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- 🤖 OpenAI Says Microsoft Isn’t Moving Fast Enough For Them
🤖 OpenAI Says Microsoft Isn’t Moving Fast Enough For Them
PLUS: AI Tells Amazon Drivers Which Packages to Deliver, AI Improves Simulation Accuracy in Computational Finance
Welcome back AI enthusiasts!
In today’s AI Report:
📦AI Tells Amazon Drivers Which Packages to Deliver
👟OpenAI Says Microsoft Isn’t Moving Fast Enough For Them
📊AI Improves Simulation Accuracy in Computational Finance
🛠Trending Tools
💰Funding Frontlines
💼Who’s Hiring?
Read Time: 3 minutes
🗞RECENT NEWS
AMAZON
📦AI Tells Amazon Drivers Which Packages to Deliver
Image Source: About Amazon News/Ivy Levine/“New AI Tech in Amazon Vans Spotlights Packages to Save Drivers Effort and Time”/Screenshot
Amazon plans to deploy 1,000 Rivian Electric Delivery Vans (EDVs) with Vision-Assisted Package Retrieval (VAPR) by early 2025.
Key Details:
Amazon drivers spend several minutes at each stop looking for packages, which can add up when delivering to hundreds of customers.
VAPR is a new AI-powered device that prevents Amazon drivers from having to manually locate a customer’s name and address on the packages. Instead, they look for VAPR’s green light, then grab and go!
When packages are loaded into the EDV, they’re scanned and uploaded to a database.
VAPR is equipped with Computer Vision (CV), which enables it to interpret, analyze, and extract visual data on the packages and compare it to the database.
Then, VAPR projects a green “O” on all the packages that should be delivered at a stop and a red “X” on all the other packages.
Why It’s Important:
In early tests, VAPR has saved Amazon drivers more than 30 minutes during deliveries.
“Before, organizing packages for the next stop took me up to five minutes. VAPR takes about a minute. It’s made my life so much easier,” said Bobby Garcia, a driver for Bloomfield Logistics testing the AI-powered device.
OPENAI
👟OpenAI Says Microsoft Isn’t Moving Fast Enough For Them
Image Source: Canva’s AI Image Generators/Magic Media
According to The Information, OpenAI is reducing reliance on Microsoft for computing power by partnering with Oracle and building their own AI hardware.
Key Details:
OpenAI CFO Sarah Friar told shareholders that Microsoft “hasn’t moved fast enough” to supply computing power, slowing down AI projects.
Tensions are rising between OpenAI and Microsoft over the budget, design, and timeline of their joint venture called “Fairwater.”
“Fairwater” involves building new data centers equipped with over 300,000 Nvidia GB200 Grace Blackwell Superchips to handle the demanding computational requirements to run AI applications like ChatGPT.
OpenAI plans to lease an entire data center in Abilene, TX, from Oracle, a major competitor to Microsoft that specializes in offering cloud infrastructure and enterprise software.
Microsoft likely had to “bless” OpenAI’s plans with Oracle. However, Microsoft had limited input in the decision-making process.
Why It’s Important:
OpenAI is diversifying the company’s computing resources to gain more control over AI development timelines and reduce reliance on Microsoft.
Despite these shifting dynamics, OpenAI maintains that its relationship with Microsoft remains unchanged. However, this situation highlights the ongoing complexities of their partnership.
🩺 PULSE CHECK
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AI RESEARCH
📊AI Improves Simulation Accuracy in Computational Finance
Image Source: MIT CSAIL/“Message-Passing Monte Carlo (MPMC): Generating Low-Discrepancy Point Sets via Graph Neural Networks (GNNs)”/Screenshot
Imagine you’re tasked with sending firefighters into a skyscraper to inspect for fire hazards. If you randomly assign their positions, they might cluster together on certain floors, potentially missing fire hazards. However, if you outline a strategy, like spreading out uniformly across each floor, you’d better assess the skyscraper’s fire hazards.
Now, imagine needing to spread out uniformly across hundreds of dimensions. To accomplish this, MIT CSAIL researchers developed Message-Passing Monte Carlo (MPMC), which generates uniformly spaced points across hundreds of dimensions to improve simulation accuracy.
In computational finance, accurate simulations of complex financial systems are crucial. These complex financial systems often involve multiple underlying assets and market conditions.
Consider a portfolio optimization problem involving hundreds of assets. To accurately assess the portfolio’s Risk-Return (R/R) ratio, MPMC can accurately simulate its performance under various market conditions.
R/R measures the potential risk and potential return of an investment. A lower ratio means the potential return is greater than the potential risk.
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🔮Browse our always Up-To-Date AI Tools Database.
💰FUNDING FRONTLINES
VanEck launches a $30M Venture Fund to back AI startups.
Distributional raises a $19M Series A to automate AI model testing.
Restoke AI secures a $5.1M Funding Round to offer restaurant process automation solutions.
💼WHO’S HIRING?
Force Factor (Boston, MA): AI Integration Intern, Summer 2025
Qualcomm (San Diego, CA): Machine Learning {ML} Engineering Intern, Summer 2025
Codazen (Irvine, CA): Full-Stack Engineer, New College Grad 2024/2025
Universal Music GRP. (New York City, NY): Junior Software Developer, New College Grad 2024/2025
NVIDIA (Santa Clara, CA): Mixed Signal Design Engineer, New College Grad 2024
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