Looking for a valuable way to use your data? With anomaly detection, you can use it to stop a minor issue from becoming a widespread, time-consuming problem. By proactively detecting abnormal behavior, your company can ensure the right people are alerted to unexpected changes and are able to make faster decisions about what actions need to be taken. Join us for this "ask me anything" webinar to hear from a panel of data scientists on the basics of anomaly detection, common use cases, and some key techniques to keep in mind as you get started. We've got the answers to all your questions about anomaly detection.

Turns out we could and it was a great opportunity for me to learn more about Machine Learning and neural networks and artificial intelligence from my friend Jabril.

At the work recently, I wanted to make some interesting start-up pitch (presentation) ready animated visualization and got some first experience with spatial data (e.g. I enjoyed working with such a type of data and I wanted to improve on working with them, so I decided to try to visualize something interesting with Bratislava (Slovakia) open-data and OpenStreetMaps. I ended with animated maps of violations on Bratislava streets through the time of 2 and a half years. Since spatial time series are analyzed in this post, it still sticks with the blog domain and it is time series data mining You can read more about time series forecasting, representations and clustering in my previous blog posts here. The ultimate goal is to show where and when are the most dangerous places in the capital of Slovakia – Bratislava.

A 6 Step Field Guide for Building Machine Learning Projects Have data and want to know how you can use machine learning with it? Sep 21 · 19 min read I listened to Korn's new album on repeat for 6-hours the other day and wrote out a list of things I think about when it comes to the modelling phase of machine learning projects. Thank you Sam Bourke for the photo. The media makes it sound like magic. Reading this article will change that. It will give you an overview of the most common types of problems machine learning can be used for. And at the same time give you a framework to approach your future machine learning proof of concept projects. How is machine learning, artificial intelligence and data science different? These three topics can be hard to understand because there are no formal definitions. Even after being a machine learning engineer for over a year, I don't have a good answer to this question. I'd be suspicious of anyone who claims they do. To avoid confusion, we'll keep it simple. For this article, you can consider machine learning the process of finding patterns in data to understand something more or to predict some kind of future event. The following steps have a bias towards building something and seeing how it works. You may start a project by collecting data, model it, realise the data you collected was poor, go back to collecting data, model it again, find a good model, deploy it, find it doesn't work, make another model, deploy it, find it doesn't work again, go back to data collection.

Massive IoT including the large number of resource-constrained IoT devices has gained great attention. IoT devices generate enormous traffic, which causes network congestion. To manage network congestion, multi-channel-based algorithms are proposed. However, most of the existing multi-channel algorithms require strict synchronization, an extra overhead for negotiating channel assignment, which poses significant challenges to resource-constrained IoT devices. In this paper, a distributed channel selection algorithm utilizing the tug-of-war (TOW) dynamics is proposed for improving successful frame delivery of the whole network by letting IoT devices always select suitable channels for communication adaptively.

Inc to highly-complex products from IBM, artificial intelligence (AI) has become central to corporate strategy. While the use of AI is mixed across organizations and industries, early adopters are quickly realizing that building trustworthy AI programs – using related data and technologies ethically – can have both short- and long-term benefits when properly supported by leadership. CorpGov: How do leaders at AI-using organizations manage ethics? Mr. Saif: We polled over 550 C-suite and other executives working at organizations using AI, and found that nearly half expect to increase AI use for risk management and compliance efforts in the coming 12 months. Yet, just one in five said their organizations have an ethical framework in place for such use of AI.

While big data has long been harnessed by leaders across virtually every industry to make key business decisions, today, the field is a proven and established subset of tech. With an ever-growing list of professions and use-cases surrounding big data, trends have emerged in how that data is collected, organized and used. Obviously, developments in big data mean different things to different companies. We spoke to data pros at 11 tech companies to learn more about the big data trends they're keeping up with and how they're impacting business operations. Trusting one's gut is a practice that can lead to success in many professions. But data science is not typically seen as one of them, since trusting numbers is generally considered safer than trusting instincts. However, Data Science Manager David Thompson at Pareto Intelligence said that sentiment is changing. The leader at the healthtech company said intuition plays an increasingly large role in how industry pros work with big data. What are the top big data trends you're watching that are significantly impacting the industry? Lately, and rightfully so, there has been a significant focus on social determinants of health -- the conditions in which people are born, grow up, live, work and age that impact health status.

How can we bridge the gap between patient expectations and the complex reality of medical diagnosis? The answer, and the future of healthcare, lies in making self-service work through AI, AR, and video. The first two decades of this century saw an information revolution, thanks to widespread internet access. That, in turn, has led to a self-service revolution. We use mobile apps to book theatre tickets, check our bank balances, find the best restaurants.

Filing a patent is the clerical equivalent of pulling teeth -- at least in the U.S. It first requires inventors to determine the type of intellectual property (IP) protection they require (i.e., utility, design, or plant). Then they're on the hook to conduct a United States Patent and Trademark Office (USPTO) database search for similar inventions. If and only if the novelty of their idea passes muster are they allowed to proceed to the next step, which is preparing an application and fees. The system has motivated people like former aerospace engineer Dr. Stephen Thaler to turn to AI in pursuit of a better way. He, along with a team of legal experts and engineers, developed DABUS, a "creativity machine" that's able to generate ideas without human intervention.

We communicate with artificial intelligence via user interface, or UI. AI innovator Erica Lee joins Dirk and Jon to talk about the present and future of people communication with our smart machines. AI innovator Erica Lee joins Dirk and Jon to explore user interface and artificial intelligence. We talk about on-trend inputs like gestural and voice and how they contribute to augmentation and automation driven by artificial intelligence.