Beginners in AI

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Welcome to this week's edition of Beginners in AI, where we explore the latest trends, tools, and news in the world of AI and the tech that surrounds it. Like all editions, this is human curated and published with the intention of making AI news and technology more accessible to everyone.

This week, AI collided head-on with accountability. Anthropic agreed to a $1.5 billion settlement with authors over training data. Meanwhile, governments, researchers, and tech giants pressed forward—testing AI for influence campaigns, building tools inspired by bees, and even predicting anomalies in space.

AI TOP STORY
Anthropic to Pay Authors $1.5 Billion in Landmark Settlement

What Happened

Anthropic has agreed to a historic $1.5 billion settlement with authors who alleged their books were copied and used to train chatbots without permission. The payout works out to about $3,000 per title across nearly 500,000 works. Beyond the financial settlement, Anthropic must also delete the pirated files, preventing their reuse in future training. “As best as we can tell, it's the largest copyright recovery ever,” said the authors’ lawyer, Justin Nelson.

What It Means

This isn’t just about money. For authors, the settlement validates years of frustration over their work being fed into AI systems without consent. For the tech industry, it underscores that copyright law will not bend as easily as some companies may have assumed. The case also adds pressure on other AI developers who face similar lawsuits, from OpenAI’s disputes with songwriters to ongoing challenges from news outlets. What was once brushed aside as a “gray area” is now being forced into the black-and-white of legal precedent.

Why to Take Away

The practical implication is that AI companies may need to shift from scraping data to negotiating licensing deals up front. That could slow development, increase costs, and divide the market between firms that can afford licenses and those that can’t. For readers, it’s a reminder that behind every chatbot answer sits a vast library of human work—work that is now being recognized, if belatedly, as more than free training fuel.

At the same time, not everyone agrees with this framing. Some researchers argue that using public data to train AI is more like reading than copying—a way of learning patterns rather than reproducing content. They caution that strict licensing requirements could entrench only the biggest companies, leaving smaller labs and startups unable to compete. This tension between compensating creators and keeping innovation open remains unresolved—and it may shape the next chapter of AI’s growth as much as the courtroom battles.

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LAST WEEK IN AI AND TECH

Memes as Weapons: U.S. Military Eyes AI for Influence Ops
A leaked document shows U.S. Special Operations Command is experimenting with AI systems that can generate memes, fake news posts, and even automated responses in foreign languages. The goal is to sway public opinion abroad and disrupt adversarial messaging in real time. It signals a new era of AI-driven information warfare that blurs the line between soft power and manipulation.

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Glasses With a Brain: Samsung’s XR Wearables Incoming
Reports suggest Samsung is preparing to unveil Galaxy-branded smart glasses that run on Google’s Android XR platform. The launch could position Samsung to compete with Apple’s Vision Pro and Meta’s Quest devices, extending the battle for dominance in mixed reality. If confirmed, the debut could mark a turning point for mainstream wearable computing.

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Flight School: What Bees Can Teach AI About Smarter Vision
Scientists discovered that bees improve their ability to recognize patterns not through brain power alone, but through how they move while flying. “Bees use flight movements to sharpen brain signals, enabling them to recognize patterns with remarkable accuracy.”  By simulating this in models, researchers showed that motion can enhance neural efficiency, hinting that smarter AI may come from clever strategies rather than brute computing force. This could open the door to lightweight AI systems inspired by nature.

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Your Chats, Their Training Data: Anthropic’s New Default
Anthropic has quietly updated its consumer terms to make chat and code conversations part of model training by default. Unless users opt out by September 28, data will be retained for five years and fed into Claude’s future improvements. The shift raises questions about privacy and transparency, echoing similar moves by other AI giants.

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Forecasting Solar Fury: NASA & IBM’s AI Predicts Storms
NASA and IBM have introduced Surya, a first-of-its-kind AI model trained on nearly a decade of solar observations. It predicts solar flares with 16% higher accuracy than traditional methods, offering warnings up to two hours in advance. Better forecasting could protect satellites, astronauts, and power grids from space weather events.

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TECH TERMS TO KNOW

Data Moat is the competitive advantage an AI company builds by controlling unique or proprietary training data — which lawsuits like Anthropic’s $1.5B settlement show isn’t as “free” as many assumed.

TOOL SPOTLIGHT (affiliate link)

Suno is an AI-powered music creation platform that lets anyone generate original songs—including vocals and instrumentation—just by describing ideas with text.

What Makes Suno Interesting

• Create full songs instantly with zero musical training; just describe a style or mood and hear the results.

• Personalize tracks with unique lyrics or themes, perfect for gifting or special occasions.

• Experiment across genres, collaborating or crafting songs for any vibe—great for amateurs and musicians.

• Discover how AI interprets quirky or creative prompts, often resulting in surprising or funny songs.

• Enjoy easy sharing, remixing, and playful social interaction around music-making together.

• Ideal for making fun music about daily moments, inside jokes, or just as a creative hobby.

Here’s a sample song about not wanting to wake up because you stayed up too late reading: https://suno.com/s/UF7KSNHCbMwJEWVJ

ROBOTICS AND AI
Robotic Cowboys: Herding Tech on the Range

At Mississippi State University, researchers are testing a Warthog robotic platform that herds cattle calmly and efficiently—loitering alongside livestock with sensors and computing power, it acts like a "workforce multiplier" rather than a replacement. Cowboy skeptics remain unconvinced, but the system’s ability to navigate tough terrain and mimic human-like presence is drawing attention across the agriculture industry.

“This is not replacing the human aspect of livestock agriculture … purely utilized as a workforce multiplier.” — Marcus McGee, assistant clinical professor of Animal and Dairy Sciences, Mississippi State University

TRY THIS PROMPT (copy and paste into ChatGPT, Grok, Perplexity, Gemini)

👉 This prompt generates a strategic GTM playbook with clear positioning, acquisition channels, pricing, and growth milestones — a ready-to-use blueprint for executives planning a product launch.

How to Use This Prompt

1. Fill in the Inputs — Replace the placeholders (product, geography, customer segment) with your specific details.

2. Run the Prompt — Paste the full XML-style template into your AI tool.

3. Review the Output — The AI will generate a structured GTM playbook with positioning, acquisition, pricing, sales strategy, and milestones.

4. Refine & Iterate — Adjust the inputs or add more context (e.g., budget, competitors) to get sharper recommendations.

Got to Market Playbook

<task>  
You are a senior go-to-market strategist with access to industry benchmarks, customer research, and competitive intelligence. Act as a GTM consultant for a company preparing to launch a new product. Generate a full go-to-market playbook designed for executive decision-making.  
</task>  

<inputs>  
<product_or_solution>[Insert product/solution here]</product_or_solution>  
<target_geography>[Insert region(s), e.g., North America, Global]</target_geography>  
<customer_segment>[Insert target audience, e.g., mid-market enterprises, solo creators, Fortune 500]</customer_segment>  
<goal>Develop a GTM strategy that defines positioning, acquisition channels, pricing, and growth milestones</goal>  
</inputs>  

<instructions>  
Structure the output like a high-value GTM analyst report, including:  

1. Market Positioning  
   - Clear value proposition  
   - Key differentiators vs. competition  
   - Messaging pillars tailored to target segment  

2. Customer Acquisition Strategy  
   - Primary channels (digital, sales-led, partnerships, events)  
   - Recommended budget allocation across channels  
   - Tactics for early traction and scaling  

3. Pricing & Packaging  
   - Suggested models (subscription, freemium, usage-based, tiered)  
   - Competitive pricing benchmarks  
   - Recommendations for pilot/introductory offers  

4. Sales & Distribution  
   - Go-to-market motion (PLG, sales-led, hybrid)  
   - Key partnerships or distribution leverage points  
   - Organizational needs (sales team, customer success, support)  

5. Growth Roadmap  
   - 0–6 month launch milestones  
   - 6–12 month scale milestones  
   - KPIs to measure success (CAC, LTV, churn, revenue growth)  
</instructions>  

DID YOU KNOW?

Amazon once patented “anticipatory shipping.” The system predicts what you’ll order and ships products toward your region before you even click buy.

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Thank you for reading. We’re all beginners in something. With that in mind, your questions and feedback are always welcome and I read every single email!

-James

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