Image Understanding

Kaia Health caught our attention last year with an app that tracks your motion using your phone's camera in a bid to help you achieve perfect squat form, though we found it didn't quite hit the mark. Still, Kaia is elevating the concept with an updated version called Kaia Personal Trainer. It says the app will track your exercises and reps, create workout plans tailored to you and offer audio feedback in real time. It doesn't need any equipment other than an iPhone or iPad running iOS 12 (an Android version will arrive in the next few months), though you might still opt to use a fitness tracker. Once you get into position around seven feet away from your device, the app's AI uses a 16-point system to compare the way you move to optimal movement, looking at factors including the positions and angles of your limbs and joints.

Polarr, a six-year-old San Jose computer vision startup cofounded by Stanford graduate and Google veterans Borui Wang and Derek Yan, today announced that it has secured $11.5 million in series A funding led by Threshold Ventures, with participation from Cota Capital and Pear Ventures. Wang said the fresh capital -- which brings its total raised to $13.5 million, according to Crunchbase -- will be used to accelerate research and development; expand platform and service support; and grow its technology partnerships in drone, home appliance, ecommerce, and image storage verticals. "As deep learning compute shifts from the cloud to edge devices, there is a growing opportunity to provide sophisticated and creative edge AI technologies to mobile devices," said Wang, who serves as CEO. "This new round of financing is a tangible endorsement of our approach to enable and inspire everyone to make beautiful creations." Threshold Ventures' Chris Kelley and Pear Ventures' Mar Hershenson will join Polarr's board of directors as part of the round.

TELLING a yellow taxi and a pair of binoculars apart is so easy most people could do it standing on their head. Not so for an artificial intelligence: flip the cab upside down and it sees binoculars. This is just one of dozens of examples that show AI is a lot worse at identifying objects by sight than many people realise.

Combine Python & TensorFlow powers to build projects. In this course, you will learn how to code in Python, calculate linear regression with TensorFlow, and use AI for automation. Together with a professional you will perform CIFAR 10 image data and recognition and analyze credit card fraud by building practical projects. We explain everything in a straightforward teaching style that is easy to understand. Join Mammoth Interactive in this course, where we blend theoretical knowledge with hands-on coding projects to teach you everything you need to know as a beginner to credit card fraud detection What you'll learn Learn how to code in Python, a popular coding language used for websites like YouTube and Instagram.

The AI focus in Joget DX is to simplify the integration of pre-trained AI models into end user applications. As rationalized in the previous article, the training of AI models are best left to machine learning experts so once a trained model is available, the goal is to make it as accessible as possible to app designers. With the bundled TensorFlow AI plugin, you essentially: Upload a pre-trained TensorFlow model exported in protobuf (.pb) format Configure the inputs and outputs Configure optional post processing The following sections showcases how a sample app on Joget DX incorporates some well known models for several common AI use cases: Image Classification Audio Classification Text Sentiment Analysis Sample No-Code AI Apps on Joget DX Image Classification Inception v3 is a widely-used image recognition model that has been shown to attain greater than 78.1% accuracy on the ImageNet dataset. ImageNet is a dataset containing for image classification containing than 14 million labeled images. COCO is a large-scale dataset for object detection that contains 1.5 million object instances.

Summary: In the literal blink of an eye, image-based AI has gone from high cost, high risk projects to quick and reasonably reliable. C-level execs looking for AI techniques to exploit need to revisit their assumptions and move these up the list. For data scientists these are miraculous times. We tend to think of miracles as something that occurs instantaneously but in our world that's not quite so. Still the rate of change in deep learning, particularly in image recognition is mind boggling and way up there on the miraculous scale.

Image recognition and classification is a rapidly growing field in the area of machine learning. In particular, object recognition is a key feature of image classification, and the commercial implications of this are vast. These are just a few of many examples of how image classification will ultimately shape the future of the world we live in. So, let's take a look at an example of how we can build our own image classifier. VGG16 is a built-in neural network in Keras that is pre-trained for image recognition.

Imagine the added safety if semis are equipped with road marking recognition technology that can adjust a vehicle's steering if a tired operator fails to do so. Imagine a world where image recognition becomes more available and more efficient for organizations needing to harness the technology, helping to catch more bad guys. At the Embedded World 2019 Exhibition & Conference, February 26-28 in Nuremberg, Western Digital, in collaboration with Mipsology, will demonstrate an impressive, innovative new prototype in Booth #3A-429. Western Digital's small form factor (SFF) accelerator offers a highly optimized platform for inference at the edge of the data center, with performance and power optimized for the most common mid-range networks workloads. Able to quickly and dynamically process multiple in-bound neural networks at once, it has the potential to improve the intelligence and analytics of a wide range of deep learning and neural network-enabled applications, including smart surveillance and image classification and recognition.

TLDR; This series is based on the work detecting complex policies in the following real life code story. Code for the series can be found here. In the previous tutorials we outlined our policy classification challenge and showed how we can approach it using the Custom Vision Cognitive Service. This tutorial introduces deep transfer learning as a means to leverage multiple data sources to overcome data scarcity problem. Before we try to build a classifier for our complex policy let's first look at the MNIST dataset to better understand key image classification concepts such as One Hot Encoding, Linear Modeling, Multi Layer Perception, Masking and Convolutions then we will put these concepts together and apply them to our own dataset.

In this Feb. 1, 2019 photo, surveillance cameras are seen near the spot where "Empire" actor Jussie Smollett allegedly staged the attack in Chicago. Chicago police tapped into a vast network of surveillance cameras _ and some homeowners' doorbell cameras _ to help determine the identities of two brothers who later claimed they were paid by "Empire" actor Jussie Smollett to stage a racist and homophobic attack. CHICAGO (AP) -- Police tapped into Chicago's vast network of surveillance cameras -- and even some homeowners' doorbell cameras -- to track down two brothers who later claimed they were paid by "Empire" actor Jussie Smollett to stage an attack on him, the latest example of the city's high-tech approach to public safety. Officers said they reviewed video from more than four dozen cameras to trace the brothers' movements before and after the reported attack, determining where they lived and who they were before arresting them a little more than two weeks later. Smollett reported being beaten up by two men who shouted racist and anti-gay slurs and threw bleach on him.