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Real Gemini demo built using GPT4 Vision, Whisper and TTS

Real Gemini demo built using GPT4V and Whisper and TTS

If like me you were a little disappointed to learn that the Google Gemini demonstration released earlier this month was more about clever editing rather than technology advancements. You will be pleased to know that perhaps we won’t have to wait too long before something similar is available to use.

After seeing the Google Gemini demonstration  and the revelation from the blog post revealing its secrets. Julien De Luca asked himself “Could the ‘gemini’ experience showcased by Google be more than just a scripted demo?” He then went about creating a fun experiment to explore the feasibility of real-time AI interactions similar to those portrayed in the Gemini demonstration.  Here are a few restrictions he put on the project to keep it in line with Google’s original demonstration.

  • It must happen in real time
  • User must be able to stream a video
  • User must be able to talk to the assistant without interacting with the UI
  • The assistant must use the video input to reason about user’s questions
  • The assistant must respond by talking

Due to the current ability of Chat GPT  Vision to only accept individual images De Luca needed to upload a series of images and screenshots taken from the video at regular intervals for the GPT to understand what was happening. 

“KABOOM ! We now have a single image representing a video stream. Now we’re talking. I needed to fine tune the system prompt a lot to make it “understand” this was from a video. Otherwise it kept mentioning “patterns”, “strips” or “grid”. I also insisted on the temporality of the images, so it would reason using the sequence of images. It definitely could be improved, but for this experiment it works well enough” explains De Luca. To learn more about this process jump over to the Crafters.ai website or GitHub for more details.

Real Google Gemini demo created

AI Jason has also created a example combining GPT-4, Whisper, and Text-to-Speech (TTS) technologies. Check out the video below for a demonstration and to learn more about creating one yourself using different AI technologies combined together.

Here are some other articles you may find of interest on the subject of  ChatGPT Vision :

To create a demo that emulates the original Gemini with the integration of GPT-4V, Whisper, and TTS, developers embark on a complex technical journey. This process begins with setting up a Next.js project, which serves as the foundation for incorporating features such as video recording, audio transcription, and image grid generation. The implementation of API calls to OpenAI is crucial, as it allows the AI to engage in conversation with users, answer their inquiries, and provide real-time responses.

The design of the user experience is at the heart of the demo, with a focus on creating an intuitive interface that facilitates natural interactions with the AI, akin to having a conversation with another human being. This includes the AI’s ability to understand and respond to visual cues in an appropriate manner.

The reconstruction of the Gemini demo with GPT-4V, Whisper, and Text-To-Speech is a clear indication of the progress being made towards a future where AI can comprehend and interact with us through multiple senses. This development promises to deliver a more natural and immersive experience. The continued contributions and ideas from the AI community will be crucial in shaping the future of multimodal applications.

Image Credit : Julien De Luca

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Gemini Pro vs GPT-3.5 vs GPT-4

Gemini Pro vs GPT-3.5 vs GPT-4

The world of artificial intelligence is evolving at an impressive pace, with new models emerging that are capable of performing a wide array of tasks. One of the  more recent releases has been made by Google in the form of its new Gemini artificial intelligence. Google’s Gemini Pro is now directly competing with the likes of OpenAI’s GPT-3.5 and GPT-4 which are also leading the field in AI, each offering a suite of features that cater to different needs.

Google’s Gemini Pro features multimodal capabilities similar to that of ChatGPT, which allow it to understand and generate responses based on both text and images. This unique feature opens up a world of possibilities for more dynamic interactions and applications, distinguishing it from other AI models that are limited to text-only inputs.

On the other hand, OpenAI’s GPT-3.5 and GPT-4 are making a name for themselves in the realm of natural language processing together with the enhancements added by the release of ChatGPT-4 Vision and DallE 3. These models have significantly enhanced the way chatbots and customer support systems operate by providing conversations that are remarkably similar to those with a human. Their ability to understand and generate text has transformed the way we interact with machines.

A standout feature of both Gemini Pro and the GPT models is their streamed responses. This allows for a conversational flow that is both natural and immediate, which is essential for creating engaging and seamless user experiences. Whether it’s for casual conversation or more complex customer service inquiries, this feature is a key factor in the success of AI-driven interactions.

Gemini Pro vs GPT-3.5 vs GPT-4

If you are interested in learning more about the differences between the three major AI models currently battling it out for supremacy. You might be interested in an interesting comparison created by Tina Huang.

Here are some other articles you may find of interest on the subject of Google Gemini :

When it comes to embedding services in tasks like semantic search and text classification, these AI models are powerful tools. They can be seamlessly integrated into existing systems, enhancing their capabilities in language understanding and generation. This demonstrates the advanced potential of these AI technologies.

However, it’s important to be aware of certain limitations and requirements associated with these models, such as input token limits. These constraints can impact the complexity of the interactions and the depth of content that can be generated, which is an important consideration when choosing the right AI model for a specific task.

The performance of Gemini Pro, GPT-3.5, and GPT-4 varies depending on the task at hand. For instance, Gemini Pro excels in tasks that involve images, thanks to its multimodal nature. Meanwhile, GPT-3.5 and GPT-4 are more adept at handling text-based challenges, such as storytelling, search, and humor. While each model has its strengths and weaknesses, here’s a comprehensive overview of how they stack up against each other:

Gemini Pro

Gemini Pro, developed by Google AI, is a LLM that aims to address the limitations of previous generations of language models. It boasts a significant improvement in fluency and coherence, particularly in generating long-form text formats like essays, poems, and scripts. Additionally, Gemini Pro demonstrates enhanced creativity and ability to produce novel and original text formats, making it a valuable tool for creative writing and content creation.

One of the unique features of Gemini Pro is its ability to integrate with Google Maps, providing location-based responses. This is particularly useful for applications that require geographical context, offering a level of specificity that text-only models cannot match.

GPT-3.5

GPT-3.5, the latest iteration of OpenAI’s GPT-3 series, represents a significant leap forward in language processing capabilities. It introduces several improvements, including better semantic understanding, more nuanced responses, and enhanced ability to engage in open-ended conversations. GPT-3.5 also excels in tasks involving factual knowledge and reasoning, making it a powerful tool for research and information retrieval.

GPT-4

GPT-4, developed by OpenAI, is the most advanced LLM to date. It introduces a novel architecture that allows for deeper language understanding and more context-aware responses. GPT-4 demonstrates exceptional performance in tasks like summarization, translation, and code generation, setting a new benchmark for LLM capabilities.

As we compare Gemini Pro, GPT-3.5, and GPT-4, it becomes clear that the AI landscape is diverse, with each model carving out its own niche. Whether you’re looking for an AI that can handle both text and images or one that specializes in crafting engaging narratives, there’s a model designed to meet those specific needs. As these technologies continue to develop, they are set to unlock new possibilities and redefine the boundaries of AI’s capabilities.

Each of these LLMs offers unique strengths and capabilities. Gemini Pro excels in fluency, creativity, and originality, making it a great choice for creative writing and content creation. GPT-3.5 shines in factual knowledge, reasoning, and open-ended conversations, making it ideal for research and information gathering. GPT-4 stands at the pinnacle of language processing technology, offering exceptional performance across a wide range of tasks.

The choice between these LLMs depends on the specific needs and preferences of the user. For creative endeavors, Gemini Pro might be the preferred choice. For tasks involving factual knowledge and reasoning, GPT-3.5 could be more suitable. And for those seeking the ultimate in language processing capabilities, GPT-4 is the clear frontrunner.

Ultimately, all three LLMs represent significant advancements in artificial intelligence and are poised to revolutionize the way we interact with language and technology. As these models continue to evolve, we can expect even more impressive capabilities and applications in the years to come.

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How to use Aider AI coding assistant powered by OpenAI GPT-4

How to use Aider AI coding assistant powered by OpenAI GPT-4

Improving your software development workflow has never been easier thanks to the explosion of AI tools and large language models such as Copilot, , ChatGPT and others. Aider as another AI-powered coding assistant well worth checking out  as it has been designed to streamline your coding process. Powered by sophisticated technology of OpenAI’s GPT-4 and GPT-3.5 APIs, Aider offers a command-line interface that can significantly enhance your coding tasks. This tool is not just another addition to your toolkit; it’s a smart assistant that can help you manage your code more effectively.

Aider stands out with its seamless Git integration, which allows you to commit code directly to your repositories. This integration is a time-saver, cutting down on the steps you need to take to manage your version control. It’s not just about saving a few keystrokes; Aider ‘s integration can help prevent errors that often occur when handling code manually. The AI’s ability to generate code snippets, run unit tests, and refactor code is a testament to its advanced capabilities.

Using Aider AI coding assistant

Getting started with Aider is straightforward. You can install it using pip, the Python package manager, which is a familiar process for many developers. Once installed, you’ll need to set up your environment with an OpenAI API key. This key is crucial as it allows Ida to communicate with the GPT-4 and GPT-3.5 APIs. For those looking to get the most out of Ida, integrating ctags can enhance your experience by enabling efficient source code indexing and navigation.

Features of Aider

– Chat with GPT about your code by launching aider from the command line with set of source files to discuss and edit together. Aider lets GPT see and edit the content of those files.
– GPT can write and edit code in most popular languages: python, javascript, typescript, html, css, etc.
– Request new features, changes, improvements, or bug fixes to your code. Ask for new test cases, updated documentation or code refactors.
– Aider will apply the edits suggested by GPT directly to your source files.
– Aider will automatically commit each changeset to your local git repo with a descriptive commit message. These frequent, automatic commits provide a safety net. It’s easy to undo changes or use standard git workflows to manage longer sequences of changes.
– You can use aider with multiple source files at once, so GPT can make coordinated code changes across all of them in a single changeset/commit.
– Aider can give GPT-4 a map of your entire git repo, which helps it understand and modify large codebases.
– You can also edit files by hand using your editor while chatting with aider. Aider will notice these out-of-band edits and keep GPT up to date with the latest versions of your files. This lets you bounce back and forth between the aider chat and your editor, to collaboratively code with GPT.

Aider ‘s compatibility with popular code editors like Visual Studio Code is a significant advantage. Whether you’re a fan of Visual Studio Code’s extensive features or Vim’s minimalist approach, Aider can fit into your existing workflow. This adaptability means you don’t have to change your coding habits to benefit from AI-powered enhancements.

The automation of routine coding tasks is where Aider truly shines. By handling the mundane aspects of coding, Aider not only saves you time but also reduces the likelihood of human error. This can lead to substantial cost savings, as you can allocate your resources to more complex and creative tasks. The result is a higher value delivered to your clients, as you can focus on innovation rather than routine code management.

AI coding tools like Aider are at the forefront of a shift in development practices. As AI technology continues to evolve, these tools will become even more integral to improving code quality and developer productivity. By adopting AI assistants early on, developers can stay ahead of the curve and be prepared for the industry’s technological advancements.

Aider AI’s IDE  is more than just a coding tool; it’s a partner in your development journey. Integrating Aider into your daily routine can elevate your productivity and improve the quality of your code. In the competitive field of software development, utilizing AI tools is not just about keeping up with trends—it’s about excelling and providing exceptional value to your clients. With Aider , you’re not just coding; you’re crafting the future of software development.

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Gemini vs GPT-4 vs Grok AI models performance compared

Gemini vs GPT-4 vs Grok AI models compared

If you are interested in learning more about the performance and capabilities of the latest AI models designed and created by Google, OpenAI and X AI Elon Musk’s AI assistant.  You will be pleased to know that these three advanced models have recently been put through their paces Gemini vs GPT-4 vs Grok AI to determine their capabilities across a range of tasks.

The AI models, known as Gemini Pro, GPT-4, and Grok respectively, have been scrutinized for their performance in writing, reasoning, humor, vision, coding, and music generation. For those curious about which AI might come out on top, a comprehensive Gemini vs GPT-4 vs Grok AI comparison has been made by Wes Roth to highlight their individual strengths and areas where they may fall short.

Writing performance

When it comes to writing, GPT-4 takes the lead with its ability to generate text that is not only coherent but also contextually aware. Gemini Pro is not far behind, with a strong showing in creativity and innovation in its written work. Grok, while not as focused on writing, still manages to produce respectable results. The ability to write effectively is crucial for AI, as it reflects the machine’s understanding of human language and its nuances.

Reasoning performance

Reasoning is another critical aspect of AI performance, and all three models have shown impressive abilities in this area. They can participate in complex conversations and tackle problems with a level of sophistication that might surprise many. However, each AI has its unique way of approaching abstract thinking, which highlights their different capabilities.

Gemini vs GPT-4 vs Grok

Here are some other articles you may find of interest on the subject of other AI model comparisons :

AI personality

When it comes to humor, an area that has traditionally been challenging for AI, Grok stands out. It has a nuanced understanding of human idiosyncrasies, which allows it to engage in humorous exchanges that feel surprisingly natural.

AI vision

In tasks that involve vision, such as image recognition, the models show varying levels of success. GPT-4 is particularly adept, demonstrating consistent accuracy, while Gemini Pro struggles somewhat. This highlights the significance of being able to interpret visual data, an area where GPT-4’s versatility is particularly noticeable.

Coding abilities

The AI models’ coding abilities have also been tested, with tasks that include creating browser games and writing JavaScript code. This is an area of great potential for AI in software development. Both GPT-4 and Gemini Pro exhibit strong coding skills, but GPT-4 often comes out ahead, producing code that is generally more efficient and contains fewer errors.

Musicality and composing skills

Music creation is yet another arena where these AI models have been tested. They all have the capability to compose tunes using ABC notation, but GPT-4 distinguishes itself by creating musical pieces that are both harmonious and complex, showcasing its extensive creative abilities.

The evaluation of these AI models concludes with a scoring system that ranks them based on their performance in the aforementioned areas. This system helps to clearly identify where each model excels and where there is room for improvement. If you’d like to learn more about the latest Google Gemini AI and more comparison data compared to OpenAI’s ChatGPT-4 jump over to our previous article.

What is Grok AI?

Grok is an AI model designed with a unique personality and purpose, inspired by the whimsical and insightful nature of the “Hitchhiker’s Guide to the Galaxy.” This inspiration is not just thematic but also functional, as Grok aims to provide answers to a wide array of questions, coupled with a touch of humor and a rebellious streak. This approach is a departure from traditional AI models, which often prioritize neutral and purely factual responses.

Grok’s standout feature is its real-time knowledge capability, enabled by the platform. This gives it a distinct edge, as it can access and process current information, a feature not commonly found in standard AI models. Furthermore, Grok is designed to tackle “spicy” questions, those that are usually sidestepped by conventional AI systems, potentially making it a more versatile and engaging tool for users seeking unconventional or candid responses.

Despite its innovative features, Grok is in its early beta phase, having undergone only two months of training. This indicates that while Grok shows promise, users should anticipate ongoing development and improvements. The xAI team emphasizes that user feedback will play a crucial role in shaping Grok’s evolution, highlighting their commitment to creating AI tools that are beneficial and accessible to a diverse range of users.

The journey to creating Grok-1, the engine behind Grok, involved significant advancements over a four-month period. The initial prototype, Grok-0, demonstrated impressive capabilities with fewer resources compared to models like LLaMA 2. However, it’s the subsequent development of Grok-1 that showcases substantial improvements in reasoning and coding abilities, positioning it as a state-of-the-art language model. These advancements are evident in its performance on benchmarks like the HumanEval coding task and the MMLU.

Results

Overall, GPT-4 emerges as a versatile and reliable AI across a variety of tasks. Gemini Pro is particularly noteworthy for its writing and creative contributions, although it does not perform as well in vision and music-related tasks. Grok, on the other hand, impresses with its humor and problem-solving skills, even if it doesn’t lead in every category. This analysis offers a detailed look at where each AI model stands, providing valuable insights into the complex and sophisticated world of modern artificial intelligence technology.

This Gemini vs GPT-4 vs Grok AI comparison not only serves as a benchmark for the current state of AI but also as a guide for future developments in the field. As AI continues to advance, understanding the specific capabilities and limitations of different models becomes increasingly important for both developers and users. Whether it’s for writing, reasoning, or even creating music, these AI models represent the cutting edge of technology, and their ongoing development will undoubtedly shape the future of artificial intelligence. As always we’ll keep you up to speed on all the latest developments in the world of AI.

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GPT-4 vs GPT-4-Turbo vs GPT-3.5-Turbo performance comparison

GPT-4 vs GPT-4-Turbo vs GPT-3.5-Turbo speed and performance tested

Picking the right OpenAI language model for your project can be crucial when it comes to performance, costs and implementation. OpenAI’s suite, which includes the likes of GPT-3.5, GPT-4, and their respective Turbo versions, offers a spectrum of capabilities that can greatly affect the outcome of your application and the strain on your budget. This GPT-4 vs GPT-4-Turbo vs GPT-3.5-Turbo guide provides an overview of what you can expect from the performance of each and the speeds of response.

The cutting-edge API access provided by OpenAI to its language models, such as the sophisticated GPT-4 and its Turbo variant, comes with the advantage of larger context windows. This feature allows for more complex and nuanced interactions. However, the cost of using these models, which is calculated based on the number of tokens used, can accumulate quickly, making it a significant factor in your project’s financial considerations.

To make a well-informed choice, it’s important to consider the size of the context window and the processing speed of the models. The Turbo models, in particular, are designed for rapid processing, which is crucial for applications where time is of the essence.

GPT-4 vs GPT-4-Turbo vs GPT-3.5-Turbo

When you conduct a comparative analysis, you’ll observe differences in response times and output sizes between the models. For instance, a smaller output size can lead to improved response times, which might make GPT-3.5 Turbo a more attractive option for applications that prioritize speed.

Evaluating models based on their response rate, or words per second, provides insight into how quickly they can generate text. This is particularly important for applications that need instant text generation.

 

The rate at which tokens are consumed during interactions is another key factor to keep in mind. More advanced models, while offering superior capabilities, tend to use up more tokens with each interaction, potentially leading to increased costs. For example, the advanced features of GPT-4 come with a higher token price tag than those of GPT-3.5.

Testing the models is an essential step to accurately assess their performance. By using tools such as Python and the Lang chain library, you can benchmark the models to determine their response times and the size of their outputs. It’s important to remember that these metrics can be affected by external factors, such as server performance and network latency.

Quick overview of the different AI models from OpenAI

GPT-4

  • Model Size: Larger than GPT-3.5, offering more advanced capabilities in terms of understanding and generating human-like text.
  • Capabilities: Enhanced understanding of nuanced text, more accurate and contextually aware responses.
  • Performance: Generally more reliable in producing coherent and contextually relevant text across a wide range of topics.
  • Use Cases: Ideal for complex tasks requiring in-depth responses, detailed explanations, and creative content generation.
  • Response Time: Potentially slower due to the larger model size and complexity.
  • Resource Intensity: Higher computational requirements due to its size and complexity.

GPT-4-Turbo

  • Model Size: Based on GPT-4, but optimized for faster response times.
  • Capabilities: Retains most of the advanced capabilities of GPT-4 but is optimized for speed and efficiency.
  • Performance: Offers a balance between the advanced capabilities of GPT-4 and the need for quicker responses.
  • Use Cases: Suitable for applications where response time is critical, such as chatbots, interactive applications, and real-time assistance.
  • Response Time: Faster than standard GPT-4, optimized for quick interactions.
  • Resource Intensity: Lower than GPT-4, due to optimizations for efficiency.

GPT-3.5-Turbo

  • Model Size: Based on GPT-3.5, smaller than GPT-4, optimized for speed.
  • Capabilities: Good understanding and generation of human-like text, but less nuanced compared to GPT-4.
  • Performance: Efficient in providing coherent and relevant responses, but may not handle highly complex or nuanced queries as well as GPT-4.
  • Use Cases: Ideal for applications requiring fast responses but not the full depth of GPT-4’s capabilities, like standard customer service chatbots.
  • Response Time: Fastest among the three, prioritizing speed.
  • Resource Intensity: Least resource-intensive, due to smaller model size and focus on speed.

Common Features

  • Multimodal Capabilities: All versions can process and generate text-based responses, but their capabilities in handling multimodal inputs and outputs may vary.
  • Customizability: All can be fine-tuned or adapted to specific tasks or domains, with varying degrees of complexity and effectiveness.
  • Scalability: Each version can be scaled for different applications, though the cost and efficiency will vary based on the model’s size and complexity.
  • API Access: Accessible via OpenAI’s API, with differences in API call structure and cost-efficiency based on the model.

Summary

  • GPT-4 offers the most advanced capabilities but at the cost of response time and resource intensity.
  • GPT-4-Turbo balances advanced capabilities with faster response times, suitable for interactive applications.
  • GPT-3.5-Turbo prioritizes speed and efficiency, making it ideal for applications where quick, reliable responses are needed but with less complexity than GPT-4.

Choosing the right model involves finding a balance between the need for speed, cost-efficiency, and the quality of the output. If your application requires quick responses and you’re mindful of costs, GPT-3.5 Turbo could be the best fit. On the other hand, for more complex tasks that require a broader context, investing in GPT-4 or its Turbo version might be the right move. Through careful assessment of your application’s requirements and by testing each model’s performance, you can select a solution that strikes the right balance between speed, cost, and the ability to handle advanced functionalities.

Here are some other articles you may find of interest on the subject of ChatGPT

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GPT-4 Turbo vs Orca-2-13B AI models compared

GPT-4 Turbo vs Orca-2-13B large language AI models compared

In the ever-evolving world of artificial intelligence (AI), there’s a lot of talk about how we should build and share AI technologies. Two main approaches are often discussed: open-source AI and proprietary AI. A recent experiment that compared an open-source AI model called Orca-2-13B with a proprietary model known as GPT-4 Turbo has sparked a lively debate. This debate is not just about which model is better but about what each approach means for the future of AI.

The open-source AI model, Orca-2-13B, is a shining example of transparency, collaboration, and innovation. Open-source AI is all about sharing code and ideas so that everyone can work together to make AI better. This approach believes that when we make AI technology open for all, we create a space where anyone with the right skills can help improve it. One of the best things about open-source AI is that you can see how the AI makes decisions, which is really important for trusting AI systems. Plus, open-source AI benefits from communities like GitHub, where developers from all over can work together to make AI models even better.

Orca 2 is Microsoft’s latest development in its efforts to explore the capabilities of smaller LMs (on the order of 10 billion parameters or less). With Orca 2, it demonstrates that improved training signals and methods can empower smaller language models to achieve enhanced reasoning abilities, which are typically found only in much larger language models.

Orca-2-13B large language AI model comparison chart

On the other side, we have proprietary AI, like GPT-4 Turbo, which focuses on security, investment, and accountability. Proprietary AI is usually made by companies that spend a lot of money on research and development. They argue that this investment is key to making AI smarter and more capable. With proprietary AI, the code isn’t shared openly, which helps protect it from being used in the wrong way. Companies that make proprietary AI are also in charge of making sure the AI works well and meets ethical standards, which is really important for making sure AI is safe and effective.

GPT-4 Turbo vs Orca-2-13B

  • Orca-2-13B (Open-Source AI)
    • Focus: Emphasizes transparency, collaboration, and innovation.
    • Benefits:
      • Encourages widespread participation and idea sharing.
      • Increases trust through transparent decision-making processes.
      • Fosters innovation by allowing communal input and improvements.
    • Challenges:
      • Potential for fragmented efforts and resource dilution.
      • Quality assurance can be inconsistent without structured oversight.
  • GPT-4 Turbo (Proprietary AI)
    • Focus: Concentrates on security, investment, and accountability.
    • Benefits:
      • Higher investment leads to advanced research and development.
      • Greater control over AI, ensuring security and ethical compliance.
      • More consistent quality assurance and product refinement.
    • Challenges:
      • Limited accessibility and collaboration due to closed-source nature.
      • Might induce skepticism due to lack of transparency in decision-making.

The discussion around Orca-2-13B and GPT-4 Turbo has highlighted the strengths and weaknesses of both approaches. Open-source AI is great for driving innovation, but it can lead to a lot of similar projects that spread resources thin. Proprietary AI might give us more polished and secure products, but it can lack the openness that makes people feel comfortable using it.

Another important thing to think about is accessibility. Open-source AI is usually easier for developers around the world to get their hands on, which means more people can bring new ideas and improvements to the table. However, without strict quality checks, open-source AI might not always be reliable.

After much debate, there seems to be a slight preference for the open-source AI model, Orca-2-13B. The idea of an AI world that’s more inclusive, creative, and open is really appealing. But it’s also clear that we need to have strong communities and good quality checks to make sure open-source AI stays on the right track.

For those interested in open-source AI, there’s a GitHub repository available that has all the details of the experiment. It even includes a guide on how to use open-source models. This is a great opportunity for anyone who wants to dive into AI and be part of the ongoing conversation about where AI is headed.

The debate between open-source and proprietary AI models is about more than just code. It’s about deciding how we want to shape the development of AI. Whether you like the idea of working together in the open-source world or prefer the structured environment of proprietary AI, it’s clear that both ways of doing things will have a big impact on building an AI future that’s skilled, secure, and trustworthy.

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How to combine GPT-4 Turbo with Google web browsing

How to combine GPT-4 Turbo with Google web browsing using the Assistants API

Being able to combine the power of OpenAI’s latest GPT-4 Turbo AI model and Google web browsing using the Assistants API opens up a wide variety of new applications that can take your business, SaaS or ideas to the next level. Search engines, like Google, have become the gatekeepers of vast amounts of data, and AI is the key to unlocking the most relevant and personalized results. Let’s explore the sophisticated technologies that are enhancing the way we search for information, making it a more intuitive and efficient experience.

When you type a query into a search bar, you expect more than just a list of links. You want answers that are tailored to your needs and preferences. This is where AI modeling comes into play. By integrating advanced AI, such as OpenAI’s GPT-4 Turbo, search engines can interpret your queries with a deeper understanding. This means that the results you get are not just related to your question, but they also take into account the context of your search.

Browsing the web with GPT-4 Turbo

The backbone of this seamless integration lies in Application Programming Interfaces (APIs), like the Google Search API. These APIs allow the AI model to quickly process your questions and fetch the most relevant search results. Alongside APIs, web scraping tools, such as Beautiful Soup, are used to gather data from the web pages that appear in your search results. This combination ensures that the information you receive is both up-to-date and comprehensive.

But how does it all start? With your questions. The AI system takes your queries and optimizes them for the search engine, ensuring that the essence of what you’re asking for is captured. Then, it retrieves URLs from the search results, diving into the web’s vast pool of information. The real magic happens in how the AI presents the information to you. Whether you prefer quick bullet points or a detailed JSON format for integrating data, the AI adapts to your needs. It can even add a touch of creativity to the responses, making the process of finding information more engaging.

Building apps with GPTs web browsing functionality

Other articles we have written that you may find of interest on the subject of coding with AI models, ChatGPT and Copilot :

Conversion of user queries into optimized search queries

Let’s consider a practical example. Say you’re looking to find out the latest on Sam Altman’s role at OpenAI. The AI system doesn’t just give you the latest news; it organizes it in a way that’s easy to digest. Or perhaps you’re curious about who won the Las Vegas F1 Grand Prix. The AI quickly scans the web for the latest results, keeping you updated as events happen.

The integration of AI with search engines is revolutionizing how we access information. By leveraging cutting-edge technologies like GPT-4 Turbo, the Google Search API, and web scraping tools such as Beautiful Soup, the system provides search results that are not only optimized and current but also customized to your preferences. As you journey through the vast information superhighway, AI stands as a powerful companion, delivering the knowledge you seek with unprecedented efficiency.

The potent power of GPTs and web browsing

This advanced integration of AI into search engines is not just about getting answers; it’s about getting the right answers quickly and in a way that resonates with you. It’s about having a digital assistant that understands not just the words you type, but the intent behind them. With these AI-enhanced search capabilities, the world’s information is at your fingertips, ready to be accessed and utilized in ways that were once unimaginable.

Combining ChatGPT with web browsing capabilities for creating custom Software as a Service (SaaS) applications and enhancing business services and websites offers several powerful advantages:

  • Access to Real-Time Information: ChatGPT, when integrated with web browsing, can access and retrieve the most current data from the web. This is crucial for businesses that rely on up-to-date information, such as market trends, news updates, or regulatory changes.
  • Enhanced User Experience: ChatGPT can provide interactive and personalized experiences for users. By combining this with web browsing, the interaction becomes even more relevant and engaging, as it can pull in live data or additional context from the web to enhance the conversation.
  • Automated Research and Data Gathering: For SaaS applications, especially those involving data analysis, market research, or competitive intelligence, the ability to automatically gather and process information from the web is invaluable. This reduces manual effort and increases efficiency.
  • Dynamic Content Generation: Businesses can use ChatGPT to generate content dynamically for websites or applications. When combined with web browsing, this content can be tailored to current events, user preferences, or specific queries, keeping the content fresh and relevant.
  • Customer Support and Engagement: ChatGPT can provide immediate, 24/7 customer support. By integrating web browsing, it can pull specific information, such as FAQs, product details, or policy information, directly from the business’s website or other relevant sources, offering more accurate and helpful responses.
  • Scalability and Cost-Efficiency: Automating tasks like customer service, data gathering, and content creation with ChatGPT and web browsing can significantly reduce costs and allow for easy scaling as the business grows.
  • Informed Decision Making: For business analytics and decision support systems, combining ChatGPT’s ability to reason and explain with real-time data from the web can lead to more informed and timely decisions.
  • Personalization and Targeting: SaaS applications can use this combination to better understand user needs and preferences, customizing services and content accordingly, which enhances user satisfaction and engagement.
  • Continuous Learning and Improvement: As ChatGPT interacts with users and web content, it can learn from these interactions, leading to continuous improvement in its responses and recommendations.
  • Seamless Integration with Existing Systems: ChatGPT can be integrated with existing business systems and workflows, enhancing them with web browsing capabilities without the need for major overhauls or disruptions.

As we continue to rely on the internet for knowledge, entertainment, and communication, the importance of efficient search capabilities cannot be overstated. The AI-driven search is a testament to the incredible progress we’ve made in technology, and it’s a glimpse into a future where our interactions with machines are more natural and productive.

The implications of this technology are vast. For businesses, it means being able to provide customers with instant, accurate information. For researchers, it streamlines the process of sifting through endless data to find relevant studies and data. For the everyday user, it simplifies the quest for knowledge, whether it’s for learning a new skill, keeping up with current events, or just satisfying a curious mind.

As we look ahead, the potential for AI to further enhance our search experiences is boundless. We can expect even more personalized results, faster response times, and perhaps even predictive search capabilities that anticipate our questions before we even ask them. The integration of AI into search engines is a significant step forward in our digital evolution, making information more accessible and useful for everyone.

So, the next time you find yourself typing a question into a search bar, take a moment to appreciate the complex technology at work behind the scenes. AI is not just changing the way we search; it’s changing the way we interact with the world’s knowledge. It’s a powerful tool that, when used wisely, can help us make more informed decisions, spark new ideas, and continue to push the boundaries of what’s possible.

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GPT-4 Turbo 128K context length performance tested

GPT-4 Turbo 128K context length performance tested

Recently, OpenAI unveiled its latest advancement in the realm of artificial intelligence: the GPT-4 Turbo. This new AI model boasts a substantial 128K context length, offering users the ability to process and interact with a much larger swath of information in a single instance. The introduction of GPT-4 Turbo invites a critical question: How well does it actually perform in practical applications?

Before delving into the specifics of GPT-4 Turbo, it’s important to contextualize its place in the lineage of Generative Pretrained Transformers (GPTs). The GPT series has been a cornerstone in the AI field, known for its ability to generate human-like text based on the input it receives. Each iteration of the GPT models has brought enhancements in processing power, complexity, and efficiency, culminating in the latest GPT-4 Turbo.

The 128K context window of GPT-4 Turbo is its most notable feature, representing a massive increase from previous versions. This capability allows the model to consider approximately 300 pages of text at once, providing a broader scope for understanding and generating responses. Additionally, GPT-4 Turbo is designed to be more economical, reducing costs for both input and output tokens significantly compared to its predecessor, the original GPT-4. This cost efficiency, combined with its ability to produce up to 4096 output tokens, makes it a potent tool for extensive text generation tasks.

GPT-4 Turbo 128K context length performance tested

Check out the video below to learn more about the new GPT-4 Turbo 128K context length and its implications and applications.

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However, advancements in technology often come with new challenges. One of the primary issues with GPT-4 Turbo, and indeed many large language models, is the “lost in the middle” phenomenon. This refers to the difficulty these models have in processing information that is neither at the very beginning nor at the end of a given context. While GPT-4 Turbo can handle vast amounts of data, its efficacy in navigating and utilizing information located in the middle of this data is still under scrutiny. Early tests and observations suggest that despite its expanded capabilities, GPT-4 Turbo may still struggle with comprehending and integrating details from the central portions of large data sets.

This challenge is not unique to GPT-4 Turbo. It reflects a broader pattern observed in the field of language modeling. Even with advanced architectures and training methods, many language models exhibit decreased performance when dealing with longer contexts. This suggests that the issue is a fundamental one in the realm of language processing, transcending specific model limitations.

Interestingly, the solution to this problem might not lie in continually increasing the context window size. The relationship between the size of the context window and the accuracy of information retrieval is complex and not always linear. In some cases, smaller context windows can yield more accurate and relevant outputs. This counterintuitive finding underscores the intricacies of language processing and the need for careful calibration of model parameters based on the specific application.

As the AI community continues to explore and refine models like GPT-4 Turbo, the focus remains on improving their ability to handle extensive contexts effectively. The journey of GPT models is characterized by continuous learning and adaptation, with each version bringing us closer to more sophisticated and nuanced language processing capabilities.

For those considering integrating GPT-4 Turbo into their workflows or products, it’s crucial to weigh its impressive capabilities against its current limitations. The model’s expanded context window and cost efficiency make it a compelling choice for a variety of applications, but understanding how it performs with different types and lengths of data is key to making the most out of its advanced features. GPT-4 Turbo represents a significant stride in the ongoing evolution of language models. Its expanded context window and cost efficiency are remarkable, but as with any technology, it’s essential to approach its use with a clear understanding of both its strengths and areas for improvement.

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Building AI sports commentators using GPT4 Vision and TTS

Coding AI sports commentators using GPT4 Vision and OpenAI Text to Speech

In the ever-evolving domain of sports and Esports, the introduction of AI commentary is reshaping how we experience these events. Unlike human commentators, AI brings a level of consistency and reliability that is unaffected by fatigue or emotional bias. This translates into a steady, quality commentary throughout an event, ensuring that every moment is captured with precision.

Unlike humans, AI commentators have the ability to process and interpret large volumes of data in real-time. This capability allows for the provision of insightful statistics, historical comparisons, and tactical analysis at a level of efficiency and depth that human commentators might find challenging. This data-driven approach enriches the viewing experience, offering insights that might otherwise be missed.

Moreover, the ability of AI to provide commentary in multiple languages and adapt to various dialects and accents significantly broadens the accessibility of sports and Esports events. This multi-lingual capacity helps in breaking down language barriers, making these events more inclusive for a global audience. Additionally, AI commentators can be programmed to cater to different levels of audience expertise, offering basic explanations for novices and complex analyses for enthusiasts, thus customizing the experience for viewers with varying levels of understanding of the game.

How to build an AI sports commentator using GPT4 Vision

The journey begins with the use of GPT-4 with vision, a sophisticated AI model adept at interpreting images. In sports commentary, this technology is employed to analyze video frames and generate detailed descriptions. These descriptions form the foundation of the script for your AI commentator, bridging the gap between visual action and verbal narration.

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The next step in this process involves transforming these scripts into speech, which is where OpenAI’s text-to-speech API enters the scene. This powerful tool can convert text into speech that closely mirrors human tones, inflections, and nuances, making it an ideal choice for crafting realistic and engaging sports commentary.

Converting videos into frames

A critical stage in this process is the initial conversion of video into frames. This is achieved using OpenCV, a highly esteemed video processing technology. By breaking down the video into individual frames, the AI model can meticulously examine each segment, ensuring precise and relevant commentary for every moment of the game. The art of crafting these frame descriptions is a testament to the capabilities of GPT-4 with vision. The model scrutinizes each frame, identifying key moments, movements, and tactics in the game, and converts these observations into coherent, descriptive scripts. This level of detail in the commentary not only enhances the viewing experience but also provides insights that might be overlooked in traditional commentary.

Voice communication

Once the descriptions are ready, they are voiced using OpenAI’s text-to-speech API. This API excels at producing speech that is not only clear and intelligible but also engaging and dynamic, vital qualities for maintaining viewer interest throughout the sports event. The entire procedure is streamlined through the use of Google Colab, a cloud-based coding platform. Google Colab offers an interactive environment that simplifies the process, making it accessible even for those who may not be experts in coding.

Combining audio and video together

The final step involves merging the generated audio with the original video. This is where video editing software comes into play. The synchronization of audio with video is crucial, as it ensures that the narration aligns perfectly with the on-screen action, providing a seamless viewing experience. During this process, you may encounter the need to adjust the code to accommodate changes in API calls. These modifications are usually minor and can be seamlessly integrated into the existing framework. Another aspect to consider is the token limitations inherent in data processing. This constraint can impact the length of the descriptions generated by the AI model, but with strategic planning and tweaking, you can effectively manage these limitations.

The creation of an AI sports commentator using GPT-4 with vision and OpenAI’s text-to-speech API is a fascinating venture. By following these steps, you can craft engaging and informative sports commentary that not only enhances the viewer’s experience but also adds a new dimension to the game. The possibilities are endless, from offering in-depth analysis to providing multilingual commentary, making sports events more accessible and enjoyable for a global audience.

Financial considerations

When considering the financial aspects, AI commentators, despite the initial investment in development and deployment, can prove to be more cost-effective in the long run. Their ability to cover a wide range of events across different locations and languages makes them a financially viable alternative to human commentators. Furthermore, AI commentators are designed to work alongside human commentators, enhancing broadcasts by handling specific tasks and allowing human commentators to focus on aspects where they excel, like providing emotional depth and personal insights.

Another significant advantage of AI is its precision, which reduces the likelihood of errors in recalling statistics or player histories. This accuracy is crucial in maintaining the integrity and quality of the commentary. In terms of scalability, AI can easily manage to cover multiple events simultaneously, a feat that is both challenging and resource-intensive for human commentators.

The human element

AI commentators are not only about efficiency and accuracy; they also open the door to innovative viewing experiences. They enable new forms of interactive and personalized viewing, allowing viewers to choose the type of commentary that suits their preference. Also, AI can be trained to notice and comment on non-traditional aspects of the game, offering unique perspectives that might be overlooked by human commentators. However, it’s important to acknowledge that AI cannot replace the human element in commentary, which brings emotion and personal insight. The ideal scenario is a blend of AI and human commentators, leveraging the strengths of both to provide a comprehensive and engaging viewing experience.

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Creating website user interfaces using AI GPT-4 Vision

Creating website user interfaces using AI GPT-4 Vision and Draw a UI app

Website and user interface designers might be interested in a new application that allows you to transform sketches into coded user interfaces. Currently in its early development stages the AI Draw a UI app provides an insight into how AI can be used to create user interfaces for a wide variety of different applications from website designs to mobile apps. The creation of user interfaces (UI) stands out as a task blending aesthetics, functionality, and user engagement. The introduction of Draw a UI marks a unique moment in UI design, showcasing the intricate relationship between creativity and technology.

In the world of digital applications, the user interface (UI) is akin to a bridge that connects human interaction with the digital realm. It’s the first thing users encounter and, consequently, forms the cornerstone of their experience. In this digital age, where applications pervade every aspect of our lives, understanding the nuances of UI design becomes imperative.

This innovative tool takes UI design to unprecedented heights by converting UI sketches into deployable HTML code in real-time, directly within your web browser. This advancement is a significant stride in making UI design more accessible and efficient for everyone. Its drag-and-drop feature streamlines the design process, especially for those with limited coding expertise, making the creation of UI designs more intuitive. You’ll be pleased to know that it also offers code customization options, allowing you to tailor the generated code to your specific needs and preferences.

AI user interfaces design

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GPT-4 Vision to make UIs

One of the most impressive features of Draw a UI user interfaces design app is its integration with the GPT-4 Vision API. This cutting-edge technology augments the tool’s capability to interpret visual content, enabling it to produce corresponding HTML code with exceptional accuracy. This feature is particularly beneficial for those who prefer to sketch their UI designs manually before converting them into code. The HTML output from Draw a UI is structured using Tailwind CSS, a modern, utility-first CSS framework. This ensures that the designs are not only visually appealing but also responsive, adapting effortlessly to various screen sizes and devices.

While Draw a UI showcases remarkable potential, it’s important to note that it is currently a demo tool and not yet intended for production use. The absence of authentication methods, crucial for code security and integrity, is a key limitation at this stage. For those curious about exploring “Draw a UI,” the tool can be installed locally. This process requires access to the GPT-4 Vision API, which powers the tool’s ability to interpret visual content. Detailed instructions are provided to ensure a smooth setup experience.

The Role user interfaces design in user experience

  • First Impressions: The UI is often the first point of contact between the user and the application. A well-designed interface not only captivates users but also establishes a tone for their entire experience.
  • Usability: At its core, a good UI is about usability. It’s about creating a seamless path for users to accomplish their goals, whether it’s booking a flight or checking the weather.
  • Accessibility: Inclusivity is key. UI design should cater to a diverse audience, ensuring accessibility for people with different abilities.

Considerations in UI Design

  • Simplicity: The mantra ‘less is more’ holds particularly true in UI design. A clutter-free, intuitive design is paramount.
  • Consistency: Keeping design elements consistent across the application enhances user familiarity and comfort.
  • Feedback: Immediate feedback for user actions, like a confirmation after a button press, is crucial in keeping users informed and engaged.

The technical side of user interface design

When designing the user interface it’s important to consider a wide variety of different factors some of which are listed below. Each area must be considered to create an ergonomic and user-friendly user interface that can be used across a wide variety of different devices and platforms.

The digital world today is a mosaic of devices, each with varying screen sizes and resolutions. Responsive design in UI is not just a feature; it’s a necessity. It ensures that a digital application is accessible and functional across different devices, from the smallest smartphones to the largest desktop monitors.

Responsive design employs fluid grid layouts that adjust to the screen size, ensuring content is readable and accessible regardless of the device. Media queries, a staple of responsive design, allow designers to apply specific styles based on the device’s characteristics, such as its width, height, or orientation. This adaptability enhances the user experience by providing a seamless interaction across all platforms.

Animations in UI design are not just decorative elements; they serve functional purposes as well. Subtle animations can guide users through tasks, provide feedback on their actions, and clarify the flow of application usage. When implemented thoughtfully, animations can make complex interactions feel simple and intuitive.

By incorporating animations, designers can create a more engaging and interactive experience. Animations like button expansions, loading indicators, and transition effects not only add aesthetic value but also provide useful cues to the user, making the digital experience more dynamic and responsive to their actions.

In the world of UI design, performance is synonymous with user satisfaction. A UI that is slow to respond can lead to user frustration, abandonment of the application, and negative perceptions of the brand. Ensuring that the UI is optimized for performance, with minimal load times and quick response to user inputs, is as crucial as its visual design.

Optimizing for Efficiency

Performance optimization involves various techniques, from reducing image sizes and using efficient code to leveraging browser caching and minimizing HTTP requests. A well-optimized UI ensures that resources are used judiciously, leading to faster interactions and a smoother user experience.

Responsive design, animation, and performance are integral components of modern UI design. Each plays a unique role in enhancing the user experience, ensuring that digital applications are not only visually appealing but also functionally robust and user-friendly. In the rapidly evolving digital landscape, attention to these aspects is paramount in creating interfaces that resonate with users and stand the test of time.

A/B Testing: The Art of Comparison and Choice

A/B testing, at its core, is a comparative analysis method. It involves creating two versions of a UI – Version A and Version B. These versions are typically similar, with one or two key differences that could impact user behavior. For instance, Version A might feature a green call-to-action button, while Version B uses a red one.

Users are randomly exposed to either version without prior knowledge of the test. Their interactions with each version are closely monitored and analyzed. Metrics like click-through rates, conversion rates, time spent on the page, and user engagement levels are gathered to determine which version performs better.

The outcome of A/B testing provides concrete, data-driven insights. It helps in making informed decisions about which elements of the UI work best in achieving desired user actions and improving overall user experience. This method takes guesswork out of the equation, allowing designers to base their decisions on actual user data.

Gathering Insights

User feedback is an indispensable part of the UI design process. It involves collecting opinions and experiences directly from the users. This can be done through various means such as surveys, interviews, user testing sessions, or feedback forms within the application.

The Role of Feedback in UI Refinement

Incorporating user feedback is crucial for several reasons:

  • Identifying Pain Points: Users can highlight issues and pain points that designers might not have foreseen.
  • Understanding User Needs: Feedback provides a deeper understanding of what users actually need and value in the UI.
  • Continuous Improvement: UI design is not a one-time task but a continuous process of iteration. User feedback is the driving force behind this iterative process, ensuring that the UI evolves to meet changing user needs and preferences.

By prioritizing user feedback, designers cultivate a user-centric approach to UI design. This approach ensures that the final product is not just aesthetically pleasing but also functionally relevant and user-friendly.

While a visually appealing UI can draw users in, its true effectiveness lies in its functionality. The goal is to strike a balance where the design is not only pleasing to the eye but also facilitates ease of use.

A/B testing and user feedback are instrumental in the UI design process. They provide a structured approach to understanding user preferences and behaviors, allowing designers to make informed decisions and continuously improve the UI. In the dynamic field of digital applications, these methods are key to creating interfaces that resonate with users and drive engagement.

The Business Implications of UI Design

  • Brand Identity: The UI is a reflection of a company’s brand. A distinctive and thoughtful design can set an application apart in a crowded market.
  • User Retention: An intuitive and efficient UI can significantly enhance user satisfaction, leading to higher retention rates.
  • Conversion Rates: In eCommerce, for example, a well-designed UI can streamline the shopping process, directly impacting conversion rates.

Draw a UI harnesses the capabilities of the OpenAI GPT model and the GPT-4 Vision API, providing instant code generation, drag-and-drop design functionality, and customization options. Although currently a demo, its potential for future development and application is immense. This tool not only symbolizes the ongoing evolution in web development but also opens doors to exciting future possibilities in this domain.

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