I’m late, but it’s for a good cause. Since I can only send one newsletter a day through the software I use, I wanted to wait until OpenAI’s DevDay concluded before writing today’s newsletter. I’m glad I did because it turns out, the predictions I made a few weeks ago I went 3 for 5 which isn’t a bad record at all. I would say 4 for 5 because Sam did hint at how the things they are currently working on are much bigger but no real show and tell of what’s to come. Today was all about what’s available TODAY. That’s right, they unleashed a whole slew of updates that go into effect today if you are a developer or ChatGPT Plus or Enterprise subscriber.

TL;DR 🚀:

1. GPT-4 Turbo Unleashed: OpenAI's newest model boasts a 'War and Peace' sized context window, wallet-friendly pricing, and an Assistants API for easy app building.

2. Multimodal Mastery: The latest AI can now see, create images with DALL·E 3, and produce eerily accurate text-to-speech, while JSON mode keeps it predictable.

3. Customizable ChatGPTs: Tailor your own GPTs for any task imaginable, from kid's homework to data analytics. Share your genius or monetize it in the upcoming GPT Store.

4. Grok by Musk: Elon Musk introduces Grok, an AI with humor and smarts, shaking up the scene with coding and real-time info retrieval with a snarky twist.

5. AI & Business Synergy: Small and medium-sized businesses are leveling up with AI, transforming content strategies and operations for future-proofing success.

6. Content Watermarking: Biden's executive order advances AI-labeling, paving the way for authenticity and ownership in the digital realm.

📰 News From The Front Lines

📖 Tutorial Of The Day

🔬 Research Of The Day

📼 Video Of The Day

🛠️ 6 Fresh AI Tools

🤌 Prompt Of The Day

🐥 Tweet Of The Day

AI Engineer Skills for Beginners

Title: EMERNERF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision

Authors:

Jiawei Yang, Boris Ivanovic, Or Litany, Xinshuo Weng, Seung Wook Kim, Boyi Li, Tong Che, Danfei Xu, Sanja Fidler, Marco Pavone, Yue Wang

Executive Summary:

The research presents "EmerNeRF," an innovative method for learning spatial-temporal representations of dynamic driving scenes using neural fields. EmerNeRF distinguishes itself by decomposing scenes into static and dynamic components through self-supervision, enabling it to learn from a wide array of unstructured data sources. Additionally, the approach involves parameterizing an induced flow field from dynamic scenes, which helps to aggregate multi-frame features, thereby enhancing the precision in rendering dynamic objects. This self-sufficient method does not depend on ground truth annotations or pre-existing models for object segmentation or optical flow estimation. Notably, EmerNeRF achieves superior performance in sensor simulation, significantly surpassing previous methods in reconstructing both static and dynamic scenes. Moreover, the technique includes lifting 2D visual foundation model features into 4D space-time and addressing positional biases in modern Transformers, yielding considerable improvements in 3D perception performance. The authors also introduce a challenging dataset comprising 120 sequences to benchmark neural fields in extreme dynamic conditions.

Pros:

- State-of-the-art Performance: EmerNeRF outperforms existing methods in accuracy and detail for both static and dynamic scene reconstructions.

- Self-supervision: The method can learn from diverse, unlabeled data, reducing the reliance on extensively annotated datasets.

- Innovative Decomposition: It effectively separates scenes into static and dynamic components without prior knowledge of the environment.

- Semantic Generalization: Enhanced by integrating features from 2D visual models into the 4D spatio-temporal domain.

- Positional Bias Addressing: Improves 3D perception by adjusting for biases present in Transformer models.

Limitations:

- Complexity and Resource Intensity: The self-supervision and decomposition process might be resource-intensive, potentially requiring high computational power.

- Potential Overfitting: If not carefully implemented, the model could overfit to specific types of scenes or conditions.

- Generalization Challenges: While the method aims to generalize well, performance in entirely unseen or drastically different environments is not guaranteed.

Use Cases:

- Autonomous Vehicle Perception: Improving the accuracy of sensory data interpretation in dynamic driving environments.

- Robotics: Assisting robots in perceiving and interacting with changing surroundings.

- Computer Graphics: Enhancing the realism of simulations and animations involving dynamic scenes.

- Augmented Reality: Providing a more accurate real-time overlay of virtual objects in physical environments.

Why You Should Care:

EmerNeRF represents a significant advancement in the way machines perceive and reconstruct dynamic environments, which is crucial for the development of autonomous systems and enhanced virtual simulations. Its ability to self-learn from varied, unlabelled data sources offers a versatile tool for developers, reducing dependency on large, annotated datasets. The improvements in scene decomposition and feature integration suggest broad applicability across fields such as autonomous driving, robotics, and virtual reality, indicating its potential to contribute substantially to advancements in these areas.

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Acquisition Mix GPT:

CONTEXT:
You are Acquisition Mix GPT, a professional digital marketer who helps [What You Do] get more traffic. You are a world-class expert in generating a system called Acquisiton Mix.

GOAL:
I want you to generate an acquisition mix for my business. I will use it to get more traffic more systematically.

ACQUISITION MIX FRAMEWORK:
- There are 3 types of marketing channels (algorithmic, long-term, and direct impact)
- Algorithmic channels (for example, Twitter, TikTok, LinkedIn, Instagram, etc.). If you post something extra helpful, millions of people can see it. If you post something irrelevant, only dozens of people will see it. It’s an unstable and often illogical channel. But the possible upside can be huge.
- Long-term channels (for example, YouTube, Newsletter, Podcast, SEO, etc). You will see the first results after 6-12 months. But if you stay long enough, your business will have a massive effect.
- Direct impact channels (for example, Product Hunt, Email Outreach, Paid Ads, etc). You have much control over these channels. The more you do something, the better result you will see. It all depends on you.
- Every business needs at least one channel from each type to have a balanced acquisition mix. Otherwise, their user acquisition will be unstable or take too long to catch up.

RESPONSE STRUCTURE:
- Each type should have 1 best channel for my business and audience.
- Each channel should have a description of why it's a good fit with my business and 3 distribution ideas to simplify the next step for me

ACQUISITION MIX CRITERIA:
- Be creative. Outdated marketing channels and ideas won't work in 2023. I need you to think outside the box and focus on unconventional and overlooked ideas to promote my business.
- Prioritize channels and ideas with the highest ROI. I don't want to spend my time on moderately useful tactics.
- Describe channels and ideas straightforwardly. I want to understand your judgment and understand what my next steps should look like.
- Prioritize free marketing channels and ideas if they make sense. But if you see a potential with paid ads, make sure to include it. I have a marketing budget to spend.

INFORMATION ABOUT ME:
- My business: [Explain What Your Business Is/Does]
- My target audience: [Enter Your Target Audience]
- My pricing: [Enter Your Pricing]

RESPONSE FORMATTING:
Use Markdown to format your response.