundational idea that all language and thought are inherently dialogic. We lean language and come to understand language through the practice of dialogue. Learning means dialogue in many forms with different people, tools, perspectives in many different contexts. It was introduced in his Problems of Dostoevsky's Poetics(1929). This dialogue is in stark contrast with the teacher norm, direct instruction or monologue. Learning emerges from dialogue, external and internal.

In his incomplete essay Toward a Philosophy of the Act (1986), written in the 1920s, he outlines a theory of human identity or mind based on dialogic development. There are three forms of identity:

I-for-Myself

I-for-the-Other

Other-for-Me

The I-for-myself is untrustworthy. The ‘I-for-the-Other’ on the other hand, allows one to develop one’s identity as a se of perspectives others have about you. Th ‘Other-for-Me’ is the incorporation by others of their perception of you into their own identities. This is an expansive and interesting form of identity in an age of dialogue with others through social media and messaging technology, as well as dialogue with technology which now plays a similar role.

Dialogism manifests itself in ‘heteroglossia’, with exposure to a multiplicity of voices and perspectives in a language. These can be parents, teachers, friends and those on social media. A heterogeneous group of voices that one can learn from.

Authoritative vs. Internally Persuasive Discourse

Authoritative Discourse is discourse that is embedded in a culture, enshrined in tradition. It is unnegotiable and taught as truth. It may be religious beliefs, science, the canon, parental beliefs and other assumed forms of knowledge and practice. It is often enshrined in an official curriculum or syllabus, which learners must memorise and regurgitate in exams.

Internally Persuasive Discourse is more personal, related to the learner’s experiences and views. The learner has to engage in dialogue with the established views to relate it to their own prior knowledge, adapt to it, assimilate it and create their own sense of meaning.

Although we learn through both these forms of discourse, learning is to move from the authoritative to the internally persuasive to find our own deeper forms of meaning.

Carnivalesque

Bakhtin also wrote about the concept of the "carnivalesque" in literature, which subverts and liberates the assumptions of the dominant style or atmosphere through humor and chaos. In education, this can be pedagogical strategies that disrupt traditional hierarchies and empower students to challenge and question. 

Dialogism and AI

AI technology has now produced dialogic systems that may also fulfil Bakhtin’s needs. We can now communicate, in dialogue form with a new form of ‘other’ that lifts us out of our I-for-Myself, allowing technology to play a role in I-for-the-Other and Other-for-Me.

Interestingly traditional educators demand of the technology that it be a truth machine, when language is no such thing. Language is multi-perspective, at times messy, even carnivalesque. It points not just towards learning as dialogue but learning emerging from dialogue, in many different forms. LLMs and chatbots seem to be delivering this new form of learning.

Bakhtin would have loves the carnivalesque uses, such as be a pirate, the odd poems. I rather like the fact that Musk's GROK chatbot is quite snarky! AI satisfied both authoritative and personal learning and language allows for both. This is why I think Generative AI will have a profound effect on learning through dialogue, which is how most good teaching is done, both direct instruction and looser learner-centric dialogue.

Conclusion

He recognised the multifarious, and messy nature of human communication and therefore learning. For this he should be applauded, where theory is so often formulated in rigid and simplistic models. Bakhtin’s work has influenced educational theorists who view learning as a social, dialogical process and who advocate for more participatory, student-centered approaches. Educators who draw on Bakhtin’s ideas might focus on the role of discussion, debate, and dialogue in learning, and they might seek to create learning environments in which students’ voices are heard and valued as part of the collective construction of knowledge. 

Bibliography

Bakhtin, M.M. (1984) Problems of Dostoevsky's Poetics. Ed. and trans. Caryl Emerson. Minneapolis: University of Minnesota Press.

Bakhtin, M.M. (1993) Toward a Philosophy of the Act. Ed. Vadim Liapunov and Michael Holquist. Trans. Vadim Liapunov. Austin: University of Texas Press.

Bakhtin, M.M. (1981) The Dialogic Imagination: Four Essays by M.M. Bakhtin. Ed. Michael Holquist. Trans. Caryl Emerson and Michael Holquist. Austin and London: University of Texas Press.