Wish your network could predict its own problems and fix them automatically? Cisco believes it has the technology you need. The networking tech giant announced today what it said is the culmination of two years of work: an analytics engine that can predict network issues before they happen, and with enough integration and training even fix problems itself, Cisco said. Citing data from an in-house study, Cisco said that 45 percent of IT leaders it surveyed cited responding to disruptions as their biggest networking challenge of 2021. Predictive analytics technology, coupled with "enormous amounts of historical [networking] data," is a potential solution, Cisco said.

At Kinaxis, who we are is grounded in our common belief that people matter. Each one of us plays an important part in accomplishing our work, building our culture and making a global impact. Every day, we're empowered to work together to help our customers make fast, confident planning decisions. This is how we create a better planet – for each other, for our customers and for generations to come. Our cloud-based platform RapidResponse ensures that the products we need – everything from medicine and cars, to day-to-day items like toothpaste – make it to market and into our hands when we need them with minimal ecological footprint.

Baskerville is a machine operating on the Deflect network that protects sites from hounding, malicious bots. It's also an open source project that, in time, will be able to reduce bad behaviour on your networks too. Baskerville responds to web traffic, analyzing requests in real-time, and challenging those acting suspiciously. A few months ago, Baskerville passed an important milestone – making its own decisions on traffic deemed anomalous. The quality of these decisions (recall) is high and Baskerville has already successfully mitigated many sophisticated real-life attacks.

As Chief Data Officer for the OSTHUS Group, Eric Little co-founded LeapAnalysis, a new approach to AI, data integration and analytics. LeapAnalysis is the first fully federated and virtualised search and analytics engine that runs on semantic metadata. It allows users to combine semantic models (ontologies) with machine learning algorithms to provide customers with unparalleled flexibility in utilizing their data. Nearly all technologies surrounding AI and analytics are purely statistical in nature, using algorithmic approaches that are not incredibly novel, such as decision trees, neural networks, etc. The logical framework that contextualises these things is often missing.

AI will play a large role in the era of Big Data. We have no doubt because the future of HVAC reveals AI and IoT. The debate about IoT market strategies will continue because of the expansive, even wild projections for the IoT market. Unfortunately, hype leads to myth, and myth leads to confusion. Moving forward means taking a step back to look for clues about how the IoT market could evolve.

Apache Spark is a leading platform for large-scale data mining, batch processing and stream processing. Touted as a "lightning-fast unified analytics engine," Spark modernizes data analytics with machine learning to help businesses uncover patterns at new levels. Best of all, Spark is included within many other software solutions, so this powerful tool may already be part of your modern data analytics infrastructure. From its inception at the AMPLab at U.C. Berkeley in 2009, Spark has become one of the key big data distributed processing frameworks in the world. It's used by banks, telecommunications companies, games companies, governments, and nearly all major tech giants, including Apple, Facebook, and Microsoft.

The last few years have seen an explosion in the number of global companies that have started leveraging machine learning (ML) to convert data to business opportunities. Is there a role of machine learning in enterprise networking? The answer is a loud and resounding YES. However, more often than not, I see a number of IT companies starting this exploration of ML because it makes headlines and helps creates a buzz. This approach would fizzle out as it would not solve any major problems for their customers.

We hear a lot these days about how AI is changing the marketing technology landscape, helping us sell products and services in the 21st century. But during my two-week journey through Israel's startup scene, it was a medtech company that struck a nerve with me -- a deep learning imaging analytics startup called Zebra Medical Vision. Using AI to recommend related products, build a million split tests, or determine the optimal time to push a discount voucher may be effective from a marketing standpoint, but these applications are hardly life-changing. And they wouldn't have helped save my dad's life. He passed away from lung cancer in February this year, and there is a good chance that an early and accurate diagnosis could have given him a better chance at beating "the big C." Zebra's technology is making it possible to catch misdiagnosed diseases, early-stage cancers, and other life-threatening ailments, and the company today announced a significant change in its business model that makes its AI-powered medical scan recognition for hospitals more affordable.

A while ago Angela Lipscomb from SAS got in touch with me to get me introduced to SAS's concept of a Customer Decision Hub. Their Customer Decision Hub is a solution concept that shall allow organizations to derive insights and to trigger actions from interactions with external parties, like customers based upon rules and the derived insights. At the same time standard communications can get suppressed based upon the same set of rules. In other words, the Customer Decision Hub fosters customer engagement based upon inbound signals that get analyzed and processed through the organization. Why is this remarkable, I hear you asking?

Intermountain Healthcare joined forces with Israeli startup Zebra Medical Vision to develop imaging algorithms that can improve patient care. Zebra sells an engine for physicians and healthcare providers to analyze imaging records. The company said its platform has already yielded insights that have been validated using hundreds of thousands of cases in the fields of bone health, cardiovascular analysis, liver and lung indications. Intermountain said that Zebra's Analytics Engine will enable it to perform large-scale analytics against imaging data for the first time and essentially apply machine learning algorithms to those data sets. Under terms of the deal, Intermountain also led a!2 million Series B round of funding, which Zebra CEO Elad Benjamin wrote in a blog the company will use to populate its Analytics Engine with "hundreds of algorithmic insights needed in order to automatically diagnose imaging scans."