Exactly. IT orgs should put defenses in place to catch the majority of fishing emsils, using sender info,AI to recognize patterns etc. You won’t catch everything but can greatly reduce the chan crew s they get through.

It was probably the other O'Neil, with one L. He has no sense of humour at all.

I could understand bots and mailing lists. The thing I never understood with LinkedIn was how many real people (usually recruitment agents) had apparently spent the time to read my profile and send me a personalised message, yet still thought I'd be interested in a job paying half the market rate for someone with my skills and experience.

You don't know how to argue, or debate, or even answer comments, do you? Mindless passion is still mindless.

Try rebutting what I said. Can you do that for me? I'd appreciate it.

Corals have been regenerating themselves for several hundred million years. They don't need human help.

Corals have been around for several hundred million years. They survived the comet which killed off the dinosaurs. They survived ice ages and glaciations, the most recent being a 400-500 foot sea level rise just 10-15,000 years ago. They routinely bleach themselves while shifting populations of the critters which give them color.

We puny humans are no threat to corals.

I don't know what you mean. Look at the improvement in hiring when you can advertise an open position on LinkedIn and get maybe 100x as many applicants as before! OK, probably 99% of them will be completely unqualified for the role and barely capable of pushing the instant application button, but how is that a problem, really? Surely hiring managers can easily filter them out and no qualified applicant will ever get lost in the noise?

It's like a video I saw on YouTube the other week. Someone was talking all about "skills" for their AI agents and how they'd automated so much of their workflow using them. They started the video by kicking off four different tasks for separate agents to show how productive it was to have all this stuff happening in parallel and how it was so quick and easy to organise.

One of the tasks involved producing an infographic about a certain topic, and the AI duly produced one based on a one-liner prompt, taking only a few seconds of the presenter's time and with no further input from them about the content. The infographic had exactly the level of quality you'd expect from this: a load of boxes and arrows (with some of those arrows starting or finishing floating in space without connecting anything) and some words (a few of which might have been related to the topic mentioned in the prompt).

That infographic was the only substantial output from the whole exercise. It was the worst kind of AI slop imaginable, generated with literally no thought or oversight from the presenter beyond a couple of words about the topic as part of that one-liner prompt. This was probably about the average level of the LinkedIn posts I used to see before I left as well. As you said, I'm not sure you can be a "thought leader" without doing any thinking.

I stopped using LinkedIn when they seemingly decided my account wasn't about me (despite having obvious and publicly verifiable details right there in the description) and wanted me to verify my ID using one of the US-based services with a questionable reputation to get my access back. That's not going to happen, so I just don't use the site any longer.

As far as I can tell, that has made zero difference to my career, and it's one fewer drain on my time. The only thing I have missed is seeing the occasional good news story where a past colleague has been promoted or the like, but I'm not giving up my government-issued credentials to the US-based data harvesting industry for that.

I meet Jerry at a computer show I was covering. I saw him in the press room, introduced myself, said I really like his style and said I copied it for my column. He looked at me and said steal, only amateurs copy, we professionals steal. Had an interesting conversation. Great guy who will be missed.

Considering they likely used credit, /,, and a bunch of other sites to train the model I think the cruelty has long since been baked in...

Yes, and it requires a multi-layerd approach to how they system raises issues. One simple prompt pass is not enough, however a multi-tiered approach can increase teh success rate. It'll never catch everything, and neither will a human, the tool is merely a way to remove much of the grunt work searching though documents so teh analyst can focus on what is important and make it easier ot find where things are referenced.

There are two different types of reports to develop - one that validates meeting requirements and one that is merely reporting results or periodic required reporting. The former is challenging, the later a lot less so, and reducing the time spent on the later frees up time for the former. Of course, many such reports do not need AI since they merely compile data, and that is easy to program in any of a number of tools. AI just adds hype and market cap...

Of course, which is why for special purpose models this can work; especially since you can train it to be more accurate once you have enough real life data. This tool is not likely using the commercial ChatGpt but a bespoke setup.

While I agree that AI is not truly deterministic, that doesn't mean you will get widely divergent results from teh same data. There is some run to run variant, but in my experience, working with very specific criteria, it tends to be in the ranking order and not a failure to identify relationships. That can be addressed in a number of ways. While positive hits are easier, negative ones can be checked in a number of ways to increase the confidence level that they truly are negative, in the sense they are not relevant to the tested data set. To me, one of teh keys is to work with a well defined set of criteria, something I think is possible with tehy type of information they are dealing with, especially since much of the input data will be well structured and in a consistent format.

A human must always be the final arbitrator, and needs to realize the AI is not fool proof and you can't accept everything at face value.

I was think more along the lines of "Here is the periodic report on X" or "Here all the similar events and corrective actions..." to basically act like a research librarian; not to review techical documentation to identify design issues.