by Jim Sinur
History reveals that there have been two major AI winters: the first in the mid-1970s, followed by another in the late 1980s. While there were very clear reasons for the last two AI winters, most of the technical and cost-related issues have since been resolved. The wild card in our hands now is the extent to which humans accept AI this time around.
My bet? We won’t be seeing another brutal winter. This doesn’t mean there won’t be cold snaps, but a 15 to 20-year freeze-out doesn’t seem likely now.
The First AI Winter
This first freeze was caused by a couple of major factors. First and foremost, the mid-1970s experienced a lack of computer power so the examples put forward were “toy” solutions that really did not appeal to investors. This set off a period of infighting about Natural Language Processing (NLP) in the AI community, which scared off investors for the following years.
The Second AI Winter
Round two was catalyzed by the fact that the AI portion of an application had to run on a detached and expensive LISP machine that wasn’t much more powerful than newer computers. To compound the situation, the AI systems were brittle and expensive to create and maintain. The isolation and expense were too much for users and investors alike, thus kicking off an even longer AI winter.
What’s Different Now?
- AI now has a great Natural Language Processing (NLP) base to leverage, and soon we will see NLP services for front-ending legacy transactions and systems.
- AI is data-focused now, and not as rule and algorithm-focused. This means that as data changes, AI can change right along with the data. This reduces costs of maintenance.
- Computing capability is strong enough—and getting stronger—to take on even more ambitious uses. We see many instances of parallelism on the rise in Quantum Computing that are necessary for more inventive uses of AI.
- With the exception of dangerous and low-level work, AI is working hard to assist people rather than displace them.
- AI can consume large amounts of data from vastly different locations, and provide valuable advice that can be leveraged by users.
Bottom Line
If we can maintain AI’s ease of use as it takes on more challenging tasks, we can avoid another AI winter. The economic and computing power issues seem to be under control, so now it is our job to make AI interface well with people to make them feel secure.
The bottom line is that AI is here to help us; modern AI enables enterprises to automate data to get better insights than their human competitors. Enterprises who leverage it sooner than their competitors will have a strategic advantage.