It will likely go down representing many things, but probably none as significant as rightsizing IT. Rightsizing is not a new IT term, nor a new function. As companies at still facing uncertainty with revenue, staffing and technology direction, rightsizing comes into focus. While rightsizing is an ongoing normal function in highly mature IT organizations, it bubbles up more as a project in less mature organizations. Downturns are often a catalyst for rightsizing efforts within IT organizations.

Microsoft has announced the general availability of the Azure OpenAI Service and plans to add ChatGPT in the near future. Currently, Azure OpenAI Service provides access to some of the most powerful AI models in the world--including Codex and DALL-E 2. A "fine-tuned" version of GPT-3.5 will also be available through Azure OpenAI Service soon. We've learned a lot from the ChatGPT research preview and have been making important updates based on user feedback. ChatGPT will be coming to our API and Microsoft's Azure OpenAI Service soon. Azure OpenAI Service was unveiled in November 2021.

Chatbots have a checkered past of often not delivering the performance their providers have promised. This is especially true in the IT service management (ITSM) and multilingual NLP spaces, where service desks found support teams deluged with complaints -- yes, about the support chatbots. Just getting English language nuance right and how enterprises communicate often require chatbots to be custom programmed with constraint and logic workflows supported with natural language processing (NLP) and machine learning. If that sounds like a science project, it is, and IT users are the test subjects. Because of their complexity, chatbots were contributing to already overflowing trouble-ticket queues.

Companies rely on real-world data to train artificial-intelligence models that can identify anomalies, make predictions and generate insights. To detect credit-card fraud, for example, researchers train AI models to look for specific patterns of known suspicious behavior, gleaned from troves of data. But unique, or rare, types of fraud are difficult to detect when there isn't enough data to support the algorithm's training. To get around that, companies are learning to fake it, building so-called synthetic data sets designed to augment training data. At American Express Co., machine-learning and data scientists have been experimenting with synthetic data for nearly two years in hopes of improving the company's AI-based fraud-detection models, said Dmitry Efimov, head of the company's Machine Learning Center of Excellence. The credit-card company uses an advanced form of AI to generate fake fraud patterns aimed at bolstering the real training data.

Computers and humans have never spoken the same language. Over and above speech recognition, we also need computers to understand the semantics of written human language. We need this capability because we are building the Artificial Intelligence (AI)-powered chatbots that now form the intelligence layers in Robot Process Automation (RPA) systems and beyond. Known formally as Natural Language Understanding (NLU), early attempts (as recently as the 1980s) to give computers the ability to interpret human text were comically terrible. This was a huge frustration to both the developers attempting to make these systems work and the users exposed to these systems.

Moveworks founding team from left to right Vaibhav Nivargi, CTO; Bhavin Shah, CEO; Varun Singh, VP ... [ ] of Product; Jiang Chen, VP of Machine Learning. Computers and humans have never spoken the same language. Over and above speech recognition, we also need computers to understand the semantics of written human language. We need this capability because we are building the Artificial Intelligence (AI)-powered chatbots that now form the intelligence layers in Robot Process Automation (RPA) systems and beyond. Known formally as Natural Language Understanding (NLU), early attempts (as recently as the 1980s) to give computers the ability to interpret human text were comically terrible.

Moveworks founding team from left to right Vaibhav Nivargi, CTO; Bhavin Shah, CEO; Varun Singh, VP ... [ ] of Product; Jiang Chen, VP of Machine Learning. Computers and humans have never spoken the same language. Over and above speech recognition, we also need computers to understand the semantics of written human language. We need this capability because we are building the Artificial Intelligence (AI)-powered chatbots that now form the intelligence layers in Robot Process Automation (RPA) systems and beyond. Known formally as Natural Language Understanding (NLU), early attempts (as recently as the 1980s) to give computers the ability to interpret human text were comically terrible.

It's no secret that deep learning lets data science practitioners reach new levels of accuracy with predictive models. However, one of the drawbacks of deep learning is it typically requires huge data sets (not to mention big clusters). But with a little skill, practitioners with smaller data sets can still partake of deep learning riches. Deep learning has exploded in popularity, with good reason: Deep learning approaches, such as convolutional neural networks for computer (used primarily for image data) and recurrent neural networks (used primarily for language and textual data) can deliver higher accuracy and precision compared to "classical" machine learning approaches, like regression algorithms, gradient-boosted trees, and support vector machines. But that higher accuracy comes at a cost.

IT support has plagued enterprises for years. And for good reason: existing solutions haven't changed for almost two decades. Employees typically either send an email or navigate to an archaic web portal to file an IT ticket and endure long wait times (often days). What's interesting is that the syntax behind IT support tickets hasn't changed much over time: "I can't seem to reset my salesforce password?"; "My laptop screen got cracked"; "My email is not working."

This week I'm joined by Yousuf Khan, CIO and VP of Customer Success at Moveworks. Yousuf has served as CIO for multiple companies and shares his perspective on the changing CIO role. CIOs are finding themselves moving beyond operational functions and taking on customer-facing responsibilities. In this episode, we discuss the importance customer experience plays for enterprises and the pillars that enable the CIO to play a significant role. Yousuf outlines three keys to building relationships and the importance that community plays in CIO decision making. Tim Crawford: Hello, and welcome to the CIO In The Know podcast, where I take a provocative but pragmatic look at the intersection between business and technology. Tim Crawford: This week I'm joined by Yousuf Khan, CIO and VP of Customer Success at Moveworks. Yousuf has served as CIO for multiple companies and shares his perspective on the changing CIO role. CIOs are finding themselves moving beyond operational functions and taking on customer-facing responsibilities. In this episode, we discuss the importance customer experience plays for enterprises and the pillars that enable the CIO to play a significant role.