Artificial Intelligence (AI) is pivotal in advancing mobile network systems by facilitating smart capabilities and automation. The transition from 4G to 5G has substantial implications for AI in consolidating a network predominantly geared towards business verticals. In this context, 3GPP has specified and introduced the Network Data Analytics Function (NWDAF) entity at the network's core to provide insights based on AI algorithms to benefit network orchestration. This paper proposes a framework for evolving NWDAF that presents the interfaces necessary to further empower the core network with AI capabilities B5G and 6G. In addition, we identify a set of research directions for realizing a distributed e-NWDAF.

TomTom just announced a "fully integrated, AI-powered conversational automotive assistant" which should start popping up in dashboard infotainment platforms in the near-ish future. The company has issued some bold claims for the AI, saying it'll offer "more sophisticated voice interaction" and allow users to converse naturally to navigate, find stops along a route, control onboard systems, open windows and just about anything else you find yourself doing while driving. The company, best known for GPS platforms, partnered up with Microsoft to develop this AI assistant. Cosmos DB is a multi-model database and Cognitive Services is a set of APIs for use in AI applications, so this should be a capable assistant that draws from the latest advancements. TomTom promises that the voice assistant will integrate into a variety of interfaces offered by major automobile manufacturers, stating that the auto company will retain ownership of its branding.

Azure AI is driving innovation and improving experiences for employees, users, and customers in a variety of ways, from increasing workday productivity to promoting inclusion and accessibility. The success of Azure AI--featuring Azure Cognitive Services, Azure Machine Learning, and Azure OpenAI Service--is built on a foundation of Microsoft Research, a wide range of Azure products that have been tested at scale within Microsoft apps, and Azure customers who use these services for the benefit of their end users. As 2023 begins, we are excited to highlight 10 use cases where Azure AI is utilized within Microsoft and beyond. Speech transcription and captioning in Microsoft Teams is powered by Azure Cognitive Services for Speech. Microsoft achieved human parity in conversational speech recognition when it reached an error rate of 5.9 percent.

Did you miss a session from MetaBeat 2022? Head over to the on-demand library for all of our featured sessions here. Today Microsoft Azure announced a variety of enhancements across its AI services at Ignite 2022. The company says the updates will help people "work smarter, not harder by bringing more intelligence, insights and value to the hands of customers." The product updates include new innovations in Azure Applied AI Services to help customers automate mundane tasks and serve end-users in multiple languages worldwide; updates to Azure Cognitive Services to "enrich and simplify" the creation of AI apps with pre-built models and text-to-image generation; and new capabilities in Azure Machine Learning that boost the productivity of developers and data scientists of all skill levels, and help further responsible AI deployment.

Microsoft and Meta are extending their ongoing AI partnership, with Meta selecting Azure as "a strategic cloud provider" to accelerate its own AI research and development. Microsoft officials shared more details about the latest on the Microsoft-Meta partnership on Day 2 of the Microsoft Build 2022 developers conference. Microsoft and Meta -- back when it was still known as Facebook -- announced the ONNX (Open Neural Network Exchange) format in 2017 in the name of enabling developers to move deep-learning models between different AI frameworks. Microsoft open sourced the ONNX Runtime, which is the inference engine for models in the ONNX format, in 2018. Today, Meta officials said they'll be using Azure to accelerate research and development across the Meta AI group.

If you want to begin using machine learning in your applications, Microsoft offers several different ways to jumpstart development. One key technology, Microsoft's Azure Cognitive Services, offers a set of managed machine learning services with pretrained models and REST API endpoints. These models offer most of the common use cases, from working with text and language, to recognizing speech and images. Machine learning is still evolving, with new models being released and new hardware to help speed up inferencing, and so Microsoft regularly updates its Cognitive Services. The latest major update, announced at Build 2022, features a lot of changes to its tools for working with text, bringing three different services under one umbrella.

Azure Machine Learning, Azure Synapse Analytics and Spark using Azure Databricks are cloud services that you can use to train, deploy, automate, and manage machine learning models, all at the broad scale that the cloud provides. Azure Cognitive Services are APIs/SDKs/services available to help developers build intelligent applications without the need for AI or data science skills/knowledge. Azure Cognitive Services enable developers to easily add cognitive features such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding – into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The event will be held in English.

The financial services sector has been an eager adopter of robotic process automation (RPA): by one estimate, it accounts for 29% of the RPA market, more than any other sector. So it stands to reason that the industry is an early adopter of intelligent automation, the combination of RPA with AI. "Financial services [institutions] have always been among of the top adopters of intelligent automation," says Sarah Burnett, industry analyst and evangelist at process mining vendor KYP.ai. Financial institutions have adopted a range of use cases for intelligent automation, from simple integrations of cognitive services into RPA systems to, in a few cases, AI-powered decision making. As such, they have also encountered the security risks and governance challenges that arise from intelligent automation sooner than most. Intelligent automation is a broad term, representing a range of possibilities for integrating AI and machine learning into process automation.

In the 16th episode of the business intelligence report in partnership with Bright Talk, we'll be taking a deeper look into machine learning solutions, in particular, data-centric machine learning and machine learning pipelines, in conversation with Rajdeep Biswas, Director, Advanced Analytics & Machine Learning at Microsoft and Nik Spirin, Co-founder & CEO at Metapixel AI Machine learning is an application of artificial intelligence, that includes algorithms that parses the data, learn from the data, and then apply what they have learned to make informed decisions. There are hundreds of examples of machine learning; the music streaming service, for instance, uses ML to make a decision about which new song or artist to recommend to a listener. Machine learning algorithms associate the listeners' preferences with other listeners who have similar musical tastes. This technique is used in many services that offer an automatic recommendation. More specifically, right now deep learning (DL) is considered an evolution of machine learning and it uses programmable neural networks that enable machines to make accurate decisions without the help of humans so it's, it's more sophisticated.

Azure, combined with Microsoft Cognitive Services, are a huge opportunity for developers. Has Microsoft's Cognitive services piqued your interest, but you haven't been able to find a decent course that will teach you how to use those services effectively? Or maybe you have just recognised how a valuable skill like machine learning can open up big opportunities for you as a developer. Perhaps you just wanted to find out how to add "superpowers" to your programs to do amazing things like face detection, but had no idea how to go about it. Whatever the reason that has brought you to this page, one thing is for sure; the information you are looking for is contained in this course!