Blocks, a Theano framework for training neural networks Caffe, a deep learning framework made with expression, speed, and modularity in mind. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm. ConvNet, a Matlab based convolutional neural network toolbox - a type of deep learning, can learn useful features from raw data by itself.
TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Our goal is to extend the TensorFlow API to accept raw DICOM images as input; 1513 DaTscan DICOM images were obtained from the Parkinson's Progression Markers Initiative (PPMI) database. DICOM pixel intensities were extracted and shaped into tensors, or n-dimensional arrays, to populate the training, validation, and test input datasets for machine learning. We implemented a neural network classifier that produces diagnostic accuracies on par with excellent results from previous machine learning models.
Amazon Web Services and Microsoft have teamed up to launch an open-source, deep learning library called Gluon that the companies say will make machine learning accessible to more developers. The library gives developers an interface where they can prototype, build, train and deploy machine learning models for cloud and mobile apps, the companies explained in a joint press release. "We created the Gluon interface so building neural networks and training models can be as easy as building an app." The Cortana Skills Kit allows developers to make use of services and/or bots created with the Microsoft Bot Framework and publish them to Cortana as a new skill.
Box unveiled a set of new tools today that are designed to make it easier for companies to derive insights from the files they have stored with the cloud content management company. Called Box Skills, the features let customers use the power of machine learning to do things like automatically parse images, video, and audio data using machine learning. Those include audio transcription that uses IBM's Watson APIs, video analysis with Microsoft's Cognitive Services, and Box's already-announced image processing capabilities that use Google's Cloud Image API. In addition, Box also launched new iterations of its file management capabilities, including enhanced comments and visual version histories that will let users see how updates impacted a piece of work.
Today, AWS and Microsoft announced Gluon, a new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components. Gluon is available in Apache MXNet today, a forthcoming Microsoft Cognitive Toolkit release, and in more frameworks over time. Neural Networks vs Developers Machine learning with neural networks (including'deep learning') has three main components: data for training; a neural network model, and an algorithm which trains the neural network.
In April, Elon Musk announced a secretive new brain-interface company called Neuralink. "We decode realistic synthetic birdsong directly from neural activity," the scientists announced in a new report published on the website bioRxiv. The final result, say the authors: "We decode realistic synthetic birdsong directly from neural activity." At Elon Musk's Neuralink, bird scientists were among the first key hires.
AI has become the next major battleground in a wide range of software and service markets, including aspects of enterprise resource planning, Clearley explains. Packaged software and service providers should outline how they'll be using AI to add business value in new versions in the form of advanced analytics, intelligent processes and advanced user experiences. Intelligent things are physical things that go beyond the execution of rigid programming models to exploit AI to deliver advanced behaviors and interact more naturally with their surroundings and with people, Clearley explains. While conversational interfaces are changing how people control the digital world, virtual reality, augmented reality and mixed reality are changing the way that people perceive and interact with the digital world.
New interfaces will dramatically change the way consumers and employees access computing resources, Andrews said. Specifically, the new wave of interfaces relies on natural language processing and generation, visual analytics and gesture interpretation -- technologies powered by AI. In a client example Andrews titled the "Warehouse of Babel," artificial intelligence is bridging a language barrier for a European-based warehouse. The warehouse is now using a natural language interpretation system so that employees, who come from all corners of Eastern Europe, don't have to speak the same language to communicate or access applications "in a comparatively unified way," Andrews said.
According to a survey of 83 Gartner clients, 60% of respondents reported to be in an AI "knowledge-gathering phase," 25% said they are piloting an AI solution and a mere 5% of respondents said they have implemented an AI solution. New interfaces will dramatically change the way consumers and employees access computing resources, Andrews said. Specifically, the new wave of interfaces relies on natural language processing and generation, visual analytics and gesture interpretation -- technologies powered by AI. The warehouse is now using a natural language interpretation system so that employees, who come from all corners of Eastern Europe, don't have to speak the same language to communicate or access applications "in a comparatively unified way," Andrews said.
Even just five years back, Artificial Intelligence (AI) was still the stuff of science fiction, confined to research labs and tech giants' showcases. Processing power capacity, availability of representative data, development of more powerful algorithms, adaption of user interfaces, and, last but not least, a willingness to get the right policies in place. These single-chip processors were originally designed for video games but their capacity now to handle parallelization of multi-data processing means they're lending themselves to the use of complex AI algorithms and neural networks. This kind of complex machine learning though represents a big user challenge.