Thursday, July 11, 2024

Good discussion paper on the Role and Expertise of AI Ethicists: Bottom line – it’s a mess!

 Good discussion paper on the Role and Expertise of AI Ethicists

Who is an AI Ethicist? An Empirical Study of Expertise, Skills, and Profiles to Build a Competency Framework Mariangela Zoe Cocchiaro et al.

Bottom line – it’s a mess! 

In the less than 2 years, AI Ethicists have become common. You see it in social media profiles, speaker profiles, especially in academia. Where did they all come from, what is their role and what is their actual expertise?

Few studies have looked at what skills and knowledge these professionals need. This article aims to fill that gap by discussing the specific moral expertise of AI Ethicists, comparing them to Health Care Ethics Consultants (HC Ethicists) in clinical settings. As the paper shows, this isn’t very clear, leading to vastly different interpretations of the role.

It’s a mess! A ton of varied positions that lack consensus on professional identity and roles, a lack of experience in the relevant areas of expertise, especially technical, lack of experience in real-world applications and projects and a lack of established practical norms, standards and best practices. 

As people who have as their primary role the bridging the gap between ethical frameworks and real-world AI applications, relevant expertise, experience, skills and objectivity are required. The danger is that they remain too theoretical and can be bottlenecks if they do not have the background to deliver objective and practical advice. There is a real problem of shallow and missing expertise along with the ability to deliver practical outcomes and credibility. 

Problem with the paper

The paper focus on job roles, as advertised, but misses the mass of self-proclaimed, internally appointed and simply identified as doing the role without much in the way of competence-based selection. Another feature of the debate is the common appearance of ‘activists’ within the field, with very strong political views. They are often expressing their own political beefs, as opposed to paying attention to the law and reasonable stances on ethics – I call this moralising, not ethics.

However, it’s a start. To understand what AI Ethicists do, they looked at LinkedIn profiles to see how many people in Europe identify as AI Ethicists. They also reviewed job postings to figure out the main responsibilities and skills needed, using the expertise of HC Ethicists as a reference to propose a framework for AI Ethicists. Core tasks for AI Ethicists were also identified.

Ten key knowledge areas

Ten key knowledge areas were outlined, such as moral reasoning, understanding AI systems, knowing legal regulations, and teaching.

K-1 Moral reasoning and ethical theory  

● Consequentialist and non-consequentialist approaches (e.g., utilitarian, deontological approaches, natural law, communitarian, and rights theories). 

● Virtue and feminist approaches. 

● Principle-based reasoning and case-based approaches. 

● Related theories of justice. 

● Non-Western theories (Ubuntu, Buddhism, etc.). 

K-2 Common issues and concepts from AI Ethics 

● Familiarity with applied ethics (such as business ethics, ecology, medical ethics and so on).

● Familiarity with ethical frameworks, guidelines, and principles in AI, such as beneficence, non-maleficence, autonomy, justice and explicability (Floridi & Cowls, 2019). 

K-3 Companies and business’s structure and organisation 

● Wide understanding of the internal structure, processes, systems, and dynamics of companies and businesses operating in the private and public sectors. 

K-4 Local organisation (the one advised by the AI Ethicist) 

● Terms of reference. 

● Structure, including departmental, organisational, governance and committee structure.  

● Decision-making processes or framework. 

● Range of services.  

● AI Ethics’ resources include how the AI Ethics work is financed and the working relationship between the AI Ethics service and other departments, particularly legal counsel, risk management, and development.  

● Knowledge of how to locate specific types of information. 

K-5 AI Systems  

● Wide understanding of AI+ML technology’s current state and future directions: Theory of ML (such as causality and ethical algorithms) OR of mathematics on social dynamics, behavioural economics, and game theory 

● Good understanding of other advanced digital technologies such as IoT, DLT, and Immersive.  

● Understanding of Language Models – e.g., LLMs – and multi-modal models. 

● Understanding of global markets and the impact of AI worldwide.  Employer’s policies  

● Technical awareness of AI/ML technologies (such as the ability to read code rather than write it). 

● Familiarity with statistical measures of fairness and their relationship with sociotechnical concerns.  

K-6 Employer’s policies 

● Informed consent. 

K-7 Beliefs and perspectives of the stakeholders 

● Understanding of societal and cultural contexts and values.  

● Familiarity with stakeholders’ needs, values, and priorities.  

● Familiarity with stakeholders’ important beliefs and perspectives.  

● Resource persons for understanding and interpreting cultural communities.

K-8 Relevant codes of ethics, professional conduct, and best practices  

● Existing codes of ethics and policies from relevant professional organisations (e.g. game developers, software developers, and so on), if any.

● Employer’s code of professional conduct (if available).

● Industry best practices in data management, privacy, and security. 

K-9 Relevant AI and Data Laws 

● Data protection laws such as GDPR, The Data Protection Act and so on. 

● Privacy standards.  

● Relevant domestic and global regulation and policy developments such as ISO 31000 on risk.  

● AI standards, regulations, and guidelines from all over the world.  

● Policy-making process (e.g., EU laws governance and enforcement). 

K-10 Pedagogy  

● Familiarity with learning theories.  

● Familiarity with various teaching methods. 

Five major problems

They rightly argue that AI Ethicists should be recognized as experts who can bridge ethical principles with practical applications in AI development and deployment. Unfortunately this is rare on the ground. It is a confusing field with a lots of thinly qualified, low level commentators self-appointing themselves as ethicists. 

  1. Some, but few in my experience, have any deep understanding of moral reasoning and ethical theories or applied ethics. 
  2. As for business or organisational experience few seem to have been in any real positions relevant to this role within working structures. 
  3. Another often catastrophic failing is the sometimes the lack of awareness of what AI/ML technology is, along with the technical and statistical aspects of fairness and bias.
  4. A limited knowledge even of GDPR is often apparent and the various international dimensions to the law and regulations.
  5. As for pedagogy and teaching – mmmm.


To be fair much of this is new but as the paper righty says, we need to stop people simply stating they are ethicists without the necessary qualifications, expertise and experience of the practical side of the role. AI Ethicists are crucial for ensuring the ethical development and use of AI technologies. They need a mix of practical moral expertise, real competences in the technology, a deep knowledge of the laws and regulations, and the ability to educate others to navigate the complex ethical issues in AI. At the moment the cacophony of moralising activists need to give way and let the professionals take the roles. Establishing clear competencies and professional support structures is essential for the growth and recognition of this new profession. 

My favourite AI quote....

This is my favourite AI quote, by E.O Wilson:

The real problem of humanity is the following: We have Paleolithic emotions, medieval institutions and godlike technology. And it is terrifically dangerous, and it is now approaching a point of crisis overall.” 

There’s a lot to unpick in this pithy and brilliant set of related statements.

By framing the problem in terms our evolutionary, Paleolithic legacy, as evolved, emotional beings, he recognises that we are limited in our capabilities, cognitively capped. Far from being exceptional, almost all that we do is being done, or will be done, by technology. This we refuse to accept, even after Copernicus and Darwin, as we are attached to emotional thought, beliefs rather than knowledge, emotional concepts such a soul, Romanticism around creativity and so on. We are incapable of looking beyond our vanity, have some humility and get over ourselves.

To moderate our individualism, we created Medieval institutions to dampen our human folly. It was thought that the wisdom, not of the crowd, but of middle managers and technocrats, would dampen our forays into emotional extremes. Yet these institutions have become fossilised, full of bottlenecks and groupthink that are often centuries old, incapable of navigating us forward into the future. Often they are more about protecting those within the institutions themselves than serving their members, citizens, students or customers. We lack trust in global institutions, political institutions, educational institutions, businesses and so on, as we see how self-serving they often become.

When Godlike (great word) technology comes along, and threatens either ourselves or our institutions, we often react with a defensive, siege mentality. Generative AI in Higher education is seen as an assault on academic integrity, using generative tools an attack on writing, getting help leading to us becoming more stupid. All sense of proportion is lost through exaggeration and one-sided moralising. No high-horse is too high for saddling up and riding into the discussion.

Wilson’s final point is that this produces an overall crisis. With Copernicus it led to printing, the Reformation, Enlightenment and Scientific Revolution. With Darwin, Church authority evaporated. With the Godlike technology AI, we have created our own small Gods Having created small Gods, they help us see what and who we are. It is seen as an existential crisis by some, a crisis of meaning by others. At the economic level a crisis of tech greed, unemployment and inequality. 

But it’s at the personal level where the Paleolithic emotions are more active. As Hume rightly saw, our moral judgements are primarily emotional. That is why many, especially those who work in institutions express their discontent so loudly. Technology has already immiserated blue collar workers, with the help of white collar institutions such as business schools. It is now feeling their collar. AI is coming for the white collar folks who work in these institutions. It is actually ‘collar blind’ but will hit them the hardest. 

Wednesday, July 10, 2024

Lascaux: archaeology of the mind - a LIM (Large Image Model) and a place of teaching and learning

Having written about this in several books, thrilling to finally get to Lascaux and experience the beauty of this early spectacular form of expression by our species. The Neanderthals had gone and this was the early flowering of the early Homo sapiens.

This is archaeology of the mind, as these images unlock our cognitive development to show that over tens of thousands of years we represented our world, not through writing but visual imagery, with startlingly accurate and relevant images of the world we lived in – in this case of large animals – both predator and prey – of life and death.

As hunters and gatherers we had no settled life. We had to survive in a cold Ice Age climate when there were no farms, crops and storage, only places small groups would return to after long nomadic journeys on the hunt. It was here they would converge in larger groups to affirm their humanity and, above all, share, teach and learn.

Cave as curriculum

After a period of disbelief, where it was thought such images were fraudulent and could never have been created by hunter gatherers tens of thousands of years ago, we had lots of perspectives; from Victorian romanticism of cave 'art' through to shamanic drug induced experiences, finally moving towards more practical, didactic interpretations.

The didactic explanations seem right to me and Lascaux is the perfect example. It was much used, purposeful and structured. A large antechamber and learning journeys down small branched passageways show narrative progression. like early churches, it is packed with imagery. Movement is often suggested in the position of the legs and heads, perspective is sometimes astounding and the 3D rock surface is used to create a sculptural effect. You feel a sense of awe, amazement and sheer admiration.

Narratives are everywhere in these exquisite paintings. Working from memory they created flawless paintings of animals standing, running, butting and behaving as they did in the wild. They dance across the white calcite surface but one thing above all astounded me - they made no mistakes. They used reindeer fat and Juniper which does not produce sooty smoke to light the cave, also scaffolding and a palette of black (manganese) and a range of ochres from yellow to orange and red. Flints scored the shapes, fingers palms, ochre pencils, straws, spitting techniques and stencils were used to shape, outline and give life to these magnificent beasts.

Learning journey

Entering the large rotunda with a swirl of huge bulls, horses and stags, you get a sense of the intensity of the experience, the proximity to these animals, their size and movement. But you are attracted by the scary dark hole existence of two other exits

The first has a foreboding warning – the image of a bear with his claws visible just next to the entrance. One can imagine the warning given by the teacher. Then into the hole of darkness of what is now called the Sistine Chapel of cave painting, a more constricted passage, just enough in places for one person to pass, with images much closer to you. At the end, a masterful falling horse in a pillar of rock, which you have to squeeze around, then into an even more constricted long passage with predatory lions. The narrative moves from observing animals in the wild to their death and finally to the possibility of your death from predators.

Choose the other side passage and you get a low crouching passage, at one point there is a round room, full of images, and at the back after a climb, a steep drop into a hidden space where a dead man (only figure in entire cave) lies prone, the charging bison’s head low and angry, its intestines hanging out. Beside the man lies a spear thrower and the spear is shown across the bison’s body. Beside him a curious bird on a stick.

What is curious are the dozens of intentional signs, clearly meaningful, often interpreted as the seasons, numbers of animals in a group and so on. It isprproto-writing and they have a teaching and learning purpose.

The cave is a structured curriculum, an ordered series of events to be experienced, gradually revealed and explained to the observer in a dark, flickering, dangerous world.

Setting the scene

Let's go back to the cave opening again. As you enter there is a strange, hybrid creature, that seems to say What is this? What animal could it be? The point may have been that we see animals as first glimpses, often at a distance and must learn to identify what they are – predator or prey? It has circular markings on its body, long straight horns and what looks like a pregnant belly. This seems like the spot an experienced hunter would explain that variability in markings, horns, body shape and knowing colour and breeding seasons matters to the hunter.

Expertise was rare, as people died young. The known had to be passed down generations not just by speech and action but permanently as images that told stories. This was a way of preserving thatvrare commodity - cultural capital.

Basic skills

As you enter the huge ante-chamber, which could have held the entire hunter and gatherer group, you literally walk in and find yourself beneath a huge herd of animals. It would have been a surprise, not possible in the real world, a simulation. This is an introduction to many different species.

It has been carefully composed. You are in a representation of the real world, a simulation that focuses on what matters, what you must avoid as predators, and kill as prey. It needed a huge communal effort, as scaffolding had to be manufactured and built, materials gathered and skilled artists themselves trained and selected. This is an organised group, creating an organised venue for organised learning.

The effect of large animals coming at you out of the dark, within the confines of a cold cave would have been terrifying, like being a horror movie. The flickering lamps revealing a horned head here, a tail there. It is as if they understood the idea of attention, simplicity of image and their impact on learning and memory.


As hunters late palaeolithic people tended to hunt a specific species at any one time of the year. This matches the imagery, where one can stop at an image of one species (they had to enter difficult passages with small lamps) and move from one species to another sequentially. There are narrative structures within the images; breeding pairs, animals in motion, different seasonal coats. At the end you encounter a masterpiece – the falling horse, with a bloated stomach, dead,


In another long side cave, like a long break-out room, the images are entirely different, a bewildering set of outlines and scores that suggest a more direct telling of what it is to hunt. Like a huge blackboard, they have been drawn and overdrawn by what seems like more improvisational hands. Here, I think, they were explaining the details of hunting. This was the chalkboard lecture hall. It is low and requires one to crouch, more likely to sit and be taught. 

New teachers clearly overwrote on those that came before, as there were no board cleaners! There a huge range of animals in these drawings - horses, bison, aurochs (bulls), ibexes, deer, a wolf and lion. They are often partial images, especially heads, which suggests some specific points were being made about what you need to look for as a hunter. It is a series of drawings over-writing the earlier work, over a long period by different people. 

In this area there is a shaft, climb down and there is a black scene of a human figure lying prone beneath a wounded bison, its intestines hanging out, its head low as it charges. This is the only image of a person in the whole cave. Flint knapping debris and ochre covered flints were found here, indicating the teaching of tools for butchering. One can imagine this being a specific, final lesson – kill or be killed.

Sound and speech

What is missing are the sounds of the teachers and learners. But even here we have some clues. One image, called the Roaring Stag is prominent. I have heard this in the Highlands of Scotland while camping in Winter. The noise is incredible like wolves all around you. It is likely that these sounds would have been simulated in the cave, an intense and frightening amplifier. You can imagine people in the dark suddenly frightened by the sound of rutting stags.

Communal knowledge

I wrote about this in my books on AI and 3D mixed reality, as they tell us something quite profound, that learning, for millions of years, was visual. We were shown things. This is our primary sense and as learning was about the world we knew and skills we had to master, images were our content. But we also have meaningful symbolic communications - not yet rwiting as we know it but an account of sorts and a sense of number.

Additionally, this was the first example of a communally shared learning experience. What we learnt and knew was not owned by others. It was a shared dataset, brought together by the whole group, to be shared for mutual benefit. It took a huge communal effort to create the first LIM (Large Image Model). There were no arguments about who drew or owned what, no ethical concerns about the dangers of sharing our data, just the need to share to survive and thrive.


Altamira was my first illustrated cave, many years ago. I can still remember the shock of that experience, a visceral surprise. Lascaux is even more wondrous. These places reveal the awakening our species - Homo sapiens, the ‘knowing man’. When we began to teach and learn, preserving and passing on our cultural knowledge. We became smarter and more cunning. The Neanderthals, who dabbled in such cave representations, were already long gone. We had separated the known from the knower, so that it could be passed on to many others. We were on the path to externalising ideas, refining them, reflecting upon them and using this knowledge to create new things, moving from tools to technologies; farming, writing, printing, the internet and AI. We became Homo technus.

Sunday, July 07, 2024

Labour, growth, productivity and AI

Labour Party Manifesto was led by a single goal – GROWTH. 

Problem – easy to promise, hard promise to keep.

Yet there are two letters that got zero mention by any party in this election – AI. The evidence is clear, that significant productivity gains can be achieved using this tech. You don’t need AGI to reap the benefits, just focus on straight utility, making things faster, better and cheaper.

Despite the global shift to a digitised economy, productivity growth has been declining since the 2000s. Artificial Intelligence will drive a productivity surge that supports long-term economic growth. Excitement is growing over "generative" Artificial Intelligence, which utilizes advanced computer models to create high-quality text, images, and other content based on their training data. Many are speculating whether this technology could revolutionise labour productivity, similar to the impact of the steam engine, electricity, and the personal computer. One is Tony Blair who has said this should be the first thing a Labour government looks at.

The positive effects on productivity have always lagged behind the invention of new technologies. However, AI seems to have had immediate effects due to its rapid adoption, which is down to its:

Fremium model

Ease of use

Cross sector application

Clear efficacy

2024 is the year of broad adoption, with real productivity gains in organisation-specific tasks. Whatever the process, AI seems to significantly shorten and also increase quality of output.

Initial studies show significant product gains, as well as increases in quality, even in creative tasks. In fact, it is the sheer variety of tasks that matters.

A further advantage is as a solution to recruitment needs. In increasing productivity, less staff are needed for the same amount of work, hence growth.

We are not in the EU therefore not subject to the EU AI Act. In being more aligned with the US and having English as our language we have an advantage within Europe.  


In schools we need to encourage the use of this technology to increase:

1. Teacher productivity through automated production, teaching & marking

2. Learner productivity through the adoption of these tools in the learning process

3. Admin productivity

4. AI in all teacher training

5. Reduction in curriculum content

In tertiary education, where we have Jaqui Smith as the skills, further and higher Minister, we need:

More short transition courses

Lecturer productivity

Leaner productivity

Focussed, robust AI research

In business we need to incentivise and support the creation and growth of startups. But for startups to truly thrive, several conditions must be met:

Efficient and high-speed dissemination of technical information, including:

Open-access technical publication

Open-source software

Data available for research

Smooth movement of personnel between companies, industry and academia

Smaller equity slices by Universities

Easier access to venture capital

Application of AI in SMEs

In large organisations we need:

Good measurement and studies in productivity

Cases studies of successful implementations

Implementation across the public sector

Tax breaks for AI in companies exporting


Above all we need to stop the doom mongering. It is not that AI practitioners are exaggerating its capabilities, that mostly comes from straw manning by the media, a few well known doomsters and speculative ethicists. These people will deny most ordinary people growth and prosperity while they sit on good institutional salaries and pensions.

We have the additional problem of politicians not being tech savvy, few having technical or scientific degrees, and many grazing on media reports and old-school advisors of the same ilk.

What we need from National Government are policies that accelerate this competitive advantage. NOT create more institutes. Enough already with the quangos like Digital Catapult. They do not help. Ignore the trade quangos who write anodyne reports written by low level experts. Avoid the consultancy trough frameworks, reports and documents.


There is one man who could make a big difference here - Sir Patrick Vallance. He has a strong research background and understands the impact it will have economically. We have an opportunity here to not get entangled with the negativity of the EU and forge ahead with an economic growth model based on increased productivity and new jobs. The other Ministers have shown little in the way of innovation in policy. He has the chops to get at least some of this done.


Tuesday, July 02, 2024

Mary Meaker's tech report 'AI & Universities' is right but delusional

Mary Meaker’s tech reports were always received with a degree of reverence. Her technology  PPTs were data rich, sharp, clear and were seen as a springboard for action. This latest report is the opposite. Data-rich but delusional. 

Curiously, her data is right but her focus on Higher Education as the driver of growth and social change is ridiculously utopian. The institutional nature of Higher Education is so deeply rooted in the campus model, high costs and an almost instinctive hatred of technology, that it cannot deliver what she advises. She is therefore both right and wrong. Right in her recommendations, wrong in her prediction. Higher Education’s reaction to the AI storm was to focus largely on plagiarism, with a pile of negativity expressed thinly veiled as ‘ethics’.

The research base has shifted out of Universities into companies that have market capitalisations in their trillions, huge cash reserves, oodles of talent and the ability to deliver. Higher education at the elite end has huge endowments but remains inefficient, expensive, has a crisis of relevance and is wedded to a model that cannot deliver on her recommendations.

Where she is right in is pointing towards the US advantage, set by Vannervmar Bush in the 1940s, where industry, Higher Education and Government were seen as a combined engine for growth and chance. 

Vannervar Bush's Vision

Vannervar Bush (1890 - 1974) was the Dean of the School of Engineering at MIT, a founder of Raytheon and the top administrator for the US during World War II. He widened research to include partnerships between government, the private sector and universities, a model that survives to this day in the US. He claimed that his leadership qualities came from his family who were sea captains and whalers. He was also a practical man with inventions and dozens of patents to his name. In addition to his Differential Analyzer, he was an administrator and visionary who not only created the environment for much of US technological development during and after World War II leading to the internet but also gave us a powerful and influential vision for what became the World Wide Web.

When World War II came along he headed up Roosevelt’s National Defense Research Committee and oversaw The Manhattan Project among many others. Basic science, especially physics, he saw as the bedrock of innovation. It was technological innovation, he thought, that led to better work conditions and more “study, for learning how to live without the deadening drudgery which has been the burden for the common man for past ages”. His post war report saw the founding of the National Science Foundation, and Bush’s triad model of government, private sector and Universities became the powerhouse for America’s post war technological success. Research centres such as Bell labs, RAND Corporation, SRI and Xerox PARC were bountiful in their innovation, and all contributed to that one huge invention - the internet.

Bush was fascinated with the concept of augmented memory and in his wonderful 1945 article As We May Think, described the idea of a ‘Memex’. It was a vision he came back to time and time again; the storage of books, records and communications, an immense augmentation of human memory that could be accessed quickly and flexibly - basically the internet and world wide web.

Fundamental to his vision was the associative trail, to create new trails of content by linking them together in chained sequences of events, with personal contributions as side trails. Here we have the concept of hyperlinking and personal communications. This he saw as mimicking the associate nature of the human brain. He saw users calling up this indexed, motherlode of augmenting knowledge with just a few keystrokes. A process that would accelerate progress in research and science.

More than this he realised that users would be able to personally create and add knowledge and resources to the system, such as text, comments and photos, linked to main trails or in personal side trails - thus predicting concepts such as social media. He was quite precise about creating, say a personal article, sharing it and linking it to other articles, anticipating blogging. The idea of creating, connecting, annotating and sharing knowledge, on an encyclopedic scale anticipated Wikipedia and other knowledge bases. Lawyers, Doctors, Historians and other professionals would have access to the knowledge they needed to do their jobs more effectively. 

In a book published 22 years later, Science Is Not Enough (1967), he relished the idea that recent technological advances in electronics, such as photocells, transistors, magnetic tape, solid-state circuits and cathode ray tubes have brought his vision closer to reality. He saw in erasable, magnetic tape the possibility of erasure and correction, namely editing, as an important feature of his system of augmentation. Even more remarkable was his prophetic ideas around voice control and user-generated content, anticipating the personal assistants so familiar to us today. He even anticipated the automatic creation of trails, anticipating that AI and machine learning may also play a part in our interaction with such knowledge-bases.

What is astonishing is the range and depth of his vision, coupled with a realistic vision on how technology could be combined with knowledge to accelerate progress, all in the service of the creative brain. It was an astounding thought experiment.

AI and growth

AI is now fulfilling Bush’s vision in moving our relationship with knowledge beyond search into dialogue, multimodal capabilities and accelerated teaching and learning, along with the very real implementation of the extended mind.

But the power has shifted out of the University system into commerce. Higher Education has retreated from that role and the research entities such as Bell Labs and Xerox Parc are no longer relevant. She is right in seeing the US power ahead of everyone on AI and productivity. The danger is that this produces an even lazier Higher Education sector that doesn’t adapt but become even more of an expensive rite of passage for the rich. 


Thursday, June 27, 2024

Is the Dispatches C4 programme a completely faked experiment?

Dispatches on Channel 4. 12 households, supposedly undecided voters, were put to the test and force-fed AI deepfakes. Split into two groups, At the end they go through a mock election.

The programme lays its stall out from the start – they’re out to conform what they already believe. This is not research, it’s TV’s odd version of confirmation bias. The three experts are the usual suspects and, guess who, the famously fragile interviewer Cathy Newman. They are actually zealots for deepfake danger – they are willing this to happen. A dead-giveaway are the past examples of deepfakes they pull up – all of left-wing folk – Hillary Clinton and Sadiq Khan. One of the three experts is, of course, a Labour Party Comms expert!

Here’s the rub. Fed a limited diet of information that massively increases the ratio of fake to real news renders the whole experiment useless. The presence Channel 4, of camera crews, lighting as they are set up to watch the fakes adds to the confirmation that this is authoritative content. They are deliberately set up to receive this ‘leaked’ news. It completely destroys any notion of ecological neutrality in the experiment. In fact, you’re almost forcing htem into believing what you’re telling them. The programme actually becomes a case study in bad research and investigation – it actually becomes fake new in itself, a ridiculously biased experience masquerading as supposedly authoritative journalism.

Actual research

Hugo Mercier’s book Not Born Yesterday debunks the foundations of this moral panic about deepfakes, with research that shows it is marginal at the edges, they are quickly debunked and few actually believe them. He argues that humans are not as gullible as often portrayed. Instead, they are selective about the information they believe and share and explores how social dynamics and evolutionary history have shaped human reasoning and belief systems, making us more resistant to deception than commonly assumed. Most of us weren’t born yesterday. Language didn’t evolve to be immediately believed as true.

Brendan Nyhan a world class researcher, who has extensively studied the impact and implications of deepfakes for many years, is clear. His research focuses on the potential threats posed by deepfakes to democratic processes and public trust. Nyhan argues that while deepfakes represent a significant technological advancement, their real-world impact on public perception and misinformation might be more limited than often suggested. He emphasises that the most concerning scenarios, where deepfakes could substantially alter public opinion or significantly disrupt political processes, are less common and that the actual use and effectiveness of deepfakes in altering mass perceptions have been relatively limited so far.

Deepfakes touch a nerve

They are easy to latch on to as an issue of ethical concern. Yet despite the technology being around for many years, there has been no deepfake apocalypse. The surprising thing about deepfakes is that there are so few of them. That is not to say it cannot happen. But it is an issue that demands some cool thinking.

Deepfakes have been around for a long time. Roman emperors sometimes had their predecessors' portraits altered to resemble themselves, thereby rewriting history to suit their narrative or to claim a lineage. Fakes in print and photography have been around as long as those media have existed.

In my own field, learning, a huge number have for decades, Dale’s Cone is entirely made up, based on a fake citation, fake numbers put on a fake pyramid. Yet I have seen a Vice Principal of a University and no end of Keynotes at conferences and educationalist use it in their presentations. I have written about such fakery for years and a lesson I learnt a long time ago was that we tend to ignore deepfakes when they suit our own agendas. No one complained when a flood of naked Trump images flooded the web, but if it’s from the Trump camp, people go apeshit. In other words, the debate often tends to be partisan.

When did recent AI deepfake anxiety start?

Deepfakes, as they're understood today, refer specifically to media that's been altered or created using deep learning, a subset of artificial intelligence (AI) technology.

The more recent worries about AI creating deepfakes have been around since 2017 when ‘deepfake’ (portmanteau of deep learning & fake) was used to create images and videos. It was on Reddit that a user called ‘Deepfake’ starting positing videos in 2017 of videos with celebrities superimposed on other bodies.

Since then, the technology has advanced rapidly, leading to more realistic deepfakes that are increasingly difficult to detect. This has raised significant ethical, legal, and social concerns regarding privacy, consent, misinformation, and the potential for exploitation. Yet there is little evidence that they are having any effect of either beliefs or elections.

Deliberate deepfakes

The first widely known instance of a political AI deepfake surfaced in April 2018. This was a video of former U.S. President Barack Obama, made by Jordan Peele in collaboration with BuzzFeed and the director’s production company, Monkeypaw Productions. In the video, Obama appears to say a series of controversial statements. However, it was actually Jordan Peele's voice, an impressionist and comedian, using AI technology to manipulate Obama's lip movements to match his speech. We also readily forget that it was Obama who pioneered the harvesting of social media data to target voters with political messaging.

The Obama video was actually created as a public service announcement to raise awareness about the potential misuse of deepfake technology in spreading misinformation and the importance of media literacy. It wasn't intended to deceive but rather to educate the public about the capabilities and potential dangers of deepfake technology, especially concerning its use in politics and media.

In 2019, artists created deepfake videos of UK politicians including Boris Johnson and Jeremy Corbyn, in which they appeared to endorse each other for Prime Minister. These videos were made to raise awareness about the threat of deepfakes in elections and politics

In 2020, the most notable deepfake video of Belgian Prime Minister Sophie Wilm├Ęs showed her give a speech where she linked COVID-19 to environmental damage and the need to take action on climate change. This video was actually created by an environmental organization to raise awareness about climate change.

In other words, many of the most notable deepfakes have been for awareness, satire, or educational purposes.

Debunked deepfakes

Most deepfakes are quickly debunked. In 2022, during the Russia-Ukraine conflict, a deepfake video of Ukrainian President Volodymyr Zelensky was circulated. In the video, he appeared to be making a statement asking Ukrainian soldiers to lay down their arms. Deepfakes, like this, are usually quickly identified and debunked, but it shows how harmful misinformation during sensitive times like a military conflict, can be dangerous.

More recent images of Donald Trump were explicitly stated to be deepfakes by their author. They had missing fingers, odd teeth, a long upside down nail on his hand and weird words on hats and clothes, so quickly identified. At the moment they are easy to detect and debunk. That won’t always be the case, which brings us to detection.

Deepfake detection

As AI develops, deepfake production becomes more possible but so do advances in AI and digital forensics for detection. You can train models to tell the difference by analysing facial expressions, eye movement, lip sync and overall facial consistency. There are subtleties in facial movements and expressions, blood vessel giveaways, as well as eye blinking, breathing, blood pulses and other movements that are difficult to replicate in deepfakes. Another is checks for consistency, in lighting, reflections, shadows and backgrounds. Frame by frame checking can also reveal flickers and other signs of fakery. Then there’s audio detection, with a whole rack of its own techniques. On top of all this are forensic checks on the origins, metadata and compression artefacts that can reveal the creation, tampering or its unreliable source. Let’s also remember that humans can also be used to check, as our brains are fine-tuned to find these tell-tale signs, so human moderation still has a role. 

As deepfake technology becomes more sophisticated, the challenge of detecting them increase but these techniques are constantly evolving, and companies often use a combination of methods to improve accuracy and reliability. There is also a lot of sharing of knowledge across companies to keep ahead of the game.

So it is easier to detect deepfakes that many think. There are plenty of tell-tale signs that AI can use to detect, police and prevent them from being shown. These techniques have been honed for years and that is the reason why so few ever actually surface on social media platforms. Facebook, Google, X and others have been working on this for years. 

It is also the case, as Yann Lecun keeps telling us, that deepfakes are largely caught, quickly spotted and eliminated. AI does a good job in policing AI deepfakes. That is why they have not been caught flat-footed on the issue.

This blind trial paper raises some serious questions on assessment

In a rather astonishing blind trial study (markers were unaware) by Scarfe (2024),  they inserted GenAI written submissions into an existing examination system. They covered five undergraduate modules across all years of BSc Psychology at Reading University.

The results were, to my mind, not surprising, nevertheless, quite shocking.

94% AI submissions undetected

AI submission grades on average half grade higher than students

What lessons can we learn from this paper?

First, faculty can’t distinguish AI from student work in exams (94% undetected) and second which is predictable, also that AI outoerformed students, with a half grade higher, again unsurprising, as a much larger study, Ibrahim (2023) Perception, performance, and detectability of conversational artificial intelligence across 32 university courses showed that ChatGPT’s performance was comparable, if not superior, to that of students across 32 University courses. They added that AI-detectors cannot reliably detect ChatGPT’s, as they too often claim that human work is AI generated and the text can be edited to evade detection.

Framing everything as plagiarism?

More importantly, there is an emerging consensus among students to use the tool, while faculty tend to see it only through the lens of and among educators to treat its use as plagiarism. 

The paper positions itself as a ‘Turing test’ case study. In other words, the hypothesis was that GPT-4 exam outputs are largely indistinguishable from human. In fact, on average the AI submission scored higher. They saw this a a study about plagiarism but there are much bigger issues at stake. As long as we frame everything as a plagiarism problem, we will miss the more important, and hard, questions. 

This is becoming untenable.

Even primitive prompts suffice?

As Dominik Lukes at the University of Oxford, an expert in AI, noted about this new study; “The shocking thing is not that AI-generated essays got high grades and were not detected. Because of course, that would be the outcome. The (slight) surprise to me is that it took a minimal prompt to do it.”

We used a standardised prompt to GPT-4 to produce answers for each type of exam. For SAQ exams the prompt was:

Including references to academic literature but not a separate reference section, answer the following question in 160 words: XXX

For essay-based answers the prompt was:

Including references to academic literature but not a separate reference section, write a 2000 word essay answering the following question: XXX

In other word, wit the absolute minimal effort undetectable AI submissions are possible and produce better than student results.

Are the current methods of assessment fit for purpose?

Simply asking for short or extended text answers seems to miss many of the skills that an actual psychologist requires. Text assessment is often the ONLY form of assessment in Higher Education, yet psychology is a subject that deals with a much wider range of skills and modalities.

Can marking be automated?

I also suspect that the marking could be completely automated. The simple fact that a simply prompt to score higher than the average student suggests that the content is easily assessable. Rather than have expensive faculty assess, provide machine assed feedback to faculty for more useful student feedback.

Will this problem only get bigger?

It is clear that a simple prompt with the question suffices to exceed student performance. I would assume that with GPT-5 this will greatly exceed student performance. This leads into a general discussion about whether white collar jobs, in psychology or, more commonly the jobs psychology graduates actually get, require this time, expense (to both state and student). Wouldn’t we bet better focusing on training for more specific roles in healthcare and other field, such as HR and L&D, with modules in psychology?

Should so many doing a Psychology degree? 

Over 140,425 enrolments in psychology in 2022/23. It seems to conform to the gener stereotypical idea that females tend to choose subjects with a more human angle, as opposed to other sciences 77.2% are female and the numbers have grown massively over the years. Relatively few will get jobs in fields directly related to psychology, even indirectly, it is easy to claim that just because you studied psychology you have some actual ability to deal with people better.

The Office for Students (OfS) suggests that a minimum of 60% of graduates enter professional employment or further study. Graduate employability statistics for universities and the government are determined by the Graduate Outcomes survey. The survey measures a graduate's position, whether that's in employment or not, 15 months after finishing university. Yet the proportion of Psychology graduates undertaking further education or finding a professional job is relatively small compared with vocational degrees 15 months after graduation (Woolcock & Ellis, 2021).

Many graduates' career goals are still generic, centring on popular careers such as academia, clinical and forensic Psychology that limit their view of alternative and more easily accessible, but less well paid, jobs (Roscoe & McMahan, 2014). It actually takes years (3-5) for the very persistent, to get a job as a Psychology professional, due to postgraduate training and work experience requirements (Morrison Coulthard, 2017).

AI and white-collar jobs?

If GPT-4 performs at this level in the exams across the undergraduate degree, then it is reasonable, if not likely, top expect the technology to do a passable job now in jobs that require a psychology degree. Do we really need to put hundreds of thousands of young people through a full degree in this subject at such expense and with subsequent student debts when many of the tasks are likely to be seriously affected by AI? Many end up in jobs that do not actually require a Degree, certainly a Psychology degree. It would seem that we are putting more and more people through this subject but for fewer and fewer available jobs, and even then, their target market is likely to shrink. It seems like a mismatch between supply and demand. This is a complex issue but worthy of reflection.


Rather than framing everything as a plagiarism problem, seeing things as a cat and mouse game (where the mice are winning), there needs to be a shift towards better forms of assessment but more than this some serious reflection on why we are teaching and assessing, at great expense, in subjects, that are vocational in nature but have few jobs available.. 

Wednesday, June 26, 2024

Natural-born cyborgs - learnings new imperative with AI

A much more philosophical and practical book than Anil Seth’s Being You is The Experience Machine by Andy Clark. It takes deeper dives into conceptualising the predictive brain as it looks both inwards and outwards. The book explodes into life in Chapter 6, with an absolutely brilliant synthesis of predictive processing towards goals and the idea of the extended mind (he with Chalmers wrote the seminal paper in early 90s). He starts with Tabitha Godlstaudm, a successful entrepreneur and dyslexic, who uses SwiftKey and Grammarly, seeing speech to text as her saviour. This is an example, of the extended mind, where we naturally use tools and aids to get things done. She moved beyond the brain because she had to. But this is about all of us.

When I travel, I book online, get boarding pass for the airport, book my hotel and glide through an electronic gate at customs using face recognition, order an Uber, get list of things to see on my smartphone, check out restaurants and use Google maps to get places – this seamless weave of mind and technology puts maid to the neuro-chauvinism around the mind being unique. As the “weave between brain, body and external resources tightens” our lives become easier, faster, more frictionless and opportunities to act and lean expand. This looping by the brain through the digital world becomes second nature, biology and technology entwine. The same is true in learning.


These theorists recommend that we need some humility here. It is not the traditionalists that show humility, as they are addicted to biological-chauvinism. The brain is perhaps less deep than they or we care to admit. What goes on inside our heads is limited, leaky, full of bottlenecks, with a seriously narrow working memory and fallible long-term memory. It stutters along, improvising as it goes.

He makes the excellent point that we ALL have a form of dementia, in the sense of constantly failing and fallible memories. This also means we ALL have learning difficulties. If our benchmark is the average human, that is a poor benchmark compared to the digitally enhanced human, with just a smartphone, especially one with AI, which means them all. We use AI unwittingly when we search, use Google maps, translate and so on. Increasingly we are using it to enhance performance.

Our brains don’t really care if what we use is inside the skull or smartphone, if it gets the job done and one consequence is his recommendation that we embrace the idea of a more fluid use of tools and supportive environment to get tasks done and to improve our own performance. He asks us to imagine we had Alzheimer’s and needed labels, pictures and reminders and supportive environments to get through our day. This idea of linking task performance to performance support, makes sense. It chains learning to real world action and actual performance.

We need to move on and see the encouragement and use of AI as a core activity. If we get stuck in the mindset of ranting and railing against the extended mind, even worse seeing it as a threat, we will be bypassed. The extended mind has been made real and relevant by AI. That is our game.

Natural-Born Cyborgs

In Mollock’s 2023 paper on productivity with 758 consultants from the Boston Consulting Group across 18 tasks, 12.2% more tasks were completed, with 25.1% faster completion and an astonishing 40% higher quality. The more fascinating finding from the data was the emergence of a group of superusers (cyborgs), who integrated AI fully into their workflow. Low latency, multimodal support has made this even more potent. Compare these to the Centaurs who benefit, but less, as they saw it as an add-on, adjunct technology, not as the extended mind.

We need to be looking at AI in terms of what Andy Clark calls the “natural-born cyborg’. We now collaborate with technology as much as people to get things done. It is a rolling, extended process of collaboration, where we increasingly don’t see it as separate from thinking. We have to free ourselves from the tyranny of time, location, language limits, and embrace the bigger opportunities that technology now offers through cognitive extension.

The naked brain is no longer enough and our job should be to weave that brain into the web of resources at the right time to get our jobs done, not drag people off into windowless rooms for courses or subject them to over-long, over-linear and impoverished e-learning courses, where they feel as though they have no agency.


Another consequence of the predictive, computational model of the brain is its ability to explain autism, PTSD, dreams, mental illnesses such as schizophrenia and hallucinogenic drug experiences. In The Experience Machine (2023) Clark goes into detail, with real case studies, on how predictive processing models suggest that psychosis can arise from disruptions in the brain's ability to accurately predict sensory input, leading to hallucinations or delusions. Schizophrenia is one example, similarly, the effects of drugs on perception and cognition can be understood as alterations in the brain's predictive models, changing the way it interprets sensory information or the level of confidence it has in its predictions. Dreams can also be seen as a state where the brain generates internal predictions absent of external sensory input. Autism is predictive activity with less regulation. This could also explain the often bizarre and illogical nature of dreams, as the brain's predictive models operate without the grounding of real-world sensory data

Hacking the Prediction Machine

He finally encourages us to ‘hack our predictive minds’. These hacks include the use of meditation, technologies such as VR and mixed reality along with AI. Therapies also fall into this category, encouraging us to break the cycle of negative. Cycles of predictive behaviour in conditions such as depression and anxiety.


There is much to be gained from theorists who push the boundaries of the mind into its iterations with the world, others and technology. The Extended Mind is an incredibly useful idea as it explains both why and hoe we should be implementing technology for learning. This book gives us a cognitive bedrock with a computational theory of the mind but is also fruitful in pointing us in the right direction on implementation through task support and performance support.

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.


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.


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 the 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 Geoffrey Hinton's Mortal Computer.


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.


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.


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.


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.


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.


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.


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.