Web developers: you have a fantastic opportunity to make your web UIs more intelligent and productive than before. But don’t just throw on a chat pane and call it done, as people may not even use or like it. Let's explore how language models can integrate into your existing web UIs, anticipating your users' needs and completing their tasks faster.This is a technical, demo-centric talk with examples in Blazor, MVC/Razor Pages, and plain old C#, but the concepts would apply in other stacks too. We’re not going to get into the depths of how large language models (LLMs) work internally, nor will we focus on specifics of OpenAI APIs. Instead, we’ll focus on practical, complete, end-to-end usage patterns for AI in web UIs, making your users happier and more productive.

Ref: https://www.youtube.com/watch?v=TSNAvFJoP4M&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=29

In this session, we will explore what this model can do, and rather than just showing a perfect polished final demo, I will walk you through my entire journey of trying to use the model to solve Wordle puzzles, starting with "Hello World". Along the way, you will gain a good understanding of the model's capabilities, along with learning some prompt engineering techniques that drove progress in this journey (along with what didn't work!). We'll close with a live demo to attempt to solve today's Wordle! This session will tackle a fun problem, but the underlying prompt engineering techniques for image understanding that you will learn are applicable to a wide variety of business problems.

Ref: https://www.youtube.com/watch?v=sfpk4wQeS_g&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=37

The focus upon AI continues to be the predominant technology subject of the day; it’s the must-have feature of any new product or service; it’s at the forefront of many discussions about ethics, attribution and indeed our own future employment prospects.But increasingly, the term “AI” has become synonymous with only one flavour of artificial intelligence - that being “machine learning” (ML) - e.g. generative AI, applied AI, large language models (LLMs) and the such.However, there are many other types of AI, which until recently, were the mainstay algorithms found behind automated decision making solutions.Many of these concepts can be found in the video games we know and love.Are these other types of AI still relevant? Do they risk being drowned out, or forgotten, in the rush to embrace machine learning solutions?In this session, intended for the enterprise/business application developer, we’ll open a window into the world of the video game development.We’ll explore the type of algorithms that are a staple of game development: pathfinding, state machines, decision trees, and goal-oriented action planning.We’ll delve into some of the performance considerations necessary to keep these algorithms running efficiently.We’ll circle back to how the business application developer can use this type of AI in applications, and how the lessons learnt making video games can help us write better software.

Ref: https://www.youtube.com/watch?v=w30cK2ga42M&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=36

AI CoPilots are all the rage - but none quite offer that personalised butler service SciFi told us we might one day have.To understand what it takes to train a CoPilot, we will see how training a model works under-the-hood; discuss the importance of quality training data to craft a truly powerful and personalised experience, and safety or security concerns to consider when training a model on a public service.Moving beyond the (chat) box, we will leverage Azure's OpenAI Service and Semantic Kernel in .NET to create a custom AI CoPilot for internal applications or data. We will see how to train our own custom Codex model, for generating code and commands to perform bespoke tasks against a non-public API, plus some innovative ways to glue this together with a nice user experience.You will leave feeling excited about the power of custom CoPilots, and armed with the knowledge to build your own!

Ref: https://www.youtube.com/watch?v=QXPlIYd408A&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=35

As we enter the age of AI, the roles of programmers and designers are evolving. The convergence of design and code signals a narrowing gap, prompting us to question the future landscape of design. Will AI-driven innovation lead us to chart a new course, or will it see us walking down familiar paths?Drawing from my experience leading Design at GitHub, I’ll delve into my journey starting out as a designer who codes, to building and leading teams of hybrid designer-developers. I'll examine how blurring the traditional boundaries between design and engineering has shaped the role of Design Engineering in the future of software design. Join me as I explore the dynamic interplay between AI, design, and programming, and consider the exciting possibilities that lie ahead.

Ref: https://www.youtube.com/watch?v=c1uEHfmJMTM&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=34

It’s 1998. It’s the year of Britney Spears, The Spice Girls, the first Google Doodle, and the year Titanic dominated the box office.It’s also the year Hasbro gifted us with the Furby, the first successful attempt at an interactive robot pet. It divided the playground, created a generation of spooky sleepover stories and sparked the xmas riots of 99’.Two decades on, creatives and engineers have started to crack the secrets of the Furby, evolving and unlocking its full potential using today’s technology.

https://www.youtube.com/watch?v=2cW9KeFkMnE&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=33

It's time you meet your AI pair programmer. Do you find yourself stuck on a chunk of code? Unsure of how best to center a div? GitHub Copilot can help. Get unstuck by seeing suggested lines or code, whole functions, and learn more about your development journey through having code explained, and even translate your code into other languages.

Ref: https://www.youtube.com/watch?v=Mlviuph9QX4&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=32

Natural language processing using generative pre-trained transformers (GPT) algorithms is a rapidly evolving field that offers many opportunities and challenges for application developers. But what is a generative pre-trained transformer, and how does it work? How can you leverage the latest advances in GPT algorithms to create engaging and useful applications? Can my business benefit from creating a GPT powered chat bot?In this demo intensive session Alan will take a deep dive into the architecture of GPT algorithms and the inner workings of ChatGPT. The journey will begin by looking at the fundamental concepts of natural language processing, such as word embedding, vectorization and tokenization. He will then demonstrate how you can apply these techniques to train a GPT2 model that can generate song lyrics, showing the internals of how word sequences are predicted.Alan will then shift the focus to larger language models, such as ChatGPT and GPT4, demonstrating their power, capabilities, and limitations. The use of hyperparameters such as temperature and frequency penalty will be explained and their effect on the generated output demonstrated. He will then cover the concepts of prompt engineering and demonstrate how Retrieval Augmented Generation (RAG) patterns can be leveraged to create a ChatGPT experience based on your own textual data.

Ref: https://www.youtube.com/watch?v=P2cTtiirPnU&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=31

AI is due to revolutionize the life of a developer, with Microsoft leading the way, combining the public code base of GitHub.com with ChatGPT to product Copilot to speed code generation and increase developer productivity.However, this is just the latest in a set of tools and frameworks that have all had the goal of improving productivity. But have we lost something along the way?As soon as we start using tools, they will directly influence the way we work, and we need to be aware of when they are useful and when we should use them. This is Conway's Law applied to tools - the very tools you use change how you developer, and not necessarily for the better.This includes ORMs for DB access like NHibernate and EF, mocking frameworks, IoC frameworks, refactoring tools like ReSharper all the way to Copilot.Should we always lean so heavily on these tools? Will they be supported in the future? Are we deskilling future generations of programmers? Am I just an old grumpy developer? Will Jon Skeet no longer be required?Some of these questions may be answered in this talk.

Ref: https://www.youtube.com/watch?v=rLN9kSvMRXI&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=30

Web developers: you have a fantastic opportunity to make your web UIs more intelligent and productive than before. But don’t just throw on a chat pane and call it done, as people may not even use or like it. Let's explore how language models can integrate into your existing web UIs, anticipating your users' needs and completing their tasks faster.This is a technical, demo-centric talk with examples in Blazor, MVC/Razor Pages, and plain old C#, but the concepts would apply in other stacks too. We’re not going to get into the depths of how large language models (LLMs) work internally, nor will we focus on specifics of OpenAI APIs. Instead, we’ll focus on practical, complete, end-to-end usage patterns for AI in web UIs, making your users happier and more productive.

Ref: https://www.youtube.com/watch?v=TSNAvFJoP4M&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=29