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OpenAI Developers explain how to use GPTs and Assistants API

OpenAI Developers explain how to use GPTs and Assistants API

At the forefront of AI research and development, OpenAI’s DevDay presented an exciting session that offered a deep dive into the latest advancements in artificial intelligence. The session, aimed at exploring the evolution and future potential of AI, particularly focused on agent-like technologies, a rapidly developing area in AI research. Central to this discussion were two of OpenAI’s groundbreaking products: GPTs and ChatGPT.

The session was led by two of OpenAI’s prominent figures – Thomas, the lead engineer on the GPTs project, and Nick, who oversees product management for ChatGPT. Together, they embarked on narrating the compelling journey of ChatGPT, a conversational AI that has made significant strides since its inception.

Their presentation underscored how ChatGPT, powered by GPT-4, represents a new era in AI with its advanced capabilities in processing natural language, understanding speech, interpreting code, and even interacting with visual inputs. The duo emphasized how these developments have not only expanded the technical horizons of AI but also its practical applicability, making it an invaluable tool for developers and users worldwide.

The Three Pillars of GPTs

The core of the session revolved around the intricate architecture of GPTs, revealing how they are constructed from three fundamental components: instructions, actions, and knowledge. This triad forms the backbone of GPTs, providing a versatile framework that can be adapted and customized according to diverse requirements.

  1. Instructions (System Messages): This element serves as the guiding force for GPTs, shaping their interaction style and response mechanisms. Instructions are akin to giving the AI a specific personality or directive, enabling it to respond in a manner tailored to the context or theme of the application.
  2. Actions: Actions are the dynamic component of GPTs that allow them to interact with external systems and data. This connectivity extends the functionality of GPTs beyond mere conversation, enabling them to perform tasks, manage data, and even control other software systems, thus adding a layer of practical utility.
  3. Knowledge: The final element is the vast repository of information and data that GPTs can access and utilize. This knowledge base is not static; it can be expanded and refined to include specific datasets, allowing GPTs to deliver informed and contextually relevant responses.

Through this tripartite structure, developers can create customized versions of ChatGPT, tailoring them to specific themes, tasks, or user needs. The session highlighted how this flexibility opens up endless possibilities for innovation in AI applications, making GPTs a powerful tool in the arsenal of modern technology.

Delving into GPTs and ChatGPT

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Live Demonstrations: Bringing Concepts to Life

The presentation included live demos, showcasing the flexibility and power of GPTs. For instance, a pirate-themed GPT was created to illustrate how instructions can give unique personalities to the AI. Another demonstration involved Tasky Make Task Face, a GPT connected to the Asana API through actions, showing the practical application in task management.

Additionally, a GPT named Danny DevDay, equipped with specific knowledge about the event, was shown to demonstrate the integration of external information into AI responses.

Introducing Mood Tunes: A Creative Application

A particularly intriguing demo was ‘Mood Tunes’, a mixtape maestro. It combined vision, knowledge, and music suggestions to create a mixtape based on an uploaded image, showcasing the multi-modal capabilities of the AI.

The Assistance API: A New Frontier

Olivier and Michelle, leading figures at OpenAI, introduced the Assistance API. This new API is designed to build AI assistants within applications, incorporating tools like code interpreters, retrieval systems, and function calling. The API simplifies creating personalized and efficient AI assistants, as demonstrated through various practical examples.

What’s Next for OpenAI?

The session concluded with a promise of more advancements, including making the API multi-modal by default, allowing custom code execution, and introducing asynchronous support for real-time applications. OpenAI’s commitment to evolving AI technology was clear, as they invited feedback and ideas from the developer community.

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How to use the OpenAI Assistants API to build AI agents & apps

Learn how to use the OpenAI Assistants API

Hubel Labs has created a fantastic introduction to the new OpenAI Assistants API which were recently unveiled at OpenAI’s very first DevDay. The new API tool has been specifically designed to dramatically simplified the process of building custom chatbots, offering more advanced features when compared to the ChatGPT custom GPT Builder which is integrated into the ChatGPT online service.

The API’s advanced features have the potential to significantly streamline the process of retrieving and using information. This quick overview guide and instructional videos created by Hubel Labs will provide more insight into the features of OpenAI’s Assistance API, the new GPTs product, and how developers can use the API to create and manage chatbots.

What is an Assistance API

The Assistants API allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling. In the future, we plan to release more OpenAI-built tools, and allow you to provide your own tools on our platform.

diagram of what is an Assistance API

Using Assistants API to build ChatGPT apps

The Assistants API is a powerful tool built on the same capabilities that enable the new GPTs product, custom instructions, and tools such as the code interpreter, retriever, and function calling. Essentially, it allows developers to build custom chatbots on top of the GPT large language model. It eliminates the need for developers to separate files into chunks, use an embedding API to turn chunks into embeddings, and put embeddings into a vector database for a cosine similarity search.

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The API operates on two key concepts: an assistant and a thread. The assistant defines how the custom chatbot works and what resources it has access to, while the thread stores user messages and assistant responses. This structure allows for efficient communication and data retrieval, enhancing the functionality and usability of the chatbot.

Creating an assistant and a thread is a straightforward process. Developers can authenticate with an organization ID and an API key, upload files to give the assistant access to, and create the assistant with specific instructions, model, tools, and file IDs. They can also update the assistant’s configuration, retrieve an existing assistant, create an empty thread, run the assistant to get a response, retrieve the full list of messages from the thread, and delete the assistant. Notably, OpenAI’s platform allows developers to perform all these tasks without any code, making it accessible for people who don’t code.

Creating custom GPT’s with agents

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One of the standout features of the Assistance API is its function calling capability. This feature allows the chatbot to call agents and execute backend tasks, such as fetching user IDs, sending emails, and manually adding game subscriptions to user accounts. The setup for function calling is similar to the retrieval mode, with an assistant that has a name, description, and an underlying model. The assistant can be given up to 128 different tools, which can be proprietary to a company.

OpenAI Assistants API

The assistant can be given files, such as FAQs, that it can refer to. It can also be given functions, such as fetching user IDs, sending emails, and manually adding game subscriptions. The assistant can be given a thread with a user message, which it will run and then pause if it requires action. The assistant will indicate which functions need to be called and what parameters need to be passed in. The assistant will then wait for the output from the called functions before completing the run process and adding a message to the thread.

The Assistance API’s thread management feature helps truncate long threads to fit into the context window. This ensures that the chatbot can effectively handle queries that require information from files, as well as those that require function calls, even if they require multiple function calls.

However, it should be noted that the Assistance API currently does not allow developers to create a chatbot that only answers questions about their knowledge base and nothing else. Despite this limitation, the Assistance API is a groundbreaking tool that has the potential to revolutionize the way developers build and manage chatbots. Its advanced features and user-friendly interface make it a promising addition to OpenAI’s suite of AI tools.

Image Credit : Hubel Labs

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OpenAI Data Partnerships announced for AI training with diverse global data

OpenAI Data Partnerships announced

OpenAI, a leading artificial intelligence research lab, has recently launched the OpenAI Data Partnerships program. This new initiative is designed to encourage collaboration with a variety of organizations to create both public and private datasets for AI model training. The program’s main goal is to improve the understanding of AI models across a wide range of subjects, industries, cultures, and languages. This is achieved by training the models on a diverse and comprehensive dataset.

OpenAI is particularly interested in large-scale datasets that reflect the complexities of human society. These datasets, which are often not easily accessible online, are invaluable for AI training. The company can work with any type of data, including text, images, audio, or video. This multi-modal approach to AI training allows for a more comprehensive understanding of the data, leading to the development of more accurate and effective AI models.

Data Partnerships

One of OpenAI’s strengths is its ability to assist with the digitization and structuring of data. This is done using advanced technologies such as Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR). OCR technology is used to digitize text, converting printed or handwritten characters into machine-readable text. This makes it easier to process and analyze large amounts of text data. ASR technology, on the other hand, is used to convert spoken words into written text, which is especially useful for processing audio data.

OpenAI has made it clear that it is not interested in datasets that contain sensitive or personal information, in line with its commitment to privacy and data protection. Instead, the focus is on data that reflects human intention, which can provide valuable insights into human behavior and decision-making, thereby enhancing the training of AI models.

Datasets

The OpenAI Data Partnerships program is not limited to public datasets. The company is also interested in confidential data for AI training. These private datasets can be used to train proprietary AI models, providing a competitive edge for businesses and organizations. However, the use of such datasets is subject to strict confidentiality and data protection measures.

OpenAI’s commitment to improving AI understanding through comprehensive training datasets is evident in its partnerships with various organizations. For instance, the company has partnered with the Icelandic Government, Miðeind ehf, and the Free Law Project to access and use their datasets. These partnerships highlight the potential of collaborative efforts in advancing AI technology.

In summary, the OpenAI Data Partnerships program represents a significant step forward in AI research. By using both public and private datasets, the company aims to enhance the understanding and effectiveness of AI models. This could lead to the development of more accurate and reliable AI applications, benefiting various industries and sectors. This initiative demonstrates OpenAI’s strategy to pushing the boundaries of AI technology.

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What are OpenAI GPTs and how do they work?

What are OpenAI GPTs

During the first-ever OpenAI developer conference this week Sam Altman introduced a wealth of new services, features and enhancements to its AI models. One new product was the announcement of the new OpenAI GPTs. But what are GPTs, how do they work and how do you create them?

If you our curious about the new AI model personalization techniques that are rolling out to ChatGPT users. This quick guide will provide more insight into what OpenAI GPTs AI models are and how they function. GPT’s have been created to provide a fresh approach to the way users can customise ChatGPT reshaping how we interact with OpenAI’s AI model, extending its capabilities to better serve our individual needs, whether at home or in the office.

What are OpenAI GPTs

GPTs, simply put, are custom versions of ChatGPT or personalized bots that you can mold to perform tasks as varied as teaching math to kids, helping you navigate the complexities of board games, or even designing creative stickers. These custom versions of ChatGPT are not just a figment of the future; they are here, and they’re transforming the way we utilize AI in our daily lives.

Creating your very own GPT is a breeze. Imagine crafting a digital assistant with no more technical knowledge than it takes to hold a conversation. Whether for personal use, internal company operations, or sharing with the world, these GPTs are crafted by simply chatting, instructing, and deciding their capabilities, which can range from web searches to graphic design.

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The beauty of GPTs lies in their accessibility. OpenAI has simplified the process of fine tuning AI models, allowing anyone, regardless of coding expertise, to become a creator. For instance educators, hobbyists, and professionals alike contributing to a expanding library of GPTs, each bringing their unique flair and expertise to the table and making them available for others. Either for free or at a cost depending on the time and effort that has been implemented into creating the GPT.

Intrigued by the potential? You can dip your toes in the water right now. Certain GPT examples are already up for grabs for ChatGPT Plus and Enterprise users. These include integrations with Canva and Zapier AI Actions, paving the way for a more hands-on AI experience. And if patience is your virtue, the GPT Store is on the horizon, slated to open later this month. Here, verified creators will showcase their GPTs, which will be up for discovery and possibly, top the leaderboards.

The concept of customization isn’t new; since the inception of ChatGPT, users have sought ways to tailor the experience using AI model fine tuning. OpenAI responded with Custom Instructions, and now, GPTs are the next step in this evolution, doing away with the need for manual prompt lists and offering a more seamless, automated personalization process.

Data privacy and safety are not afterthoughts but foundations upon which GPTs are built. OpenAI ensures that interactions with GPTs remain private and that builders do not access your chats. Further safeguards are in place to prevent the dissemination of GPTs with harmful content. And for those who are conscientious about data use, there are options to limit how your conversations are utilized for model improvement.

As GPTs grow in sophistication, they will begin to take on more tangible tasks, acting as ‘agents’ in the AI lexicon. This gradual progression acknowledges the delicate balance between technological advancement and societal readiness.

Developers and businesses

Developers are not left out of this narrative. With the ability to integrate APIs, GPTs can connect to databases, emails, and even manage e-commerce transactions. This capability builds upon the insights gained from the plugin beta, offering developers a tighter rein over the model’s integration with their APIs.

For businesses, GPTs offer a unique proposition. Companies like Amgen, Bain, and Square are already leveraging internal GPTs for various applications, from branding to customer support. OpenAI has designed an ecosystem where enterprise customers can deploy GPTs tailored to their specific needs, ensuring a secure, code-free development environment within their own workspaces.

OpenAI’s commitment to collaboration is clear. By inviting more people to contribute to AI’s development, the technology becomes more robust, aligned, and safe. This inclusive approach is not just a nice-to-have; it’s essential to building artificial general intelligence (AGI) that benefits all of humanity. So, if you’re pondering the future of AI, or perhaps how you can be a part of it, GPTs offer a fascinating glimpse into what’s possible when technology is crafted not just for the people but by them.

 

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How to use OpenAI Assistants API

a team of developers building AI applications using OpenAI Assistants API

During the recent OpenAI developer conference Sam Altman introduced the company’s new Assistants API, offering a robust toolset for developers aiming to integrate intelligent assistants into their own creations. If you’ve ever envisioned crafting an application that benefits from AI’s responsiveness and adaptability, OpenAI’s new Assistants API might just be the missing piece you’ve been searching for.

At the core of the Assistants API are three key functionalities that it supports: Code Interpreter, Retrieval, and Function calling. These tools are instrumental in equipping your AI assistant with the capability to comprehend and execute code, fetch information effectively, and perform specific functions upon request. What’s more, the horizon is broadening, with OpenAI planning to introduce a wider range of tools, including the exciting possibility for developers to contribute their own.

Three key functionalities

Let’s go through the fundamental features that the OpenAI Assistants API offers in more detail. These are important parts of the customization and functionality of AI assistants within the various applications you might building or have wanted to build but didn’t have the expertise or skill to create the AI structure yourself.

Code Interpreter

First up, the Code Interpreter is essentially the brain that allows the AI to understand and run code. This is quite the game-changer for developers who aim to integrate computational problem-solving within their applications. Imagine an assistant that not only grasps mathematical queries but can also churn out executable code to solve complex equations on the fly. This tool bridges the gap between conversational input and technical output, bringing a level of interactivity and functionality that’s quite unique.

Retrieval

Moving on to Retrieval, this is the AI’s adept librarian. It can sift through vast amounts of data to retrieve the exact piece of information needed to respond to user queries. Whether it’s a historical fact, a code snippet, or a statistical figure, the Retrieval tool ensures that the assistant has a wealth of knowledge at its disposal and can provide responses that are informed and accurate. This isn’t just about pulling data; it’s about pulling the right data at the right time, which is critical for creating an assistant that’s both reliable and resourceful.

Function calling

The third pillar, Function calling, grants the assistant the power to perform predefined actions in response to user requests. This could range from scheduling a meeting to processing a payment. It’s akin to giving your AI the ability to not just converse but also to take actions based on that conversation, providing a tangible utility that can automate tasks and streamline user interactions.

Moreover, OpenAI isn’t just stopping there. The vision includes expanding these tools even further, opening up the floor for developers to potentially introduce their own custom tools. This means that in the future, the Assistants API could become a sandbox of sorts, where developers can experiment with and deploy bespoke functionalities tailored to their application’s specific needs. This level of customization is poised to push the boundaries of what AI assistants can do, turning them into truly versatile and adaptable components of the software ecosystem.

How to use OpenAI Assistants API

In essence, these three functionalities form the backbone of the Assistants API, and their significance cannot be overstated. They are what make the platform not just a static interface but a dynamic environment where interaction, information retrieval, and task execution all come together to create AI assistants that are as responsive as they are intelligent.

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To get a feel for what the Assistants API can do, you have two avenues: the Assistants playground for a quick hands-on experience, or a more in-depth step-by-step guide. Let’s walk through a typical integration flow of the API:

  1. Create an Assistant: This is where you define the essence of your AI assistant. You’ll decide on its instructions and choose a model that best fits your needs. The models at your disposal range from GPT-3.5 to the latest GPT-4, and you can even opt for fine-tuned variants. If you’re looking to enable functionalities like Code Interpreter or Retrieval, this is the stage where you’ll set those wheels in motion.
  2. Initiate a Thread: Think of a Thread as the birthplace of a conversation with a user. It’s recommended to create a unique Thread for each user, right when they start interacting with your application. This is also the stage where you can infuse user-specific context or pass along any files needed for the conversation.
  3. Inject a Message into the Thread: Every user interaction, be it a question or a command, is encapsulated in a Message. Currently, you can pass along text and soon, images will join the party, broadening the spectrum of interactions.
  4. Engage the Assistant: Now, for the Assistant to spring into action, you’ll trigger a Run. This process involves the Assistant assessing the Thread, deciding if it needs to leverage any of the enabled tools, and then generating a response. The Assistant’s responses are also posted back into the Thread as Messages.
  5. Showcase the Assistant’s Response: After a Run has been completed, the Assistant’s responses are ready for you to display back to the user. This is where the conversation truly comes to life, with the Assistant now fully engaging in the dialogue.

Threads are crucial for preserving the context of a conversation with the AI. They enable the AI to remember past interactions and respond in a relevant and appropriate manner. The polling mechanism, on the other hand, is used to monitor the status of a task. It sends a request to the server and waits for a response, allowing you to track your tasks’ progress.

To interact with the Assistants API, you’ll need the OpenAI API key. This access credential authenticates your requests, ensuring they’re valid. This key can be securely stored in a .env file, an environment variable handler designed to protect your credentials.

If you’re curious about the specifics, let’s say you want to create an Assistant that’s a personal math tutor. This Assistant would not only understand math queries but also execute code to provide solutions. The user could, for instance, ask for help with an equation, and the Assistant would respond with the correct solution.

In this beta phase, the Assistants API is a canvas of possibilities, and OpenAI invites developers to provide their valuable feedback via the Developer Forum. OpenAI has also created documentation for its new API system which is deftly with reading before you start your journey in creating your next AI powered application or service.

OpenAI Assistants API is a bridge between your application and the intelligent, responsive world of AI. It’s a platform that not only answers the ‘how’ but also expands the ‘what can be done’ in AI-assisted applications. As you navigate this journey of integration, you will be pleased to know that the process is designed to be as seamless as possible and OpenAI provides plenty of help and insight, ensuring that even those new to AI can find their footing quickly and build powerful AI applications.

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OpenAI ChatGPT-4 Turbo tested and other important updates

OpenAI GPT-4 Turbo tested

If you are interested in learning more about all the new updates released by OpenAI to its GPT-4 AI model and other services and news from the first ever OpenAI Dev Day Keynote event. This quick overview provides more insight into what you can expect from the performance of the latest ChatGPT-4 Turbo AI model and also an overview of the other enhancements, features and services announced by OpenAI that will soon be available for ChatGPT users to enjoy.

During the conference Sam Altman announced the imminent release of ChatGPT-4 Turbo, an upgraded version of the already sophisticated language model GPT-4, represents a significant leap forward in the field of AI development. This improved model introduces six key enhancements that are set to transform how developers work with AI.

 ChatGPT-4 Turbo

These enhancements include a longer context length, greater user control, improved knowledge, the addition of new modalities, customization options, and increased rate limits. The cut-off date for the new ChatGPT-4 Turbo AI model has also been increased to April 2023 allowing the AI model to expand its knowledge from the original September 2021 cut-off date for ChatGPT-4 released back in March 2023.

The new 128k context window, when used with the ChatGPT-4 Turbo engine, allows for extensive text processing. This means you can process larger amounts of text at once, making it easier to analyze and generate text-based content. This is especially useful for tasks such as document analysis, content generation, and machine translation.

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OpenAI GPT Price reductions

In an effort to make AI more accessible, OpenAI has reduced the price of GPT-4 Turbo. This strategic move is designed to make AI more affordable and accessible to a wider range of developers. By reducing the financial barrier to entry, OpenAI is encouraging more developers to explore and use AI technologies. This could potentially lead to an increase in the number of AI apps on the market, offering a wider range of solutions for end-users and expanding the possibilities of AI.

Copyright Shield

Another major update is the OpenAI Copyright Shield, a legal protection tool designed to safeguard you from potential copyright issues when using AI technologies. This is particularly important if you’re developing AI bots or custom AI models that could unintentionally infringe on copyrighted material. This tool provides a safety net, allowing you to innovate without worrying about legal issues.

OpenAI GPTs customizable AI models

Another significant update is the introduction of General Purpose Transforms (GPTs). GPTs give you the ability to build customized versions of Chat GPT, complete with instructions, expanded knowledge, and actions. This means you can create AI assistants like Matbot 3000, tailored to specific needs and requirements. Additionally, you can use the Assistance API to create AI assistants, and the persistent threads feature to maintain conversation history, enhancing the user experience and making your AI applications more user-friendly.

GPT-4 upgrades

OpenAI APIs

The Assistance API is another noteworthy feature. It aids in the creation of chatbots, providing a way to automate conversations. This is particularly useful in customer service, where chatbots can handle routine inquiries, freeing up human agents to deal with more complex issues.

– GPT Vision API is a powerful tool for image analysis, providing detailed insights into image content. This is particularly useful in fields such as surveillance, medical imaging, and content moderation, where accurate image analysis is crucial.

– DallE 3 API is designed for image generation. It can create images based on user prompts, making it a valuable tool for graphic designers, artists, and anyone needing to quickly and efficiently generate images. This API can greatly reduce your workload, allowing you to focus on the creative aspects of your work, thereby increasing productivity.

– Text to Speech API is another key feature. This tool converts written text into spoken words, providing a way to create audio content from written material. This is especially useful for creating audiobooks, podcasts, or any other form of audio content. The API supports a variety of languages and voices, allowing you to customize the output to meet your specific needs.

For developers, these advancements mean you now have the tools to build more complex and nuanced AI applications. For instance, the JSON mode feature enables you to generate valid JSON responses, while the reproducible outputs feature ensures consistent model outputs, enhancing the reliability of your AI applications.

Everything announced by OpenAI

GPT Store

OpenAI also launched the GPT Store, a marketplace specifically for GPT models. This platform functions similarly to an app store, allowing you to sell your GPTs and providing a platform for monetizing your AI developments. This could potentially lead to an increase in the number of AI solutions available on the market, offering a wider range of options for end-users and fostering a more competitive AI landscape.

However, these updates could also have implications for existing SaaS startups. OpenAI appears to be incorporating many features that were previously offered by third-party tools. This could potentially make some existing SaaS startups obsolete, as developers might prefer to use OpenAI’s integrated tools instead, leading to a shift in the SaaS landscape.

OpenAI’s Dev Day announcements represent significant advancements in AI development. The introduction of ChatGPT-4 Turbo, the Copyright Shield, the reduction in pricing, the launch of GPTs, and the GPT Store, as well as the Assistance API and persistent threads, are all expected to make AI app development more accessible and affordable. However, these updates could also disrupt the existing landscape of SaaS startups. As a developer, it’s crucial to stay updated with these advancements and consider how they might impact your work, shaping your strategies and decisions in the rapidly evolving world of AI.

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New OpenAI GPTs custom versions of ChatGPT roll-out this week

New OpenAI GPTs models,custom versions of ChatGPT

At the first ever OpenAI DevDay Keynote Sam Altman introduced a new addition to the services offered by OpenAI in the form of customizable versions of its ChatGPT, simply called GPTs. These adaptable AI models offer you the ability to shape OpenAI’s AI models to fit specific tasks or objectives. Providing the ability for to create your own GPTs without needing any coding expertise. This broadening of AI technology makes it more accessible to a larger audience, removing obstacles and unlocking new opportunities. For instance, you could employ these GPTs for a variety of tasks, from explaining mathematical concepts to crafting creative stickers, all based on your unique needs and preferences. You can even try it out for yourself here.

OpenAI explain more : “We’re rolling out custom versions of ChatGPT that you can create for a specific purpose—called GPTs. GPTs are a new way for anyone to create a tailored version of ChatGPT to be more helpful in their daily life, at specific tasks, at work, or at home—and then share that creation with others. For example, GPTs can help you learn the rules to any board game, help teach your kids math, or design stickers.

Anyone can easily build their own GPT—no coding is required. You can make them for yourself, just for your company’s internal use, or for everyone. Creating one is as easy as starting a conversation, giving it instructions and extra knowledge, and picking what it can do, like searching the web, making images or analyzing data.”

GPT Store

Looking forward, OpenAI plans to launch a GPT Store soon. This new e-commerce platform will serve as a marketplace where you can display and monetize your custom GPTs. This initiative not only offers a platform for you to present your AI models to a larger audience but also introduces a new path for monetizing your creative and technical endeavors. It’s a distinctive opportunity to transform your AI creations into a potential income source.

Alongside these advancements, OpenAI has also put in place strong privacy and safety measures to safeguard user data. These data protection measures are crucial in preserving user trust and ensuring the ethical use of AI technology. The company has set forth clear and comprehensive usage policies that provide guidelines for GPT usage. These guidelines ensure that the technology is used responsibly and ethically, underscoring OpenAI’s dedication to the ethical use of AI.

GPTs APIs

For developers, OpenAI has enabled the connection of GPTs to real-world applications via third-party APIs. This external data integration allows GPTs to interact with other software and services, greatly extending their functionality and usability. For example, you could integrate a GPT with a weather forecasting service to provide personalized weather updates, enhancing the user experience with a touch of personalization.

For enterprise customers, OpenAI provides the option to deploy internal-only GPTs for specific business needs. These business-specific AI models can be shaped to perform specific tasks, such as analyzing customer feedback or predicting market trends. This allows businesses to utilize AI technology in a way that is directly relevant and beneficial to their operations, boosting efficiency and productivity. OpenAI also offers an admin console, a comprehensive management tool for GPTs. This console allows you to manage your GPTs, including setting custom actions, installing plugins, and monitoring performance. This tool is crucial for maintaining control over your AI models and ensuring they perform as expected, providing you with the necessary oversight to manage your AI models effectively.

OpenAI’s rollout of customizable versions of ChatGPT, the forthcoming launch of the GPT Store, and the implementation of robust privacy and safety measures represent significant progress in the field of AI. These developments not only make AI more accessible but also ensure that it is used responsibly and ethically. By engaging the community in AI development, OpenAI is ensuring that AI technology is developed in a way that benefits all of humanity, reinforcing its commitment to the ethical and responsible use of AI. Other articles you may find of interest on the subject of OpenAI :

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OpenAI safety measures for advanced AI systems

OpenAI AI safety measures for advanced artificial intelligence systems

The creation of advanced artificial intelligence (AI) systems brings with it a host of opportunities, but also significant risks. In response to this, OpenAI has embarked on the development of AI safety measures and risk preparedness for advanced artificial intelligence systems. The aim is to ensure that as AI capabilities increase, the potential for catastrophic risks is mitigated, and the benefits of AI are maximized.

OpenAI’s approach to managing these risks is multi-faceted. The company is developing a comprehensive strategy to address the full spectrum of safety risks related to AI, from the concerns posed by current systems to the potential challenges of superintelligence. This strategy includes the creation of a Preparedness team and the initiation of a challenge to foster innovative solutions to AI safety issues.

OpenAI’s approach to catastrophic risk preparedness

“As part of our mission of building safe AGI, we take seriously the full spectrum of safety risks related to AI, from the systems we have today to the furthest reaches of superintelligence. In July, we joined other leading AI labs in making a set of voluntary commitments to promote safety, security and trust in AI. These commitments encompassed a range of risk areas, centrally including the frontier risks that are the focus of the UK AI Safety Summit. As part of our contributions to the Summit, we have detailed our progress on frontier AI safety, including work within the scope of our voluntary commitments.”

In July, OpenAI joined other leading AI labs in making voluntary commitments to promote safety, security, and trust in AI. This commitment focuses on frontier risks, which are the potential dangers posed by frontier AI models. These models, which exceed the capabilities of current AI systems, have tremendous potential to benefit humanity. However, they also pose significant risks, including the possibility of misuse by malicious actors.

To address these concerns, OpenAI is working diligently to answer key questions about the dangers of frontier AI systems. The company is developing a robust framework for monitoring and protection, and is exploring how stolen AI model weights might be misused. This work is crucial for ensuring that the benefits of frontier AI models can be realized, while the risks are effectively managed.

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Protecting against catastrophic risks

The Preparedness team, led by Aleksander Madry, is at the forefront of these efforts. This team is tasked with connecting capability assessment, evaluations, and internal red teaming for frontier models. Their work involves tracking, evaluating, forecasting, and protecting against catastrophic risks in multiple categories. This includes individual persuasion, cybersecurity, Chemical, Biological, Radiological, and Nuclear (CBRN) threats, and Adversarial Robustness and Assurance (ARA).

The Preparedness team is also responsible for developing and maintaining a Risk-Informed Development Policy (RDP). This policy details OpenAI’s approach to developing rigorous frontier model capability evaluations and monitoring. It outlines the steps for creating protective actions and establishing a governance structure for accountability and oversight. The RDP is designed to complement and extend existing risk mitigation work, contributing to the safety and alignment of new, highly capable systems, both before and after deployment.

The development of AI safety measures and risk preparedness for advanced artificial intelligence systems is a complex and ongoing process. It requires a deep understanding of the potential risks and a robust approach to mitigating them. OpenAI’s commitment to this work, as evidenced by the formation of the Preparedness team and the development of the RDP, demonstrates the company’s dedication to ensuring that AI is developed and used in a way that is safe, secure, and beneficial for all of humanity. It is a clear testament to the company’s commitment to promoting AI safety, security, and trust.

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The future of AI interview with OpenAI CEO Sam Altman

OpenAI CEO Sam Altman and CTO Mira Murati on the Future of AI 2023

The future of Artificial Intelligence (AI) and Artificial General Intelligence (AGI) has been a topic of hot debate, with experts from various fields offering their insights and predictions. Among the leading voices in this discussion are OpenAI CEO Sam Altman and CTO Mira Murati, who have shared their visions for the future of AI, its capabilities, applications, and the ethical considerations surrounding its use.

AI’s ability to mimic human traits like humor and emotion has been a fascinating development in recent years. OpenAI has been at the forefront of this, working on creating AI models that not only understand and generate text but can perceive the world in a similar way to humans, incorporating images and sounds. As these models become smarter, they are expected to require less training data, a shift that could revolutionize how AI is developed and applied.

OpenAI CEO Sam Altman and CTO Mira Murati

The potential for AI to replace jobs and tasks traditionally performed by humans is a concern that has been voiced by many. OpenAI acknowledges this, predicting that the future of work will be significantly impacted by AI. However, they also believe that while some jobs may change or disappear, new and better jobs will be created, and that people will continue to find satisfaction in work. This mirrors the historical trend of technology creating more jobs than it displaces, albeit in different sectors and requiring different skills.

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OpenAI is also focusing on the development of AGI, a form of AI that can generalize across many domains equivalent to human work. They predict that AGI, which they believe will arrive within the next decade, will significantly improve the human condition by providing abundant and inexpensive intelligence and energy. However, they also acknowledge that the definition of intelligence is evolving and will continue to do so as AI develops.

AI training

Data plays a crucial role in training AI models. OpenAI is considering the use of data that people are comfortable with and exploring partnerships to achieve this. They are also working on personalizing AI models, allowing them to learn user preferences and provide more tailored responses. This level of personalization could transform the way we interact with technology, making it more intuitive and user-friendly.

Ethical considerations

The ethical considerations and potential societal impact of AI are issues that OpenAI takes seriously. As AI systems increase in capability, they are working on improving their reliability and safety, with a focus on reducing the “hallucination issue” where AI systems generate false or misleading information. OpenAI also emphasizes the importance of users knowing when they are interacting with AI, an essential aspect of transparency and accountability in AI.

Responsibility

The company acknowledges the responsibility they have in shaping the future of AI and believe that decisions about AI should be made by society as a whole. This underscores the importance of public engagement and accessibility in AI development, a principle that OpenAI is committed to upholding.

The future of AI

Looking ahead, OpenAI suggests that a new computing platform may be needed to fully utilize the potential of AI. While they did not provide specific details, this indicates that the technological infrastructure for AI could undergo significant changes in the future. To meet the scale of demand they anticipate for their AI technology, OpenAI is considering making its own chips, but currently, they have strong partnerships with other companies.

The future of AI and AGI is a complex and multifaceted issue, with numerous potential benefits and challenges. Through their work, OpenAI CEO Sam Altman and CTO Mira Murati are striving to ensure that advancements in AI are made responsibly and ethically, with the aim of improving the human condition and empowering individuals to influence the future as AI technology advances. Their insights provide a valuable perspective on the potential of AI and AGI, the impact on society and the workforce, and the importance of ethical considerations and safety measures.

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OpenAI ChatGPT API rate limits explained

Understanding ChatGPT API rate limits from OpenAI

If you are creating programs and applications linked to OpenAI’s services such as ChatGPT it is important that you understand the rate limits which have been set for your particular AI model and how you can increase them if needed as well as the costs involved. Understanding the intricacies of an API’s rate limits is crucial for developers, businesses, and organizations that rely on that service for their operations. One such API is the ChatGPT API, which has its own set of rate limits that users must adhere to. This article will delve into the specifics of the ChatGPT API rate limits and explain why they are in place.

What are API rate limits?

Rate limits, in essence, are restrictions that an API imposes on the number of times a user or client can access the server within a specific period. They are common practice in the world of APIs and are implemented for several reasons. Firstly, rate limits help protect against abuse or misuse of the API. They act as a safeguard against malicious actors who might flood the API with requests in an attempt to overload it or disrupt its service. By setting rate limits, OpenAI can prevent such activities.

Secondly, rate limits ensure that everyone has fair access to the API. If one user or organization makes an excessive number of requests, it can slow down the API for everyone else. By controlling the number of requests a single user can make, OpenAI ensures that the maximum number of people can use the API without experiencing slowdowns.

Understanding OpenAI ChatGPT API rate limits

Rate limits help OpenAI manage the aggregate load on its infrastructure. A sudden surge in API requests could stress the servers and cause performance issues. By setting rate limits, OpenAI can maintain a smooth and consistent experience for all users.

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The ChatGPT API rate limits are enforced at the organization level, not the user level, and they depend on the specific endpoint used and the type of account. They are measured in three ways: RPM (requests per minute), RPD (requests per day), and TPM (tokens per minute). A user can hit the rate limit by any of these three options depending on what occurs first.

For instance, if a user sends 20 requests with only 100 tokens to the Completions endpoint and their RPM limit is 20, they will hit the limit, even if they did not send 150k tokens within those 20 requests. OpenAI automatically adjusts the rate limit and spending limit (quota) based on several factors. As a user’s usage of the OpenAI API increases and they successfully pay the bill, their usage tier is automatically increased.

For example, the first three usage tiers are as follows:

  • Free Tier: The user must be in an allowed geography. They have a maximum credit of $100 and request limits of 3 RPM and 200 RPD. The token limit is 20K TPM for GPT-3.5 and 4K TPM for GPT-4.
  • Tier 1: The user must have paid $5. They have a maximum credit of $100 and request limits of 500 RPM and 10K RPD. The token limit is 40K TPM for GPT-3.5 and 10K TPM for GPT-4.
  • Tier 2: The user must have paid $50 and it must be 7+ days since their first successful payment. They have a maximum credit of $250 and a request limit of 5000 RPM. The token limit is 80K TPM for GPT-3.5 and 20K TPM for GPT-4.

In practice, if a user’s rate limit is 60 requests per minute and 150k tokens per minute, they’ll be limited either by reaching the requests/min cap or running out of tokens—whichever happens first. For instance, if their max requests/min is 60, they should be able to send 1 request per second. If they send 1 request every 800ms, once they hit the rate limit, they’d only need to make their program sleep 200ms in order to send one more request. Otherwise, subsequent requests would fail.

Understanding and adhering to the ChatGPT API rate limits is crucial for the smooth operation of any application or service that relies on it. The limits are in place to prevent misuse, ensure fair access, and manage the load on the infrastructure, thus ensuring a consistent and efficient experience for all users.

OpenAI enforces rate limits on the requests you can make to the API. These are applied over tokens-per-minute, requests-per-minute (in some cases requests-per-day), or in the case of image models, images-per-minute.

Increasing rate limits

OpenAI explains a little more about its API rate limits and when you should consider applying for an increase if needed:

“Our default rate limits help us maximize stability and prevent abuse of our API. We increase limits to enable high-traffic applications, so the best time to apply for a rate limit increase is when you feel that you have the necessary traffic data to support a strong case for increasing the rate limit. Large rate limit increase requests without supporting data are not likely to be approved. If you’re gearing up for a product launch, please obtain the relevant data through a phased release over 10 days.”

For more information on the OpenAI rate limits for its services such as ChatGPT jump over to the official guide documents website for more information and figures.

How to manage API rate limits :

  • Understanding the Limits – Firstly, you need to understand the specifics of the rate limits imposed by the ChatGPT API. Usually, there are different types of limits such as per-minute, per-hour, and per-day limits, as well as concurrency limits.
  • Caching Results – For frequently repeated queries, consider caching the results locally. This will reduce the number of API calls you need to make and can improve the responsiveness of your application.
  • Rate-Limiting Libraries – There are rate-limiting libraries and modules available in various programming languages that can help you manage API rate limits. They can automatically throttle your requests to ensure you stay within the limit.
  • Queuing Mechanism – Implementing a queuing mechanism can help you handle bursts of traffic efficiently. This ensures that excess requests are put in a queue and processed when the rate limit allows for it.
  • Monitoring and Alerts – Keep an eye on your API usage statistics, and set up alerts for when you are nearing the limit. This can help you take timely action, either by upgrading your plan or optimizing your usage.
  • Graceful Degradation – Design your system to degrade gracefully in case you hit the API rate limit. This could mean showing a user-friendly error message or falling back to a less optimal operation mode.
  • Load Balancing – If you have multiple API keys or accounts, you can distribute the load among them to maximize your allowed requests.
  • Business Considerations – Sometimes, it might be more cost-effective to upgrade to a higher tier of the API that allows for more requests, rather than spending engineering resources to micro-optimize API usage.

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