Systems that use data to learn and improve from experience are getting more pilots and trials in large and medium enterprises. Here are five reasons why. Large and medium-sized enterprises will step up their use of machine learning in 2018, Deloitte Global predicts, doubling the number of implementations and pilot projects that were underway in 2017. By 2020, that number will likely have doubled again. And as enabling technologies such as machine learning APIs and specialized hardware become available in the cloud, these advances will likely be increasingly within reach for small companies as well.

Booz Allen and NVIDIA are offering deep learning training. NVIDIA is working with Booz Allen Hamilton to rapidly build solutions that are needed in cyberdefense for both government and commercial customers. Now, certified Deep Learning Institute instructors from NVIDIA and Booz Allen are offering training to a variety of customers on how to build your own effective deep learning and data-driven solutions. 'Deep Learning Demystified,' hosted by Booz Allen and NVIDIA, will provide an introduction to deep learning, explore key fundamentals and opportunities, and how to best address current challenges. If you can't make our June 7th, 7:30AM - 11:00AM course, this course will also be offered over another two dates: "Together with NVIDIA, we've already seen through the Data Science Bowl how deep learning can speed cancer and heart disease diagnoses. Machine intelligence, powered by deep learning and other techniques, will help organizations -- public and private sector alike -- supercharge human ingenuity to uncover new revelations about the complex systems in which we live and work," said Dr. Josh Sullivan, senior vice president of data science at Booz Allen.

Mr. Jones is planning a vacation for the summer. He looks up travel destinations, flight tickets, and travel sites offering special summer vacation deals. Skyscanner, or Airbnb, he forms a customer touch point with that business. And, the sum total of all his interactions with that business, from the first interaction i.e. browsing through their site to the last interaction i.e. booking his holiday package, forms his customer journey. Ensuring a pleasant and fruitful customer journey for customers like Mr. Jones is imperative to business success.

As part of our guest blog series, Accenture Nordic Banking Practice Lead Satu Pulkkinen explores how banks can take the next step in evolving customer relations. First came online and mobile banks, with Nordic banks leading the way. The conversational bank that operates within messaging applications. Technology that can see and hear us, continuously growing ecosystems around increasingly popular messaging applications, as well as the amazing progress of artificial intelligence (AI), are enabling personalized, fully digital banking assistants that you can talk to anytime, anyplace. Technology is an integral part of our daily lives. Increasingly, devices that used to simply respond to our commands and actions can now also hear and see us.

Two key characteristics of artificial intelligence (AI) will help us address the challenges we face, says Celine Herweijer, Partner, Innovation and Sustainability, at PwC UK. First, AI helps increase productivity, and second, it speeds up the process of learning and understanding, which enables scientific discovery. Last week's AI for Good Global Summit at ITU headquarters in Geneva, Switzerland gathered top minds across various sectors to discuss the potential of AI to solve some of the most urgent challenges and transform our future. For Ms. Herweijer, the health of our planet is one of the most pressing issues this generation faces. "Our planet has never been under so much strain, whether it's climate change, oceans, air and water quality or the extinction crisis with 1 in 5 species facing extinction." She then posed the question, "How can we transform our human systems, our cities, our transport networks, using technologies so that we don't create the negative impacts that past industrial revolutions have created?"

While the chief data science officer role is common at technology firms, it's not standard at accounting and consulting firms. "I'm excited for this opportunity and the commitment from Crowe to push forward into this area," said Bass, who joined Crowe in 2015 as the firm's only data scientist. "With a team of world-class data scientists, we've already set the groundwork and incorporated machine learning into a few solutions in a very short amount of time." Bass has extensive experience applying advanced analytic, automation and machine learning techniques to a variety of industries. He is a continual learner, as shown through five combined degrees in aerospace engineering, statistics and finance from the University of Michigan, Purdue University and the Pennsylvania State University.

Over the last decade, knowledge has become the key driver for productivity and economic growth. Professional services providers like accountants and lawyers have benefited from this strong knowledge economy. These professionals have a combination of knowledge and expertise that makes them uniquely qualified for solving specific problems. Until recently, this industry has been relatively untouched by disruption. Machine learning is changing this equation.

As industries journey towards the Fourth Industrial Revolution, where people and robots will work together seamlessly, it's becoming more important than ever for organisations to integrate artificial intelligence (AI), robotics and the internet of things (IoT) into their digital game plan. However, much like policy makers, business leaders are struggling to keep up with these advancements. They're failing to leverage technology in areas such as human resources, payroll and workforce management. According to Deloitte's Human Capital Trends Report 2018, almost 95% of organisations worldwide don't know how to manage their workforce effectively. It's a confronting statistic and it's one Jarrod McGrath, founder and CEO of strategic workforce management consultancy Smart WFM, has set out to change. "I couldn't believe how high this figure was," says Jarrod.

Banks Must Invest in Reskilling Their Workforces to Seize AI-driven Growth Opportunities, Accenture Report Finds Stronger commitment to AI could boost revenues 34 percent and employment 14 percent by 2022 NEW YORK; May 2, 2018 – Although bank leaders recognize that intelligent technologies are reshaping the core banking process and can transform customer experiences, few plan to significantly increase investments in reskilling their workforces to enable these technologies in the near-term, according to a report by Accenture (NYSE: ACN). Based on two surveys – one of 100 banking executives and another of 1,300 non-executive bank employees – the report, "Future Workforce Survey - Banking: Realizing the Full Value of AI," estimates that if banks invest in artificial intelligence (AI) and human-machine collaboration at the same rate as top-performing companies do, they could boost revenues by 34 percent and raise employment levels 14 percent by 2022. "As AI becomes more nuanced, its role in banks is moving beyond automation to elevating human capabilities," said Alan McIntyre, a senior managing director at Accenture and head of the company's Banking practice. "To benefit from the potential of AI, banks need to implement'applied intelligence' – combining technology and human ingenuity – across all areas of their core business. To achieve this, they will need commitment from the highest levels of leadership and an understanding that this evolution will require a dramatic change in their workforce."

Bangalore: An in-depth study on talent in the Machine Learning (ML) space by Zinnov, a global management consulting firm, revealed that while a few startups have a had success stories in their AI (artificial intelligence)/ML journeys, there still exists a deep chasm, and most startups and global enterprises haven't been able to succeed and/or scale, their ML initiatives. The AI/ML spend is predicted to touch $400 billion by 2020, according to industry estimates. Given this, it is more important for organizations to invest in the talent that will capitalize on this niche technology. However, acquiring and retaining, the right kind of ML talent continues to remain a significant challenge for organizations. Zinnov's study explained that a large contributor to this challenge is the skewed concentration of the niche ML talent.