The ongoing evolution in advanced analytics and other emerging technologies is transforming the operational processes in government and public service agencies throughout the world. With the help of these technologies, such organisations are trying to address citizen demands, helping to overcome persistent challenges such as regulatory compliance, outdated legacy IT infrastructures and organisational cultures, according to a new study report from Accenture. In its latest report titled, Emerging Technologies in Public Service, Accenture surveyed nearly 800 public service technology professionals across nine countries to identify emerging technologies being implemented or piloted. The technologies include advanced analytics/predictive modelling, the IoT, intelligent process automation, video analytics, biometrics/identity analytics, machine learning, and natural language processing/generation. The survey found that while more than two-thirds, (70%) of public sector agencies are evaluating the potential of emerging technologies, only a small percentage (25%) is moving beyond the pilot phase to full implementation.

Not long ago, audits could be performed only by teams of accountants manually scouring reams of financial information. But given the explosion of data in today's digital world, it's critical that the audit profession evolves its traditional processes and embraces advanced technology tools--including robotics, automation and cognitive technology. By doing so, it can uncover insights that allow the audit to continue to be relevant and effective in helping investors make important financial decisions. Learn how cognitive technology is changing the financial statement audit. Cognitive technology--also known as artificial intelligence--can go through a vast amount of data faster and more precisely than any person.

Artificial intelligence is the yin and blockchains are the yang of digital business. Artificial intelligence is the yin and blockchains are the yang of digital business. While AI helps us assess, understand, recognize and decide, blockchains can help us verify, execute and record. While the machine learning methods that are a part of AI help us find opportunity and improve decision making, smart contracts and blockchains can automate verification of the transactional parts of the process. AI and blockchains in that sense are complementary and synergistic.

Get the big picture of how machine learning works, its relationship to artificial intelligence, and where companies are putting it to work. Machine learning brings humans and machines closer together by enabling humans to "teach" machines. Machines learn by processing a training data set, which tunes an algorithm. A valid training set contains the features necessary to tune the algorithm. The algorithm allows machines to execute a task, such as classifying email.

Software's immersion within businesses, process and objects has expanded the scope of what could be automated. When you add in rapid advances in artificial intelligence, you start seeing a lot more interest in technologies that are increasing how well machine sense, learn and act. Investors are taking notice--investments in artificial intelligence start-ups by venture capitalists have increased roughly 20x in the last four years. What's your Intelligent Automation play?

By 2030, technology will have made banks and banking invisible to customers, hidden by Siri-like personal assistants that cull data from our connected lives to fulfil daily personal and financial obligations, claims KPMG.

But what if you are not one of these? I received the following job ad in my mailbox (see below in italics), from a third-party recruiter, and it's probably for a data science position at Nike near Portland, Oregon (my guess). Basically, it's a 6-month gig to build an A/B platform. I discuss here a few aspects that make this job ad unlikely to attract talent, as well as remedies. This skills mix is not found in typical employees.

There is much hype over big data and new analytical technologies, some of it justified, much of it not. In this article, we explore the possibilities and the limitations of technology, and discuss how it can practically improve your business strategy. We believe man's ability to think and formulate hypotheses is an art that is difficult for machines to replace. Analytics cannot yet do your strategizing for you. Nor can artificial intelligence (AI) and cognitive computing engage creatively and emotionally with employees and stakeholders.

Succeeding in today's fast-paced business economy requires companies to harness data quickly and at scale. As the volume, velocity, and variety of data increase, it's becoming necessary to use machine learning and artificial intelligence (AI) to sift through all the incoming information, make sense of it, and accurately predict future business direction. It takes the right expertise, the right tools, and the right data to achieve the promise of machine learning. Even with all of those factors in place, it's still easy to get it wrong. "Machine learning gives us a very powerful set of techniques for making predictions, but it can also lead to disastrous results if you don't understand what your machine learning algorithm is doing," said Spencer Greenberg, a mathematician and founder of decision-making website ClearerThinking.org, in an interview.