Deep learning, a field in machine learning relying on learning data representations as opposed to task-specific algorithms, is experiencing a great and fairly sudden storm of enthusiasm and excitement after over twenty years of relative quietness. Delivering big technology breakthroughs recently, it has been regarded as a major driver towards artificial intelligence by many observers. The AI industry has spurred countless investments and acquisitions over the past years. In 2017, AI startups in the UK raised £488m, according to Pitchbook, more than twice as much as in the previous year. Furthermore, the country hosted four of the biggest acquisitions of AI startups over the past five years, including Google/DeepMind, Apple/VocalIQ, Microsoft/SwiftKey and Twitter/Magic Pony. Very generally speaking, deep learning models are based on information processing and communication patterns in biological nervous systems and represent coding attempts to define relationships between certain stimuli and associated responses in the brain in a mathematically convenient way.

By its own reckoning, Bitmain built 70 percent of all the computers on the Bitcoin network. It makes specialized chips to perform the critical hash functions involved in mining and trading bitcoin and packages those chips into the top mining rig; the Antminer S9. Now the tech giant is looking to Artificial Intelligence. Apart from Bitcoin and cryptocurrency mining, Jihan Wu, the Co-founder of Bitmain envisions a use-case for Bitmain beyond blockchain and cryptocurrency. In an interview with the Institute of Electronics and Electrical Engineers (IEEE), Wu expressed that while Bitcoin's success is personal to him, Bitmain can't solely rely on Bitcoin and has to search other avenues for continual success.

Paperspace, a Brooklyn-based startup has launched an AI PaaS offering called Gradient. Based on the serverless delivery model, Gradient removes the friction involved in launching and configuring GPU-backed VMs to train machine learning and deep learning models. Data scientists and developers spend a lot of time configuring the right environment needed for creating machine learning experiments and models. Firstly, they need to launch a Linux virtual machine powered by a GPU. This step is followed by installing required NVIDIA tools such as graphic drivers, CUDA runtime, and cuDNN libraries.

In my first blog post on the topic – Customer Experience and Machine Learning: Practical Applications – I discussed how machine learning techniques are being used today by financial services organizations to achieve business benefit. Insurers and retail banks are using machine learning to improve personalization by being able to better analyze and predict customer behavior, and deliver the optimal marketing offer, message, or price. But what is coming in the future? Based on the research we are doing – we are seeing a few capabilities come to forefront. These include augmented analytics, collaborative machine learning, and the introduction of decision trees and neural networks within deep learning.

These days, organizations are creating and storing massive amounts of data, and in theory this data can be used to drive business decisions through application development, particularly with new techniques such as machine learning. Data is arguably the most important asset, and it is also probably the most difficult thing to manage. It can be structured or unstructured, and it is increasingly scattered in different locations – in on-premises infrastructure, in a public cloud, on a mobile device. It is a challenge to move, thanks to the costs in everything from bandwidth to latency to infrastructure. It has a zillion different formats, sometimes chunks of data are missing, and usually it is unorganized and alarmingly often ungoverned.

Few industries have been hit as hard by the technological changes of recent times as the media industry. The same trends that have improved the lives of billions -- the growth of the internet, the spread of social media, and the proliferation of smartphones -- have instead disrupted the business models of every major media company, diluting their ability to sustainably fund their core operations. Most media companies have identified a'shift to video' as a critical pathway out of this digital dilemma. Digital video content is five times more engaging for consumers and four times more valuable for advertisers than text content alone. However, despite huge investments by media companies in increased video production, these shifts to video have so far failed to deliver meaningful bottom-line results.

If you go to college and take a course "Machine learning 101", this might be the first example of machine learning your teacher will show you: Imagine you work for a real estate agency, and you want to predict, for how much a house will sell. You have some historical data -- you know that house A has been sold for $500 000, house B for $600 000, and house C for $550 000. You also know something about properties of the houses -- you know the size of the house in square meters, number of rooms in the house, and the year the house was build. The goal of the real estate agency is to predict, for how much a new house D will sell, given its known properties (size, age and number of rooms of the house). In ML terminology, the known properties of the house are called "features" or "indicators" (we use the term "indicators" in Signals, because this term has been historically used in trading).

At its Think 2018 conference in Las Vegas on Tuesday, IBM rolled out its Deep Learning as a Service (DLaaS) program for artificial intelligence (AI) developers. The service is available through Watson Studio, and is aimed at helping developers run hundreds of deep learning training models at the same time while building out their neural networks, according to a press release. The firm has been working on the service since at least the middle of last year, according to a white paper from IBM researchers. By using the power of the cloud to deliver AI capabilities like deep learning, IBM and other vendors that have similar services are democratizing access to these tools. Since companies won't have to build and maintain costly hardware to experiment with deep learning, it could mean more companies are able to leverage the power of AI in their own products and services.

Multiscale methods, in which a dataset is viewed and analyzed at different scales,are becoming more commonplace in machine learning recently and are proving to be valuable tools. At their core, multiscale methods capture the local geometry of neighborhoods defined by a series of distances between points or sets of nearest neighbors. This is a bit like viewing a part of a slide through a series of microscope resolutions. At high resolutions, very small features are captured in a small space within the sample. At lower resolutions, more of the slide is visible, and a person can investigate bigger features.Main advantages of multiscale methods include improved performance relative to state-of-the-art methods and dramatic reductions in necessary sample size to achieve these results.