Posts With Tag: ai

Insights, Shallow-Work, and the Next Correction

Person in front of a computer looking at a down market chart. PD from

2020 was a rough year. Much of my income had come from live performances as a magician, live events as a speaker and trainer, and in-person consulting gigs. By March of 2020, every live event was being dubbed a “super spreader” event, markets were cooling, and enterprises were preparing to “batten down the hatches” in anticipation of unknown societal and financial disruption. My livelihood was in grave jeopardy. My wife and I tightened our belts hoping to ride out the storm but, by the summer, it became clear that this particular storm had an indefinite duration. I eventually accepted a job as a principal software architect at an intriguing startup within the enterprise and quickly rose through the ranks to become their Chief Architect.

As my responsibilities grew, I realized it would be crucial to scale myself and my contributions, so my focus turned to hiring and I had never seen a market like it. Salary expectations had inflated 100%+ since I was in the market. Signing bonuses were being offered at a scale I had never seen before. As it became harder and harder to attract talent, compensation only continued to increase. I won’t lie, I was a little bitter and a little envious. My direct reports I hired were earning substantially more than I. I expressed these feelings to a colleague who asked, “Have you considered looking, yourself?” My response was “It crossed my mind, but I think it would be a bad idea. ‘Those who live by the sword, die by the sword.’” In other words, the tech market looked an awful lot like a bubble. I didn’t know when it would burst, but I knew it would. Companies were responding to the market changes by “warchesting” talent; proactively hiring for roles that didn’t even exist yet. 18 months later came some of the most brutal layoffs I’ve seen since the 2000-2001 era and almost all of those who found themselves swept up in the cuts were downsized through no fault of their own.

I believe another correction is imminent, but this might be a good thing, depending on how we navigate it.

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The Future is Here (it's Just not Evenly Distributed)

A screenshot of a jupyter notebook demonstrating connecting GPT to a knowledge graph.

A few weeks ago I wrote an article on how investing in structured, semantic data can help move tools like ChatGPT from the “Trough of disillusionment” to the “plateau of productivity” and create intelligent agents that are actually intelligent. The core was that standardizing on REST Level 1 (or better) and beginning to layer in JSON-LD could provide a more meaningful and factual foundation for generative AI like GPT3 to deliver revolutionary value to organizations.

Less that three weeks later, Tony Seale, a Knowledge graph engineer, posted a brief demo video of these ideas in action.

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The Future Beyond 'Spicy Autocomplete'

Michael Gartenberg

The stages of playing with GPT-3:

- OMG this can do anything
- There goes my job
- I should start a business around this
- Some of the responses aren’t too good
- Actually, some of these responses are just awful
- This isn’t really intelligence
- This is just spicy autocomplete

Since ChatGPT became publicly available last November there has been an explosion of interest, articles, blogs, videos, arguments for-and-against; it can be difficult to separate out the hype from the reality. If you haven’t yet played with the public beta, it’s worth taking a look; first impressions are often downright startling.

One of the most impressive capabilities might be ChatGPT’s ability to seemingly answer questions asked in a casual, conversational manner and many hailed this as “the future of search” with Microsoft and Google both scrambling to integrate these capabilities into their search engines. A mind-bogglingly complex language model trained on a web-sized corpus of text boasts stunning capabilities although it doesn’t take long to discover that beneath ChatGPT’s impressive grasp of language there is a serious lack of knowledge. Google’s parent company, Alphabet, recently lost 8% of its market cap–roughly $100b USD–after their live-stream conference demonstrated Bard, their language model, returning incorrect answers.

The Mastodon post above summarizes my–and so many other’s–experience. Never in my 20+ years in the industry have I seen a technology move from the “Peak of Inflated Expectations” to the “Trough of Disillusionment” so quickly (see Gartner Hype Cycle). There is something powerful here, especially if it can be integrated with actual knowledge. Forward-thinking organizations are adopting the existing standards and architecture that just might be the key to unlocking the dream that GPT-hype represents. The first step may be as simple as evolving your API strategy.

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