When we look up “AI” and “artificial intelligence” in Google Ngram viewer, we see that the narrative for artificial intelligence comes and goes, just as narrative economics describes. Two important peaks, and a third peak forming for AI, and one less for artificial intelligence.
The first peak dates from 1967, and it is no coincidence. In 1956, the term "artificial intelligence" (AI) was officially coined, marking a significant moment in the history of the field. The term was introduced during a seminal conference at Dartmouth College in Hanover, New Hampshire, known as the Dartmouth Conference. The conference, held in the summer of 1956, is often considered the birthplace of artificial intelligence.
The term "artificial intelligence" was proposed by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, who were among the leading figures in the emerging field of AI. McCarthy, in particular, is credited with coining the term. The Dartmouth Conference aimed to bring together researchers interested in exploring ways to simulate human intelligence using machines.
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The introduction of the term "artificial intelligence" in 1956 marked the formalization of the field as a distinct area of research and study. This moment is crucial in the history of AI as it helped shape the narrative and direction of research in the subsequent years. Since then, AI has undergone significant advancements, with periods of growth and periods of reduced enthusiasm, known as AI winters. The term and the Dartmouth Conference played a foundational role in establishing AI as a field of study and research that continues to evolve and impact various aspects of technology, science, and society today.
After a wile, enthusiasm, funding, and public interest in the AI field experienced a significant decline. The so-called first AI winter. There is a perception that the promises and expectations of AI did not match the actual achievements, leading to a cooling off of investment, research, and development in the field. To challenges such as the complexity of problems, limited computational power, and insufficient understanding of AI techniques funding decreased, and many projects were abandoned, resulting in skepticism and reduced support.
After the first AI winter, interest in artificial intelligence (AI) revived due to several factors. Advances in machine learning techniques, such as backpropagation and reinforcement learning, overcame earlier limitations. Expert systems demonstrated practical applications, garnering interest and investment. Government and corporate funding, including support from agencies like DARPA, continued to flow into AI research. Improved computing power enabled researchers to tackle more complex problems, particularly in machine learning and neural networks.
Real-world applications and commercialization of AI technologies gained momentum as businesses recognized their practical value. International collaboration and conferences facilitated the exchange of ideas, contributing to the collective progress of the field. Successes in speech recognition and computer vision showcased the tangible capabilities of AI systems. These combined factors marked the end of the first AI winter and initiated a renewed era of growth and exploration in AI leading to the peak on 1987 on the chart.
The second AI winter was initiated by a resurgence of skepticism and reduced interest in artificial intelligence during the late 1980s and 1990s. This period was characterized by disappointments in the practical applications of expert systems, limitations in the capabilities of existing AI technologies, and a perception that AI had been overhyped. The publication of "Perceptrons" by Minsky and Papert, which highlighted the constraints of certain neural network approaches, contributed to the declining enthusiasm. Funding cuts, a lack of significant breakthroughs, and the absence of clear, tangible results in AI research further fueled the onset of the second AI winter.
The third AI revival was initiated by breakthroughs in deep learning, particularly with the development of powerful GPUs. Advances in handling large datasets and improved data processing capabilities became crucial. Open source collaboration and the sharing of knowledge on platforms like GitHub accelerated progress. Real-world successes in applications like speech recognition and image classification demonstrated AI's practical value, leading to increased adoption by businesses. Entrepreneurial efforts and industry investment, especially in startups, fostered innovation. Progress in natural language processing, driven by models like BERT and GPT, improved language understanding. Developments in autonomous vehicles and robotics showcased AI's potential in real-world scenarios. These factors collectively fueled the third AI revival, marked by unprecedented growth, breakthroughs, and widespread adoption across various industries.
Crypto and AI
Examining Google Trends data for the search term "AI crypto" reveals patterns starting in 2017, marking a significant period in the evolution of the cryptocurrency space: crypto's cambium. During this phase, the crypto landscape witnessed explosive growth, transforming from a limited number of projects to a myriad of initiatives. This expansion not only resulted in increased diversity within the crypto space but also saw the emergence of projects integrating artificial intelligence (AI) technologies. The intersection of AI and crypto became more pronounced, reflecting the innovative endeavors that have come to define this dynamic and rapidly evolving sector.
After a minor peak around the cycle peak in December 2021, “AI crypto” searches declined. However, breakthroughs in AI use cases marked a turning point. In July 2022, Midjourney, and in August 2022, Stable Diffusion, launched their initial public versions, enabling users, including myself, to generate photorealistic images, paintings, and videos at minimal or no cost. AI began fulfilling its promises.
The technology used by Stable Diffusion to translate text into images sparked interest in AI within the crypto community, particularly ChatGPT. The first public version of ChatGPT was released at the end of November 2022, leading people to realize the immense potential of AI. Subsequently, there was a rapid surge in searches for "AI crypto," and the trend remains upward. Some perceive it as a developing bubble, raising questions about its eventual culmination and the factors that might lead to its end.
The purposes of AI in crypto can be read in my newsletter AI-Infused Crypto Projects.
The future
Learning from the history of AI winters provides valuable insights into potential indicators for the conclusion of an AI crypto phase. Similar to previous AI winters, an end could be signaled by overinflated expectations driven by speculative hype rather than tangible developments. Disillusionment may set in if the technology fails to deliver on initial promises, with failed projects and technical limitations contributing to a decline in interest. Currently, individuals are turning to applications such as ChatGPT and Bard as an oracle, attempting to predict future events; however, it's important to note that these applications lack the capability to do so, occasionally generating nonsensical outputs.
Market corrections, akin to those in traditional financial markets, may occur as speculative valuations readjust to more realistic levels. Such corrections can be part of the natural market cycle. Shifts in public perception, influenced by negative narratives or a loss of confidence in the potential of AI crypto, could impact adoption and investment.
While historical patterns offer lessons, each technological cycle is unique, and the AI crypto space may be influenced by a combination of factors. Economic climates and recessions can have complex effects on the development and sustainability of emerging technologies. Monitoring these indicators and maintaining a realistic understanding of the technology's capabilities will be crucial for navigating the evolution of AI in the cryptocurrency space.
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