As companies continue to pivot and adapt in response to the pandemic, more of them have turned toward automation, artificial intelligence (AI), and machine learning (ML)--the trifecta behind intelligent automation--to help them streamline their business processes and better prepare for future "what if" scenarios. In a recent refresh of its Automating with Intelligence study, Deloitte saw a significant uptick in the adoption of intelligent automation in 2020, with 73 percent starting their intelligent automation journey--a 15 percent increase over 2019. Of those, 37 percent are piloting (1–10 automations), 23 percent are implementing (11–50 automations), and 13 percent are scaling (51 automations). According to the study, companies deploying new intelligent automation initiatives expect a 15 percent revenue increase in the targeted areas and a 24 percent average cost reduction over the next three years. The number of organizations deploying at scale nearly doubled, and Deloitte expects a bigger return on investment versus the 2019 study.

Addressing roadblocks in machine learning adoption can help enterprises industrialize and scale AI, and ultimately embed it into businesses processes and new products and services. We are entering the golden age of machine learning (ML), with adoption increasing across all customer segments. Once considered peripheral, ML technology is becoming a core part of many business strategies around the world. From health care to manufacturing, fintech to media and entertainment, ML holds great promise for many industries. Driven by the wide availability of cloud-based computing power, storage capacity, and easy-to-use AI toolsets, the normalization of AI and ML continues at a rapid pace.

I was at work as a postman. I hated my job, but I had to do it to fund my way through college. Whenever I was on my post walks, I'd listen to interviews of people I admired. One day, I just so happened to be listening to a Bill Gates interview, and someone from the audience asked him what he'd be doing if he hadn't created Microsoft. He responded along the lines of "I'd be testing the limits with Natural Language data," and that he'd be an AI researcher.

Accenture's partnership with AI4ALL gives emerging leaders exposure to Responsible AI in practice. The field of AI is changing rapidly, making the need for responsible AI greater than ever. While only 18% of data science students reported learning about ethics in a recent industry survey, examples of AI products with unintended negative consequences continue to grow. Marisa Tricarico, the North America Practice Lead for Responsible AI at Accenture, has a unique perspective on the rapid expansion of this field, as she works with a growing roster of Accenture clients as they develop and deploy AI. Marisa and Accenture's work intersects with AI4ALL's work to train the next generation of responsible AI leaders as well.

Robot-led automation has the potential to transform today's workplace as dramatically as the machines of the Industrial Revolution changed the factory floor. Both Robotic Process Automation (RPA) and Intelligent Automation (IA) have the potential to make business processes smarter and more efficient, in very different ways. Both have significant advantages over traditional IT implementations. Robotic process automation tools are best suited for processes with repeatable, predictable interactions with IT applications. These processes typically lack the scale or value to warrant automation via IT transformation.

Disruptive new technologies and methodologies have already gained a foothold in most organizations. Cloud, Machine Learning, Edge Computing, IoT, Cybersecurity Best Practices, Additive Manufacturing, Augmented Reality, DevOps and more are enabling new business processes and obscuring traditional functional boundaries. OT, IT, and ET teams are growing their skills and capabilities and transforming real-time operations. Executives charged with driving transformation are seizing this moment to innovate and deliver real value. By using data, digital technologies, and machine learning, organizations can ask questions about their interactions with customers, then map those learnings back to how assets are deployed and managed in operations.

Deloitte announced a collaboration today with Automation Anywhere to drive further adoption of cloud deployments on Automation 360, the first cloud-native, AI-powered robotic process automation (RPA) platform. Deloitte will combine its leading capabilities in cloud infrastructure and automation to provide a first-of-its-kind solution that enables a successful migration of client automations to the cloud, helping organizations accelerate the rate and delivery of business performance while effectively limiting costs. Mutual customers, both first-time RPA and existing Automation Anywhere users, will experience a smooth transition to the cloud platform with Deloitte's migration as a service capabilities. "The need for digital transformation is more prevalent than ever as organizations continue to navigate the effects of the pandemic and pivot to cloud-based solutions that can seamlessly integrate with their existing systems," said Douglas Williams, managing director, Deloitte Consulting LLP. "Our solutions are designed for Automation 360 to help customers through the migration process to get the most value out of their RPA investment with minimal disruption, all while finding efficiencies and reducing investment costs."

Engineers and computer scientists spent decades perfecting computers' abilities to solve classical math and logic problems. But as it would turn out, a huge set of real-world decision-making isn't readily framed as a tidy math problem. Machine learning (ML) earns its paycheck in these kinds of situations: When we're unable to logically or cost-effectively use math to tell a computer what to do, we can use ML to teach a computer what to do by showing it examples of how it's been done. This current AI/ML "Cambrian explosion" is resulting in a radical rethink of what computers can realistically learn. Startups and incumbents alike are teaching machines to emulate an ever-increasing share of capabilities once thought of as "uniquely human." Other frontiers of AI advancement include sensation and discernment (the five "senses"); creativity (reading, writing, and the arts); and congeniality (emotional intelligence).

PwC announced that it was cited as a Leader in The Forrester Wave: AI Consultancies, Q1 2021. In the report, Forrester notes that "AI consultancy customers should look for providers that: Commenting on PwC, the report states that: "The PwC backstory has two facets -- client transformations and its own. PwC helps transform client businesses, but its own transformation is part of its story. PwC doubled down on its own upskilling and IP-building platform and then launched this for clients. One-off simulation projects are now scaled offerings for strategic planning, operations, and continuous scaling of business models. Even strategic innovation partnerships are points of excellence; one client specifically selected PwC because of the consultancy's relationship with Carnegie Mellon."

IDSIA has a very broad range of research interests, spanning most of Artificial Intelligence as it is understood today: machine learning, including deep learning/neural networks, control and signal processing, natural language processing, robotics, computer vision, search and optimisation, and more fundamental questions in uncertainty, probability, statistics, causal inference. To give an example, we have a 4-year Data project funded by the National Science Foundation as part of Switzerland's National Research Programme 75 "Big Data". In this project we deal with Gaussian processes, which can be understood as statistical neural networks, which can then provide uncertainty estimates relating to their own predictions – unlike traditional neural nets. This is very important in applications where we are evaluating risks. For example, a self-driving car needs to know whether the car's sensors are reliably warning of a potential accident ahead rather than a a person safely crossing the street.