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Using GPT-4 Turbo and GPTs to create next generation AI apps

Using GPT-4 Turbo and GPTs to create next generation AI apps

OpenAI has recently introduced an advanced model called GPT-4 Turbo together with a wealth of other new enhancements and services one of which is the creation of new GPTs. These GPTs have been specifically created to enable anyone to create custom AI models for a wide variety of different applications and sell these on the upcoming GPT Store. This innovative AI model creation system is a significant step towards the development of AI agents that can carry out tasks on your behalf.

Although these AI agents are not fully autonomous, they represent a major leap towards the creation of completely independent AI entities, signaling a new phase in the AI industry. It’s also worth mentioning that at the end of the very first OpenAI Developer Conference, OpenAI’s CEO Sam Altman said “What we launched today is going to look very quaint relative to what we’re busy creating for you now.” Suggesting that perhaps more amazing and autonomous AI functionality is on its way.

The GPT-4 Turbo model is a key part of OpenAI’s broader mission to make AI technology accessible to a wider audience. To support this goal, OpenAI has launched an AI app store, a platform where you can sell your AI bots. This online platform is carefully designed to be user-friendly, enabling individuals without a background in coding or machine learning to easily create and deploy AI agents.

The future of creating AI apps

Check out the video below kindly created by Wes Roth  to learn more about what we can expect from the new GTPs  and how they are going to change the landscape of app development and the integration of  artificial intelligence into our everyday lives.

Other articles you may find of interest on the subject of ChatGPT-4 Turbo :

building GPTs apps

The OpenAI API is the main interface for interacting with OpenAI’s AI models. This API allows you to engage with the AI models, making it easier to create AI agents, use advanced analytics, and even generate images using AI, such as with the innovative DallE 3 image generation feature. One other key features of the GPT-4 Turbo model is its ability to smoothly integrate with third-party services. For example, you can use Zapier, a popular automation tool, to create automated workflows or “Zaps” that include your AI agents. This integration allows you to automate tasks and processes, freeing up your time for other activities.

The introduction of GPT-4 Turbo and the AI app store is expected to significantly impact various sectors. In e-commerce, for instance, AI bots can be used to automate tasks like customer service or inventory management, improving efficiency and productivity. On social media platforms, AI bots can automate posts or interact with followers, changing the way businesses communicate with their audience. In education, AI models like Chad GPT can act as AI mentors, providing personalized learning support to students and transforming the learning experience.

 GPT-4 Turbo

GPT-4 Turbo is OpenAI’s latest generation model. It’s more capable, has an updated knowledge cutoff of April 2023 and introduces a 128k context window (the equivalent of 300 pages of text in a single prompt). The model is also 3X cheaper for input tokens and 2X cheaper for output tokens compared to the original GPT-4 model. The maximum number of output tokens for this model is 4096.

In addition to these features, OpenAI’s recent developments also have implications for the field of machine learning. The GPT-4 Turbo model includes a memory system that allows it to learn from past experiences and improve its performance over time. This model also has a code interpreter, enabling it to understand and execute code, a feature that could potentially transform the field of AI-powered programming.

OpenAI’s recent advancements also have potential uses in the field of advanced analytics. By using AI to analyze large datasets, businesses and researchers can discover insights that would be difficult, if not impossible, to find using traditional methods. For example, an AI-powered program could examine social media posts to identify trends in consumer behavior, or it could analyze financial data to predict market trends, providing valuable insights for decision-making.

OpenAI’s launch of the GPT-4 Turbo model and the AI app store represents a significant advancement in the field of AI. By enabling users to create AI agents and sell them on a user-friendly platform, OpenAI is making AI technology more accessible. Furthermore, by integrating with third-party services and using advanced machine learning techniques, these developments have the potential to transform various sectors, from e-commerce to education, marking a new phase in the application of AI.

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Build a virtual AI workforce using AutoGen and GPT-4

Build a virtual workforce of AI helpers using AutoGen and GPT-4

We have covered plenty of projects that have been created over the past few months using the new Microsoft AutoGen framework which was quietly rolled out to GitHub. offering a framework that enables the development of LLM applications using multiple agents, capable of communicating with each other to solve tasks. The beauty of AutoGen agents is that they are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.

If you’ve ever been captivated by the idea of automating complex workflows using artificial intelligence, you will be pleased to know that AutoGen is at the forefront of this emerging landscape. Imagine a world where your projects are not just assisted by a single language model, but an entire team of specialized AI agents, conversing amongst themselves and executing tasks at an unprecedented scale. Intrigued? Let’s delve deeper into how you can build a virtual workforce of AI helpers using AutoGen and GPT-4.

“GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.”

Team of AI agents working together

At the core of AutoGen lies its capability to simplify the orchestration, automation, and optimization of intricate workflows involving language models like GPT-4. While there are other contenders in this space—think MetaGPT or ChatDev—AutoGen stands out for its focus on multi-agent conversations. What this means is that you can have several agents, each programmed for specific roles or tasks, working in concert. Not only does this make the system more robust by offsetting individual limitations of single agents, but it also enables a level of customization that is hard to match.

Other articles we have written that you may find of interest on the subject of Microsoft’s AutoGen AI Agent framework :

Microsoft AutoGen AI agent framework

If you are wondering how to adapt this to suit your specific needs, AutoGen provides tools for customizing the conversational patterns of your agents. Whether you’re considering one-to-one, multi-agent, or even complex tree-like conversational topologies, it’s all within reach. You get to decide the number of agents involved and the degree to which they can converse autonomously. This is highly beneficial for applications requiring a diversity of conversational styles and structures, from customer service to project management and beyond.

AutoGen is versatile in its application, able to accommodate a multitude of use-cases across various sectors. Be it healthcare, finance, or retail, the framework has pre-built, working systems that can be adapted to different complexities and requirements. This is an invaluable asset for those wanting to integrate AI into specialized domains without reinventing the wheel.

In terms of technical infrastructure, AutoGen brings several advantages to the table. It offers enhanced performance tuning options, API unification, and caching functionalities. Advanced features like error handling, multi-config inference, and context programming are also part of the package. Essentially, you get a plethora of utilities to ensure that your virtual workforce performs optimally.

How to build a virtual AI workforce

If you’re eager to dive in, the easiest entry point is through Github Codespace. Simply copy the sample OAI_CONFIG_LIST to the /notebook folder, rename it to OAI_CONFIG_LIST, and set the configurations as needed. From there, you’re all set to explore and experiment with the example notebooks. Full instructions on how to use Microsoft’s AutoGen and Codespaces can be found over on GitHub.

“Create a codespace to start developing in a secure, configurable, and dedicated development environment that works how and where you want it to.”

While automating tasks is compelling, there are instances when human intuition and expertise cannot be replicated by machines. Recognizing this, AutoGen is designed to seamlessly integrate human input and feedback into the system. You, or any other human user, can interact with the agents, guiding them towards better solutions or intervening when necessary.

So there you have it—an intricate yet user-friendly guide to creating a virtual team of AI helpers, effortlessly amalgamating the individual strengths of multiple agents into a coherent and efficient workforce. If you are invested in leveraging AI for complex problem-solving, AutoGen, coupled with GPT-4, offers a promising avenue to make this a reality.

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