What is AI? Ingredients for Intelligence

When I tell people that I work at an AI company, they often follow up with “So what kind of machine learning/deep learning do you do?” This isn’t surprising, as most of the market attention (and hype) in and around AI has been centered around Machine Learning, and its high profile subset Deep Learning, and around Natural Language Processing, with the rise of the chatbot and virtual assistants. But while machine learning is a core component for artificial intelligence, AI is in fact more than just ML.


The Competitive Landscape for Machine Intelligence

Three years ago, our venture capital firm began studying startups in artificial intelligence. AI felt misunderstood, burdened by expectations from science fiction, and so for the last two years we’ve tried to capture the most-important startups in the space in a one-page landscape. (We prefer the more neutral term “machine intelligence” over “AI.”)


Electricity demand forecasting

Forecasting of power demand plays an essential role in the electric industry, as it provides the basis for making decisions in power system planning and operation. A great variety of methods for predicting electricity demand are being used by electrical companies, which are applicable to short-term, medium-term or long-term forecasting.


LinkedIn Knowledge Graph Enriches Data Value

LinkedIn data represents the world’s largest online professional network, with relationships among more than 467M members, 290M jobs and 9M organizations through professional entities and attributes. This data provides the foundation of consumer products for our members and monetization products for premium members. Data value is usually measured by revenue and user engagement with the products, both of which depend on the accuracy and comprehensiveness of the data. For example, the successfulness of LinkedIn Sales Navigator is determined by how accurately it finds the right decision makers in a company for salespeople to contact, and how many such candidates are discovered.


Customising Shiny Server HTML Pages

At Mango we work with a great many clients using the Shiny framework for R. Many of those use Shiny Server or Shiny Server Pro to publish their shiny apps within their organisations. Shiny Server Pro in particular is a great product, but some of the stock html pages are a little plain, so I asked the good folks at RStudio if it was possible to customise them to match corporate themes and so on. It turns out that it’s a documented feature that’s been available for around 3 years now!
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