AI

How can clients reap AI’s greatest rewards while reducing risks? The answer: Trust!

The state of artificial intelligence (AI) and advanced automation covering areas of opportunity, bank strategies, adoption, implementation challenges, use cases and impact.

AI has the potential to overcome the biggest challenges governments face and dramatically improve life for citizens.

Digital transformation has the potential to deliver enormous benefits in the public sector. However, despite some pockets of excellence, most governments are lagging behind the corporate world in harnessing the power of digital.

To understand the latest in artificial intelligence and its applications in P&U.

AI is changing health care, reshaping the business models of life sciences and health care companies as access to data is increasingly democratized.

The oil and gas sector faces challenges on multiple fronts. Embracing AI may provide a way to address them.

Digital transactions insight deck on how digital disruption is impacting life sciences companies and how to raise capital and drive M&A to accelerate digital transformation programs.

Artificial intelligence (AI) refers broadly to a spectrum of technologies and research that aim to improve the cognitive capabilities of machines and software. This tutorial provides a high-level overview of AI, including the different types of AI and the drivers of growth, particularly in the context of the financial industry.

The tutorial also outlines how future breakthroughs in AI are expected to expand its current narrow applications, allowing AI algorithms to broaden in scope and interact actively across every aspect of human life. Prerequisite Knowledge Robotic Process Automation (RPA) Level: Introductory.

The world of artificial intelligence (AI) includes many areas in computing which makes it a complex field. This course provides a useful description of AI which will allow you to describe real-world problems as artificial environments.

Artificial intelligence (AI) refers broadly to a spectrum of technologies and research that aim to improve the cognitive capabilities of machines and software. This tutorial provides a high-level overview of AI, including the different types of AI and the drivers of growth, particularly in the context of the financial industry.

The tutorial also outlines how future breakthroughs in AI are expected to expand its current narrow applications, allowing AI algorithms to broaden in scope and interact actively across every aspect of human life. Prerequisite Knowledge Robotic Process Automation (RPA) Level: Introductory.

The world of artificial intelligence (AI) includes many areas in computing which makes it a complex field. This course provides a useful description of AI which will allow you to describe real-world problems as artificial environments.

Robotic process automation (RPA) refers to the use of software to replicate process steps that are typically performed by humans. This tutorial provides a high-level overview of RPA, including its benefits and limitations, particularly in the context of the financial industry. The tutorial also outlines how concepts such as machine learning and artificial intelligence are expected to enable the next stage of automation after RPA. Prerequisite Knowledge None Level: Introductory.

The world of artificial intelligence (AI) includes many areas in computing which makes it a complex field. This course provides a useful description of AI which will allow you to describe real-world problems as artificial environments.

Ever wonder how Twitter, LinkedIn, Google and Facebook infuse intelligence into their products to predict your preferences, populate newsfeeds personalized to you, and understand you better?

This course if for EY practitioners wanting to understand how businesses can build an analytics capability that can support all of their business functions and how the supporting platforms are architected to enabled. We define the journey from strategy definition all the way to the design and build of micro-serves architecture and ML/AI model creation and deployment.

High level overview on artificial intelligence and "cognitive" and what it means for our business and the future. Overview of the big bet, high level client pain points, capabilities overview, firm resources available, case studies / engagement spotlights, problem solved at the client.

Complete guide to artificial intelligence and machine learning, prep for deep reinforcement learning

Decision-makers in financial services have considerations that are particular to their industry to help them realize the true transformational impact of AI in the enterprise. It is critical to understand the components of a strategy that will help the financial services sector create business value with AI.

In government, decision-makers must take into account specific public sector considerations before they can realize the true transformational power of AI. It's critical to understand the components of a strategy that will help the public sector create value with AI. This learning path is designed for government decision-makers to illustrate the true transformational impact of AI in the public sector.

Decision-makers in healthcare have considerations that are particular to their industry to help them realize the true transformational impact of AI in the enterprise. This learning path is designed for healthcare industry decision-makers to illustrate the true transformational impact of AI in the enterprise.

Retail industry decision-makers have considerations that are particular to their industry to help them realize the true transformational impact of AI in the enterprise.

he interplay between AI, cloud, and edge is a rapidly evolving domain. Currently, many IoT solutions are based on basic telemetry. The telemetry function captures data from edge devices and stores it in a data store. Our approach extends beyond basic telemetry. We aim to model problems in the real world through machine learning and deep learning algorithms and implement the model through AI and Cloud on to edge devices. The model is trained in the cloud and deployed on the edge device. The deployment to the edge provides a feedback loop to improve the business process (digital transformation).

Azure Machine Learning is a cloud platform for creating and managing machine learning models. Learn how to apply your existing data science skills and build cloud-scale machine learning services that provide the foundation for artificial intelligence (AI) solutions.

In this learning path, you will hear from Peter Zemsky, INSEAD's Eli Lilly Chaired Professor of Strategy and Innovation, about how AI is driving business value across industries and companies as well as Microsoft customers and partners. You will be able to discuss how technical teams are brining AI into business applications and how business leaders of different industries are thinking about AI for their own businesses.

In this learning path, you will hear from top Microsoft executives about a framework to drive the key changes that are necessary to become an AI -ready organization. You will be able to identify the successful implementations of AI and articulate relevant scenarios. You will be able to discuss a line of business specific and relevant uses cases in finance, marketing, sales, and customer service. And finally, you will be able to identify AI solutions to implement in your organization based upon its maturity.

In this learning path, you will be provided with a high-level overview of the primary concepts of AI. You will be able to describe what AI happens to be and the technologies that underpin it. You will be able to share how Microsoft is turning the latest advancements in AI into tools, products, and services that can be leveraged by organizations.

Hear from our AI@leaders on the topic of Machine Learning and its applications at in this introductory webcast.

Hear from our AI@leaders on the topic of Natural Language Processing and its applications at in this introductory webcast.

This module provides an overview of Azure AI and demonstrates how Microsoft tools, services, and infrastructure can help make AI real for your organization, whether you want to unlock insights from your latent data with knowledge mining, develop your own AI models with machine learning, or build immersive apps using AI.

This course is all about the application of deep learning and neural networks to reinforcement learning.

If you’ve taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI.

Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level.

Reinforcement learning has been around since the 70s but none of this has been possible until now.

The world is changing at a very fast pace. The state of California is changing their regulations so that self-driving car companies can test their cars without a human in the car to supervise.

We’ve seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.

Supervised and unsupervised machine learning algorithms are for analysing and making predictions about data, whereas reinforcement learning is about training an agent to interact with an environment and maximize its reward.

Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus - they want to reach a goal.

introduces you to deep learning: the state-of-the-art approach to building artificial intelligence algorithms. We cover the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks. A major focus of this course will be to not only understand how to build the necessary components of these algorithms, but also how to apply them for exploring creative applications.