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Carlos Eduardo
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Using a new tool from MIT’s called PixelPlayer, you can click on a music instrument in a video and PixelPlayer will isolate the sound coming from that instrument and hear it louder than the other instruments. PixelPlayer uses self-supervised deep learning technique and 3 neural networks (NN) trained on over 60 hours of videos: one network for video, one network for audio, and a last one to associate specific pixels regions to specific sounds in order to separate out the different sounds. For the moment, PixelPlayer can only identify the sounds of 20 instruments.
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China's first AI chip has been produced by Baidu. The processor is called Kunlun and "is capable of handling both datacentre and edge workloads." "The company said it started developing a field-programmable gate array AI accelerator in 2011, and that Kunlun is almost 30 times faster. The chips are made with Samsung's 14nm process, have 512GBps memory bandwidth, and are capable of 260 tera operations per second at 100 watts."
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#NVIDIA researchers have developed #Noise2Noise AI that is capable of taking a noisy image and transforming it to near-perfection.
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More about Google's Duplex phone assistant by Ron Amadeo in Ars Technica. Duplex wouldn't pass a Turing test, but in the limited context of a reservation phone call, you can believe the hype, Duplex is as impressive as promised. And every call started with disclosure that Duplex is a robot, and that the call is recorded.
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Object Detection with 10 lines of code
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.
Object detection refers to the capability of computer and software systems to locate objects in an image/scene and identify each object. Object detection has been widely used for face detection, vehicle detection, pedestrian counting, web images, security systems and driverless cars. There are many ways object detection can be used as well in many fields of practice. Like every other computer technology, a wide range of creative and amazing uses of object detection will definitely come from the efforts of computer programmers and software developers.
Getting to use modern object detection methods in applications and systems, as well as building new applications based on these methods is not a straight forward task. Early implementations of object detection involved the use of classical algorithms, like the ones supported in OpenCV, the popular computer vision library. However, these classical algorithms could not achieve enough performance to work under different conditions.
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"DLTK, the Deep Learning Toolkit for Medical Imaging, extends TensorFlow to enable deep learning on biomedical images." "Biomedical images are typically volumetric images (3D) and sometimes have an additional time dimension (4D) and/or multiple channels (4-5D) (e.g. multi-sequence MR images). The variation in biomedical images is quite different from that of a natural image (e.g. a photograph), as clinical protocols aim to stratify how an image is acquired (e.g. a patient is lying on his/her back, the head is not tilted, etc.). In their analysis, we aim to detect subtle differences (i.e. some small region indicating an abnormal finding)."

"The main reasons for creating DLTK were to include speciality tools for this domain out of the box. While many deep learning libraries expose low-level operations (e.g. tensor multiplications, etc.) to the developers, a lot of the higher-level specialty operations are missing for their use on volumetric images (e.g. differentiable 3D upsampling layers, etc.), and due to the additional spatial dimension(s) of the images, we can run into memory issues (e.g. storing a single copy of a database of 1k CT images, with image dimensions of 512x512x256 voxels in float32 is ~268 GB). Due to the different nature of acquisition, some images will require special pre-processing (e.g. intensity normalization, bias-field correction, de-noising, spatial normalization/registration, etc)."
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"Children with autism spectrum conditions often have trouble recognizing the emotional states of people around them -- distinguishing a happy face from a fearful face, for instance. To remedy this, some therapists use a kid-friendly robot to demonstrate those emotions and to engage the children in imitating the emotions and responding to them in appropriate ways."

"This type of therapy works best, however, if the robot can smoothly interpret the child's own behavior -- whether he or she is interested and excited or paying attention -- during the therapy. Researchers at the MIT Media Lab have now developed a type of personalized machine learning that helps robots estimate the engagement and interest of each child during these interactions, using data that are unique to that child."

"Armed with this personalized 'deep learning' network, the robots' perception of the children's responses agreed with assessments by human experts, with a correlation score of 60 percent."
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Scalable Deep Reinforcement Learning for Robotic Manipulation

How can robots acquire skills that generalize effectively to diverse, real-world objects and situations? While designing robotic systems that effectively perform repetitive tasks in controlled environments, like building products on an assembly line, is fairly routine, designing robots that can observe their surroundings and decide the best course of action while reacting to unexpected outcomes is exceptionally difficult. However, there are two tools that can help robots acquire such skills from experience: deep learning, which is excellent at handling unstructured real-world scenarios, and reinforcement learning, which enables longer-term reasoning while exhibiting more complex and robust sequential decision making. Combining these two techniques has the potential to enable robots to learn continuously from their experience, allowing them to master basic sensorimotor skills using data rather than manual engineering.
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Learning from humans: what is inverse reinforcement learning?

One of the goals of AI research is to teach machines how to do the same things people do, but better. However, there’s still a massive list of problems where humans outperform machines. Although we can no longer claim to beat machines at tasks like Go and image classification, we have a distinct advantage in solving problems that aren’t as well-defined, like judging a well-executed backflip, cleaning a room while preventing accidents, and perhaps the most human problem of all: reasoning about people’s values. Since all these tasks contain some degree of subjectivity, machines need information about the world as well as a way to learn about the people within it in order to solve these problems. The two tasks of inverse reinforcement learning and apprenticeship learning, formulated almost two decades ago, are closely related to these discrepancies. And solutions to these tasks can be an important step towards our larger goal of learning from humans.
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The 10 coolest papers from CVPR 2018

Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization. This paper comes from Nvidia and goes full throttle on using synthetic data to train Convolutional Neural Networks (CNNs). They created a plugin for Unreal Engine 4 which will generate synthetic training data.
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