Then the AI algorithm integrates this information with other information, such as imaging information and general lab test information, and puts it all together to make a more comprehensive prediction about the condition of the patient.
Professor Lequan Yu is the director of the Medical AI Lab at HKU and his work lies at the intersection of AI and healthcare. Before joining HKU, Professor Yu was a postdoctoral research fellow at Stanford University. In this interview he explains to our science editor, Dr Pavel Toropov, how AI can revolutionise healthcare.
❓ Dr Pavel Toropov: How do you use artificial intelligence in your research?
💬 Professor Lequan Yu: We use AI technology, AI algorithms to solve problems related to healthcare and medicine. We rely on multimodal AI models, using AI to analyse and integrate different medical data, such as medical images, medical reports, lab test results and genomic data. The aim is to interpret and integrate them together to help doctors make decisions.
❓ Could you provide an example?
💬 For example, using an algorithm to see if the patient has cancer or not from computed tomography (CT) images. This can reduce the doctor’s workload. Also, we want to do precision medicine, especially for cancer patients. Currently, treatment strategies are not really tailored for individual patients. We want to use AI algorithms to integrate the diverse information about each individual patient and then let AI make recommendations for doctors.
❓ What data would AI integrate?
💬 Radiology data such as CT scans, MRI and also pathology images – microscopic images. Recently, we have been exploring how to integrate genomic data.
❓ What do you mean by “genomic data”？
💬 Broadly speaking, this refers to DNA, RNA or protein data. For example, we work on gastric cancer. We get samples of cancerous tissue, and do genetic sequencing or molecular testing to obtain molecular information, for example, about what subtype of cancer it is.
Then the AI algorithm integrates this information with other information, such as imaging information and general lab test information, and puts it all together to make a more comprehensive prediction about the condition of the patient.
For cancer, take gastric cancer for example, there are different treatment strategies, for example immunotherapy. But, we do not know whether this strategy and treatment would benefit this particular patient. Because some strategies may not. So our AI algorithm can predict treatment response in this particular patient and also provide survival analysis.
❓ In healthcare, what does using AI allow humans to do that they cannot do alone?
💬 Two examples. One is chest X-rays. A doctor can do the analysis very well, detecting pneumonia, for example. AI can also do it, and the reason to use AI is to help reduce the doctors’ effort and workload.
But for cancer image analysis, that is different. Doctors can estimate potential survival or potential treatment response from the image. But this is quite subjective, based on the doctor’s experience. AI has the potential to evaluate this more quantitatively and objectively.
❓ Can this technology be used in clinical practice now?
💬 Currently for oncology this is frontier research. There is still a way to go before putting it in clinical practice. But, after we incorporate genomic data, we think it will be more workable. Perhaps in to 10 years this will be applied in clinical practice.
❓ Other than cancer, in the treatment of what other conditions can AI help?
💬 Cardiovascular disease. Here AI can play an important role. When it comes to certain heart risk disease predictions, it is less challenging than with cancer. Moreover, AI can integrate and analyse chest X-ray images, and here the accuracy of AI is very high, over 90%.
But still we have issues regarding privacy, ethics and medical regulations before we can apply it in clinical practice.
Collaboration is very important – we must collaborate with doctors, hospitals, medical schools. It is the best way to apply AI technology to solve real-world problems, address real-world needs, and help our society, medicine and economy.
Professor Yuan Xiaoming works in the Department of Mathematics at HKU. An accomplished mathematician and scientist, he was three times voted highly cited researcher by Clarivate Analytics. Professor Yuan’s main speciality is optimisation, and he applies this expertise in the field of artificial intelligence. In this interview with our science editor Dr Pavel Toropov, Professor Yuan explains the importance of mathematics in AI.
❓ Dr Pavel Toropov: What is the role of a mathematician in AI?
💬 Professor Yuan Xiaoming: Our role is fundamental, a foundation. I can prove it – when people talk about AI, you usually only hear about the engineering side, the programming, the implementation of the product. But – breakthroughs in AI depend on how good you are at maths! What does AI mean? Intelligence created by people. And how do you get intelligence? One answer is maths!
❓ What do you mean by this?
💬 AI is artificial – fake intelligence, intelligence in the machine. AI is not fake, of course, but it is computational intelligence. And, who tells the computer to generate this intelligence? Humans, people. So, you need human intelligence – in your brain – to do AI better. And maths is the best way to improve your intelligence, your level of thinking, your logic.
❓ So mathematics is like training, going to the gym, but for the brain?
💬 Yes. Mathematics is brain training.
❓ What is your main area in the field of AI?
💬 Optimisation (algorithms). There are a lot of optimization problems in the AI industry. For example, if we want to minimise the bandwidth cost for a livestreaming business, that’s an optimisation problem.
You understand the questions from a maths perspective and design fast, efficient and robust algorithms to solve them. These are purely maths problems. We work on such problems.
❓ You also work a lot with the industry, designing algorithms for commercial application. How do you use maths there?
💬 For example, something that everyone is talking about now – LLM, large language models, like Chat GPT and variants of that. In LLM there are several stages – pre-training, post-training, fine-tuning. Each stage has a lot of optimisation problems.
For example, LLM has a lot of connections between different neurons, and in the post-training stage you have to cut some of these connections to save the hardware computation facilities, like memory. Which neurones can be cut off? We design a mathematical model to help do this and it can save a lot of computation facilities – and that’s money.
❓ You also work with AI chips. What is an AI chip?
💬 Traditionally the concept of computer chips is just hardware. But now people integrate algorithms into the chip so that it works faster. We design algorithms into the chip to accelerate the computation in the chip.
One type of standard, representative work is vectors and matrix decomposition, matrix and vector multiplication. We design specific algorithms to try to make the computation of vectors and matrix more efficient.
Sometimes a structure has to be introduced into the chip. For example, the most popular chips, like Nvidia A100 and H100, have specific structures – that’s why they work so well. One typical structure is sparse tensor core, designed to accelerate sparse matrix computations, and we have to design algorithms to fit into such hardware structures.
❓ Where does HKU compare to other institutions when it comes to innovation in the field of AI and mathematics?
💬 HKU is a good place to do AI-related topics. I am a maths professor, and for AI we need to do a lot of maths. Theory-wise we are quite strong – HKU attracts good postgrad students, and I am very happy to be working with such good students. I also launched an AI program with the Department of Statistics and the Department of Computer Science. This program provides new resources, manpower for our projects.
❓ Tech jobs – software engineers, programmers, are coveted because they offer good pay and are in demand. Are there career opportunities for mathematicians?
💬 I can see a direct link between maths and commercial value. I know that many people do not have any idea about this – maths is just formulas on paper, they have nothing to do with business, but I am an example – I save money for industry.
I am a mathematician, and I can help industry earn a lot of money. I recently helped Huawei, with my algorithms, to save USD108 million, in less than three years. That’s commercial value! I designed the algorithms which helped them save money because of benefits costs for their livestream business, which is one of the most important parts of the digital economy, as business professors like to call it.
And – what is the specific meaning of “digital economy”? Digital means numbers, numbers on the computer. Creating meaningful knowledge from numbers depends on numerical algorithms, so it depends on mathematical knowledge.
Therefore, the mathematical foundation for AI is really important. We are not doing AI incrementally, little by little – mathematicians can help industry with breakthrough ideas! The importance of mathematicians in AI must be highlighted.
❓ Final question: would you recommend mathematics as a career?
The final project is a 3-5 minute short film, using different AI applications. They put all their AI skills together to assemble something complex, working with text prompts and reference images to create video sequences. The main Gen AI applications that we use are ChatGPT, Midjourney and RUNWAY Gen3 Alpha.
Ulrich Gaulke is an award-winning documentary filmmaker who has taught his art across the world, from Bosnia to Bolivia. Last year he took a position as a senior lecturer at the Media and Journalism Center at the University of Hong Kong. One of the courses he teaches is Generative AI for Media Applications, for which he has been awarded the Social Science Outstanding Teaching Award. In this interview with our science editor Dr Pavel Toropov, Ulrich Gaulke talks about this new course, the role of AI in filmmaking, and why AI cannot yet replace a human storyteller.
❓How did this course – Generative AI for Media Applications, come about?
💬I studied computer science – a long time ago! And I am interested in technical things – like AI. I had an idea – how I can develop a new, pioneering class where students are storytellers, but they develop story-telling skills by using all the newest applications of Gen AI.
❓What do students do in your course?
💬First we do introduction to AI – how large language models work, how a diffusion model that creates images works… basic understanding of neural networks. Students learn what happens with the data inside an AI model and how the model creates a proper outcome.
Then, the students build, and feed, their own AI model with data – pictures. The students take pictures of Hong Kong with mobile phones to feed their own diffusion AI model. Based on this AI model we can create more Hong Kong-related stuff.
These are the beginnings. Then, step by step, we go through Gen AI applications: text to text, text to image, text to video, text to animation, text to speech, and text to music. After this the students can create stories and video sequences, and can start working on the final project.
The final project is a 3-5 minute short film, using different AI applications. They put all their AI skills together to assemble something complex, working with text prompts and reference images to create video sequences. The main Gen AI applications that we use are ChatGPT, Midjourney and RUNWAY Gen3 Alpha.
But first, the students must create a proper story, then divide it in different parts – like a story board in a fiction film. Each chapter, location, characters, everything must be visualised. I let students start with the final project only when I agree with the story. If their story has something poetic, revealing, touching, then I let them create characters, set ups, visuals.
We also have weekly assignments – to create something. We also discuss the ethical aspects of AI and technical background. I wanted to include people working with AI and combine their experience with my perspective of a storyteller. So, I have guest speakers from the computer science department.
I also invite guest speakers who are leading figures in AI and media, for example Professor Sylvia Rothe, Chair of Artificial Intelligence from Munich University of Television and Film in Germany.
We established a pioneering course, nobody else does this kind of work.
❓How popular is this course?
💬It was booked out immediately. But more students want to attend! Journalism and media students are a priority, and so we established a summer class in June. It has the same content, but the course is open to students from other departments and faculties.
❓You teach this course at the Media and Journalism Center at HKU. Is the course for filmmakers or journalists?
💬The course is open to both journalism students and filmmaker students, but our work is focused on the creation of works of fiction, not journalistic work.
Journalism is fact-based. If you are a journalist, you have to be very responsible in your use of generative AI. An AI model is not a research tool like Google. We teach that with AI, you cannot trust the outcome – an AI model does what it wants, creates its own patterns. The result may look very detailed, but it needs to be checked.
❓So, what is the main use of AI in filmmaking?
💬Filmmakers can use it creatively as a visualisation tool – for visualising something that is not possible to shoot, for example something that happened in the past, something that you have no video materials for.
Animation has traditionally been used for this purpose, in combination with powerful, real stories. For example (the animated film) FLEE, about a family from Afghanistan escaping to Europe, was nominated for three Oscars. It is based on a real story but is fully animated.
❓Do you use AI in your own work as a filmmaker?
💬I am now using it for historical re-enactments in my latest documentary about five 100-years-old ladies. They are talking about their past, but they only have still pictures from the time when they were very young. So, I can create video sequences based on these still images.
❓Storytelling is key to making films. Do you think this will be done by AI at some point?
💬Yes and No. It depends on what your expectations are. ChatGPT can help you write a story, but the main constellation – the plot, the characters, this must come from you! If you let AI create everything by itself, then you will see that it is just mimicking something that already exists. Stories are what humans use to communicate with each other. A good story must include something unique, surprising, that has something to do with your own life.
AI is based on patterns, these patterns come from the learning material, and the learning material is based on what has already been made… A script for a TV soap opera is based on very simple elements, and writers write the same stuff every year, so that is something that AI can do.
❓A little while ago, when text to video applications came out, there was talk that AI will now make films for us, eliminating the need for actors, directors…. This does not seem to be happening.
💬A lot of people are giving up on that idea.
❓Why?
💬The expectations are too high. Try giving AI a simple story to do. For example, a teacher is angry at a pupil, a little girl. Try to keep consistency with both characters, try to bring them into a serious conversation – for example, the angry teacher tells the pupil that there is something wrong with her homework. It is a very simple story but try to create it with AI – and it becomes very complicated!
Try to find a video on YouTube, one that can make you forget that it was created by AI. It is always more than obvious that a video was created by AI – a character disappears, another appears randomly, there are many random actions, there are aliens…
❓So, you see AI as a tool to create visuals for a creatively written story, one done by a human?
💬If you let AI do something on its own, then it goes weird, random. AI-created work is totally different from our idea of creativity. It´s more like a dream.
If you want to use AI application as a tool to create something that is based on our understanding of storytelling, of creating characters, of emotional expression, then it is very hard. Consistency is the problem – the movement, and the facial expressions of characters are not consistent.
It is very hard to develop a character using an AI model. You can create a realistic photo using Midjourney – for example of an old guy who is looking sad. The AI model will create an image of an old guy looking sad, but is this the old guy that you want to use in your story? Or is he completely different?
❓Does AI allow you to fine-tune these discrepancies?
💬Gen AI can do very impressive things, but it is not like applications such as Photoshop or After Effects, where you have direct control of the outcome by changing the parameters.
With Gen AI, if the result is something that you cannot use for your work – then try to change something, and – it is impossible! You can instead create something else, something new, but it can differ, again, from what you expect. You can become more and more frustrated, because you do not have direct control of the outcome.
What you can do with AI is write another prompt. But you cannot be sure that the AI model will give you exactly what you want.
❓Where would the skill of operating AI tools be? We know what a good photoshop operator can do, what is the AI equivalent?
💬The equivalent is an AI operator who is very experienced in writing prompts. The communication with an AI model needs AI communication skills and this means prompting, prompt design: how can I design the prompt to make the AI model to fulfil my expectations?
❓Do you teach prompt design in your course?
💬I try, in each lecture, to talk about prompt design. But, the more complex the outcome needs to be, the more skills you need to write a prompt.
There are prompt design tools: you can write something that is not really good as a prompt, and this tool can turn your idea into a proper prompt. I teach that too.
The students must keep in mind that it takes a lot of work to design prompts. So, they must be prepared – have a proper story ready, and only then spend time to create, using AI, the right visuals for that story.
❓What is the plan for the future of this course?
💬The AI applications keep improving. Last year we were mostly focused on managing the challenges and the difficulties of all the AI applications. This year we can expect more from them. Runway, the video application, is more advanced, so now we can do more with storytelling.
I want to make sure that the students develop better storytelling skills. Then, they can use the AI applications that are now more advanced, which means that we can create more sophisticated visuals.
“AI can help us greatly in finding patterns in this data. We recently discovered that we can use AI inspired procedures to read emotions from the brain. AI can find out if someone feels afraid or feels disgusted, for example.” – Prof. Benjamin Becker
Professor Benjamin Becker studies the human brain. His innovative, cutting-edge research has been published in top journals such as Nature Human Behaviour and the American Journal of Psychiatry. In this interview with our science editor Dr Pavel Toropov, Professor Becker talks about the breakthroughs that Artificial Intelligence made possible.
❓ Dr Pavel Toropov: What is the main direction of your research?
💬 Professor Benjamin Becker: Trying to find out how the human brain generates emotions, and what happens with these processes in people with mental disorders and how we can make this better.
❓ How do you use AI?
💬 AI allows us to make very big progress in analyzing brain images. The brain is a highly complex structure, probably the most complex structure in the Universe. We are looking at the biological architecture of the brain made from billions of neurons with billions of connections. Humans, because our cognitive capacities are very limited, struggle to make sense of these very complex patterns.
AI can help us greatly in finding patterns in this data. We recently discovered that we can use AI inspired procedures to read emotions from the brain. AI can find out if someone feels afraid or feels disgusted, for example. Using human brain power this is nearly impossible. We need complex algorithms to help us make sense of this complex data.
❓ How is this done?
💬 We put individuals in MRI scanners to image their brain activity while we induce specific emotions. What we, humans, can only see (in the brain scan images) is that particular brain regions become active. But this is too simple, and AI allows us to see more complex patterns and read out the emotions that the individual experiences.
❓ Can you specify?
💬 We, humans, see that specific regions in the brain have become active, but these are rather big structures, and what AI can do is screen those structures on a much finer level than humans can, and then use the data to generate complex patterns – like the fingerprints that specific emotions have left on the brain.
Most amazingly, based on these patterns that it sees in the brain, the AI can read out what the person feels at a given moment. For humans this data is too noisy and too complex. A human interpretation is just not possible. AI gives us the cutting edge.
❓ So, for humans, basically, a brain scan is too noisy – blurry, messy, to see pattern in it. But AI can look at a series of such brain scans, see through this noise, and say – these are all the same, and these people are all feeling, say – fear?
💬 Yes. AI can even take advantage of this noise to make very good predictions.
❓ How cutting edge is this? This was not possible just a few years ago, right?
💬 Yes. I think there has been progress on three sides. Progress with imaging technology, MRI. Then we have progress in terms of what we know about human emotions, and the third is the progress in machine learning and AI.
❓ Where can this take us in the future?
💬 When MRI was developed 30 years ago, people said: in ten years we will have understood the entire brain. This did not happen. It was too optimistic. The brain is still the most complex structure in the Universe, so to understand it will still take some time.
I see more progress from AI in applications. In basic research we look at how emotions are processed in the brain, we look at mental disorders, because that’s when emotions dysregulate. Patients with depression, addiction, they have problems controlling their emotions and they feel these negative emotions very strongly.
Our hope is to map these emotions in a healthy brain, and then apply AI to support the diagnostics of mental disorders. We now see advances where AI can help make good diagnosis – for a medical doctor it is difficult to decide – does this patient have depression, anxiety or something else?
AI could provide us with a probability value – for example, 80%, that this patient will respond to this treatment and not to this one. We will be able to make huge progress, reducing the duration of patients’ suffering and also reducing the cost for the health care systems.
The second thing – using AI, you can have subgroups of patients and make better recommendations for their treatment.
❓ What do you mean by subgroups of patients?
💬 Working as part of a large collaboration, we have recently shown a lot of variation in the symptoms and brain alterations in adolescents with depression. Using findings like these we could target different brain areas, or provide different treatments (according to the subgroups). Some patients would, for example, respond better to behavioral therapy, others to medication, others to brain stimulations.
❓ Can this technology be used clinically, in the real world?
💬 What we see is that there is good progress, but right now AI is not precise enough for clinical diagnosis level. This is about human life. Perhaps soon we will be able to use AI to make recommendations, but now the predictions are not precise enough to enter clinical practice, we need to have a high level of certainty.
❓ There is a lot of fear about AI replacing humans in many jobs. Do you think that in the future AI can replace psychologists?
💬 I think in the next 10 years I will be able to get away with it! I am not concerned for psychology and I would recommend students who have an interest in psychology to pursue it.
❓ Why are you so confident?
💬 One area where AI will not overtake us is in understanding other humans, communicating with other humans, bringing humans together and treating humans as therapy.
❓ Is there a scientific basis to this?
💬 Yes. An area where we see more and more research is AI interaction with humans. We recently did a study about our trust in other humans and in AI.
From very early on in our lives, we are very sensitive to whom we can trust. Evolutionary this is very deep. If you don’t have this skill, your ancestors probably did not survive for long, because they trusted the wrong people or because they did not trust anyone.
We showed that there is a clear brain basis for our trust in other humans. At the same we assessed people’s trust in AI. We asked them – do you trust AI? We saw that these two “trusts” are not related! Moreover, trust in humans was associated with specific brain systems, but we did not see a brain basis for AI.
We have learned as species to trust each other. This is ingrained in our biology. But with AI, even though it is somehow human-like, it has only been there for a couple of years. How can we know whether to trust it or not?
“Do you feel that Chat GPT understands you? Microsoft Copilot? To a certain degree, I do, yes… I did not expect this just three years ago, but it happened. But is it true understanding? I don’t know. “- Prof. Lingpeng Kong
Professor Lingpeng Kong’s research at HKU’s Department of Computer Science focuses on natural language processing (NLP). Before joining HKU, Professor Kong worked at the AI research laboratory Google DeepMind in London.
❓ Dr Pavel Toropov: Your research profile says that you “tackle core problems in natural language processing by designing representation learning algorithms that exploit linguistic structures.” What does it mean, in simple terms?
💬 Professor Lingpeng Kong: We teach computers how to understand human language and speak like a human.
❓ And what is the main difficulty for a computer in doing that?
💬 The ambiguity of the human language. Humans have a lot of ambiguities in our speech, for example: “the man is looking at a woman with a telescope”. Does the woman have a telescope? Is the man using a telescope to look at the woman?
This is called proposition attachment problem. Modern language processing is built around the idea of statistical methods, but you always have a lot of boundary cases that you cannot fully and efficiently model.
❓ Humans figure out such boundary cases easily, from context. Why cannot computers do that?
💬 Because there is an exponentially large space to search. We must find efficient space within the boundaries of computation recourse and memory. That’s the difficult part, to build a statistical method to model that stuff.
Also, it is difficult with low resource languages. For example, Swahili – we don’t have enough data to train the system to work efficiently.
I think the good thing is that with current development of deep learning we can build models with large exponential value, and we can solve a lot of problems that in the past we could not imagine that we would be able to solve. That is why people are excited about AI.
You learn about things and you learn to generalise into things you have not thought before.
It is a matter of what model, what algorithm can generalise the best from less data, less computations. Nowadays we need very large data to train systems, basically the whole of the Internet.
❓ You also work on machine translation. The quality of machine translation seems very good now, much better than just a few years ago.
💬 I feel like the problems with machine translations have been solved! It has been developing very fast. Ten years ago there were translation ambiguities that you could not solve well, but today we have large language models.
Chat GPT translates really, really well! I think when it comes to technical documents and daily use email, it does better than me in Chinese to English translation. Nowadays, if I write an email in Chinese and translate to English, I only have to modify very, very few things.
❓ So will translators be replaced by AI?
💬 I think it is already happening now. Technology has advanced so far that some of the very difficult things in the past are not that difficult today.
Machine translation is just conditional language generation – for example, conditioning on the Chinese part to generate the English part and represent the same meaning. There are a lot of conditional generation problems like this – condition on your prompts to generate the next thing.
Everything is inside one model now, the big language model. Before, question answering has its own system, machine translation had its own system, so did creative writing… but now it is all the same system, it is only the prompt that is different.
❓ What prevents machines from understanding humans?
💬 Nothing, but here is always a philosophical debate about what the true understanding is. Do you feel that Chat GPT understands you? Microsoft Copilot? To a certain degree, I do, yes… I did not expect this just three years ago, but it happened. But is it true understanding? I don’t know.
I like to do tests – I give song lyrics (to AI) and I ask: what does this mean? And it tells me, for example: sometimes times are hard, but things will be better. I still feel that it is not quite a human being talking to me, but maybe because I know that the result is coming from a lot of computation.
But if you do what is called Turing Test – differentiate between talking to Chat GPT and talking to a human being, then it is hard, really hard. I don’t think I can guess right more than 60 or 70% of the time.
❓ What allowed the AI to be able to communicate like that?
💬 We had never, in human history, trained a model of that size before. Before COVID, the largest language model had roughly 600 million parameters. Today, we have the model, the open source one, with 405 billion parameters. We never had the chance before to turn this quantity of data, such large amount of computation, into knowledge inside computers, and now we can.
❓ What is the current direction of your research?
💬 Our group works mainly on discovering new machine learning architecture. When you talk with Chat GPT, after about 4000 words, it forgets. The longer you talk to it, the more likely it is not to remember things. These are fundamental problems with machine learning architecture sites. This is one of the things we are trying to solve.
The machine learning model behind Chat GPT is called Transformer. It is a neural network. It can model sequences used everywhere, for example in the AI program called AlphaFold that works with proteins.
One direction of our work is – making Transformer better in terms of efficiency, in terms of modelling power, so that we can we have Transformer that works with ultra-long sequences and does not forget.
The second direction is pushing the boundary of reasoning limits of the current language models. I have a team working on problems from the International Mathematics Olympiad. We can now use large language models to solve those problems. It is doing really well.
Does AI have beliefs, desires, plans even emotions? Does it want anything? Does it want to answer our questions? Is it capable of having a desire and goal? – Prof. Herman Cappelen
A leading authority in the philosophy of artificial intelligence, Chair Professor Herman Cappelen is the director of AI, Ethics and Society MA, the director of AI&Humanity-Lab, and co-director of ConceptLab at the University of Hong Kong. Author of some of the most influential papers in this field, he has also written several books on the philosophy of AI. In this extensive interview with our science editor Dr Pavel Toropov, Professor Cappelen explains how AI is changing us and – how the humanity can survive AI.
❓ Dr Pavel Toropov: You are a philosopher. It is easy to see why we need a computer scientist, an engineer or a mathematician when it comes to AI, but what is the role of a philosopher?
💬 Professor Herman Cappelen: We, philosophers, have spent centuries trying to understand what it means to have a thought, a desire, a goal, a fear, and – love. Now we use those theories to investigate whether AI can have thoughts, desires, hopes, fears, and love. You are not trained to answer these questions if you are a computer scientist.
As we build more and more powerful AI, we want to know about its capacities. For example, if you create something that has desires, consciousness, awareness and emotions, then you might have made something that’s an agent. You then might have moral and ethical responsibilities towards it. Again, these moral questions are not something computer scientists are experts on or even trained to think about.
If you create something that has its own goals and desires, is really, really smart, and is running our economy, electricity system and the military, you’ve created a risk. That’s why people talk about AI risk and develop strategies to make us safe from potentially dangerous AI.
❓ So, are philosophers “psychologists” who help us understand what is going on “in the head” of the AI by providing analysis beyond what algorithms and mathematics can give us?
💬 Psychologists are primarily trained to answer questions about humans, human fear, human anxiety, human development… They are not trained to think about or experiment on things made from silicon. With AI, there is now a whole new field that takes the concepts we use to describe the human mind and apply them to a new kind of creature. The bridge, from talking about human cognition to AI cognition, is built in large part by philosophers.
It might be that we need a whole new terminology for the “psychology” of AI. Maybe AI does not want things in the way that we do. Maybe AI does not hope or fear the way humans do, maybe it has other (psychological) states, different from ours.
❓ What does your work on AI involve?
💬 One part of my work is about understanding the way things like Chat GPT understand language, the ways AI can and cannot communicate, the ways in which it can be similar to or different from humans in its grasp and construction of language. We want to know if they are communicative agents as we are, or if they are a totally different thing.
❓ What do you mean by “totally different thing”?
💬 Some theorists claim that AI systems don’t understand anything of language. On that view, they are like parrots that produce human speech, but don’t understand what they produce. Something goes in, something goes out, but there is nothing there.
I worked on the nature of language and the nature of communication long before anything like Chat GPT came about. When Chat GPT came out I thought it was incredibly interesting from the point of view of my research, because now we could have created systems totally different from us, that can communicate. This gave us new insights into the nature of language and communication.
❓ What kind of insights?
💬 Almost everything we know about language and communication comes from studying humans, but now it turns out that there are many other non-human ways to communicate. ChatGPT is doing it, and maybe even better than us! I wrote a book on this topic: “Making AI Intelligible”.
❓ In what ways can Chat GPT be better at communicating than humans?
💬 It does not get tired. It is not going to get up and leave because it has another meeting to go to. It remembers. It always talks about what you want to talk about. Its ability to process and produce conversation is much better and faster than ours. Chat GPT can produce everything I will say in the next half hour in ten seconds, and it could process this entire conversation in 2 seconds.
❓ In addition to language, what else interests you, a philosopher, about AI?
💬 Does AI have beliefs, desires, plans even emotions? Does it want anything? Does it want to answer our questions? Is it capable of having a desire and goal? Can it be held morally accountable? Do we have moral obligations towards AI? Are they potentially our intellectual superiors and if so, how do we react to that? My next book on AI: “The Philosophy of AI: An Opinionated Methodological Guide” is in large part on these issues.
❓ So, does AI have goals?
💬 I think the answer to this is yes.
❓ Why?
💬 Because AI exhibits the kind of structural behaviour that we would consider as having a goal in humans. It behaves structurally as if it had a goal and plan and some beliefs and then acts on those beliefs.
❓ Could you clarify?
💬 For example, if you ask it questions, and think – why is it giving me answers? Well, one way is to say AI wants to answer your questions, it understood your questions and it thinks that the answer is “bla bla bla”, and so it says it “bla bla bla”. That’s the kind of explanation that we use for humans.
The people who disagree say: “Yeah, but that’s not really what’s going on, because it is really just processing symbols and just predicting what the next word is.” I think this is a horrible argument because you can say that about humans too: “What’s really going on is just processing in some fatty tissue inside the brain”. That would be a bad argument for the view that humans lack goal directed behaviour. It’s an equally bad argument in the case of AI.
❓ So, do you think that AI can have a mental life?
💬 I’m confident that people who say: “AI cannot speak a language, it cannot have a mental life”, are basing their views on bad arguments. The big picture is that I don’t think we can be sure about the answers until we have resolved some extremely hard philosophical questions.
We used to think that non-human animals couldn’t think, plan, or have emotions. Now we are much happier to accept that elephants and dolphins, cats and dogs, can communicate and have rich cognitive, emotional and social lives. You don’t have to be a human to have these. Mental and cognitive life can exist in things very different from us. But then there is a big leap – could it also exist in something non-biological? I am not convinced by the arguments that it cannot, and I think there are strong arguments that it can.
❓ One of the main worries about AI is that it will replace humans in every job imaginable. What is your view on that?
💬 Some jobs will go very fast. There are a lot of skills AI cannot do now, but this will change fast. People say that AI makes mistakes, but I am completely unmoved by that. Humans make mistakes much more than AI and the rate at which it improves is unbelievable. Chances are that those deficiencies will disappear really fast.
But surprisingly, much will stay because we want human to human interactions. And in more areas than you might think! Let’s look at chess. Humans are much, much worse at chess than even a cheap computer. The best chess player, Magnus Carlsen, can never beat the best computer.
But the important thing is that nobody wants to watch two computers play each other. People want to play people and watch other people doing the same.
I asked my 13-year-old daughter: would you listen to a musician who is an avatar that creates the same music that you like? (She said) Of course not! We care about people and a lot of the things we do is about people. I would not talk to you and care much about this interview if you were an avatar sitting there in front of me.
❓ Let’s talk about the risks that AI poses to humanity, this is another area of your work, correct?
💬 Will the super intelligence turn on us? I don’t know if it will do that, but it is possible.
I have a new paper that’s co-authored with Simon Goldstein. It’s called “AI Survival Stories”. It is about the ways in which we can survive AI.
There are two broad categories of how we can survive – one is what we call plateau stories and the other non-plateau stories.
One kind of plateau is technical. We keep developing AI over the next few years, but nothing significantly better happens. For some reason that we could not foresee, the technology does not keep evolving, it flatlines.
Another plateau story is cultural. Maybe we’ll eventually treat dangerous AI the way we now treat biological or chemical weapons. One way that could happen is that someone uses AI in very dangerous ways that has horrific consequences. After that, AI is perceived as a threat to humanity and there’s a worldwide effort to block the creation of dangerous AI.
The other scenario is that there is no plateau, and the AI becomes super intelligent and super powerful. And then there are two ways in which we can survive. Either we make sure that AIs are aligned with our values and are always nice to us. The other option is that we can control the AI systems and somehow make sure that they can’t harm us.
Technical plateau is not super likely. Cultural plateau is not that likely. Alignment is incredibly difficult. Trying to control a super intelligent AI seems hopelessly difficult. What you think of the probability of humanity surviving AI depends how likely you think these various survival stories are.
❓ Replacing human jobs and threatening the humanity aside, AI is now affecting our daily and emotional lives. In the 2013 film “Her,” a man falls in love with an AI chatbot. This seemed like pure fiction then, but now we are not so sure. Can AI change us?
💬 AI can change us in very unpredictable ways. It changes our language, changes how we classify the world, changes what we think is important in the world, and changes how we think about each other. It is all very new, only two years old, but the change is unbelievably fast. I have never seen anything change that fast.
There is now a huge market for romantic chatbots – for boyfriend, girlfriend, partner, whatever bots. People can now develop a super emotional, romantic relationship with a chatbot.
I was just at a conference in Germany where one of the speakers had created an AI robot to help interact with people with dementia. It was remarkable how the patients often prefer that to the overworked and stressed nurses.
There is something great about these chatbot friends. They are never tired; they are never in a bad mood. They don’t ask for anything in return. If we get used to that as a model of friendship and intimacy, might we expect that from humans too: why is he or she not like a chatbot?
Therefore, one thing that I think is happening already, words like “friendly”, “empathy”, “relationship” are changing in meaning. Our classification of emotions and cognitive states is in flux, changed by our interaction with AI systems.
Our ConceptLab at HKU is about how language changes. And these kinds of language changes happen when we are in new situations. Now we are in a new situation – one where we can talk and engage with AI systems.
❓ Continuing on the subject of AI films, which films about AI do you think come closest to reality?
💬 The Matrix is an amazing film, a brilliant illustration of a very important philosophical issue.
AI has created an illusion of life, and you are not a person but a figment of a big computer system. It is something that philosophers think about, and the Matrix is an ingenious illustration of that possibility. Philosopher Nick Bostrom has a super famous paper arguing that the probability that we are in a matrix is pretty high.
It is a very simple calculation – what is the probability that AI systems will be able to generate a completely realistic world? Pretty high. How many of these are there going to be? Probably a lot. How many real worlds are there? One. So, what’s the probability you are in a real one? Pretty small.
❓ And finally, will AI replace philosophers?
💬 I’m not sure. There will probably be AI systems that can generate philosophical arguments better and faster than any human. What I’m less sure about is whether people will want to talk to and interact with this system.
Recall the point from before: lots of people want to pay attention to Magnus Carlson playing chess and no one cares about the games played between chess computers. Maybe philosophy will be like that? I think we’ll be harder to replace than, say, computer scientists. Their function is to produce a certain output and if something can produce it cheaper, faster, and better, replacement is likely.
Philosophy has human connection at its core and so is harder to replace. But maybe not impossible.
👏 Thank you, Professor Cappelen!
🔍 Many of the questions discussed in this interview are part of the curriculum of the AI, Ethics and Society MA at the University of Hong Kong.
For more information: https://admissions.hku.hk/tpg/programme/master-arts-field-ai-ethics-and-society
