OpenAI has released GPT-4, a multi-modal language model that outperforms humans on certain tasks, but the lack of technical information and proprietary data raises concerns about transparency and accountability.
GPT4 outperformed humans on LSAT and bar exam simulations, but human-designed tests may not fully reflect language model capabilities.
GPT4 was evaluated on human tests, including chemistry and algebra tests, and performed better than 88% of humans on the LSAT and better than 90% of humans on a simulated bar exam.
Language models are good at recanting knowledge in a verbose and elaborate way, but human tests are specifically designed for humans and may not necessarily reflect how well the models perform in comparison to humans.
Humans need to interact with clients, make connections, and reason about situations to succeed in a job, while newer AI models like GPT-4 outperform older ones in tasks like describing humor in images.
To be successful in a job, humans need to do a variety of important tasks, including interacting with clients, making connections, and reasoning about situations.
Evaluating AI models on benchmarks designed for humans may not accurately reflect their capabilities, but GPT4 with vision outperforms GPT3.5.
Newer models of OpenAI outperform older models, with examples including GPT-4's ability to describe the humor in an image, and the technical report discusses limitations and safety measures taken during the training process.
Human reinforcement learning helps OpenAI's language model become better assistants, but it doesn't necessarily improve their ability to learn new skills.
OpenAI's language model is trained to predict the next word in a document using publicly available data and is fine-tuned using reinforcement learning with human feedback to align it with the user's intent within guardrails, and it has a system parameter that allows users to specify how the model should act.
The model's capabilities come from the pre-training process and the rlhf does not improve exam performance without active effort.
Human reinforcement learning helps models become assistants and helpers, but it doesn't necessarily make them better at learning new skills, and it reduces the need for extensive prompt engineering and context tokens.
OpenAI's technical report lacks meaningful research details, as they want to keep their proprietary data and models to themselves.
The technical report lacks any meaningful research details on the architecture, model size, hardware, training, or data set construction, and only mentions the competitive landscape and safety implications of large-scale models like gpt4.
OpenAI wants to keep their proprietary data and models to themselves, using recent advances in the field to create a good model that they don't want others to replicate.
Big research projects like pharmaceutical companies invest a lot of money and effort into getting a good model, but they don't want to share their knowledge to maintain their competitive advantage, so they shouldn't claim to be open or democratizing everything for the benefit of humanity.
Training a model for longer is more effective than making it bigger, and the quality of the model comes from proprietary data.
By analyzing older models, we can predict GPT-4's performance and make better investment decisions, but the exact amount of compute used is unclear.
By analyzing older models, we can accurately predict the performance of GPT4 and make better investment decisions, although the exact amount of compute used is unclear, it is estimated to be between 100-10,000 times more than GPT3.
OpenAI's GPT-4 model is trained for longer and reverses the trend of the inverse scaling price, which asked for contributions of data sets where bigger models get worse.
The lecture discusses a betting scenario where the expected value favors one answer, but the outcome may not align with the correct decision according to expected value, leading to a philosophical debate on determinism.
OpenAI releases GPT-4, an improved language model, with limited access to image inputs and concerns about data security.
OpenAI's technical report, which should be cited as OpenAI 2023, used GPT-4 for formatting and styling, and includes an unusual image of a man ironing clothes on an ironing board attached to the roof of a moving taxi.
The GPT-4 model's knowledge cutoff is claimed to be 2001, but it is assumed that many evaluation sets are also part of the training data, and there is uncertainty about the model's true performance.
OpenAI released an open source repository for evaluating language models and benchmarks, granting access to GPT for those who contribute high quality evals.
GPT-4 API is currently usable for text-only requests with a waitlist, but image inputs are still in limited Alpha, and it's unclear why it was released now.
The new GPT model is better than GPT 3.5, but it's not a revolutionary breakthrough and the release was disappointing.
Open AI's use of real production data raises concerns about data security and privacy for businesses and clients.