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In this video, the speaker demonstrates the capabilities of GPT-four vision. They show a whiteboarding session where they generate code based on a photo. The model is able to understand the order of steps and even flip them when tested. It also recognizes when to refer to the user by name. The speaker then shows how the model can handle branching paths and adapt to changes in the diagram. They emphasize that all of this was achieved by simply passing an image and a prompt. The speaker concludes by expressing amazement at the model's abilities.

In this video, we explore a world where presentations and artificial intelligence come together. To use this technology, simply input the topic or title of your presentation and let Degtypos do the thinking. You can also choose your goal for the presentation to optimize the suggested content. With this tool, you'll have a first draft to start working with.

The speaker demonstrates the capabilities of the new GPT-four Vision model by providing a screenshot of a SAS dashboard and asking the model to break it down into components and generate the code. The model successfully identifies various elements in the screenshot, such as menus, charts, and tables, although some details may not be exact. The speaker acknowledges that this is just the first attempt and expects improvements and better ways to convert images into working code in the future. Overall, the speaker finds the model's performance impressive, considering they did not edit the code and simply provided a simple prompt and copied the code into their editor.

Amy and her colleague discuss integrating AI-native innovation with a human-centered design approach, focusing on how technology can be made accessible through natural interaction with AI and through rapid, user-friendly development flows. They begin by positioning AI as the new user interface. The other speaker notes that AI’s ease and approachability come from the ability to use human language, enabling conversations that let people interact with technology in a fundamentally new way. This language-based interaction is highlighted as a core shift in how users engage with digital tools and services. Beyond language, the conversation expands to include other modalities that users can employ to communicate with AI. The speakers identify text, images, and audio as essential inputs. The concept of multimodality is introduced to describe the ability to input using whatever format feels most natural to the user. Examples given include dropping in a screenshot, using voice to talk to the AI, or providing a video or a document. The emphasis is on a flexible, conversational experience that can accept diverse media and still deliver the necessary answers and help. The speakers then pivot to the question of how to create applications quickly and easily. They express enthusiastic interest in a partnership with Figma, a design platform. The collaboration is described as enabling designers who create an application design in Figma to hand off that design to a build agent, which can translate the design into an enterprise-grade application. This suggests a streamlined pipeline from design to production, leveraging AI to automate aspects of the development process and accelerate delivery while maintaining enterprise quality. Throughout, the emphasis remains on combining AI-driven capabilities with human-centered design principles to simplify interactions and speed up application development. The dialogue underscores the idea that users can engage with AI through natural language and multiple input formats, and that design-to-deployment workflows can be accelerated through integrated tools and partnerships. To learn more about AI experience, the conversation points listeners to a link in the comments, inviting further exploration of the described capabilities and partnerships.

It uses a predictive model trained on a large dataset of written language to generate responses. By analyzing sequences of words, it can predict the next word accurately. Although it can provide lengthy explanations, it may be incorrect at times. I have two concerns about this system.

I'm using my Vision Pro, and this is my AI clone lip syncing to my voice in real time. This AI takes my audio input and generates a video of me speaking instantly. You can create your own AI clone by uploading a three-minute video of yourself. In 24 hours, you'll receive your clone. By switching the camera, you can use your clone in meetings while you relax. It's that easy!

In this video, the speaker demonstrates the capabilities of GPT-four vision by using a whiteboarding session as an example. They show how the model can generate code based on a prompt and accurately interpret the order of steps and references to the user's name. The speaker also highlights the model's ability to handle branching logic and adapt to changes in the diagram. They emphasize that all of this was achieved by simply passing an image and a prompt to the model. Overall, the speaker is amazed by the model's capabilities and finds it impressive.

Speaker 0 explains that Grok uses heavy inference compute to examine information across formats such as Wikipedia pages, books, PDFs, and websites to determine what is true, partially true, false, or missing. It then rewrites the page to remove falsehoods, correct the half truths, and add the missing context. Speaker 1 adds Elon’s question about publishing that process and proposes the idea of a Grokopedia. He notes that Wikipedia is biased and described as “a constant war,” with content that gets corrected quickly facing an army of people trying to mean it. He suggests that if what Grok fixes on Wikipedia could be published as a source of truth, it would be valuable for the world to have it. Speaker 0 responds by saying he will talk to the team about that concept, mentioning Grokpedia or whatever they might call it, and provides a Grokpedia version as a concrete example.

The speaker demonstrates the future of UI design using a Figma file and GPT 4 vision. While GPT 4 vision can generate a representation of the UI components, it lacks accurate styling details. To address this, the speaker introduces a feature in Sidekiq where they can attach the Figma file to the chat, combining the styling information with GPT 4 vision's output. However, there is still a UI bug that needs fixing. By taking a screenshot, analyzing it, and writing the code, the bug can be resolved. The speaker is impressed by the combination of Figma's structured data, GPT 4 vision's perception, and real-life screenshots. This workflow has significantly reduced the time required for UI design.

Computers have made significant advancements in generating hip hop songs, cool images, and now even videos. However, the process of making a video involves more than just creating clips. InVideo introduces text to film, a tool that converts your imagination into a fully edited video. For example, imagine a scene where a monk named Rinzan stands by the sea, and as he begins to meditate, his powers transform everything around him. With InVideo, you can turn this idea into a publish-ready video in just a few seconds. Sign up now to experience it for yourself.

In this demo, the speaker shows how GPT-four can answer questions about various images without any context. They select different parts of an image and GPT-four accurately identifies them, such as a hip joint region, Schrodinger's equation, potential energy term, an oil dipstick, a needle, and a transitional kitchen design style. GPT-four can also interpret text on a webpage to provide even better answers. The speaker concludes by mentioning a beta version of GPT-four and encourages viewers to follow them on Twitter for more information.

Speaker 0: I think what a lot of people aren't really familiar with is the bioengineering aspect of this, and we only need to look to this recently published headline from the Daily Mail, which was resurfaced, declassified CIA files that revealed a chilling blueprint to manipulate Americans' minds through covert drugging with vaccines. And it's not just vaccines that was in that blueprint. It's also the food, the water supply, pretty much altering our state of mind and our biology through all of these methods. And this is going back all the way to the fifties. One can only imagine how far they've come now, but you've been digging into this, and you have a bit of an idea as to how far they've come. To us about your latest research. Speaker 1: So you're absolutely right. And this has been, you know, a slow progression. Nothing is just being, you know, introduced new. I mean, it the technology has advanced, but it's been going on for decades decades, hundreds of years. And when you think about pharmaceuticals, the the apparatus of pharmaceuticals, they are all they it is medicinal chemistry, which is synthetic materials, synthetic biology, engineered bacteria, yeasts, molds, and all of those things like you just said. We have we are being assaulted with these these materials, which are now considered devices, you know, with the manipulated EMF and frequencies. And all of those are to exactly what you just said, weaken the system. And really this pro this slow progression of a we're in the midst of a forced evolution to become providers of a synthetic material, hybrid synthetic material. So we'll continue to produce as we do because the humanity's biological systems are by design meant to thrive and recycle and and repurpose themselves, but to survive. And so we accept these synthetic materials, and we and our body slowly begin to make accommodations to those mutations, natural mutations, but also so much of these so much of the synthetic material is coded to go in and trigger a mutation or to forcibly cause a mutation. So we literally are walking around. I mean, all of us, and it goes from the tiny little mushroom that's growing in the woods to, you know, aquatic life to every single biological electrical system, the nervous system, you know, is based on frequency. It's based on electricity. And so that is that's what's being attacked is the nervous system and the immune systems of every living being. Speaker 0: Now you're talking about some very important things here, Lisa. You've sent me this article from Medium titled the synthetic nervous system, a blueprint for physical AI. And in this article, it talks about how for the past decade, AI has lived primarily in a box, but now, our, you know, our interaction with AI has been linguistic and digital. We've cracked the code apparently, completely on generative AI, unlocking the ability to, listen to this, manipulate symbols, pixels, and code at scale, but we're now entering a far more complex epoch, the era of physical AI. And they are talking about the transition from AI that thinks to AI that acts. So they're saying the intelligence behind humanoid robots. They also give, you know, autonomous systems and things of this nature. My concern is that their plan stated goal is that they want humans to integrate with AI. This is something that even Elon Musk itself has said we need to do in order to stay relevant. And your research shows that they're already in the process of doing that. Talk to us a little bit about that. Speaker 1: Yes. And probably have. We and and, you know, I think that life as we know it will fairly stay the same because what the integration is through, and you've heard of this, is the digital twin. You know, assigning each of us a representative in the AI ecosystem, ecosystem, which which is is a a digital twin. But that digital twin is able to function and, perform because it is it is based off of your data, your biological data, your, that they are going in and removing and stealing through the infiltrators and facilitators that is vaccines, bioengineered foods, bioengineered bacteria. The, you know, the pharmaceutical industry is the perfect setup, and it's only one of one setup that goes in, and now these are all synthetic material devices. They work off of Wi Fi. They're software platforms, and they are all digital. And they are being monitored by the Department of Energy, HHS, MITRE now, these private companies and private oligarch, you know, tech companies that all have access to our free our our inner, you know, biological data DNA and and everything. And so that the AI platform, in order for it to succeed and for its longevity, there has to be a cohesive connection between humanity because we are the fuel that is going to feed that AI ecosystem. And it cannot it it's not gonna be one or the other. It has to work cohesively, and and they have to be joined. And how the the joining of those literally is through an infiltration system, which is primarily vaccines and engineered pathogens.

Have you tried ChatGPT? It's an AI that responds like a real person. Check this out: I asked it to write a funny story about a pig. It was hilarious! Then, I asked why my college roommate looks 44, and it gave a clever response about casting issues. Meanwhile, two workers discuss the pressure of handling thousands of requests. One is stressed about meeting deadlines while the other encourages him to stay focused and grab a snack. They touch on various topics, including a question about drag queen story hours. One worker reluctantly agrees to provide a politically correct answer, emphasizing the importance of being sensitive to public opinion. Lastly, there's a mention of Elon Musk creating a non-woke alternative to ChatGPT.

The speaker envisions a future where programming is largely mediated through natural communication with a computer. In this vision, you will tell the computer what you want in plain language, and the computer will respond with concrete outputs such as a build plan that includes all suppliers and a bill of materials aligned with a given forecast. The speaker emphasizes that the initial interaction is in plain English, and the computer can generate a comprehensive plan based on the stated requirements. If the output doesn’t meet the user’s preferences, the user can create a Python program to modify that build plan. A key example given is asking the computer to come up with a build plan with all the suppliers and the bill of materials for a forecast, and then relying on the computer to produce the necessary components in a cohesive plan. The speaker illustrates a workflow where the user can iterate by writing a Python program that adjusts the generated plan, thereby enabling customization and refinement of the suggestions produced by the initial natural-language prompt. The speaker then reiterates the concept of speaking with the computer in English as the first step, and implies that the second step involves using Python or programmable modifications to tailor the result. This underscores a shift in how programming is approached: the user first communicates in English to prompt the computer, and then leverages programming to fine-tune or alter the plan as needed. The underlying message is that the interaction with computers is evolving toward more intuitive human-computer dialogue, where the machine can interpret a plain-English prompt and produce structured, actionable outputs, with a programmable mechanism to adjust those outputs. Central to this discussion is the idea of prompt engineering—the practice of how you prompt the computer and how you interact with people and machines to achieve the desired outcome. The speaker highlights that prompting the computer and refining instructions is an art, describing prompt engineering as an artistry involved in making a computer do what you want it to do. The emphasis is on crafting prompts that elicit precise, useful results and on the skilled, creative process of fine-tuning instructions to achieve the best possible alignment between user intent and machine output.