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Source: Prism Skylabs
photo: Prism Skylabs

One of the big themes at our Structure Data conference in March is the advent of new techniques to make sense of new data sources. One of the most-promising is video, which had value well beyond capturing crimes and making us laugh on YouTube. Read more »

In Brief

The National Football League and General Electric announced on Thursday a list of 16 projects that will each receive $300,000 to advance their research in the field of diagnosing and preventing head injuries. Among the selected projects is a collaboration between the University of California, San Francisco, and machine learning startup Ayasdi to analyze CAT scan data to predict which players might have persistent symptoms. Another involves the Purdue Neurotrama Group and a company called BrainScope that uses machine learning algorithms to power a device that it hopes can detect head injuries on the sidelines. As everything from algorithms to computing power improve, machine learning is actually becoming fairly common in medical research.

In Brief

A Cambridge, Mass.-based startup called Kensho has raised a $10 million seed round from a group of investors that includes General Catalyst, NEA, Accel Partners and Google Ventures. The company’s product, called Warren (as in Warren Buffett, I presume), is a natural-language search engine for data on financial markets. You (assuming you’re a banker or very sophisticated day trader) type in a question — an example from the company’s website is “Which aerospace companies rally following major breakthroughs in drone technology?” — and it returns results in the form of data. It looks like a smart product from a smart team, especially if the UI and visualizations are as good as the algorithms.

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In Brief

The Wall Street Journal is reporting that Dropbox has raised “about $250 million” at a valuation “close to $10 billion.” Given the crazy valuations of other startups, such as Snapchat, such a high number for Dropbox isn’t too surprising. It has hundreds millions of users and has the personnel in place to start adding value beyond just syncing and storing data. Of course, it was just down for a couple days, which is something that can’t happen too frequently if you’re also planning a move into the lucrative business-user market, which Dropbox is.

HughEWilliams (1)

EMC-VMware spinoff Pivotal has hired Hugh Williams as its senior vice president of research and development. Williams was most recently a VP at eBay responsible for the technological platforms that underpin the site’s customer experience. Read more »


You can’t talk about data without talking Hadoop. That’s why three CEOs — Rob Bearden of Hortonworks, Tom Reilly of Cloudera and Paul Maritz of Pivotal — will take the stage to talk about where the market it headed and how their companies are helping steer its direction. Read more »

On The Web

This post from the MIT Technology Review discusses how Google used deep learning to recognize houses numbers and make Street View more useful (the research paper it cites is here). It’s just the latest example of applied deep learning from Google, which already uses the technique to power speech recognition on Android phones and image recognition in Google+. And, as we’ve been noting for the past few months, other web companies are now getting on board, applying various forms of machine learning to take advantage of the immeasurable volumes of images and text they’ve accumulated over the years.

In Brief

An Oakland-based startup called Omicia has raised a $6.8 million series A round of venture capital, led by Artis Ventures, for a cloud service that lets doctors analyze whole human genomes in order to identify the presence of diseases. The basic service is free (while more-advanced analyses and capabilities cost $99), and the whole process takes less than 3 hours for a whole genome (or less than 1 hour for an exome). Advances in sequencing, algorithms, data storage and cloud computing have been rapidly driving down the cost of genomic analysis over the past few years, leading to an uptick of of startup activity in the space.

In Brief

Two new platforms for storing and analyzing genomic data have raised venture capital recently, with Curoverse announcing $1.5 million in seed funding in mid-December and Tute Genomics announcing $1.5 million in seed funding on Dec. 31. Curoverse is a specialized private-cloud system, while Tute Genomics is a pure cloud service. Both are riding the waves of cheaper gene-sequencing costs, data storage and computing power, assuming it will result in a deluge of demand for genomic analysis over the next few years. They’re not alone: We’ve covered numerous startups trying to do the same thing, including DNAnexus, Bina Technologies, Spiral Genetics and Appistry.

In Brief

People Pattern, an Austin, Texas-based startup focused on targeted marketing, has raised a $4.5 million series A round of venture capital from Mohr Davidow and a collection of private investors. It’s hardly the first company to think about applying new big data and data science techniques in the name of better understanding customers — that’s arguably the most popular use for these techniques — but People Pattern’s approach is noteworthy. It lets users mine both their internal data stores (e.g., and external sources (e.g., social media) to predict the highest-value users and the best ways to target them.

On The Web

This article in IEEE Spectrum highlights some interesting research into making cloud computing more efficient by balancing the carbon footprints of global data centers and the latency of serving requests from those data centers. It’s an admittedly incomplete study, and one that’s probably more important to large web companies (e.g., Facebook) and cloud providers than to normal businesses just consuming cloud resources. Smarter load balancing could help providers cut operational costs and pass the savings onto users. Of course, large cloud users such as Netflix might want to research and develop their own systems, as well.

In Brief

Researchers from the National University of Singapore and the King Abdullah University of Science and Technology in Saudi Arabia have developed a new method of storing data on magnetoresistive random attached memory, or MRAM, chips that they claim can store data for at least 20 years. Some believe MRAM has promise in future consumer devices and in embedded systems because it’s faster, denser and longer-lasting than traditional DRAM and flash memory. However, it’s not exactly clear how revolutionary the researchers’ work is: An Arizona-based company called Everspin already produces MRAM technology it claims can last more than 20 years.

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