Broadly speaking, there are three types of artificial intelligence models: reinforcement learning, unsupervised learning and supervised learning.

In a reinforcement learning model, trainers set a series of algorithms against a given problem, watch to see which perform the best and then continue to refine the programs as they get closer and closer to the target. In an unsupervised learning model, a large set of data — typically unlabeled — is fed into an algorithm, and the model begins sorting things into different categories on its own.

Neural networks are an example of a supervised AI model. Trainers feed labeled data into an algorithm, teaching it to recognize the subtle changes that distinguish, for instance, a dog from a cat in a photograph. Neural networks are designed to continually learn over time, gradually reducing their “loss” (or miss rate).

Neural networks are already creating value across numerous industries.

2020 is over. And while it was an unprecedented year, the As a Service companies like Five9 helped make the widespread adoption of remote work a reality. For more As a Service information, check out my other blog series.

It takes a catastrophic event to stress test a system. Last year was just that event, as we saw massive amounts of internet traffic, cloud services up and down and sometimes degraded. Through it all, Five9 continued to function and moved forward with mergers and acquisitions.

City leaders may know they need a smart city strategy to attract and grow business, and to connect and serve citizens. But often, achieving the desired outcomes is harder than it sounds. These initiatives vary widely, with some struggling to get off the ground, while others achieve significant successes, such as aligning disparate, siloed systems.

To help establish the kind of success that drives further investment, I often suggest focusing on the development of the smart city “nervous system” platforms — the place where humans and data intersect for the purpose of better decision-making.

Let‘s face it: Cybersecurity teams are overwhelmed with work. There simply is not enough time in the day to perform all the high-value tasks they are responsible for, such as consulting with project teams, conducting risk analyses and developing strategic security plans. To make matters worse, security teams often find themselves consumed with time-sensitive and repetitive operational work. Anything they can do to reduce this burden increases their ability to add value to the organization.

Automation has the potential to help with this heavy lifting. Any time security teams find themselves performing work that requires following a repetitive process, it is likely that automation can play an important role in reducing the operational burden. In our work with CDW customers, we have found a few common areas where automation can help increase the efficiency and effectiveness of cybersecurity teams. Let’s look at a few examples.

In a previous blog post, I discussed using AI in the call center with virtual assistance. In this post, I want to go further to dissect the various pieces, components and decisions that need to be made to roll out a successful chatbot solution.

For many business and educational leaders, the resumption of work and learning amid the COVID-19 pandemic continues to be a challenge. Enforcing health and safety policies and detecting noncompliance can be difficult to accomplish, especially in high-traffic areas, but it’s crucial. Enhanced video surveillance can help. In recent months, many organizations have repurposed existing EVS systems, typically deployed for physical security, to support screening measures related to COVID-19, such as thermal imaging scans, occupancy management and contact tracing.

Layering face mask detection into video analytics, and potentially integrating it with access control systems, provides an additional layer of protection. As with occupancy management and contact tracing, many companies find that automated solutions are far more effective at mask monitoring than humans are.