The world's top Go player will take on an artificial intelligence opponent again this spring, but this time it will not be Google's DeepMind that provides the machine rival. Ke Jie had previously vowed never to play against AI again after repeatedly losing to DeepMind's AlphaGo. But according to Chinese media reports, he will take on a range AI opponents, including one from China's Tencent. The man-versus-machine series will take place in China in April 2018. The matches will form part of the the World AI Go Tournament.

The release goes on, "Belle's integration with AI technology makes it stand out among its competitors. Leveraging AI deep learning and voice interaction, Belle is capable of having intelligent interaction with people by constantly studying and adapting to human behavior and speech pattern. Supported by the facial recognition technology, Belle is able to remember and identify frequent visitors. Besides greeting them with basic conversation, Belle will distribute visiting requests via Netvue's mobile app to the respective home member for remote instructions. As a smart doorbell passing on fun and joy, it even comes in choices of three personalities to choose from. Beyond its HD live feed and AI integration, Belle has also offered advanced motion alert and cloud video recording service. Its motion detection zones and sensibility can be customized to avoid false alerts like street traffic and focus on objects getting close to the front door."

If my model is not learning I have a better idea of what to address rather than blindly wasting time switching optimisers (or even frameworks).

Semi-supervised machine learning is getting ready for primetime. In this article we review a number of common semi-supervised algorithms, capped by a presentation of our own Mean Teacher [arxiv, github], presented at NIPS 2017. Deep learning models have delivered superhuman performance for many years. However, training with standard supervised techniques requires huge amounts of correctly labeled data. Being able to use unlabeled data would open doors to many new applications in e.g.

Back in May, AlphaGo from Google, an AI algorithm that is part of DeepMind, defeated the human world champion Ke Jie in a three-part match. After it was over, Jie vowed never to play a computer again. But apparently something has changed his mind because Chinese news sources report that Jie will once again play an artificial intelligence at an AI tournament to be held in China in April 2018. Ke Jie is one of the tournament's ambassadors, and he will play against the AI Tianrang. Normally, a human representative places pieces on behalf of the AI, but in this case, a robotic arm developed by Fuzhou University will fulfill that role.

Machine learning (ML) is taking cybersecurity by storm nowadays as well as other tech fields. In the past year, there has been ample information on the use of machine learning in both defense and attacks. While the defense was covered in most articles (I recommend reading "The Truth about Machine Learning in Cybersecurity"), Machine Learning for Cybercriminals seems to be overshadowed and not unanimous. The recent findings show how cybercriminals can deploy machine learning to make attacks better, faster, and much cheaper to perform. The objective of this article is systemizing information on possible or real-life methods of machine learning deployment in malicious cyberspace. It is intended to help members of the Information Security teams to prepare for imminent threats.

About this course: This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. This is the fifth and final course of the Deep Learning Specialization.

Over the past few years, data science has been all the rage. If you are a newbie in the field of data science, chances are that you have got to hear a lot of business jargon every other day. Two of the latest buzz words, which are misunderstood (misused) in today's B2B landscape are deep learning and machine learning. Even you might have noticed that all of a sudden everybody has started talking about deep learning and machine learning -- irrespective of whether they know the difference between them or not. If you are also wondering what the difference deep learning and machine learning is, keep reading this post to find a comparison between them in simple language.

We have seen a rise of AI technologies for image and video processing. Even though things tend to take a little while longer making it to the world of audio, here we have also seen impressive technological advances. In this article, I will summarize some of these advances, outline further potentials of AI in audio processing as well as describe some of the possible pitfalls and challenges we might encounter in pursuing this cause. The kicker for my interest in AI use cases for audio processing was the publication of Google Deepmind's "WaveNet" -- A deep learning model for generating audio recordings [1] which was released during the end of 2016. Using an adapted network architecture, a dilated convolutional neural network, Deepmind researchers succeeded in generating very convincing text-to-speech and some interesting music-like recordings trained from classical piano recordings.

Humans have officially given their voice to machines. A research paper published by Google this month--which has not been peer reviewed--details a text-to-speech system called Tacotron 2, which claims near-human accuracy at imitating audio of a person speaking from text. The system is Google's second official generation of the technology, which consists of two deep neural networks. The first network translates the text into a spectrogram (pdf), a visual way to represent audio frequencies over time. That spectrogram is then fed into WaveNet, a system from Alphabet's AI research lab DeepMind, which reads the chart and generates the corresponding audio elements accordingly.