AI book club pt 1
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On this week’s podcast episode (linked here), I had a great conversation with Seth Earley, writer of The AI-Powered Enterprise about how organizations can implement AI models more efficiently by utilizing ontologies.
Having read his book in preparation for our interview, I got to thinking about some of the other books I’ve found valuable in my years-long quest to separate the sci-fi conception of AI from the impact that AI tools are having and will have here in the real world. So, I decided to list out some of the books that I think are actually worth your time, plus my reviews, and a little motivation for why I picked them.
Book 1 - A World Without Work: Technology, Automation, and How We Should Respond by Daniel Susskind
If you're interested in how AI is going to impact the future of work and employment in society.
There are two popular outcomes envisioned in talks about AI-instigated mass unemployment. Outcome number one: AI as the great liberator from work (see, Aaron Bastani’s take that: “luxury will pervade everything as society based on waged work becomes as much a relic as the feudal peasant and medieval knight”). On the other side of the spectrum is outcome number two: the complete economic devastation of the working class.
Daniel Susskind’s work does not rely on extreme modes of thinking like the two examples I just gave you. In fact, I wouldn’t say that he has any firm personal opinions on the ‘good’ or ‘bad’ of mass unemployment caused by AI, but rather views it as a broad inevitability with many forking paths leading to and from it. What he does point out is that the broad arc of culture has, for many years positioned work as the primary determinant of an individual’s value... and that many in the workforce today have already lost faith in the religion of work (eg, r/antiwork).
One of my main takeaways from Susskind’s book is that what a “world without work” looks like from our current vantage point is a highly subjective thing - one that’s been skewed by the fact that commentators on the subject tend to actually enjoy their professions (unlike a large proportion of typical working people). This of course is in and of itself somewhat of a controversial assertion in countries like the US where work and personal value are so intimately intertwined, but don’t be fooled into believing that belief is any meaningful aspect of Susskind’s arguments - his approach is purely pragmatic.
The mechanisms he recommends using to forge a favorable path to the death of work include shortening the average work week, robust new leisure policies, and a major cultural shift in the way we look at what makes our lives meaningful. But, the key value of “A World Without Work”, and the reason I think it’s worth reading is that it treats the issue as a pragmatic reality rather than an ideological thought experiment. It’s one of the only pieces on the subject which has been debated since at least 1891 with the release of Oscar Wilde’s The Soul of Man Under Socialism, that approaches the issue from an economist’s perspective, and it avoids all the political muck typical of other explorations thanks to this unique characteristic.
Book 2 - Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
If you're interested in what AGI might actually look like and want to differentiate between superintelligence and generative AI.
“Superintelligence” is probably the pre-eminent work exploring the existential risk that is artificial general intelligence (at least, if you ask YouTube). It’s reputation amongst sci-fi fans and content creators is relatively well-deserved. While occasionally dense, Bostrom goes out of his way to make technical examples more easily understandable to a non-technical audience, making it a book worth anybody’s time.
To set the stage, “Superintelligence” is specifically about its namesake, and explores in great detail the differing outcomes that may be set in motion upon reaching that mythical moment in which true artificial general intelligence is achieved. Obviously, superintelligence is in and of itself a fantasy, and therefore his exploration entails a good amount of world-building and speculation regarding what paths will be successful in producing such technology.
Written years before the explosion of LLMs onto the scene, the book feels more hypothetical and speculative than one would like today. But, importantly, it consistently makes the reader aware that the scenarios it presents are in fact thought experiments and that real-life, incremental achievements in AI like those exemplified by LLMs do not come remotely close to the reality of a true synthetic sentience.
So, why read Superintelligence today? In my opinion, while less concretely tethered to the real world than the other two books on this list, Superintelligence explores the outer reaches of synthetic intelligence in a way that clearly showcases the difference between today’s AI technologies and what a truly superintelligent computer would look like. To be clear, we’re not close to AGI, we’re only marginally closer than we were before generative AI, and it’s very possible that we’ll never actually achieve true superintelligence. That may sound disappointing but trust me, once you read some of Bostrom’s predictions for what might happen when we do achieve it, you’ll be rooting against it too.
Book 3 - Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian and Tom Griffiths
If you want a unique take on how algorithms can be applied to actual daily decision-making.
At the end of the day, all machine learning comes down to algorithms. While the underlying algorithms of life like the formula for DNA, the electrical processes of the brain and more are a too abstract to be tangibly useful to the average person, by understanding the algorithms that make machines work, we can also apply them to our own decision making to simplify complex questions and return more logical answers.
In “Algorithms to Live By”, the authors present a series of complex computer science concepts, such as sorting, caching, scheduling, and game theory, among others, in a highly digestible manner. What makes it truly interesting though is the authors' ability to link these concepts seamlessly to everyday human decision-making scenarios - such as deciding when to stop looking for a parking spot or when to settle down in relationships. The end effect is both an understanding of how the concept works, and how it can be applied to a familiar example - which I’ve found incredibly beneficial to comprehending computer science topics as a non-coder.
Christian and Griffiths present each concept with sufficient background, explaining their origins in computer science, and gradually build up to their applications in daily life. They succeed in making complex theories comprehensible and engaging for the layperson, all while maintaining academic rigor. Their approach illuminates our understanding of decision-making, showing that there is in fact an 'optimal' way to make decisions if one considers and properly quantifies the trade-offs. They argue that understanding these algorithms can lead to better decision-making, reduced stress, and ultimately improve life.
While "Algorithms to Live By" is a well-written and thought-provoking book, it's not without its limitations. For one, the link between computer algorithms and human decisions may seem a bit stretched or oversimplified at times. While it's an interesting premise, human decisions often involve emotions and unpredictability, aspects that algorithms don't usually handle.
Additionally, readers with a solid background in computer science might find the explanations of basic concepts too detailed. Conversely, complete beginners might require a re-read of some sections to fully grasp the underlying principles.