If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
NXP Semiconductors N.V. (NASDAQ:NXPI) announced a comprehensive, easy-to-use machine learning (ML) environment for building innovative applications with cutting-edge capabilities. Customers can now easily implement ML functionality on NXP's breadth of devices from low-cost microcontrollers (MCUs) to breakthrough crossover i.MX RT processors and high-performance application processors. The ML environment provides turnkey enablement for choosing the optimum execution engine from among Arm Cortex cores to high-performance GPU/DSP (Graphics Processing Unit/Digital Signal Processor) complexes and tools for deploying machine learning models, including neural nets, on those engines. Embedded Artificial Intelligence (AI) is quickly becoming an essential capability for edge processing, gives'smart' devices an ability to become'aware' of its surroundings and make decisions on the input received with little or no human intervention. NXP's ML environment enables fast-growing machine learning use-cases in vision, voice, and anomaly detections.
One of the important fields of Artificial Intelligence is Computer Vision. Computer Vision is the science of computers and software systems that can recognize and understand images and scenes. Computer Vision is also composed of various aspects such as image recognition, object detection, image generation, image super-resolution and more. Object detection is probably the most profound aspect of computer vision due the number practical use cases. In this tutorial, I will briefly introduce the concept of modern object detection, challenges faced by software developers, the solution my team has provided as well as code tutorials to perform high performance object detection.
This post is co-authored by Mary Wahl, Data Scientist, Xiaoyong Zhu, Program Manager, Siyu Yang, Software Development Engineer, and Wee Hyong Tok, Principal Data Scientist Manager, at Microsoft. Object detection powers some of the most widely adopted computer vision applications, from people counting in crowd control to pedestrian detection used by self-driving cars. Training an object detection model can take up to weeks on a single GPU, a prohibitively long time for experimenting with hyperparameters and model architectures. This blog will show how you can train an object detection model by distributing deep learning training to multiple GPUs. These GPUs can be on a single machine or several machines.
With warnings coming fast and furious from tech luminaries as diverse as Bill Gates, Elon Musk, and the late Stephen Hawking, to name a few, most of us are conditioned to think of the potential dangers of artificial intelligence in the hands of bad actors. While armies of AI-powered humanoid bots have not yet materialized, AI is being harnessed by hackers in the creation of "smart malware" that overcomes traditional defenses by using predictive technology. But if this is true, can't the opposite be also? Can AI be harnessed to turn against malware? Experts emphatically say yes, and some, like Barracuda and Cylance, are already doing it.
Man has always been fascinated with a view of the world from the top -- building watch-towers, high fortwalls, capturing the highest mountain peak. To capture a glimpse and share it with the world, people went to great lengths to defy gravity, enlisting the help of ladders, tall buildings, kites, balloons, planes, and rockets. Today, access to drones that can fly as high as 2kms is possible even for the general public. These drones have high resolution cameras attached to them that are capable of acquiring quality images which can be used for various kinds of analysis. With easier access to drones, we're seeing a lot of interest and activity by photographers & hobbyists, who are using it to make creative projects such as capturing inequality in South Africa or breathtaking views of New York which might make Woody Allen proud.
Last year Custom Vision was released to classify your own objects on images. Yesterday at Build 2018 a new Project Type was added to enable Object Detection in images. In this blog we are going to take a closer look and see what this new feature can do. Detect multiple objects in an image and draw boxes around these objects. As for every Machine Learning project you need a dataset, Kaggle is a great resource for that and I have downloaded The Simpsons dataset.
E-commerce is a rising giant in a number of fields like lifestyle, fashion, culinary arts, décor and utilities. With e-commerce, comes the expected breakthrough of online frauds. Cyber-crime is a critical issue. In the midst of different tasks and more than a million transactions, manpower is futile. For the very same reason, AI has made a breakthrough in the finance department.
This blog post survey the attacks techniques that target AI (artificial intelligence) systems and how to protect against them. This post explores each of these classes of attack in turn, providing concrete examples and discussing potential mitigation techniques. This post is the fourth, and last, post in a series of four dedicated to providing a concise overview of how to use AI to build robust anti-abuse protections. The first post explained why AI is key to building robust protection that meets user expectations and increasingly sophisticated attacks. Following the natural progression of building and launching an AI-based defense system, the second post covered the challenges related to training classifiers.
As a visual platform, the ability to learn from images to understand our content is important. In order to detect near-duplicate images we use the NearDup system, a Spark- and TensorFlow-based pipeline. At the core of the pipeline is a Spark implementation of batch LSH (locality-sensitive hashing) search and a TensorFlow-based classifier. Every day, the pipeline compares billions of items and incrementally updates clusters. In this post, we'll explain how we use this technology to better understand images and improve the accuracy and density of recommendations and search results across our production surfaces.
I gave a talk at MaRS on this topic. The event was put on by Steve O'Neil and his team, who all did an excellent job. The venue was packed to standing-room only with a fantastic audience of 300-400 people. The goal of the event was to have a discussion around "Rational AI in the Enterprise." I think all of the speakers did a wonderful job of honoring the topic.