neural network

How AI is helping to save the media industry – Daniel Burke – Medium


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.

What is Machine Learning and how do we use it in Signals?


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).

Apple Enables Watson AI to Run on iOS Core ML - PaymentsJournal


IBM and Apple have agreed to link Watson's machine learning development platform so that trained models in Watson can be executed on the iOS platform, but apparently only within the Mobile First initiative which restricts availability: "Integrating Watson tech into iOS is a fairly straightforward workflow. Clients first build a machine learning model with Watson, which taps into an offsite data repository. The model is converted into Core ML, implemented in a custom app, then distributed through IBM's MobileFirst platform. Introduced at the Worldwide Developers Conference last year, Core ML is a platform tool that facilitates integration of trained neural network models built with third party tools into an iOS app. The framework is part of Apple's push into machine learning, which began in earnest with iOS 11 and the A11 Bionic chip.

IBM's Deep Learning as a Service uses the cloud to democratize AI for developers


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.

12 Breakthroughs That Shaped today's Artificial Intelligence


Artificial intelligence is suddenly in people's homes, driving their cars, and running their security systems. Users interact with chatbots, sometimes unaware they're not talking to live people. Designers and marketing agencies trust computer-generated insights and machine learning over human input in making business decisions. Artificial intelligence development seemed to happen overnight, but it has been a series of developments that stretches back hundreds of years. It's hard to imagine that, 381 years ago, anyone could have conceived of artificial intelligence.

Multiscale Methods and Machine Learning


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.

IBM Tool Seeks to Bridge AI Skills Gap


Deep-Learning-as-a-Service, unveiled at IBM's annual IT industry conference in Las Vegas, seeks to lower barriers to deploying AI and deep-learning tools, a complex and painstakingly repetitive process that requires large amounts of computing power, the company said. The new service allows companies to upload data in Watson Studio, IBM's cloud-native platform for data scientists, developers and business analysts. There, they can create deep-learning algorithms for datasets – known in AI parlance as a "neural network" – using a drag-and-drop interface to select, configure, design and code the network. IBM also has automated the repetitive process of fine-tuning deep-learning algorithms, with successive training runs started, monitored and stopped automatically. For many firms, the complexity of creating smart algorithms from scratch has kept them from leveraging AI to parse massive stores of data for business value, the company said.

We Need Defensive AI to Protect Us From AI Attacks


Note: This is an edited version of an article I wrote for RT Insights. I'm convinced we are entering the Golden Age of artificial intelligence (AI); with so much promise and potential in front of us, I am feeling a little like Neo in The Matrix as he swallows the red pill. However, rather than science fiction, my recent work at FICO to make AI better has drawn upon my background in theoretical physics to create what we call defensive AI. This is needed, because AI-based attacks are not science fiction -- they are happening today. Businesses have relied on AI to fight fraud and financial crime for more than 25 years.

AI could alleviate China's doctor shortage

MIT Technology Review

On a recent day at a hospital in western Beijing, a cancer radiologist named Chongchong Wu loaded a suspicious-looking lung scan into a computer program resembling Photoshop. A neural network trained on thousands of example scans highlighted nodules in red squares, which she examined carefully. She corrected two false positives where the network mistakenly identified blood vessels as potential malignancies. But she also found a nodule that she'd previously overlooked, perhaps indicating an early sign of disease. China is embarking on a big initiative to add AI to health care with tools like this one.