or Nash Equilibrium. If EU wants to get away from MS and GOP's flakey tribalism, they have to agree to all (or most) moving at once, and accept a learning curve.
I'd go with Nash Equilibrium here. DVORK is not inherently better than QWERTY for typing speed, although it is less prone to causing RSI. The big downside is the learning curve and making shortcuts like Ctrl-V harder to press.
To that one user: "Tough shit. You're getting and using the updated software or you can find yourself a new job." Assuming you have that power, of course.
User: The annual report is nit ready because IT forced a software update and it broke the report generation. I tried to warn them but they did it anyway...
No browser is going to bring back the good old days.
I want Ludwig Plutonium, AFU, Kibo, Serdar Argjic and the Zumabots, Ted Frank, and Joel Furr vs. The Green Card Lawyers...
These organizations are offloading their technology deficiencies onto the poor and incapable user.
I, as a seasoned professional that has seen every trick email that has ever been sent, cannot determine if certain messages are malicious or not. The users will never be able to do it themselves. And punishing them, whether that's reprimand, scolding, mocking, making them resit training, is NOT the right way to do it.
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.
Anthropic is likely training its models with your data. If your data is cruel then the models can become cruel as well.
Considering they likely used credit,
not to review techical documentation to identify design issues
But this is exactly where AI can actually be useful — so long as you don't depend on it to identify all of the design issues. It can review mountains of information and find things in it you might not have caught. You just can't assume that it will catch everything it "should" — that is, what it should be capable of catching is not necessarily what you are capable of catching.
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.
Meanwhile, that periodic report has requirements, so you're going to have to ask it to write the report, then to review it to see if it met the requirements, then to review its own statements about whether it successfully met each requirement, because it won't do any of that sanity checking without being told to do so.
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...
While I agree that AI is not truly deterministic, that doesn't mean you will get widely divergent results from teh same data.
Widely? No. Divergent? Yes. Run the same query multiple times and you'll get different answers. The more input you give, the more subtle the differences are likely to be, but there's always a chance it will go completely off its nut and follow some line of "reasoning" that goes someplace utterly unrelated to what you asked for. This is less likely in special-purpose models, but never impossible.
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.
Tailoring the LLM to taht can save a lot of time for an analyst/engineer/operator by identifying and collecting teh information needed for analysis and reporting
It can't, though, and for two reasons. One, a person is required to check off on the data. Two, LLMs don't faithfully represent data. They ingest it as tokens and then what they crap out is influenced by random numbers produced internally as part of the token selection process. That means a person cannot certify that the data was faithfully reproduced without checking literally every value, because that's simply not how LLMs work.
You can potentially address this with a hybrid approach, where the LLM uses a tool to collect and manipulate the data, but this is shifting the problem to whether the LLM used the tool correctly. It might be more obvious when it doesn't, though, so it might still be a win.
What LLMs seem best at when it comes to text processing, and they are still not infallible, is summarization. But you can't trust its negatives, so it's only useful for identifying other possible positives. That's not worthless, it can help you catch things, but you can't trust it to do the parts you'd most want it to do.
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 wouldn't want them to be relying on AI to do reviews though. Maybe two AI, looking only for issues and reporting them back, like you can have AI look for security issues with computer code. And a proper review done by a human.
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.
Flock doesn't solve violent crime, it might provide evidence for a policeman to solve violent crime.
It's not evil to solve violent crime. You are getting over excited dear.
However Flock could be used for evil. And there is a consumer backlash because of that, it's the wrong product.
And that is the conundrum. If there were real efective data protections to ensure private data was secure from any cop wanting to do a search and searches required teh same effort as those in the real world, people might have fewer issues with it. Collecting massive data bases of people is inherently bad beacuse it will be missued, as events have already shown.
More interesting is why is there is so much violent crime in your culture? If you are an American, you appear to have a violent culture, perhaps the most violent in history,
The US culture certainly grew out of violent struggles to expand its territory across the continent, and no doubt shaped its current one to a degree; and has it roots in European culture. Most violent in history? There are plenty of cultures that can easily compete for that title, from European ones to Asian to African and everywhere between. Europeans, for example, routinely massacred or enslaved defeated opponents in their history.
the only one to have dropped a nuke.
The moral of that story is don't keep fighting a war you can't win, and when it is obvious you are going to lose, surrender.
This is when you, the accused, either demand a lawyer be present to represent you, or clam up and invoke your right to remain silent (and let them prove their case on their own).
The mistaken color is another common police tactic. t.
Always ask for a lawyer immediately when questioned in such cases. Do not wait, do not pass go, do not collect $200.
I saw part of the testimony. This all comes down to human over-aggressiveness—not even mistakes.
The detective used Flock to look for all Dodge Durangos within a radius of the accident. They located her at her apartment. So far, so good. But her car is a different color than what eyewitnesses said. Other details did not match up with eyewitness accounts. There was NO damage to her vehicle, but they arrested her anyway. The detective doubled down and the police continued there prosecution of her.
I'm no fan of Flock, but in this case it's simply bad police.
Correct. Flock provided data for teh cops to review and make informed decisions. Part of the problem is often pressure ot close cases, so an easy arrest is one way to do that, which then points to poor leadership who wants good stats. We can argue all day about Flok's role, and personally I am against Flock's use without very strict guardrails (which will never be imposed), but this is simply shoddy police work.
No amount of genius can overcome a preoccupation with detail.
