The AI talent crisis

Mistral’s Edge Models Bring Generative AI to Laptops and Phones

French AI startup Mistral just released its first set of generative AI models, named Les Ministraux. These models are tailored for laptops, phones, and other edge devices. With growing concerns over cloud-based AI and data privacy, Mistral is angling for smaller, more efficient models that run directly on devices.

AI on the edge: Running AI models on devices ensures faster processing with reduced reliance on cloud servers, offering users privacy. This opens doors for tasks like on-device translation, autonomous robotics, and local analytics without sending sensitive data to the cloud.

Smaller, faster, smarter: Mistral’s models can handle tasks that require the equivalent of processing a 50-page book in one go. These models are quicker and cheaper to train and deploy than their larger counterparts.

• Privacy-first AI: Ensures data security with local device processing.

• Low-latency processing: Instant results without relying on the cloud.

• Wide applications: From smart assistants to autonomous robotics.

What’s next? Tech giants like Google and Microsoft have already jumped on this bandwagon with their smaller models. With the edge becoming more relevant, expect a boom in mobile AI applications that prioritize speed, privacy, and efficiency.

Why the Chip Industry Can’t Find Enough Talent

The semiconductor industry is on track to hit $1 trillion globally in revenue by 2030, but a talent crisis threatens this growth. With the rise of Generative AI, the demand for skilled workers is at an all-time high. The problem? The industry’s workforce is shrinking, aging, and lacking the necessary expertise to keep up with these advances. Without addressing this, the industry's ambitions could stall.

An aging workforce: More than half of the U.S. semiconductor workforce is over age 45, and with fewer younger workers entering the field, the talent pipeline is drying up.

GenAI demands talent: The rise of Generative AI has made it essential to develop new talent skilled in AI technologies. 

• A race against the clock: Without the right workforce, innovations like chip design, AI training, and manufacturing advancements will slow down, leaving the industry struggling to meet global demands.

• Case in point: 67,000 jobs in the U.S. semiconductor industry may go unfilled by 2030.

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Boston Dynamics and Toyota Push AI Boundaries with Atlas

Boston Dynamics and Toyota Research Institute have partnered to bring AI-driven intelligence to Boston Dynamics' electric Atlas humanoid robot. This alliance marks a key step in building humanoid robots capable of learning and executing complex human tasks.

AI meets robotics: Toyota brings to the table its work on large behavior models (LBMs), which are designed similarly to large language models like ChatGPT. Boston Dynamics, known for its cutting-edge robotic hardware, is combining its expertise with Toyota’s AI capabilities to advance the Atlas humanoid’s intelligence and autonomy.

• Building general-purpose robots: This partnership aims to develop humanoids that not only perform specific tasks but can learn and adapt in real-time. 

• Imagine: A robot that can help in the kitchen one day and assist with construction the next — that’s the goal.