Saturday, November 09, 2019

Doug – a meditation

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

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Sunday, November 03, 2019

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.

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Monday, October 07, 2019

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 HaidtBoth 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.

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Sunday, October 06, 2019

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 6thyear 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!

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Sunday, September 22, 2019

Greatest learning experience of my life… it will surprise you…

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.
O 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. 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.

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Monday, August 26, 2019

Why we need polymaths like Dennett in the AI ethics debate

Daniel Dennett, is a philosopher, but also a polymath, with a deep understanding of 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.
Holistic view of AI
He takes a holistic vies 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. And in AI, Watson may have won Jeopardy! But it didn’t know it had won. 
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. 
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.
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, 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 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 limits.
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.
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”.

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Tuesday, August 20, 2019

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.
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.
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.

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Wednesday, July 31, 2019

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.
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

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Sunday, June 09, 2019

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 punssarcasm, and irony to similes, metaphors, analogieswitticisms 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?

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…

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Friday, June 07, 2019

Podcasts - 20 reasons why we should be using more podcasts in learning

Even the Obamas are in on the podcast act, signing a deal with Spotify. Hardly surprising, as over 50% of Americans have now listened to a podcast, very much a medium for people of working age, with the listening figures dropping off in the 55+ age group. Reuters Journalism, Media and Technology trends highlights audio as a significant growth area and Spotify are investing $500 million in the medium. 
I’m a podcast fan myself. Whether it is Talking Politics, where some of the best minds in political science discuss a contemporary political topic, or In Our Time, where history, philosophy, art and science is brought alive with a trio of academics. If I want an in-depth learning experience, this is often my medium of choice. For real depth I prefer text – books, papers, articles and blogs. For practical learning, video. For really practical learning – doing stuff and experience. But podcasts lie in that niche between long-form text and short-form video and  have their own special allure, as well as being so very convenient. So why use podcasts in learning? What type of content is suitable? How does one make one?
Podcasts tend to be long form and content rich. They are, in a sense , the opposite of microlearning or the tendency to reduce things into small pieces. They also have different cognitive affordances from video, text or graphics. Video is great for ‘showing’ things such as drama, objects, places, processes and procedures with more of an emphasis on attitudinal or practical learning. Text may be better for semantic and symbolic knowledge, where the art of the wordsmith comes into play and subjects like maths. Graphics, of course can visualise data, show schemas; diagrams can illustrate what you want to teach and photographs give a sense of realism. Podcasts, however, tend to deal with more conceptual knowledge, where ideas and discussion matter. They seem better at allowing experts or leaders to explain more complex thoughts and issues, where genuine discussion or stories can reveal the learning, with deeper levels of reflection and different perspectives. Relying on spoken language alone often gives them a depth that other media don't carry.
Many like to listen to podcasts when walking, running, in the gym, car or commuting. The sheer convenience of time shifting the experience, of using this dead time, in what Marc Augur called ‘non-places’, even if only to hold off boredom, is what makes podcasts a form of productive, mobile learning. I personally, like to listen while sitting down, with headphones, as I’m a note taker but many listen when they are doing other things. This convenience factor is a big plus.
Oral communication is more natural and feels more authentic than written text, as it has many of the human flavours of the speaker, such as tone, intonation, accent, emotion and emphasis. Technically, we are grammatical geniuses aged three, able to listen and understand complex language, without formal learning. This makes such content easy to access, especially for those with lower levels of reading literacy. It is, in this sense, a very natural form of learning. This more frictionless form of communication allows us to take deeper dives, through attentive listening (as opposed to hearing), making them potent learning experiences.
Listening to a podcast, especially with headphones, can be an intense, intimate and private affair. Many podcast fans report that sense of eavesdropping into an intimate conversation, you feel as though you get to know the people over time. There is a sense of focus and attention that the learner feels, as if one was part of the conversation, literally sitting there next to the participant(s). So in this process of eavesdropping, how many participants should one have in a podcast? 
It is hard to hold full attention for long when it is a single podcaster. Imagine sitting in a plane, asking someone a question and they come back with a 40 minute reply. Although, as a fan of the comedian Bill Burr’s podcasts, it can be done. The difference is that Bill has decades of experience as a stand-up comic and can hold an audience’s attention.
A more popular format is the interview. Joe Rogan is a good example, with massive audiences - there are many others. He interviews an individual, drawing out their stories and anecdotes. The questions in an interview format act as breaks, chunking the content down into meaningful pieces, making them easier to learn. It sometimes feels as if it is you, as the learner is asking the questions, and in that sense, feels like a personal dialogue.
Some of my favourite podcasts, the BBCs In Our Time and Talking Politics, often have three or four participants. Interestingly, they both have an anchor, Melvyn Bragg and David Runciman, who hold the discussion together and give it shape and direction. The advantage of this format is that it provides different angles on the same subject, sometimes different areas of expertise, even disagreements.
Some of the most popular podcasts have been series, where they’ve built an audience over time. These segment the content and often have cliff hangers, to make you want to listen to the next one. In learning, of course, this has the advantage of splitting material over time, introducing spaced practice, by taking just a minute or so to recap on the previous episode and summarise that the end, to top and tail, improves retention.
Media rich is not mind rich
Mayer and others have, over decades, shown us, through pinpoint research with good controls, that rich media, used unwisely, can inhibit learning, as in learning 'less if often more'. This has much to do with the limitations of working memory but also with using up cognitive channels. Yet online learning seems to ignore that simple, popular, single-channel medium – the podcast. Podcasts have the advantage of low cognitive bandwidth and low costs, along with several other advantages in learning.
Audio has the advantage of taking up only one channel, the auditory channel, leaving the mind free to generate, through the imagination, your own interpretation, allowing the brain to integrate new knowledge with your prior existing knowledge. As working memory has a limit of 4 or so registers, which we can hold for around 20 seconds, keeping some free from imagery can, for some types of knowledge, be a powerful advantage, especially for conceptual content, as it gives your working memory some time to interpret, even manipulate ideas.
Take notes
Podcasts have one great advantage over video or text/graphics. For active learners, the simple fact that you don’t have to look at a screen allows you to take notes. Research shows that note taking can increase retention from 20-30%. In learning podcasts it is important that you recommend note taking, as you are hands and eye free, allowing more sophisticated notes in your own words.
Speed control
Many listen to podcasts at 1.5 times normal speed as they can still understand what is being said. We read faster than we listen and many find that they can still get the full meaning at speeds beyond that of spoken delivery. This variability of speed allows different learners to listen at different rates, giving learning, almost personalised advantages,
Content control
Another form of control is stop, back, forward and control over a visible timeline. Most find themselves doing a lot of this when using podcasts to learn, when you don’t understand something, want to reflect more, skip extraneous material, take more detailed notes and so on. This, again, allows the learner to process content at a much deeper level for retention.
Audio quality
Nass and Reeves, in research in their book, The Media Equation, showed that although one can get away with low fidelity images in video, this doesn’t work for audio. Poor quality audio has a significantly detrimental effect on learning, lowering retention. We have evolved to have visual systems that can adapt to twilight and distance. Our auditory systems are less forgiving, and expect high fidelity audio, as if delivered by a person speaking in front of you. Distance, volume, tinny sound, mechanical delivery, all diminish attention and learning. Experienced podcast producers will recommend either a studio or as quiet as possible an environment, with a good microphone to get best results. Some avoid table-top mikes and prefer lapel or head mounted.
Reading content from a script can be a killer as delivery really does matter. Listeners want energy, passion and expert or academic gravitas. Humour often helps to punctuate, dwell, then move on. It is that sense of listening to an ‘expert’, also shown by Nass and Reeves to increase retention, that is so important in learning. Above all, podcasts seem to give authenticity to the ‘voice’ of the speaker. It must be and sound natural. Over-produced podcasts can often be counter-productive.
A good podcast also needs god preparation. Make sure your technical set-up is clear. Then prep the participants. A structured script is useful, even if it just a series of agreed questions, along with advice on short answers to questions. Test the levels, make sure the atmosphere is relaxed to encourage good discussion.
There’s an argument for having music as a lead-in, even leading out at the end, as it helps brands podcasts, especially if it is a series of episodes, but avoid laying down a music track behind the speakers – it just kills attention and retention.
While recording
‘Go again… this time shorter” if often good advice, editing out the longer version. Try to avoid recording over several session, as it is difficult to get the same levels and sound the second and third time around. And if you think you can simply drop a word into a sentence that may have been mispronounced or the wrong word, think again – this is notoriously difficult. For 'learning' podcasts, there is something to be said for more structure in the content and clear edit points for different learning objectives. There are also strong arguments for more recaps, summaries and repetition to increase retention.
AI generated podcasts
One can already generate speech from text with relative ease. This is passable but still a little ‘mechanical’. However, we are reaching a position where it will feel very natural, so automatic podcasts from text scripts will be quick and cheap to produce and one can change and update them by simply changing the text, without going back into a recording studio. We already do this in WildFire for short introductions to pieces of learning.
Google is introducing real time transcription. This is a boon for note taking, as you can annotate, add your own words, summarise, mind-map, whatever. This is often difficult when you have to ‘watch’ a lecturer, PowerPoint or video. With WildFire we have also grabbed podcast transcripts, used AI to generate active online content to supplement the listening experience and solidify knowledge.
Before commissioning or producing podcasts, listen to a few. They’re everywhere on the web. But listen to those that are most popular. You will find all sorts of subjects, by all sorts of people and variations on formats. For learning, listen to some of the more serious podcasts, although there’s nothing wrong with lightening things up. I know of several companies who do ‘learning’ podcasts and have been on the end of quite a few. Given that it is a massively popular medium, cheap to produce, with significant advantages in terms of learning, why not give them a try?
Edison Report. (2019)
Llinares D. (2018) Podcasting: New Aural Cultures and Digital Media 
Nass and Reeves. The Media Equation
Newman N. (2019) Reuters. Journalism, Media and Technology Trends and Predictions 
For some interesting, and detailed research on podcasts, try Steve Rayson's blog. He has a strong learning background and is doing detailed research on who uses podcasts and why. Some of the ideas in this piece have come from his blog.

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