My mate Doug - things I've learnt Part III....

My mate Doug…

“Hell is other people” he said, that French bloke… but heaven is time on your own with your dog. Sometimes it’s just him lying there on the carpet, throwing the occasional glance my way. Sometimes it’s on a walk, him steaming ahead, a glance back to see that I’m still there. Sometimes we step into The Flour Pot, a café where he sits beneath the table checking all who enter. We like it there. Mostly it’s just the satisfying sense that we’re together, mates really.

We got locked out the other day – went for a walk and forgot our keys. Had to sit on the doorstep for about 40 minutes waiting on Gil to return. Doug sat coiled in my lap and fell asleep. We kept each other warm. A woman stopped, ‘Are you alright?’ she said…. ‘Never better’ was my reply…

There are times, on my own, when Doug brings his orange lama toy to me. It squeaks when he presses it in his jaw. I pull, he pulls, and we have a little tug of war. Then it comes free and I throw it to the other end of the room. He turns and zooms down to grab it, shakes it, and brings it to me. I haven’t really played for years, now I play every day and you know what… it’s OK to play. 

He stole Gil's blusher the other day - chewed it to bits (see pic). She went mental but when she left the room we had a good laugh together. You see I also speak to him… a lot. Sure he hasn’t a clue what I’m saying but that’s not the point. The point is that I’m saying stuff I really mean, not that chit-chat you get between humans but the real deal – mate to mate.

At times Doug looks at me with his black eyes then tilt his head and we commune. I have no idea what’s in there but it’s something simple, nice and loving. There’s no narcissism in dogs… it’s more give than take. Descartes thought that dogs had no soul, no consciousness, just cold machines. Descartes was a damn fool. John Searle has four dogs Frege, Russell, Ludwig, Tarski... and he loves them, although, as he admits, they are all hopeless with logic.

Part I
Part II

Video for learning –15 things the research says – some may shock you…

Video for learning is great at some things, not so great at others. Some time back I attended a session on video for learning and found that neither the commissioners nor the video production company had looked at a single piece of research on video and learning. They told us that people loved the work and since then I’ve seen a lot of 'Netflix for learning’ delivery. That’s fine but let’s step back a minute..What I didn’t see was much evidence that people were actually ‘learning’ much. I have been involved in video production for 35 years, from corporate videos delivered on VHS cassettes, interactive videos on Laservision (in many ways way beyond what we currently see), CD-ROMs and now streamed. We even made a feature film The Killer Tongue – lost a pile of money on that one and lesson learnt.

Video is great for emotional impact and so attitudinal shift, showing processes and procedures, moving objects, going inside things down to the micro-level or to places we can’t get to like space or the inside of a nuclear reactor. It can also present people, talking to you. But what works best for LEARNING? The evidence shows that many of the things we do in video are just plain wrong. There’s decades of research on the subject, that mostly remains unread and unloved. For a good summary, read Brahme (2016). So let’s surface a few of these evidence-based pointers.

What can we learn from Netflix?

It wiped out Blockbuster and has become a behemoth in the entertainment industry because it adopted a demand-AI-driven, technological and personalised solution in a world that was stuck in scheduled, supply-side delivery. Netflix is easy to use, searchable and personalized, extensively A/B tested, tiled and has an AI recommendation engine behind the interface. It is also time-shifted, streamed and multi-device. But what really makes it sing is the data-driven algorithmic delivery. This is why learning and video folk need to pay attention to technology, especially AI. That other behemoth, YouTube, the largest learning platform on the planet, is also searchable, personalised and time-shifted.

But Netflix is entertainment, not learning content, so what more do we have to learn, from cognitive science about what makes great learning video?

Episodic v semantic memory

Many can recall famous scenes from their favourite film. Mine is the Rutger Hauer scene from Bladerunner, where he sits, dying on the roof in the rain and gives a moving soliloquy about the joys of life, forgiveness and death. Many recall the scene well but few can remember what he actually said. That is because our episodic memory is strong and video appeals to that form of memory, but video is terrible on semantic knowledge, what we need to know in terms of language. I can remember the scene and literally play it back in my head but I can’t remember the speech. This is why video is not so good at imparting detail and knowledge. There is a big difference between recalling episodes and knowing stuff. Yet so often we see talking heads and text on screen that tries to do exactly that.

Shooting star

Learning is a lasting change in long-term memory and video suffers from the lack of opportunity to encode and consolidate memories. Your working memory lasts about 20 seconds, can only hold three or four things in the mind at one time and without the time to encode it is quickly forgotten Sweller (1988). Our minds move through video like a shooting star, where the memories burn up behind us. Without additional active, effortful learning, we quite simply forget. A big warning here, people think they have learnt from video but as Bjork (2013) and many others have shown, much of this is illusory learning, mistaking the feeling that you’ve learnt things but when tested, you have not.

How long do learners engage?

We know from Guo (2014), who had a large data set of learning video data gathered from MOOCs, that learners drop out in large numbers at around six minutes. It drops dramatically down to 50% at 9-12 minutes and 20% beyond this. Evidence from other studies on attention, using eye-tracking, confirm this quite rapid drop in arousal Risko (2012). Keep videos at 6 minutes or less – the less the better.

Student control of video?

Should you give learners control of video? Yes. In fact giving them 'chapter' points beyond the usual forward, back and pause controls proves useful, as it matches the structure of the learning experience Zhang (2006).

Video, audio, text on screen?

It is vital to reduce cognitive load in video. Mayer (2003) and others have shown that text plus audio plus video on the screen, commonly seen in lecture capture, actually inhibits learning. Don’t put captions, text or scripts on the screen while the narrator or person on the screen is talking. One exception to this (rules are there to be broken) is in language learning, where the ability to jump from voice to text can prove useful. Also avoid complex backgrounds and background music, as they also inhibit learning Brahme (2016).

Talking heads or not?

This comes as a bit of shock to many but talking heads, that staple of lecture capture and MOOCs, is often not such a great idea. It can be useful if the person is a respected and well-known expert and there is evidence that the mere knowledge of them as famous experts increases retention and there is a social learning effect. Inspirational talks, well-delivered with emotional impact can also work as do short introductions, links and goodbyes, to take advantage of the social effect. But there is nothing worse, in terms of learning that an autocued talking head, mechanically delivering a scripted piece to camera in the expectation that people will remember what he or she has said – they won’t Chaohua (2019). Fiorrella and Mayer (2018). We have ample evidence for this in the Khan Academy where you don’t see Salmon Khan’s face, only the working through of examples or images. Cognitively, the face is often just noise, what you want to see is the actual maths, with the hand drawing and writing on screen. For subjects that are semantically rich, getting rid of the face is a good thing as it reduces cognitive load. Social cueing does seem to work, for example, at start and end of lesson and, of course, for subjects where visuals are less useful - story-telling, philosophy and so on.

Recommendations on speech?

Avoid over-formal, TV-news like presentation from autocue. The evidence suggests that  informal is good, enthusiastic is good, personal ‘I’ ‘you’ ‘your’ is good. Speaking between 185–254 words per minute seems to be optimal and no background music Mayer (2008), Brahme (2006).

Perspectives in learning video?

When showing the manipulation of objects by hand or, in general, procedures and processes, video shot from the perspective of the learner, is better that third-person viewpoints. This may upset traditional Directors but it means that you get cognitive congruence Florella (2017).

How does image size affect learning?

This should worry those whose video as a great medium on smartphones. The smaller the screen the less you learn. Or the larger the screen the more you learn. Nass and Reeves (1996) showed 60 video segments to 125 adults on different screen sizes and tested them a week later. Those that had watched the larger screen remembered “significantly more”.

How does video quality affect learning?

Video quality doesn’t appear to matter as much as audio quality. That’s because our vision has evolved to cope with twilight, different light conditions and so on. Our hearing, on the other hand, is designed for close proximity speech Nass and reeves (1996). Their research showed that on attention, memory and evaluation, video quality had “no psychological advantages” whereas audio quality was critical.

How do video cuts and pace affect learning?

Cuts raise attention, about 1 sec after the cut but too many cuts lowers attention

So be measured with cuts and make sure they match the ‘need to know’ learning objectives. It is worth going at a slower pace with cuts, as the learners needs to process information before it goes from working to long-term memory Nass and Reeves (1996).

Should we chunk video?

OK here’s the big one. Learning improves when there are “visual rests” and memory is enhanced when ”people have a chance to stop and think about the information presented". We need to stop and think about that a little. It seems as though micro-chunking video down to smaller segments is a good thing for learning Florella (2019).

What should the relationship be between video and active learning?

This may seem obvious but it is often ignored in practice. After video the effortful learning must be very closely related to video content. A large data mining exercise showed that this is not always the case and that when the two are too loosely related it affects student attainment MacHardy (2015).

What do you do after/between video chunks?

Don’t get them to draw stuff, get them to explain what they think they’ve learnt. You can do multiple choice but this is the weakest of the techniques. Retrieving key concepts is better Szpunar (2013), Roediger (2006), Vural (2013) and typing in actual explanations, that can be semantically interpreted by AI is even better.

Wildfire uses AI to grab the transcript of the video and produce retrieval-based online learning. It does it quickly and cheaply with links to external content, making the learner type in concepts, numbers or full sentences, which are interpreted by AI.

Conclusion

To produce effective videos for learning we must look at learning research. That not only tells us about the cognitive basis of good learning, it also gives us sound recommendations about what we need to do to increase retention, some of it surprising and counterintuitive. You have to break some of the rules of traditional video production and directing to adjust video towards learning. Oh... and rules are there to be sometimes broken... but only with good reason.

Bibliography

Sweller, J., 1988. Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), pp.257-285

Bjork, R.A., Dunlosky, J. and Kornell, N., 2013. Self-regulated learning: Beliefs, techniques, and illusions. Annual review of psychology, 64, pp.417-444.

Brame, C.J., 2016. Effective educational videos: Principles and guidelines for maximizing student learning from video content. CBE—Life Sciences Education, 15(4), p.es6.

Guo PJ, Kim J, Robin R.  L@S’14 Proceedings of the First ACM Conference on Learning at Scale.New York: ACM; 2014. How video production affects student engagement: an empirical study of MOOC videos; pp. 41–50.

Risko, E.F., Anderson, N., Sarwal, A., Engelhardt, M. and Kingstone, A., 2012. Everyday attention: Variation in mind wandering and memory in a lecture. Applied Cognitive Psychology, 26(2), pp.234-242.

Zhang, D., Zhou, L., Briggs, R.O. and Nunamaker Jr, J.F., 2006. Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & management, 43(1), pp.15-27.

Mayer, R.E. and Moreno, R., 2003. Nine ways to reduce cognitive load in multimedia learning. Educational psychologist, 38(1), pp.43-52.

Chaohua, O, Joyner, D, Goel, A., 2019. Developing Videos for Online Learning: A 7-Principle Model. Online Learning

Mayer, R.E., 2008. Applying the science of learning: Evidence-based principles for the design of multimedia instruction. American psychologist, 63(8), p.760.

Brame, C.J., 2016. Effective educational videos: Principles and guidelines for maximizing student learning from video content. CBE—Life Sciences Education, 15(4), p.es6.

Fiorella, L., van Gog, T., Hoogerheide, V. and Mayer, R.E., 2017. It’s all a matter of perspective: Viewing first-person video modeling examples promotes learning of an assembly task. Journal of Educational Psychology, 109(5), p.653.

Reeves, B. and Nass, C.I., 1996. The media equation: How people treat computers, television, and new media like real people and places. Cambridge university press.

Fiorella, L., Stull, A.T., Kuhlmann, S. and Mayer, R.E., 2019. Fostering generative learning from video lessons: Benefits of instructor-generated drawings and learner-generated explanations. Journal of Educational Psychology.

MacHardy Z, Pardos ZA. Evaluating the relevance of educational videos using BKT and big data. In: Santos OC, Boticario JG, Romero C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, Desmarais M, editors. Proceedings of the 8th International Conference on Educational Data Mining, Madrid, Spain. 2015

Szpunar, K.K., Khan, N.Y. and Schacter, D.L., 2013. Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proceedings of the National Academy of Sciences, 110(16), pp.6313-6317.

Roediger III, H.L. and Karpicke, J.D., 2006. The power of testing memory: Basic research and implications for educational practice. Perspectives on psychological science, 1(3), pp.181-210.

Vural, O.F., 2013. The Impact of a Question-Embedded Video-based Learning Tool on E-learning. Educational Sciences: Theory and Practice, 13(2), pp.1315-1323.

OEB Berlin… AI, video, learning analytics, data, and schnapps!

Been going for many years… and like the vibe… fruity mixture of tech, academic, government from 73 countries. There's the added attraction of Berlin, a Christmas market right across the street and free drinks at the Marlene Bar!

Gave three talks, all on AI theme… AI and ethics (the overblown hysteria), Learning Analytics (how to and real examples), Video and AI (research and how to). A number of the sponsors were companies that use AI and it was a solid theme this year, rightly, as it has already changed why, what and how we learn.

As is usually the case at conferences I found the smaller presentations and conversations more useful than the keynotes. Great start with Julian Stodd, who was his usual articulate and incisive self. He talked about the weirdness of HR trying to ‘impose’ values and compliance training on people, attacking people’s sense of self and agency. But one phrase that really resonated with me was the ‘humility to listen’. There’s a lot of depth to those three words…

The opening keynotes were a trio of very different fruits. The Max Planc/MIT guy gave a solid talk, showed the Frey and Osborne report (2013), but got the date wrong – it wasn’t 2016 – this matters as it was a paper that predicted the 47% jobs at risk of automation over a decade in the US. We are 6 years in and there is pretty much full employment in the US. Toby Walsh eviscerated this report and talked at this conference two years ago – so we seemed to be going backwards. The Chinese guy was clearly giving a sales pitch but at least he had data and citations to back up his case. Audrey Watters gave her standard  ‘it’s largely agitprop, ideology and propaganda’ replete with soviet posters. Oddly she mentioned being jeered at a summit in Iceland. I was there - it was a very small audience and the first question she was asked (by a woman) was whether she was throwing the baby out with the bathwater, neither was there a 3D cat. She rightly showed some claims that were unsubstantiated but out of context and actually several are evidence-based but Audrey’s was so keen to show that everyone else was ideological that she missed the fact that hers was the most ideological talk of the three. But oh how academe clapped.

The keynotes on the second day (HE session) tackled the future of HE. Professor Shirley Alexander showed the shocking costs, debts and default rates of HE – it is basically out of control on costs. But her solution, literally on the next slide was a huge, spanking new building they’ve just erected and some writing feedback software. I was convinced by nether the erection nor software, which has been around for decades. Bryan Alexander is always up for some fun and opened his talk in a Death Metal voice. Had a great conversation with Bryan about AI afterwards and he did the futurist thing – 3D printing, drones etc…didn’t really see any scalable solutions that tackled the cost issue.

One feature of learning conferences is a general refusal to face up to political issues such as cost and inequality. It is assumed that education is an intrinsic good, no matter what the cost.  No reflection on WHY Brexit, Trump, Gilet Jaunes and other political upheavals are happening, only a firm belief that we keep on doing what we do, no matter the cost. This is myopic. Bryan Caplan tried with his keynote last year, with real evidence, but once again we seemed to have gone backwards. I had a ton of conversations in the bar, in restaurants and over coffees on these issues. A refreshingly straight talk with Mirjam Neelen was one of many.

I liked the practical sessions on learning analytics. It is complex subject but offers a way forward that builds on a platform of data that can be used to describe, analyse, predict and prescribe learning solutions. With smart software (AI), it frees us from the fairly static delivery of media, which online learning has done for over 30 years. Speaking with the wonderfully named Thor and Christian Glahn, we opened up the world of xAPI, LXPs, LRSs and adaptive learning. Here lies some real solutions to the problems posed by the keynotes. 

Sure there are ethical issues and I gave a session explaining that AI is not as good as you think and not as bad as you fear. We went through a menu of ethical issues: Existential, employment, bias, race, gender and transparency. Every man, woman and their dog is setting up and ethics and AI committee, pouring out recommendations and edicts, often based on a thin understanding of ethics and the technology. Many seem designed to give people an excuse to avoid it and do nothing.

Enjoyed Mathew Day’s session on the use of video which is uploaded to the International Space Station, which they use just before they do a task. That’s what I call cosmic, performance support. I was on just before him and showed the evidence in learning theory on why video on its own is rarely enough for deep learning, as well as key evidence on what makes a good learning video, much of it counterintuitive – POV, slower pace, edit points, not so much talking heads, maximum length, adding active learning and so on.

So many interesting chats with people I knew and met for the first time. What I did walk away with was a sense that people are waking up to the possibilities of AI in learning, especially for teaching, Henri Palmer of TUI gave a great case study, showing how one can deliver a large project, super-fast at a fraction of the cost using AI created online content. Great to hear that her team won a Gold Award for that project the night before in London. 

Final dinner in Lutter and Wegner, an old German restaurant was great. Harold did his pitch-perfect Ian Paisley impression at full volume with much clinking of glasses… wine and schnapps. When you’re sitting next to people from Norway, Poland, France, Belgium and Trinidad – you can’t go far wrong.

BIG thanks to Channa, Astrid, Rosa, Rebecca, Harold and the team for inviting me… open people who not only do a great job organising this event but are also open-minded enough to encourage critical thinking…

Learning - lessons from AI...

AI in one sense means doing what human do when they learn. The field is thick with references to ‘learning’, the most common being machine learning, deep learning and reinforcement learning.
Machine learning uses algorithms and statistical models and applies patterns and inferences to perform tasks, so that it learns from experience.
Deep learning is a machine learning technique that uses layered neural networks with data, supervised, semi-supervised or unsupervised, to perform tasks.
Reinforcement learning operates on maximising reward by exploring existing knowledge and exploring new knowledge. 

Digging deeper there is decision-tree learning, lazy and eager learning, supervised and unsupervised learning, incremental, feature, federated and ontology learning. The noun is used so frequently, in so many contexts, for so many techniques that it has become a general term for software that gets better as it proceeds. This is why it AI is a topic of immediate interest to those in the learning game. 

John McCarthy and Marvin Minsky convened a conference in 1956, whose aim was to 

“to proceed on the basis of the conjecture that every aspect of learning (my bold) or any other features of intelligence can in principle be so precisely described that a machine can be made to simulate it”. 

There were successes, like Arthur Samuel’s checkers software, but the promise was never realised and the first AI winter arrived in the 60s. The early 80s saw a resurgence of interest in expert systems and a second winter came. It was only when probability and statistics were literally introduced into the equations that deep learning gave us translation, speech recognition and image recognition. But AI, with exponential growth in processing power, data and devices can now deliver on some of those early promises.

We are now revisiting, and implementing that 1956 objective, with a focus on how AI can be used to accelerate learning. AI makes us rethink learning. It holds the possibility that we do not have to learn some old knowledge and skills, it may help us learn new knowledge and skills, even improve the process and speed of learning. What greater objective that AI helping us to learn; learn to deal with the consequences of this bountiful technology, learn to solve intractable problems, learn to control AI, if necessary. 

Stuart Russell, a major figure in AI, rightly claims in 'Human Compatible', that 

“With AI tutors, the potential of each child, no matter how poor can be realised. The cost per child would negligible and that child would live a far richer and more productive life.” I think he’s right. Even if he’s only part right, this is the right direction of travel.

Doug – a meditation - Things I've learnt from Doug Part II

I’ve never been one for meditation, mindfulness or mentors, never been one for fads… but you know what I've learnt from Doug… to be a little more contented...

I sometimes wake during the night and feel him curled up on the bed, warm asleep. Even better when I open my eyes in the morning and there’s Doug, looking down on me, waiting… you know the day will be all the better for having him around.

Doug, of course, loves a walk. I say a walk, it’s more like being pulled forward by a small locomotive. He springs along like a lamb, checks this, checks that, a judicious sniff here or there, no tree left unexamined, always ahead but often looks back to see that I’m still there. We’re a team, Doug and I. Occasionally he’ll walk behind me, usually mulling over some new scent but we had an ‘incident’ my son and I, when I heard a strange sound behind me, an odd bumping and scraping. Looking round we found that Doug has slipped his harness and was off for a sniff in the bushes. We were literally walking his empty lead and harness for about 30 yards. Lesson learnt.

On walks to Preston Park, we pass the Preston Park Tavern and he veers off, as if on dog autopilot, towards the door of the pub. This is no accident. You see, my sons and I have been taking him to the pub. He loves a flop on the warm, wooden floor, gets the occasional head rub from a punter and generally keeps an eye on what’s going on. But I think what he really loves is that warm, hoppy, beery smell. Good pubs need a dog. I don’t mean bars, I mean pubs. Doug likes pubs, not places with music, too many young people, fishbowls of gin, but pubs and punters. So… you know that old guy in the pub on his own… looking perfectly content… that’s me that is…

Then there’s The Flour Pot, a little café up at the Fiveways. We go there, sometimes, after our morning walk, for a coffee and a read of the paper… Doug will snuggle down on the velvety cushion next to me and just watch, head perfectly still, eyes checking out anyone who passes our spot, like a two little searchlights… they have a little jar of dog treats. He likes that.

Now there comes a point in the day, when I sit down and try to stop the tumble of thoughts in the washing machine that is my mind. As I get older I’ve even taken to the occasional nap in the afternoon. Doug has this covered. He loves a nap. And a nap is all the better for doing it together. It helps when Doug flops down on the sofa next to me, his body pushed up against me in a show of solidarity, even better when he places his head gently across my thigh and closes his eyes. That is a moment of pure joy. Sometimes I catch him dream; a quiver, little chew, a wee moan. It’s good to know that we both have dreams. The difference, I suspect, is that his always come true.

For my previous piece on Doug - see here

Don’t lecture me!

Lectures are an hour long (some astoundingly 2/3 hours) because the Sumerians had a base-60 number system. It is for the convenience of timetabling, not the psychology of attention and retention.

One of the saddest learning stories I’ve ever heard was from the actress Tilda Swinton. She was the only student who turned up to a lecture at Oxford by Raymond Williams where he read out his lecture, from notes, from behind the lectern, and neither of them even acknowledged each other. Studies show quick drop-out from lectures across all subjects. Even at Harvard, where you may be laying out a large five figure sum per year, where GoPro cameras recorded attendance across ten courses, lecture attendance dropped off to only 60% (average) of students attended any given lecture and attendance declined over the semester from 79% to 43%. Imagine running a restaurant, where people pay for the food up front and 40% fail to turn up. You’d surely question the quality of the food.

The bog standard lecture is still the lazy, primary teaching technique in Higher Education. I say lazy because it is based on convention and habit. It is embedded practice as opposed to tased on evidence-based research. Higher Education values research over teaching, yet studiously ignores the research around teaching. Research on attendance is worrying. Research on retention is clear. Research on why researchers don’t make great teachers is also clear. Astin’s longitudinal data on 24,847 students at 309 different institutions found a strongly negative correlation between orientation towards research and teaching. 

It’s an inconvenient truth but researchers are systematic, obsessed by detail and often lack the social skills to be good teachers. Witness the 20 stab-point PowerPoint slides, the often dull delivery, the lack of engagement. Teaching skills demand social skills, communication skills, the ability to hold an audience, keep to the right level, avoid cognitive overload, good pedagogic skills and the ability to deliver constructive feedback. 

If you do lecture then recording is an important stopgap. Suppose that journalists read out pieces once a day in the local square, a novelist reads his book only once and didn’t publish it in book form. That’s unrecorded lectures for you. To deny students second and subsequent bites of the cherry is an act of conceit. Little is learnt on first exposure, most is learnt from subsequent effort. Original lectures are often delivered too fast, especially if it is the learner’s second language. Students can stop, drill down on a point through research, then resume rewind, repeat, watch at any time at any place, take notes second time round, watch after illness,  and move through the course at their own pace.

But the real solution is to largely stop lecturing. In practice, students increasingly learn via Google, YouTube and other online educational tools. At Stanford University this year, I sat with academics and medical students in the same room. Many lecturers were surprised at the range of online tools and apps used intensely by students, fine-tuned to active learning, retrieval, reinforcement, spaced practice, retention and recall. The lecture is increasingly under attack from superior tools, sensitive proven research and how we really learn. We see the rise of smart, AI-driven technology that focuses on the well-researched area of retrieval practice and adaptive learning designed to educate everyone uniquely. Designed to raise attainment and stop drop-out, these tools increase efficacy in learning, yet few academic are even aware of their existence.

Note that I’m not wholly against lectures. Students want to see academics in the flesh. To see a practicising philosopher or physicist, be inspired by that person and subject. This type of inspiring, spot lecture is important. Slabbing lectures out over a term, by researchers who struggle to present, never mind teach, is not just lazy, it is dishonest. Teach me, don’t lecture me.

The Madness of Crowds – why training may be tilting at windmills

The Madness of Crowds by Douglas Murray is a good companion piece to The Coddling of the American Mind: How Good Intentions and Bad Ideas Are Setting Up a Generation for Failure by Haidt. Both put the brakes on what they call the ‘ideology’ of atomising identity, then policing oppression through language and restrictions on freedom of speech. They both believe that much of this (not all) is harmful, producing a culture of complaint, grievance and victimhood that actually damages people and institutions. They also have a go at the training industry.

The reason HR and L&D people may want to read these books is that they lay a charge against organisational learning that needs to be discussed. Could it be that we are now using invalid instruments to diagnose our 'unconscious bias', even when those who designed those instruments tell us they are unsuitable? Could we be defaulting into simply protecting the organisation against its own employees? Are concepts like ‘triggering’ and ‘safe spaces’ limiting open and free discussion and learning? Are voices being silenced in this process? Good questions.

Murray rightly questions the role of training, especially that of ‘unconscious bias’ training, which he sees as a futile attempt to diagnose and ‘rewire our attitudes’. I have already written about, what I regard as the foolishness of ‘unconscious bias’ training as being the wrong target, unreliable, the wrong target, not predictive and doesn't actually change behaviour. Since when did HR and L and D managers have the permission, or even imagine they have the skills, to probe my unconscious? They really know nothing about the scientific validity of the tools they’re using or the dangers of such training. It is overreach on an astounding scale. 

An even bolder question, put forward by both authors, is that we may, inadvertently, be doing damage to people, especially the young, by making them less resilient and less capable of coping with adversity. Like Don Quixote, we go tilting at windmills but quickly turn to tilting at anything that moves, rather than being careful on both the methods we use in such titling and the targets. Large studies suggest that this type of training is often counter-productive with no measurable effect on the hypothetical problem.

Murray points out that the slicing and dicing of identity eventually results in a hierarch of oppression, where the groups turn on each other. Feminists turn on Germaine Greer, a central thinker for decades in feminism, preventing her from speaking. Women find themselves being criticised by people who were men for being transphobic, if they even question the use of certain language. It is as important to know the limits of one’s professional reach and competence. Much of what passes for training in this area is actually incompetent and places us outside of our reasonable sphere of influence.

I’m aware of the fact that even writing this places one on the plain of La Mancha, waiting to be skewered by the lance of a delusional Don Quixote. But these issues need to be discussed and debated and I don’t apologise for that.

EdCrunch – cracking event in Moscow

Fascinating week in Moscow. It is so easy to get bounded in the proverbial nutshell and I’ve broken out by visiting Moscow for a week. I was there three times in the late seventies and early eighties, during Soviet times, and travelled all over the then Soviet Union, so memories came flooding back and the city reminded me of a cake, the old cake still there but with a new layer of icing – more colour, restaurants, shops and wealth…

EdCruch is in its 6^thyear and is very different from other learning technology conferences I’ve attended. It had an interesting intensity and focus. Let me explain. 

The opening session was a piece of modern dance representing the union of mind and machine. Rather beautiful and whet the palette for the Bolshoi two nights later. I had been there 37 years ago and remember the inside of this stunning theatre along with the jaw-dropping performance. My son was with me this time and he had the same experience. His first ballet performance and he was thrilled to get a like from one of the Bolshoi ballerinas, when he posted a pic online. We met Tim O’Reilly outside after the performance… 

Back to the event. The first two speakers were world class – both talked about AI in learning and Tim O’Reilly (a God-like figure in IT) gave a masterclass in how learning needs both structured, linear experiences as well as opportunities to learn by exploring and doing, showing Jupyter… if you don’t know what this is check it out. Our encounter outside of the Bolshoi – thrilling for my son who has spent years reading his books, was one of then highlights of the trip. 

When I said the event was intense, I meant there were no fluffy futurists, no anti-tech sceptics, no-one babbling on about ‘creativity’… just experienced folk talking about what a lifetime of experience has taught them about learning and technology. Refreshing, as I’ve had a bellyful of keynotes who have done little in the field, often with a book to sell, giving it large through anecdote.

Let’s talk about chatbots…

We did an afternoon on chatbots, running a workshop, discussing the pedagogic advantages, looking at literally dozens of real examples of their use in learning, designing several, then talking a deep dive into the production process and technology. Pretty intense but found the audiences for all the sessions I gave, both intense and interested.

Also took part as a judge in an EdTech competition where Russian companies, some using AI, were tackling problems of delivery… found it a useful window into the Russian tech scene. The investment environment may be weak but there’s no shortage of good ideas and young entrepreneurs willing to give things a go. There’s an energy here…

Was also the UK representative on a panel comparing UK, US, Russian and and Chinese EdTech markets. I started with the old joke that ‘size isn’t everything’.. pleased to say the joke travels! Then showed that the UK has a good innovative ecosystem of investment, EdTech startups, mature companies, research, large conferences, supportive institutions, and government support. Showed Don Taylor’s research on EdTech futures.

But my main presentation was on AI for learning, a practical talk on how AI can be used to help teaching and learning. AI was a strong theme in this conference and I found that forward thinking. Russia gave us Markov, Sergei Brin (Google founder) had parents who both graduated from Moscow State University. They have depth in maths, and AI. and strong links with China.

We also managed to interleave all of this with trips into the city – Red Square on the first night where we ate at the old Soviet-style restaurant in GUM. Travelling on the Metro is a joy in itself, with its magnificent architecture. The Space Park is huge and again, the Russians are rightly proud of their achievements in this field. Pushkin Museum has Schliemann’s treasure from Troy and some fine paintings.

Also enjoyed some fine dining… a Georgian restaurant, Ruski the highest restaurant in the world, Korean and a restaurant with an entire farm on the second floor – cow, goat, pig and chickens… no idea why.

Moscow really is one of the great cities of the world, easy to get around and this conference took me out of my proverbial comfort zone. Came back having learnt a lot from other speakers, audiences and people we met at all the social events. We also enjoyed spending time with our minder – Alexandra, only 17 years old, but mature beyond her years. She was fun, informative and if she’s representative of her country’s young people, its future is in good hands. Also thanks to Diana Obukhova, who so reminded me of those other hard working women at Online Educa, Rebecca Stromeyer, Channa van der Brug and Astrid Jaeger – incredibly well organised and look after their speakers well. You guys should meet!

Greatest learning experience of my life… it will surprise you… Things I've learnt from Doug Part I

I had no idea. In fact I had the wrong idea. I never ‘got’ it. Why would anyone want a dog? Then I got one… and ‘got’ it. It’s been one of the deepest and significant learning experiences of my entire life.

First up is the emotion. Now I’m a bit of an old Calvinist, Scottish to boot, and as PJ Wodehouse said “It is never difficult to distinguish between with a Scotsman with a grievance and a ray of sunshine.” But from first grasp, holding that tiny thing, as it looked into my eyes, I was, as they say… smitten. It only got worse. I became besotted. 

People, who have known me forever, will tell you that it made me calmer. It also, and I say this with no evidence whatsoever, made me more empathetic. You have to work with a dog. It doesn’t speak, so you need to get into its mind a little – which is clearly a mind that is free from most human concerns. Doug knows nothing of good or evil, heaven or hell. Socrates said that "the unexamined life is not worth living”. I look at Doug, and think, “the over-examined life ain’t all it’s cracked-up to be either”. I honestly think a dog can make you a better person.

Of course, you start out with high hopes – that your dog is so smart that it may just squeeze out a GCSE or two… then you realise that it is, well just let’s say cognitively capped. This had a sort of philosophical effect on me as I became more aware that we humans are most likely in the same predicament. You learn humility.

Yes, there’s the exercise, getting out more, nice walks and meeting people. I met a big, hairy-arsed builder who told me that when he comes back after a hard day’s graft, his teenage kids ignore him, his wife nods and seems tired of him, but when that first crack of the door opens, his dog greets him as if he had just won the dog lottery – every damn day. There’s the lovely Ethiopian guy, who recued his Huskie from China. Apparently there’s a growing middle-class who buy expensive breeds but can’t handle them in their little apartments, and dump them on the outskirts of the cities. There’s the teenagers who are happy to talk about school and sometimes… their hopes. 

Then there’s the dog talk – tons of it and I can tell you I love it. I’m becoming quite the dog-man. My grandfather had a Jack Russell and was called Jack Russell – both, I hear, were characters. He was a stand-up comedian back in the day and used the dog in his act. I’ve learnt that Corgis were bred to herd cattle – they snapped at their heels apparently. 
Brings your family together. We gather around Doug, take him for walks together. Talk about him. Buy him stuff - loads of stuff. Actually it's not that he brings your family together he is your family.

Most of all there’s Doug. He’s there all the time, prancing around the house, sleeping, bringing his soft toy (replica of a rat) and wanting to play. But the greatest joy is when he comes and sits next to you or better with his head on you. It would melt an obsidian heart.

So there it is… I flipped. I learnt that you can change your mind on something – big time. What it takes is not reading about dogs, researching dogs, going on a course about dogs – it’s about getting a dog. They say you can’t teach an old dog new tricks but you bloody well can. Thanks Doug. You don’t know it but you sort of changed my life mate.

Dennett - why we need polymaths in the AI ethics debate

Daniel Dennett, is a renowned philosopher, but also a polymath, with a deep understanding of psychology, science and maths. His book Bacteria to Bach and Back is essential reading for those who want to see a synthesis of human evolution and AI, and its consequences for the ethics of AI. Dennett has the backgrounded depth in philosophy to understand the exaggerations and limits of AI and its consequences for ethics. He also grounds his views in biology an humans as the benchmark and progenitor of AI.

Competence without comprehension

He takes a holistic view of AI. Just as the Darwinian evolution of life over nearly 4 billion years has been ‘competence without comprehension’ the result of blind design, what Dawkins called the ‘blind watchmaker’, so cultural evolution and AI is often competence without comprehension. We have all sorts of memes in our brains but it is not clear that we know why they are there. Similarly with AI, Watson may have won Jeopardy! But it didn’t know it had won. This basic concept of competence without comprehension is something that has to be understood and assumes as there is far too much exaggeration and anthropomorphism around in the subject, especially in the ethics debate. His view, which I agree with, is that AI is not as good as you think it is and not as bad as you fear.

Bayesian brain

His vision, which has gained some traction in cognitive science, is that the brain uses Bayesian hierarchical coding (Hinton 2007; Clark 2013; Hohwy 2013), a prediction machine, constantly modelling forward. Interestingly, he sees this as the cause of dreams and hallucinations – random and arbitrary attempts at Bayesian prediction. This is an interesting species of the computational model of the brain and explains why the brain has been a productive, intuitive source of inspiration for AI, especially neural networks. 

Cultural evolution

He then examines cultural evolution as the invasion or infection of the brain by memes, primarily words, and that these memes operate in a sort of Bayesian marketplace, without a single soul or executive function. These informational memes, like Darwinian evolution, also show competence without comprehension and fitness in the sense of being advantageous to themselves. That brings us back to the ethical considerations around AI. He surfaces the contribution of Baldwin as an evolutionary theorist who saw 'learning' as an evolutionary accelerator.

AI

As he rightly says, we make children without actually understanding the entirety of the process, so it is with generative technology. Almost all AI is parasitic on human achievements, corpuses of text, images, music, maths and so on. He is rightly sceptical about Strong AI, master algorithms and super-intelligent agents.

We already trust systems that are way more competent than we humans and so we should. His call is for us to keep an eye on the boundaries between mind and machine, as we have a tendency to over-estimate the 'comprehension' of the machines, way beyond their actual competence, and investing in or anthropomorphising comprehension. We see this with even the most casual encounters with chatbots and devices such as Alexa or Google Home. We all too readily read intentionality, comprehension, even consciousness into technology when it is completely absent.

AI ethics

By adopting regulatory rules around false claims of anthropomorphism, especially in advertising and marketing, we can steer ourselves through the ethical concerns around AI. Over reach and concealing anthropomorphism and false claims should be illegal, just as exaggeration and side effects are regulated in the pharmaceutical industry. Tests, such as variations of Turing’s test, can be used to test their upper limit and actual competence. He is no fan of the demand for full transparency, which, he thinks, and I agree, are utopian. Many use Google Scholar, Google and other tools without knowing how they work. Competence without comprehension is not unusual.

Learning

His hope is that machines will open up “notorious pedagogical bottlenecks” even “imagination prostheses” working with and for us to solve big problems. We must recognise that the future is only partly, yet largely, in our control. Let our artificial intelligences depend on us even ”as we become more warily dependent on them”.

Neurotech – mind-boggling final frontier between mind & machine – and possible impact on learning

A slew of companies are working on neural interfaces in what could (emphasis here) change the future of our species through a deeper understanding of how we learn and the ability to accelerate learning.

The brakes on learning are well known, what Dennett (2017) calls the 'notorious pedagogic bottlenecks'; working memory, forgetting, inattention, distraction, interference, crude interfaces, low bandwidth interface of our meat fingers, inability to upload, download and network. Learning is often like trying to squeeze and elephant through a porthole. Our minds can only deal with a tiny fraction of the available sensory and other information that is available. So consciousness, and therefore learning, is severely limited by the evolved apparatus of our current, organic minds. Neural interfaces may free us from some of these holdups and blockages.

There is no shortage of brainpower, companies and investment behind the push. The stakes are high and the people working in this field are well-funded, multidisciplinary (neurologists, engineers, computer scientists, AI experts, mathematicians). They want to radically improve the interface, some non-invasive, some invasive, to improve cognition and performance.
This is a sort of cognitive moonshot, where frictionless movement between mind and machine could be possible or at least sophisticated hacks that make our minds more potent and efficient. Isn’t it curious, even wonderful, that the organ itself is now pushing for its enhancement?

On hearing about this stuff, many are immediately dismissive, without realising that much progress has already been made in animal studies and humans with cognitive enhancement and implants. In addition to animal studies, which may worry many, neurons grown from human stem cells are being used to develop the technology and new implant technology is awaiting approval.

Non-invasive technology

Mark Zuckerberg’s group, is perhaps the best known in this area, He has hired a high-powered team to create a non-invasive device targeted at ‘speech to text’. They use imaging to identify words as they are formed in the brain. We have this ability to rehearse and hear silently. Read that sentence again, internally, in a Scottish accent or in a squeaky voice. Your brain has this ability to rehearse silently and internally. Tapping into this phonological loop may allow us to by-pass our fingers, or actual speech, and type at speeds up to 100 words a minute. This is an admirable goal, of frictionless communication, but others are much more ambitious.

Serial entrepreneur Bryan Johnson has pumped $100 million into Kernel, which hopes to read and write to the brain. Kernel has its eye on serious medical conditions, such as dementia, Alzheimer’s and epilepsy. Building on decades of work on rats by Theodore Berger, the neuro-prosthesis technology has already been tried on real patients. They are developing algorithms which help brains learn faster or develop memories quicker – in other words to learn. 

His aim is to have a commercial product at an attainable price and the stakes are high, not just in the treatment of disease but in education and training. Education and training is an expensive and long-winded business. Young people spend nearly two decades in classrooms and lecture theatres to be even remotely ready for work, and that’s only the start, as once in work the learning continues. Medical treatment may be the stepping stone to enhanced learning.

Invasive technology

DARPA and others have been involved in some very strange research involving implants in insects, rats and sharks.  Humans have been able to control these living beings through electrical impulses controlled by humans. But in human Neurostimulators have long been used to relieve symptoms in neurological disorders, such as Parkinson’s ands epilepsy. 

A pioneer of micro-electrodes implanted in the brain, BrainGate can ‘decode’ the intentional signals that make cursors and limbs move. They are also building wireless devices that allow physicians to monitor brain activity to help diagnose and treat neurological diseases. Another company that is working towards building an implanted chip, that is a modem between mind and machine, is Paradromics. Their focus is also on healthcare.

Looking further ahead, as Musk tends to do, is Neurolink. As well as escaping from our planet and boring into it, Musk wants to bore into our brains. His is a technology play, with arrays of flexible threads, a neural lace, that can be inserted into the brain without tissue damage. They have also developed a robot for the automated insertion of these tiny threads. This builds on the success of neuroprosthetic control in cursor, limb and speech control. But he sees the problem of non-invasive techniques as one of fidelity. With non-invasive techniques, the skull distorts the data so that one is recording the noise of averages. This BMI (Brain Machine Interface) is designed to provide high-bandwidth communications, as the problem Musk is trying to overcome is low bandwidth human interfaces. It can be placed on different parts of the brain and should provide cleaner and more relevant data.

AI in learning

Much of the attention in this filed goes into the hardware – helmets, neural laces and implants but the real challenge is actually in software. These organisations are developing algorithms to interpret the data they receive from brains then constructing data that can be fed back into the brain. Recent advances in AI, and machine learning, especially neural networks, literally extend the brain by using silicon-based neuron-like structures. Daniel Dennett’s rather clumsily titled book ‘From Bacteria to Bach and Back’ makes the case for brains to be evolving Bayesian engines, both physically and culturally. It is this technology that seems to be interfacing and merging mind and machine.

Problems

We have made great progress in technology that relieves symptoms, makes the deaf hear with Cochlear implants, the blind see and the physically disabled walk and type. But the problems behind accelerated learning are immense. The brain has 86 billion neurons and the complex interactions between different types of neurons is still largely unknown. This complex Bayesian inference engine works as a huge parallel processor, so complex that we may be doing no more that reading the hum one hears from a large computer. The formation of memories in the brain may work in ways that are not possible to read from or write to. The technology may be literally like using a fork to do brain surgery.

Then there are the moral issues, such as privacy, identity and personal safety. It is vital that the patients do this voluntarily. That last bastion of privacy, your own thoughts, may now be open to examination, analysis and manipulation. As Daniel Dennett says, the brain is open to infection by memes, that is his definition of cultural evolution. But this means being open to both good and bad memes. We must make sure that these systems cannot be hacked and that they are under strict control.

Conclusion

The two major prizes, are in two sectors – healthcare and education. It seems likely that non-invasive techniques will produce results but limited results, as the channels and bandwidth are diffuse and noisy. Invasive techniques promise cleaner, high-bandwidth data, for two-way communication and the interpretation of data, along with memory formation. In terms of learning, this holds the greater promise, but is much harder to achieve. 

As science fiction becomes reality, we may reflect on what the future holds here. Is it cures for disabilities, so that they allow one to operate as an able-minded and bodied person would? Or is it a world where networked mind-control becomes possible? Will memories and skills be implantable? At the very least the process of learning may be enhanced. We have seen examples of this already. It’s all, literally, mind boggling.

LXP, data analysis and actionable insights

Opinion mining
AI is good at precision tasks, not general teaching or training. With that mantra in mind, 'opinion mining' is one such precision technique. We can now mine social learning data for actionable insights.
With the rise of LXP systems the evaluation of deliverables and data jumps from the impoverished world of happy sheets and SCORM completion to a completely different level. We literally go from 10 to 500 miles an hour. 

We can also evaluate relatively unstructured data, getting rid of surveys and time consuming questionnaires. AI NLP (Natural Language Processing) techniques are far more suitable, as you will harvest data and opinions that are much richer than traditional survey data, completion rates and scores. You will be delivering dynamic learning that needs dynamic tracking. and evaluation.

How do we do this?

At WildFire we have been working on this for some time, looking for the most suitable techniques to use in learning. The level of granularity is important here. Let’s say you want insights on the content, teaching, time taken, relevance, or more generally anecdotal and miscellaneous opinions, you may need to be very granular, down to the word level. There is a pay-off here as the more granular you get the less context you have.

So there’s a whole number of separate steps, where one has to use different forms of software to get rid of extraneous words (such as pronouns), tag with aspects, set sentiment scores to opinion words, watch for the presence of negation words, and so on. Some of this software will be pre-trained using large corpuses of text. It is useful to know whether, for example, Wikipedia was used, as it is more relevant to knowledge based learning courses. Or one can build your own corpus of data to make this more targeted over time.

Actionable insights

You run your social data through this process, which can take a while (optimization is usually necessary) and out pops positive and negative sentiments on the aspects you identified.

Note that this can be applied to all of your social data, just one module a subset of your learners or individual learners. You have to be clear about what you want to get out of this in terms of decision making. Actionable insights can be selected in terms of their ranked importance; positive, negative, miscellaneous and so on. 

We are on the verge of a revolution here but it needs careful handling and expectation setting. This mat, at last be the door through which learning professionals can walk, with data, techniques and insights relevant to learners, teachers, senior managers and employers. Actionable insights that allow decision making to happen, rather than the reactive delivery of yet another course, without really knowing what is actually going on.

Conclusion

Technology is always ahead of sociology, which is always ahead of pedagogy. This allows us to more align all three, as the technology feeds actionable insights to real people, quickly. This is not turning humans and learners into data points, it is identifying all that is human about their learning journeys. If you’re interested in using this service, contact us here… WildFire

Can AI be creative?

A friend, Mark Harrison, filmed me for his film on AI and creativity. It made me think - really think. Can AI be creative? Easy to ask and difficult to answer, because it involves complex philosophical, aesthetic and technical issues. Whenever the subject is brought up in conversation, I can feel the visceral reaction among many - that creativity is that last bastion of humanity, what makes us human-all-too-human, and not the domain of machines. Yet...

Problems with definition

The problem with ‘creativity’ is that it is so difficult to pin down. The word ‘creative’ is a bit like Wittgenstein’s word ‘game’. A game can be a sport, board game, card game, even just bouncing a ball against a wall. It defies exact definition, as words are not defined by dictionaries but use. So it is with ‘creativity’, as it can be the product of a creative work of art, creative play in sport, creative decision making, even creative accounting.

This is also a problem with AI, the phrase created by John McCarthy in 1956. There is no exact mathematical or computing definition for AI and it is many different things. Like ‘Ethics and AI’, “Aesthetics and AI’ suffers from difficulties in definition, anthropomorphism and often a failure to discuss the philosophical complexities of the issue. That, of course, does not prevent us from trying.

Is AI intrinsically creative?

Language, poetry, music and image generators have used many AI techniques, often in combination, as well as using outside data sources; TAILSPIN, MINSTREL, BRUTUS for storytelling, JAPE, STANDUP create jokes, ASPERA for poetry, AARON, NEvAr, The Painting Fool for visual works of art. In addition, the use of AI in areas such as research, maths, business and other domains could also be seen as intrinsically creative. When problems in these fields are solved in an innovative manner, are these creative acts? There are literally dozens of systems that claim to have produced creative output.

Some AI systems, often combinations of AI techniques, claim creative output, as they can produce, in a controlled or unpredictable manner, new, innovative and useful outputs. Contemporary AI techniques such as neural networks, machine learning and semantic networks, some claim, generate creative output in themselves. Stephen Thaler claims that neural networks and deep learning have already exhibited true creativity, as they are intrinsically creative.

Recently, GPT-2, a model from the not-for-profit OpenAI, showed how potent generative AI can be, producing articles, essays and text from just general queries and requests (see more here). Google’s Deep Dream is another open-source resource that uses neural networks to produce strange psychedelic imagery, used in print and moving images, such as music videos. DeepArt produces new images in the style of famous artists.

Others, however, argue that if software has to be programmed by humans it is by definition not creative, that AI can never be creative in that it can do nothing other than transform inputs into outputs. The rebuttal being that this argument could also be applied to humans. So let’s look at some specific creative domains.

Creativity, AI and language

There is linguistic creativity using AI around many forms of language, everything from puns, sarcasm, and irony to similes, metaphors, analogies, witticisms and jokes. Sometimes linguistic creativity involves the intensification of existing rules, sometimes the breaking of these rules. Narrative Science and many other companies have been using AI to generate text for sports, financial and other articles. These have been widely syndicated, published, read and evaluated.

Creativity, AI and games

DeepMind, when it played Space Invaders, did something quite astonishing. It shot up to either side of the screen, around the invaders, so that the space invaders could be attacked from above, something humans hadn’t done. In Chess and GO, we see this a lot. Seemingly odd, unorthodox and surprising moves, that turn out to turning points that win the game, are masterfully creative. Also in computer games such as DOTA-2 AI agents are beating humans in complex team environments.

Creativity, AI and music

The one area of Computational Creativity that has received most attention is music. Could AI composed music win a Grammy? It hasn’t some argue that one day it could. Classical music, many would say, is a crowning human achievement. It is regarded as high art and its composition creative and complex. Jazz is wonderfully improvisational. Whatever the genre, music has the ability to be transformative and plays a significant role in most of our lives. But can AI compose transformative music?

At a concert in Santa Cruz the audience clapped loudly and politely praised the pieces played. It was a success. No one knew that it had all been composed by AI. Its creator, or at least the author of the composer software, was David Cope, Professor of the University of California, an expert in AI composed music. He developed software called EMI (Experiments in Musical Intelligence) and has been creating AI composed music for decades.

Prof Steve Larson, of the University of Oregon, heard about this concert and was sceptical. He challenged Cope to a showdown, a concert where three pianists would play three pieces, composed by:

   1. Bach

   2. EMI (AI)

   3. Larson himself

Bach was a natural choice as his output is enormous and style distinctive. Larson was certain of the outcome, and in front of a large audience of lecturers, students and music fans, in the University of Oregon concert Hall, they played the three pieces. The result was fascinating. The audience believed that:

   1. Bach’s was composed by Larson

   2. EMI’s piece was by Bach

   3. Larson’s piece composed by EMI.

Interesting result. (You can buy Cope’s album Classical Music Composed by Computer.) 

Iamus, named after the Greek god who could understand birdsong, created at the University of Malaga, composed a piece called Transits - Into the Abyss, which was performed by the  London Symphony Orchestra in 2012 and also released as an album. Unline Cope's software, Iamus creates original, modern pieces that are not based on any previous style or composer. Their Melamics website has an enormous catalogue of music and has an API to allow you to integrate it into your software. They even offer adaptive music which reacts to your driving habits or lulls you into sleep in bed, by reacting to your body movements.

Further examples of the Turing Test for music have been applied to work by Kulitta at Yale. But is a Turing test really necessary? One could argue that all we’re doing is fooling people into thinking this has been composed by a machine that cheats. Cope has been creating music from computers from 1975, when he used punch cards on a mainframe. He really does believe that computers are creative. Others are not so sure and argue that his AI simply mimics the great work of the past and doesn’t produce new work. Then again, most human composers also borrow and steal from the past. The debate continues, as it should. What we need to do is look beneath the surface to see how AI works when it ‘composes’.

The mathematical nature of harmony and music has been known since the Pre-Socratics and music also has strong connections with mathematics in terms of tempo, form, scales, pitch, transformations, inversions and so on. Its structural qualities makes it a good candidate for AI production.

Remember - AI is not one thing, it is very many things. Most have been used, in some form, to create music. Beyond mimicry, algorithms can be used to make compositional decisions. One of the more interesting phenomena is the idea of improvisation through algorithms that can, in a sense, randomise and play with algorithmic structures, such as Markov chains and Monte Carlo tree decisions, to create, not deterministic outcomes, but compositions that are uniquely generated. Evolutionary algorithms have been used to generate variations that are then honed towards a musical goal. Algorithms can also be combined to produce music. This use of multiple algorithms is not unusual in AI and often plays to the multiple modality of musical structure, playing to different strengths to produce aesthetically beautiful music. In a more recent development, machine learning, presents data to the algorithmic set, which then learns from that data and goes on to refine and produce composed music, bringing an extra layer of compositional sophistication.

We, and all composers, are organisms created from a bundle of organic algorithms over millions of years. These algorithms are not linked to the materials from which you create the composer. Whether the composer is man or machine, music is music. There is no fatal objection to the idea that organic composers can do things that non-organic algorithms will never be able to replicate, even surpass. 

Creativity AI and aesthetics

The AI v human composition of music also opens up several interesting debates within aesthetics. What is art? Does ‘art’ reside in the work of art itself or in the act of appreciation or interrogation by the spectator? Does art need intention by a human artist or can other forms of ‘intelligence’ create art? Does AI challenge the institutional theory of art, as new forms of intelligent creation and judgement are in play? Does beauty itself contain algorithmic acts within our brains that are determined by our evolutionary past? AI opens up new vistas in the philosophy of art that challenge (possibly refute, possibly support) existing theories of aesthetics. This may indeed be a turning point in art. If art can be anything, can it be the product of AI? 

This area is rich in innovation and pushes and challenges us to think about what art is and could be. Is the defence of the ‘artist’ or ‘composer’ just a human conceit, built on the libertarian idea of human freedom and sanctity of the individual, that makes us repel from the idea of AI generated music and art? The advent of computers, used by musicians to compose and in live performance, has produced amazing music, some created live, even through ‘live coding’. As in other areas, where AI is delivering real solutions, music is being created that is music and is liked. Early days but it may be that musical composition, with it’s strong grounding in mathematical structures, is one of those things that AI will eventually do as well, if not better, than we mere mortals.

Let’s focus on the question, ‘What is art?’ Is it defined in terms of the:

  1. aesthetic effect of the object itself 
  2. intention of the artist
  3. institutional affirmation

If it is 1. AI created art could be eminently possible, as many AI created works have already been judged to be art.
If 2. and you need intention, then we will have to abandon hope for AI or wait until AI has intention. The Intentional fallacy was written in 1946 and attempted to strip away the intention of the artist. This argument gained momentum in the 1960s with Barthes "death of the author, Foucault's abandonment of the "author function" and Derrida's "nothing but the text".
If it is 3. then the arts community may at some time agree that something created by AI is art. Put it another way, if you define ‘creative’ as something that is, in its essence ‘human’, then by definition you have to be ‘human’ to be creative. Then AI can, logically, never be creative. If, however, we accept that AI is not about copying what it is to be human, we leave room for it being called creative. We see this in technology all the time. We didn’t copy the flapping of bird wings, we invented the airplane,. We didn’t go faster by copying the legs of a cheetah but by inventing the wheel.

So how do you decide what is art when generating AI output? It is easy to mimic or appropriate existing artworks to create what many regard as pastiches. One fundamental problem here is anthropomorphism. When we say ‘Can AI be creative?’ we may already have anthropomorphized the issue. Our benchmark may already be human capabilities and output, so that creative acts may be limited to human endeavour. 

We may, literally, in our language, be unable to envisage creative acts and works beyond our human-all-too-human abilities. What would such a thing be? How would we make that judgement? Some have proposed formal criteria, such as novelty and quality to judge creative outputs. The danger of many systems is that AI has produced lots of works but a human has ‘curated’ so that only the best are selected for scrutiny. Another solution is to posit an equivalent to Turing’s Test. The problem is that this assumes that creativity is a judgment on the work itself, without requiring intention.

Beyond the human

We seem to want creative technology to be more human but this may be a red herring. It may well be that creativity is that layer that lies just beyond the edge of our normal capabilities – that’s certainly how creative acts are sometimes described, as pushing boundaries. So why not consider acts that come from another source, such as DeepMind, OpenAI or Watson? If AI transcends what it is to be human then we may have to accept that acts of creativity may do the same. Our expectations may have to change. In art we saw this with Duchamp’s urinal (Fountain). Could it be that a Duchamp-like event could take us into the next phase of art history, where it is precisely because it was NOT created by a human that it is considered art – art as a transgressive and transcendental act?

Conclusion

This is a lively field of human inquiry, that has a long way to go. It is easy to jump to conclusions and underestimate the complexity of the issues, which need careful unpacking. We need to be clear in the language we use, the claims we make and the evaluative judgments we make, as it is too easy to come to premature conclusions. Moffat and Kelly (2006) produced evidence that people are biased against machines when making judgments about creativity. Others are too quick to claim that outputs constitute art.

There are several possible futures here, where: 

   AI plays no role in creative output

   AI enhances human creative output

   AI produces creative output that is valued, appreciated and bought and accepted as creative by humans 

   AI creations transcends that of humans and that art becomes the domain of AI

Time and technology will tell…