AWS hopes a sprinkling of artificial intelligence tools and services will whet the appetites of AWS users who aren't AI experts, while also keep pace with aggressive cloud competitors. AWS unveiled five artificial intelligence (AI) services for its public cloud platform at AWS re:Invent 2017. These AWS AI services include a trio of deep learning services to perform language tasks, video capabilities for existing services and even a video recorded with onboard compute. Arguably the AI highlight was Amazon SageMaker, a managed service with 10 popular machine learning algorithms to guide developers and data scientists inexperienced with AI technology. The service uses open source Jupyter notebooks to visualize Simple Storage Service (S3) data, and it can run frameworks such as TensorFlow and Apache MXNet.

Deep learning is getting lots of attention lately, and for good reason. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as deep neural networks. The term "deep" usually refers to the number of hidden layers in the neural network.

CNNs (convolutional neural networks) are awesome. They are one of the reasons deep learning is so popular today. They can do amazing things that people used to think computers would not be capable of doing for a long, long time. Nonetheless, they have their limits and they have fundamental drawbacks. Let us consider a very simple and non-technical example. We have the face oval, two eyes, a nose and a mouth.

The following articles were recently hand-picked, and curated by one of our interns, Amelia. They quickly became popular, and cover dozens of topics of interest to data scientists. This is just a small, random selection among popular curated blogs on DSC. You can check our previous selection of curated articles here.

Generative Adversarial Networks (GAN) is one of the most promising recent developments in Deep Learning. GAN, introduced by Ian Goodfellow in 2014, attacks the problem of unsupervised learning by training two deep networks, called Generator and Discriminator, that compete and cooperate with each other. In the course of training, both networks eventually learn how to perform their tasks. GAN is almost always explained like the case of a counterfeiter (Generative) and the police (Discriminator). Initially, the counterfeiter will show the police a fake money.

DeepMind has recently announced a fresh collaborative partnership with the UK's health service, with plans for the artificial intelligence firm to develop machine learning technology to research breast cancer. DeepMind, a Google subsidiary, is perhaps best known for successfully building AI that is now better than humans at the ancient game of Go. But in recent months – when attempting to apply this tech to serious healthcare issues – it has been on the sidelines of a data breach storm. In July, DeepMind's collaboration with London's Royal Free hospital led to the NHS trust violating the UK's data protection laws. The Information Commissioner's Office (ICO) found that Royal Free's decision to share 1.6m personally identifiable patient records with DeepMind for the development of Streams – an automated kidney injury detection software – was "legally inappropriate".

In the last few years, we have experienced the resurgence of neural networks owing to availability of large data sets, increased computational power, innovation in model building via deep learning, and, most importantly, open source software libraries that ease use for non-researchers. In 2016, the rapid rise of the TensorFlow library for building deep learning models allowed application developers to take state-of-the-art models and put them into production. Deep learning-based neural network research and application development is currently a very fast moving field. As such, in 2017 we have seen the emergence of the deep learning library PyTorch. At the same time, researchers in the field of recommendation systems continue to pioneer new ways to increase performance as the number of users and items increases.

We all know that Google can instantly translate between 100 different human language, that too very quickly as if by magic. The technology behind Google Translate is called Machine Translation and has been savior for people who can't communicate with each other because of the difference in the speaking language. Now, you would be thinking that this feature has been there for a long time, so, what's new in this? Let me tell you that over the past two years, with the help of deep learning, Google has totally reformed the approach to machine translation in its Google Translate. In fact, deep learning researchers who know almost nothing about language translation are putting forward relatively simple machine learning solutions that are beating the best expert-built language translation systems in the world.

To further enhance its research capabilities Eco Marine Power announced today that it will begin using the Neural Network Console provided by Sony Network Communications Inc., as part of a strategy to incorporate Artificial Intelligence (AI) into various ongoing ship related technology projects including the further development of the patented Aquarius MRE (Marine Renewable Energy) and EnergySail. The Neural Network Console is an integrated development environment using deep learning for AI creation and has been used in deep learning applied technology development within Sony since 2015. Various functions are included such as recognition technology and a full-fledged GUI (graphical user interface) and these allow for deep learning programs to be developed. Deep learning refers to a form of machine learning that uses neural networks modelled after the human brain and is notable for its high versatility with applications in a wide variety of fields including signal processing, and robotics. An initial area of focus will be on studying how the Neural Network Console and AI can assist with the development of the automated control system for EMP's EnergySail.

Artificial intelligence (AI), once a topic only explored in science fiction movies, TV shows, and books, is something that has quickly become a part of the world of today. In 1969, management consulting firm McKinsey & Company released an article claiming that computers were not smart enough to make any decisions, but rather the human's intelligence behind the devices was powering them. With modern computers replacing skilled human labor in such fields as medicine, agriculture, and education, it is fascinating to see how incorrect this claim turned out to be. Some may even argue that artificial intelligence is the way of the future. With buzzwords like "artificial intelligence," "machine learning," and "bots" being tossed around, sometimes incorrectly interchangeably, it can be confusing to keep up with what is going on in this booming industry.