I find that questionable. What does "software engineering in a business environment" require that a competent competitive programmer couldn't also learn?
Personally, in my locale March is usually when the crocuses start peeking out and the robins return... first signs of spring. But just 100 miles east the landscape might still be a winter wonderland well into April.
If I had to hazard a guess, this article was probably written with a British audience in mind, where "spring" loosely corresponds with March through May.
Some studies/analyses are surely laundered opinions. I have a habit these days of checking if the authors have a product to sell or funding to raise. If so, pinch of salt.
Wise decision as far as I'm concerned. Unfettered AI experiments on production systems are an IP and (depending on your field) regulatory nightmare waiting to happen. Unless you have lawyer-vetted guidelines and oversight, use OpenAI products exclusively for experiments and personal projects.
The problem with your suggested approach is the resulting lack of holistic context. The problem of OP's approach (direct parsing) is cost and context-window-limits. There has to be a better way.
The usual caution applies here. The danger of data isn't in the data itself, but in how it's used and interpreted. In this particular case, a relatable comparison would be a book - it's just a bunch of words on a page, but those words can be used to inspire or to manipulate. Pretty important to be mindful of the context and the intent behind the data.
Something important I was thinking of the other day is the power dynamics that come into play with data as the new currency. Those with access to more data (money) and better tools for analysis (investment) of it end up having an unfair advantage.
I'm genuinely curious: What about push notifications (at least on desktop browsers) bothers you that much? I give permission to a few (like Gmail, etc.) and that's that; it makes my life less stressful knowing that I'll get alerted when it's time.
Yep, and that's the difference between a big profitable company doing research as a side-hustle, and a company whose business IS the research.
One interesting and somewhat scary exception seems to be Microsoft; they seem to be converting a lot of their recent research projects into commercial value.
> Lanier, 62, has worked alongside many of the web’s visionaries and power-brokers. He is both insider (he works at Microsoft as an interdisciplinary scientist
And his unique perspective on AI is all the more valuable (and courageous) considering that Microsoft, recently laid off their AI ethics team. It's super important we don't let human considerations fall by the wayside in this rush. The potential of AI is limitless, but so are the potential risks.