AI Roundup: GPT Fine Tuning, Code Llama, Jailbreaking, SeamlessM4T and more.
AI Roundup: GPT Fine Tuning, Code Llama, Jailbreaking, SeamlessM4T and more.
Week 34, 2023 AI Roundup
In this weekly roundup, let’s take a look at:
Using ChatGPT for Stock Market Predictions
A groundbreaking research paper reveals that the AI language model ChatGPT can accurately predict stock market movements by analyzing news headlines. Surprisingly, the smaller GPT-3.5 model outperformed the larger GPT-4 model in forecasting returns. The study, which used data from October 2021 to December 2022, found that ChatGPT's sentiment scores have a significant predictive power on daily stock returns, outshining traditional sentiment analysis methods. This research highlights the potential of AI in enhancing investment decision-making and could pave the way for more accurate and profitable stock market predictions.
Read more here.
SeamlessM4T: Bridging Language Barriers with Advanced AI
Meta's SeamlessM4T is a groundbreaking universal translator inspired by fictional the Babel Fish from The Hitchhiker's Guide to the Galaxy. Unlike traditional fragmented systems, it uses a unified approach to cover nearly 100 languages, offering on-demand translations. It offers both speech-to-speech and text-to-text translations. With state-of-the-art results, especially in underrepresented languages, and built-in safeguards against toxicity and bias, SeamlessM4T promises a future where language barriers are effortlessly bridged.
Read more here.
What the Heck is GPT-3.5 Fine Tuning?
Last week OpenAI released fine-tuning GPT-3.5 Trubo. The GPT-3.5 Turbo 4K model can be fine-tuned for improved and specific results. Fine-tuning helps in enhanced performance, shortened prompts, and customized outputs. The process involves preparing data, uploading it via OpenAI's API, initiating a fine-tuning job, and then using the model. Costs are based on tokens and are relatively cost-effective compared to GPT-4. Fine-tuning decisions depend on individual needs, but a fine-tuned GPT-3.5 Turbo can outperform GPT-4 and is more cost-efficient. Fine-tuning is key for those seeking enhanced AI performance.
Read more here.
Code Llama: The Future of Writing Code
Code Llama is a new open-source Large Language Model (LLM) designed to assist in coding tasks, providing an upgrade over tools like GitHub Copilot. It not only suggests code but also explains its logic. Features include code generation with natural language explanations, real-time code completion, multilingual support, and deep context understanding. Built on the Llama 2 foundation, Code Llama offers specialized versions for specific tasks. In performance tests, it outperformed multiple other AI coders. With the rise of AI tools like GitHub Copilot and now Code Llama, the future of automated coding looks bright, promising enhanced developer efficiency and innovation.
Read more here.
Getting Accurate Math Answers With ChatGPT (GPT-4)
OpenAI's GPT-4 has been fine-tuned to run Python code, introducing the GPT-4 Code Interpreter (GPT4-Code). This enhancement enables logical natural language reasoning, code generation and execution, and feedback from executed code results. Using the Code-based Self-Verification (CSV) method enhances GPT-4's mathematical reasoning further, allowing the model to generate verification code and adjust its strategies based on feedback. GPT4-Code showed a significant improvement in accuracy, scoring 84.32% on the MATH dataset with CSV, up from GPT-4's original 42.2%. Users can try GPT4-Code by activating it through ChatGPT Plus settings. The innovation is reshaping automated mathematical problem-solving.
Read more here.
Jailbreaking (Hacking) ChatGPT
Large Language Models (LLMs) like ChatGPT have significant potential but also pose risks like spreading misinformation and aiding cybercrime. OpenAI has implemented safety protocols, including mitigating jailbreaks with reinforcement learning from human feedback, to counter these risks. However, "jailbreak prompts" are a new challenge, as they can bypass safeguards to produce harmful content. A study found that these prompts are longer and more toxic than regular prompts. While some LLMs show resistance against them, no safeguard is entirely foolproof. The rise of jailbreak prompts emphasizes the need for ongoing safety research to ensure responsible LLM use.
Read more here.