ChatGPT is phenomenal. However, it is prohibitively expensive to train and refine such giant models. Fortunately, small language models are flourishing and becoming more and more competent. We call them "mini-giants". We argue that open source community like Kaggle and mini-giants will win-win in many ways, technically, ethically and socially. In this article, we present a brief yet rich background, discuss how to attain small language models, present a comparative study of small language models and a brief discussion of evaluation methods, discuss the application scenarios where small language models are most needed in the real world, and conclude with discussion and outlook.

The article gives an overview of two popular AI and ML tools -- TensorFlow and Tesseract OCR -- which have been developed by Google AI Labs for the open source community. Google AI Labs provides many services for AI and ML. These include the use of free platforms for development activities, releasing code to the open source community, and support for AI/ML related research activities. TensorFlow is a Google AI project and one of the most popular open source machine learning frameworks. It can be used to build and train ML models like Keras API.

Welcome to the December 2022 edition of Baseline, Accenture Federal Services' monthly machine learning newsletter. In Baseline, we share insights on important advances in machine learning technologies likely to impact our federal customers. This edition is a special year-end roundup - our chance to highlight some of the most interesting and impactful advances that occurred in the machine learning space this year. These developments have pushed the boundaries of what is possible with machine learning and will continue to have far-reaching ramifications next year and beyond. We're excited to see what's next.

Thank you so much for joining us in this interview series! Can you share with us the'backstory" of how you decided to pursue this career path in AI? I believe that there is enormous power in data. The more a company has, the more they're able to propel their businesses forward. But only if they're able to get meaningful insights from it.

By August, it had been cloned in the open by two master's students as OpenGPT-2 By November, OpenAI released their 1.5B parameter model, after a cautious staged release process May 2020: OpenAI released GPT-3 as a paper and a closed beta API in June 2020. Mar 2021: EleutherAI released their open GPT-Neo 1.3B and 2.7B models May 2022: Meta released OPT-175B for researchers (with logbook! and an open license) The Text-to-Image cycle took 4? months: Apr 2022: OpenAI announces DALL-E 2 with a limited "research preview" The timelines above are highly cherrypicked of course; the story is much longer if you take into account the longer development history starting from the academic papers for diffusion (2015) and transformer models (2017) and older work on GANs. But what is more interesting is what has happened since: OpenAI's audio-to-text model, Whisper, was released under MIT license in September with no API paywall. Of course, there is less scope for abuse in the audio-to-text domain, but more than a few people have speculated that the reception to Stable Diffusion's release influenced the open sourcing decision. Sufficiently advanced community is indistinguishable from magic.

Software Freedom Conservancy, a not-for-profit organization that provides support and legal services for open source software projects, has called on the open source community to ditch GitHub after quitting the code-hosting and collaboration platform itself. The move comes a week after Microsoft-owned GitHub launched the commercial version of Copilot, an AI-powered pair-programmer that collaborates with software developers by suggesting lines or functions as they type. It's a little like Gmail's Smart Compose feature, which strives to expedite your email writing by suggesting the next piece of text in your message using contextual cues. Software Freedom Conservancy is financially backed by a number of big-name companies, such as Google, Red Hat, and Mozilla, and its members span more than 40 projects, including Git (which GitHub relies heavily on), Selenium, and Godot. While the Software Freedom Conservancy's beef with GitHub predates Copilot by some margin, it seems that GitHub's latest launch is the final straw.

The research team behind Meta AI's Detectron project has recently been awarded the PAMI Mark Everingham Prize for contributions to the computer vision community. We first open-sourced the Detectron codebase five years ago as a collection of state-of-the-art algorithms for tasks such as object detection and segmentation. It has since evolved and advanced in important ways thanks to the contributions of both the open source community and many researchers here at Meta. In 2019, we released a ground-up rewrite of the codebase entirely in PyTorch to make it faster, more modular, more flexible, and easier to use in both research-first and production-oriented projects. Earlier this year, we released Detectron2Go, a state-of-the-art extension for training and deploying efficient object detection models on mobile devices and hardware, as well as significantly improved baselines based on the recently published state-of-the-art results produced by other experts in the field. Several members of the Detectron team sat down to discuss the project's origins, advances, and future.

This section highlights the impact of the AIX360 toolkit in the first two years since its release. It describes several different forms of impact on real problem domains and the open source community. This impact has resulted in improvements in multiple metrics: accuracy, semiconductor yield, satisfaction rate, and domain expert time. The current version of the AIX360 toolkit includes ten explainability algorithms described in Table 1 covering different ways of explaining. Explanation methods could be either local or global, where the former refers to explaining an AI model's decision for a single instance, while the latter refers to explaining a model in its entirety.

All the sessions from Transform 2021 are available on-demand now. Women in the AI field are making research breakthroughs, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. We created the VentureBeat Women in AI Awards to emphasize the importance of their voices, work, and experience and to shine a light on some of these leaders. In this series, publishing Fridays, we're diving deeper into conversations with this year's winners, whom we honored recently at Transform 2021. Check out last week's interview with the winner of our AI research award.

All the sessions from Transform 2021 are available on-demand now. The traditional way for a database to answer a query is with a list of rows that fit the criteria. If there's any sorting, it's done by one field at a time. Vector similarity search looks for matches by comparing the likeness of objects, as captured by machine learning models. Vector similarity search is particularly useful with real-world data because that data is often unstructured and contains similar yet not identical items.