Saturday, March 16, 2019

Ai starts to crack the critical thinking... astonishing experiment...

Just eighteen years after 2001 (older readers will know the significance of that date), the AI-debater on the left, a 6 foot high black stele, with a woman’s voice, used arguments, objections, rebuttals, even jokes, to tussle with her opponent. She lost but, in a way, she also won, as this points towards an interesting breed of critical thinking software.  This line of AI has significance in the learning world.
How does it work?
First, she creates an opening speech by searching through millions of opening gambits, removes extraneous text and looks for the highest probability claims and arguments, based on solid evidence, she then arranges these arguments thematically to give a four minute speech.  In critical conversation,  she then listens to your response and responds, debating the point step by step. This where it gets clever as it she has to cope with logical dilemmas and structured debate and argument, drawing on a huge corpus of knowledge, way beyond what any human could read and remember.
Debate
In learning, working through a topic through dialogue, debate and discussion is often useful. Putting your ideas to the test, in an assignment or research task or when writing an article for publication and so on, would be a useful skill for my Alexa to be able to deliver. It raises the game, as it pushes AI generated responses beyond knowledge into reasoned argument and checks on evidence from trusted sources. But a debate is not the great win here. There are other more interesting and scalable uses.
Critical thinking
Much of the talk about 21st century skills is rather cliched, with little in the way of evidence-based debate. The research suggests that these skills, far from being separate 'skills' are largely domain specific. You don't get far in being a creative, critical and problem solving thinker in, say Data Science, if you don't know a lot about...  well... Data science. What's interesting about this experiment is the degree to which general debating skills,, let's call it stating and defending or attacking a proposition, shows how one can untangle, say critical thinking, into its components, as it has to be captured and delivered as software.
There are some key lessons here, as the logo of debate is actually the logic we know from Aristotle onwards, syllogistic and complex, often beyond the capability of the human brain. On the other hand the heuristics we humans use are a real challenge for AI. But AI is rising to this challenge with all sorts of techniques, many species of supervised and unsupervised AI that learns through machine learning, fuzzy logic to cope (largely with the impreciseness of language and human expression) and a battery of statistical theory and probability theory to determine certainty.
This, along with GTP-2 (I've written about this here), which creates content, along with techniques embedded in Google Duplex around complex conversational rules, are moving learning AI into new territory, with real dialogue based on structured creation of content, voice and the flow of conversations and debate. Why is this important?
1. Teaching
When it reaches a certain standard, we can see how it starts to behave like a teacher, to engage with a learner in dialogue and interpret the strengths of arguments, debate with the student, even teach and assess critical thinking and problem solving. In a sense it may transform normal teaching, in being able to deliver personalised learning at this level, on scale. The skills of a good teacher or lecturer are to introduce a subject, engage learners, support learners, assess learners. Even if it does not perform the job of an experienced teacher, one could see how it could support teachers.
2. Communication skills
There is also the ability to raise one’s game by using it as a foil to improve one’s communication skills, as a learner, teacher, presenter, interviewer, coach, therapist or sales person. Being able to persuade it that you are right, based on evidence, is something we could all benefit from. It strikes me that it could in time, also identify and help correct various human biases, especially confirmation bias but many others. Daniel Kahneman, in his Thinking Fast and Slow makes an excellent point at the very end of the book when he says that these biases are basically 'uneducable'. In other words, they are there, and rather than trying to change them, which is near impossible, we must tame them.
3. Expert
With access to over 300 million articles it has digested more than any human can read and remember in a lifetime. But this is just for reference. The degree to which it can use this as evidence for argument and advice is interesting. The experiment seems to support the idea that domain knowledge really does matter in critical thinking, something largely ignored in the superficial debate at conferences on 21st century skills. This may untangle this complex area by showing us how trues expertise is developed and executed.
4. Practice
The advantages the machine has over humans is the consistent access and use of very large knowledge bases. One can foresee a system that is an expert in a multitude of subjects and able to deliver scalable and sophisticated practice in not only knowledge but higher order skills across a range of subjects. The development of expertise takes time application and practice. This offers the opportunity to accelerate expertise. Of course, it also suggests that expertise may be replaces may machines. Read that sentence again, as it has huge consequences.
5. Assessment
If successful, such software could be a sophisticated way to assess learner’s work, whether written work, essays or oral, as it puts their arguments to the test. This is the equivalent to a VIVA or oral exam. With more structured questions, one could see how more sophisticated and objective assessment, free from essay mills and cheating, could be delivered.
6. Decision making
One could also see a use in decision-making, where evidence-based arguments would be at least worth exploring, while humans still make the decisions. I’d love, as a manager, to make a decision based on what has been found to work, rather than guessing or relying on faddish decision making.
Conclusion
This will, eventually, be invaluable for a teaching assistant that never gets tired, inattentive, demotivated, crabby and delivers quality learning experiences, not just answering questions. It may also help eliminate human bias in educational processes, making them more meritocratic. Above all it holds the promise of high level teaching that is scalable and cheap. At the very least it may lift often crass debate around 21st century skills beyond their cliched presentation as lists in bad PowerPoint presentations at conferences.

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