recognition


Getting Started with Deep Learning

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Editor's note: Please note that, while this chart and post was up to date when it was first published, the landscape has changed in such a way that the table below is not depict a fully accurate picture at this point (e.g. Keras now supports a greater number of frameworks). The post is still beneficial, however, with this caveat noted. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the model performance.


Huawei Mate 10 hands-on: Faster, more helpful AI

Engadget

The most important phones of the year have already been announced, but one company might still be able to pique our interest. Huawei unveiled its AI-focused Kirin 970 processor at IFA, saying the chip's real world benefits would be shared at the launch of its next flagship. Now, the company is ready to reveal how the Kirin 970 performs in a phone. The Huawei Mate 10 and Mate 10 Pro were designed around AI -- so much so that Huawei wants to call them "intelligent machines." We don't know how much these intelligent machines will cost yet, but Huawei told Engadget to expect the prices to be competitive.


The CIO's journey to artificial intelligence

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AI will play an increasingly important role in the top three business objectives often cited by CEOs -- greater customer intimacy, increasing competitive advantage and improving efficiency. But commercial uses of AI are in specialised industry-specific applications such as actuarial forecasts and medical diagnosis -- making CIOs understandably cautious about promoting AI's potential business value. While most organisations may not pursue these leading-edge uses of AI, it will play an increasingly important role in the top three business objectives often cited by CEOs -- greater customer intimacy, increasing competitive advantage and improving efficiency. These skills include technical knowledge in specific AI technologies, data science, quality data maintenance, problem domain expertise, as well as skills to monitor, maintain and govern the environment.


How to Train your Self-Driving Car to Steer – Towards Data Science – Medium

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Neural networks, and particularly deep learning research, have obtained many breakthroughs recently in the field of computer vision and other important fields in computer science. Deep neural networks, especially in the field of computer vision, object recognition and so on, have often a lot of parameters, millions of them. It's a quite recent model that achieved remarkable performances on object recognition tasks with very few parameters, and weighting just some megabytes. I added a recurrent layer to the output of one of the first densely connected layers of SqueezeNet: the network now takes as input 5 consecutive frames, and then the recurrent layers outputs a single real-valued number, the steering angle.


Evolution of Neural Networks

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Today, AI lives its golden age whereas neural networks make a great contribution to it. Previous applications such as AND, OR and OR logic functions are linear problems whereas XOR function is non-linear problem. He transforms the concept of neural networks to deep learning which includes too many hidden layers. Today, deep learning stands in back of most of challenging technologies such as speech recognition, image recognition, language translation.


Top 10 hottest Artificial Intelligence (AI) Technologies to look upon

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By providing algorithms, APIs (application programming interface), development and training tools, big data, applications and other machines, machine learning platforms are gaining more and more traction every day. Companies focused in machine learning include Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree and Leverton "The current darling of the media," from simple chatbots to advanced systems that can network with humans. Some of the companies that provide virtual agents include Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi. A Mechanical Engineer and MBA by education, a Digital Business Transformation & Automation Consultant by profession, he is essentially a Technology Evangelist by passion.


trio-ai-ceo-zhuoran-wang-make-human-language-understandable-machines

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Q&A is even more straightforward than task-oriented spoken dialogue, as chatbots can provide answers directly to users' questions, such as "How much does an adult panda weigh?" In terms of IoT, Trio's technologies have been applied to a wide range of smart devices, including Xiaomi's Mi TVs, Mi AI speakers, and Smartisan smartphones. They enable Mi TVs and Mi AI speakers to interact with users through voice recognition. Microsoft XiaoIce remains a non-task oriented chatbot, while Baidu's Duer has developed into a platform called DuerOS, similar to Amazon's voice interaction platform, Alexa.


We Almost Gave Up On Building Artificial Brains - The Crux

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Today artificial neural networks are making art, writing speeches, identifying faces and even driving cars. Nils Nilsson, a retired Stanford University computer science professor, worked on these early generations of artificial intelligence. He says Marvin Minsky's Perceptron paper showed that other artificial intelligence research areas were needed -- and that the technology wasn't there yet. The neural network thus began to recede from the public imagination, ushering in what's been called "AI Winter," where artificial intelligence research funding dried up, and many lines of research ground to a halt.


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Download our Machine Learning Industry Guide to identify specific ways in which machine learning software and platforms can benefit your business with industry insight. We covered this in an earlier blog post, to quickly recap (if you haven't read the earlier post), supervised learning involves training an algorithm with specific samples of A B data and can be used to classify vast quantities new data to identify which category it belongs to. Before running machine learning algorithms, training data must be selected, pre-processed and cleansed. A system based on machine learning and artificial intelligence operates on rules and probabilities to solve problems.