Do bears shit in the woods? 7 reasons why data analytics a misleading, myopic use of AI in HE

I’m increasingly convinced that HE is being pulled in the wrong with its obsession with data analytics, at the expense of more fruitful uses of AI in learning. Sure it has some efficacy but the money being spent at present, may be mostly wasted.

1. Bears in woods

Much of what is being paid for here is what I’d say was answers to the question, ‘Do bears shit in the woods?’ What insights are being uncovered here? That drop-out is being caused by poor teaching and poor student support? That students with English as a second language struggle? Ask yourself whether these insights really are insights or whether they’re something everyone knew in the first place.

2. You call that data?

The problem here is the paucity of data. Most Universities don’t even know how many students attend lectures (few record attendance), as they’re scared of the results. I can tell you that the actual data, when collected, paints a picture of catastrophic absence. That’s the first problem – poor data. Other data sources are similarly flawed, as there's little in the way of fine-grained feedback. It's small data sets, often messy, poorly structured and not understood.

3. Easier ways

Much of this so-called use of AI is like going over top of head with your right hand to scratch your left ear. Complex algorithmic approaches are likely to be more expensive and far less reliable and verifiable than simple measures like using a spreadsheet or making what little data you have available, in a digestible form, to faculty.

4. Better uses of resources

The problem with spending all of your money on diagnosis, especially when the diagnosis is an obvious limited set of possible causes, that were probably already known, is that the money is usually better spent on treatment. Look at improving student support, teaching and learning, not dodgy diagnosis.

5. Action not analytics

In practice, when those amazing insights come through, what do institutions actually do? Do they record lectures because students with English as a foreign language find some lecturers difficult and the psychology of learning screams at us to let students have repeated access to resources? Do they tackle the issue of poor teaching by specific lecturers? Do they question the use of lectures? (Easily the most important intervention, as the research shows)  is the shift to active learning. Do they increase response times on feedback to students? Do they drop the essay as a lazy and monolithic form of assessment? Or do they waffle on about improving the ‘student experience’ where nothing much changes?

6. Evaluation

I see a lot of presentations about why one should do data analytics  - mostly around preventing drop-out. I don’t see much in the way of verifiable analysis that data analytics has been the actual causal factor in preventing future drop-out. I mean a cost-effectiveness analysis. This is not easy but it would convince me,

7.  Myopic view of AI

AI is many things and a far better use of AI in HE, is, in my opinion, to improve teaching through personalised, adaptive learning, better feedback, student support, active learning, content creation and and assessment. All of these are available right now. They address the REAL problem – teaching and learning.

Conclusion

To be fair I applaud efforts from the likes of JISC to offer a data locker, so that institutions can store, share and use bigger data sets. This solves some legal problems but looks at addressing the issue of small data. But this is, as yet, a wholly unproven approach.

I work in AI in learning, have an AI learning company, invest in AI EdTech companies, am on the board of an AI learning company, speak on the subject all over the world, write constantly on the subject . You’d expect me to be a big fan of data analytics in HE – I’m not. Not yet. I’d never say never but so much of this seems like playing around with the problem, rather than facing up to solving the problem.

AI is the new UI: 7 ways AI shapes your online experience

HAL stands for ‘Heuristically programmed ALgorithmic computer’. Turns out that HAL has become a reality. Indeed we deal with thousands of useful HALs every time we go online. Whenever you are online, you are using AI. As the online revolution has accelerated, the often invisible application of AI and algorithms has crept into a vast range of our online activities. A brief history of algorithms includes the Sumerians, Euclid, the origins of the term (Al Khwarismi), Fibonacci, Leibniz, Gauss, Laplace, Boole and Bayes but in the 21st century ubiquitous computing and the internet has taken algorithms into the homes and minds of everyone who uses the web.

You’re reading this from a network, using software, on a device, all of which rely fundamentally on algorithms and AI. The vast portion of the software iceberg that lies beneath the surface, doing its clever but invisible thing, the real building blocks of contemporary computing – are algorithms and AI. Whenever you search, get online recommendations, engage with social media, buy, do online banking, online dating, see online ads; algorithms are doing their devilishly clever work.

BCG’s ten most innovative companies 2016

Boston Consulting Group publish this list every year:

1. Apple
2. Google
3. Tesla
4. Microsoft
5. Amazon
6. Netflix
7. Samsung
8. Toyota
9. Facebook
10. IBM

Note how it is dominated by companies that deliver access and services online. Note that all, apart perhaps from Toyota, are turning themselves into AI companies. Some, such as IBM, Google and Microsoft have been explicit on this strategy. Others, such as Apple, Samsung, Netflix and Facebook have been acquiring skills and have huge research resources in AI. Note also that Tesla, albeit a car company, is really an AI company. Their cars are always on, learning robots. We are seeing a shift in technology towards ubiquitous AI.

1. Search

We have all been immersed in AI since we first started using Google. Google is AI. Google exemplifies the success of AI in having created one of the most successful companies even on the back of AI. Beyond simple search, they also enable more specific AI-driven search through Google Scholar, Google Maps and other services. Whether it is documents, videos, images, audio or maps, search has become the ubiquitous mode of access. AI is the real enabler when it comes to access. Search Engine Indexing finds needles in the world’s biggest haystack. Search for something on the web and you’re ‘indexing’ billions of documents and images. Not a trivial task and it needs smart algorithms to do it at all, never mind in a tiny fraction of a second. PageRank was the technology that made Google one of the biggest companies in the world. Google has moved on, nevertheless, the multiple algorithms that rank results when you search are very smart. We all have, at our fingertips, the ability to research and find the things that only a tiny elite had access to only 20 years ago.

2. Recommendations

Amazon has built the world’s largest retail company with a raw focus on the user experience, presented by their recommendation engine. Their AI platform, Alexa, now delivers a range of services but it was made famous by its recommendations on first books, now other goods. But recommendation engines are now everywhere on the web. You are more often than not presented with choices that are pre-selected, rather than the result of a search. Netflix is a good example, where the tiling is tailored to your needs. Most social media feeds are now AI-driven, as are many online services, where, what you (and others) do, determines what you see.

3. Communications

Siri, VIV, Cortana, Alexa… voice recognition, enabled by advanced in AI through Natural Language Programming, has changed the way we communicate with technology. As speech is our natural form of communication, it is a more natural interface, giving significant advantages in some contexts. We are now in a position of seeing speech recognition move from being a topic of research to real commercial application as AI, in many forms but particularly deep learning and large data sets, have allowed some of the world’s largest tech companies to use it with hundreds of millions of customers; Apple, Amazon, Google, Microsoft, Samsung and others.

4. Translation

In translation, the recent shift in approach from large scale pattern matching to more focused AI techniques gave Google a gear change in efficacy. Deep learning translation is so powerful that it now works with any new languages, without the need for huge data sets. Ready translation on social media, real time translation on Skype are here now. Language hurdles can be overcome with realtime online translation, available for voice calls and instant messaging. Skype Translator uses AI, machine learning, so the more you use it, the better it gets.

5. Social

This is the age of algorithms. We open a file, it is decompressed, we save a file, it is compressed, we send a file, it is managed across a global network. We Skype, WebX, SnapChat, WhatsApp, Facetime – all of this is enabled by smart AI in terms of compression, networks and decompression. The underlying technology is fundamentally algorithmic. When we zip files, compress for transmission, decompress for use. Lossless and lossy compression and decompression magically squeeze big files into little files for transfer. On top of this are error correcting codes, mistakes that fix themselves, so that sound, pictures and videos can be saved, stored and retrieved without loss, especially across networks, where these clever algorithms maintain quality. Beyond this is the work of AI in determining news feeds and ads in social media.

6. Databases

The advent of big data means that the balance, in some contexts, has swung away from algorithms, towards the power of massive data sets. Nevertheless, when you use a database you use some clever algorithms. Databases are used for many forms of content storage and, although you may not know it most of the time, whenever you access a learning management system, VLE or learning content, you will have been using algorithm-driven databases. In other words, algorithms already lie at the heart of learning, albeit in an almost invisible and indirect way. We now see the emergence of blockchain, a distributed, hackproof, database structure, that may enable finance and learning applications of a different order.

7. Commerce

Public key cryptography is how encryption works and keeps your credit card details safe when buying stuff. Amazon, Ebay, PayPal, credit cards and the entire world of online retail would not exist without this algorithm. Spam filters, phishing, even higher order cyber-threats, are all handled by AI.

Learning

Going back to our top ten list of innovative companies. They all see software that learns, as an integral part of their products and services. Machine ‘learning’, products and services that, the more you use them, the better they get, places ‘learning’ at the core of their businesses. Yet there is another sense in which AI can deliver ‘learning.

As most learning is informal, not through formal online learning courses, most online learning, through search, social media, communications and other online services, can be said to be AI-driven and mediated. AI has enabled informal online learning. AI now also delivers AI-driven content creation, curation, chat and consolidation through tools such as WildFire. Adaptive learning is also being delivered in large formal courses. Adaptive assessment, automated essay marking, face recognition, typing recognition are also AI-driven. Even plagiarism checking is now AI driven. AI is the new UI. AI is also the new UI for learning.

Conclusion

Bright young AI mathematicians and coders, no longer yearn to work on Wall Street or in banks but in start-ups, incubators and business creation. This has been a long time coming but at last human talent is being directed, not towards the mere management of money, but the creation of new ways of creating jobs and shaping the future. The question remains, that the Age of the Algorithm may destroy more jobs than it creates. Nevertheless, for the moment, it holds the promise of getting us out of boom-bust cycles where maths was forever blowing financial bubbles, into maths that make things work. As we have revealed the potency of algorithms, one can’t fail to admire the elegance of these carefully constructed, magic, mathematical spells. They are stunningly clever.

7 myths about AI

Big Blue beat Kasparov in 1997 but chess is thriving. AI remains our servant not master. Yet mention AI and people jump to the far end of the conceptual spectrum with big-picture, dystopian and usually exaggerated visions of humanoid robots, the singularity and the existential threat to our species. This is fuelled by cultural messages going back to the Greek Prometheus myth, propelled by Mary Shelly’s Frankenstein (subtitled The Modern Prometheus) to nearly a century of movies from Metropolis onwards that portray created intelligence as a threat. The truth is more prosaic.

Work with people in AI and you’ll quickly be brought back from sci-fi visions to more practical matters. Most practical and even theoretical AI is what is called ‘weak’ or ‘narrow’ AI. It has no ‘cognitive’ ability. I blame IBM and the consultancies for this  hyperbole. There is no ‘consciousness’. IMBs Watson may have beaten the Jeopardy Champions, Google’s AlphaGO may have beaten the GO Champion – but neither knew they had won.
The danger is that people over-promise and under-deliver, so that there's disappointment in the market. We need to keep a level head here and not see AI as the solution to everything. In fact, many problems need far simpler solutions.

AI is the new UI

AI is everywhere. You use it every day when you use Google, Amazon, social media, onine dating, Netflix, music streamming services, your mobile and any file  you create, store or open. Our online experineces are largely of AI driven services. It's just not that visible. AI is the new UI. However, there are several things we need to know about AI if we are to understand and use it well in our specific domain, and in this case it is teaching and learning.

1. AI is ‘intelligent’
2. AI is all about the brain
3. AI is conscious
4. AI is strong
5. AI is general
6. AI is one thing
7. AI doesn’t affect me

1. AI is not ‘intelligent’

I have argued that the word ‘intelligent’ is misleading in AI. It pulls us toward a too anthropomorphic view of AI, suggesting that it is ‘intelligent’ in the sense of human intelligence. This, is a mistake as the word ‘intelligence is misleading. It is better to see AI in terms of general tasks and competences, not as being intrinsically intelligent, as that word is loaded with human preconceptions.

http://donaldclarkplanb.blogspot.co.uk/search?q=Why+AI+needs+to+drop+the+word+‘intelligence’

2. AI is not about the brain

AI is coded and as such, most of it does not reflect what happens in the human brain. Even the so-called ‘neural network’ approach is loosely modelled on the networked structure of the brain. It is more analogy that replication. It’s a well work argument but we did not learn to fly by copying the flapping of birds’ wings and we didn’t learn to go faster by copying the legs of a cheetah – we invented the wheel. Similarly with AI.

3. AI is not cognitive

IBMs marketing of AI as ‘cognitive technology’ is way off. Fine if they mean it can perform or mimic certain cognitive tasks but they go further, suggesting that it is in many senses ‘cognitive’. This is quite simply wrong. It has no consciousness, nor real general problem solving abilities, none of the many cognitive qualities of human minds. It is maths. This is nit necessarily a bad thing, as it is free from forgetting, racism, sexism, cognitive biases, doesn’t need to sleep, networks and doesn’t die. In other words AI is about doing things better than brains but  by other means.

4. AI is weak

There is little to fear from threatening independence and autonomy in the short term.  Almost all AI is what is called ‘weak’ AI, programmes, run on computers that simulate what humans can do. Strong AI is the idea that it actually does what the brain does. My own view is that we are very firmly at the ‘weak’ stage but that the distinction is actually on a spectrum, like cool to hot. That’s not to say that ‘strong’ AI is not on its way, just that it’s not here yet.

5. AI is narrow

AI applications do specific things well and general things badly. They play chess and GO well but that piece of AI does very little else.  That is nit to say that AI will not get to the position of being a general problem solver. It will just atke time. We can see, from driverless cars, that an array of sensing, decision making and learning software can do remarkable things when working in tandem.

6. AI is not one thing

Far from being one thing, AI is many things. There are also many different approaches to AI. This is well covered in Pedro Domingos’s book The Master Algorithm, with chapters on Symbolists, Connectivists, Evolutionists, Bayesians and Analogizers. From the learning perspective, there’s machine learning (supervises and unsupervised), reinforcement learning and  and deep learning. Other ways of looking at AI is through areas of problem solving, such as NLP (Natural Language Programming) or robotics. There are many other ways of slicing the AI cake. The important point is to see it as a set of very different technologies or tools that solve problems.

7. AI doesn’t affect me

In practice AI is very good, often better than humans, in very narrow applications and domains. The most pervasive example is Google, which is brilliant at searching for relevant links and information – whether it be websites, scholarly papers, images, videos, audio, maps and so on. This has revolutionised how we store, access and use media and knowledge. It was a huge pedagogic shift. Another specific, but immensely powerful use, is in buying, where Amazon’s algorithmic power constantly recommends and shapes our buying habits – especially in books. Then there’s social media, where the algorithmic power of personalised news shapes out timelines on facebook or Tweets. On top of this are the algorithmic recommendations on Netflix and many streaming music sites. AI is the new UI. Most of our online experience is shaped by AI, as it gets to know us better and delivers a better service. We live in the age of algorithms.

Conclusion

AI is the wonder of our age. Something so exciting that it tends to produce extreme reactions in commentators. This is fine but we must, for the moment not fall into dystopian or utopian visions of what it can achieve. We must be realistic. That is not to say we should be complacent. We have already seen the power of AI to transform online services, automate factories and seriously impact employment. Political shocks, like Brexit and Trump can, I believe, be partly attributed to this phenomenon. We must therefore be vigilant on regulation and its political consequences. 

Elon Musk – the Bowie of Business

Having just finished Morley’s brilliant biography of Bowie, it struck me that Musk is the Bowie of business. Constantly reinventing himself; Paypal hero, Tesla road warrior, Solar City sungod, Starman with Space X and now the sci-fi Hyperloop hipster- and he’s still only in his forties. Strange fact this but the first Tesla car was codenamed DarkStar.

But let’s not stretch Bowies leg warmers too far. Ashlee Vance’s biography of Elon Musk is magnificent for mostly other reasons. It’s about Musk the man, his psychology. There’s a manic intensity to Musk, but it’s directed, purposeful and, as Vance says, it’s not about making money. Time and time again he puts everything he’s made into the next, even weirder and riskier project. Neither is he a classic business guy or entrepreneur. For him questions come first and everything he does is about finding answers. He despises the waste of intellect that gets sucked into the law and finance, as he’s a child of the Enlightenment and sees as his destiny the need to accelerate progress. He doesn’t want to oil the wheels, he wants to drive, foot to the metal, the fastest electric car ever made then ride a rocket all the way to Mars. As he says, he wants to die there – just not on impact. Always on the edge of chaos, like a kite that does its best work when it stalls and falls but then it soars.

Time and time again experience tells me, and I read, about actual leaders who bear no resemblance to the utopian model presented by the bandwagon ‘Leadership’ industry. The one exception is Stanford’s Pfeffer, who also sees the leadership industry as peddling unreal, utopian platitudes. Musk has a string of business successes behind him, including PayPal, and is the major shareholder in three massive, public companies, all of which are innovative, successful and global. He has taken on the aerospace, car and energy industries at breathtaking speed, with mind-blowing innovation. Yet he is known to be mercurial, cantankerous, eccentric, mean, capricious, demanding, blunt, delivers vicious barbs, swears like a trooper, takes things personally, lacks loyalty and has what Vance calls a ‘cruel stoicism’ –all of these terms taken from the book. He demands long hours and devotion to the cause and is cavalier in firing people. “Working at Tesla was like being Kurtz in Apocalypse Now”. So, for those acolytes of ‘Leadership’ and all the bullshit that goes with that domain, he breaks every damn rule – then again so do most of them – in fact that’s exactly why they succeed. They’re up against woozies who believe all that shit about leading from behind.

So why are people loyal to him and why does he attract the best talent in the field? Well, he has vision. He also has a deep knowledge of technology, is obsessive about detail, takes rapid decisions, doesn’t like burdensome reports and bureaucracy, likes shortcuts and is a fanatic when it comes to keeping costs down. Two small asides – he likes people to turn up at the same time in the morning and hates acronyms. I like this. His employees are not playing pool or darts mid-morning and don’t lie around being mindful on brightly coloured bean bags. It’s relentless brainwork to solve problems against insane deadlines.

You may disagree but he does think that it is only technology that will deliver us from climate change, the dependence on oil and allow us to inhabit planets other than our own and his businesses form a nexus of energy production, storage and utilisation that, he thinks, will save our species. He may be right.