Amazon Web Services Inc. is not only infusing its own tools with machine learning capabilities, the cloud giant is also providing technology for developers, scientists and engineers to run and manage thousands of complex machine learning workloads at the same time. An example can be found in how Ampersand Inc., a data-driven TV advertising sales and technology firm, uses AWS Batch to run 50,000 concurrent machine learning models in less than one day. A look at how AWS enables enterprises to put data to work with artificial intelligence and machine learning-powered low-code and automation initiatives will be the focus for theCUBE, SiliconANGLE Media's livestreaming studio, during its coverage of the AWS Summit San Francisco event, airing April 21. Ampersand's business model delivers data-driven insights to assist TV advertisers in planning and measuring campaigns for apps and networks in all U.S. markets. It is complicated, data-heavy work that requires the creation of machine learning models at scale to produce viewer insights.

Artificial intelligence and machine learning requires huge amounts of processing capacity and data storage, making the cloud the preferred option. That raises the specter of a few cloud giants dominating AI applications and platforms. Could the tech giants take control of the AI narrative and reduce choices for enterprises? Not necessarily, but with some caveats, AI experts emphasize. But the large cloud providers are definitely in a position to control the AI narrative from several perspectives.

The cloud-computing boom over the past 15 years dramatically lowered the barrier to accessing the virtually unlimited computing power of Amazon, Microsoft, and Google. But rather than simply renting out computing power for developers to run their own code, these cloud giants also sell pre-built tools for developers to integrate into their own apps or platforms. Artificial intelligence has become the largest category of these tools. Algorithms once reserved for deep-learning experts are now available to anyone with an internet connection and a credit card. Jerry Chen, a Greylock partner, called AI services the "whales of the cloud" in a report on the sector's landscape, since the billions invested by tech companies and venture capitalists far outpaces other cloud-computing categories.

The next generation of wireless networks, dubbed 5G, will have more capacity, faster speeds, and lower latency than its predecessor 4G. As a result, it's expected to bring technologies like augmented reality, self-driving cars, data-crunching Internet of Things devices, and even smart cities closer to the mainstream than ever before. Deeply entwined with cloud computing, 5G is expected to be the backbone of so many future products and services that it has the potential to power economic growth for decades to come, analysts predict. At the moment, 5G networks are still being rolled out by wireless carriers, and the public has yet to fully realize its benefits. But there are plenty of opportunities for startups and major companies alike, including in partnering with wireless carriers, deploying private and enterprise 5G networks, and developing 5G-enabled applications.

Every week, my team at Invector Labs publishes a newsletter that covers the most recent developments in AI research and technology. You can find this week's issue below. You can sign up for it below. Amazon, Microsoft and Google have embarked in a frantic race to dominate the artificial intelligence(AI) market. To some extent, AI has become one of the few areas of differentiation between the three cloud giants.

Compared with all the attention given to the giant AI-related investments made by tech giants, the investments being made by many enterprise software firms don't get as much attention. But as talks with industry execs often drive home, investments in software features that leverage AI/machine learning have also become a key R&D priority for many publicly-traded enterprise software firms -- even if their spending on such work can't compare with what tech giants are doing. When looking at how enterprise software firms are using "AI" to strengthen their offerings, it's worth remembering that there's a difference between broader investments in machine learning, and investments in deep learning in particular. Machine learning (ML) covers the general use of algorithms that analyze data to make conclusions and predictions, and which get better as more data is analyzed. Deep learning (DL) is a computationally demanding subset of machine learning that involves creating artificial neural networks (ANN) that in some respects function like a human brain.

You know the story already. Artificial intelligence (AI) and the machine learning (ML) engines that drive it are about to reinvent every aspect of business and propel us forward into a new era of intelligent business systems, ultra-connected networks and sentient machines that can help manage and direct our workloads for us. AI is arguably already the over-talked technology subject of the decade in spite of its undeniable second era now showing us real applications for the real world -- it's first era being the fanciful stuff of Hollywood Sci-Fi movies throughout the 1970s and 1980s. One of the main stumbling blocks is business tradition, that is – all too many enterprises will do'things' the way they've always been done because those systems just kind of work. These same firms run with a'why fix it if it's not broken' mentality.