Dataconomy

APRIL 5, 2017

Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down. The post Investing, fast and slow – Part 1: The Present and the Future of AI in Investment appeared first on Dataconomy.

Algorithm 172

Algorithm AI AI Artificial Intelligence 172

Distill

APRIL 4, 2017

We often think of optimization with momentum as a ball rolling down a hill. This isn't wrong, but there is much more to the story.

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Explosion

APRIL 1, 2017

Short of Artificial General Intelligence, we'll always need some way of specifying what we're trying to compute. Labelled examples are a great way to do that, but the process is often tedious. However, the dissatisfaction with supervised learning is misplaced. Instead of waiting for the unsupervised messiah to arrive, we need to fix the way we're collecting and reusing human knowledge.

Supervised Learning 40

Supervised Learning 40

Dataconomy

APRIL 6, 2017

Big data is big news at the moment. The latest Yahoo! data breach, which affected data from over 500 million customers, continues to be discussed by press and public alike, while the role of big data in predicting and even influencing last year’s US election brought the term firmly into. The post Big Data Symbiosis: Using Lessons from Big Data to Protect Big Data appeared first on Dataconomy.

Big Data 150

Big Data Big Data 150

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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

AI

Dataconomy

APRIL 3, 2017

As the collectors of actionable input information, networked smart devices with embedded sensors, software and electronics are a key driving force behind the Internet of Things (IoT). However, they do not generate value for organizations on their own. Powerful, fast database technologies are required to create meaningful insight from the. The post Three Key Facts About Sensors That Are Driving IoT Forward appeared first on Dataconomy.

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Internet of Things Database 148

Dataconomy

APRIL 7, 2017

For those of you similarly interested (obsessed?) with the changing role of government statistics relative to the explosion of highly dimensional private sector data, I recommend having a look at Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy from the National Academy of Sciences. It’s an easy read and offers a solid.

Data Science 132

Data Science 132