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Continuous Feedback Is Key To Taking Your AI From “Good to Great”

Deploying AI instantly brought value and growth to many businesses. However, it is well established that sustaining the value over time, not to mention maximizing it, could be quite challenging. Continuous optimization is the key to successful AI deployments. Begin with a product that’s good enough, learn from how it performs in the real world (especially as the data environment changes), and then improve; then learn and improve again, and so on. It’s a bit of an obvious insight, but it is rare for AI-driven products to be perfect from day one. 

To accomplish continuous optimization you need continuous feedback. You need “eyes and ears” observing your data and models and telling you whether they’re performing well. That’s easier said than done, for various reasons. These reasons are outlined below. 

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Using Machine Learning for Log Analysis and Anomaly Detection: A Practical Approach to Finding the Root Cause

There are many articles on applying machine learning for log analysis. However, most of them are dated, academic in nature, or don’t focus on practical outcomes. On DZone, the last time an article covering how ML can be used for log analysis was published 5 years ago.

In this article, we want to share our real-life experience on using ML/AI for log analysis and anomaly detection with the specific purpose of automatically uncovering the root cause of software issues.

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7 Things You Probably Didn’t Know About AI Engineering

As we all know, the business world is changing at a rate faster than you can imagine. To cope up with it, we need to adopt dynamic technologies and engineering practices so our businesses keep running seamlessly. Out of the numerous emerging technologies, one of the most important and significant ones is Artificial Intelligence Engineering or AI Engineering. If you are new to this field, do not worry, as everything that you need to know about AI Engineering will be discussed in this blog. 

Let us start with the basics: What is AI Engineering?

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How Machine Learning Helps Analytics Be More Proactive

"Many organizations claim that their business decisions are data-driven. But they often use the term "data-driven" to mean reporting key performance metrics based on historical data — and using analysis of these metrics to support and justify business decisions that will, hopefully, lead to desired business outcomes. While this is a good start, it is no longer enough".

The traditional role of data and analytics has always been in supporting decision-making. Now, they are applied where they have never been before. Today data and analytics are not only used for describing, diagnosing, predicting, or even recommending the best actions but also triggering those actions automatically. The motivation behind this new area of application is the goal of many businesses to reduce task performance time and the volume of human labor.

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Cognitive Framework for Wildlife Monitoring and Management

Wildlife Issues

Despite significant work to protect wildlife and manage national parks and forests, many incidents continue to occur every year, either causing loss to human beings or to wildlife. 

While we are using Artificial Intelligence to solve complex problems such as predicting failures of complex equipment, performing natural language processing, making driver-less cars, and so on, applying technology to protect wildlife needs more work. Presented here is a non-evasive method and framework which effectively uses IoT enabled cognitive systems to make a drastic improvement in this domain.

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How AI & Machine Learning Have Impacted the COVID-19 Pandemic

In recent years, machine learning has found applications in new and often unexpected areas. With the novel coronavirus outbreak in 2019 and 2020, it makes sense that many have tried to apply machine learning and artificial intelligence to various problems relating to the disease. From modeling the spread of the disease to searching for possible drugs and vaccines, machine learning has been integral to understanding many of the problems caused by the COVID-19 pandemic.

Case Study: Disease Dynamics

A simple internet search will lead you to hundreds of dashboards showing the current number of coronavirus cases around the world. This stems from how easy it is to access data relating to the virus, especially from reputable sources like Kaggle or Johns Hopkins. This data, along with sophisticated models for disease dynamics has, for example, enabled predictive modeling for the number of people who actually have the virus and the risk of hosting an event in any county in the US. 

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Creating a Non-English Chatbot Solution in Teneo

When you created your first solution you chose English as the bot’s language and the tutorials on this site assume your bot understands English. However, Teneo supports many more languages, which you can read about here: Languages.

The evaluation environment that is created for you when you sign up contains the resources needed that offer advanced support for Dutch, English, French, German, Norwegian and Swedish. On this page we will show you how you can create a German solution, but the same principle applies to French as well.

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Job Hunting in the Age of AI: How to Upskill for the 5 Hottest New Jobs

You could worry about the jobs AI will obliterate or focus on the exciting new jobs it will create. The latter will take you places.

AI is transforming global job markets. From reshaping career paths to developing new markets, it is an exciting time for people who wish to learn new skills and persevere. A report from the World Economic Forum (WEF) states that AI will create 58 million new jobs by 2022. Those who wish to capitalize on this enormous opportunity need to focus on reskilling and upskilling and take a proactive approach to learning so they can land some of the most sought-after jobs in the modern AI era.