Deck for session entitled "Unleashing the Power of Generative AI: Python API Integration with ChatGPT, DALL-E, and D-ID Studio" presented at PyCon Ireland Conference on November 11th 2023
OpenAI’s GPT 3 Language Model - guest Steve OmohundroNumenta
In this research meeting, guest Stephen Omohundro gave a fascinating talk on GPT-3, the new massive OpenAI Natural Language Processing model. He reviewed the network architecture, training process, and results in the context of past work. There was extensive discussion on the implications for NLP and for Machine Intelligence / AGI.
Link to GPT-3 paper: https://arxiv.org/abs/2005.14165
Link to YouTube recording of Steve's talk: https://youtu.be/0ZVOmBp29E0
Artificial Intelligence has unleashed a wave of innovation, from effortlessly summarizing
articles to engaging in deep, thought-provoking conversations — with large language
models taking on the primary workload.
Enter the extraordinary realm of large language models (LLMs), the brainchild of deep
learning algorithms. These powerhouses not only decipher and grasp massive amounts
of data but also possess the uncanny ability to recognize, summarize, translate, predict,
and even generate a diverse range of textual and coding content.
Retrieval Augmented Generation in Practice: Scalable GenAI platforms with k8s...Mihai Criveti
Mihai is the Principal Architect for Platform Engineering and Technology Solutions at IBM, responsible for Cloud Native and AI Solutions. He is a Red Hat Certified Architect, CKA/CKS, a leader in the IBM Open Innovation community, and advocate for open source development. Mihai is driving the development of Retrieval Augmentation Generation platforms, and solutions for Generative AI at IBM that leverage WatsonX, Vector databases, LangChain, HuggingFace and open source AI models.
Mihai will share lessons learned building Retrieval Augmented Generation, or “Chat with Documents” platforms and APIs that scale, and deploy on Kubernetes. His talk will cover use cases for Generative AI, limitations of Large Language Models, use of RAG, Vector Databases and Fine Tuning to overcome model limitations and build solutions that connect to your data and provide content grounding, limit hallucinations and form the basis of explainable AI. In terms of technology, he will cover LLAMA2, HuggingFace TGIS, SentenceTransformers embedding models using Python, LangChain, and Weaviate and ChromaDB vector databases. He’ll also share tips on writing code using LLM, including building an agent for Ansible and containers.
Scaling factors for Large Language Model Architectures:
• Vector Database: consider sharding and High Availability
• Fine Tuning: collecting data to be used for fine tuning
• Governance and Model Benchmarking: how are you testing your model performance
over time, with different prompts, one-shot, and various parameters
• Chain of Reasoning and Agents
• Caching embeddings and responses
• Personalization and Conversational Memory Database
• Streaming Responses and optimizing performance. A fine tuned 13B model may
perform better than a poor 70B one!
• Calling 3rd party functions or APIs for reasoning or other type of data (ex: LLMs are
terrible at reasoning and prediction, consider calling other models)
• Fallback techniques: fallback to a different model, or default answers
• API scaling techniques, rate limiting, etc.
• Async, streaming and parallelization, multiprocessing, GPU acceleration (including
embeddings), generating your API using OpenAPI, etc.
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
The GPT-3 model architecture is a transformer-based neural network that has been fed 45TB of text data. It is non-deterministic, in the sense that given the same input, multiple runs of the engine will return different responses. Also, it is trained on massive datasets that covered the entire web and contained 500B tokens, humongous 175 Billion parameters, a more than 100x increase over GPT-2, which was considered state-of-the-art technology with 1.5 billion parameters.
OpenAI’s GPT 3 Language Model - guest Steve OmohundroNumenta
In this research meeting, guest Stephen Omohundro gave a fascinating talk on GPT-3, the new massive OpenAI Natural Language Processing model. He reviewed the network architecture, training process, and results in the context of past work. There was extensive discussion on the implications for NLP and for Machine Intelligence / AGI.
Link to GPT-3 paper: https://arxiv.org/abs/2005.14165
Link to YouTube recording of Steve's talk: https://youtu.be/0ZVOmBp29E0
Artificial Intelligence has unleashed a wave of innovation, from effortlessly summarizing
articles to engaging in deep, thought-provoking conversations — with large language
models taking on the primary workload.
Enter the extraordinary realm of large language models (LLMs), the brainchild of deep
learning algorithms. These powerhouses not only decipher and grasp massive amounts
of data but also possess the uncanny ability to recognize, summarize, translate, predict,
and even generate a diverse range of textual and coding content.
Retrieval Augmented Generation in Practice: Scalable GenAI platforms with k8s...Mihai Criveti
Mihai is the Principal Architect for Platform Engineering and Technology Solutions at IBM, responsible for Cloud Native and AI Solutions. He is a Red Hat Certified Architect, CKA/CKS, a leader in the IBM Open Innovation community, and advocate for open source development. Mihai is driving the development of Retrieval Augmentation Generation platforms, and solutions for Generative AI at IBM that leverage WatsonX, Vector databases, LangChain, HuggingFace and open source AI models.
Mihai will share lessons learned building Retrieval Augmented Generation, or “Chat with Documents” platforms and APIs that scale, and deploy on Kubernetes. His talk will cover use cases for Generative AI, limitations of Large Language Models, use of RAG, Vector Databases and Fine Tuning to overcome model limitations and build solutions that connect to your data and provide content grounding, limit hallucinations and form the basis of explainable AI. In terms of technology, he will cover LLAMA2, HuggingFace TGIS, SentenceTransformers embedding models using Python, LangChain, and Weaviate and ChromaDB vector databases. He’ll also share tips on writing code using LLM, including building an agent for Ansible and containers.
Scaling factors for Large Language Model Architectures:
• Vector Database: consider sharding and High Availability
• Fine Tuning: collecting data to be used for fine tuning
• Governance and Model Benchmarking: how are you testing your model performance
over time, with different prompts, one-shot, and various parameters
• Chain of Reasoning and Agents
• Caching embeddings and responses
• Personalization and Conversational Memory Database
• Streaming Responses and optimizing performance. A fine tuned 13B model may
perform better than a poor 70B one!
• Calling 3rd party functions or APIs for reasoning or other type of data (ex: LLMs are
terrible at reasoning and prediction, consider calling other models)
• Fallback techniques: fallback to a different model, or default answers
• API scaling techniques, rate limiting, etc.
• Async, streaming and parallelization, multiprocessing, GPU acceleration (including
embeddings), generating your API using OpenAPI, etc.
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
The GPT-3 model architecture is a transformer-based neural network that has been fed 45TB of text data. It is non-deterministic, in the sense that given the same input, multiple runs of the engine will return different responses. Also, it is trained on massive datasets that covered the entire web and contained 500B tokens, humongous 175 Billion parameters, a more than 100x increase over GPT-2, which was considered state-of-the-art technology with 1.5 billion parameters.
This is an article about Generative AI. It discusses what it is and the different techniques used to create it. It also goes into the potential uses of Generative AI. Some of the important points from this article are that Generative AI is still in its early stages but has already shown promising results. It is also important to note that Generative AI can be used to create fake data that is indistinguishable from real data.
https://www.ltimindtree.com/wp-content/uploads/2023/01/DeepPoV-Generative-AI.pdf
What Is GPT-3 And Why Is It Revolutionizing Artificial Intelligence?Bernard Marr
Could GPT-3 be the most powerful artificial intelligence ever developed? When OpenAI, a research business co-founded by Elson Musk, released the tool recently, it created a massive amount of hype. Here we look through the hype and outline what it is and what it isn’t.
How Azure helps to build better business processes and customer experiences w...Maxim Salnikov
Artificial Intelligence is not the future, it is NOW. Cloud technology empowers developers and technology leaders to benefit from AI effectively and responsibly with the models and tools they need. In this session, we go through the portfolio of Azure AI services and run some demos to showcase how AI can improve daily life, safety, productivity, accessibility, and business outcomes.
Episode 2: The LLM / GPT / AI Prompt / Data Engineer RoadmapAnant Corporation
In this episode we'll discuss the different flavors of prompt engineering in the LLM/GPT space. According to your skill level you should be able to pick up at any of the following:
Leveling up with GPT
1: Use ChatGPT / GPT Powered Apps
2: Become a Prompt Engineer on ChatGPT/GPT
3: Use GPT API with NoCode Automation, App Builders
4: Create Workflows to Automate Tasks with NoCode
5: Use GPT API with Code, make your own APIs
6: Create Workflows to Automate Tasks with Code
7: Use GPT API with your Data / a Framework
8: Use GPT API with your Data / a Framework to Make your own APIs
9: Create Workflows to Automate Tasks with your Data /a Framework
10: Use Another LLM API other than GPT (Cohere, HuggingFace)
11: Use open source LLM models on your computer
12: Finetune / Build your own models
Series: Using AI / ChatGPT at Work - GPT Automation
Are you a small business owner or web developer interested in leveraging the power of GPT (Generative Pretrained Transformer) technology to enhance your business processes?
If so, Join us for a series of events focused on using GPT in business. Whether you're a small business owner or a web developer, you'll learn how to leverage GPT to improve your workflow and provide better services to your customers.
For many decades now, the software industry has attempted to bridge the productivity gap, develop higher quality code and manage the ever growing complexity of software-intensive systems. The results have been mixed, and as a result, a great majority of today's software is still written manually by human developers. This is about to change rapidly as recent developments in the field of Artificial Intelligence show promising results. While artists and designers have been taken by surprise by OpenAI’s DALL-E 2’s capabilities in designing unique art, ChatGPT has astonished the rest of the world with its capability of understanding human interaction. AI-assisted coding solutions such as Github’s Copilot and Replit’s Ghostwriter, among many others, are rapidly developing in a direction where AI generates new code that runs fast with high quality. Little is known about the true capabilities of AI programmers and their impact on the software development industry, education, and research. This talk sheds light on the current state of ChatGPT, large language models including GPT-4, AI-assisted coding, highlights the research gaps, and proposes a way forward.
ChatGPT (Chat Generative pre-defined transformer) is OpenAI's application that performs human like interactions. GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor. Deck contains more details about ChatGPT, AI, AGI, CoPilot, OpenAI API, and use case scenarios.
Details regarding the working of chatgpt and basic use cases can be found in this presentation. The presentation also contains details regarding other Open AI products and their useability. You can also find ways in which chatgpt can be implemented in existing App and websites.
LangChain Intro, Keymate.AI Search Plugin for ChatGPT, How to use langchain library? How to implement similar functionality in programming language of your choice? Example LangChain applications.
The presentation revolves around the concept of "langChain", This innovative framework is designed to "chain" together different components to create more advanced use cases around Large Language Models (LLMs). The idea is to leverage the power of LLMs to tackle complex problems and generate solutions that are more than the sum of their parts.
One of the key features of the presentation is the application of the "Keymate.AI Search" plugin in conjunction with the Reasoning and Acting Chain of Thought (ReAct) framework. The presenter encourages the audience to utilize these tools to generate reasoning traces and actions. The ReAct framework, learned from an initial search, is then applied to these traces and actions, demonstrating the potential of LLMs to learn and apply complex frameworks.
The presentation also delves into the impact of climate change on biodiversity. The presenter prompts the audience to look up the latest research on this topic and summarize the key findings. This exercise not only highlights the importance of climate change but also demonstrates the capabilities of LLMs in researching and summarizing complex topics.
The presentation concludes with several key takeaways. The presenter emphasizes that specialized custom solutions work best and suggests a bottom-up approach to expert systems. However, they caution that over-abstraction can lead to leakages, causing time and money limits to hit early and tasks to fail or require many iterations. The presenter also notes that while prompt engineering is important, it's not necessary to over-optimize if the LLM is clever. The presentation ends on a hopeful note, expressing a need for more clever LLMs and acknowledging that good applications are rare but achievable.
Overall, the presentation provides a comprehensive overview of the LanGCHAIN framework, its applications, and the potential of LLMs in solving complex problems. It serves as a call to action for the audience to explore these tools and frameworks.
This session was presented at the AWS Community Day in Munich (September 2023). It's for builders that heard the buzz about Generative AI but can’t quite grok it yet. Useful if you are eager to connect the dots on the Generative AI terminology and get a fast start for you to explore further and navigate the space. This session is largely product agnostic and meant to give you the fundamentals to get started.
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
This session is based on my research and experiments in Prompt Engineering and is 100% relevant for cloud developers who investigate adding some LLM-powered features to their solutions. It's a guide to building proper prompts for AI to get desired results fast and cost-efficient.
This report offers an in-depth exploration of the application and potential of ChatGPT, a sophisticated AI conversational model developed by OpenAI. With over 100 practical examples of prompts, we aim to demonstrate the breadth of the model's capacity and its utility across diverse fields and industries, such as education, customer service, research, entertainment, and more.
Introduction:
ChatGPT is a highly advanced machine learning model that utilizes a transformer architecture for generating human-like text based on given prompts. It's part of OpenAI's GPT (Generative Pretrained Transformer) series, and as of our knowledge cutoff in 2021, its latest version is GPT-4. It has proven to be a transformative tool for various applications, such as drafting emails, writing code, creating content, answering queries, tutoring in various subjects, translating languages, simulating characters for video games, and more.
Chapter 1: Understanding ChatGPT
In this chapter, we delve into the basics of ChatGPT, starting with its origins and development. We touch on the model's architecture, including its use of attention mechanisms and transformer models, its training process using reinforcement learning from human feedback, and how it generates responses.
Here, we explore some of the myriad applications of ChatGPT across multiple sectors. We discuss how it's revolutionizing customer service by providing 24/7 support, aiding in education by personalizing learning, assisting researchers with literature reviews, and even creating dialogue for video games. Real-world examples and case studies are included to illustrate these applications.
This chapter serves as a comprehensive guide for utilizing ChatGPT effectively. We provide over 100 prompt examples spanning various fields, like marketing, healthcare, entertainment, etc. These prompts range from simple inquiries to complex, layered questions, giving readers a thorough understanding of how to harness the full potential of ChatGPT.
While the potential of ChatGPT is unquestionable, it's crucial to address the ethical implications of its use. This chapter delves into areas such as data privacy, the risk of misuse, and the importance of transparency. We also contemplate the future directions of AI conversation models like ChatGPT, discussing the potential for even more nuanced understanding and response generation.
In our concluding remarks, we reflect on the transformative potential of ChatGPT and similar AI models. We emphasize the model's ability to democratize access to information, offer personalized learning and support, and the broader implications for society.
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
Build an LLM-powered application using LangChain.pdfAnastasiaSteele10
LangChain is an advanced framework that allows developers to create language model-powered applications. It provides a set of tools, components, and interfaces that make building LLM-based applications easier. With LangChain, managing interactions with language models, chaining together various components, and integrating resources like APIs and databases is a breeze. The platform includes a set of APIs that can be integrated into applications, allowing developers to add language processing capabilities without having to start from scratch.
Neural Language Generation Head to Toe Hady Elsahar
This is a gentle introduction to Natural language Generation (NLG) using deep learning. If you are a computer science practitioner with basic knowledge about Machine learning. This is a gentle intuitive introduction to Language Generation using Neural Networks. It takes you in a journey from the basic intuitions behind modeling language and how to model probabilities of sequences to recurrent neural networks to large Transformers models that you have seen in the news like GPT2/GPT3. The tutorial wraps up with a summary on the ethical implications of training such large language models on uncurated text from the internet.
Unleashing the Power of Generative AI.pdfeoinhalpin99
Slide deck for session named "Unleashing the Power of Generative AI: Python API Integration with ChatGPT, DALL-E, and D-ID Studio" that was presented on Nov 11th at the PyCon Ireland 2023 Conference.
This is an article about Generative AI. It discusses what it is and the different techniques used to create it. It also goes into the potential uses of Generative AI. Some of the important points from this article are that Generative AI is still in its early stages but has already shown promising results. It is also important to note that Generative AI can be used to create fake data that is indistinguishable from real data.
https://www.ltimindtree.com/wp-content/uploads/2023/01/DeepPoV-Generative-AI.pdf
What Is GPT-3 And Why Is It Revolutionizing Artificial Intelligence?Bernard Marr
Could GPT-3 be the most powerful artificial intelligence ever developed? When OpenAI, a research business co-founded by Elson Musk, released the tool recently, it created a massive amount of hype. Here we look through the hype and outline what it is and what it isn’t.
How Azure helps to build better business processes and customer experiences w...Maxim Salnikov
Artificial Intelligence is not the future, it is NOW. Cloud technology empowers developers and technology leaders to benefit from AI effectively and responsibly with the models and tools they need. In this session, we go through the portfolio of Azure AI services and run some demos to showcase how AI can improve daily life, safety, productivity, accessibility, and business outcomes.
Episode 2: The LLM / GPT / AI Prompt / Data Engineer RoadmapAnant Corporation
In this episode we'll discuss the different flavors of prompt engineering in the LLM/GPT space. According to your skill level you should be able to pick up at any of the following:
Leveling up with GPT
1: Use ChatGPT / GPT Powered Apps
2: Become a Prompt Engineer on ChatGPT/GPT
3: Use GPT API with NoCode Automation, App Builders
4: Create Workflows to Automate Tasks with NoCode
5: Use GPT API with Code, make your own APIs
6: Create Workflows to Automate Tasks with Code
7: Use GPT API with your Data / a Framework
8: Use GPT API with your Data / a Framework to Make your own APIs
9: Create Workflows to Automate Tasks with your Data /a Framework
10: Use Another LLM API other than GPT (Cohere, HuggingFace)
11: Use open source LLM models on your computer
12: Finetune / Build your own models
Series: Using AI / ChatGPT at Work - GPT Automation
Are you a small business owner or web developer interested in leveraging the power of GPT (Generative Pretrained Transformer) technology to enhance your business processes?
If so, Join us for a series of events focused on using GPT in business. Whether you're a small business owner or a web developer, you'll learn how to leverage GPT to improve your workflow and provide better services to your customers.
For many decades now, the software industry has attempted to bridge the productivity gap, develop higher quality code and manage the ever growing complexity of software-intensive systems. The results have been mixed, and as a result, a great majority of today's software is still written manually by human developers. This is about to change rapidly as recent developments in the field of Artificial Intelligence show promising results. While artists and designers have been taken by surprise by OpenAI’s DALL-E 2’s capabilities in designing unique art, ChatGPT has astonished the rest of the world with its capability of understanding human interaction. AI-assisted coding solutions such as Github’s Copilot and Replit’s Ghostwriter, among many others, are rapidly developing in a direction where AI generates new code that runs fast with high quality. Little is known about the true capabilities of AI programmers and their impact on the software development industry, education, and research. This talk sheds light on the current state of ChatGPT, large language models including GPT-4, AI-assisted coding, highlights the research gaps, and proposes a way forward.
ChatGPT (Chat Generative pre-defined transformer) is OpenAI's application that performs human like interactions. GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor. Deck contains more details about ChatGPT, AI, AGI, CoPilot, OpenAI API, and use case scenarios.
Details regarding the working of chatgpt and basic use cases can be found in this presentation. The presentation also contains details regarding other Open AI products and their useability. You can also find ways in which chatgpt can be implemented in existing App and websites.
LangChain Intro, Keymate.AI Search Plugin for ChatGPT, How to use langchain library? How to implement similar functionality in programming language of your choice? Example LangChain applications.
The presentation revolves around the concept of "langChain", This innovative framework is designed to "chain" together different components to create more advanced use cases around Large Language Models (LLMs). The idea is to leverage the power of LLMs to tackle complex problems and generate solutions that are more than the sum of their parts.
One of the key features of the presentation is the application of the "Keymate.AI Search" plugin in conjunction with the Reasoning and Acting Chain of Thought (ReAct) framework. The presenter encourages the audience to utilize these tools to generate reasoning traces and actions. The ReAct framework, learned from an initial search, is then applied to these traces and actions, demonstrating the potential of LLMs to learn and apply complex frameworks.
The presentation also delves into the impact of climate change on biodiversity. The presenter prompts the audience to look up the latest research on this topic and summarize the key findings. This exercise not only highlights the importance of climate change but also demonstrates the capabilities of LLMs in researching and summarizing complex topics.
The presentation concludes with several key takeaways. The presenter emphasizes that specialized custom solutions work best and suggests a bottom-up approach to expert systems. However, they caution that over-abstraction can lead to leakages, causing time and money limits to hit early and tasks to fail or require many iterations. The presenter also notes that while prompt engineering is important, it's not necessary to over-optimize if the LLM is clever. The presentation ends on a hopeful note, expressing a need for more clever LLMs and acknowledging that good applications are rare but achievable.
Overall, the presentation provides a comprehensive overview of the LanGCHAIN framework, its applications, and the potential of LLMs in solving complex problems. It serves as a call to action for the audience to explore these tools and frameworks.
This session was presented at the AWS Community Day in Munich (September 2023). It's for builders that heard the buzz about Generative AI but can’t quite grok it yet. Useful if you are eager to connect the dots on the Generative AI terminology and get a fast start for you to explore further and navigate the space. This session is largely product agnostic and meant to give you the fundamentals to get started.
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
This session is based on my research and experiments in Prompt Engineering and is 100% relevant for cloud developers who investigate adding some LLM-powered features to their solutions. It's a guide to building proper prompts for AI to get desired results fast and cost-efficient.
This report offers an in-depth exploration of the application and potential of ChatGPT, a sophisticated AI conversational model developed by OpenAI. With over 100 practical examples of prompts, we aim to demonstrate the breadth of the model's capacity and its utility across diverse fields and industries, such as education, customer service, research, entertainment, and more.
Introduction:
ChatGPT is a highly advanced machine learning model that utilizes a transformer architecture for generating human-like text based on given prompts. It's part of OpenAI's GPT (Generative Pretrained Transformer) series, and as of our knowledge cutoff in 2021, its latest version is GPT-4. It has proven to be a transformative tool for various applications, such as drafting emails, writing code, creating content, answering queries, tutoring in various subjects, translating languages, simulating characters for video games, and more.
Chapter 1: Understanding ChatGPT
In this chapter, we delve into the basics of ChatGPT, starting with its origins and development. We touch on the model's architecture, including its use of attention mechanisms and transformer models, its training process using reinforcement learning from human feedback, and how it generates responses.
Here, we explore some of the myriad applications of ChatGPT across multiple sectors. We discuss how it's revolutionizing customer service by providing 24/7 support, aiding in education by personalizing learning, assisting researchers with literature reviews, and even creating dialogue for video games. Real-world examples and case studies are included to illustrate these applications.
This chapter serves as a comprehensive guide for utilizing ChatGPT effectively. We provide over 100 prompt examples spanning various fields, like marketing, healthcare, entertainment, etc. These prompts range from simple inquiries to complex, layered questions, giving readers a thorough understanding of how to harness the full potential of ChatGPT.
While the potential of ChatGPT is unquestionable, it's crucial to address the ethical implications of its use. This chapter delves into areas such as data privacy, the risk of misuse, and the importance of transparency. We also contemplate the future directions of AI conversation models like ChatGPT, discussing the potential for even more nuanced understanding and response generation.
In our concluding remarks, we reflect on the transformative potential of ChatGPT and similar AI models. We emphasize the model's ability to democratize access to information, offer personalized learning and support, and the broader implications for society.
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
Build an LLM-powered application using LangChain.pdfAnastasiaSteele10
LangChain is an advanced framework that allows developers to create language model-powered applications. It provides a set of tools, components, and interfaces that make building LLM-based applications easier. With LangChain, managing interactions with language models, chaining together various components, and integrating resources like APIs and databases is a breeze. The platform includes a set of APIs that can be integrated into applications, allowing developers to add language processing capabilities without having to start from scratch.
Neural Language Generation Head to Toe Hady Elsahar
This is a gentle introduction to Natural language Generation (NLG) using deep learning. If you are a computer science practitioner with basic knowledge about Machine learning. This is a gentle intuitive introduction to Language Generation using Neural Networks. It takes you in a journey from the basic intuitions behind modeling language and how to model probabilities of sequences to recurrent neural networks to large Transformers models that you have seen in the news like GPT2/GPT3. The tutorial wraps up with a summary on the ethical implications of training such large language models on uncurated text from the internet.
Unleashing the Power of Generative AI.pdfeoinhalpin99
Slide deck for session named "Unleashing the Power of Generative AI: Python API Integration with ChatGPT, DALL-E, and D-ID Studio" that was presented on Nov 11th at the PyCon Ireland 2023 Conference.
Building a generative AI solution involves defining the problem, collecting and processing data, selecting suitable models, training and fine-tuning them, and deploying the system effectively. It’s essential to gather high-quality data, choose appropriate algorithms, ensure security, and stay updated with advancements.
With the evolution of no-code AI, sectors such as web development are advancing while others are just emerging. Now, with these no-code AI platforms, businesses have a chance to explore the technology without needing to hire tech experts or adopting expensive strategies.
NO-CODE PLATFORMS HAVE MADE IT EASY TO CREATE PROGRAMS THAT USE ADVANCED TECHNOLOGIES. THE INTRODUCTION OF THESE PLATFORMS HAS RESULTED IN AN INCREASING NUMBER OF BUSINESSES ATTEMPTING TO USE THEIR CAPACITY TO BUILD AI SOLUTIONS.
With this, visual drag-and-drop tools come into the picture, aiding data scientists in filling the void and making AI less daunting for people with non-technical backgrounds.
This article discusses the top no-code platforms for building AI solutions.
MonkeyLearn
MonkeyLearn is an all-in-one text analysis and data visualization studio that can be used to extract topic, sentiment, intent, keywords, and other information from unstructured text-based data. Automatically tagging business data, presenting actionable insights and trends, and simplifying text classification and extraction processes are just a few of the features. It integrates with Zendesk, RapidMinder, and Google products, with a few others on the way. Also, it is one of the best blog resources for text analysis.
RunwayML
RunwayML is a tool for creators that focuses on creative work that involves dealing with pictures, videos, text, latent spaces, and segmentation masks, as well as motion capture, backdrop removal, and style transfer. They have a Generative Engine, which is a storytelling machine that generates visuals automatically as you write.
Finally
Businesses are increasingly turning to no-code platforms for a variety of reasons. Access to developers and software engineers slows project delivery, partly owing to the ripple impact on workforce management, and this is where technology can help. The unicorn we all want to catch is not only enabling your team to create solutions but also being relevant and competitive in the present context.
For more such updates and perspectives around Digital Innovation, IoT, Data
Infrastructure, AI & Cybersecurity, go to AI-Techpark.com.
Generative AI models are transforming various fields by creating realistic images, text, music, and videos. This guide will take you through the essential steps and considerations for building a generative AI model, providing a comprehensive understanding of the process.
Mohamed Amrith Project and ContributionsMuslimVoice3
I am an experienced Artificial Intelligence and Natural Language Processing professional, skilled in developing and implementing algorithms and systems.
leewayhertz.com-How to build a generative AI solution From prototyping to pro...KristiLBurns
Generative AI has gained significant attention in the tech industry, with investors, policymakers, and the society at large talking about innovative AI models like ChatGPT and Stable Diffusion.Generative AI has gained significant attention in the tech industry, with investors, policymakers, and the society at large talking about innovative AI models like ChatGPT and Stable Diffusion.
Bhadale group of companies projects portfolio - This is a list of public shareable projects for the past 10 years.Technologies used are AI / ML, Scala , Spark, Akka, Play, IoT, Hadoop, React, Javascript and several other related ones
Gen Apps on Google Cloud PaLM2 and Codey APIs in ActionMárton Kodok
Build applications with generative AI on Google Cloud! We are going to see in action what Gen App Builder is for developers to build and deploy AI-driven applications. We will explore Model Garden powered experiences, then we are going to learn more about the integration of these generative AI APIs. Vertex AI includes a suite of models that work with code. Together these code models are referred to as the PaLM and Codey APIs. The Vertex AI Codey APIs include the code generation API which supports generating code using a natural language description. We will show strategies for creating prompts that work with the model to generate code. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative AI industry trends.
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...DianaGray10
Continuous accuracy and efficiency of Large Language Models (LLM) is key to successfully building out your next AI-infused automation, regardless of business use case.
For our next Connector Corner webinar, we’ll explore how using a seamless AI integration process provides access to industry leading models, curated activities, and embeddings that help achieve operational efficiency.
Join us on March 26 to learn about:
Accessing large language models, hosted by UiPath
Reducing complexities of prompt-engineering, by using curated sets of activities
Assuring accuracy and safety, by building an AI Trust Layer to moderate the output of AI models, and their generated results.
Discovering what’s new in embeddings connectivity
Cultivating your AI knowledgebase using Vector Databases
Expect to see these use cases in action:
Leveraging UiPath hosted LLMs and activities
Document comparison using our LLM framework
Please stay tuned for additional use cases
Speakers:
Charlie Greenberg, host
George Roth, Technology Evangelist
Scott Schoenberger, Senior Product Manager
Koji Takimoto, Director Product Support
This presentation describes some of the Open Source Ai projects we are working at the Center for Open Source, Data and AI Technologies (CODAIT), including Model Asset Exchange (MAX), Fabric for Deep Learning (FfDL) and Jupyter Enterprise Gateway.
Profile Summary
14 years of Total Experience in Python Development
10 Years in Leading Teams, Scrum Master and Management
8 Years of experience as Solution Architect in multiple projects.
Open source Contributor in Python Software Foundation
Research & Development, Proof of Concepts, SDLC process
Gathering information from Clients directly and Reporting
Agile Methodology and Cloud Technology SME
Corporate Trainer for Python, Flask and Agile
Conducting Interviews for Python, Linux, C++
Domain Exposure: Banking, Finance, Digital, Network Security, Energy, CFD,
HPSA, Server Automation
The ability to keep up with the current digital revolution and ensure business continuity is dependent on the skills of Flutter App Development Services. When it comes to Flutter app development tools, businesses have many options. Additionally more agile than past methods, this one makes it simpler for engineers to write code. Google's backing is likely to directly cause Flutter's popularity to soar. To accomplish this successfully, you will require numerous extra development tools from other sources.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Your Digital Assistant.
Making complex approach simple. Straightforward process saves time. No more waiting to connect with people that matter to you. Safety first is not a cliché - Securely protect information in cloud storage to prevent any third party from accessing data.
Would you rather make your visitors feel burdened by making them wait? Or choose VizMan for a stress-free experience? VizMan is an automated visitor management system that works for any industries not limited to factories, societies, government institutes, and warehouses. A new age contactless way of logging information of visitors, employees, packages, and vehicles. VizMan is a digital logbook so it deters unnecessary use of paper or space since there is no requirement of bundles of registers that is left to collect dust in a corner of a room. Visitor’s essential details, helps in scheduling meetings for visitors and employees, and assists in supervising the attendance of the employees. With VizMan, visitors don’t need to wait for hours in long queues. VizMan handles visitors with the value they deserve because we know time is important to you.
Feasible Features
One Subscription, Four Modules – Admin, Employee, Receptionist, and Gatekeeper ensures confidentiality and prevents data from being manipulated
User Friendly – can be easily used on Android, iOS, and Web Interface
Multiple Accessibility – Log in through any device from any place at any time
One app for all industries – a Visitor Management System that works for any organisation.
Stress-free Sign-up
Visitor is registered and checked-in by the Receptionist
Host gets a notification, where they opt to Approve the meeting
Host notifies the Receptionist of the end of the meeting
Visitor is checked-out by the Receptionist
Host enters notes and remarks of the meeting
Customizable Components
Scheduling Meetings – Host can invite visitors for meetings and also approve, reject and reschedule meetings
Single/Bulk invites – Invitations can be sent individually to a visitor or collectively to many visitors
VIP Visitors – Additional security of data for VIP visitors to avoid misuse of information
Courier Management – Keeps a check on deliveries like commodities being delivered in and out of establishments
Alerts & Notifications – Get notified on SMS, email, and application
Parking Management – Manage availability of parking space
Individual log-in – Every user has their own log-in id
Visitor/Meeting Analytics – Evaluate notes and remarks of the meeting stored in the system
Visitor Management System is a secure and user friendly database manager that records, filters, tracks the visitors to your organization.
"Secure Your Premises with VizMan (VMS) – Get It Now"
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
1. Unleashing the Power of
Generative AI: Python API
Integration with ChatGPT,
DALL-E, and D-ID Studio
Eoin Halpin,
Tom Halpin 11/11/2023
2. Agenda
Presenters
AI Models
Large Language Models (LLMs) - ChatGPT
Image Generation Models – DALL-E
Image Manipulation Models – D-ID Studio
Integration Considerations
Availability of APIs
AI Model Integration Examples – ChatGPT, DALL-E, D-ID Studio
Template GitHub Repository
Pace of change
Conclusions
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3. Presenters
Tom Halpin:
Distinguished Engineer – DevOps
Enablement.
Help teams move to a DevOps model in
support of product-aligned value
streams.
Facilitate adaption of the associated
culture, practices, and tools in
organizations.
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Eoin Halpin:
Analyst - Project/Program Management
Member of agile, customer-facing teams
focused on delivering value to
stakeholders.
Help organizations and customers to
gain valuable insights from data.
4. AI Models and Categories
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AI Models:
Many diverse AI models each with unique capabilities.
Large Language Models (LLMs):
Definition: LLMs are advanced AI models that understand and generate human-like text.
Applications: Language translation, content generation, chatbots, and more.
Key Features: Multimodal capabilities (understand and generate content in multiple modes or
types of data i.e. text, images or video), natural language understanding.
Example: ChatGPT which is an LLM-based chatbot.
Importance: Transforming the way we interact with AI.
5. AI Models and Categories
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Image Generation Models:
Definition: Image generation models specialize in creating visual content.
Applications: Art creation, design, visual content generation.
Example: DALL-E, which generates images from textual descriptions.
Importance: Enabling AI to generate visual art and design.
Image Manipulation Models:
Definition: Models focused on modifying and processing images.
Applications: Privacy protection, image enhancement, facial anonymization.
Example: D-ID Studio, which anonymizes faces in images.
Importance: Enabling AI to manipulate images and enhance visual data.
6. AI Models Covered
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ChatGPT:
Overview: ChatGPT is a
conversational AI model by
OpenAI.
Use Cases: Customer support,
virtual assistants, interactive
user experiences.
Integrations: Easily integrated
into applications, websites, and
products.
DALL-E:
Overview: DALL-E is an AI
model by OpenAI.
Creativity Unleashed: Generates
images from textual
descriptions.
Diverse Applications: Art
creation, content generation,
design.
Integrations: Enable developers
to use DALL-E's creative
capabilities.
D-ID Studio:
Overview: D-ID Studio is a
creative tool by D-ID.
Functionality: Image and video
manipulation, facial
anonymization.
Applications: Privacy protection,
content creation, media editing.
Integrations: Flexible and can be
integrated into various
platforms.
7. Importance of Integrations
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Brownfield Integrations:
The red pill - stay in wonderland and see how deep
the rabbit hole goes.
Majority of companies have the challenge of
integrating new technologies with existing
technologies.
Large companies have complex IT portfolios with
hundreds of strategic applications supporting a broad
customer base via a dynamic workforce.
Massive opportunities to integrate AI Models & LLMs
into enterprise systems to unlock hereto hidden
potential.
Potential Benefits: Enhanced customer experiences,
automation, and efficiencies.
Greenfield Integrations:
The blue pill - wake up in your bed
and believe whatever you want to
believe.
The select few.
Limited only by the imagination.
8. Key Considerations for Enterprise Integrations
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Valid Use Case: The use cases chosen need to be aligned with strategic organizational
objectives.
Data Accessibility: Need to link AI Models with internal systems.
Real-time Interactions: Live data allows for up-to-the-minute decisions
Security and Compliance: Must ensure data integrity, protection and regulatory adherence.
Data Quality: Ensuring data consistency and relevancy for AI Models & LLMs is essential.
Workflow Automation: Streamline business processes with AI-powered automation.
Scalability and Maintainability: Design integrations for growth and long-term sustainability.
9. Availability of APIs
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OpenAI and D-ID API: ChatGPT, DALL-E and D-ID Studio have APIs for developers.
Ease of Access: Quick and straightforward integration into various projects.
Community Collaboration: Developers can leverage the capabilities of advanced AI models
with ease.
Developers' Portal: Access documentation and resources for integration with the AI Models.
10. ChatGPT Integration
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Site - https://chat.openai.com/
API - https://platform.openai.com/docs/guides/gpt
API Key - https://platform.openai.com/account/api-keys
GitHub Repository - https://github.com/genai-musings/chatting-with-ChatGPT
Docker Image - https://hub.docker.com/r/genaimusings/chatting-with-chatgpt
11. DALL-E Integration
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Site - https://openai.com/dall-e-2
API - https://platform.openai.com/docs/guides/images/image-generation?context=node
API Key - https://platform.openai.com/account/api-keys
GitHub Repository - https://github.com/genai-musings/dallying-with-DALL-E
Docker Image - https://hub.docker.com/r/genaimusings/dallying-with-dall-e
12. D-ID Studio Integration
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Site - https://www.d-id.com/
API - https://docs.d-id.com/reference/get-started
API Key - https://studio.d-id.com/account-settings
GitHub Repository - https://github.com/genai-musings/dawdling-with-D-ID
Docker Image - https://hub.docker.com/r/genaimusings/dawdling-with-d-id
13. Template, Repo Template
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Site - https://www.cyberdynesystems.ie
API – https://www.cyberdynesystems.ie/dev/api
API Key - https://www.cyberdynesystems.ie/dev/keys
GitHub Repository - https://github.com/genai-musings/template-repo-template
Docker Image - https://hub.docker.com/r/genaimusings/template-repo-template
14. Pace of Change
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Custom GPTs: ChatGPT-like chatbots. Empowers users
to tailor ChatGPT for specific personal or professional
use cases without needing any development/coding
knowhow. Custom GPTs can be developed for individual
or enterprise use and/or sold via the GPT Store.
GPT Store : AI App Store allowing users to create and
sell new GPTs. Equivalent of Apples’ App Store. No
coding skills required to build and monetize custom
GPTs.
Assistants API: Allows the creation of agent like
experiences within applications.
.
GPT-4 Turbo: Unveiled upgraded
LLM. Knowledge of world events up
to April 2023. More powerful and
cost-effective for developers. 128k
context window in a single prompt,
allowing book scale content
generation.
Copyright Shield: To protect
customers against potential
copyright lawsuits. Addresses
potential copyright infringement
issues related to usage of OpenAI
products.
OpenAI Inaugural DevDay (Nov 6th) Key Announcements:
15. Conclusions
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AI Models and LLMs are reshaping industries and are about to reshape even more industries.
Briefly explored features of ChatGPT, D-ID Studio, and DALL-E.
Focused on the Integrations and APIs available specifically how they can allow the power of AI Models and
LLMs to be leveraged to create exciting solutions.
Provided sample “workloads” utilizing the API available and shared the code for those workloads via open-
sourced GitHub repositories which include full CI/CD functionality.
Provided a template GitHub repository which can be used to create workloads for other AI Models via the
associated APIs.
Pace of change is astounding, shared key announcements from OpenAI’s inaugural DevDay event.