Artificial Intelligence (AI): reality and hype
Christiane Amanpour recently presented this reality-based discussion of artificial intelligence (AI) with MIT economist David Autor that is cautiously optimistic and informative about the subject.1
Parsing the hype
One guideline I use for myself in thinking about AI is a Not-Skynet-or-Mister-Data measure. If we think of AI as an android that can pass the Turing Test, i.e., humans using the system are unable to distinguish whether they are talking to a machine or a human being, then AI is still a fantasy. A well-grounded science-fiction fantasy maybe, but still a fantasy.
In the last decade, “critics have continually noted that the Turing Test is less about building an intelligent system as it is about building one that deceives people most effectively.” Being clever at mimicking human behavior is not the only element of intelligence for AI machines. Deception and intelligence are not identical, in other words.2
But neural networks that facilitate processing and use of data are real systems with real capabilities. They can evaluate large numbers of data points and make numerical calculations and derive probabilities faster than human mathematicians. And machines today like mobile phones can process the same amounts of data that decades ago would have required huge physical computers.
Yet, as Cristoph Kehl has observed, “When - and if in any case - a strong AI is realizable which comes close to human intelligence is a question over which we can only speculate.”3
Kehl takes the application of AI to elder care as a paradigmatic case of how to think about the possibilities, uses, practicality, and ethics of AI.
The idea that the rising need for elder care can be satisfied by Mister Data-type service androids is blind optimism, though it’s a favorite fantasy for AI boosters. For wealthy countries, the much more feasible and readily possible solution lies in immigration and sensible laws and educational practices to go with it.
As Kehl explains, this is a good field in which to conceptualize the barriers between humans and AI machines: “Because the role of neurotechnology is mostly limited to the therapeutic field, service robotics for geriatric care is the driving force behind the dynamics of the dissolution of boundaries.”4
That also makes the field a good way to conceive of practical applications and their limits. Robots can delivered meals to rooms in a nursing-care facility. But the field also requires the emotional interaction other humans provide and the kind of nuanced evaluations of behavior that medical samples and AI calculations cannot provide.
Kehl also notes, “The large amount of energy AI requires is also part of the differentiation between humans and machines.”
AI does require a lot of energy, which also proved to be a mare limiting factor in cryptocurrency. While AI models adapt knowledge from human brain operations, they don’t come remotely close to the human brain’s energy efficiency at this point.5
Catrin Misselhorn wrote six years ago about consciousness and will in the AI context. And though six years can be a long time in AI research, this broad observation still holds:
[A]rtificial systems do not yet actually have consciousness and free will. Consciousness in the sense of subjective experience would be required, for example, in order to be able to feel moral emotions such as compassion or guilt. Free will opens up the possibility of deciding against an option for action that is recognized as moral and of [instead] acting immorally. Artificial systems do not yet have this ability either - and should not have it, in order to protect the user.6
AI Waves of development and AI “Winters”
One thing to note in the current phase of AI excitement is that a large part of the hyperventilating about AI is actually saying, there were huge technological developments in the past that transformed the world and AI could do that, too! It could, and it is doing so to some extent.
But in those kinds of presentations, you may have to look very hard to get information about what the actual promising developments are and why they are promising. A recent example I encountered was a German book titled, Droht das Ende der Experten? ChatGPT und die Zukunft der Wissensarbeit (Is the End of Experts Impending? ChatGPT and the Future of Knowledge Work).7 But just because the cotton gin or the steam engine hat dramatic effects on the world economy, doesn’t mean that every novel development will have the same fate. Framing AI in that way is more hype than analysis.
David Autor’s interview above is an example of a meaningful Long View picture of technology developments that’s not just an instance of superficial promotional speculation. It includes a useful framework of comparing the jobs of air traffic controller and crossing guards that illustrates why some things can be more easily automated (or AI-ed) than others.
Manuela Lenzen reminds us that there have been two previous waves of AI research and development that were followed by what she calls “AI winters.”8 The current Wikipedia article on the topic notes, “There were two major winters approximately 1974–1980 and 1987–2000,” though it notes that different sources date the two slightly differently.9 It designates the early 2020s as a third AI “spring.” When ChatGPT in November 2022 was made publicly available online, that was an important moment in raising general public awareness of the current version of artificial intelligence.
As MIT Technology Review put it in 2023:
We’ve reached peak ChatGPT. Released at the end of November as a web app by the San Francisco–based firm OpenAI, the chatbot exploded into the mainstream almost overnight. According to some estimates, it is the fastest-growing internet service ever, reaching 100 million users in January, just two months after launch.
Through OpenAI’s $10 billion deal with Microsoft, the tech is now being built into Office software and the Bing search engine. Stung into action by its newly awakened onetime rival in the battle for search, Google is fast-tracking the rollout of its own chatbot, based on its large language model PaLM. Even my family WhatsApp is filled with ChatGPT chat.10
Since the current AI wave is functioning as the tech industry’s Next Big Thing at the moment, it’s worth remembering that the term “artificial intelligence” made its way into the general human vocabulary through being popularized by the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) in 1956, which was hosted by John McCarthy and Marvin Minsky, and featured the presentation of the Logic Theorist program and which was “designed to mimic the problem solving skills of a human and was funded by Research and Development (RAND) Corporation. It’s considered by many to be the first artificial intelligence program.”11
AI Could Actually Help Rebuild the Middle Class, Says MIT Economist. Democracy Now! YouTube channel 04/09/2024. (Accessed: 2024-10-04).
Lenzen, Manuela (2023): Künstliche Intelligenz. Was sie kann & was uns erwartet (4.aktualisierte Auflage), 29. München: C.H. Beck. My translation from the German.
Kehl, Christoph (2018): Entgrenzungen Zwischen Mensch und Maschine, oder Können Roboter zu Guter Pflege Beitragen?): Aus Politik und Zeitgeschichte (APuZ) 6-8. My translation from the German. “Wann – und ob überhaupt – eine starke KI realisierbar ist, die der menschlichen Intelligenz gleichkommt, ist eine Frage, über die sich nach wie vor nur spekulieren lässt.”
Kehl, Christoph, Ibid. My translation from the German. “Da sich die Rolle der Neurotechnologie hauptsächlich auf den therapeutischen Bereich beschränkt, stellt die Servicerobotik für die Altenpflege die treibende Kraft der Entgrenzungsdynamik dar.”
Whitten, Allison (2024): Ein Chip manch dem Vorbild Des Gehirns. Spektrum SPEZIAL Biologie Medizin Hirnforschung 1.24, 62-65.
Misselhorn, Catrin (2018): Mascinenethik und "Artificial Morality": Können un Sollen Maschines Moralisch Handeln? Aus Politik und Zeitgeschichte (APuZ) 6-8, 29-33. My translation from the German.
Holtel, Stefan (2024): München: Verlag Franz Vahlen.
Lenzen, Manuela (2024): Künstliche Intelligenz.Fakten, Chancen, Risiken. München: C.H. Beck.
AI winter. Wikipedia 04/29/2024. <https://en.wikipedia.org/w/index.php?title=AI_winter&oldid=1221339673> (Accessed: 2024-03-05).
Heaven, Will Douglas (2023): ChatGPT is everywhere. Here’s where it came from. MIT Technology Review 02/08/2023. <https://www.technologyreview.com/2023/02/08/1068068/chatgpt-is-everywhere-heres-where-it-came-from/> (Accessed: 2024-03-05).
Anyaha, Rockwello (2017): The History of Artificial Intelligence. Harvard.edu 08/28/2017. <https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/> (Accessed: 2024-03-05).