The technology industry is currently in the midst of an artificial intelligence (AI) renaissance. Initial work in this field fell something short of its longer term potential due to limitations in the technology platforms of the day, somewhere around the 1980s. As such, the first age of AI was ignominiously relegated to movies where it powered talking cars, humanoid cyborgs and a selection of other fancifully imagined products. The current reawakening of AI has been facilitated by advances in hardware from processing to memory to data storage; but also by our ability to now develop complex algorithmic structures capable of running on these new super-powered backbones. As IT departments start working to apply AI-enablement to the enterprise software stacks, it is worth taking a step back and examining what is actually happening inside the synaptic connections that go to make our AI "brains" so smart.

Yoshua Bengio is a grandmaster of modern artificial intelligence. Alongside Geoff Hinton and Yan LeCun, Bengio is famous for championing a technique known as deep learning that in recent years has gone from an academic curiosity to one of the most powerful technologies on the planet. Deep learning involves feeding data to large, crudely-simulated neural networks, and it has proven incredibly powerful and effective for all sorts of practical tasks, from voice recognition and image classification to controlling self-driving cars and automating business decisions. Bengio has resisted the lure of any big tech company. While Hinton and LeCun joined Google and Facebook respectively, he remains a full-time professor at the University of Montreal.

Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things--numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm. Machine learning is the process that powers many of the services we use today--recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa.

Article written by Peter Jeffcock. AI, machine learning, and deep learning – these terms overlap and are easily confused, so let's start with some short definitions. AI means getting a computer to mimic human behavior in some way. Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications. Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.

As the industry moves towards no-code artificial intelligence model training platforms, AI is being positioned as a tool for the masses. It, therefore, comes as no surprise to find that the industry bellwethers are releasing no-code or low-code platforms to build custom machine learning models that can be used with ease and security. Joining the big tech giants is China's internet major Baidu that launched an AI platform designed to make building custom ML models easier and it rules out the need for algorithmic programming. Known as EZDL, the service platform enables developers to build custom ML models with a drag-and-drop interface, Yongkang Xie, tech lead of Baidu EZDL, said in a company statement. He further emphasised developers can build deep learning models which are specific to their business needs only in four steps.

Machines learning to play games by watching humans might sound like the plot of a science fiction novel, but that's exactly what researchers at OpenAI -- a nonprofit, San Francisco-based AI research company backed by Elon Musk, Reid Hoffman, and Peter Thiel, among other tech luminaries -- and Google subsidiary DeepMind claim to have accomplished. In a paper published on the preprint server Arxiv.org Their deep neural network -- which, like other neural networks, consists of mathematical functions loosely modeled on neurons in the brain -- achieved superhuman performance on two out of the nine Atari games tested (Pong and Enduro) and beat baseline models in seven. The research was submitted to the Neural Information Processing Systems (NIPS 2018), which is scheduled to take place in Montreal, Canada during the first week in December. "To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions," the team wrote.

These terms are now among the most used in tech think tanks around the world. All devices and systems are already integrated with this technology to keep up with the demands of the human race, drastically improving the way we live. We've reached that age of time wherein men have created machines capable of thinking independently. These machines can learn and mimic human-like responses which can be applied in a variety of fields like medicine, transportation and manufacturing, among others. All of us have benefited from them for sure.

By shifting a fundamental property of computation, Princeton researchers have built a new type of computer chip that boosts the performance and slashes the energy demands of systems used for artificial intelligence. The chip, which works with standard programming languages, could be particularly useful on phones, watches or other devices that rely on high-performance computing and have limited battery life. The chip, based on a technique called in-memory computing, is designed to clear a primary computational bottleneck that forces computer processors to expend time and energy fetching data from stored memory. In-memory computing performs computation directly in the storage, allowing for greater speed and efficiency. The announcement of the new chip, along with a system to program it, follows closely on an earlier report that the researchers in collaboration with Analog Devices Inc. had fabricated circuitry for in-memory computing.

Distributed representations of words, better known as word embeddings, have become important building blocks for natural language processing tasks. Numerous studies are devoted to transferring the success of unsupervised word embeddings to sentence embeddings. In this paper, we introduce a simple representation of sentences in which a sentence embedding is represented as a weighted average of word vectors followed by a soft projection. We demonstrate the effectiveness of this proposed method on the clinical semantic textual similarity task of the BioCreative/OHNLP Challenge 2018.

Recent studies using deep neural networks have shown remarkable success in style transfer especially for artistic and photo-realistic images. However, the approaches using global feature correlations fail to capture small, intricate textures and maintain correct texture scales of the artworks, and the approaches based on local patches are defective on global effect. In this paper, we present a novel feature pyramid fusion neural network, dubbed GLStyleNet, which sufficiently takes into consideration multi-scale and multi-level pyramid features by best aggregating layers across a VGG network, and performs style transfer hierarchically with multiple losses of different scales. Our proposed method retains high-frequency pixel information and low frequency construct information of images from two aspects: loss function constraint and feature fusion. Our approach is not only flexible to adjust the trade-off between content and style, but also controllable between global and local. Compared to state-of-the-art methods, our method can transfer not just large-scale, obvious style cues but also subtle, exquisite ones, and dramatically improves the quality of style transfer. We demonstrate the effectiveness of our approach on portrait style transfer, artistic style transfer, photo-realistic style transfer and Chinese ancient painting style transfer tasks. Experimental results indicate that our unified approach improves image style transfer quality over previous state-of-the-art methods, while also accelerating the whole process in a certain extent. Our code is available at https://github.com/EndyWon/GLStyleNet.