OpenAI’s Diwali Week
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OpenAI sure knows how to host a Diwali party, featuring not only the Coca-Cola marketing ad with DALLE.3 but also setting off a series of bombs and firecrackers in the AI landscape.
Last week, during OpenAI's first ever developer conference, Sam Altman launched GPT-4 Turbo, an upgraded version of GPT-4, that boasts a 128k context window, allowing it to process an equivalent of over 300 pages of text in a single prompt, with knowledge extending up to April 2023.
He also made a slew of announcements that covered every part of the technology, such as open source models and developer tools, where the generative OpenAI was lacking against the competition.
Killing many birds with one stone
First came OpenAI (back in 2015) and then others (Anthropic, Cohere, and Langchain, among others) followed. The uniqueness of other platforms are based on the shortcomings of OpenAI. Thus, everytime, OpenAI improves itself, it ends up killing a bunch of other platforms.?
For instance, with the launch of GPT-4 Turbo, which has an improved context windows from 32k tokens to 128k tokens, Anthropic seems to be losing its relevance. Undoubtedly, GPT-4 is the most advanced large language model (LLM) out there.?
AIM spoke to various companies and realised that when it comes to LLM, their first preference is GPT-4. Now, in the case of Claude, Anthropic’s AI assistance, it had the advantage of a context window, supporting 100k tokens. Now, with GPT-4 Turbo, that advantage has vanished. Companies, which preferred GPT-4 over others, will quickly shift to their favourite LLM.
Another killer invention by OpenAI was GPTs that allow anyone to easily build their own GPT without the need of coding. With GPTs, users will have the capability to customise ChatGPT according to their specific needs, making it more supportive in their daily activities, tasks at work or home. They can then share these personalised versions with others.
Moreover, after users craft their personalised GPT, they have the option to share them publicly on the soon-to-be-launched GPT Store, slated for release later this month. Think of the GPT Store as akin to the Google Play Store or Apple's App Store, offering a platform for sharing and accessing a variety of GPT creations.
Once in the store, GPTs become easily searchable and have the potential to ascend the leaderboards. Looking ahead, developers will also have the opportunity to earn income based on the usage metrics of their GPT, creating a financial incentive tied to the popularity of their creations.
This particular invention may sound the death knell for hundreds of companies, which were providing various services built on GPT-4, like DocGPT that allows users to ask any question, from the PDF documents. There are many XGPTs (companies based on GPT-4), which are going to be redundant after this.
Open sourcing act
OpenAI, once known for closed practices, is taking steps towards open source. At DevDay, they unveiled 'large-v3,' an open-source automatic speech recognition model, Whisper, with plans for an API release soon.?
Whisper, available on GitHub under a permissive licence, excels in transcribing diverse content and is hailed as a top-notch tool. Its unique timestamp feature makes it ideal for use as subtitles on platforms like YouTube. The model, designed for researchers, segments audio into 30-second clips, leveraging an encoder and decoder for accurate text prediction. Originally intended for integration with ChatGPT, OpenAI opted for a direct public release. Notably, Whisper is currently aimed at researchers, not end users.
The Consistency Decoder, a replacement for Stable Diffusion VAE decoder, was also open-sourced, a notable move for improved stability. However, it's worth noting that the decoder currently supports versions v1 and v2, not SDXL, despite the mention of compatibility with "1.0+."
OpenAI chose to open-source Whisper large-v3 with the goal of providing a foundation for creating practical applications and advancing research in robust speech processing. The AI tool was refined through extensive training on a dataset comprising 680,000 hours of carefully supervised data from the internet. Notably, one-third of the dataset is derived from non-English sources.
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