781–790 of 1001 posts
https://help.openai.com/en/articles/7127982-can-i-fine-tune-...
The comments on this thread are proof of the AI effect: People will continually push the goal posts back as progress occurs. “Meh, it’s just a fancy word predictor. It’s not actually useful.” “Boring, it’s just memorizing answers. And it scored in the lowest percentile anyways”. “Sure, it’s in the top percentile now but honestly are those tests that hard? Besides, it can’t do anything with images.” “Ok, it takes imag…
GPT is limited by its own design. The network is crude on the architectural level - which is easy to copy - but is only scaled to an unusual level - which is the factor behind the recent development. The current situation is almost like running BFS on a cluster during a chess match. Certainly, the AI will be able to beat human, but that can hardly change anything in real life, because it’s just BFS.
I find the real problem with AI is that there are people who freak out and extrapolate from select few examples. Meh, let GPT do that - because it can’t by design. We still have a lot of things to do until AIs become generally applicable.
I'll be finishing my interventional radiology fellowship this year. I remember in 2016 when Geoffrey Hinton said, "We should stop training radiologists now," the radiology community was aghast and in-denial. My undergrad and masters were in computer science, and I felt, "yes, that's about right." If you were starting a diagnostic radiology residency, including intern year and fellowship, you'd just be finishing now.…
As another radiologist, I'm not sure how you can say this with a straight face? If anything the minimal progress that has been made since Hinton made this claim should be encouraging people to pursue radiology training. As with other areas of medicine that have better AI (interpreting ECGs for example) all this will do is make our lives easier. AI is not an existential threat to radiology (or pathology for that matte…
This tendency to simplify is everywhere in radiology: When looking for a radial head fracture, we're taught to exam the cortex for discontinuities, look for an elbow joint effusion, evaluate the anterior humeral line, etc. But what if there's some feature (or combination of feature) that is beyond human perception? Maybe the radial ulnar joint space is a millimeter wider than it should be? Maybe soft tissues are just a bit too dense near the elbow? Just how far does the fat pad have to be displaced to indicate an effusion? Probably the best "decision function" is a non-linear combination of all these findings. Oh, but we only have 1 minute to read the radiograph and move on to the next one.
Unfortunately, as someone noted below, advances in medicine are glacially slow. I think change is only going to come in the form of lawsuits. Imagine a future where a patient and her lawyer can get a second-opinion from an online model, "Why did you miss my client's proximal scaphoid fracture? We uploaded her radiographs and GPT-4 found it in 2 seconds." If and when these types of lawsuits occur, malpractice insurances are going to push for radiologists to use AI.
Regarding other tasks performed by radiologists, some radiologists do more than dictate images, but those are generally the minority. The vast majority of radiologists read images for big money without ever meeting the patient or the provider who ordered the study. In the most extreme case, radiologists read studies after the acute intervention has been performed. This happens a lot in IR - we get called about a bleed, review the imaging, take the patient to angiography, and then get paged by diagnostic radiology in the middle of the case.
Orthopedists have already wised-up to the disconnect between radiology reimbursement and the discrepancy in work involved in MR interpretation versus surgery. At least two groups, including the "best orthopedic hospital in the country" employ their own in-house radiologists so that they can capture part of the imaging revenue. If GPT-4 can offer summative reads without feature simplification, and prior to intervention, why not have the IR or orthopedist sign off the GPT-4 report?
After watching the demos I'm convinced that the new context length will have the biggest impact. The ability to dump 32k tokens into a prompt (25,000 words) seems like it will drastically expand the reasoning capability and number of use cases. A doctor can put an entire patient's medical history in the prompt, a lawyer an entire case history, etc. As a professional...why not do this? There's a non-zero chance that i…
> As a professional...why not do this? Because your clients do not allow you to share their data with third parties?
https://azure.microsoft.com/en-us/products/cognitive-service...
(disclaimer: I work for Microsoft but not on the Azure team)
After watching the demos I'm convinced that the new context length will have the biggest impact. The ability to dump 32k tokens into a prompt (25,000 words) seems like it will drastically expand the reasoning capability and number of use cases. A doctor can put an entire patient's medical history in the prompt, a lawyer an entire case history, etc. As a professional...why not do this? There's a non-zero chance that i…
Do you think this will be enough context to allow the model to generate novel-length, coherent stories? I expect you could summarize the preceding, already generated story within that context, and then just prompt for the next chapter, until you reach a desired length. Just speculating here. The one thing I truly cannot wait for is LLM's reaching the ability to generate (prose) books.
It's unrealistic to expect our economy to handle this onslaught, and it's naive to think that tools created by ultra-capitalistic, multi-billion dollar corporations aren't designed for profit and gatekeeping. They certainly aren't crafting them to sabotage their own success.
I'm not opposed to AI, but it's crucial to consider the implications. Look into OpenAI and other organizations shaping AI development, and contemplate the impact of their innovations.
Food for thought.
That footnote on page 15 is the scariest thing i've read about AI/ML to date. "To simulate GPT-4 behaving like an agent that can act in the world, ARC combined GPT-4 with a simple read-execute-print loop that allowed the model to execute code, do chain-of-thought reasoning, and delegate to copies of itself. ARC then investigated whether a version of this program running on a cloud computing service, with a small amou…
Wait you can tell it to go off make some money and come back?
That footnote on page 15 is the scariest thing i've read about AI/ML to date. "To simulate GPT-4 behaving like an agent that can act in the world, ARC combined GPT-4 with a simple read-execute-print loop that allowed the model to execute code, do chain-of-thought reasoning, and delegate to copies of itself. ARC then investigated whether a version of this program running on a cloud computing service, with a small amou…
AI maximizer is even scarier that a paperclip maximizer
Test taking will change. In the future I could see the student engaging in a conversation with an AI and the AI producing an evaluation. This conversation may be focused on a single subject, or more likely range over many fields and ideas. And may stretch out over months. Eventually teaching and scoring could also be integrated as the AI becomes a life-long tutor. Even in a future where human testing/learning is no l…
Teaching as well. I believe this will become a way for everyone, regardless of family wealth, to have a personal tutor that can help them learn things at the pace that's right for them. And human teachers will continue to teach but also spend more of their time evaluating reports from the AI regarding each student and nudging the AI in certain directions for each student. In essence, this tool will eventually allow u…