Learn to Transcribe & Summarize with OpenAI’s Guide – A Step-By-Step to Transforming Your Meetings with OpenAI’s Powerful GPT-4 and Whisper Technologies
OpenAI Publishes Tutorial For AI-Generated Meeting Minutes
OpenAI just dropped a cool tutorial on how to use their GPT-4 and Whisper models to auto-generate meeting minutes. They’re all about making AI more accessible to everyone, so this is a big deal.
Basically, the tutorial walks you through the whole process of transcribing and summarizing meeting minutes using GPT-4 and Whisper. Meeting minutes are super valuable for companies, you know.
On the other hand, GPT-4 is a language whiz that can make human-like text. It takes those transcriptions from Whisper and turns them into easy-to-understand meeting minutes. To get the best results with GPT-4, they say you gotta be specific with your instructions. Don’t be wishy-washy, just tell it what you need. Oh, and give it some reference material to avoid crazy made-up answers.
This AI meeting minutes thing is a game-changer for folks who do a lot of meetings. It saves time, money, and makes communication smoother. Plus, mastering GPT-4 can level up your business and marketing game. So give it a shot, y’all!
Google is training robots the way it trains AI chatbots
Google is making its robots smarter by using an AI learning model called Robotic Transformer (RT-2). This model helps robots understand visual and language patterns to interpret instructions and figure out the best objects for a task. It was tested with a robotic arm in a kitchen office, where it successfully chose an improvised hammer (a rock) and a drink for a tired person (a Red Bull).
The model was trained on web and robotics data, combining advances in large language models and robotic information. In the past, teaching robots required a lot of manual programming, but with models like RT-2, they can access more information and make better decisions.
AWS AI-powered video highlights feature introduces game-changing World Cup viewing experience for fans
FOX Sports, in partnership with Amazon Web Services (AWS), has introduced an AI-powered feature called “Catch Up With Highlights” to enhance the World Cup viewing experience for fans. This feature autonomously generates real-time recap videos and game highlights, helping fans stay updated on key plays they may have missed if they join the broadcast late.
The technology uses computer vision and audio detection models to identify significant moments in the game and create condensed highlight clips. The partnership between FOX and AWS has resulted in cutting-edge AI technology that dynamically retells the game’s story and keeps fans engaged no matter where they are.
The feature was initially launched for the FIFA World Cup in Qatar and has been rolled out for other sports as well. FOX plans to bring the feature to football broadcasts later this year.
Protect AI raises $35M to build a suite of AI-defending tools
Protect AI, a startup focusing on securing AI systems, has raised $35 million in a Series A funding round. The round was led by Evolution Equity Partners and included participation from Salesforce Ventures, Acrew Capital, boldstart ventures, Knollwood Capital, and Pelion Ventures. The recent funding more than doubles the size of Protect AI’s seed round, and brings the startup’s total funding to $48.5 million.
Founded by Ian Swanson and Daryan Dehghanpisheh in 2022, Protect AI offers a range of services to address the security weaknesses in AI models. Its primary tool, AI Radar, provides visibility into the components used to build an AI model. It then generates a “machine learning bill of materials,” or MLBOM, that details the data used for training, testing datasets, and code used in the model. AI Radar can also identify practical threats and risks within an enterprise’s machine learning models.
Protect AI also offers tools to defend against certain types of AI attacks. This includes attacks involving malicious prompts, or ‘prompt injection’, where an AI that relies on text-based instructions is tricked into performing tasks outside of its original objective. Protect AI also provides tools to scan Jupyter Notebook documents, a popular platform used in AI model creation and data science experiments, for common issues and vulnerabilities.
Graft is building an AI development platform for the masses
Graft, a company focused on making artificial intelligence (AI) more accessible for all businesses, has announced a $10 million seed investment and is opening its AI development platform to a larger number of companies. The initiative comes from the vision to bridge the gap between experimental AI models and production-ready applications, making AI more accessible beyond just the largest companies with abundant resources.
Co-founder and CEO Adam Oliner, formerly heading AI at Slack, acknowledges that while AI applications like OpenAI’s ChatGPT have showcased what’s possible with AI, the path to creating production-ready applications has not necessarily become simpler. Large language models, while powerful, are complex, difficult to explain, and often raise new concerns about compliance, privacy, and AI ethics.
To help customers navigate these complexities, Graft is introducing a series of predefined AI applications, or “apps”, that can serve as templates for specific use cases. These templates can be easily adjusted to work with a company’s specific data and needs, simplifying the process of implementing AI in a production environment.
AutogenAI, a generative AI tool for writing bids and pitches, secures $22.3M
London-based startup AutogenAI has secured $22.3 million in funding from Blossom Capital for its generative AI tool that assists businesses in creating stronger pitches. The investment will be used to hire more talent, expand the platform, and grow the company’s customer base.
AutogenAI’s tool uses generative AI to help clients formulate persuasive business proposals and bids. In less than a year, the startup has managed to acquire 28 clients. However, the names of these customers have not been disclosed, reflecting some resistance in the business community to using AI tools that could replace or augment human work.
The platform works by using large language models, including those from OpenAI, coupled with a client’s proprietary data. The tool creates pitches based on a company’s most successful past work. These are then reviewed and tailored by the client’s team before being submitted.