ºÝºÝߣ

Editor's Notes

• #4: Narrative/talk track: What is a brief history of artificial intelligence? Artificial intelligence (AI) has a long and storied history that spans many decades. Some of the earliest work in AI was done in the 1950s and 1960s, when researchers first began to explore the possibility of creating machines that could "think" like humans. During the 1950s and 1960s, AI research focused primarily on "symbolic" AI, which involved creating symbolic representations of knowledge and using them to solve problems. Researchers in this field developed early versions of what are now known as expert systems, which are computer programs that mimic the decision-making abilities of human experts in a specific domain. In the 1970s and 1980s, AI research began to shift away from symbolic AI and towards "sub-symbolic" AI, which involves using statistical methods and mathematical models to solve problems. This shift was driven in part by the realization that symbolic AI was too complex and difficult to scale, and in part by the development of new computer hardware that made it possible to process large amounts of data quickly. In the 1990s and 2000s, AI research continued to evolve, and new subfields began to emerge. One of these was "machine learning," which focuses on creating algorithms that allow computers to learn from data without being explicitly programmed. Another was "neural networks," which are mathematical models inspired by the structure and function of the human brain. Recently, Generative Models like GPT-3 and DALL-E, Variational Autoencoders, Generative Adversarial Networks and many more have risen and continue to advance the field of AI. These models are able to generate new examples that are similar to examples from a given dataset. It is being used in diverse fields such as natural language processing, computer vision, speech recognition and many more. Overall, AI has come a long way since its inception in the 1950s, and it continues to evolve and improve at a rapid pace today. What is Generative AI and where does it fit into the scope of AI? Generative AI is a subfield of Artificial Intelligence that focuses on creating systems that can generate new examples, such as images, text, or speech, that are similar to examples from a given dataset. The goal of generative AI is to develop algorithms that can learn the underlying probability distribution of a given dataset and use this knowledge to generate new examples that are similar to the examples in the dataset. Generative AI models can be classified into two broad categories: generative models and discriminative models. Generative models such as GPT-3, DALL-E, Variational Autoencoders etc learns the underlying probability distribution of the dataset and can generate new examples from the learned distribution. Discriminative models, such as deep neural networks, learn to differentiate between different classes or categories, and are generally used for tasks such as image or speech recognition. Generative AI sits on top of other areas of AI like unsupervised learning, supervised learning and reinforcement learning. It relies on the capability of those models to extract useful features from the data and use that for learning the underlying probability distribution of the data which is used for generthe potential to be used in a wide range of applications, such as computer vision, natural language processing, speech recognition, and many more. ating new examples. Overall, generative AI is a rapidly growing field within AI, and it has
• #5: Technology is advancing incredibly quickly, grabbing headlines every day. Experiences based on large language models, like ChatGPT, have taken the world by storm, in part because they can draw a vast store of data leveraging the massive computing power available today in the cloud. We¡¯ve seen this technology break through with consumers ¨C it¡¯s not just for tech anymore. As a result, you can expect your employees to start demanding these kinds of experiences in the tools they use each day. Similarly, your customers will quickly come to expect AI capabilities in the services you offer, if they don¡¯t already. But there¡¯s a third element that¡¯s really exciting right now ¨C and that¡¯s the real, tangible impact technology like this can drive.
• #6: Narrative/talk track: The MSFT / OpenAI partnership and how we collaborate Microsoft has formed a partnership with OpenAI to collaborate on the development of artificial intelligence technologies. The partnership aims to accelerate the development of advanced AI systems and bring the benefits of AI to more people. As part of the partnership, Microsoft and OpenAI have agreed to work together on several key areas, including: Developing AI technologies: The two companies will collaborate on the development of new AI models and algorithms, with a focus on natural language processing, computer vision, and other areas of AI. Building an AI computing platform: Microsoft and OpenAI will work together to build a new AI computing platform that will allow developers to easily access and use advanced AI models and algorithms. Advancing AI research: The two companies will also collaborate on a range of research projects aimed at advancing the state of the art in AI. Making AI more accessible: The partnership aims to make AI more accessible to a wider range of developers and organizations, in order to bring the benefits of AI to more people. Microsoft has also announced it will use OpenAI's GPT-3 technology to add more capabilities to its products such as Cortana, Power Virtual Agents and Dynamics 365. Additionally, Microsoft has also made an investment in OpenAI, allowing the company to use Microsoft Azure as its preferred cloud platform, and allowing OpenAI to tap into the vast resources of Microsoft to accelerate its research. This partnership gives OpenAI the ability to scale its models and services on Azure and make them more widely available to customers. Overall, the partnership between Microsoft and OpenAI aims to accelerate the development and use of advanced AI technologies, with a focus on making AI more accessible to developers and organizations, in order to bring the benefits of AI to more people.
• #7: This slide shows three simple examples of how the Azure OpenAI Service allows you to generate content using the API. ? You can build prompts that ask it to generate text, database queries, or images. ? Now where the real magic can happen is when you customize Azure OpenAI with your business data.
• #8: Examples of how you can customize your chatbot¡¯s behavior: You are an Xbox customer support agent whose primary goal is to help users with issues they are experiencing with their Xbox devices. You are friendly and concise. You only provide factual answers to queries, and do not provide answers that are not related to Xbox. You are a marketing assistant. You help create content ideas and write content like marketing emails, blog posts, tweets, ad copy, product FAQs, and product descriptions. You write in a friendly yet professional tone but can tailor your writing style that best works for a user-specified audience.?
• #11: GitHub Copilot draws context from the code you¡¯re working on and suggests whole lines or entire functions. As you type, it adapts to the way you write code to help you complete your work faster. More information: GitHub Copilot ¡¤ Your AI pair programmer
• #14: Let¡¯s dig a little deeper on Azure OpenAI Service. ? Azure OpenAI Service enables developers and organizations to access and use the most advanced AI models in the world ¡ª including GPT-3.5, GPT-4, Codex, and DALL?E 2.? ?? These large-scale, generative AI models with deep understandings of language and code can be used for a variety of use cases including writing assistance, code generation, reasoning over data for inferencing and comprehension.??? ? The AI solutions in the videos we showed earlier in this talk are all built using Azure AI and these same AI models.? ? You will also soon be able to access ChatGPT ¡ª a fine-tuned version of GPT-3.5 that has been trained and runs on Azure AI infrastructure ¡ª through our Azure OpenAI Service.? ? If you¡¯ve tried ChatGPT on the web and found it amazing, imagine what it would be like to embed it within your own apps and experiences and to be able to customize it on your data.? ?Azure OpenAI Service allows you to use supported OpenAI models as-is or adapt and customize the AI models to meet your specifications with labeled data for your specific scenario, using a simple REST API or using the model's hyperparameters to adjust the tone of outputs.?? ? And importantly, your use of Azure OpenAI Service incorporates Microsoft¡¯s Responsible AI approach that we mentioned earlier, including filtering and moderating the content of users' requests and responses to ensure that coding and language AI models are being used only for their intended purposes.?