June 9, 2023 12:54 am

As anticipated, generative AI took center stage at Microsoft Make, the annual developer conference hosted in Seattle. Inside a handful of minutes into his keynote, Satya Nadella, CEO of Microsoft, unveiled the new framework and platform for developers to develop and embed an AI assistant in their applications.

Kevin Scott, CTO, Microsoft


Branded as Copilot, Microsoft is extending the identical framework it is leveraging to add AI assistants to a dozen applications, like GitHub, Edge, Microsoft 365, Energy Apps, Dynamics 365, and even Windows 11.

Microsoft is recognized to add layers of API, SDK, and tools to allow developers and independent application vendors to extend the capabilities of its core solutions. The ISV ecosystem that exists about Workplace is a classic instance of this method.

Possessing been an ex-employee of Microsoft, I have observed the company’s unwavering potential to seize each chance to transform internal innovations into robust developer platforms. Interestingly, the culture of “platformization” of emerging technologies at Microsoft is nevertheless prevalent even immediately after 3 decades of launching extremely profitable platforms such as Windows, MFC, and COM.

Even though introducing the Copilot stack, Kevin Scott, Microsoft’s CTO, quoted Bill Gates – “A platform is when the financial worth of everyone that makes use of it exceeds the worth of the corporation that creates it. Then it is a platform.”

Bill Gates’ statement is exceptionally relevant and profoundly transformative for the technologies business.There are numerous examples of platforms that grew exponentially beyond the expectations of the creators. Windows in the 90s and iPhone in the 2000s are classic examples of such platforms.

The most recent platform to emerge out of Redmond is the Copilot stack, which makes it possible for developers to infuse intelligent chatbots with minimal work into any application they develop.

The rise of tools like AI chatbots like ChatGPT and Bard is altering the way finish-customers interact with the application. Rather than clicking via many screens or executing several commands, they choose interacting with an intelligent agent that is capable of effectively finishing the tasks at hand.

Microsoft was fast in realizing the significance of embedding an AI chatbot into each application. Right after arriving at a frequent framework for developing Copilots for numerous solutions, it is now extending to its developer and ISV neighborhood.

In numerous strategies, the Copilot stack is like a modern day operating technique. It runs on major of highly effective hardware primarily based on the mixture of CPUs and GPUs. The foundation models type the kernel of the stack, when the orchestration layer is like the method and memory management. The user practical experience layer is comparable to the shell of an operating technique exposing the capabilities via an interface.

Comparing Copilot Stack with an OS

Janakiram MSV

Let’s take a closer appear at how Microsoft structured the Copilot stack without the need of acquiring also technical:

The Infrastructure – The AI supercomputer operating in Azure, the public cloud, is the foundation of the platform. This goal-constructed infrastructure, which is powered by tens of thousands of state-of-the-art GPUs from NVIDIA, delivers the horsepower required to run complicated deep understanding models that can respond to prompts in seconds. The identical infrastructure powers the most profitable app of our time, ChatGPT.

Foundation Models – The foundation models are the kernel of the Copliot stack. They are educated on a huge corpus of information and can execute diverse tasks. Examples of foundation models involve GPT-four, DALL-E, and Whisper from OpenAI. Some of the open supply LLMs like BERT, Dolly, and LLaMa might be a aspect of this layer. Microsoft is partnering with Hugging Face to bring a catalog of curated open supply models to Azure.

Even though foundation models are highly effective by themselves, they can be adapted for distinct scenarios. For instance, an LLM educated on a huge corpus of generic textual content material can be fine-tuned to have an understanding of the terminology utilized in an business vertical such as healthcare, legal, or finance.

Azure ML Model Catalog


Microsoft’s Azure AI Studio hosts a variety of foundation models, fine-tuned models, and even custom models educated by enterprises outdoors of Azure.

The foundation models rely heavily on the underlying GPU infrastructure to execute inference.

Orchestration – This layer acts as a conduit among the underlying foundation models and the user. Given that generative AI is all about prompts, the orchestration layer analyzes the prompt entered by the user to have an understanding of the user’s or application’s true intent. It initial applies a moderation filter to make certain that the prompt meets the security recommendations and does not force the model to respond with irrelevant or unsafe responses. The identical layer is also accountable for filtering the model’s response that does not align with the anticipated outcome.

The subsequent step in orchestration is to complement the prompt with meta-prompting via more context that is distinct to the application. For instance, the user might not have explicitly asked for packaging the response in a distinct format, but the application’s user practical experience demands the format to render the output appropriately. Assume of this as injecting application-distinct into the prompt to make it contextual to the application.

After the prompt is constructed, more factual information might be required by the LLM to respond with an precise answer. With no this, LLMs might have a tendency to hallucinate by responding with inaccurate and imprecise info. The factual information commonly lives outdoors the realm of LLMs in external sources such as the planet wide internet, external databases, or an object storage bucket.

Two strategies are popularly utilized to bring external context into the prompt to help the LLM in responding accurately. The initial is to use a mixture of the word embeddings model and a vector database to retrieve info and selectively inject the context into the prompt. The second method is to develop a plugin that bridges the gap among the orchestration layer and the external supply. ChatGPT makes use of the plugin model to retrieve information from external sources to augment the context.

Microsoft calls the above approaches Retrieval Augmented Generation (RAG). RAGs are anticipated to bring stability and grounding to LLM’s response by constructing a prompt with factual and contextual info.

Microsoft has adopted the identical plugin architecture that ChatGPT makes use of to develop wealthy context into the prompt.

Projects such as LangChain, Microsoft’s Semantic Kernel, and Guidance develop into the important elements of the orchestration layer.

In summary, the orchestration layer adds the important guardrails to the final prompt that is getting sent to the LLMs.

The User Expertise – The UX layer of the Copilot stack redefines the human-machine interface via a simplified conversational practical experience. Lots of complicated user interface components and nested menus will be replaced by a straightforward, unassuming widget sitting in the corner of the window. This becomes the most highly effective frontend layer for accomplishing complicated tasks irrespective of what the application does. From customer internet sites to enterprise applications, the UX layer will transform forever.

Back in the mid-2000s, when Google began to develop into the default homepage of browsers, the search bar became ubiquitous. Customers began to appear for a search bar and use that as an entry point to the application. It forced Microsoft to introduce a search bar inside the Commence Menu and the Taskbar.

With the developing recognition of tools like ChatGPT and Bard, customers are now hunting for a chat window to commence interacting with an application. This is bringing a basic shift in the user practical experience. Alternatively and clicking via a series of UI components or typing commands in the terminal window, customers want to interact via a ubiquitous chat window. It does not come as a surprise that Microsoft is going to place a Copilot with a chat interface in Windows.

Microsoft Copilot stack and the plugins present a considerable chance to developers and ISVs. It will outcome in a new ecosystem firmly grounded in the foundation models and huge language models.

If LLMs and ChatGPT produced the iPhone moment for AI, it is the plugins that develop into the new apps.

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Janakiram MSV is an analyst, advisor and an architect at Janakiram &amp Associates. He was the founder and CTO of Get Cloud Prepared Consulting, a niche cloud migration and cloud operations firm that got acquired by Aditi Technologies. By means of his speaking, writing and evaluation, he aids corporations take benefit of the emerging technologies.

Janakiram is one particular of the initial handful of Microsoft Certified Azure Pros in India. He is one particular of the handful of experts with Amazon Certified Answer Architect, Amazon Certified Developer and Amazon Certified SysOps Administrator credentials. Janakiram is a Google Certified Expert Cloud Architect. He is recognised by Google as the Google Developer Specialist (GDE) for his topic matter experience in cloud and IoT technologies. He is awarded the title of Most Precious Expert and Regional Director by Microsoft Corporation. Janakiram is an Intel Software program Innovator, an award offered by Intel for neighborhood contributions in AI and IoT. Janakiram is a guest faculty at the International Institute of Facts Technologies (IIIT-H) exactly where he teaches Massive Information, Cloud Computing, Containers, and DevOps to the students enrolled for the Master’s course. He is an Ambassador for The Cloud Native Computing Foundation.

Janakiram was a senior analyst with Gigaom Study analyst network exactly where he analyzed the cloud solutions landscape. Through his 18 years of corporate profession, Janakiram worked at planet-class item corporations like Microsoft Corporation, Amazon Net Solutions and Alcatel-Lucent. His final part was with AWS as the technologies evangelist exactly where he joined them as the initial employee in India. Prior to that, Janakiram spent more than ten years at Microsoft Corporation exactly where he was involved in promoting, advertising and marketing and evangelizing the Microsoft application platform and tools. At the time of leaving Microsoft, he was the cloud architect focused on Azure.

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