It took less than a week for OpenAI's ChatGPT to reach a million users, and it crossed the 100 million user mark in under two months. The interest and excitement around this technology has been remarkable. Users around the world are seeing potential for applying these large language models to a broad range of scenarios. In the context of enterprise applications, the question we hear most often is "how do I build something like ChatGPT that uses my own data as the basis for its responses?" It integrates the enterprise-grade characteristics of Azure, the ability of Cognitive Search to index, understand and retrieve the right pieces of your own data across large knowledge bases, and ChatGPT's impressive capability for interacting in natural language to answer questions or take turns in a conversation.

Each year, Microsoft Build is like Christmas for developers, especially those focused on the Microsoft stack. It's when they make the most exciting announcements, release the best new software, and start to solidify the roadmap of innovation for the next year. While we don't get to enjoy the conference in person this year, we do get the same great collection of announcements. This year's highlight for engineers and developers interested in machine learning is the Azure Applied AI Services. Microsoft has been Democratizing AI since 2016. Applied AI Services takes this ideal another step forward. Microsoft has already made machine learning accessible to developers through their outstanding Cognitive Services, and this offering will make certain scenarios even more accessible.

This week at Microsoft Ignite, a number of new developments to Azure were in focus. While there were dozens of updates to the world's second-largest public cloud, data was once again in the spotlight. The company made a series of announcements to enable users to extract more value from the exponential increase in data. Satya Nadella, in his Ignite keynote, provided a new visionary direction, or at least a new way of expressing the company's cloud endeavors. In short, the Microsoft cloud is evolving to further embrace edge, privacy, security, AI, and developers (both coders and no coders), and to serve as an engine of job creation. On the surface, this shift appears subtle.

Powerful Indexing Cognitive search, unlike keyword search, crawls and ingests both structured and unstructured data. Keep in mind: experts estimate that as much as 80-90% of your data is unstructured, including email, customer surveys and social media. Cognitive search solutions also enable developers to embed search in other applications using SDKs, APIs, and other tools. This is important because your data isn't confined to databases: it's scattered across the enterprise. So, search has to work where your teams work -- in Slack, Salesforce, Jira, Amazon Web Services (AWS), etc. Natural Language Processing (NLP) Keyword search is basically a matching game played with digital data.

With companies struggling to survive the COVID-19 storm, new approaches and tactics have replaced existing business models and strategies. However, the biggest challenge for businesses remain ensuring superior customer experience and preserving relationships to thrive in this new normal. In fact, according to a survey conducted by Forrester, consumers now feel more fragmented, disconnected and less trustworthy of brands than before. Accompanied with minimal physical interactions, companies are in search of devising unique ways to interact with customers and keep them happy and connected. Current customer service models are hinged on availability of an army of agents to satisfactorily resolve customer queries.

Cognitive search is a new generation of enterprise search that uses artificial intelligence technologies to improve users' search queries and extract relevant information from multiple, diverse data sets. Cognitive search capabilities extend beyond those of a classic search engine to bring numerous data sources together while also providing automated tagging and personalization. It has the potential to greatly improve how an organization's employees discover and access information relevant and necessary to their work context. Cognitive search differs from previously available search products because it combines indexing technology with powerful artificial intelligence technologies -- such as natural language processing (NLP) capabilities and algorithms -- to scale a variety of data sources and types. Additionally, developers can build search applications that can be embedded into business process applications, such as pharmaceutical research tools and customer portals.

Over the years, the role of the customer support agent has evolved from simply handling customer inquiries to building customer relationships and growing the business. According to the Salesforce State of Service Report, 71 percent of agents see their role as more strategic than two years ago. This means not only are agents spending more time solving complex issues, but they're also expected to upsell, cross-sell, and provide voice of the customer input into product development. The problem is, they're being asked to take on these changing responsibilities using the same old processes and tools. "As agents become more strategic, they have the potential to add more business value, especially in the area of customer experience," said Alok Ramsisaria, CEO of Grazitti Interactive, SearchUnify's parent company based in Sunnyvale, CA. "Having successfully completed hundreds of service implementations for enterprises all over the world, I can confidently say that the key to empowering agents is to arm them with contextual knowledge about the problem they're solving. And do it in a way that's scalable and delivers consistent results. Unfortunately, support teams are being hampered by manual processes and technology silos that keep them from servicing customers in the most efficient, effective way."

Your employees have a full spectrum of content and data sourced both within and external to the enterprise. And enterprise search technologies need to keep pace and stay robust. Join this VB Live event to learn how machine-learning and search can drive efficiency and Opex savings. The internet, Google, and Amazon aren't just useful consumer tools; they changed the game in terms of what kind of data users expect to have at their fingertips, and how quickly that data can be accessed. At home, the expectation is that any search will return the right results, even if you have to do some scrolling.

Your employees have a full spectrum of content and data sourced both within and external to the enterprise. And enterprise search technologies need to keep pace and stay robust. Join this VB Live event to learn how machine-learning and search can drive efficiency and Opex savings. The internet, Google, and Amazon aren't just useful consumer tools; they changed the game in terms of what kind of data users expect to have at their fingertips, and how quickly that data can be accessed. At home, the expectation is that any search will return the right results, even if you have to do some scrolling.

Cognitive Search is powered by Azure Search with built in Cognitive Services. It pulls data from almost any data source and applies a set of composable cognitive skills which extract knowledge. This knowledge is then organized and stored in an index, enabling new experiences for exploring the data using Search. When we first applied Cognitive Search to the JFK files, it was incredible to see what emerged. Not only could we answer so many interesting questions, we could see the answers and relationships in context with the original documents.