Since the launch of ChatGPT, on November 2022, Generative AI has been the hottest topic in every conversation. It is almost two years later now, and we continue seeing new breakthroughs happening every other week. Trying to keep up with every news and opinion piece in such a context is impossible, but at the same time it is essential to be aware of the implications that this technology will have in our future. In cases like this, the best solution is to turn to the prominent voices, those who have been out there trying and thinking deeply about this topic for a long while, and being recognized as having valuable insights to offer.
Given that, I picked up the book CoIntelligence: Living And Working With AI, by Ethan Mollick, to hopefully serve as a baseline of understanding when it comes to the current trend of Artificial Intelligence. Ethan is a college professor at the University of Pennsylvania who has been experimenting and writing profusely about AI (following him on Twitter/X is among the best things you can do to keep yourself up to date with the practical side of the field) and, if you believe his cleverly AI-directed propaganda on his bio page, is a well-respected person by artificial intelligences of all kinds - who better to bring some insight to us, mere biological, natural-intelligence plebs?
I read CoIntelligence in the context of my personal studies habit, in which I set off 15-30 minutes of my free time each day to work through a technical book which is either a classic in the field or that I believe has an interesting point of view. I studied this book in 2024, from April 22nd to June 8th. In this post I offer a short review of the book, consolidating some of my notes and highlighting the things that drew my attention the most. If you are interested in the field of AI, I strongly recommend you buy the book and read it for yourself - it is worth every second you spend with it.
Motivation
I have been interested in the field of AI for as long as I remember, long before I decided to have a career in Technology. Even as a child, I was draw to videogames and wondered how might a piece of electronics have enough intelligence to put up a challenge against human players - I vividly remember mixing up the concepts of soul, intelligence and conscience, wondering if after death I could be re-incarnated as a NPC in some game. Later, when I became a Computer Science undergraduate, I chose to follow AI as my main field of interest, committed to attending as many classes in this area as possible.
Fast forward a few years, and the launch of ChatGPT coincided with the start of my graduation project at college: both happening in November 2022. I always knew I wanted to do the project in some subarea of AI, but up until then I had thought about going for AI in games. After some discussion with professors, I ended up deciding to do it about applying BERT (and older LLM by Google) to the task of poem classification. Changing the focus to the intersection of AI and NLP was serendipitous, as I confess I had never heard about LLMs or the brewing revolution that was picking up steam at the time. However, with this change I had the opportunity to follow closely the arrival and consolidation of generative AI as the defining technology of our times.
A Thousand Nights And A Night
A big part of the fascination with generative AI is that it truly feels a little magical at times. Even to experienced professionals, specialists and the people who know intimately the mathematical and technical details that make it work. It is something so new and unexpected that everyone must have a period of adaptation and reframing of their expectations with regards to what these computer systems do (these are computer programs that can write human prose and suck at mathematics! the exact opposite of what computers have been doing since their invention).
In the very beginning, the book says that it takes 3 sleepless nights to get to know AI. I disagree. I think it takes at least a thousand nights to achieve that. And I fear I might even be setting an overly optimistic estimate.
I know I am getting close to those thousand (about two years of experimenting, which is roughly in the 700s days range) and I feel like I have barely scratched the surface of what is possible with this technology. The book itself describes several techniques I had not used, and that I have incorporated since then. And as newer and more powerful models appear, crossing into different modalities such as vision and audio, the possibilities increase faster than what anyone can cover.
Summary of main ideas
Before we get too dizzy talking about the exponential amount of possibilities that generative AI brings, let's take a look at the book's main ideas. These serve as the theoretical foundation for all of the experiments and wild extrapolations discussed as the book progresses.
General Purpose Technologies
Any technology always has some impact in a society, and usually in the society's economy. Some, however, are broadly-applicable enough to have a significant impact on the entirety of the economy. Classic examples are the steam engine, electricity and computers themselves - each of which completely changed the game when it entered common usage. The book claims that Artificial Intelligence is an instance of a General Purpose Technology, and it is very hard to argue against that. We have already seen AI, either the new generative deep learning trends or more classic approaches, being applied to a vast amount of fields of our society, with several more expected to also be impacted in the near future. As a side note, the fact that General Purpose Technologies is also abbreviated GPT seems to be totally unrelated to the flagship models that power OpenAI's ChatGPT - the earliest entry I found on Wikipedia's page about the topic, already using this very name, dates from 2014, four years before the first GPT models came along.
Four Principles
The book proposes 4 principles to keep in mind when interacting with AI systems. These are:
- Always invite AI to the table.
- Be the human in the loop.
- Treat AI like a person (but tell it what kind of person it is).
- Assume this is the worst AI you will ever use.
By following these, we have a strong framework to analyse our interactions with AI, and to have a sense of where we are going with them. And this is key: everything is happening so fast with this technology, that just understanding what is going on is a major concern for anyone. I will have more to say about this later, when I talk about my takeaways.
Centaurs and Cyborgs
Another separation that the book proposes is in two modes of working with AI:
- Centaur: when there is a clear separation between person and machine, with the human doing one set of the tasks and the AI doing another set. For instance: a human delegates to an AI system the summarization of the news of the day, which the human later reads to update himself on what is going on. The analogy that gives this mode its name is with the clear separation between the top side of the centaur (which is entirely human) and its bottom side (which is entirely animal).
- Cyborg: when there is a blending of the person and the machine, with tasks being handled together and the "pilot seat" alternating frequently between AI and human. A good example is how LLMs are being used for coding in several products that have come along the last years, with inline suggestions from the AI being fed in real time while the human codes. The analogy is with the fully integrated nature of cyborgs, organisms that are composed of both organic and electronic parts as equals.
Personal Highlights
Moving on to my personal opinions about the ideas in the book, here are some of the things that most drew my attention.
A Time For Explorers
There is an old bittersweet meme that describes us as those unfortunate beings that were "born too late for the sea, too early for deep space". I admit I have often felt like that myself: someone deprived of a vast and new field for exploration, where one could get lost in the promise of adventures and riches never before dreamed. Generative AI solves that.
At least for the moment, it is very clear that we are living the early days of something extremely disruptive. It is impossible to see humanity going back to how things were before co-existing with ubiquitous artificial intelligence systems, in the same way that once the internet was introduced it was impossible to go back to an offline world. While there definitely is a large layer of hype surrounding generative AI, and we have not even started to explore the truly disruptive implications of it (both things that were true of the internet in the late 90s and early 2000s), it is clear that this technology will be a foundational part of how society will function in the future.
The book effectively captures this feeling in several places. One of the direct implications of this is that the main task of each and everyone of us, right now, is to use these systems, explore them, explore our interactions with them, find out their strengths, weaknesses, breakthrough points, etc., as the ultimate capabilities and limitations of AI are currently unknown. And I agree with the argument the book makes about this being first and foremost a task for the individuals. Large entities, such as corporations and governments, will definitely experiment and find out how to employ this technology for their own ends, but by their very size this will be a long process. As individuals, we can experiment much faster, as we have no need to coordinate efforts along chains of reporting, to make strict allocation of resources to this effort, and we have very little to lose if we decide to just drop an experiment and switch to a new, more promising one, at any time. We get to be the explorers now.
Similarly, this mindset of experimentation and sharing brings the excitement associated with fabled moments of our past. We all learned to write good prompts by reading, copying and experimenting with the prompts openly shared by anonymous enthusiasts, who themselves learned much the same way. To the point in which sometimes it is considered good behavior to share your prompts together with your results, in a sort of "open prompt" philosophy that reminded me of the early days of computer programming, where people would learn to code by checking out pieces of code that other developers had created. I have tried to share my prompts whenever I have used a relevant amount of generative AI in my personal projects, such as for the creation of the User Stories for SnakeJS. The book talks about how generative AI differs from traditional software by not coming with an operating manual or tutorial, leading to everyone sharing prompts "as if they were magical incantations rather than regular software code", a beautiful turn of phrase that reminded me of Simon Willison's "We get to be wizards now!" moment.
All of a sudden we get to be both explorers and wizards!
Potential for education
Another point I enjoyed in the book was its emphasis on the impact that generative AI can have on education. I was particularly happy with how it explored both sides of the issue.
As a professor, the author is very aware of the negative impact that easily accessible AI has on the current mainstream mechanisms used by educational systems. As an intelligent professor, he is also very aware that the right way to deal with new ideas and technologies is not to fight back against its disruption of current models, but instead to focus on how its strengths can bring us to the next level by proposing new models that achieve more than the previous ones. The book describes a few of the experiments that the author used to include generative AI as a first-class citizen in his classes, and the outcomes.
Beyond formal and group education, generative AI can be a huge boost to personal learning and growth. The current state of the technology already brings us teasingly close (but not all the way!) to the vision of everyone having a personal tutor, mentor and guide to facilitate us following our interests. This model is more aligned to the classical view of education (think Ancient Greeks and their education system - at least for the elite), in opposition to the industrial-grade "factory of standardized citizens" model that represents the current state in most western societies now. Ubiquitous, personalized, generative AI systems can finally solve the problem of scale, which held us into the non-optimal model for so long.
As a final remark about education, the book also makes the counterintuitive argument that generative AI can mean an increase in value for an education in the Humanities. For a long time, more profitable careers and a higher status has been driving people away from the Humanities and into more hard-science fields such as Computer Science and Engineering. However, since generative AI models are trained with the accumulated knowledge our species has gathered in writing, images and sounds, a deep knowledge of the cultural heritage, artistic history and philosophical traditions suddenly means having a concrete advantage in extracting the best results out of these models. I fully agree with the book in that, I think if we are wise enough as a society we will start giving more value to the study of the Humanities.
AI success in exams
Although almost everything I have to say about the book is agreeing with it, there is at least one aspect in which that is not the case. The book, as almost every other source that talks about current generative AI models, talks highly of cases in which AI models (most often LLMs) achieve high results in exams and tests commonly used to assess humans. I have to say that these cases do not impress me too much.
The cynic in me has always disregarded almost every single test I saw as a very weak proxy to evaluating real knowledge. Be it an university entry exam, a cheap IQ test or any other evaluation mechanism, they always end up confusing some operational skill with knowledge: dissertations test your writing skills, not your knowledge (much less your intelligence); multiple-choice exams test your memory skills, not your knowledge (much less your intelligence). A test only tests the subject's ability in passing the test, never the subject's knowledge or intelligence. I have passed way too many exams I was hopelessly underqualified for during my school and college years to take any such success by AI systems seriously. They have much more interesting, useful and mindblowing proofs of intelligence than passing tests.
Comparing to expectations
Finally, for each book I study I always like to make a comparison of how it stood up to my expectations. In terms of quality, CoIntelligence completely satisfied my expectations. In terms of usefulness, it by and large exceeded them. I have found in this book both inspiration and specific techniques to incorporate more AI into my daily routine.
One significant difference between me and the book is that the book focuses on frontier models, that is, the biggest and most powerful models currently available, all of them hosted by some big corporation (OpenAI, Anthropic, and so on). I am more interested in local and open models, that anyone can run on their own devices. While I understand that there is indeed a big gap in cognitive performance between these two types of models, I personally think that the customizable and private nature of open models makes them more disruptive, as they are the ones that can truly be incorporated as a personal extension of one's cognitive capabilities with no threat to individuality. I was aware of this difference in interest before I picked up the book, though, and found that it had no negative influence in my reading.
Conclusion
To wrap up, I think CoIntelligence is an outstanding book, full of interesting, inspirational and useful ideas. It is specially useful for those who are not constantly reading every piece of news around this topic, but that still want to have a solid framework with which to reason about the usage of artificial intelligence systems.
In the foreword to the book Working Effectively With Legacy Code, Uncle Bob highlights how he enjoyed the way Michael Feathers talked about having that "... then it began" moment in programming. That moment when you write a piece of code and, for the first time, you realize you will never stop doing it for the rest of your life. I think CoIntelligence captures very well the similar feeling that so many people are having with AI, that feeling of having just met the Future and wanting to dive deep into it as much as possible. It provides excellent guidance as we move forward and, together, by experimentation and open collaboration, learn how to co-exist with our new found partners, and what our shared future will look like.
And it reminds us that our nights have only just began.
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