Click here to jump to my foreword and skip the background. If you want to read the foreword in pdf, click here. If you’re interested in the tables of contents from the series, click here. Here’s my video message for their publication celebration: https://youtu.be/O78XdDYRZfc.
Background: Right before COVID-19 struck NY heavily this past Spring, K-12 teachers from Busan, Korea stopped by at NYC on their trip to US for studying various AI education strategies in US, and asked me for a short meeting. Frankly i was quite skeptical about this meeting, and was assuming it was their vacation in disguise. This skepticism of mine completely melted down when I met them in their hotel’s meeting room and began to hear what they’ve done and are doing at their schools, covering primary (1-6y), middle (7-9y) and high schools (10-12y), to teach their students what AI is, what these students can already do with it, and what they would be able to do with it in the future. it was eye-widening and has since made me realize how outdated my view of K-12 education (be it in Korea or elsewhere) is and how much K-12 education can be updated to keep up with latest developments in the society when teachers are enthusiastic and given opportunities.
This trip was a part of their effort in creating a teaching material for AI education aimed at K-12 teachers. I heard back from them a few months later that this material is ready to be published as a series of four books and was asked to write an opening remark. I was of course more glad to write one for them. Because I’m not too comfortable writing about AI in Korean (i mean.. when have i ever written anything AI in Korean?) i went ahead with English, and one of the participating teachers translated it into Korean.
Today (Nov 21 2020), i received the pdf copies of these four books and was able to take a more careful look at the content. it’s filled up with fun activities teachers can help students go through to learn about AI by experiencing a diverse set of sub-disciplines, including robotics, computer vision, natural language processing, machine learning, data science, etc. i’m so envious of these kids who will get to experience and have fun with all these activities and projects and ultimately become AI-native, unlike any of us.
And, without further ado, here it is.
Foreword: Intelligence is one of the last remaining mysteries of this universe and of ourselves that has evaded our collective attempt at uncovering its underlying mechanisms. We think every day, every hour, every minute, if not every second, effortlessly, without realizing that there are 86 billion neurons that are interacting with each other in both highly coordinated and highly chaotic manner behind this process of thinking. We perceive the surrounding world, which consists of our family, our friends and everything you can imagine and interact with each day, effortlessly, when the surrounding world never stays idle but dynamically changes its appearance non-stop. Based on our perception and pondering, we act in the surrounding environment effortlessly, although there are infinitely many possible ways in which our action could go wrong. Intelligence is behind these seemingly facile activities, driving each and every of us from one moment to another, but intelligence has largely evaded our interrogation and investigation even until now.
Despite “artificial” in artificial intelligence, artificial intelligence (AI) is a scientific discipline in which intelligence in general, not necessarily artificial one, is studied. As the first step in this direction, AI scientists ask what intelligence is. To answer this question, some are inspired by biological intelligence. To answer this question, some look into psychology. To answer this question, some look into philosophy. To answer this question, some look into mathematics. To answer this question, some, like myself, look into computer science which has a good track record of rigorously defining and understanding traditionally illusory concepts, such as information and computation, thanks to Claude Shannon, Alan Turing, who originally “propose[d] to consider the question, ‘Can machines think?” in 1950, and the like.
In this scientific pursuit of (artificial) intelligence, “learning” has been found to be a central concept to intelligence. Intelligence is not merely a bag of algorithms and knowledge for solving a fixed set of problems, but it is rather the process of learning to solve a new problem by creating a new algorithm. Every time a new problem or a variant of a known problem is given, a machine, either biological or not, must “learn” to solve it and acquire a set of sophisticated skills in this process. The question of “what is intelligence?” has suddenly morphed itself into the question of whether we can build a machine that can learn to solve any problem. If we could build one, that machine would be intelligent, and this machine itself would be our answer to the ultimate question of “what is intelligence”. Machine learning is a sub-discipline in computer science that has pursued this direction of building a learning machine to figure out what intelligence is.
Machine learning has made rapid progress in recent years, thanks to theoretical and empirical advances in learning algorithms, increased availability of data, wide adoption of open-source software and incredible advances in computing systems. A few years ago, a deep neural network learned to listen to speech in a quiet room and transcribe it almost as well as an average person could. This was quickly followed by a deep convolutional network which could detect an incredible number of different objects in a picture, rivaling humans in object recognition. A couple of years later, a deep recurrent neural network was trained to translate news articles between English and Chinese and ended up translating almost as well as average bilingual speakers could. All these results were openly shared in forms of open-access publications and open-source software packages, which led to an unprecedented level of adoption of these new technologies. Industry has rapidly implemented and deployed these AI systems in various products, including voice assistants, real-time machine translators, automatic image tagging, content recommendation, driving assistance and even automated tutoring. These AI technologies are being deployed in increasingly more challenging domains, such as healthcare, medicine and automation.
Unfortunately positive is not the only way to describe this rapid advance and wide adoption of machine learning and thereby artificial intelligence in recent years. These AI systems have been silently tested and deployed in the society, touching many, if not most, of us often without our realization. These silent, and often premature, tests have sadly revealed negative sides of AI.
Billions of people use social media regularly, and social media companies extensively use AI technology to personalize individual users’ experience, effectively censoring the flow of information. Billions of people use video streaming services and news aggregation services every day, and the providers of these services use AI to decide not only what to but also what not to recommend and display to individual users, effectively shaping the users’ opinions without their own realization. This mass adoption of AI-based content filtering has unintentionally but unmistakably resulted in deepening polarization in many societies all over the world, sometimes resulting in fatal incidents and destabilization of otherwise stable, democratic societies.
Hastily developed and prematurely deployed AI systems, such as face recognition, automated exam proctoring and automated interviewer assessment, have been found to amplify undesirable societal biases and inequalities, such as racial bias, gender bias, income inequality and geographical inequality. For instance, incorrect identification of a face recognition system, which has repeatedly been found to disproportionately associate black people and people of colour as threatening, by police in the US has recently led to the wrongful arrest of an innocent black male. The world’s largest e-commerce company recently had to drop an AI-based recruiting system, because it was giving female candidates unjustifiable disadvantages for software engineering roles. A recent study has uncovered that commercial object recognition systems’ accuracies significantly drop when presented with pictures taken from poorer countries.
For AI to truly benefit us and the society, these shortcomings must be addressed and addressed fully. Technical advances alone, often made by a small group of elite scientists, will not be enough to make AI safe, fair and beneficial for all. Safe, fair and beneficial AI will only be possible when the whole society, consisting of both AI scientists and others, is aware of AI’s capability, adoption and deployment. The society must continue to carefully watch and monitor AI’s impact on the society, and be ready to rise and intervene against unsafe, unfair and unjust use of AI. This awareness of capability, limitations and underlying technology of AI is necessary for the society to benefit from AI.
Such awareness in the society of a new technology, in particular when it is an enabling technology, does not happen overnight. It must happen carefully and patiently over many years, if not decades, to ensure the whole society possesses a rational and coherent view of AI technology and its use. For this to happen, we must go beyond the status quo in which discourse on AI happens within and across universities and industry. We must start discourse and education on AI already with K-12 students who will be the first generation in the history of humanity to grow to live in a society where AI is not a novelty but an everyday reality. As the first step toward this goal, we must educate teachers of all levels to be familiar with and comfortable with the technologies and implications of AI, and must immediately start preparing educational materials and systems for teaching AI.
I thus applaud this effort by the Busan Metropolitan City Office of Education preparing a new curriculum and accompanying educational materials on AI for both students and teachers. In doing so, the team from the Office of Education has struck perfect balance between theory and application, between history and modern practices, and between technology and ethics. I am envious of students in Busan who will learn to be native in AI according to this curriculum, and am now hopeful rather than worried about the future of AI and its impact on society.