Good morning, it’s Tuesday! SpaceX’s Starship is one step closer to full reusability with a mid-air "chopsticks" catch, Tesla’s affordable humanoids promise an era of abundance, and BitEnergy’s new algorithm could slash AI energy use by 95%. Plus, AMD Launches AI Chip to Rival NVIDIA.

Inside today’s edition:

• Top stories 🗞️ 

• Starship marks its historic 5th flight 🚀

• [FF Original] Part 2-2: The Journey to AGI 👾 

• Elon Musk unveils Robotaxi  📽️ 

🗞️ YOUR DAILY ROLLUP

Stories to Know

1. Vertical LLM Agents: The New $1 Billion SaaS Opportunity

2. Tesla's Optimus Robot Aims to Revolutionize Daily Life

3. AMD Takes on NVIDIA with New AI Chip, Eyeing $500B Market

4. Integer Addition Algorithm Slashes AI Energy Use by 95%

5. DeepMind’s Michelangelo Exposes Long-Context Reasoning Limits

6. Gmail Users Warned of Sophisticated AI Phishing Scam

7. Adobe rolls out AI video tools, challenging OpenAI and Meta

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🌌 SPACE

SpaceX Successfully Catches Returning Starship Booster in Historic Fifth Flight

The Recap: On October 13, 2024, SpaceX successfully completed its fifth Starship flight test, achieving a major milestone by “catching” the Super Heavy booster mid-air with the launch tower’s robotic "chopsticks." This marks a significant step forward in the company’s plans for a fully reusable rocket system designed for Mars and lunar missions.

Highlights:

• The test launched from SpaceX’s Starbase in Boca Chica, Texas, with Starship lifting off around 8:25 a.m. ET.

• After separation, the Super Heavy booster executed a controlled descent to the launch pad, guided by three Raptor engines.

• The launch tower’s robotic arms, known as "Mechazilla," successfully grabbed the booster—a precision maneuver aimed at rapid reusability.

• Starship’s upper stage continued its journey, re-entering Earth’s atmosphere and splashing down in the Indian Ocean.

• This successful catch represents SpaceX’s first attempt to return a booster directly to the launch pad, avoiding the need for offshore landing sites.

• The booster landing process required thousands of adjustments to ensure alignment with the narrow capture zone.

• The test was not only a major technical success but also a visual spectacle, with sonic booms marking the return of the booster to its launch pad.

Forward Future Takeaways: This test marks a leap in SpaceX’s reusability objectives, pushing the company closer to rapid turnaround flights essential for cost-effective space exploration. By perfecting mid-air booster catches, SpaceX could reduce turnaround times and increase Starship’s sustainability for frequent launches to the Moon, Mars, and beyond. This success also places SpaceX on a promising path for its upcoming crewed missions and large-scale space transportation ambitions. Read the full article here.

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👾 FORWARD FUTURE ORIGINAL

Part 2-2: The Journey to AGI

Training Data

ScaleAI CEO Alexandr Wang gave an interesting insight into the importance of training data in the podcast by venture capital firm Andressen Horowitz (a16z) titled “Human Data is Key to AI: Alex Wang from Scale AI”. He explained that since the introduction and success of GPT-3.5, it has become clear that three areas in particular need to be further developed: Compute (computational capacity), Scale (the number of parameters in a model) and Data (data quality and quantity; Wang thus distinguishes Scale from Compute and Data). Wang emphasizes that the main problem at the moment is the lack of high-quality data. His company, ScaleAI, has therefore specialized in converting unstructured data into high-quality data sets that can be used to train AI models, including through fine-tuning, reinforcement learning from human feedback (RLHF), data labeling and curation.

Wang also explains why, despite the great progress in scaling computing capacity, we do not yet have functional agents for generalized tasks. He attributes this primarily to the lack of suitable training data. While humans are naturally used to combining different tasks and processing information between different applications, AI models lack this ability as there is simply no suitable data to train such complex tasks. For example, it is natural for a human to process data in Excel, transfer it to another tool and then evaluate it further. However, this multitasking ability cannot yet be taught to AI agents, as there is no suitable training data that maps such processes. → Continue reading here.

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🛰️ NEWS

Looking Forward: More Headlines

• Abridge AI Raising $250M Funding: Abridge AI is set to secure $250M, boosting its valuation to $2.5B, with an AI platform that automates medical documentation across specialties and integrates with EHR systems.

• OpenAI Unveils Realtime API Success: Launched at DevDay SF, the API is quickly gaining traction with developers, enabling dynamic applications.

• Anduril Unveils Bolt Autonomous Air Vehicles: Anduril has launched the Bolt family of autonomous air vehicles, offering ISR and munition variants with advanced software to simplify mission execution for military operations.

• Prime Intellect Launches INTELLECT-1 AI Model: Prime Intellect has released INTELLECT-1, a 10B-parameter open-source model trained through decentralized methods, advancing efforts toward open-source AGI.

• FBI's Fake Crypto Sting: The FBI's fake cryptocurrency exposed crypto fraud, leading to multiple charges and the recovery of $25 million.

• Gemini Launches Free Image Generator: Google’s Imagen 3 is now free for all Gemini users, but generating human images requires a subscription.

📽️ VIDEO

Elon Musk Unveils Robotaxi - "We, Robot" Breakdown

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