Welcome insideBIGDATA AI News Briefs Bulletin Board, our timely new feature bringing you the latest industry insights and perspectives surrounding the field of AI including deep learning, large language models, generative AI, and transformers. We’re working tirelessly to dig up the most timely and curious tidbits underlying the day’s most popular technologies. We know this field is advancing rapidly and we want to bring you a regular resource to keep you informed and state-of-the-art. The news bites are constantly being added in reverse date order (most recent on top). With our bulletin board you can check back often to see what’s happening in our rapidly accelerating industry. Click HERE to check out previous “AI News Briefs” round-ups.
[1/6/2024] Microsoft unveiled its first major keyboard redesign in 30 years. Starting in January, some new PCs that run the Windows 11 OS will include a special “Copilot key” that launches Microsoft’s AI chatbot. Getting 3rd party PC makers to add an AI button to laptops is the latest move by the company to capitalize on its partnership with OpenAI. Microsoft has not yet indicated which PC makers are installing the Copilot button beyond the company’s own Surface devices. Some companies are expected to announce new models at CES next week.
[1/5/2024] OpenAI’s GPT Store launching next week: OpenAI plans to launch its GPT Store next week, after a delay from its initial November announcement majorly due to upheaval in the company including CEO Sam Altman’s temporary departure. This platform is strategically positioned as more than just a marketplace, but rather a significant pivot for OpenAI, shifting from a model provider to a platform enabler.
The GPT Store will allow users to share and monetize custom GPT models developed using OpenAI’s advanced GPT-4 framework. This move is significant in making AI more accessible. Integral to this initiative, the GPT Builder tool allows the creation of AI agents for various tasks, removing the need for advanced programming skills. Along side the GPT Store, OpenAI plans to implement a revenue-sharing model based on the usage of these AI tools. Additionally, a leaderboard will feature the most popular GPTs, and exceptional models will be highlighted in different categories.
[1/4/2024] OctoAI announced the private preview of fine-tuned LLMs on the OctoAI Text Gen Solution. Early access customers can:
- Bring any fine-tuned Llama 2, Code Llama, or Mi(s/x)tral models to OctoAI, and
- Run them at the same low per-token pricing and latency as the built-in Chat and Instruct models already available in the solution.
The company heard from customers that fine-tuned LLMs are the best way to create customized experiences in their applications, and that using the right fine-tuned LLMs for a use case can outperform larger alternatives in quality and cost — as the figure below from OpenPipe shows. Many also use fine-tuned versions of smaller LLMs to reduce their spending and dependence on OpenAI. But popular LLM serving platforms charge a “fine-tuning tax,” a premium of 2x or more for inference APIs against fine-tuned models
OctoAI delivers good unit economics for generative AI models, and these efficiencies extend to fine-tuned LLMs on OctoAI. Building on these, OctoAI offers one simple per-token price for inferences against a model – whether it’s the built-in option or your choice of fine-tuned LLM.
[1/3/2024] Intel Corp. (Nasdaq: INTC) and DigitalBridge Group, Inc. (NYSE: DBRG), a global investment firm, today announced the formation of Articul8 AI, Inc., an independent company offering enterprise customers a full-stack, vertically-optimized and secure generative artificial intelligence (GenAI) software platform. The platform delivers AI capabilities that keep customer data, training and inference within the enterprise security perimeter. The platform also provides customers the choice of cloud, on-prem or hybrid deployment.
Articul8 offers a turnkey GenAI software platform that delivers speed, security and cost-efficiency to help large enterprise customers operationalize and scale AI. The platform was launched and optimized on Intel hardware architectures, including Intel® Xeon® Scalable processors and Intel® Gaudi® accelerators, but will support a range of hybrid infrastructure alternatives.
“With its deep AI and HPC domain knowledge and enterprise-grade GenAI deployments, Articul8 is well positioned to deliver tangible business outcomes for Intel and our broader ecosystem of customers and partners. As Intel accelerates AI everywhere, we look forward to our continued collaboration with Articul8,” said Pat Gelsinger, Intel CEO.
[1/2/2024] GitHub repo highlight: “Large Language Model Course” – an comprehensive LLM course on GitHub paves the way for expertise in LLM technology.
[1/2/2024] Sam Altman and Jony Ive recruit iPhone design chief to build new AI device. Legendary designer Jony Ive, known for his iconic work at Apple, and Sam Altman are collaborating on a new artificial intelligence hardware project, enlisting former Apple executive Tang Tan to work at Ive’s design firm, LoveFrom.
[1/2/2024] Chegg Experiencing “Death by LLM”!?! Chegg began in 2005 as a disruptor, bringing online learning tools to students and transforming the landscape of education. But since the company stock’s (NYSE: CHGG) peak in 2021, Chegg has taken a significant nose dive of more than 90% while facing competition with the widely accessible LLMs, e.g. ChatGPT that came out on November 30, 2022. In August 2023, Chegg announced a partnership with Scale AI to transform their data into a dynamic learning experience for students after already collaborating with OpenAI on Cheggmate. A recent Harvard Business Review outtake highlights the potential value that Chegg’s specialized AI learning assistants may bring to a student’s learning experience by using feedback loops; instituting continuous model improvement; and training the model on proprietary datasets. The question remains however, can Chegg effectively associate their user data with AI to reclaim lost competitive ground and take advantage of new revenue streams, or is it fighting a losing battle against the rapidly evolving GenAI ecosystem? Personally, I have no love lost with Chegg, as I’ve discovered a number of of my Intro to Data Science students cheating on homework assignments and exams by accessing my coursework uploaded to Chegg.
[1/2/2024] AI research paper highlight: “Gemini: A Family of Highly Capable Multimodal Models,” the paper behind the new Google Gemini model release. The main problem addressed by Gemini is the challenge of creating models that can effectively understand and process multiple modalities (text, image, audio, and video) while also delivering advanced reasoning and understanding in each individual domain.
[1/2/2024] AI research paper highlight: “Generative Multimodal Models are In-Context Learners.” This research demonstrates that large multimodal models can enhance their task-agnostic in-context learning capabilities through effective scaling-up. The primary problem addressed is the struggle of multimodal systems to mimic the human ability to easily solve multimodal tasks in context – with only a few demonstrations or simple instructions. Emu2 is proposed, a new 37B generative multimodal model, trained on large-scale multimodal sequences with a unified autoregressive objective. Emu2 consists of a visual encoder/decoder, and a multimodal transformer. Images are tokenized with the visual encoder to a continuous embedding space, interleaved with text tokens for autoregressive modeling. Emu2 is initially pretrained only on the captioning task with both image-text and video-text paired datasets. Emu2’s visual decoder is initialized from SDXL-base, and can be considered a visual detokenizer through a diffusion model. VAE is kept static while the weights of a diffusion U-Net are updated. Emu-chat is derived from Emu by fine-tuning the model with conversational data, and Emu-gen is fine-tuned with complex compositional generation tasks. Results of the research suggests that Emu2 achieves state-of-the-art few-shot performance on multiple visual question-answering datasets and demonstrates a performance improvement with an increase in the number of examples in context. Emu2 also learns to follow visual prompting in context, showcasing strong multimodal reasoning capabilities for tasks in the wild. When instruction-tuned to follow specific instructions, Emu2 further achieves new benchmarks on challenging tasks such as question answering for large multimodal models and open-ended subject-driven generation.
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