Anybody who has Faraday or knows about Faraday, I’m thinking of getting a better budget laptop. I was thinking of getting the HP Omen 16. The 112 GB laptop I have now I was told it wouldn’t work, so I’m wondering if it would work on a bigger laptop(HD and RAMwise) like HP Omen 16. Will it?
Hey folks, I am excited to announce a new Open Source AI tool – Deep Chat Playground. This is a platform that enables anyone to create and use chatbot components that can connect to popular AI services without having to write any code.
What other AI tools have you tried apart from Chatgpt? Which one you liked the most? – which is better for coding/software-related things? – Which is better for English/communication-related things? – Which is better for talking to like a friend when you are getting bored? – I found that Chatgpt doesn’t answer sexual-related stuff. – Which is better if you wanna take the latest data into account? – Is there any bot that could read through the newspapers and give a summary?
– I tried giving Chatgpt a website to scan and give me results. It did not do it. Probably it’s not considered legal to do that.
Hi how are you..I want to improve or rebuild my communication skills so I search conversationalAI tool to help me practice …could you recommend me good one to mimic conversation for different situations even for love or intimidation situation? I already facing this situation but I want to build good way to talk or communicate
Do you ever wonder why LLMs Hallucinate or get things completely wrong?
Why does it happen even after training the model on your knowledge base or even after fine-tuning?
The answer lies in understanding the fundamental structure of an LLM and how it works.
One of the biggest misconceptions is in thinking that LLMs have knowledge or that they are programs.
At their core, they are a Statistical Representation of Knowledge, and understanding this can be profound.
Here is the crucial difference between both.
When you ask a knowledge base a question, it simply looks up the information and spits it out.
Conversely, an LLM is a probabilistic model of knowledge bases that generates answers; hence, it is a Generative Large Language Model. It generates responses based on language probabilities of what word should come next.
As a result, this can lead to hallucinations, self-contradictions, bias, and incorrect responses.
Now, bias goes far deeper than just LLMs, and I’ll cover that in more detail in a future email, but for now, the question is what can be done about all of this and how can we work with LLMs in such a way as to limit bias, hallucinations and incorrect responses?
Here are a few techniques we can use:
NLU: using NLU for critical areas where a specific answer is required
Knowledge Bases: Feeding the LLM information that can be used as the basis for answering questions
Prompt Engineering & Prompt-tunning: This can be used to optimize the performance and accuracy of the model.
Fine-Tuning: Training the model on your data
Want to go deeper?
We created a free Guide to LLMs that covers the basics and advanced topics like fine-tuning, and we hope to offer a model and framework for optimizing your success with LLMs.
