(Link throughs will be updated)
OpenAI launches GPT-4!
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OpenAI launches GPT-4!
OpenAI introduces Whisper and ChatGPT APIs for commercial use cases
BUGRA AKYILDIZ MAR 19
OpenAI released GPT-4 and it is a very significant improvement over GPT-3 or ChatGPT.
It is significantly better than ChatGPT(GPT3.5) in a variety of tasks through GPT-4 research.
Its main capabilities are:
Academic success as you see above graph, through visual inputs, it can solve a variety of exams.
Steerability: through socratic method, you can guide/influence and teach the model in a direction and correct some of its answers afterwards.
Visual Inputs: it accepts and can process the visual inputs.
You can learn more about doing the following things about GPT-4:
Read paper, View system card
Try on ChatGPT Plus
Join API waitlist
Rewatch demo livestream
Contribute to OpenAI Evals
My experience going through some examples:
answers become much more concise(comparing to verbosity in ChatGPT)
it can actually construct the sentences clearer and it can produce better/more readable long paragraphs comparing to ChatGPT.
Its code debugging capability is much better than ChatGPT.
The runs might take longer than ChatGPT. This might improve in future, though.
OpenAI also introduced ChatGPT and Whisper APIs officially. Engineers can now integrate ChatGPT and Whisper models into their apps and products through our API.
Google published a post on Vid2Seq, their new framework for captioning videos. The Vid2Seq architecture augments a language model with special time tokens, allowing it to seamlessly predict event boundaries and textual descriptions in the same output sequence. In order to pre-train this unified model, we leverage unlabeled narrated videos by reformulating sentence boundaries of transcribed speech as pseudo-event boundaries, and using the transcribed speech sentences as pseudo-event captions. The Vid2Seq architecture includes a visual encoder and a text encoder, which encode the video frames and the transcribed speech input, respectively. The resulting encodings are then forwarded to a text decoder, which autoregressively predicts the output sequence of dense event captions together with their temporal localization in the video. The architecture is initialized with a powerful visual backbone and a strong language model.
Yi Tay wrote about release of a new Flan 20B parameter model with UL2. In “Scaling Instruction-Finetuned language models (Chung et al.)” (also referred to sometimes as the Flan2 paper), the key idea is to train a large language model on a collection of datasets. These datasets are phrased as instructions which enable generalization across diverse tasks. Flan has been primarily trained on academic tasks. In Flan2, we released a series of T5 models ranging from 200M to 11B parameters that have been instruction tuned with Flan. UL2 is a unified framework for pretraining models that are universally effective across datasets and setups.
