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  • šŸ¤– Current AI Scaling Laws Show Diminishing Returns

šŸ¤– Current AI Scaling Laws Show Diminishing Returns

PLUS: Humanoid Robots Work In BMW Factory, New Benchmark Exposes AIā€™s Math Problem

Welcome back AI enthusiasts!

In todayā€™s AI Report:

  • šŸ“‰Current AI Scaling Laws Show Diminishing Returns

  • šŸ¦¾Humanoid Robots Work In BMW Factory

  • šŸ“ŠNew Benchmark Exposes AIā€™s Math Problem

  • šŸ› Trending Tools

  • šŸ’°Funding Frontlines

  • šŸ’¼Whoā€™s Hiring?

Read Time: 3 minutes

šŸ—žRECENT NEWS

AI TRENDS

šŸ“‰Current AI Scaling Laws Show Diminishing Returns

Image Source: Threads/@tomwarrenuk/ā€œThe Debate Around Scaling Laws for AI Modelsā€/Screenshot

OpenAI and Anthropic struggle to build more advanced AI models as current ā€œAI Scaling Lawsā€ show diminishing returns.

Key Details:
  • The ā€œAI Scaling Lawsā€ claim that as AI models are given more data, more training, and more computing power, theyā€™ll continue to improve.

  • However, as AI models grow larger and larger, the marginal gains in performance from the ā€œAI Scaling Lawsā€ are decreasing.

  • In other words, adding more data, more training, and more computing power isnā€™t improving AI models as much as it used to.

  • To address this issue, Google DeepMind introduced ā€œTest-Time Scaling,ā€ which allocates more computing power during AI Inference, which is everything that happens after you enter your prompt.

  • ā€œOpenAI o1,ā€ a new series of AI models designed to spend more time thinking before they respond, relies on ā€œTest-Time Scaling.ā€

Why Itā€™s Important:
  • During a Q3 FY25 Earnings Call, Nvidia CEO Jensen Huang called ā€œTest-Time Scalingā€ a new ā€œAI Scaling Law,ā€ 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

šŸ¦¾Humanoid Robots Work In BMW Factory

Image Source: Figure AI/YouTube/ā€œFigure AI Status Update, BMW Use Caseā€/Screenshot

Figure AI CEO Brett Adcock posted an update on X (i.e., formerly Twitter) about the companyā€™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 ā€œlabor shortages are the most severe.ā€

  • 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 Itā€™s Important:
  • Figure AI believes that as ā€œautomation continues to integrate with human life at scale,ā€ 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 ā€œbecome equivalent to the price of renting a humanoid robot, vacillating a long-term, holistic reduction in costs.ā€

šŸ©ŗ PULSE CHECK

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AI RESEARCH

šŸ“ŠNew Benchmark Exposes AIā€™s Math Problem

Image Source: Canvaā€™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 canā€™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 donā€™t inherently understand the underlying concepts behind the large amounts of data. They canā€™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 ā€œFrontierMath,ā€ a benchmark for evaluating an AI modelā€™s ability to solve complex math problems. Current AI models solve less than 2% of ā€œFrontierMathā€™sā€ complex math problems.

šŸ› TRENDING TOOLS

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šŸ”®Browse our always Up-To-Date AI Tools Database.

šŸ’°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.

šŸ’¼WHOā€™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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ā¤ļøTAIP Review of The Day

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