How would you give a talk on Artificial Intelligence (AI) to 120 students between the age of 16-18, not all of whom are necessarily interested or have a background in science? How would you define AI? What would you include (and exclude)? What is the best way to structure it? Well, surely, there are many valid answers to these questions. It was the first time that myself and Elayne Ruane, a colleague who is also a PhD researcher, attempted to give an 80 minute talk to a big crowd of such students. We didn’t find much in terms of guidance or advice on how to interact with the students or how to frame the AI discourse in a suitable manner for students who are about to embark on their college journey. We wanted to convey the excitement, hope and potential the field holds while also portraying a realistic image of its current state. Hopefully sharing our general approach might be helpful to anybody who finds themselves in a similar situation.
Mind, what worked for us might not work in different contexts, mindsets, situations, or for a different topic. AI is one of the most over-hyped and misunderstood areas of research in the minds of the general public. Furthermore, AI has been somewhat associated with a certain stereotypical archetype in the media – a white male genius computer geek. How one introduces the field and the kind of work and influential figures one includes plays a subtle but important role towards challenging these misconceptions and stereotypes. Specifically, when addressing a crowd of young people in the midst of deciding what areas of study they will pursue at university, how you present the field of AI can send implicit signals about who is welcome. For us, this is everyone.
We began our talk with a brief discussion of what a computer science degree, as one of the routes to AI research, entails (within the context of our own department at University College Dublin) and the kinds of careers that it can lead to while raising the point that there isn’t one path to follow. We then briefly talked about exemplar AI projects that are taking place within our own School. We kept this part of the talk very interactive by frequently polling the group by way of raising their hands. This was important in keeping the students engaged.
What is AI?
We discussed the general definition of AI – the common view that artificial intelligence refers to a machine that simulates human intelligence. What it means to ‘simulate’ or ‘human intelligence’ are contested and of course far from settled. However, we felt it was important to keep it simple for the purpose of this talk. ‘Machines that simulate human intelligence and exhibit human behaviour’, often comes down to abilities such as learning, problem solving, reasoning, language processing and the like.
Unlike other disciplines such as physics or biology, Artificial Intelligence is not a clearly defined and well contained discipline but rather a very broad and cross disciplinary endeavour. It draws from mathematics, engineering, biology, neuroscience, linguistics, philosophy, and many more. Although the most direct route to studying AI is through computer science (certainly within the context of UCD), one can also get to AI through other routes. Besides, AI can be synthesized with any field of enquiry, including, neuroscience, music and art. Christie’s recent AI generated art is a good example.
AI is a wide umbrella term with sub-fields including robotics, natural language processing, computer vision, machine learning and deep learning, speech recognition, machine translation and more. We tried to use examples of these relevant to the students including Google Translate, Amazon’s Alexa, PS4 games, Minecraft, facial recognition tools, and robots. We showed them the famous video of Boston Dynamic’s robot, Spot, dancing to Uptown Funk which was a huge hit.
The History of AI
AI is often thought of as a recent development, or worse, as futuristic, something that will happen in the far future. We tend to forget that dreams, aspirations and fascinations with AI go back in history back to antiquity. In this regard, Rene Descartes’s simulacrum and the Mechanical Turk are good examples. Descartes was fond of automata and had a walking and talking clockwork named after his daughter Francine. The machine apparently simulated his daughter, who died of scarlet fever at the age of 5. Similarly, the 18c Hungarian author and inventor Wolfgang von Kempelen created the Mechanical Turk, (a fake) chess-playing and speaking machine to impress the Empress Maria Theresa of Austria.
We can list an endless number of scholars who contributed to the development of AI as it is conceived today. The main towering figures we included were:
- The ninth century Persian mathematician Muḥammad ibn Mūsā al-Khwārizmī who gave us one of the earliest mathematical algorithms. The word “algorithm” comes from mispronunciation of his name.
- The English mathematician, Ada Lovelace who is often regarded as the first computer programmer.
- Alan Turing who is regarded as the father of theoretical computer science and whom most students seemed to be already aware of.
- And more recently, and perhaps scholars most influential in shaping the way we currently understand AI, are Marvin Minsky, John McCarthy, and Margaret Masterman.
We tried to make our talk as interactive as possible. We had questions and discussion points throughout. Towards the end, we had a game where students had to guess whether the AI being described on each slide was ‘sci-fi’ or ‘real’. Here are the main examples. Have a go yourself. 🙂
Sci-fi or real
- Self-aware robots
- Face recognition AI which rates people’s ‘trustworthiness’
- A bedside light that notifies you of your retweets
- Automated confession systems
If there is anything that the AI narrative is not short of, it’s hype and misconception. Clarifications, in a subtle way, both help illustrate what the actual current state of the field is as well as highlighting the challenges that arise with it. As such, the final concluding remakes were highlighting the misconceptions surrounding AI and the ethical concerns that necessarily arise with any technological advancement. The major misconceptions we mentioned are:
- AI is a distant reality. The fact is far from it. AI is deeply embedded in the infrastructure of everyday life. It is invisible and ubiquitous.
- AI equals robots or ‘self-driving’ cars. As it would have been obvious by now, robotics is simply one dimension.
- AI is neutral and can’t be biased. This again is far from reality. As AI integrates deeper into the educational, medical, legal, and other social spheres, ethical questions inevitably arise. Questions of ethics, fairness, and responsibility are inherently questions of AI.
That concludes the content of the talk.
- Keep it open and flexible. Create opportunities to hear from them. This allows you to get an idea of their awareness and knowledge (which can then help you calibrate on the fly in terms of technical detail) while also keeping them engaged.
- Pictures, more picture, and videos, are a great way to open up discussion. We showed a video of Google Assistant making a phone call which really captured their attention and got them talking. This also brought forth some ethical discussion.
- Prepare for plenty of questions around “Is AI going to take over?” and “How scared and worried should we be?”. It’s important to highlight how AI advancements can be misused but the trick is to highlight how much of what is reported on AI is overly blown hype which contributes to these unnecessary and unrealistic fears of AI when in fact much of the development in AI remains still premature. On the other hand, remember, we were talking to young science students about to embark to college. We still want to encourage them and want them to feel the dreams, excitements and hopes that have been the driving force of AI, at least in the 50s and 60s and the promising potentials that AI presents in medicine, robotics and more.
- (2016) Discussing the limits of artificial intelligence
- David Chapman (2018) How should we evaluate progress in AI?
- François Chollet (2017) The impossibility of intelligence explosion
- Gary Marcus and Ernest Davis (2018) A.I. Is Harder Than You Think
- Melanie Mitchell (2018) Artificial Intelligence Hits the Barrier of Meaning
- Oscar Schwartz (2018) ‘The discourse is unhinged’: how the media gets AI alarmingly wrong