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The Truth About OpenAI’s o1: Is It Worth the Hype?

Explore the capabilities and limitations of OpenAI’s o1 model, its impact on the AI community, and its potential role in the future of AI in Asia.

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OpenAI o1 model

TL;DR:

  • OpenAI’s o1 model excels at complex reasoning but is more expensive and slower than GPT-4o.
  • Open AI’s o1 is best suited for big, complicated tasks rather than simpler questions.
  • The AI community has mixed feelings about o1’s capabilities and its high cost.

The Arrival of OpenAI’s o1: A Step Forward or Back?

OpenAI recently released its new o1 models, nicknamed “Strawberry,” which pause to “think” before answering. While there’s been much anticipation, the model has received mixed reviews. Compared to GPT-4o, o1 is better at reasoning and complex questions but is roughly four times more expensive. It also lacks the tools, multimodal capabilities, and speed that made GPT-4o impressive. OpenAI even admits that GPT-4o is still the best option for most prompts.

Ravid Shwartz Ziv, an NYU professor studying AI models, shares, “It’s impressive, but I think the improvement is not very significant. It’s better at certain problems, but you don’t have this across-the-board improvement.”

Thinking Through Big Ideas

OpenAI o1 stands out because it breaks down big problems into small steps, attempting to identify when it gets a step right or wrong. This “multi-step reasoning” isn’t new but hasn’t been practical until recently. Kian Katanforoosh, Workera CEO and Stanford adjunct lecturer, explains, “If you can train a reinforcement learning algorithm paired with some of the language model techniques that OpenAI has, you can technically create step-by-step thinking and allow the AI model to walk backwards from big ideas you’re trying to work through.”

However, o1 is pricey. It charges for “reasoning tokens,” which are the small steps the model breaks big problems into. This makes it crucial to use o1 wisely to avoid high costs.

OpenAI o1 in Action

To test o1, I asked ChatGPT o1 preview to help plan Thanksgiving dinner for 11 people. After 12 seconds of “thinking,” it provided a detailed response, breaking down its thinking at each step. It suggested prioritizing oven space and even proposed renting a portable oven. While it performed better than GPT-4o, it also suggested overwhelming solutions for simpler tasks.

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For instance, when asked where to find cedar trees in America, o1 delivered an 800+ word response, outlining every variation of cedar tree. GPT-4o provided a concise, three-sentence answer.

Tempering Expectations

The hype around o1 started in November 2023, leading some to speculate that it was a form of AGI. However, OpenAI CEO Sam Altman clarified that o1 is not AGI and is still flawed and limited. The AI community is coming to terms with a less exciting launch than expected.

Rohan Pandey, a research engineer with AI startup ReWorkd, notes, “The hype sort of grew out of OpenAI’s control.” Mike Conover, Brightwave CEO, adds, “Everybody is waiting for a step function change for capabilities, and it is unclear that this represents that.”

The Value of OpenAI o1

The principles behind o1 date back years. Google used similar techniques in 2016 to create AlphaGo. Andy Harrison, former Googler and CEO of the venture firm S32, points out that this brings up an age-old debate in the AI world. One camp believes in automating workflows through an agentic process, while the other thinks generalized intelligence and reasoning would eliminate the need for workflows.

Katanforoosh sees o1 as a tool to question your thinking on big decisions. For example, it can help assess a data scientist’s skills in a 30-minute interview. However, the question remains whether this helpful tool is worth the hefty price tag.

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The Future of AI in Asia

The release of o1 raises questions about the future of AI, particularly in Asia. As AI models become more capable, they also become more expensive. The trade-off between cost and capability will shape how AI is adopted and used in the region.

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What are your thoughts on OpenAI’s o1 model? Have you tried it yet? Share your experiences and thoughts on the future of AI and AGI in the comments below. Don’t forget to subscribe for updates on AI and AGI developments.

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ChatGPT Canvas: The Future of AI Collaboration is Here!

ChatGPT Canvas revolutionises AI collaboration with real-time editing and inline feedback, making it easier to create and refine projects with AI.

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ChatGPT Canvas

TL;DR:

  • OpenAI launches ChatGPT Canvas, a new AI-first text and code editor.
  • Canvas allows users to collaborate with AI on projects in real-time.
  • Initially available to Plus and Team subscribers, with Enterprise and Education users getting access soon.

The Dawn of a New AI Era

Artificial Intelligence (AI) is transforming the way we work, and OpenAI’s latest innovation, ChatGPT Canvas, is set to revolutionise collaboration. This new AI-first text and code editor allows users to work side by side with AI on projects, making it easier to create and refine ideas.

What is ChatGPT Canvas?

ChatGPT Canvas is more than just a new user interface; it’s a game-changer in AI collaboration. OpenAI describes it as a “new way of working together” with ChatGPT. It’s designed to help users make small revisions or change specific elements without losing the flow of their work.

Key Features of ChatGPT Canvas

  • AI-First Interface: Canvas is an AI-first text and code editor that lets you adapt any single element or the whole project with the help of AI.
  • Real-Time Collaboration: It allows you and ChatGPT to collaborate on a project in real-time, making it easier to create and refine ideas.
  • Targeted Edits: The model knows when to open a canvas, make targeted edits, and fully rewrite. It also understands broader context to provide precise feedback and suggestions.
  • Inline Feedback: Like a copy editor or code reviewer, it can give inline feedback and suggestions with the entire project in mind.
  • Writing Controls: There will be a series of writing controls in a pop-out menu on the side, including options to adjust the length of the text and adapt the reading level.

How Does ChatGPT Canvas Work?

Canvas will be available through a new option in the model dropdown menu, labeled “ChatGPT 4o with Canvas”. When selected, it opens in a separate window, allowing you and ChatGPT to collaborate on a project. During a demo, I saw it take live data from an AI web search and adapt pieces of text from across a long article to reflect the new data — all from a single prompt.

The Future of AI Collaboration

ChatGPT Canvas represents a paradigm shift in how we collaborate with artificial intelligence. It’s a way to work on a project with AI as a partner rather than having it do all the work and then editing it later. This new way of working together is set to make AI collaboration more efficient and effective.

“A key challenge was defining when to trigger a canvas. We taught the model to open a canvas for prompts like ‘Write a blog post about the history of coffee beans’ while avoiding over-triggering for general Q&A tasks like ‘Help me cook a new recipe for dinner.’,” – OpenAI

Who Can Access ChatGPT Canvas?

ChatGPT Canvas is initially available to Plus and Team subscribers from today, with Enterprise and Education users getting access next week. This early beta version presents a “new way of working together” with ChatGPT by creating and refining ideas side by side.

What’s Next for ChatGPT Canvas?

OpenAI says this is just an initial beta release and that there are plans for rapid upgrades over the coming months. While they didn’t go into detail, I suspect this will include the addition of DALL-E images, more editing features, and potentially the ability to load multiple Canvas elements in a single chat thread.

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The Impact of ChatGPT Canvas on the Tech Industry

ChatGPT Canvas is a mixture of several popular AI products, including Anthropic’s Claude Artifacts, Cursor AI, and existing platforms like Google Docs — but with an OpenAI spin. It’s a significant improvement when it comes to making small revisions or changing specific elements, tasks that can get confusing or lose the flow when using the chat interface.

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What do you think about the future of AI collaboration with tools like ChatGPT Canvas? Share your thoughts and experiences in the comments below! Don’t forget to subscribe for updates on AI and AGI developments.

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Meta’s Movie Gen: Revolutionising Video Creation with AI

The Accenture-Nvidia partnership signals a new era of AI-centric strategies in Asia, with generative AI and open-source models playing crucial roles.

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AI video generation

TL;DR:

  • Meta introduces Movie Gen, an AI model that generates realistic videos with sound.
  • Movie Gen can edit existing videos and create new ones based on user prompts.
  • This technology rivals tools from leading startups like OpenAI and ElevenLabs.

In the rapidly evolving world of artificial intelligence, Meta, the company behind Facebook, has made a groundbreaking announcement. They have developed a new AI model called Movie Gen, which can create realistic videos complete with sound. This innovative tool is set to challenge leading media generation startups like OpenAI and ElevenLabs. Let’s dive into what makes Movie Gen so exciting and how it could change the landscape of video creation.

What is Movie Gen?

Movie Gen is an AI model that can generate realistic-seeming video and audio clips based on user prompts. This means you can describe a scene, and Movie Gen will create a video that matches your description. Whether it’s animals swimming or people performing actions like painting, Movie Gen can bring your ideas to life.

How Does Movie Gen Work?

Movie Gen uses advanced AI algorithms to understand and generate video content. It can create background music and sound effects that are perfectly synced with the video content. This makes the generated videos not only visually impressive but also aurally engaging.

Key Features of Movie Gen

  • Realistic Video Generation: Movie Gen can create videos that look and sound realistic. From animals swimming to people painting, the possibilities are endless.
  • Audio Synchronisation: The model generates background music and sound effects that match the video content, creating a seamless experience.
  • Video Editing: Movie Gen can also edit existing videos. For example, it can insert objects into a video or change the environment, such as transforming a dry parking lot into one covered by a splashing puddle.

Examples of Movie Gen’s Creations

Meta has provided several samples to showcase Movie Gen’s capabilities. In one example, the AI model inserted pom-poms into the hands of a man running in the desert. In another, it transformed a dry parking lot into one covered by a splashing puddle, adding an extra layer of realism to the skateboarding video.

The Impact of Movie Gen

Movie Gen has the potential to revolutionise various industries, from entertainment to education. Here are a few ways it could make a significant impact:

  • Film and Television: Movie Gen could be used to create realistic special effects and animations, reducing the time and cost of production.
  • Advertising: Brands could use Movie Gen to create engaging and personalised video content for their marketing campaigns.
  • Education: Teachers could use Movie Gen to create interactive and immersive learning experiences for students.

Challenging the Competition

Meta’s Movie Gen is set to challenge tools from leading media generation startups like OpenAI and ElevenLabs. OpenAI is known for its advanced language models, while ElevenLabs focuses on generating realistic voices. Movie Gen combines both video and audio generation, making it a powerful competitor in the AI space.

The Future of Video Creation

With the introduction of Movie Gen, the future of video creation looks brighter than ever. This technology could democratise video production, making it accessible to anyone with a creative idea. As AI continues to advance, we can expect even more innovative tools that push the boundaries of what’s possible.

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Create a Video with Movie Gen

Imagine you want to create a video of a cat playing the piano. You can use Movie Gen to generate this video by simply describing the scene. The AI model will create a realistic video of a cat playing the piano, complete with background music and sound effects.

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What do you think about Meta’s Movie Gen? How do you see it impacting the future of video creation? Share your thoughts and experiences in the comments below. Don’t forget to subscribe for updates on AI and AGI developments.

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Accenture and Nvidia’s AI Power Play in Asia

The Accenture-Nvidia partnership signals a new era of AI-centric strategies in Asia, with generative AI and open-source models playing crucial roles.

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Accenture and Nvidia

TL;DR:

  • Accenture and Nvidia partner to create a 30,000-person AI business unit.
  • Enterprises must embrace generative AI to stay competitive.
  • Open-source models like Llama offer flexibility and reduced vendor lock-in.

In the rapidly evolving world of enterprise IT, one truth has become increasingly clear: generative artificial intelligence (gen AI) is rewriting the rules. The recent partnership between Accenture and Nvidia is a testament to this shift, signalling a new era of AI-centric strategies that businesses cannot afford to ignore. Let’s dive into the details and understand why this deal is a game-changer, especially for the tech-savvy landscape of Asia.

The Accenture-Nvidia Partnership: A Glimpse into the Future

On Wednesday, Accenture unveiled a groundbreaking partnership with Nvidia, including the creation of a 30,000-person Nvidia Business Group. This new business unit will leverage Accenture’s AI Refinery platform and Nvidia’s full AI stack, marking a significant step forward in the enterprise IT landscape.

Why This Deal Matters

The partnership is crucial because the enterprise IT world is now heavily dependent on generative AI development. Nvidia’s dominance in AI chip development has made it almost impossible for enterprises to avoid vendor lock-in. With no viable alternatives, CIOs must choose between building their AI efforts in-house or outsourcing to major players like Accenture.

The Role of Open-Source Models

One intriguing aspect of Accenture’s AI strategy is its commitment to helping clients build custom large language models (LLMs) using the Llama 3.1 collection of openly available models. This partnership with Meta’s open-source offering could be particularly attractive to enterprise CIOs looking to reduce vendor lock-in risks.

The Changing Landscape of AI in Asia

The Rise of Generative AI

Generative AI is transforming industries across Asia. From healthcare to finance, enterprises are increasingly looking to customise AI models to meet their specific needs. This trend is driving the demand for partnerships like the one between Accenture and Nvidia, which offer the expertise and resources needed to develop domain-specific AI solutions.

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The Importance of Speed and Efficiency

In the fast-paced world of AI, speed and efficiency are paramount. Enterprises are realising that outsourcing their AI efforts to major players like Accenture can help them stay ahead of the curve. With a team of 30,000 people already working on AI projects, Accenture is well-positioned to meet the growing demand for customised AI solutions.

Navigating Vendor Lock-In

The Reality of Nvidia’s Dominance

Nvidia’s near-monopoly in AI chip development means that enterprises have little choice but to secure their GPUs from Nvidia. This reality has led to a shift in how CIOs approach vendor lock-in. Rather than avoiding it, they must now focus on reducing the risks associated with it.

The Attraction of Open-Source Models

Open-source models like Llama offer a way for enterprises to build proprietary AI models without being fully dependent on a single vendor. This flexibility is particularly appealing to CIOs who are looking to future-proof their AI strategies.

The Future of AI Pricing

The Shift to Performance-Based Pricing

As AI continues to evolve, so too will the pricing models for AI services and products. Traditional time and materials-based pricing may give way to performance-based pricing, reflecting the changing nature of AI development and deployment.

The Need for Strategic Partnerships

In this new world of AI, strategic partnerships will be more important than ever. Enterprises will need to choose their partners carefully, looking for those with the expertise and resources to help them navigate the complexities of AI development and deployment.

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By embracing the opportunities presented by generative AI and strategic partnerships, enterprises in Asia can stay at the forefront of technological innovation. The Accenture-Nvidia deal is just the beginning of a new era in AI, one that promises to reshape industries and drive growth across the region.

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