Monday, June 24, 2024

Being You by Anil Seth - brilliant introduction to contemporary neuroscience

I’ve seen a lot of ‘Neuroscience’ talks at learning conferences, and am a bit weary of the old-school serotonin-dopamine story, strong conclusions and recommendations based what often seems to be correlation not causation (beware of slides with scans) and claims about neuroscience that are often cognitive science. I’ve also found a lack of real knowledge about the explosion in computational, cognitive and contemporary neuroscience in relation to new theorists and theory, the Connectionists, such as Daniel Dennett, Nick Chater, Karl Friston, Josh Tenenbaum, Andy Clark and Anil Seth.

Copernican inversion 

By far the best introductory book on this new movement in neuroscience, what I call the ‘Connectionists’, is Being You by Anil Seth. It is readable, explains some difficult, dense and opaque concepts in plain English, is comprehensive and all about what Seth calls a ‘Copernican inversion’ in neuroscience.

Starting with a stunning reflection on the complete dissolution of consciousness during general anaesthetics, he outlines the philosophical backdrop of idealism, dualism, panpsychism, transcendental realism, physicalism, functionalism and, what I really liked, the more obscure mysterianism (often ignored).

He’s also clear on the fields that prefigure and inform this new movement; NCC (Neural Correlates of Consciousness) and IIT (Integrated Information Theory). After a fascinating discussion of his LSD experiences, along with an explanation for their weirdness, he shows that the brain is a highly integrated entity, embodied and embedded in its environment.

Controlled Hallucination 

His Copernican Revolution in brain theory, that consciousness is ‘Controlled Hallucination’, builds on Plato, Kant, then Helmholtz’s idea of ‘perception as inference’. The brain is constantly making predictions, and sensory information provides data that we try to match against our existing models in a continual process of error minimalisation. This Copernican Inversion leaves the world as it is but sees the brain as an active, creative inferencing machine, not a passive receiver of sensory data. 

There is the usual, but informative notion that colour is in the hallucination not the real world and a series of illusions that prove active, predictive processing and active attention including the famous invisible Gorilla video experiment. 

He then covers most of the theories and concepts in this new area of neuroscience informed by the computational theory of the mind; abductive reasoning, generative modelling, Bayesian inference (particularly good), prediction error minimalization, free energy principle (also brilliantly explained), all under the unifying idea of a controlled hallucination as the explanation for consciousness.

Asides

There are some really well written asides in the book, one on art expanding on Riegel and Gombridge’s idea of the ‘Beholder’s Share’, where artists, such as the impressionists and cubists demand active interpretation by the viewer, confirming the perceptual inference he presents as his theory of perception and consciousness. Art surfaces this phenomenon. Another is a series of fascinating experiments on time, showing that it is related to perception and not an inner clock.

AI

The section on AI is measured. He separates intelligence from consciousness (rightly) as he is suspicious of functionalism, the basis for much of this theorising and is sceptical about runaway conscious AI, as an overextension. However hte book was published in 2018 and AI has progressed faster on the intelligence scale than the book suggests. At the end of the section he introduces 'cerebral organoids', anticipating Geoffry Hinton's Mortal Computer.

Conclusion

The only weak part of the book is his treatment of the ‘Self’. It is less substantial, not really dealing with the rich work of those who have looked at personal identity in detail, philosophically and psychologically. I was also surprised that he doesn’t mention Andy Clark, another ex-Sussex University theorist in the field, especially as he is closely associated with David Chalmers, who rightly gets lots of plaudits in the book. 

However, the fact that Anil lives in my home town Brighton is a plus! It covers a lot of the bases in the field and interleaves the hard stuff with more digestible introductions. A really fascinating and brilliant read.

PS

If you are generally interested in the theorists in this new field, John Helmer and I did a podcast on the Connectionists in the Netherlands, in front of a live audience. It was fun and covers many of the ideas presented in this book.


Friday, June 21, 2024

The DATA is in… AI is happening BIG TIME in organisations…

2024 is the year AI is having a massive impact on organisations in terms of productivity and use. Two reports from Microsoft and Duke, show massive take up. I showed this data for the first time this week at an event in London, where I also heard about GPT5 being tested as we speak.

The shift has been rapid, beyond the massive wave of initial adoption where people were largely playing with the technology. During this phase, some were also building product (that takes time). We’ve built several products for organisations, pushing fast from prototype to product, now in the market being used by real users in 2024. That's the shift.

The M&A activity is also at fever pitch. The problem is that most buyers don’t fully understand that startups are unlikely to have proven revenue streams in just 12 months. The analysts are miles behind, as they drive with their eyes in the rear-vie mirror. Don’t look to them for help. Large companies are looking for acquisitions but the sharper ones are getting on with it.

Microsoft - AI is Here

The Microsoft and Linked in report ‘AI is Here’ surprised even me.

The Survey & data of 31,000 people 31 countries covers labour & hiring trends, trillions of productivity signals and Fortune500 customers. The results clearly show that 2024 is year AI at work gets real and that employees are bringing AI to work. 75% of people are already using AI at work.



Who are using it? Everyone, the data shows everyone from Gen. Z to Boomers have jumped on board. 


And looking to the future, it is becoming a key skill on recruitment.

We have moved from employees informally bringing AI to work, to formal adoption, especially in large organisations. There's a serious interest in getting to know what to do and how to do it on scale. Next year will see the move from specific use cases, such as increasing productivity in processes to enterprise wide adoption. Some have already made that move.

Duke

CFOs that reported automating were also asked about whether their firms had utilised artificial intelligence (AI) to automate tasks over the last 12 months. 


CFOs that plan to automate over the next 12 months were asked about their plans to adopt AI over the this period. Fifty-four percent of all firms, and 76 percent of large firms, anticipate utilising AI to automate tasks, with a skew towards larger firms.

Conclusion

Anyone who thinks this is hype or a fad, needs to pay attention to the emerging data.

The problem is that it has a US skew. We’re all doing it but the US is doing it faster. As they shoot for the stars we’re shooting ourselves in both feet through negativity and bad regulation. The growth upside and savings in education and health are being ignored while we hold conferences on Ai and Ethics, where few even understand what an ‘ethical’ analysis means. It’s largely moralising, not ethics, with little understanding of the technology or actual ethics.

 

Thursday, June 20, 2024

British Library. Books look like museum pieces as that it what they are becoming?

Make it real! Can we actually deliver AI through current networks?


A talk and chat at the Nokia event held in the British Library. Wonderful venue and I made the point that we first abstracted our ideas onto shells 500000 years ago, invented writing 5000 years ago, printing 500 years ago and here we are discussing a technology that may eclipse them all – AI.

Bo heads up Nokia’s Bell Labs, who are working on lots of edge computing and other network research and we did what we do with ChatGPT – engaged in dialogue. I like this format, as it’s closer to a podcast, more informal and seems more real than a traditional keynote.

It was also great to be among real technology experts discussing the supply problems. There's something about focused practitioner events that make them more relevant. Microsoft telling us about GPT5 testing and some great case studies showing the massive impact AI is having on productivity.

Quantum computing was shown and discussed and an interesting focus on the backend network and telco problems in delivering AI. We have unprecedented demand for compute and the delivery of data at lower levels of latency. Yet much of the system was never designed for this purpose. 

Energy solutions

The race is on to find energy solutions such as:

Fusion is now on the horizon

Battery innovation progresses

AI to optimise power use now common

Low power Quantum computing begiining to be realised

Compute solutions

Models have to be trained but low latency dialogue also has to be delivered: 

Chip wars with increasing capability at lower costs

Quantum computers with massive compute power

Application-Specific Integrated Circuits (ASICs) and Field-Programmable Gate Arrays (FPGAs), optimised for AI workloads with lower power consumption

Edge computing moves processing closer to the data source at the edge of the network, reducing the need for centralised compute resources and it lowers latency

Federated learning allows multiple decentralised devices to collaboratively train models while keeping the data localised

Neuromorphic computing with chips that mimic neural structures, offering potential efficiency gains for AI workloads

Software efficiency

There’s also a ton of stuff on software and algorithmic efficiency, such as:

Model Compression through pruning, quantisation, and distillation to reduce the size and computational requirements of AI models

More efficient training methods like transfer learning, few-shot learning, and reinforcement learning to reduce the computational cost of building AI models.

Delivery

Network infrastructure moves towards 5G to provide high-speed, low-latency connectivity, essential for real-time AI applications and global delivery. Content Delivery Networks (CDNs) can cache AI models and results closer to users, reducing latency and bandwidth usage.

Two-horse race

Of course all of this has to be delivered and it is now clear that the biggest companies in the world are now AI companies. NVIDIA are now the most valuable company on the planet, at 3.34 Trillion delivering the spades to the gold miners, Microsoft at $3.32, Apple a touch less at $3.29, Google at $2.17 and Facebook at $1.27. In China Tencent $3.65 Trillion, Alibaba £1.43. This is a two horse race with Us well ahead and China chasing and copying. Europe is still in the paddock.

Conclusion

Afterwards, I went to the British Library’s Treasures of the British Library Collection. There lay the first books, written, then printed. A 2000 year old homework book, early Korans, The Gutenberg Bible. We made this work by developing paper and printing technologies, block printing, moveable type, book formats, networks for publishing and distribution. This was undermined by the internet but something much more profound has just happened.



It struck me that I that same building we had just witnessed a revolution that surpasses both. The sum total of all that written material, globally, is now being used to train a new technology, AI, that allows us to have dialogue with it to make the next leap in cultural advancement. We have invented a technology (books and printing were also technologies) that transcend even the digital presentation of print into a world where the limitations of that format are clear. We are almost returning to an oral world where we talk with our past achievements to move forward into the future.

We are no longer passive consumers of print but in active dialogue with its legacy. These books really did look like museum pieces as that is what print has become.

 

Friday, June 14, 2024

The 'Netflix of AI' that makes you a movie Director

Film and video production is big business. Movies are still going strong, Netflix, Prime, Disney, Apple and others have created a renaissance in Television. Box sets are the new movies. Social media has also embraced video with the meteoric rise of Tik Tok, Instagram, Facebook shorts and so on. YouTube is now an entertainment channel.

Similarly in learning. Video is everywhere. But it is still relatively time consuming and expensive to produce. Cut to AI…

We are on the lip of a revolution in video production. As part of a video production company then using Laserdiscs with video in interactive simulations, I used make corporate videos and interactive video simulations in the 80s/90s. The camera alone cost £35k, a full crew had to be hired, voiceovers in a professional studio (eventualy built our own in our basement), an edit suite in London. We even made a full feature film The Killer Tongue (don’t ask!).

With glimpses and demos of generated video, we are now seeing it move faster into full production, unsurprisingly from the US, where they have embraced AI and are applying it faster than any other nation.

1. Video animating an Image or prompt

I first started playing around with AI generated video from stills and it was pretty good. It’s now very good. Here’s a few examples. 

Now just type in a few words and it's done.

Turned this painting of my dog into a real dog...

Made skull turn towards viewer...


Pretty good so far...

2. Video from a Prompt

Then came prompted video, from text only. This got really good, really fast, with Sora and new players entering the market such as Luna.


Great for short video but no real long-form capability. In learning these short one-scene videos could be useful for performance support and single tasks or brief processes, even triger video as patients, customer, employees and so on. This is already happening with avatar production.

3. Netflix of AI

Meet Showrunner, where you can create your own show. Remember the Southpark episode created from AI? Same company has launched 10 shows where you can create your own episodes.

Showrunner released two episodes of Exit Valley, a Silicon Valley satire starring iconic figures like Musk, Zuck and Sam Altman. The show is an animated comedy targeting 22 episodes in its first season, some made by their own studio, the rest made by users and selected by a jury of filmmakers and creatives. The other shows, like Ikiru Shinu and Shadows over Shinjuku, are set in Neo-Tokyo, are set in distinct anime worlds, and will be available later this year.

They are using LLMs, as well as custom state-of-the art diffusion models, but what makes this different is the use of multi-agent simulation. Agents (we’ve been using these in learning projects) can build story progression and behavioural control.

This gives us a glimpse of what will be possible in learning. Tools such as these will be able to create any form of instructional video and drama, as it will be a ‘guided’ process, with the best writing, direction and editing built into the process. You are driving the creative car but there will be a ton of AI in the engine and self-driving features that allows the tricky stuff to be done to a high standard behind the scenes. Learners may even be able to create or ask for this stuff through nothing more than text requests, even spoken as you create your movie.

The AI uses character history, goals and emotions, simulation events and localities to generate scenes and image assets that are coherent and consistent with the existing story world. There is also behavioural control over agents, their actions and intentions, also in interactive conversations. The user's expectations and intentions are formed then funneled into a simple prompt to kick off the generation process.

You may think this is easy but the ‘slot-machine effect’, where things become too disjoined and random to be seen as a story, is a really difficult problem. So long-term goals and arcs are used to guide the process. Behind the scenes there is also a hidden ‘trial and error’ process, so that you do not see the misfires, wrong edits etc. The researchers likened this to Kahneman’s System 1 v System 2 thinking. Most LLM and diffusion models play to fast, quick, System 1 responses to prompts. For long-form media, you need System 2 thinking, so that more complex intentions, goals, coherence and consistency are given precedence.

Interestingly hallucinations can introduce created uncertainty, a positive thing, as happy accidents seem to be part of the creative process, as long as it does not lead to implausible outcomes. This is interesting – how to create non-deterministic creative works that are predictable but exciting, novel works.


This is what I meant by a POSTCREATION world, where creativity is not a simple sampling or remixing but a process of re-creation.

4. Live action videos

The next step, and we are surely on that Yellow Brick Road is to create your own live action movies from text and image prompts. Just prompt it with 10 to 15 words and you can generate scenes and episodes from 2 - 16 minutes. This includes AI dialogue, voice, editing, different shot types, consistent characters and story development. You can take it to another level by editing the episodes’ scripts, shots, voices and remaking episodes. We can all be live-action movie Directors.

Conclusion

With LLMs, in the beginning was the ‘word’, then image generation, audio generation, then short form video, now full-form creative storytelling. Using the strengths of the simulation, co-creating with the user, and the AI model, rich, interactive, and engaging storytelling experience are possible.

This is a good example of how AI has opened up a broad front attracting investment, innovation and entrepreneurship. At its hear are generative techniques but there are also lots of other approaches that form an ensemble of orchestrated approaches to solve problems.

You have probably already asked the question. Does it actually need us? Will wonderful, novel creative movies emerge without any human intervention. Some would say ‘I fear so’. I say ‘bring it on’. 


Wednesday, June 12, 2024

Apple solves privacy and security concerns around AI?


Apple Intelligence launched a set of AI features that had OpenAI’s GPT4 at the heart. It was a typical Apple move – focus on personalisation, integration and user performance.

The one thing that stood out for me was the announcement on privacy and ‘Edge’ computing. Their solution is clever and may give them real advantages in the real market. AI smartphones will be huge. Google led the way with the Pixel – I have one – it is excellent and cheap. But the real battle is between Apple and Samsung. The Galaxy is packed with AI features, as is the iPhone, but he who wins the AI device battle (currently 170 million units in 2024 and about to soar), will inherit users and revenue.

Privacy and security are now a big deal in AI. Whenever you shoot off to use a cloud service there is always the possibility of cybersecurity risks, losing data, even having your personal data looted.

Apple sell devices, so their device solution makes sense. It gives them ‘edge’ through ‘Edge Computing’.  A massive investment in their M3 chip and other hardware may give them further edge in the market.

In order to deliver real value to users the device needs to know what software and services you use across your devices, your emails, texts, messages, documents, emails, photos, audio files, videos, images, contacts, calendars, search history and AI chatbot use. Context really matters as if you are my ‘persona;’ assistant you need to know who I am, your friends and family, what I am doing and my present needs.

So what is Apple’s solution? They want to keep privacy on both device and when the cloud is accessed. Let’s be clear, Google, Microsoft, Meta, OpenAI and others will also solve this problem but it is apple who have been first above the parapet. This is because , unlike some of the others, they don’t sell ads and don’t sell your data. It pitches Apple against Microsoft but they are in different markets - one consumer, the other corporate.

‘Private Cloud Compute’ promises to use your data but not store and allow anyone access to your data, even Apple itself. Apple have promised to be transparent and have invited cybersecurity experts to scrutinise their solution. Note that they are not launching Apple Intelligence until the fall and even then only in the US. This makes sense, as this needs some serious scrutiny and testing.

Devices matter. Edge compute matters. As the new currency of ‘trust’ becomes a factor in sales, privacy and security matter. As always, technology finds a way to solve these problems, which is why I generally ignore superficial talk about ethics in AI, especially the doomsters. At almost every conference I attend I head misconceptions around data and privacy. Hope this small note helps.


Tuesday, June 11, 2024

Ethan Mollick’s 'CO-INTELLIGENCE' - a review

Just finished Ethan Mollick’s CO-INTELLIGENCE book. I like Ethan, as he shares stuff. His X feed is excellent, so was eager to give this a go.

It wasn’t what I expected, but that’s fine, because it’s pretty good. Ethan’s a Stanford academic, so I thought it would be a research-rich book with lots of examples but it is actually aimed at the basic, general reader, who knows little or nothing about AI; big font, big line spacing and no index but it does have some good, useful research.

It opens with his Three Nights Without Sleep revelation, that this shit is amazing! Why? Because it is a ‘General Purpose Technology’ pregnant with possibilities. I liked this. He writes well and is enthusiastic about its potential.

PART I 

That sense of wonder continues over PART I, with his musings on the Scary? Smart? Scary-smart? Nature of GenAI, seeing it as a sort of alien mind. Alignment he thinks is necessary but is not a doomster and avoids the sort of speculative sci-fi stuff that often appears whenever AI and ethics is mentioned. He ends this section with his Four Rules for Co-Intelligence:

Always invite AI to the table – like this

Be the human in the loop – OK but…

Treat AI like a person (but tell it what kind of person it is) – like this

Assume this is the worst AI you will ever use – yip!

PART II

This is the bulk of the book, with five chapters, where he sees AI as a:

Person

Creative

Coworker

Tutor

Coach

I have lots of quibbles but that’s fine. These are good, short readable discussions that open doors on its applications and potential. Each was well worth the read. I won’t go into detail, as I’d be in danger of providing one of those summaries that stops people buying the book!

It rounds off with a Chapter on AI as our future with four Scenarios; As Good As It Gets, Slow Growth, Exponential Growth or The Machine God. Then a short Epilogue, completed using ChatGPT – AI is US.

My own view is that the premise ‘CO-INTELLIGENCE’ is too simplistic and that it will do lots of things that will surprise us beyond the idea of just augmentation, a tool to enhance human creativity, decision-making, and productivity.

The problem with any book on AI, is that it is out of date before it is even printed. There were many points when I was thinking Yes… but… This is normal. The AI mindset demands fluidity and a recognition of the point Ethan makes in PART I – Assume this is the worst you will ever use.

Good introductory text – well worth a buy – but not for those who are looking for detail and depth of expertise.

 

Monday, June 10, 2024

Sam has ditched Satya for Tim – he’s so louche that lad! Apple Intelligence is here! New Siri and more...


Apple event features REALLY annoying presenters, but they finally join the GenAI club with Apple Intelligence. After showing the now compulsory ‘help me with my maths‘ example, they cut to the quick… it’s ChatGPT4 folks! 

Personal Intelligence

TTheir core idea is 'personal intelligence' as it understands your personal context. iPhone prioritises notifications, new writing tools (review, write and proofread etc) across all apps, even third party. Its email improvements, summaries of emails and so on, are super-cool. It will also intelligently prioritise your emails. Great for something I’ve been banging on about in learning – performance support. Apple are basically providing powerful, personal support across your entire online experience.

Images and video

On images it allows you to create images, including images of yourself, your friends and relatives, Sketch, Illustration and Animation styles built into apps across the system. Genmoji is a personal emoji creator, even an emoji that looks like your mates... that will be super-annoying. Image playground generation gives you styles, themes, costumes. Photoediting is super smart, getting things to disappear. Image wands allows you to circle, suggest and manipulate stuff.

There's also sound to text – great for student note taking and taking notes at work.

Search in video – clever. Great for performance support. Stories can be selected to a person and theme then strung together with music. Oh and there’s an API.

You can ask for personal stuff - remember that email I sent to... that picture I took of X last week... as it is personalising tools using personal data, the ‘who, what and whens’ of your life. 

Data privacy

This personal data is on-device processing so personal data is local. It can therefore use your personal data but with super-privacy features. An on-device semantic index helps keep it all local. Private cloud compute uses only data necessary for the task. It reaches out while still keeping your data private.

Siri

She’s gone from stupid to smart. Basically she’s now a chatbot that knows what you mean when you use ‘this’ and ‘that’ in sentences. It also has on-screen awareness and memory of what you have done. Siri knows your personal context – hotel bookings, photos you’ve taken, emails you’ve sent… porn you’ve accessed… no not that! 

Agentic

What’s interesting is its agentic capabilities. It goes off and find stuff relevant to your request, flight info, external websites, things you’ve done locally. This has legs.

This is Apple, so it is integrated, user friendly and personal

Conclusion

One thing they have done well is the M3 chip, giving device AI functionality - that lay behind much of what was delivered here and may be critical in terms of practical and secure delivery of AI. It literally gives them 'edge' in the market. They're reallly a consumer company, unlike Microsoft (apart from games), which makes edge computing and iPhone delivery more important. Lots of the features were consumer oriented.

This is AI for the rest of us – not just work but performance support for life. Well done. Every generation needs a revolution and through this revolution we become more of ourselves.

Saturday, June 08, 2024

7 success factors in real 'AI in learning' projects

With AI we are in the most interesting decade in the history of our species. I can think of no better field in which to think, write and work.

Ideas are easy, implementation hard

My first AI-like project was in the early 90s when I designed an intelligent tutoring system to teach interviewing skills. It had sentence construction as input and adaptivity in the sense of harvesting data as the learner used the system. Written in Pascal, it was clever but not yet smart, as the limitations of the hardware, in terms of processing power and memory, were extreme by today’s standards. Much of the effort went into making things work within these brutal constraints. Even then, we had controlled access to video clips (36 mins), thousands of stills and two audio tracks 112 mins) on Laserdiscs, which we used to good effect, simulating full interviews. You could feel the power of potential intelligence in software.

Jump to 2014 and those hardware limitations had gone. You could build an adaptive, personalised system, which we did at CogBooks. I invested personally in this system (twice) and brought investment in. We did oodles of research at Arizona State University and it was sold to University of Cambridge in 2021. It worked. For many years we had also been playing with AI within Learning Pool having bought an AI company. But my real project journey with modern AI started in 2014, when we build Wildfire, using 'entity analysis', open input and the semantic interpretation of open text answers. The whole thing was starting to taker shape.

Jump to November 2022 and things went a little crazy. I have barely been off the road speaking about GenAI in learning, written books on the subject, blogged like crazy, and recorded dozens of podcasts. Far more important, has been the real projects and product we have built for a number of clients and companies. This is the hard part, the really hard part. Ideas are easy, implementation is hard.

Optimal AI project

What makes a successful AI project? What are the factors that make them a success? The good news is that we have just completed a fascinating project in healthcare that had all the hallmarks of the optimal project. This was our experience.

1. Top-down support

The project started with top-down support, a goal and budget. Without top-down support, projects are always at risk of running out of support. That’s why I’m suspicious of Higher Education projects, grant-aided projects, hackathon stuff and so on. I prefer CEO, Senior Management or Entrepreneur driven initiatives, with real budgets. They tend to have push behind them, clear goals and, above all, they tend to be STRATEGIC. Far too many projects are mosquito project that fail because they end when the budget runs out and have no real impact or compelling use. Choose your use case(s) carefully and strategically. We have been through this with large Global companies - a rational approach to use cases and their prioritisation. Interestingly, AI can help.

2. Bottom-up understanding

This project also had a great client, grounded in a real workplace (a large teaching hospital), a clear budget and solid team. We made sure that everyone was on the same page, understanding what this technology was and could do. The two non-technical team members knew their process inside out but here’s where they really scored – they made the effort to understand the technology and did their homework. This meant we could get on with the work and not get bogged down in explaining basic concepts such as how an LLM works, context window and the need for clean data and data management.

Many AI projects flounder when the team has non-technical members that don’t know the technology, namely AI. It is not that they need competence in building AI, just that they need to understand what it is, the fact that it evolves quickly and that its capabilities grow rapidly.

3. Optimal team

The team also had a top-notch AI developer who has been though years of learning projects. This combination was useful, He had already built products in the learning field, understood the language of learning and its goals. The team was just three people. This really matters. Use Occam’s Razor to determine team size – the minimum number of team members to reach your stated goal. Too many AI projects include people with little or no knowledge of the basic technology. They often come with misconceptions about what they think it is and does, along with several myths.

4. Mindset matters

More than knowledge, is mindset. What cripples projects are people within the organisation who act as bottlenecks – sceptics, legal departments who do not understand data issues, old-school learning people who actually don’t like tech and anyone who is straight up sceptical of the power of AI to increase efficacy. Believe me there are plenty of those folk around. 

The mindset that leads to success is one that accepts and understands that the technology is probabilistic, data-driven, that functionality will increase during the project and things change very fast. I’d sum this up by saying you need team members who are both willing to learn fast and keep their minds open to rapid change. It also means accepting that most processes are too manual, that bottlenecks are hard to identify and that processes CAN be automated. 

5. Agency shift

You also have to let go and see that this technology has ‘agency’ and that you will have to hand agency over to AI. The technology itself will reveal the bottlenecks and insights. Don’t assume you know at the start of the project, they will be revealed if you use the technology well. This is no time for an obsession with fixed Gantt charts and designs that are fossilised then simply executed. It is like ‘agile on steroids’.

6. Manage expectations

AI is a strange, mercurial and fast moving technology. You have to dispel lots of myths about how it works, the data issues and its capabilities. You also have to communicate this to the people that matter.
You need to understand that what is hard is sometimes easy and what is easy, sometimes hard. The fact that things change quickly, for example, costs, is another problem. This happens to be a good problem as people often don't understand that token costs for fixed output are very low and even token costs for a service have plummeted in price. Expectations need to be managed by being clearly communicated.

7. Push beyond prototype to product

I can’t go into a huge amount of detail about the client but the topic was Surgical Support  - a life and death topic, with little room for error. It involved training and taking source material and turning it into usable support (not a course) for hospital staff. Processes were automated, SME (Subject Matter Expert) time reduced, delivery time to launch massively reduced so the team had more time to focus on quality, as opposed to just process and easier to maintain, as just updating the documents means the system is always current. The savings were enormous and increases in quality clear.

This success meant we could call upon the top-down support to push the project beyond prototypes into product with a broader set of goals and more focus on data management. It has given the organisation, management and team the confidence to forge ahead. With massive amounts of time saved and increased efficacy, we saw that success begets success.

Conclusion


If you don't have both TOP-DOWN and BOTTOM-UP support along with a tight team with the right mindset, you will struggle, even fail. This is a radically new and different species of technology, with immense power. It needs careful handling. The small team remained fixed on the strategic goal but was flexible enough to choose the optimal technology. Without all of the above the project would have floundered in no-man's land, with scope creep, longer timescales and the usual drop in momentum, even disappointment. 

The project exceeded expectations. How often can you say that about a learning technology project? This was a young team, astounded at what they had done, and this week, when they presented it at a learning conference, their authentic joy when expressing how it went, was truly heartening. “It was crazy!” said one when she describing the first results, then further inroads into automating in minutes, jobs that had traditionally taken them days, weeks even months. Everyone in the room felt the thrill of having achieved something. In 2024 AI has suddenly got very real.

PS

So many commentators and speakers on AI have never actually delivered a project or product. We need far more focus on practitioners who share what they think works and does not work.


Saturday, June 01, 2024

Postcreation: a new world. AI is not the machine, it is now ‘us’ speaking to ‘ourselves’, in fruitful dialogue.


Postproduction

There is an interesting idea from the French writer Bourriaud, that we’ve entered a new era, where art and cultural activity now interprets, reproduces, re-exhibits or utilises works made by others or from already available cultural products. He calls it ‘Postproduction’ I thank Rod J. Naquin for introducing me to this thinker and idea. 

Postproduction. Culture as Screenplay: How Art Reprograms the World (2002) was Bourriaud’s essay which examines the trend, emerging since the early 1990s, where a growing number of artists create art based on pre-existing works. He suggests that this "art of postproduction" is a response to the overwhelming abundance of cultural material in the global information age.

The proliferation of artworks and the art world's inclusion of previously ignored or disdained forms characterise this chaotic cultural landscape. Through postproduction, artists navigate and make sense of this cultural excess by reworking existing materials into new creations.

Postcreation

I’d like to universalise this idea of Postproduction to all forms of human endeavour that can now draw upon a vast common pool of culture; all text, images, audio and video, all of human knowledge and achievements – basically the fruits of all past human production to produce, in a way that can be described as ‘Postcreation’.

This is inspired by the arrival of multimodal LLMs, where vast pools of media representing the sum total of all history, all cultural output from our species, has been captured and used to train huge multimodal models that allow our species to create a new future. With new forms of AI, we are borrowing to create the new. It is a new beginning, a fresh start using technology that we have never seen before in the history of our species, something that seems strange but oddly familiar, thrilling but terrifying – AI.

Palimpsests

AI, along with us, does not simply copy, sample or parrot things from the past – together we create new outputs. Neither do they remix, reassemble or reappropriate the past – together we recreate the future. This moves us beyond simple curation, collages and mashups into genuinely new forms of production and expression. We should also avoid seeing it as the reproduction of hybrids, reinterpretations or simple syntheses.

Like a ‘palimpsest’, a page from a scroll or book that has been scraped clean for reuse, we can recover the original text if we scan it carefully enough, but it is the ground for a genuinely new work. It should not be too readily reduced to one word, rather pre-fixed with ‘re-’; to reimagine, reenvision, reconceptualise, recontextualise, revise, rework, revamp, reinterpret, reframe, remodel, redefine and reinvent new cultural capital. We should not pin it down like a broken butterfly with a simple pin, one word, but let the idea flutter and fly free from the prison of language.

Dialogue

We have also moved beyond seeing prompt engineering as some sort of way of translating what we humans do into AI speak. It is now, quite simply, about explaining. We really do engage and speak wto and with these systems. The move towards multimodality with generated and semantically understood audio, is a huge leap forward, especially in learning. That’s how we humans interact.

Romantic illusion

We have been doing this on a small scale for a long time under the illusion, reinforced by late 18th and 19th century Romanticism, that creation is a uniquely human endeavour, when all along it has been a drawing upon the past, therefore deeply rooted in what the brain has experienced and takes from its memories to create anything new. We are now, together, taking things from the entire memory of our cultural past to create the new in acts of Postcreation.

Communal future

This new world or new dawn is more communal, drawing from the well of a vast shared, public collective. We can have a common purpose of mutual effort that leads to a more co-operative, collaborative and unified effort. There were some historical dawns that hinted at this future, the Library at Alexandria, open to all containing the known world's knowledge, Wikipedia a huge, free communal knowledge base, but this is something much more profoundly communal.

The many peoples, cultures and languages of the world can be in this communal effort, not to fix some utopian idea of a common set of values or cultural output but creation beyond what just one group sees as good and evil. This was Nietzsche’s re-evaluative vision. Utopias are always fixed and narrow dystopias. This could be a more innovative and transformative era, a future of openness, a genuine recognition that the future is created by us, not determined wholly by the past. AI is not the machine, it is now ‘us’ speaking to ‘ourselves’, in fruitful dialogue.


Thursday, May 30, 2024

Any sufficiently advanced technology is indistinguishable from magic.

Conference in Trondheim opened with a blast! A young brass band, confident and accomplished wakened us all up, followed by a primary school choir, not stuffed into school uniforms treated like recruits into the army cade core, but natural, willing and confident – it made the heart soar. They gave it their all, as did we in the audience – with thunderous applause. Their faces at hearing adults appreciate them was one of pure innocent wonder., as it should be. This is, after all, what education should be about, growing young people into being confident, autonomous people and giving them the knowledge and skills to thrive.

The Director of Education was up next and talked about the difficulties of having a National Policy and implementing such a policy, as like good teaching, it can’t be too didactic and lecturing and must have a tension between what is directed and what is devolved out to regions and schools. It was a balanced and honest talk, so unlike the hectoring we get from our own politicians and Civil Servants. He did, however, have a title for his talk which irked me – ‘There is no magic in the machine’. 

I understood his talk, in Norwegian, because the magic in the machine (smartphone) was translating his talk in real time. I understood the text on his slides as I was using Google Lens. Both of these, for me, are real but magical. Indeed, my whole keynote was about how technology has now become quite magical. I even had a slide saying as much, showing Arthur Clarke and his quote “Any sufficiently advanced technology is indistinguishable from magic.”

My turn next and with 1200 Norwegian teachers in front of me, like the kids, I praised than, as having seen their output the kids, they must be doing something right – and I meant it. Norwegians are relaxed, open folk who relish the fact that they live in a wild and mountainous country, so I cracked a joke about we Scots getting the Norwegian Vikings, while the English got the Swedish and Danish Vikings – adding that we got the real Vikings – they gave me a round of applause! Then added that my mother never called my three sisters and I ‘children’ or ‘kids’, always ‘bairns’ – the Norse word for children. I feel at home here and we Scots may be as close to their culture as England.


Anyway, I started with some avatar stuff, my Digital-Don with all of my writing in a chatbot, then OpenAIs recent magical release of ChatGPT4o being a maths teacher heling solve a linear equation, which it did in voiced dialogue. This was followed by another example showing the same software’s ability to teach trigonometry, this time recognising things drawn by the learner. Finally, I showed Google’s Project Astra, where the context is understood from just using a smartphone to recognise what is in the room, including objects, even interpreting code it is shown on a student’s screen. 

My point was that real teaching, or at least teaching support, is here NOW.  For the first time in the history of our species, a teacher can teach that most difficult subjects to teach and learn, maths, using what real teachers use in classrooms – voice, dialogue, structured feedback based on learner’s output. The difference is that it can teach any subject anytime, at any level, anywhere in almost any language. It is a UNIVERSAL TEACHER, something I’ve written about in my latest book ‘AI for Learning’. Learners will use it, parents will use it, teachers should be using it. The rest of my talk was about the affordances of this new AI tech, its engagement, the interface which is now speech, real dialogue and multimodal. I also showed real examples of learner support, learner deliver case studies, even its role in wellbeing. This is the real deal. 

Honestly, this was a great audience, none of that uptight, uniformed, boot-camp conformity, none of that lazy scepticism. They don’t have school uniforms, less crowded curriculum, have an enlightened set of routes out of schools, with a good, well-funded, vocational system.

I travel to a lot of countries and get to meet a lot of great people working in education and workplace learning. Travel, I think, does broaden the mind, and in this one respect, shows how others educate and train their young people. Sure others have their problems, like us, but they often have a more sophisticated view of schooling; not cramming them into uniforms, making them follow lines around the school in silence, an overcrowded curriculum, Sisyphean levels of administration for teachers, obsession with examinations and a brutal demand for conformity. Out and about every single young person I met as servers in bars and restaurants, in the hotel were confident, fluid in English and seemed happy at work. We seem to be trapped in some shadowing of public-schools nightmare, where neither teachers, politicians, learners nor parents are happy – it’s all so fraught. Harry Potter be damned.

Sunday, May 26, 2024

AI as response to digital transformation

Gave a Keynote in Cyprus, after the Minster gave a rather sobering speech about how Cyprus went bankrupt in 2013. The treasury was as empty as a robbed tomb and its three core banks collapsed in a greedy borrowing spree. It took a decade to recover but Digital Transformation, he reminded us, along with climate change and nearby wars, was one of their new challenges. 

I responded by saying the Digital Challenge just got more difficult as it is now led by AI. The European tech industry and organisations are being eclipsed by the US and the political response has been a flag-waving bucket of regulation. On top of this the EU has allowed most of the US Tech giants to evade tax by holing themselves up in tax havens like Ireland, stealing other countries’ tax revenues. Ignore AI and you have no Digital Transformation strategy as you become less productive and competitive in almost every vertical.

My talk focussed on recent announcements, real examples, things you can do now, fact that ChatGPT4 will soon be free, productivity research, using AI for recruitment, as a tutor and the need for top down as well as bottom up activity. They had asked for a 'wake-up call' talk. That was my aim.

Strategic implementation

Sripada Vittala, from Dubai, followed with a great talk in the implementation of AI projects. We had a great chat the next day and seemed to agree on everything, as we have both been through real projects and real implementation issues. 

He gave a great case study on how AI had dramatically impacted productivity, recruitment and retention in huge construction company. Trading notes was useful as we’ve been through several AI transformation projects and some common themes emerged. We both had the same experience in dealing with large companies. The need for a precise and structured workshop, where the participants DO things, then a vision identifying and prioritising use cases, pushing through beyond prototypes. Turns out our processes were near identical.

Toxic leadership

The second half, after lunch, focussed on other topics. Leadership, the topic of one talk, I think, is exaggerated and often badly implemented in workplace training. Neither am I convinced that slotting ‘Leaders’ into the toxic categories of Psychopaths, Sociopaths and Narcissists is right. Most bad management is much more mundane, to do with over-zealous drive to get things done, a sense of self-importance and, commonly, a lack of competence. We have seen this with Paula Vennels and the Post Office crew, including HR, along with their supplier Fujitsu, their PR folk and their Lawyers. We saw this at The Royal Bank of Scotland, the case discussed. They were a client of mine and the managers got away with truly toxic and incompetent behaviour but none were punished and the organisation was bailed out. That’s why Cyprus’s banks collapsed five years later – there was no effective regulation and punishment – still true today.

Wellbeing

As for wellbeing, another talk, the evidence suggests that most workplace initiatives have little or no actual impact. I do feel that this constant focus on mental health is now a problem in itself. That is not to say it is not a problem, just that over-reflection on feelings, anxiety and mental health may not be the solution – it may make things worse. Denying that there are risks to therapy and interventions is a mistake, especially in your people with social anxiety. Thinking and talking about your problems all the time may not be good for you. CBT therapy confirms this, you need to break the spiral of negativity, not rise on its thermals. Being sad is not depression. A little anxiety isn’t always a bad thing. Being nervous is natural. You will get hurt emotionally. Getting hurt is not always trauma. Let’s focus on those with real chronic problems, not imaginary, self-perpetuated feelings that are often normal.

Conclusion

I'm not too sure why HR has got so obsessed by negativity and deficit thinking. I find that all a bit depressing! Nevertheless, this was a well-organised conference, short and sharp. The organisers were really efficient and hospitable. Sure there was a mix of speakers and topics, and we don’t have to agree with everyone, that’s good. The point is to make one think.

 

Thursday, May 23, 2024

Context matters? Yes and AI has made its move...

Just back from Japan and  our‘point and read’ Google Lens on the smartphone was a saviour. Japanese inscriptions in Museums, gardens, menus in restaurants, products in shops… using Lens was easy and reliable. No sooner are we were back in the UK, a rack of announcements from OpenAI, Google and Microsoft that put ‘contextual computing’ on the map. One of the primary problems in learning and performance support is CONTEXT. By that I mean, the teachers (human or AI) need to know WHO, WHAT, WHERE, WHEN to deliver great support. Context always matters.

RAG is a good example in AI and shows the direction of travel. Retrieval Augmentation Genreation, says take a look at the additional context then prompt to get your answer. Yann Lecun takes this further and sees a multimodal space for contextual analysis as being essential for AGI.

Smartphone as remote control for life 

Where AI has been lacking is in knowing about your immediate context and intent. This nut is now being cracked, as AI’s multimodal abilities have now hit the market. By this I mean its ability to ‘hear’ things, ‘see’ through your camera, identify things from video or know what’s on your screen. Both OpenAI and Google have started to crack multimodal delivery. Whether it is text, images, speech or video – it understands. Microsoft have gone after your work tasks; what application are you using, what’s local on your PC. Copilot plus knows what you are up to so it can provide the right support. Agents are starting to understand you and your context so they can act as your assistant.

Your smartphone has become a robot that knows you. You can point at anything and ask questions. It’s a hearing, seeing, understanding thing, almost sentient thing. In a sense your smartphone is fast becoming a remote control for your life.

Context aids cognition

Context is a particular boon in learning, to help deliver learning, formative assessment and assessments in situ, even in imagined 3D spaces. We can transcend the tyranny of text to do so much more in the real world doing real things, with real people in real places.

In terms of performance support, you will be able to point your camera at anything and ask a question – what is this, give me more detail how I use this, teach me to use it…. You may need new knowledge, more knowledge, learn unfamiliar tasks, apply things, learn changes to familiar tasks… all of these famous ‘moments of need’ can be satisfied by contextual analysis.

If you are working on a screen, it knows what software you are using, what is in your document, image or PPT, what you need to improve in that document, image, PPT or spreadsheet. It may either help you or do that task itself. In a hospital you may get help in that specific radiology room, with that specific medical apparatus. In a factory it may know the machines, how to use them, get real health and safety in that specific area, show you what to do when things go wrong. You get the idea. Context aids cognition.

Agents

Widening the perspective, the introduction of ‘agents’ signals something else. These are entities that act on your behalf and may know what your overall goal, sub-goals and tasks are. It is here that agents come in. This is clearly a direction of travel with smart GPTs and recent announcements show agentic workflow, a fancy name for giving AI agents control to support you in specific tasks or goals. 

Moderna and other are using up to 400 agents within their organisations to help on specific tasks. With new agentic workflow, we will see significant productivity gains, as they literally automate what humans used to do. 

Context options

When discussing the context that computers can capture, we generally refer to the various dimensions that provide situational information to enhance the interaction between the user and the computer. It is worth identifying the sheer range of contextual support that is now possible:

1. Physical Context

Your smartphone has long used GPS, as well as local movements using accelerometer and gyroscope data to determine motion and orientation. It also knows what’s in the vicinity, that a traffic jam may hold you up. It also knows the time and date, holidays, calendar events, timezones, even the weather.

2. Personal Context

It knows your identity, not just information such as your name, age, gender, and possibly user profiles with data about your socio-economic status. A step further is what you are currently doing, often inferred from app usage, keyboard activity or wearable devices. Then there’s your ‘preferences’, such as likes, dislikes and preferences based on past actions, purchases and explicit settings. At the physiological level, your heart rate, stress level and other biometrics captured via wearable devices such as bands, watches and rings are knowable. Eye, head, face and gesture tracking can all be used to read you movements but also intentions. Voice may also be read for signs of emotion, stress, interest and intent. This can provide health and activity insights. You are a literally a mass of fixed and variable data as you move through the world.

3. Social Context

Don’t think it stops there, your social context inferred from social media identifies your friends, connections and interactions on social media platforms. Information about other users in the physical vicinity, which can be detected through Bluetooth, Wi-Fi, or other proximity technologies is also usable. Then there’s your long and detailed communication history on email, chat, call history, search and other forms of communication.

4. Cultural Context:

Not difficult to know the language settings of the device and the content you typically engage with to determines your socio-economic position and cultural interests. Bourdieu identified the forms this cultural capital takes in terms of your memberships, credentials, associations, the language you use, accent, sports you like and credentials you have and value.

5. Task Context

More specific task or application data can be gleaned from the screen, supplication or activity you are engaged in at that moment. Inferences can also be made from recently completed tasks or frequently performed actions, even transcripts from meetings. What are you watching, did you pause, cut out, review – all of these micro-behaviours are useful. Menus can be adapted to suit your preferences.

6. Device Context

What device are you on? A smartphone, tablet, laptop, desktop, wearable device, along with current battery status, can influence how the device manages resources. We may even know what bandwidth you have available on wi-fi, telecoms and so on, even available space on your device, CPU use and available memory.

7. Application Context:

What applications are currently in use and what is their state? Analysis of frequency and duration of tool and app usage can help predict future behaviour, even permissions tell a story.

All of the above can be used to provide personalised notifications and reminders or sell you stuff with targeted advertising. By integrating these various dimensions of context, computers can offer more intuitive, responsive, and personalized user experiences.

Conclusion

Context is the new frontier for computing and with edge computing, local AI and agents, we can see how much work can be made more efficient. This scales people, which is what most organisations, wallowing in flat of slums of productivity need. We also have to be honest and admi that they may automate what humans thought they needed to do.


Tuesday, May 21, 2024

AI Business Boats don’t rise with these models, they remain tethered to the old dock and sink.

Sinking businesses?


AI businesses are getting steamrollered by OpenAI & others. Business cases have to be moated against attack by the new releases, as the new model will do what you do but quicker, better & cheaper. Boats don’t rise with these models, they remain tethered to the old dock and sink.

The trend is towards huge compute, large models with massive investment, effort and expertise to build and role out. Your service, built on top of the model is simply done better by using the model directly. This makes AI different from all other technology we have ever invented.

The internet created millions of businesses as it was an empty platform. AI is abundantly rich, deep & wide, trained on content that takes hundreds of 1000s of years to read. We will never catch up. The internet was a sea on which we could all sail. A LLM is an ocean that can gobble us up.

The problem is that many businesses are also tethered to this old internet model, with proprietary content and platforms,. which are slow, shallow, difficult to use and functionally limited. Intelligence has been unleashed on a new platform tat is easy to use, multimodal and fast.

ChatGPT4o will make GPT4 available for free. This is astonishing, so much power, for free, perhaps the one feature that will change things the most in their announcement.

You have to make some big bet strategic decisions as a startup.

Bet 1: LLM scalability and progress will run our of steam, allowing faster and betters products to build upon their limited success. If you get this wrong you sink.

Bet 2: Open source products will be available that have the same functionality as closed proprietary models. Here you have a chance to float on the open source movement. 

Bet 3: Build around the success of the behemoths , with consultancy, projects for large companies and so on but that doesn't give you the big revenues you imagine.

In the end you are betting against entities that have huge amounts of capital, the ears of investors and the ability to deliver. Talent is also in short supply.

So what is the new business model(s)?

Hackathons are interesting as they tend to flush out all the low hanging fruit and use the existing models and services to get things done. Most of the this low hanging fruit is rotten by the time it hits the ground. What it does do is establish business demand and use cases.

Multimodality in all its forms, huge context windows and ability to read the screen through Co-pilot have laid waste to developed applications.

So one must identify where you lie on the supply chain.

If you are building some extra functionality on top of a model, but using that model for delivery, the danger is obvious, that an improvement in the model can eat you alive.

If you have a unique asset that can be used for access, such as a large publisher, you stand a chance if you can prevent leakage.

If you have part of a service that AI does not deliver but enhances, you have a chance.

Devices - forget it.

Road to monopolies

This is a worry, as this is the road to monopolies, possibly even a single monopoly, as we may get a runaway effect where one model excels and outperforms all others. The singularity may be in business. This is worrying and governments are ill-prepared to deal with this.

They compete with each other to offer low tax regimes and companies take advantage of this. Even in supra-national entities, like the EU, have massive tax theft by allowing the large US tech companies to set up shop in the lowest tax regimes, largely Ireland.

Some may succeed but, by and large (literally), the trend is towards huge compute, large models, fine-tuned that take massive investment, effort and expertise to build and role out. Your service, built on top of the model is simply done better by users using the model directly. This is what makes this technology different from any other technology we have ever invented. 

Altman and many others have observed the disaster that is the European Tech industry. It gets crushed by negativity, bought out by the US and strangled by regulation. The problem is not AI but politics and economics. We need to get this sorted and fast.