Feb 22, 2023
Carlota Perez is a researcher who has studied hype cycles for much of her career. She’s affiliated with the University College London, the University of Sussex, The Tallinn University of Technology in Astonia and has worked with some influential organizations around technology and innovation. As a neo-Schumpeterian, she sees technology as a cornerstone of innovation. Her book Technological Revolutions and Financial Capital is a must-read for anyone who works in an industry that includes any of those four words, including revolutionaries.
Connecticut-based Gartner Research was founded by GideonGartner in 1979. He emigrated to the United States from Tel Aviv at three years old in 1938 and graduated in the 1956 class from MIT, where he got his Master’s at the Sloan School of Management. He went on to work at the software company System Development Corporation (SDC), the US military defense industry, and IBM over the next 13 years before starting his first company. After that failed, he moved into analysis work and quickly became known as a top mind in the technology industry analysts. He often bucked the trends to pick winners and made banks, funds, and investors lots of money. He was able to parlay that into founding the Gartner Group in 1979.
Gartner hired senior people in different industry segments to aid in competitive intelligence, industry research, and of course, to help Wall Street. They wrote reports on industries, dove deeply into new technologies, and got to understand what we now call hype cycles in the ensuing decades. They now boast a few billion dollars in revenue per year and serve well over 10,000 customers in more than 100 countries.
Gartner has developed a number of tools to make it easier to take in the types of analysis they create. One is a Magic Quadrant, reports that identify leaders in categories of companies by a vision (or a completeness of vision to be more specific) and the ability to execute, which includes things like go-to-market activities, support, etc. They lump companies into a standard four-box as Leaders, Challengers, Visionaries, and Niche Players. There’s certainly an observer effect and those they put in the top right of their four box often enjoy added growth as companies want to be with the most visionary and best when picking a tool.
Another of Gartner’s graphical design patterns to display technology advances is what they call the “hype cycle”. The hype cycle simplifies research from career academics like Perez into five phases.
* The first is the technology trigger, which is when a
breakthrough is found and PoCs, or proof-of-concepts begin to
emerge in the world that get press interested in the new
technology. Sometimes the new technology isn’t even usable, but
* The second is the Peak of Inflated Expectations, when the press picks up the story and companies are born, capital invested, and a large number of projects around the new techology fail.
* The third is the Trough of Disillusionment, where interest falls off after those failures. Some companies suceeded and can show real productivity, and they continue to get investment.
* The fourth is the Slope of Enlightenment, where the go-to-market activities of the surviving companies (or even a new generation) begin to have real productivity gains. Every company or IT department now runs a pilot and expectations are lower, but now achievable.
* The fifth is the Plateau of Productivity, when those pilots become deployments and purchase orders. The mainstream industries embrace the new technology and case studies prove the promised productivity increases. Provided there’s enough market, companies now find success.
There are issues with the hype cycle. Not all technologies will follow the cycle. The Gartner approach focuses on financials and productivity rather than true adoption. It involves a lot of guesswork around subjective, synthetical, and often unsystematic research. There’s also the ever-resent observer effect. However, more often than not, the hype is seperated from the tech that can give organizations (and sometimes all of humanity) real productivity gains. Further, the term cycle denotes a series of events when it should in fact be cyclical as out of the end of the fifth phase a new cycle is born, or even a set of cycles if industries grow enough to diverge.
ChatGPT is all over the news feeds these days, igniting yet another cycle in the cycles of AI hype that have been prevalent since the 1950s. The concept of computer intelligence dates back to the 1942 with Alan Turing and Isaac Asimov with “Runaround” where the three laws of robotics initially emerged from. By 1952 computers could play themselves in checkers and by 1955, Arthur Samuel had written a heuristic learning algorthm he called “temporal-difference learning” to play Chess. Academics around the world worked on similar projects and by 1956 John McCarthy introduced the term “artificial intelligence” when he gathered some of the top minds in the field together for the McCarthy workshop. They tinkered and a generation of researchers began to join them. By 1964, Joseph Weizenbaum’s "ELIZA" debuted. ELIZA was a computer program that used early forms of natural language processing to run what they called a “DOCTOR” script that acted as a psychotherapist.
ELIZA was one of a few technologies that triggered the media to pick up AI in the second stage of the hype cycle. Others came into the industry and expectations soared, now predictably followed by dilsillusionment. Weizenbaum wrote a book called Computer Power and Human Reason: From Judgment to Calculation in 1976, in response to the critiques and some of the early successes were able to then go to wider markets as the fourth phase of the hype cycle began. ELIZA was seen by people who worked on similar software, including some games, for Apple, Atari, and Commodore.
Still, in the aftermath of ELIZA, the machine translation movement in AI had failed in the eyes of those who funded the attempts because going further required more than some fancy case statements. Another similar movement called connectionism, or mostly node-based artificial neural networks is widely seen as the impetus to deep learning. David Hunter Hubel and Torsten Nils Wiesel focused on the idea of convultional neural networks in human vision, which culminated in a 1968 paper called "Receptive fields and functional architecture of monkey striate cortex.” That built on the original deep learning paper from Frank Rosenblatt of Cornell University called "Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms" in 1962 and work done behind the iron curtain by Alexey Ivakhnenko on learning algorithms in 1967. After early successes, though, connectionism - which when paired with machine learning would be called deep learning when Rina Dechter coined the term in 1986, went through a similar trough of disillusionment that kicked off in 1970.
Funding for these projects shot up after the early successes and petered out ofter there wasn’t much to show for them. Some had so much promise that former presidents can be seen in old photographs going through the models with the statiticians who were moving into computing. But organizations like DARPA would pull back funding, as seen with their speech recognition projects with Cargegie Mellon University in the early 1970s.
These hype cycles weren’t just seen in the United States. The British applied mathemetician James Lighthill wrote a report for the British Science Research Council, which was published in 1973. The paper was called “Artificial Intelligence: A General Survey” and analyzed the progress made based on the amount of money spent on artificial intelligence programs. He found none of the research had resulted in any “major impact” in fields that the academics had undertaken. Much of the work had been done at the University of Edinbourgh and funding was drastically cut, based on his findings, for AI research around the UK. Turing, Von Neumann, McCarthy, and others had either intentially or not, set an expectation that became a check the academic research community just couldn’t cash. For example, the New York Times claimed Rosenblatt’s perceptron would let the US Navy build computers that could “walk, talk, see, write, reproduce itself, and be conscious of its existence” in the 1950s - a goal not likely to be achieved in the near future even seventy years later.
Funding was cut in the US, the UK, and even in the USSR, or Union of the Soviet Socialist Republic. Yet many persisted. Languages like Lisp had become common in the late 1970s, after engineers like Richard Greenblatt helped to make McCarthy’s ideas for computer languages a reality. The MIT AI Lab developed a Lisp Machine Project and as AI work was picked up at other schools like Stanford began to look for ways to buy commercially built computers ideal to be Lisp Machines. After the post-war spending, the idea that AI could become a more commercial endeavor was attractive to many. But after plenty of hype, the Lisp machine market never materialized. The next hype cycle had begun in 1983 when the US Department of Defense pumped a billion dollars into AI, but that spending was cancelled in 1987, just after the collapse of the Lisp machine market. Another AI winter was about to begin.
Another trend that began in the 1950s but picked up steam in the 1980s was expert systems. These attempt to emulate the ways that humans make decisions. Some of this work came out of the Stanford Heuristic Programming Project, pioneered by Edward Feigenbaum. Some commercial companies took the mantle and after running into barriers with CPUs, by the 1980s those got fast enough. There were inflated expectations after great papers like Richard Karp’s “Reducibility among Combinatorial Problems” out of UC Berkeley in 1972. Countries like Japan dumped hundreds of millions of dollars (or yen) into projects like “Fifth Generation Computer Systems” in 1982, a 10 year project to build up massively parallel computing systems. IBM spent around the same amount on their own projects. However, while these types of projects helped to improve computing, they didn’t live up to the expectations and by the early 1990s funding was cut following commercial failures.
By the mid-2000s, some of the researchers in AI began to use new terms, after generations of artificial intelligence projects led to subsequent AI winters. Yet research continued on, with varying degrees of funding. Organizations like DARPA began to use challenges rather than funding large projects in some cases. Over time, successes were found yet again. Google Translate, Google Image Search, IBM’s Watson, AWS options for AI/ML, home voice assistants, and various machine learning projects in the open source world led to the start of yet another AI spring in the early 2010s. New chips have built-in machine learning cores and programming languages have frameworks and new technologies like Jupyter notebooks to help organize and train data sets.
By 2006, academic works and open source projects had hit a turning point, this time quietly. The Association of Computer Linguistics was founded in 1962, initially as the Association for Machine Translation and Computational Linguistics (AMTCL). As with the ACM, they have a number of special interest groups that include natural language learning, machine translation, typology, natural language generation, and the list goes on. The 2006 proceedings on the Workshop of Statistical Machine Translation began a series of dozens of workshops attended by hundreds of papers and presenters. The academic work was then able to be consumed by all, inlcuding contributions to achieve English-to-German and Frnech tasks from 2014. Deep learning models spread and become more accessible - democratic if you will. RNNs, CNNs, DNNs, GANs.
Training data sets was still one of the most human intensive and slow aspects of machine learning. GANs, or Generative Adversarial Networks were one of those machine learning frameworks, initially designed by Ian Goodfellow and others in 2014. GANs use zero-sum game techniques from game theory to generate new data sets - a genrative model. This allowed for more unsupervised training of data. Now it was possible to get further, faster with AI.
This brings us into the current hype cycle. ChatGPT was launched in November of 2022 by OpenAI. OpenAI was founded as a non-profit in 2015 by Sam Altman (former cofounder of location-based social network app Loopt and former president of Y Combinator) and a cast of veritable all-stars in the startup world that included:
* Reid Hoffman, former Paypal COO, LinkedIn founder and venture
* Peter Thiel, former cofounder of Paypal and Palantir, as well as one of the top investors in Silicon Valley.
* Jessica Livingston, founding partner at Y Combinator.
* Greg Brockman, an AI researcher who had worked on projects at MIT and Harvard
OpenAI spent the next few years as a non-profit and worked on GPT, or Generative Pre-trained Transformer autoregression models. GPT uses deep learning models to process human text and produce text that’s more human than previous models. Not only is it capable of natural language processing but the generative pre-training of models has allowed it to take a lot of unlabeled text so people don’t have to hand label weights, thus automated fine tuning of results. OpenAI dumped millions into public betas by 2016 and were ready to build products to take to market by 2019. That’s when they switched from a non-profit to a for-profit. Microsoft pumped $1 billion into the company and they released DALL-E to produce generative images, which helped lead to a new generation of applications that could produce artwork on the fly. Then they released ChatGPT towards the end of 2022, which led to more media coverage and prognostication of world-changing technological breakthrough than most other hype cycles for any industry in recent memory. This, with GPT-4 to be released later in 2023.
ChatGPT is most interesting through the lens of the hype cycle. There have been plenty of peaks and plateaus and valleys in artificial intelligence over the last 7+ decades. Most have been hyped up in the hallowed halls of academia and defense research. ChatGPT has hit mainstream media. The AI winter following each seems to be based on the reach of audience and depth of expectations. Science fiction continues to conflate expectations. Early prototypes that make it seem as though science fiction will be in our hands in a matter of weeks lead media to conjecture. The reckoning could be substantial. Meanwhile, projects like TinyML - with smaller potential impacts for each use but wider use cases, could become the real benefit to humanity beyond research, when it comes to everyday productivity gains.
The moral of this story is as old as time. Control expectations. Undersell and overdeliver. That doesn’t lead to massive valuations pumped up by hype cycles. Many CEOs and CFOs know that a jump in profits doesn’t always mean the increase will continue. Some intentially slow expectations in their quarterly reports and calls with analysts. Those are the smart ones.