Artificial Intelligence (AI) is an area that’s getting a lot of attention lately. Many companies are thinking about how they can incorporate AI products and services into their company, and how AI can help enhance their workforce. There is a lot of hype right now, but is this technology around to stay or is it just another short-lived season of adoption? In order to answer this question it’s important to learn about AI’s beginnings and past. In order to understand where we think it’s going it’s important to understand it’s beginning, background, and the reasons for the last two AI Winters. Will it be one of those great ideas that we can never seem to attain, or will it finally conquer the technological, economic, and societal hurdles to achieve widespread adoption?
The First Wave of AI Adoption, and the First AI Winter
The first major wave of AI interest and investment occurred from the early 1950s through the early 1970s. Much of the early AI research and development stemmed from the burgeoning field of computer science, and AI research built upon these exponential improvements in computing technology. This combined with funding from government, academic, and military sources produced some of the earliest and most impressive advancements in AI. Yet, while progress around computing technology continued to mature and progress, the AI innovations developed during those heady decades of the early computer years ground to a near halt in the mid 1970s. This period of decline in interest, funding, and research is known in the industry as the AI Winter. So, what exactly caused this AI Winter?
AI Winter Reason #1: Overpromising, Underdelivering
The early days of AI seemed to promise everything. Computers that could play chess, navigate their surroundings, have conversations with humans, and practically think and behave as people do. It’s no wonder that HAL in 2001: A Space Odyssey didn’t seem so far fetched to the audiences in 1969. Yet as it turned out, those over-promises came to a head with the backers with misaligned expectations.
Winter Reason #2: Lack of Diversity in Funding
AI funding in general was too dependent on government and non-commercial sources. When governments worldwide pulled back on academic research in the mid 1970s fueled by budgetary cutbacks and changes in strategic focus, AI suffered the most. In research settings, this is made worse by the fact that AI tends to be very much inter-disciplinary, involving different departments in computing, philosophy and logic, mathematics, brain & cognitive sciences, and others. When funding drops in one department, it impacts the ability of AI research as a whole. This is perhaps one of the most learned lessons from this era: find more consistent and reliable sources of funding so that research won’t come to an end.
The Second Wave of AI Adoption, and the Second AI Winter
Interest in AI research was rekindled in the mid 1980’s with the development of Expert Systems. Adopted by corporations, expert systems leveraged the emerging power of desktop computers and cheap servers to do the work that had previously been assigned to expensive mainframes. Expert systems helped industries across the board automate and simplify decision-making on Main Street and juice-up the electronic trading systems on Wall Street. Soon people saw the intelligent computer on the rise again. If it could be a trusted decision-maker in the corporation, surely we can have the smart computer in our lives again..
Winter Reason #3: Technological hurdles
Expert systems are very dependent on data. And, in the 1980s storage was still expensive. Corporations also needed to develop their own data and decision flows. Without a global, connected, almost infinite database and knowledge gleaned from that data, corporations were hamstrung by technology limitations.
Winter Reason #4: Complexity and Brittleness
As the expert systems became more and more complex, maintaining those data and flows became increasingly more difficult. Expert systems developed a reputation of being too brittle, depending on specific inputs to get desired outputs. The combination of the labor required for updating these systems with increasing application challenges resulted in businesses re-evaluating their need for expert systems. Simply put, expensive complex systems were replaced by cheaper, simpler systems, even though they could not meet overall AI goals.
The Third Wave of AI Adoption… Where We Stand Now
The past waves of AI overpromised and under-delivered, causing the winters we outlined above. Why then do we have a recent resurgence and interest in AI now? It revolves around three key concepts: advancement in technology (big data and GPUs in particular), acceptance of human-machine interaction in our daily lives, and integration of intelligence in everyday devices from cars to phones.
Thawing Reason #1: Advancement in Technology
The dramatic growth of Big Data and our ability to handle almost infinite amounts of data in the cloud combined with specialized computing power of Graphical Processing Units (GPUs) is resulting in a renaissance of ability to deal with previously intractable computing problems. Not only does the average technology consumer now have access to almost limitless amounts of computing power and data storage at ridiculously cheap rates, but we also have access to large amounts of data that allow organizations to share and build upon each other’s learnings at exceptionally fast rates.
Thawing Reason #2: Acceptance of Human-Machine interaction
Non-technical people are getting accustomed to talking and interacting with computer interfaces in daily life with growth of chatbots and other technologies. This sort of acceptance gives investors, companies, and governments confidence in pursuing AI-related technologies. If it’s been proven that the average Joe or Jane will gladly talk to a computer and interact with a bot, then more development on that path makes sense.
Thawing Reason #3: Integration of Intelligence in Everyday Technology
We are now starting to see evidence of more intelligent, AI-enabled systems everywhere. Cars are starting to drive and park themselves. Customer support is becoming bot-centric. Problems of all shapes and sizes are being enabled with AI capabilities, even if they aren’t necessarily warranted. Just as in the early days of the Web and mobile computing, we’re starting to see AI everywhere. Perhaps we’ve crossed some threshold of acceptance.
It is important to note that the phenomenon of the AI Winter is primarily a psychological one. Our expectation and hope is that the next AI winter will never come. Many companies are now taking an AI-first approach which has never been the case before. This should continue to push the needle forward with practical AI solutions. With AI also becoming more integrated in everyday use cases, which was not the case in the past, it will become too difficult to just pull the plug on AI as had happened in AI Winters past. For these reasons, we expect and hope another AI winter will not come.