Here's what caught my eye in the world of AI this week:

1. OpenAI Introduces Canvas, A New Way to Collaborate and Iterate with ChatGPT

   I just started using Canvas today, and it's much easier to interact with content. You can directly engage with specific sections and use a graphical interface for editing, making the entire experience of content creation and refinement more streamlined. It's definitely a step forward for editing and collaboration.

   OpenAI has launched a new feature called Canvas, available to ChatGPT Plus and Teams users 10/3/24, with wider rollout planned later. Canvas provides a workspace alongside the chat window, making it easier to handle complex writing and coding tasks. It allows users to generate content, refine it, and request targeted AI-assisted edits, addressing common challenges of chatbots handling multi-step processes. Canvas also offers intuitive tools for writers and coders to streamline productivity. OpenAI trained GPT-4 to be more collaborative for Canvas, aligning with a broader trend of making AI more usable, as seen with similar releases by companies like Anthropic and Cursor.

   Author - Chris Mckay

Quote of the week

2. The world is full of ideas, but making them happen is an entirely different journey! Don’t fear someone else will take your idea—get on the field and start executing!

3. Sense and Respond

If you know me, you know I'm a BIG John Cutler fanboy :-) But how does what he shares below apply to AI? If you can't connect the dots from your vision and mission to the AI work happening in the trenches, there's a high chance you'll miss the mark and end up implementing AI solutions that won't truly move the needle for your company. Here's a simple framework to make sure those dots are connected.

The concept revolves around using a simple yet powerful framework called "1s and 3s" to link short-term actions to long-term goals, ensuring coherence at every level. The framework breaks down time scales into:

• 1-3 hours

• 1-3 days

• 1-3 weeks

• 1-3 months

• 1-3 quarters

• 1-3 years

• 1-3 decades

Each smaller task is connected to the next larger time scale, forming a chain that goes all the way up to the company's biggest, long-term bets. For example, a 1-3 hour task like researching transaction types connects to a 1-3 day task of adding a new transaction feature, which in turn supports a 1-3 week improvement goal, and so on up to the company’s 1-3 year strategic ambitions.

The goal is to make sure that team members understand how their immediate actions (like tasks they complete in a few hours) contribute to broader company goals over months and years. This approach encourages teams to trace each action all the way up the chain, avoiding a common "messy middle" problem, where short-term tasks are clear, long-term goals look good on slides, but the mid-term strategy is often vague.

This exercise not only helps ensure alignment and continuity across time horizons but also allows teams to think bigger and smaller simultaneously. It’s a great tool for balancing immediate sprints with overarching company missions, giving clarity from the everyday tasks to the decade-long ambitions.

Author - John Cutler

4. The Intelligence Age

I'm 100% aligned with Sam Altman's vision for the future of AI. His perspective is both inspiring and deeply thought-provoking. In his latest post, Sam talks about the incredible potential of AI to bring about prosperity that seems unimaginable today—much like how none of us wish we were lamplighters anymore. AI will push humanity forward into a new era where our capabilities are amplified beyond anything we can fathom.

Want to know more about what Sam thinks? Dive into his short post on his personal blog and share your thoughts: How do you feel about the future he envisions?

Author - Sam Altman

5. ML and LLM system design: 450 case studies to learn from

   How do companies like Netflix, Airbnb, and Doordash apply AI to improve their products and processes? Evidently AI put together a database of 450 case studies from 100+ companies that share practical ML use cases, including applications built with LLMs and Generative AI, and learnings from designing ML and LLM systems.

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6. Friday Funnies 🤣 .

Riffing on Sam's post - You've got to take inspiration for memes wherever you can find it!

7. What comes after?

Governor Newsom recently vetoed a bill aimed at regulating AI models, which had previously passed the California Assembly with overwhelming support. This decision matters beyond California's borders, as it highlights the challenges in regulating AI technology effectively while balancing innovation and safety. Newsom's key concern was that the bill focused on regulating large-scale models without considering the specific contexts of their deployment, potentially giving the public a false sense of control over this fast-evolving technology. He emphasized the need for adaptable regulation and called for a more targeted approach that takes into account the risks of each use case.

Looking ahead, it seems inevitable that more AI regulations will emerge, perhaps better crafted and more specific. But this raises a fundamental question: How do we ensure AI regulation optimizes for human flourishing while fostering innovation rather than stifling it?

Author - Rohit Krishnan

Writer RAG tool: build production-ready RAG apps in minutes

RAG in just a few lines of code? We’ve launched a predefined RAG tool on our developer platform, making it easy to bring your data into a Knowledge Graph and interact with it with AI. With a single API call, writer LLMs will intelligently call the RAG tool to chat with your data.

Integrated into Writer’s full-stack platform, it eliminates the need for complex vendor RAG setups, making it quick to build scalable, highly accurate AI workflows just by passing a graph ID of your data as a parameter to your RAG tool.

Learn more about our production ready RAG tooling here.

8. How GM is using AI to boost performance on the racetrack

Car racing isn't just about speed; it's about relentless iteration and discovery, much like the technology design process. Quick decisions made on the racetrack, powered by AI, go through rapid design-test-iterate cycles that lead to breakthrough insights. These innovations aren't limited to winning races—they directly shape the future of production vehicles, making them smarter and more efficient. Read on to explore how AI and quick iteration cycles in motorsports are creating direct feedback loops into the cars we drive every day.

Author - Jordyn Grzelewski

Until next time, take it one bit at a time!

Rob

P.S.

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