Bloomberg GPT / GitHub Copilot X / AI Index Report 2023
We are excited to share with you the most recent updates in the realm of digital marketing. In this edition of our newsletter, we will be discussing the following topics:
???#AI Video of the Week: Harry Potter by BALENCIAGA
?? Text to Video: #ChatGPT Plugin
?? Meet BloombergGPT: Bloomberg's New Large-Scale Natural Language Processing Model Built for Finance
?? The Generative AI Landscape by AiBreakfast
?? AI Index Report 2023 by Stanford University: The Rise of Corporate Control in AI Development
We hope you find these topics informative and insightful.
Introducing GitHub Copilot X: The Future of AI-Powered Coding
GitHub, the popular open-source platform for software development, has unveiled an upgraded version of its AI coding tool, Copilot X, that integrates OpenAI's GPT-4 model and offers a range of new features. This latest iteration brings natural language processing and voice support, making it possible for users to interact with the tool using chat and speech input. Copilot X is no longer limited to just code completion, but can be used across the entire code creation lifecycle.
The new Copilot X offers a chat interface and interactive voice-to-code extension that recognizes the code a developer has typed and provides assistance with error messages. Moreover, the tool offers AI-generated descriptions for pull requests on Github, adds support for AI-powered tags in pull request descriptions, and has a chatbot assistant for developers.
“What we are doing with AI is to fundamentally redefine developer productivity, reduce boilerplate and manual tasks, and make complex work easier across the developer lifecycle,” says Thomas Dohmke, CEO of GitHub. He added that internal teams at GitHub are working on features to enable Copilot X to automatically suggest sentences and paragraphs and warn developers if they are missing sufficient testing for a pull request.
GitHub Copilot's new chatbot can recognize and explain code, quickly analyze code for security vulnerabilities, and even assist with rewriting parts or adding useful comments for other developers' reference. This functionality is powered by OpenAI's GPT-4 model and tailored to developers' needs. Additionally, Copilot X will automatically complete tags in pull request descriptions and help generate unit tests.
Copilot X is integrated with OpenAI models to offer various features, such as fast model response for each keystroke in the editor, accurate chat function through the use of larger models like GPT-4, and the ability to scan open-source repositories to help developers get answers. Additionally, Copilot X has a command line interface for developers, enabling them to enter commands effortlessly.
The new Copilot X, which is available in Microsoft's Visual Studio and Visual Studio Code applications, is designed to assist developers with their entire coding journey. GitHub plans to expand to other IDEs in the future.
GitHub's new AI-powered chatbot has already helped increase developer productivity and reduce the time it takes to write code by up to 55%, according to GitHub. This new vision of Copilot, as the "AI at every step" of the developer lifecycle, could change the way.
AI Video of the Week: Balenciaga Potter
Text to Video: Chat GPT Plugin
Have you heard the latest buzz around Text-to-Video technology? Zahid Khawaja has just introduced a Chat GPT plugin that can generate videos from just text! It's a remarkable achievement and a fantastic begining for marketers who want to create engaging video content without the hassle of scripting, filming and editing.
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Meet BloombergGPT: Bloomberg's New Large-Scale Natural Language Processing Model Built for Finance
At the forefront of artificial intelligence-based machine learning, Bloomberg has recently released its new large language model (LLM) called BloombergGPT. This advancement has been specifically developed for the financial industry, as it helps to support a diverse set of natural language processing (NLP) tasks. The BloombergGPT has outperformed similarly-sized open models on financial NLP tasks by significant margins, without sacrificing performance on general LLM benchmarks.?
Introducing BloombergGPT
BloombergGPT represents the first significant development towards a new domain-specific language model specifically built for financial tasks. Shawn Edwards, Bloomberg's CTO, notes that the development of BloombergGPT marks an essential milestone for the financial industry as it brings the full potential of AI technology into the financial domain. This LLM will enable Bloomberg to improve existing financial NLP tasks and unlock new opportunities to harness the vast quantities of data available on the Bloomberg Terminal.?
Technical Details on the Creation of BloombergGPT
To create the BloombergGPT model, the ML Product and Research team at Bloomberg worked closely with the AI engineering team to build one of the largest domain-specific datasets ever seen. Drawing on the vast data curation resources and the extensive archive of over 363 billion tokens of English financial documents that Bloomberg has collected and maintained for over forty years, the team created a comprehensive, clean dataset to train the LLM.?
Additionally, the team augmented the financial data with a 345 billion token public dataset, resulting in a large training corpus with over 700 billion tokens. This dataset allowed the team to train a 50-billion parameter decoder-only causal language model, the results of which were then validated on existing finance-specific NLP benchmarks, broad categories of general NLP tasks, and internal benchmarks at Bloomberg.?
Benefits and Performance of BloombergGPT
The BloombergGPT model has shown superior performance on financial NLP tasks, outperforming existing open models of a similar size by a large margin. The model still performs on par or better on general NLP benchmarks. The model's development was not an easy feat, but it will allow Bloomberg to tackle new financial applications and deliver higher performance out-of-the-box than custom models for each application.?
Gideon Mann, Head of the ML Product and Research team at Bloomberg, explains that the quality of machine learning and NLP.
The Generative AI Landscape by AiBreakfast
AiBreakfast shared The Generative AI Landscape, a summary of various AI platforms for image, code, video, and text. It's an impressive collection of tools and technologies that are pushing the boundaries of what's possible with AI.
The Rise of Corporate Control in AI Development
As AI technology continues to evolve at a rapid pace, a new report by Stanford University reveals that the development of state-of-the-art AI systems is now firmly in the hands of Big Tech companies. The 2023 AI Index report highlights that academia, which previously led the way in AI development, has been overtaken by industry due to the large resource demands required to create such applications. This shift in power towards corporate players has raised concerns about the potential for dangerous outcomes as companies rush out products and sideline safety concerns in an effort to outmaneuver rivals.
The Increasing Resource Requirements of AI Development
The report notes that the increasing resource requirements of AI development have firmly shifted the balance of power towards corporate players. For example, OpenAI's GPT-2, an early large language model, cost roughly $50,000 to train and contains 1.5 billion parameters. In contrast, Google's state-of-the-art LLM, called PaLM, cost an estimated $8 million to train and contains 540 billion parameters, making it 360 times larger than GPT-2 and 160 times more expensive. This trend has put research beyond the reach of academia and has raised concerns about the incentives of the business world leading to dangerous outcomes.
Incidents of Misuse in AI Development
As AI tools become more widely deployed, the number of incidents of ethical misuse has also increased. The report notes that the number of incidents of AI misuse has increased 26-fold between 2021 and 2012. Such incidents include fatalities involving Tesla's self-driving software, the use of audio deepfakes in corporate scams, the creation of nonconsensual deepfake nudes, and numerous cases of mistaken arrests caused by faulty facial recognition software, which is often plagued by racial biases. The report suggests that the trend for firms like Microsoft and Google to cut their AI safety and ethics teams could be contributing to this issue.
Interest in AI Regulation from Legislators and Policymakers
The report notes that interest in AI regulation from legislators and policymakers is rising. An analysis of legislative records in 127 countries noted that the number of bills containing the phrase "artificial intelligence" increased from just one passed in 2016 to 37 passed in 2022. In the US, scrutiny is also increasing at the state level, with five such bills proposed in 2015 to 60 AI.
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