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PLUS: Ghost Autonomy uses LLMs with OpenAI for self-driving technology.
Casey Clifton
November 10, 2023
Welcome back. Today is Friday, November 10th and today we're looking at OpenAI's plan to create better training data and the need to highlight diverse voices shaping the future of AI. New to The Intelligence Age? Sign up here.
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News & Insights
OpenAI Wants To Work With Organizations To Build New AI Training Data Sets
Aiming to rectify flaws in current data, OpenAI's ambitious plan involves creating both public and private data sets to improve AI model training. This approach intends to broaden the scope of domains AI models understand, making them more beneficial to diverse industries, cultures, and languages. They are particularly keen on data expressing human intention across various languages and formats.
OpenAI's initiative also includes digitizing necessary training data, ensuring sensitive or personal information is appropriately removed. The focus isn't just on open-source data, but also on private data sets for proprietary AI models. OpenAI has already collaborated with the Icelandic Government and Mieind ehf to enhance GPT-4's Icelandic language proficiency and with the Free Law Project to advance its models' comprehension of legal documents. techcrunch
These People Already Know How To Build Better AI. We Just Need To Pay Attention To Them
The key to a more trustworthy and less centralized AI lies in paying attention to those already doing it. A new report from Root Cause suggests that media narratives around AI align more with tech company interests. It's time we amplify voices that help us rethink what technology can be and who it should serve. Sasha Luccioni from Hugging Face, for instance, is developing energy-efficient AI, while Andriy Mulyar, GPT4All cofounder, is working on an offline privacy-preserving alternative to ChatGPT. These open-source challengers are proving that generative AI can have multiple trajectories, not just the market-domineering purposes of large corporations.
We also need to hear from those often overlooked - those at the receiving end of AI's global impact. Consider the content moderators in Kenya forming a labor union after being exploited by global tech companies, or the startup Karya in India advocating for fair compensation for data work. Keoni Mahelona, an Indigenous language tech developer in New Zealand, and Kathleen Siminyu, who collaborates with grassroots AI builders across Africa, are examples of local innovators working on community-based values, providing a stark contrast to Silicon Valley's priorities. fastcompany
Why does this matter?
The AI narrative is often dominated by major tech companies, but there's an emerging undercurrent of groups and individuals offering alternative perspectives and solutions. By amplifying these voices, we could usher in an era of AI that is not only more equitable but also more innovative and responsive to global needs.
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Google's Performance Max is getting a makeover, integrating fresh AI features to ramp up the campaign creation process. These updates are result of customer feedback highlighting the challenge in creating and scaling assets for campaigns. The solution? A clever blend of user expertise, performance data, and AI.
Performance Max now offers AI-generated campaign assets such as headlines, descriptions, and images, all conjured up in a matter of seconds. This isn't just a boon for businesses with limited creative resources; it's also a game-changer for those looking to test new variations of their typical hero images. And with a commitment to uniqueness, you can rest assured knowing your AI-crafted images will be one-of-a-kind.
Why does this matter?
These advancements in Google's Performance Max aren't just levelling up ad creation - they're potentially paving the way for a new standard in campaign creation across industries. Imagine a future where AI doesn't just provide the tools, but becomes an integral partner in the creative process.
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Nov 9, 2023
Research Review
Ghost Autonomy, in partnership with OpenAI, plans to leverage Large Language Models (LLMs) to tackle complex scenarios in self-driving technology. A novel approach, it's derived from the belief that LLMs can add reasoning to intricate scenes where current models aren't quite making the grade. Ghost is developing software that deploys multimodal models for high complexity scene interpretation, making road decisions based on visuals from car-mounted cameras.
Yet, some experts are skeptical. They argue that LLMs, designed primarily to explain images and generate text, might not be the optimal tool for autonomous driving. Critics suggest that LLMs' application in vehicular autonomy could be premature, even misguided, given their unpredictability and the ongoing struggle to validate their safety. techrepublic
Written by Isaac R. Ward, Casey Clifton, and Alex Brogan.
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