Welcome!

SDN Journal Authors: Yeshim Deniz, Liz McMillan, Elizabeth White, Pat Romanski, TJ Randall

Related Topics: @CloudExpo, Cognitive Computing , Machine Learning , Artificial Intelligence, @DXWorldExpo, FinTech Journal, @ThingsExpo

@CloudExpo: Article

Megatrend of #ArtificialIntelligence | @CloudExpo #BigData #AI #ML #DL #IoT

Artificial Intelligence is the discipline of thinking machines

There are seven key megatrends driving the future of enterprise IT. You can remember them all with the helpful mnemonic acronym CAMBRIC, which stands for Cloud ComputingArtificial IntelligenceMobilityBig DataRoboticsInternet of ThingsCyberSecurity.

In this post we dive deeper into Artificial Intelligence.

Artificial Intelligence is the discipline of thinking machines. The field is growing dramatically with the proliferation of high powered computers into homes and businesses and especially with the growing power of smartphones and other mobile devices. Artificial intelligence software is assisting people in most every discipline. The many functions of AI are considered by many to be threatening many human jobs across multiple industries, but others consider it a great producer of jobs since it will help create entirely new industries and free more humans to innovate and create.

artificial-intelligence-head

You can see our reference to Truly Useful AI You Can Use Right Today. Follow this link to track the highest ranked, enterprise ready Artificial Intelligence Companies.

A snapshot of the trend right now indicates:

  • With business models returning profit now, all indications are AI will continue to improve.
  • AI and Machine Learning and Cognitive Computing are being coupled with incredibly low cost cloud computing
  • No accepted protocol for security in AI. This is a huge negative.
  • Creators use a “generate and test” approach to creating functionality.
  • AI seems easier to deceive than current computer software.

Open questions decision-makers should track include:

  • Will job displacement be a crisis?
  • Will companies use AI for good?
  • Will there ever be a widely-accepted security framework for AI?
  • Can behavioral analytics enhance security?

Books we appreciated for context around Artificial Intelligence include:

For deeper considerations of the impact of Artificial Intelligence on enterprise IT it is important to track all seven MegaTrends and consider them together. Dive deeper into all the trends and examine their impact on your organization via a CTOvision Pro membership, available for enterprises and individuals.

You can launch your examination of the MegaTrends through the categories menu at CTOvision, or directly via these links:  Cloud ComputingArtificial IntelligenceMobilityBig DataRoboticsInternet of ThingsCyberSecurity.

More Stories By Bob Gourley

Bob Gourley writes on enterprise IT. He is a founder of Crucial Point and publisher of CTOvision.com

CloudEXPO Stories
While some developers care passionately about how data centers and clouds are architected, for most, it is only the end result that matters. To the majority of companies, technology exists to solve a business problem, and only delivers value when it is solving that problem. 2017 brings the mainstream adoption of containers for production workloads. In his session at 21st Cloud Expo, Ben McCormack, VP of Operations at Evernote, discussed how data centers of the future will be managed, how the public cloud best suits your organization, and what the future holds for operations and infrastructure engineers in a post-container world. Is a serverless world inevitable?
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-centric compute for the most data-intensive applications. Hyperconverged systems already in place can be revitalized with vendor-agnostic, PCIe-deployed, disaggregated approach to composable, maximizing the value of previous investments.
Wooed by the promise of faster innovation, lower TCO, and greater agility, businesses of every shape and size have embraced the cloud at every layer of the IT stack – from apps to file sharing to infrastructure. The typical organization currently uses more than a dozen sanctioned cloud apps and will shift more than half of all workloads to the cloud by 2018. Such cloud investments have delivered measurable benefits. But they’ve also resulted in some unintended side-effects: complexity and risk. End users now struggle to navigate multiple environments with varying degrees of performance. Companies are unclear on the security of their data and network access. And IT squads are overwhelmed trying to monitor and manage it all.
Machine learning provides predictive models which a business can apply in countless ways to better understand its customers and operations. Since machine learning was first developed with flat, tabular data in mind, it is still not widely understood: when does it make sense to use graph databases and machine learning in combination? This talk tackles the question from two ends: classifying predictive analytics methods and assessing graph database attributes. It also examines the ongoing lifecycle for machine learning in production. From this analysis it builds a framework for seeing where machine learning on a graph can be advantageous.'
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.