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- š¤ Current AI Scaling Laws Show Diminishing Returns
š¤ Current AI Scaling Laws Show Diminishing Returns
PLUS: Humanoid Robots Work In BMW Factory, New Benchmark Exposes AIās Math Problem
Welcome back AI enthusiasts!
In todayās AI Report:
šCurrent AI Scaling Laws Show Diminishing Returns
š¦¾Humanoid Robots Work In BMW Factory
šNew Benchmark Exposes AIās Math Problem
š Trending Tools
š°Funding Frontlines
š¼Whoās Hiring?
Read Time: 3 minutes
šRECENT NEWS
AI TRENDS
šCurrent AI Scaling Laws Show Diminishing Returns
Image Source: Threads/@tomwarrenuk/āThe Debate Around Scaling Laws for AI Modelsā/Screenshot
OpenAI and Anthropic struggle to build more advanced AI models as current āAI Scaling Lawsā show diminishing returns.
Key Details:
The āAI Scaling Lawsā claim that as AI models are given more data, more training, and more computing power, theyāll continue to improve.
However, as AI models grow larger and larger, the marginal gains in performance from the āAI Scaling Lawsā are decreasing.
In other words, adding more data, more training, and more computing power isnāt improving AI models as much as it used to.
To address this issue, Google DeepMind introduced āTest-Time Scaling,ā which allocates more computing power during AI Inference, which is everything that happens after you enter your prompt.
āOpenAI o1,ā a new series of AI models designed to spend more time thinking before they respond, relies on āTest-Time Scaling.ā
Why Itās Important:
During a Q3 FY25 Earnings Call, Nvidia CEO Jensen Huang called āTest-Time Scalingā a new āAI 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
š¦¾Humanoid Robots Work In BMW Factory
Image Source: Figure AI/YouTube/āFigure AI Status Update, BMW Use Caseā/Screenshot
Figure AI CEO Brett Adcock posted an update on X (i.e., formerly Twitter) about the companyās 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 ālabor 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 Itās Important:
Figure AI believes that as āautomation 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 ābecome equivalent to the price of renting a humanoid robot, vacillating a long-term, holistic reduction in costs.ā
š©ŗ PULSE CHECK
How will the rise of humanoid robots impact the job market?Vote Below to View Live Results |
AI RESEARCH
šNew Benchmark Exposes AIās Math Problem
Image Source: Canvaās 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 canāt 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 donāt inherently understand the underlying concepts behind the large amounts of data. They canāt 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 āFrontierMath,ā a benchmark for evaluating an AI modelās ability to solve complex math problems. Current AI models solve less than 2% of āFrontierMathāsā complex math problems.
š TRENDING TOOLS
š¤Audo finds your dream job for you.
āļøChatling enables anyone to build chatbots in minutes.
š£Vozo translates, rewrites, redubs, and lip-syncs your videos.
āļøZilliz allows you to build GenAI apps without infrastructure worries.
š¬Superchat offers an all-in-one messaging software for your business.
š®Browse our always Up-To-Date AI Tools Database.
š°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.
š¼WHOāS 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
š¤PROMPT OF THE DAY
A/B TESTING
āļøA/B Testing Email Campaigns
Craft two distinct subject lines for A/B Testing [Email Campaign]. Explain how different subject lines impact a recipientās likelihood to open an email.
Email Campaign = [Insert Here]
šFINAL NOTE
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ā¤ļøTAIP Review of The Day
āAI will takeover Hollywood. Itās inevitable!ā
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