Welcome back AI enthusiasts!
In today’s AI Report:
📉Current AI Scaling Laws Show Diminishing Returns
🦾Humanoid Robots Work In BMW Factory
📊New Benchmark Exposes AI’s Math Problem
🛠Trending Tools
💰Funding Frontlines
💼Who’s Hiring?
Read Time: 3 minutes
🗞RECENT NEWS
AI TRENDS
📉Current AI Scaling Laws Show Diminishing Returns
Image Source: Threads/@tomwarrenuk/“The Debate Around Scaling Laws for AI Models”/Screenshot
OpenAI and Anthropic struggle to build more advanced AI models as current “AI Scaling Laws” show diminishing returns.
Key Details:
The “AI Scaling Laws” claim that as AI models are given more data, more training, and more computing power, they’ll continue to improve.
However, as AI models grow larger and larger, the marginal gains in performance from the “AI Scaling Laws” are decreasing.
In other words, adding more data, more training, and more computing power isn’t improving AI models as much as it used to.
To address this issue, Google DeepMind introduced “Test-Time Scaling,” which allocates more computing power during AI Inference, which is everything that happens after you enter your prompt.
“OpenAI o1,” a new series of AI models designed to spend more time thinking before they respond, relies on “Test-Time Scaling.”
Why It’s Important:
During a Q3 FY25 Earnings Call, Nvidia CEO Jensen Huang called “Test-Time Scaling” a new “AI Scaling Law,” stating that Nvidia is well-positioned for the change.
However, this could be a more competitive space for Nvidia to operate in, as well-funded startups like Groq, with their Language Processing Units (LPUs), offer lightning-fast AI Inference.
FIGURE AI
🦾Humanoid Robots Work In BMW Factory
Image Source: Figure AI/YouTube/“Figure AI Status Update, BMW Use Case”/Screenshot
Figure AI CEO Brett Adcock posted an update on X (i.e., formerly Twitter) about the company’s fleet of humanoid robots working in the BMW factory.
Key Details:
Figure AI, a robotics company designing humanoid robots to perform dangerous and undesirable jobs, recently released Figure 02 (F.02).
F.02 is a general-purpose humanoid robot optimized for manufacturing, warehousing, and retail positions where “labor shortages are the most severe.”
F.02 performs 1,000 fully autonomous car component placements every day in the BMW factory. In the past three months, F.02 has become 4X faster and 7X more accurate when installing battery cells.
Why It’s Important:
Figure AI believes that as “automation continues to integrate with human life at scale,” it can minimize the impact of labor-based shortages on the economy.
As humanoid robots integrate into the workforce, labor costs will decrease until they “become equivalent to the price of renting a humanoid robot, vacillating a long-term, holistic reduction in costs.”
🩺 PULSE CHECK
How will the rise of humanoid robots impact the job market?
AI RESEARCH
📊New Benchmark Exposes AI’s Math Problem
Image Source: Canva’s AI Image Generators/Magic Media
AI models are great at generating text, recognizing images, and even solving basic math problems. However, they struggle with complex math problems because of their reasoning abilities.
AI models are primarily designed to recognize patterns in large amounts of data. They can’t inherently plan a sequence of actions over time toward a goal. Complex math problems require breaking down calculations into smaller steps to find a solution.
AI models don’t inherently understand the underlying concepts behind the large amounts of data. They can’t explain why a particular formula works or how different equations relate to each other.
For example, even after being trained on a vast dataset to solve three-digit multiplication, AI models failed to solve five-digit multiplication. This example suggests that while AI models can perform well on familiar tasks, they may lack the ability to truly understand the underlying principles and apply them to new situations.
To measure these flaws, Epoch AI developed “FrontierMath,” a benchmark for evaluating an AI model’s ability to solve complex math problems. Current AI models solve less than 2% of “FrontierMath’s” complex math problems.
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🔮Browse our always Up-To-Date AI Tools Database.
💰FUNDING FRONTLINES
CommBox raises a $15M Funding Round to prioritize AI in the customer experience.
H lands a $220M Seed Round to create, run, and scale web automations.
New Lantern secures a $19M Series A to help radiologists work smarter with AI.
💼WHO’S HIRING?
Cleric (San Francisco, CA): Software Engineering Intern, Summer 2025
Riot Games (Los Angeles, CA): Research Scientist Intern, Game AI, Summer 2025
Microsoft (Redmond, WA): Research Intern, AI Mediated Sensemaking, Summer 2025
Nvidia (Santa Clara, CA): Sales Operations Intern, Customer Success, Summer 2025
Nissan Motor Corp. (Silicon Valley, CA): Machine Learning {ML} Intern, Summer 2025
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