The digital economy requires increased data-driven decision-making based on AI. The adoption speed of deep learning in business analytics is surprisingly low. Deep learning is benchmarked against traditional machine learning models. Deep learning does not show superior performance on structured data. GBM is the go-to model for predictionsin business analytics.

The U.S. Army is a large organization with many stakeholders, moving parts, and money inflows and outflows. One could say it's not too different from a large private sector company. It faces many of the same stressors of a large private sector company as well, including deciding how to allocate its budget. In recent years, the Army turned to artificial intelligence (AI) to help tackle budget allocation. The challenge was to find a solution for contracting officers trying to accurately predict which contracts were most likely to end up underspending their funding.

In the early 19th century, textile workers in Nottingham rebelled against their factory owners As factory owners began to use new machinery that reduced the number of employees and factories they needed, workers felt that their skillset was being wasted and their livelihoods threatened. This rebellion was the Luddite movement. The term ‘Luddite’ has since been used to describe those who opposed industrialisation, automation, and in more recent times some cutting-edge technologies threatening to disrupt the mainstream. When it comes to artificial intelligence (AI), you can sympathise with the Luddite philosophy to an extent. The idea that we can teach

Artificial Intelligence promises an exciting future and tremendous growth, provided that legal professionals able to navigate their business in this novel environment. Many companies make massive investments in artificial intelligence (AI), and more and more AI products and technologies are being launched by companies that are not traditional software companies. This signals a transition where traditional engineering companies invest in software capabilities and position AI as a critical way to disrupt their markets and gain market share. That transition does not come without challenges for legal teams. Lawyers need to keep abreast of new and fast-evolving technologies and familiarise themselves with novel technical concepts like "machine learning" or "black box AI".

I trained as an electrical engineer and had a career that used that skillset across multiple industries including finance, oil & gas and aerospace. I eventually settled into the healthcare sector where I've spent the last 13 years. I realised that my career has been dominated by a common theme: the importance of data. Specifically, I've come to appreciate that in any industry, data can and should play a central role in decision-making and demand prediction. At GE, I realised that drawing inspiration from other industries' use of advanced AI-driven analytics could transform the healthcare sector.

Data is a guiding light. Staring directly into it can blind us, but with it, we can see everything else. The facts and figures present in any given dataset tell stories, identify trends, and chart courses of action. But none of these come to fruition by the mere presence of information. Empirical evidence usually has this intended effect when those in possession of it commit to data analytics -- a critical foundational need in every company that wants to analyze data and use it to make decisions.

In today's digital revolution, the realm of data is growing at an unprecedented rate and will continue to rise as businesses will leverage more smart technologies or devices. However, maintaining and processing these myriad amounts of data require massive computing power and the knowledge to use it. Moreover, companies these days are utilizing data to make data-driven decisions and this pursuit of data-driven decision-making can make them to seek out data science. In the modern business context, business leaders often claim unfamiliarity with the basics of data science. However, they don't need to consider intimate details of data science processes.

Adrienne Gormley, head of EMEA and VP of global customer experience at Dropbox "There is a lot of attention, rightfully so, on AI having the ability to predict user needs and analyse data patterns in a more effective manner. For instance, machine intelligence can surface the content you will need for a meeting or recognise images, so you don't have to remember file names. Machines can do the more structured tasks, which will enable humans to focus on the work that matters. "In the future, we will see AI take on more complicated tasks. As AI continues to develop, humans will have more natural and seamless interactions.

Big data can mean big business. But as Texas copes with the destruction of Hurricane Harvey, which ravaged the state late last month, and with Irma barreling over the Caribbean toward Florida, and Mexico shaken by the most powerful earthquake in 100 years, can mining vast amounts of data also help save lives from the fury of natural disasters? Find and rescue victims from rubble? Governments are looking to the same sophisticated analytics techniques that are predicting -- with fast-improving accuracy -- the paths and destruction potential of increasingly fearsome storms to better prepare for and tend to their constituents' needs during calamities. But whether such data-driven decision-making is actually making a difference is, in 2017, an open question.

Data Program Management (DPM) is the intelligent application of data management tools, technologies, and processes to improve the usefulness of an organization's data.