Goto

Collaborating Authors

mapping


DeepMap: Implementing Artificial Intelligence into Safe Autonomous Vehicle Industry

#artificialintelligence

DeepMap leverages cutting-edge technologies like artificial intelligence to enhance mapping capabilities for the autonomous vehicle industry. It is a part of NVIDIA for scaling worldwide map operations and expanding the full-self driving expertise of NVIDIA. AI models are used to build high-definition maps to navigate the world without any potential accident. AI strategies of DeepMap are useful for NVIDIA to keep up with the unique vision and technology. Let's explore the implementation of AI in DeepMap to enhance the automotive industry efficiently. The integration of artificial intelligence in mapping can help in proper localization with constant upgradations.


Understanding The Importance Of Data For Machine Learning

#artificialintelligence

Data for machine learning is food. It just consumes it and then learns the relations between different data rather than understanding the data. So basically, all machines do, is find the relations between the different data. Be with me to understand why data is important and how come machines don't understand the data but find the relations between the data. Data is crucial for machine learning, and without data, machine learning is not possible. It requires data in one form or the other. Just like we humans need food for our development of mind and then when we get another type of data by visualizing, hearing, etc., and get experience from such data. That data plays a vital role in the type of human we will be in the future. In the same way, data for machine learning is important to grow its experience and ability to make decisions based on the data fed to it.


The Unintended Benefit of Mapping a GAN's Latent Space

#artificialintelligence

While trying to improve the quality and fidelity of AI-generated images, a group of researchers from China and Australia have inadvertently discovered a method to interactively control the latent space of a Generative Adversarial Network (GAN) – the mysterious calculative matrix behind the new wave of image synthesis techniques that are set to revolutionize movies, gaming, and social media, and many other sectors in entertainment and research. Their discovery, a by-product of the project's central goal, allows a user to arbitrarily and interactively explore a GAN's latent space with a mouse, as if scrubbing through a video, or leafing through a book. An excerpt from the researchers' accompanying video (see embed at end of article for many more examples). Note that the user is manipulating the transformations with a'grab' cursor (top left). The method uses'heat maps' to indicate which areas of an image should be improved as the GAN runs through the same dataset thousands (or hundreds of thousands) of times.


The ILIFE V3s Pro is a cheap robot vacuum for hands-off spot cleaning

Mashable

When you're used to doing everything from an app, it's easy to disregard a device that's not super connected when it could be. Take robot vacuums, for example: Shiny new features like self-emptying docks, smart mapping of your home's layout, and scheduling from your phone are almost expected at this point. The ILIFE V3s Pro is one of the most popular budget bots on Amazon. It's basic as hell, but given its more than 11,000 five-star reviews, it must be meeting expectations of some sort. After testing high-end robot vacuums like the Roomba s9 and Samsung Jet Bot, I have to ask: How well does a cheap robot vacuum like the ILIFE V3s Pro actually work?


A Beginner's Guide to End to End Machine Learning

#artificialintelligence

Supervised machine learning is a technique that maps a series of inputs (X) to some known outputs (y) without being explicitly programmed. Training a machine learning model refers to the process where a machine learns a mapping between X and y. Once trained the model can be used to make predictions on new inputs where the output is unknown. The training of a machine learning model is only one element of the end to end machine learning lifecycle. For a model to be truly useful this mapping needs to be stored and deployed for use.


A Beginner's Guide to End to End Machine Learning

#artificialintelligence

Supervised machine learning is a technique that maps a series of inputs (X) to some known outputs (y) without being explicitly programmed. Training a machine learning model refers to the process where a machine learns a mapping between X and y. Once trained the model can be used to make predictions on new inputs where the output is unknown. The training of a machine learning model is only one element of the end to end machine learning lifecycle. For a model to be truly useful this mapping needs to be stored and deployed for use.


David Chalmers on the Abstract-Concrete Interface in Artificial Intelligence

#artificialintelligence

It's a good thing that the abstract and the concrete (or abstract objects in "mathematical space" and the "real world") are brought together in David Chalmers' account of Strong Artificial Intelligence (AI). Often it's almost (or literally) as if AI theorists believe that (as it were) disembodied computations can themselves bring about mind or even consciousness.


A Close Look at Application Solution Architecture

#artificialintelligence

An application architecture describes the patterns and techniques used to design and build an application. The architecture gives you a roadmap and best practices to follow when building an application so that you end up with a well-structured app. Application Architecture depicts different architecture aspects such as Functional Analysis, Implementation Architecture, Tools & Technology, Data, Non-Functional, Deployment Architecture, views of an Application. It enables you to envision the big picture and reduce cost by removing redundancies. Integrating components in the application and other systems are also clearly demarcated for everyone to visualize.


Machine learning in earth sciences - Wikipedia

#artificialintelligence

Application of machine learning in earth sciences is the use of computer systems to classify, cluster, identify and analyze vast and complex data in earth science study, for example, geological mapping, gas leakage detection and geological features identification. Machine learning (ML) is a type of Artificial Intelligence (AI) that allows computer systems to interpret data while eliminating the need for explicit instructions and programming. The Earth system can be subdivided into four major components including the solid earth, atmosphere, hydrosphere and biosphere[3]. A variety of algorithms may be applied depending on the nature of the earth science exploration. Some algorithms may perform significantly better than others for particular objectives. For example, Convolutional Neural Networks (CNN) are good at interpreting images, Artificial Neural Network (ANN) performs well in soil classification[4] but more computationally expensive to train than Support Vector Machine (SVM) learning.


Utilizing Machine Learning and AI in Your GRC Practice

#artificialintelligence

I recently had the chance to visit with Andrew Robinson to discuss utilizing ML and AI into your GRC practice for a sponsored podcast. Robinson is the co-founder and Chief Information Security Officer at 6clicks. You can check out Robinson's podcast episode here. We began with the very basic proposition that many compliance professionals and others are scared by AI in the GRC space. Robinson believes it is based on the fear of the unknown, both to many inside and outside of GRC.