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3 Things I Learned Last Week #65 – AI Fashion, Smart Learning, and Google’s Latest

Welcome to the 65th edition of “3 Things I Learned Last Week”! 🌟

Join me on my weekly journey of discovery as I delve into various sources of knowledge. This newsletter is a blend of insights and key takeaways from my recent explorations. I’m thrilled to share these gems with you and hope they inspire and inform as much as they have me. Feel free to share the love and spread this newsletter to friends who might enjoy it!

Here’s what I’ve got for you this week:

  1. Future of E-commerce?! Virtual Clothing Try-on Agent
  2. New Summarization via In Context Learning with a New Class of Models
  3. How Google is Expanding the Gemini Era

Let’s dive in!

🛒 Future of E-commerce?! Virtual Clothing Try-on Agent

Link: Watch Here

🔑 Key Takeaways:

The future of e-commerce is being reshaped by the emergence of AI-generated influencer models, which are gaining traction on platforms like Instagram. These virtual models, though not real people, have amassed substantial followings and are proving to be lucrative for businesses.

Case in point: A small business owner in China is using AI-generated social media posts featuring people (or, well, not-people) wearing his clothes to enhance customer confidence and drive sales. Think of it as hiring models who don’t need lunch breaks or ask for raises!

While there is skepticism about the authenticity of such posts (because who wants to buy from a mannequin?), the potential for AI-powered models in the fashion industry is undeniable. These models can effectively showcase clothing, offering a more immersive experience than static images and catering to diverse customer preferences.

Technical jargon incoming: AI image generation techniques like Stable Diffusion and D models transform noise images into high-fidelity representations. Imagine training a model to recognize cats, then using that model to create cats from a mess of pixels. Now replace cats with fashion models. Voilà!

Cool tools: Confy AI allows for the seamless integration of new elements, like clothing, into existing images. Platforms like Replicate host AI models such as O-Diffusion, enabling users to upload images of clothing and generate new visuals with virtual models. Soon, you might just snap a pic, pick an outfit, and voilà, your AI alter-ego is strutting the virtual runway!

📝 New Summarization via In Context Learning with a New Class of Models

Link: Watch Here

🔑 Key Takeaways:

The landscape of language models (LLMs) has evolved significantly, with a focus on personalization and curation. Personalization tailors interactions and results to individual users, offering a more customized experience. Imagine your smart assistant knowing you like cat memes over dog videos—exactly that, but for learning.

Curation: This acts like a well-trained butler, managing data overload by providing relevant information when needed. For example, a note-taking app can pull together a comprehensive set of notes from various sources, including action items, and support different user profiles for personalized curation.

Smaller is better: New proprietary models, like Gemini Ultra 1.0 and Gemini Pro 1.0, are designed to handle specific tasks efficiently. They’re the tech equivalent of a pocket knife—compact, but oh-so-handy.

The latest models, such as Haiku and Meta’s Llama 3, are designed to address challenges like multimodality, long context windows, and cost-effectiveness. Haiku, for example, is priced competitively and offers features like many-shot in-context learning and personalized summarization.

Sectioning: This is where the model identifies topic changes and creates individual summaries for each section, like a smart book club that summarizes each chapter.

In this rapidly evolving field, evaluating language models based on practical suitability rather than academic benchmarks is crucial. Smaller, faster, and cheaper models are often more practical for most applications. Think of it like picking a fuel-efficient car over a gas guzzler for your daily commute.

🚀 How Google is Expanding the Gemini Era

Link: Watch Here

🔑 Key Takeaways:

Google’s Gemini update, particularly the 1.5 version, has seen substantial adoption, with over 1.5 million developers utilizing it. This update has been integrated into various Google products, extending its reach to over 2 billion users. Yes, billion with a “B.”

The Gemini Advanced model incorporates the 1.5 Pro model, offering enhanced features like the ability for users to upload their own documents. It aims for a 2 million token capacity—because why not think big?

Context caching: This feature allows users to reuse cached tokens for multiple queries, aligning with Google’s vision of an “infinite context window.” In other words, your AI assistant won’t just remember your last question, but possibly your entire last conversation.

New toys: Google has introduced the Gemini 1.5 Flash Model, which is like the sportscar version—smaller, faster, more efficient, and budget-friendly. It’s priced at 35 cents for a million tokens and is available for testing in Google AI Studio and Vertex AI.

Looking ahead, Google is focusing on current and upcoming projects, hinting at new content on Google Agents, NotebookLM, and potential international expansion. The Gemini models, both 1.5 Flash and 1.5 Pro versions, are designed to offer distinct advantages, emphasizing speed, efficiency, and multimodal capabilities. Users are encouraged to explore and compare the results of both models, available for testing in Google’s AI platforms.

Thank you for joining me on this weekly learning journey. I hope you found the three things I shared insightful and valuable. Remember, continuous learning is essential for personal and professional growth, and I’m honored to be a part of your learning process.

I wish you a great week filled with new opportunities, growth, and joy. And if you received this newsletter forwarded by a friend, subscribe to get your own copy every week. Just click the link below and enter your email address, and you’ll be all set.

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Thank you for being so supportive, and I’ll see you next week with more exciting insights to share!

Best regards,

~ Nathan

The author partially generated this content with GPT-4 & ChatGPT, Claude 3, Gemini Advanced, and other large-scale language-generation models. Upon developing the draft, the author reviewed, edited, and revised the content to their liking and took ultimate responsibility for the content of this publication.






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