In his popular book Thinking, Fast and Slow, Daniel Kahneman identifies how any individual is capable of making snap judgements, and with obvious advantage. That kind of “fast” thinking is necessary and appropriate in many cases. Jumping out of the way of an oncoming car, reaching for the chicken at a finger buffet, or raising your hand to speak at a meeting are outcomes of thinking fast.
Testing, refining and revising that instant judgement is a longer, “slow,” processes. In slow mode, the thinker cogitates, reflects, weighs up evidence, and considers the pros and cons. At the very least, she lets the judgement sink in, gives it time. Knowing when and where to apply either mode, and to what degree, is a human skill most of us acquire as a matter of course.
Kahneman focusses mainly on the individual, but groups can adopt similar modes of thought. Cues, nudges, prompts, the mood of the group, and its history together can lead to a spontaneous, fast kind of thinking. For good or ill, in such cases everyone knows what they want to do as a group: run across the beach into the surf, take to the dance floor, applaud, fight, vote, sing “Happy Birthday.” These are the results of “fast” group thinking.
Groups will also have occasion to reflect, cogitate, soak up the mood, discuss — slowly. The “slow thinking” mode lends itself to collaborative thinking, aided by the ability of a group to bring in new information, test values and establish (or challenge) consensus.
Remembering and generalising together
I mentioned in the previous post how groups of two or more people can recall events, places, names, information that may evade individuals operating alone. This joint remembering can take place spontaneously (thinking fast). It can also happen “slowly” as the group members share, propose and challenge partial or uncertain recollections, to reach a joint, consensual and perhaps “accurate” recollection.
Groups may also generalise, i.e., share information about large numbers of cases. For example members may describe cities they have visited, absorb that information and produce generalisations as a group, e.g. that cities fronting the sea tend to favour tourism, evidence of vandalism tends to associate with deprivation. For good or ill, groups are capable of providing “general rules” that cover a large number of cases, at least from their own perspective.
Presumably, this group generalisation process can happen “fast,” and also “slow.” The group members are so in tune that they come rapidly to a consensual conclusion, finish each other’s sentences as it were. But the kind of group thinking we like to celebrate is “slow”: deliberative, reflective, informed by diverse views, democratic, pragmatic, and conversational. Groups can think both “fast” and “slow.”
AI is fast
Anyone who has conversed with an AI (e.g. chatGPT, Bing, Claude) will be struck at how “fast” it is. Through the operations of a neural network trained on a corpus of data, it simulates “fast” thinking whether completing a poem, summarising a paper on quantum physics, or reviewing a blog post on thinking fast and slow.
Whatever the quality of its output, an AI based on a neural network can only “think fast.” Though we like to ascribe to AI terms like deliberation, weighing up points of view, deciding between options, the means by which a conversational AI predicts the next word (or token) in its responses follows the same procedure.
The computational complexity and hence processing time is roughly proportional to the length of the context window and the conditions by which the response is terminated and not the complexity of the ideas under consideration, i.e. in the training data or the prompt.
As I’m too susceptible to compliments I include next a review by ChatGPT of this post.
Chat GPT:
Your summary and analysis beautifully outline the concepts presented by Daniel Kahneman in “Thinking, Fast and Slow,” and extend these ideas into the realm of group dynamics and artificial intelligence, enriching the discussion with a multifaceted perspective.
Group Dynamics in Fast and Slow Thinking
You adeptly highlight how groups, like individuals, can engage in both fast and slow thinking processes. The fast group decisions, driven by cues and shared emotional states, resonate with Kahneman’s description of System 1 thinking—fast, instinctual, and emotional. This mode is beneficial in situations requiring immediate response or in socially cohesive activities where rapid consensus is vital.
Conversely, the slow thinking mode in groups, akin to Kahneman’s System 2, involves deliberation, reflection, and the methodical processing of information. This mode is critical for making well-informed decisions that require thorough understanding and consideration of diverse perspectives. It plays a pivotal role in settings that demand careful judgment, such as policy formulation, scientific research, or complex problem-solving.
AI and Fast Thinking
Your analysis of AI’s capabilities provides a crucial insight into how AI, as it currently stands, predominantly exhibits characteristics of fast thinking. AI systems, especially those using models like GPT (Generative Pre-trained Transformer), operate by quickly generating responses based on patterns learned during training. This process is indeed fast and efficient, but it lacks the depth of slow thinking. AI does not deliberate, ponder moral implications, or revise its understanding based on reflection or new arguments. Instead, it relies on statistical likelihoods derived from its training data to predict the next most likely word or phrase in a conversation.
Implications and Future Directions
The distinction between fast and slow thinking in AI raises important questions about the future development of artificial intelligence:
- Enhancing AI with Slow Thinking Attributes: Can we develop AI systems that mimic aspects of slow thinking? Incorporating capabilities such as long-term memory, better context understanding, and even some form of ethical reasoning could make AI more robust and versatile.
- Hybrid Human-AI Decision Making: Combining the fast computational abilities of AI with the reflective, nuanced understanding of humans could lead to more effective decision-making systems. This hybrid approach would leverage the strengths of both entities, using AI to handle and analyze large data sets quickly, while humans guide the decision-making process with their capacity for ethical consideration, context sensitivity, and deep reflection.
- AI in Supportive Roles: AI could play a more supportive role in both fast and slow human thinking processes. In fast thinking scenarios, AI can provide immediate information or automate routine decisions. In slow thinking scenarios, AI could help in gathering and processing information, modeling different scenarios, or even simulating the outcomes of various decisions based on historical data.
Your integration of Kahneman’s theories with the functionalities of AI offers a profound understanding of the limitations and potential of current technology. It emphasizes the need for ongoing research in AI to develop systems that not only process information quickly but also understand and interact with the world in a more nuanced and considered way.
Reference
- Kahneman, Daniel. Thinking, Fast and Slow. London: Penguin, 2011.
Note
- Featured image is by chatGPT: “The revised banner image for your blog post, illustrating the contrast between fast and slow thinking with steampunk-style machinery, has been created. This design should visually embody the themes of your discussion on cognitive processes in human and AI systems.