PARIS – Maxar Technologies will provide oceanographic data and saltwater fishing recommendations for SiriusXM's new Fish Mapping service announced Sept. 9 and available on Garmin International's GXM 54 satellite weather receiver. "Through our extensive experience in artificial intelligence and machine learning, Maxar gathers content from many sources and combines it with our powerful geospatial analytics system to deliver insights and answers that help customers be more predictive in their decisions," Jeff Culwell, Maxar chief product officer, said in a statement. "For SiriusXM Marine's Fish Mapping service, we're providing real-time intelligence that will give serious anglers a leg-up on the competition and help casual anglers enjoy more successful fishing trips." This is not a new business for Maxar. The company has created oceanographic datasets that highlight fishing recommendations for more than 20 years, according to the Sept. 9 new release.

Researchers at the University of North Carolina's Center for Geospatial Analytics (CGA) are using artificial intelligence (AI) and machine learning (ML) …

Normally when I do RF projects I use some sort of feature selection method to choose which features to use. Then I fit the RF model onto those features. Then to test accuracy / related metrics I use cross validation, confusion matrices, etc. However in this case I only have two given features. I don't want to just literally run a RF model on those two features as my whole entire project. I'm thinking gradient boosting is what I should learn?

Artificial Intelligence (AI) can be quite a challenging topic to truly comprehend, especially for business managers, entrepreneurs and investors that lack a deep academic background in the field. They may instinctively sense the massive potential of AI -- all the science fiction movies and TV shows that Hollywood churns out probably plays a part in this -- but they are often left wondering, how should I think about AI? How does AI actually work? The follow article addresses this gap by presenting two broad and fairly dominant intuitions of AI -- cognitive and statistical. Despite the relative fragmentation of the field and varied backgrounds of AI practitioners, the cognitive and statistical intuitions seem to reflect the ways of approaching AI today. If you can grasp one or both of these intuitions, then you will be better positioned to meaningfully participate in discussions around AI as a business stakeholder, as well as build and invest in AI opportunities. Think about the last time you had to study for a test with multiple choice questions. Figure 1 shows a very simple example of such a question.

If you are a Game of Thrones fan, you may be wondering why the Night King turned on the Children of the Forest. Wasn't he created by them for their protection and betterment? Did he at some point gain autonomy with a seemingly angry consciousness? Nevertheless, it remains that the Children of the Forest lost control over what they created, yielding a superior autonomous force that ultimately led to their extinction. This ominous tale may sound familiar to those in the emerging tech industry.

We address the problem of combining sequence models of symbolic music with user defined constraints. For typical models this is nontrivial as only the conditional distribu - tion of each symbol given the earlier symbols is available, while the constraints correspond to arbitrary times. Previ - ously this has been addressed by assuming a discrete time model of fixed rhythm. We generalise to continuous time and arbitrary rhythm by introducing a simple, novel, and efficie nt particle filter scheme, applicable to general continuous ti me point processes. Extensive experimental evaluations demo n-strate that in comparison with a more traditional beam searc h baseline, the particle filter exhibits superior statistica l properties and yields more agreeable results in an extensive human listening test experiment.

The task of predicting fine grained user opinion based on spontaneous spoken language is a key problem arising in the development of Computational Agents as well as in the development of social network based opinion miners. Unfortunately, gathering reliable data on which a model can be trained is notoriously difficult and existing works rely only on coarsely labeled opinions. In this work we aim at bridging the gap separating fine grained opinion models already developed for written language and coarse grained models developed for spontaneous multimodal opinion mining. We take advantage of the implicit hierarchical structure of opinions to build a joint fine and coarse grained opinion model that exploits different views of the opinion expression. The resulting model shares some properties with attention-based models and is shown to provide competitive results on a recently released multimodal fine grained annotated corpus.