- 4 AI Things
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Belated labour day wishes, everyone! The impact of AI on the job market is increasingly apparent, with recent events such as the Google layoffs underscoring this reality. As companies seek to optimize operations and reduce costs, they are turning to automation and AI-driven solutions, leading to shifts in workforce dynamics. While some roles may become obsolete, new opportunities are also emerging, particularly in areas that require specialized AI expertise. However, the transition is not without challenges, as workers may require reskilling or upskilling to remain competitive in the evolving job market.
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Here are the 4 interesting AI things that I learned and enjoyed this week.
4 AI Things
A new AI model called Reka Core is challenging big names like ChatGPT and Claude. It stands out with its proficiency in the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark, a tool designed to evaluate AI's ability to process and reason with various types of data, much like a human expert. This AI model showcases multimodal capabilities, handling text, images, and video, and excels in reasoning and language fluency across multiple languages. It can generate code and is available via a user-friendly chatbot interface for free, or through an API that charges $10 per million tokens for input and $25 for output.
Recruiters are swamped because thousands of people are applying for tech jobs using new AI tools that make applying easy. These tools also help write resumes and screen candidates quickly. But sometimes, these tools might recommend people who aren't quite right for the job because they're based on what looks good on paper, not real fit. Recruiters and job seekers are both figuring out how to deal with this flood of applications and automated hiring systems. The tech is still new, and everyone's learning how it changes the game.
Massive Multi-discipline Multimodal Understanding (MMMU) is a concept in AI that involves training a single model to handle a vast array of tasks across different disciplines and modalities (like text, images, and audio). This approach allows the model to develop a deep and comprehensive understanding of complex information from multiple sources, using the combined context to enhance its predictions and responses. By training on diverse data types and problem sets, MMMU models can apply learned knowledge from one domain to another, significantly improving versatility and adaptability. MMMU is a useful metric because it gauges a model's ability to process and integrate information from various fields and formats.
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Elon Musk cheekily warns that any company not dropping $11 billion on AI like Tesla is basically staying in the past. It's go big or go home in the race to the future with robots and self-driving cars!
China's new AI, Vidu, created by Shengshu Technology and Tsinghua University, can generate 16-second videos in HD with just one click. Although it's shorter than its competitor Sora's videos, Vidu introduces a unique architecture blending two AI models for enhanced video creation. It's designed to handle complex scenarios with realistic visuals and dynamic camera movements. This tool represents significant progress in China's AI capabilities, focusing on multimodal applications like video, where it can incorporate native cultural elements effectively.
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