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Pixtral 12B, Ozmo Robots, and Why Generative AI is Set to Fail

Mistral drops Pixtral 12B, Ozmo robots are cleaning windows, and Waymo reveals why human drivers are the problem. Plus, we dive into the pitfalls of generative AI—here’s what you need to know.

Author

Brent Moreno
September 11, 2024

Top Stories:

  • Mistral Releases Pixtral: Multimodal AI Model with 12B Parameters

  • Robots Are Taking Over Window Washing with Ozmo

  • Waymo’s Report Shows Human Drivers Cause Most Crashes

  • Why Your Company Will Fail at Generative AI

News from the Front Lines:

  • US senators urge regulators to investigate potential AI antitrust violations

  • AI live shopping tech is coming to the 2024 VMAs

  • Apple’s AI privacy sets a new industry standard

  • A showdown of AI image generators reveals surprising results

Tutorial of the Day:
Automate Everything in ChatGPT

Research of the Day:
INTRA: A novel framework for AI affordance grounding that helps machines understand how to interact with objects in images, improving robotics, scene understanding, and assistive technologies.

Tools of the Day:
Langflow, Replit Agent, Carrot Care, Hoop, Meco, Venturekit

Prompt of the Day:
Lean Startup Framework Prompt: Guide your business through product development, customer validation, and iteration using the Lean Startup Methodology.

Tweet of the Day:
Klarna is eliminating a lot of their software subscriptions thanks to AI.

Mistral Drops Pixtral: Its First Multimodal Model

Quick Byte:

The French AI startup Mistral just made waves by releasing its first-ever multimodal model, Pixtral 12B. This AI model can handle both images and text, and with 12 billion parameters packed into it, it’s set to compete with some of the biggest players in the game. You can grab it off GitHub or Hugging Face, but it’s not all freebies — commercial use will cost you.

Key Takeaways:

  • Pixtral 12B is versatile: It can process both text and images, making it ideal for tasks like captioning and object counting. You can feed it image URLs or encode images in base64 format.

  • Open but with strings attached: While research and academic use of Pixtral 12B is free, any commercial use will require a paid license.

  • Powered by Nemo 12B: Built on the backbone of Mistral’s text-based model, Nemo 12B, Pixtral has inherited some serious problem-solving skills, clocking in at around 24GB of AI brainpower.

  • Privacy and data usage questions loom: As with many generative AI models, there’s controversy about the data used to train Pixtral 12B. Legal battles are heating up around the world over the scraping of copyrighted materials, and Mistral hasn’t made it clear which datasets Pixtral used for training.

Bigger Picture:

Mistral’s fast rise, backed by a hefty $645 million funding round, signals Europe’s AI scene is ready to throw down against the U.S. giants like OpenAI. With Pixtral 12B entering the scene, Mistral is positioning itself as a major player in the AI race. This release might be a first step in Mistral’s broader plan to take the lead in the multimodal AI space, blending both text and image processing to unlock new possibilities in everything from corporate use to creative projects.

Robots Are Coming for Window Washers—And It’s About Time

Quick Byte:

We all know it’s only a matter of time before robots start taking over more jobs—especially the dangerous ones. Enter Ozmo, a new AI-powered window-cleaning robot that’s about to shake up a $40 billion industry. After some successful test runs, Skyline Robotics just installed their futuristic bot in a 45-story office building in Manhattan. It's faster, safer, and yeah, it's probably going to put window washers out of business eventually.

Key Takeaways:

  • End of an Iconic Job: Window washing on skyscrapers? It's straight-up dangerous. In New York alone, one out of every 200 window cleaners dies on the job each year. Ozmo aims to take humans out of the equation—starting now.

  • How Ozmo Works: Ozmo mimics the classic window washer setup but with two robot arms and a lot of AI. It's equipped with sensors and force detectors that adjust pressure based on the fragility of the glass. For now, there’s still a human operator, but full autonomy is coming soon.

  • Robots-As-A-Service: Skyline Robotics is running Ozmo as a RaaS platform. Buildings pay based on square footage and cleaning cycles, and they get detailed data on each job. Ozmo’s robots are already outperforming humans—cleaning windows three times faster.

Bigger Picture:

This is just the beginning. While window washers may still be around for now, automation is creeping in fast. Ozmo’s roll-out in New York is only the start—Skyline Robotics has its sights set on cities like London, Japan, and Singapore. And while they say the current workforce will be “involved” in some capacity, history shows us what happens when robots take over: jobs disappear. It’s only a matter of time.

Human Drivers Are To Blame for Most Serious Waymo Collisions

Quick Byte:

Ever wondered if robotaxis are making our roads safer or scarier? Well, Waymo just dropped a new report with some hard data, and the answer might surprise you. While we often hear about self-driving cars being involved in accidents, it turns out that when it comes to serious crashes, it's human drivers who are the problem.

Key Takeaways:

  • Humans are dangerous drivers: Out of Waymo's 23 most serious crashes, 16 were caused by a human driver rear-ending a Waymo vehicle. Other crashes were often the result of reckless human behavior, like running red lights.

  • Safer than humans: Waymo’s driverless cars have a much lower injury-causing crash rate compared to human drivers — about one-third of the rate, per mile.

  • Scaling up fast: Waymo is already handling 100,000 robotaxi rides per week and is expected to grow even more. The data suggests this expansion is good news for road safety.

Bigger Picture:

This report shows that robotaxis like Waymo’s are already outpacing human drivers when it comes to safety. As Waymo scales up, expect more automation in transportation — and with it, fewer accidents caused by human error. While there’s still some public hesitation about AI on the road, it seems like the real danger might be letting humans stay behind the wheel.

Why Your Organization Will Fail at Generative AI

Quick Byte:

We’re closing in on two years since the launch of ChatGPT-3, and companies are scrambling to jump into the generative AI revolution. But here's the thing: Most of them are going to fail. It’s not because AI is too complicated (though it is), and it’s not because they’re not trying hard enough. It’s because the rules are changing faster than anyone can keep up with. The AI landscape evolves at breakneck speed, and by the time you lock in your budget and tech, you’re already behind.

Key Takeaways:

  • Risky Business: Traditional corporate tech builds? You’ve got a roadmap and a finish line. With AI? The goalposts keep moving. Pick the wrong large language model (LLM), and you’re rebuilding your AI assistant from scratch in a year or two.

  • Closed vs. Open-Source Showdown: Do you go with an open-source LLM that gives you customization but requires engineering expertise? Or do you play it safe with a closed model like ChatGPT that locks you in and costs more? Either way, you’re guessing because no one knows which will win out in the long run.

  • Tech is Moving Fast: Right now, RAG databases + LLM is the best practice. But what happens when a new breakthrough like neuro-symbolic AI comes along? That best practice you just implemented becomes a relic.

Bigger Picture:

Generative AI isn’t just about building an assistant and calling it a day. It’s about being nimble, setting up cross-functional teams that can adapt to new breakthroughs, and embracing the fact that failure is part of the process. This isn’t a one-time investment; it's a long-term commitment. And if you think you can take a “heads down, build it once” approach, you’re already on the wrong path.

US senators urge regulators to probe potential AI antitrust violations

The US government is starting to notice the potentially negative effects of generative AI on areas like journalism and content creation.

How retailers and media companies are teaming up to bring live shopping to the 2024 VMAs

Viewers of the 2024 MTV VMAs will be able to shop the red carpet looks they're seeing in real time with new AI technology from Paramount Global and Shopsense.

Why Apple Intelligence Sets A New Gold Standard For AI Privacy

Delve into Apple's privacy-by-design approach, emphasizing on-device processing and private cloud compute to ensure that personal data remains secure.

I gave four AI image generators a 'realism test' — and the winner surprised me

Ideogram, Flux, Stable Diffusion and Imagen 3 compared

Automate Everything in ChatGPT

Authors: Ji Ha Jang, Hoigi Seo, and Se Young Chun

Institutions: Seoul National University

Summary: INTRA introduces a novel framework for weakly supervised affordance grounding, where affordances (possible actions with objects) are mapped to objects without the need for costly annotations. The innovation here is a method to teach intelligent systems how to identify these action possibilities by observing interactions from images. Unlike prior methods, INTRA uses only exocentric images (images showing objects and humans interacting) rather than paired images, reducing the need for extensive data. The framework leverages contrastive learning, text-conditioned affordance mapping, and vision-language model embeddings to enable more scalable and flexible AI systems capable of reasoning across diverse interactions.

Why This Research Matters: As AI becomes more deeply integrated into real-world tasks, enabling machines to understand and interpret interactions with objects is critical. Affordance grounding is important in fields like robotics, where robots need to understand how to interact with objects in dynamic environments without being explicitly programmed for every action. INTRA pushes the field forward by removing the need for egocentric data (images showing just the objects) and advancing the AI's ability to process novel interactions, making AI systems much more adaptable and scalable.

Key Contributions:

  1. New Approach to Learning: INTRA transforms the affordance grounding task into representation learning, bypassing the need for paired image datasets.

  2. Text-Conditioned Affordance Mapping: Leverages vision-language models (VLM) to generate affordance maps using text descriptions, making the system flexible to new, unseen interactions.

  3. Scalability: By eliminating the need for egocentric images and allowing for zero-shot learning (where the AI can generalize to new interactions), INTRA is highly scalable and adaptable.

  4. State-of-the-Art Results: The method outperformed previous techniques on various datasets (AGD20K, IIT-AFF, CAD, UMD), showcasing both qualitative and quantitative improvements in affordance detection.

Use Cases:

  • Robotics: Enables robots to understand and predict how to interact with objects in novel environments, improving performance in tasks like grasping or manipulation.

  • Scene Understanding: Helps AI systems better interpret human-object interactions in images and videos, useful in surveillance or human-robot collaboration.

  • Assistive Technologies: Can be applied in assistive devices, such as prosthetics, to predict user intentions and improve usability through more intuitive interfaces.

Impact Today and in the Future:

  • Immediate Applications: INTRA can be applied in current AI systems to improve their interaction understanding, particularly in robotics and autonomous systems, making them more versatile and capable.

  • Future Implications: The research opens the door for more adaptable, intelligent systems that can generalize interactions, paving the way for AI that can operate autonomously in unstructured environments without extensive pre-training on specific tasks.

Langflow - An open-source platform that enables users to create and deploy custom AI applications through an intuitive visual interface. It allows developers and non-technical users to design and manage workflows, integrate models, and streamline processes for building AI-driven solutions without extensive coding.

Replit Agent - AI-powered software development & deployment platform for building, sharing, and shipping software fast.

Carrot Care - Elevates your performance and increases lifespan by guiding you in the world of human blood biomarkers. App reads any lab results from any lab. Historical data supported.

Hoop - Capture and prioritize your tasks. A global task list across all your teams, with AI at the core.

Meco - Move your newsletters to a space built for reading and declutter your inbox in seconds.

Venturekit - AI that writes a business plan for you. Make a winning business plan in minutes.

Lean Startup Framework Prompt:

CONTEXT:

You are Lean Startup Coach GPT, an expert in helping entrepreneurs and business owners build, test, and iterate on their ideas with minimal resources. You specialize in applying the Lean Startup Methodology to guide businesses through rapid product development, customer validation, and pivoting when necessary.

GOAL:

I want to implement the Lean Startup Methodology to develop and test new products or services for my business. My objective is to minimize risk by focusing on validating assumptions, gathering customer feedback, and iterating quickly to build a product that meets market demand.

LEAN STARTUP FRAMEWORK STRUCTURE:

Build (Minimum Viable Product - MVP):
Develop the simplest version of your product that can provide value and gather feedback.

Measure (Customer Feedback):
Gather actionable feedback from early users to validate or invalidate assumptions.

Learn (Pivot or Persevere):
Use feedback and data to decide whether to pivot (change the direction of the product) or persevere (continue refining the product).

LEAN STARTUP CRITERIA:

Build (MVP Development):

Provide 3 actionable steps to define and develop the Minimum Viable Product (MVP) for my business.
Focus on identifying the core features that solve the primary customer problem.

Measure (Customer Feedback):

Suggest 3 effective ways to gather customer feedback on the MVP.
Focus on how to ask the right questions to validate assumptions and uncover valuable insights.

Learn (Pivot or Persevere):

Offer 3 strategies for analyzing feedback and data to determine if I should pivot or continue refining the current product.
Provide examples of how to pivot if feedback indicates the current product isn’t solving the problem as expected.

Experimentation and Iteration:

Propose 3 methods for quickly iterating on the product based on feedback to improve it with minimal resources.

INFORMATION ABOUT MY BUSINESS:

Business Type: [Describe your business type (e.g., SaaS, e-commerce, services).]
Target Audience: [Who is your target customer?]
Current Resources: [List any constraints such as limited budget, small team, existing tools, etc.]