What do I mean by the Artificial Intelligence (AI) revolution? With all the AI hype, it is worth explaining it again from my point of view. Coined by Stanford University researcher John McCarthy, AI is the ability of a machine or a computer to think and learn – and therefore act in ways that are smart. The broad concept or idea here is to build machines capable of thinking, acting and learning like humans. In the past decade, AI has been cited as one of the transformative technologies that have made big strides in many industries including retail, healthcare, banking and finance, agriculture, manufacturing, travel and entertainment, education, public administration and many more.

Congress wants to pour hundreds of billions (yes with a B) of dollars into the federal government to increase the nation's competitiveness in emerging technology and, in particular, to accelerate the development of artificial intelligence (AI) technologies that are vital to protecting our national security. The bipartisan support shown for the U.S. Innovation and Competition Act (USICA) – the bill that provides these funds – is a noteworthy and important step in ensuring the United States is resilient and competitive in the 21st century. And that kind of money is nothing to sneeze at. But can the federal government manage to spend it? Thanks to China's aggressive, whole-of-nation approach to emerging technology and the ubiquity of AI technologies that adversaries big and small are now poised to exploit, there is a sudden urgency around AI and national security.

Business intelligence is prime for revolution -- we're calling it Data as a Service. After decades of investing in business intelligence (BI), most employees still don't have access to trusted, real-time insights. Traditional business intelligence relies on the neatly choreographed arrangement of staging servers, pre-calculated cubes, batch report servers, static dashboards, PDF and Powerpoint reports, desktop visualization tools, and many other convoluted and ungoverned methods of sharing data in the enterprise. What do all of these have in common? They reflect the limitations of the existing on-prem analytics infrastructure.

Enterprises are looking for applications and Cloud Service Providers to help them operate more efficiently through the use of Machine Learning (ML) and Artificial Intelligence (AI) to deliver more effective resource management. Achieving this will require the environment or application to understand when it needs more resources and then automatically scaling up those resources to meet the increased demand. Conversely, the technology will need to understand when specific resources are no longer needed and safely turn them off to minimize costs. Today such dynamic resource allocation can be unreliable or must employ an inefficient manual process, forcing Cloud customers to either spend more than necessary or fall short of meeting service levels during periods of peak demand. Enterprises will seek to take full advantage of the Cloud's agility by re-architecting their application/technology stacks to optimize them specifically for the Cloud environment.

The Oil & Gas sector is ripe for innovation, particularly when it comes to Artificial Intelligence (AI). A recent report conducted by Markets & Markets suggested that the value of AI within the Oil and Gas industry could reach a monumental $2.85 billion by 2022 – with an astonishing compound annual growth rate (CAGR) of 13%. Right now, the potential application of AI in Oil and Gas is broad and diverse, from process efficiencies and facilities management and safety, to forecasting, planning and surveying. We recently explored how augmented reality (AR) is already revolutionising the oil and gas sector and AR in the new enterprise. One fantastic example of how AI is impacting the Oil and Gas industry is a recent initiative conducted by ExxonMobil.

While the popular image of artificial intelligence seems to come from a Hollywood blockbuster, the reality is less epic and more pervasive. "AI is like a spreadsheet on steroids," explains Xaxis VP of product engineering and AI expert Sara Robertson in the new report "Digital Strategy in the Age of Artificial Intelligence." The result is an improved way for ad buyers to find and define audiences, refine creative messaging, generate audience personas and develop bidding strategies. But the most powerful potential of AI lies in its ability to optimize toward business outcomes rather than simple metrics. That requires advertisers to conceive and build campaigns around the unique opportunities and strategies afforded by AI. The truth is, AI isn't about robots.

In this opinion piece, Melotti Media founder Christopher Melotti (pictured below) explores how voice-activated virtual assistants will significantly change the landscape of marketing, content creation and copywriting. The next revolution in technology and marketing is voice. The age of voice-activated virtual assistants and vocal command technology is upon us – Apple's Siri, Google's Home, Amazon's Alexa, Samsung's Bixby, Microsoft's Cortana, Ozlo, X.ai, just to name a few. History shows that the businesses who embrace new opportunities like this are the ones to reap to rewards, while those who lag behind become quickly ignored by customers. My message to brands: don't get left behind.

During the digitization process, most of the knowledge is lost, and it is nearly impossibly to re-capture the lost knowledge using existing data alone. Often, the input of an individual familiar with the situation is required to understand the context surrounding the data. The ability to go beyond existing data and capture implied knowledge significantly improves the performance of the machine learning systems. Today, intelligent systems are designed and built by engineers and data scientists. Statistically significant relationships and patterns are revealed and used to create an information model.

With Version 6.0, Kinetica introduces user-defined functions (UDFs), enabling GPU-accelerated data science logic to power advanced business analytics, on a single database platform. User-defined functions (UDFs) enable compute as well as data-processing, within the database. Such'in-database processing' is available on several high-end databases such as Oracle, Teradata, Vertica and others, but this is the first time such functionality has been made available on a database that fully utilizes the parallel compute power of the GPU on a distributed platform. In-database processing in Kinetica creates a highly flexible means of doing advanced compute-to-grid analytics. This industry-first functionality stands to help democratize data science.

Artificial Intelligence (AI) and robotics are no longer distant concepts but are fast becoming a reality. These new technologies will present a range of benefits for a number of industries, including those beyond the manufacturing sector where these are already being used. Forward thinking companies are embracing the new technological revolution – Industry 4.0 – by adopting AI and robotics. Take the example of chatbots. Businesses are increasingly using AI to allow computers to understand and respond to queries from customers.