Gartner is an organization that classifies different technologies into their “hype cycle” framework. (1) basically, any piece of technology may go through 5 stages:
1. Technology Trigger
·
A
technology reaches a proof of concept, a successful experiment, people get
excited.
2. Peak of Inflated Expectations
·
Given
the excitement, some companies jump in and experiment, some succeed, most do
not.
3. Through of Disillusionment
·
Given
failures, some technology versions fail, and investment into the space gets hit
and will only recover if providers iron out main issues.
4. Slope of enlightenment
·
As
technology becomes production ready, more successes are created and the usage and
limits of the technology are better understood. New generation products appear.
5. Plateau of Productivity
·
Mainstream
adoption, what was successful niche spreads.
Guess where
Gartner placed Gen AI in its 2023 AI hype cycle?
(2)
That’s right, right at the peak of inflated expectations. Plus, they only see that plateau of productivity being reached in 5 to 10 years.
On the other hand, something like Computer Vision, where we use machines to process images to extract meaningful information is close to the plateau of productivity. There are many pieces of software/APIs that help you analyse images very efficiently, and very importantly there are proven use cases in production for computer vision, from facial recognition to control access, to recognizing who is not correctly wearing masks (useful during COVID), to detecting anomalies in x-rays/MRIs, to identifying and tracking people from public cameras (ahum…).
GenAI, on the other hand, has made a big splash, people around the world, especially including non data professionals are raving about the possibilities that GenAI can bring. AI is already being used whether we are aware/like it or not, for example in the UK (3), now imagine GenAI (in an earlier blog I listed a few well known issues with LLMs)
So what have people been doing with GenAI. One of the avenues that is being explored is helping humans write code. And there are many many exampes of this; for example the ubiquitous GitHub CoPilot (4). But as I asked in an earlier blog, do you think the code that is written is of very high quality since it is built on ‘everyone’s’ coding…
There have also been efforts to help manage GenAI. Actually, apart from the coding co pilot, the other development from Microsoft Build (5) earlier this year is the guardrails Microsoft put around GenAI. And this can be leveraged, as OCBC has done (6) with MS Azure to allow fact checking, not blindly following the answers generated: curation! (7)
The reality is, I believe GenAI is a very useful tool to have in your arsenal. More ‘traditional’/’tried and tested’ methods may be more suitable for your problem at hand. I have had customers saying “I just want GenAI” whether their use case suits or not. I would just point to the “peak of inflated expectations”.
I am someone
who enjoys building stuff that work and enables organisations to hit business
KPIs, and to do that, choosing the right tool is very important, and this is
something I can help with. You can use a sledgehammer to open a can of beans,
you can use a can opener too; guess which, currently, more efficiently gets you
to the beans and deal with your hunger?
- https://en.wikipedia.org/wiki/Gartner_hype_cycle
- https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2023-gartner-hype-cycle
- https://www.theguardian.com/technology/2023/oct/23/uk-officials-use-ai-to-decide-on-issues-from-benefits-to-marriage-licences
- https://github.blog/2023-03-22-github-copilot-x-the-ai-powered-developer-experience/
- https://news.microsoft.com/build-2023/
- https://www.straitstimes.com/business/ocbc-to-deploy-generative-ai-bot-for-all-30000-staff-globally
- Interestingly, if you look again at the AI hype cycle 2023 diagram above, "Responsible AI" is also at the peak of inflated expectations, humans still, fortunately, have more thinking to do...
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