Google has one of the largest machine learning stacks in the industry, currently centering on its Google Cloud AI and Machine Learning Platform. Google spun out TensorFlow as open source years ago, but TensorFlow is still the most mature and widely cited deep learning framework. Similarly, Google spun out Kubernetes as open source years ago, but it is still the dominant container management system. Google is one of the top sources of tools and infrastructure for developers, data scientists, and machine learning experts, but historically Google AI hasn't been all that attractive to business analysts who lack serious data science or programming backgrounds. The Google Cloud AI and Machine Learning Platform includes AI building blocks, the AI platform and accelerators, and AI solutions.

What best describes the scope of your process automation program? Primary drivers for investment center around: - Improving operations (optimization and reducing process errors) - Enable workers to better serve customers Base: n105 manager level or above from operations groups, shared services, finance/accounting and other lines of business Source: A commissioned study conducted by Forrester Consulting on behalf of November 2017 Q14. What are the primary drivers for your investment in RPA? 68% 58% 43% 40% 34% 28% 15% 14% Improve the optimization of operations Reduce process errors Augment human intelligence to free up workers to focus on more strategic tasks To complete tasks for internal employees who can better support customers Lower costs by replacing humans performing low value tasks Reduce cycle time for revenue generation transactions Improve compliance with regulations / regulatory bodies according to the country the office is based Link RPA with chatbots and self-service support to raise customer self-service experiences 6. 6 2017 FORRESTER. RPA Definitions › Attended RPA is defined as: "Automation that interacts in real time with humans who initiate and control robot tasks, often embedding functions within apps., generally associated with front-office, agent-led activities." What are your organization's plans when it comes to digital workers for intelligence augmentation (IA)?

A new Forrester Research report, Predictions 2018: Automation Alters The Global Workforce, outlines 10 predictions about the impact of AI and automation on jobs, work processes and tasks, business success and failure, and software development, cybersecurity, and regulatory compliance. We will see a surge in white-collar automation, half a million new digital workers (bots) in the US, and a shift from manual to automated IT and data management. "Companies that master automation will dominate their industries," Forrester says. Here's my summary of what Forrester predicts will be the impact of automation in 2018: Automation will eliminate 9% of US jobs but will create 2% more. In 2018, 9% of US jobs will be lost to automation, partly offset by a 2% growth in jobs supporting the "automation economy."

Setting up the data-to-action process is hard work. Companies cannot effectively buy it in the marketplace, and those that try to avoid the work or take shortcuts will be disappointed. The MIT Sloan Management Review article, which BCG co-authored, cites one pharma executive who described the products and services that AI vendors provide as "very young children." The vendors "require us to give them tons of information to allow them to learn," he said, reflecting his frustration. "The amount of effort it takes to get the AI-based service to age 17 or 18 or 21 does not appear worth it yet. We believe the juice is not worth the squeeze."

There's a high-stakes race under way in Silicon Valley to develop software that makes it easy to weave artificial intelligence technology into almost everything, and Google has sprinted into the lead. Google computer scientists including Jeff Dean and Greg Corrado built software called TensorFlow, which simplifies the programming of key systems that underpin artificial intelligence. That helps Google make its products smarter and more responsive. It's important for other companies too because the software makes it dramatically easier to create computer programs that learn and improve automatically. What's more, Google gives it away.

Forrester surveyed business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems. This gap reflects growing interest in AI, but little actual use at this time. We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models. But the burning question is: how can your enterprise use AI today to crush it?

As I read this post from the World Economic Forum, This is why China has the edge in Artificial Intelligence, what struck me wasn't whether China has an edge in AI, or even if I care. It made me wonder, are these factors essential to building a solid foundation for AI? Does high performance in these areas give an edge to AI projects? And, overall, my answer was: somewhat, but misleading. Your focus should not be on the amount of data, but the data available that could apply to the problem you have defined.

Forrester surveyed business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems. This gap reflects growing interest in AI, but little actual use at this time. We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models. But the burning question is: how can your enterprise use AI today to crush it?

Forrester surveyed business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems. This gap reflects growing interest in AI, but little actual use at this time. We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models. But the burning question is: how can your enterprise use AI today to crush it?

There's a high-stakes race under way in Silicon Valley to develop software that makes it easy to weave artificial intelligence technology into almost everything, and Google has sprinted into the lead. Google computer scientists including Jeff Dean and Greg Corrado built software called TensorFlow, which simplifies the programming of key systems that underpin artificial intelligence. That helps Google make its products smarter and more responsive. It's important for other companies too because the software makes it dramatically easier to create computer programs that learn and improve automatically. What's more, Google gives it away.