61–70 of 104 posts
This seems more of a course about how to use Generative AI - does anyone have a good recommendation of a course or book about how they actually work?
This seems more of a course about how to use Generative AI - does anyone have a good recommendation of a course or book about how they actually work?
This seems more of a course about how to use Generative AI - does anyone have a good recommendation of a course or book about how they actually work?
Andrew Ng's courses on Coursera are helpful to learn about the basics of deep learning. The "Generative AI for Everyone" course and other short courses offer some basic insight, and you can continue from there.
https://www.coursera.org/specializations/deep-learning
https://www.deeplearning.ai/courses/generative-ai-for-everyo...
HuggingFace has some nice courses as well: https://huggingface.co/learn/nlp-course/
Jay Allamer has a nice blog post on the Transformer architecture: https://www.deeplearning.ai/short-courses/
And eventually you will probably end up reading papers on arxiv.org :)
This seems more of a course about how to use Generative AI - does anyone have a good recommendation of a course or book about how they actually work?
Until I found this:
https://www.youtube.com/@algorithmicsimplicity
Instantly clicked. Both convolution and transformer networks.
EDIT: for the purpose of visualization, I highly recommend following channel: https://www.youtube.com/watch?v=eMXuk97NeSI&t=207s
It nicely explains and shows concepts of stride, features, window size, input to output size relation - in convolutional NN
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I've been finding the recently coined term "AI engineer" useful, as a role that's different from machine learning engineering and AI research. AI engineers build things on top of AI models such as LLMs. They don't train new models, and they don't need a PhD. It's still a discipline with a surprising amount of depth to it. Knowing how best to apply LLMs isn't nearly as straight forward as some people assume. I wrote a…
So in a similar vein as, data engineers being people who USE things like Redshift/Snowflake/Spark/etc., but are distinct from the category of people who actually build those underlying frameworks or databases? In some sense, the expansion of the role of data engineering as a discipline unto itself is largely enabled by the commoditization of cloud data warehouses and open source tooling supporting the function of dat…
* Fine tune this prompt/prompt chain to suggest X instead of Y.
* A/B test and show the summarized results of implementing this LoRA that our Data Engineer trained against our current LLM implementation.
* A/B test and show the summarized results of specific quantization levels on specific steps of our LLM chain.
All of with requires common sense, basic statistics and patience instead of heavy ML knowledge.
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I used the assistant API for about 2 weeks before I realized I could do a better job with the raw completion API. For me, the Assistant API now feels like training wheels. The manner in which long threads are managed over time will be domain-specific if we are seeking an ideal agent. I've got methods that can selectively omit data that is less relevant in our specific case. I doubt that OAI's solution can be this pre…
I've noticed the assistents api is a lot slower and the fact you need to "poll" for when a run is completed is annoying. There a few good points though, you can tweat the system document on the dashboard without needing to re start the app and you can switch which model is being used too.
This is another good point. If everything happens in one synchronous call chain, it's likely to finish in a few seconds. With polling, I saw some threads take up to a minute.
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I'm trying to understand the vulnerability you are pointing out; in the example of an AI assistant w/ access to your email, is that AI assistant also reading it's instructions from your email?
The key problem is that an LLM can't distinguish between instructions from a trusted source and instructions embedded in other text it is exposed to. You might build your AI assistant with pseudo code like this: prompt = "Summarize the following messages:" emails = get_latest_emails(5) for email in emails: prompt += email.body response = gpt4(prompt) That first line was your instruction to the LLM - but there's no cu…
gpt4.prompt(prompt)
gpt4.data(email_data)
response = gpt4.response()