AI in the Enterprise

Diego Oppenheimer explains why machine learning is a natural fit for serverless computing, shares a general architecture for scalable ML, discusses issues he ran into when implementing on-demand scaling over GPU clusters at Algorithmia, and provides general solutions and a vision for the future of cloud-based ML. Read more.

In 2016, the Alan Turing Institute, the UK’s new national institute for data science and AI, announced a funded strategic multiyear research partnership with HSBC. Christine Foster and Rakshit Kapoor share insights and use cases that emerged while making this ambitious and innovative cross-sector partnership work. Read more.

What do the world's most innovative and fastest growing companies have in common? They are in industries with a high level of VC funding. Accenture has analyzed five years of VC investment data to discover the AI use cases and technologies that are attracting the most money and will drive enterprise AI innovation. Diego Saenz explains where the top 10 investors in AI are placing big bets. Read more.

Martin Goodson and Mark St. John Qualter share the results of a yearlong feasibility study on the introduction of AI into the onboarding process at the Royal Bank of Scotland (RBS). Along the way, Martin and Mark share their experiences in translating this complex business process into a high-performance computational system. Read more.

By some estimates, soon it will require eight million people doing nothing but looking at satellite imagery 24/7 in order to ensure every photo taken on a daily basis is viewed. James Crawford explains how artificial intelligence solves this problem of scale, allowing us to accurately analyze reams of satellite imagery and detect patterns of socioeconomic change in a timely fashion. Read more.

Many industries, including banking, financial sectors, and insurance, continuously face the problem of detecting fraudulent activities. Giorgia Fortuna explores state-of-the-art innovations in fraud detection and explains how unsupervised ML fits into the picture, focusing on signature checks and face recognition. Read more.

Does good technology equal a good product? Not necessarily. Instead of taking only technology into account, you may need to deep dive into the AI ecosystem and look at other players and factors. Weiyue Wu explains how such analysis can help in predicting AI implementation schedules, prioritizing corporate tasks, and allocating resources efficiently. Read more.

Christopher Nguyen shares lessons learned implementing multiple AI commercial projects at Panasonic. Along the way, Christopher discusses a number of use cases at various stages of implementation maturity and explains what AI really means today in enterprise products, where the key opportunities are, their impact, and key success factors in the adoption of AI across the enterprise. Read more.

AI is a key innovation accelerator for digital business transformation. To help you with your strategic roadmap, Philip Carnelley shares IDC's research into the AI market across hundreds of European organizations and explains why organizations should establish a digital platform based on big data, AI, and cloud technologies, with an intelligent core, as part of their transformation strategy. Read more.

Chris Boyd and John Wiley explain how the Wall Street Journal uses machine learning and a proprietary algorithm to predict the likelihood for someone subscribing, which in turn dictates the paywall experience that customer receives. Read more.

We're in the year of the AI fake out. "Fake news" is the order of the day, as nebulous chatbots have become significant political actors. Startups peddle robotically handwritten notes and algorithmically personalized gifts for our loved ones. Soon we won't even be able to tell if a customer service agent is a real person. Aileen Nielsen asks, How should we redefine intelligence as fakes flourish? Read more.

With the more applications of machine learning-based applications, the complex algorithms that automate behaviors can get out of control. Ira Cohen explains how to catch problems and glitches early on by using machine learning algorithms to monitor these algorithms for anomalous behavior. Read more.

Archisman Majumdar and Jai Ganesh describe the effects of AI techniques on frontend GUI development—specifically, the use of automatically generated code and architecture from text descriptions—and share deep learning techniques for text-to-image creation and template-to-code generation, along with cloud technologies in automated deployment, management, and scaling of such applications. Read more.

Christopher Cho details how to leverage Kubernetes and the mighty Kubernetes APIs to build a complete deep learning pipeline, from data ingestion and aggregation to preprocessing and ML training to serving. Along the way, Christopher covers Kubeflow, a Google open source solution for managing machine learning with TensorFlow in a portable, scalable manner. Read more.