231–235 of 235 posts
Earlier quoted context omitted.
That's exactly the kind of attitude I am talking about. You can rationalise pretty much everything and keep moving the goal post. I've seen AI do something nobody thought was possible. If that doesn't influence how you think about AI I cannot help you.
Nobody?
Earlier quoted context omitted.
That's not how LLMs work. Including the NICE, if it actually isn't already, will not guaranty a "correct" result. It will increase the chance that the response is directly coming from the training but there is no guarante. If you are interested in why this is the case you can read this [1] post from Stephen Wolfram on how ChatGPT and in general LLMs work. This might give some insight on how and when to to use it more…
Could we instead have the LLM use NICE similar to how Bing uses the web as a reference instead? It still wouldn't guarantee a correct result, but it would that increase the reliability, right?
On one hand if you already have the NICE data at hand, you already have your answer. There won't be a need for a search enginge or a chat bot other than to perhaps, summerize the data (which is valuable on its own). On the other hand, if you don't have the NICE data at hand, the correctness of the response relies on the accuracy of the search method in order to feed the correct page to LLM. This is an issue additional to LLMs accuracy.
At first it might seem like an easy problem to solve but when one wants to engineer a solution to a nice streamlined product, it's more challenging; unsurprisingly.
With little kids I am increasingly conscious about how to best equip them for the world when they are adults. I think previous playbook will need to be redrafted. 'Resilience' and 'Critical Thinking' are two things I am thinking are key any others?
It's often said that critical thinking is the cornerstone of a well-rounded education. Universities and colleges around the world extol the virtues of critical thinking and its ability to elevate our intellectual prowess. However, I'd like to argue that critical thinking, or more specifically, the way it's often employed in certain academic contexts, has become overrated and counterproductive to meaningful, construct…
Total trash cloaked in a complicated story. What they actually did is ask 5 random people to rate what thought a language model could do to help different professions. These 5 random people don't know anything about the professions they're rating, just what anyone off the street knows, and they know as much about GPT as anyone who has briefly played with it. The title should have been "We asked 5 friends to see what…
Total trash cloaked in a complicated story. What they actually did is ask 5 random people to rate what thought a language model could do to help different professions. These 5 random people don't know anything about the professions they're rating, just what anyone off the street knows, and they know as much about GPT as anyone who has briefly played with it. The title should have been "We asked 5 friends to see what…
I can't find how many people labeled the DWA task descriptions, where did you got that number? The article seems to describing the labeling here: > Human Ratings: We obtained human annotations by applying the rubric to each O NET Detailed Worker Activity (DWA) and a subset of all O NET tasks and then aggregated those DWA and task scores at the task and occupation levels. To ensure the quality of these annotations, th…