All posts in “Data”

Inflect raises $3M seed round to make buying internet infrastructure easier

Inflect, a startup that wants to make it easier for businesses to buy their own internet infrastructure, today announced that it has raised a $3 million seed funding round. The service, which is still in preview, provides business with the necessary data to make their purchasing decisions when they go out and look for their own data center space, networking services and exchange providers.

Investors in this round include the likes of Greenpoint Technologies’ Jon Buccola Sr, Weebly CTO Chris Fanini, Server Central CEO Jordan Lowe, Global Communications Network CEO Chris Palermo and — somewhat surprisingly — Cruise Automation CTO Kyle Vogt (who was also Twitch’s former CEO).

“We’re fortunate to have investors who understand both the buy and sell sides of our industry,” said Inflect co-founder and CEO Mike Nguyen in a canned statement. “They understand how challenging it is to buy colocation, managed and network services. As industry insiders, they’ve all wasted time and money due to lack of access to accurate data and the right service provider contacts needed to source the right solutions.”

Inflect itself remains in preview, but the company says that it now includes verified data from over 40 service providers and 4,000 data centers around the world. The company itself estimates that this covers about 80 percent of the globally available public infrastructure.

It’s worth noting that this kind of data is typically very hard to get — and most companies never had an incentive to offer an API to allow others to aggregate their data (Cruise Automation’s Kyle Vogt describes the industry as “infuriatingly opaque“). Data and telco services have traditionally been bought manually, but Inflect, which was founded by a team of infrastructure veterans, aims to change this. For now, it’s mostly aggregating this data, but over time, it will surely try to allow its users to buy services right from its catalog, too.

Featured Image: Dean Mouhtaropoulos/Getty Images

How data helps medical professionals implement patient personalisation

In recent years, the scientific community has come to think about cancer — and cancer treatment — differently.

Whereas in past decades, cancer was diagnosed, classified and treated according to the specific types of tissues it affected — breast cancer, for example, has traditionally been treated with drugs developed specifically for tumours in the breast — modern medicine takes a decidedly more personalised approach.

Today, explains Dr Warren Kaplan, the Chief of Informatics at the Garvan Institute of Medical Research, cancer is thought of as a disease of DNA, rather than something that easily fits into “buckets” based on where in the tissue it originates.

Different treatments for different genetic profiles

“The three billion base pairs that make up our DNA — our genome — make each and every person in the world unique,” says Kaplan. “But this is also what makes each person’s cancer unique.”

By understanding a patient’s genome, doctors can determine the specific combination of drugs that will best suit their patient.

Researchers and scientists at Garvan and all over the globe are working to decode and sequence genomes in order to investigate the genetic makeup of cancers. The end goal, Kaplan explains, is to deviate from a “one size fits all” approach to treatment options. Once genomes are sequenced, it becomes easier for doctors and scientists to construct tailored treatment programs based on patients’ individual genetic profiles.

The benefit of this approach to treatment is twofold, says Kaplan.

“First, tumours with certain genetic profiles may respond to certain anti-cancer drugs better than others,” he says, citing an example of a pancreatic tumour that responds better to a drug traditionally prescribed for breast cancer. “Secondly, this information can also help tailor a patient’s treatment plan. By understanding a patient’s genome, doctors can determine the specific combination of drugs that will best suit their patient and avoid any harmful side effects.”

The role of data 

One of the obstacles to this approach, however, is the sheer amount of data it takes to sequence a single person’s genome: about 500 gigabytes. That’s equivalent to streaming about 100 HD movies.   

In order to lend a hand – and a byte – Vodafone Foundation has released a mobile app, DreamLab, that crowd-sources data from willing donors. All users have to do is download the app (which is now available on iOS and Android), select a project they want to contribute to, and then charge their phone as they do normally. The app then goes to work downloading small bits of information from the cloud, which helps fuel cancer research such as the work being done by Garvan.

To date, users around the world have taken up the night shift as a ‘cancer researcher,’ and “crunched” about 70% of the first research project, which focuses on comparing genetic profiles of patients with four types of cancer (breast, ovarian, prostate and pancreatic). DreamLab now has 165,000 active users – the more people that use the app, the faster researchers can complete projects which lead to discoveries.

Kaplan has high hopes that this data holds at least a few of the answers for solving cancer.

“We hope that in the future, those diagnosed with cancer will have their genomes sequenced and compared to this library, so that they can benefit from much more effective and accurate assessments of their illness,” he explains. “This way, doctors will be able to develop customised treatment plans that are known to be effective for a patient’s specific genetic profile.”

Download the DreamLab app now on iOS from the App Store or on Android from Google Play to help fight cancer.

Disclaimer: Downloading DreamLab uses data. DreamLab can be used when your device is charging and has mobile network or WiFi connectivity. Mobile data to use DreamLab is free for Vodafone Australia customers on the Vodafone Australia network. Roaming incurs international rates. 

Your smartphone could help power future cancer cures

In the field of potentially life-saving cancer research, data is more than just a buzzy term deployed by marketers — it’s a fundamental part of the search for answers.

Computing power, says Dr Warren Kaplan, the Chief of Informatics at the Garvan Institute of Medical Research, is quickly emerging as a precious resource in the quest to solve cancer and other complex diseases.

DreamLab, a mobile app and initiative dreamed up by The Vodafone Foundation Australia, is just one example of how data can make a difference. Instead of fundraising in the most literal sense, the app collects a different type of donation: your data.

Below are a few eye-opening facts about data’s role in cancer research and how DreamLab is making an impact.

The amount of data associated with cancer research is staggering

To paint a picture of the sheer amount of data we’re talking about, when it comes to cancer research such as the work being done at the Garvan Institute, it helps to think in terms with which we’re familiar. For example, according to Kaplan, sequencing one person’s genome — the three billion base pairs (or DNA letters) that act as the instruction manual for our body — requires roughly 500 gigabytes of data. This is equivalent to about half a million minutes of streaming music.

Sequencing one person’s genome requires roughly 500 gigabytes of data.

If you multiply this number by many thousands — the number of individuals whose genomes must be analysed to gain meaningful insights into cancer — that’s the amount of data processing power it takes to begin making a dent.

“Increasingly, we researchers are depending on supercomputers to crunch immense amounts of data in order to learn more about cancer and other serious illnesses,” says Kaplan. “A choke point in this research has been the sheer quantities of computing power required. The more computing power that’s available, the faster genomes can be analysed and potential new treatments discovered.”

Donate data simply by charging your device  

Millions of us today are walking around with tiny, powerful computers inside our pockets. Now, we can put those devices to use for the greater good.

Supporting the research being conducted by Kaplan and his colleagues is as simple as downloading DreamLab and performing an action you already do dozens of times every week — plugging in your device.

DreamLab is simple to use: You download it, choose a cancer research project you’d like to support and then select how much data to donate. (The mobile data to use the app itself is free if you’re a customer of Vodafone Australia). Then, whenever you charge your phone, the app downloads small bits of information from the cloud about specific types of cancer.

Kaplan elaborates about the app’s process: “Using your phone’s computer processor, the app then compares these genetic profiles to identify their similarities and differences between different cancers and sends the answer back to our team at the Garvan Institute.”

“DreamLab provides dedicated, free access to what is essentially a smartphone supercomputer,” says Kaplan. “By harnessing this power, complex data can be crunched faster and research completed sooner — speeding up the chance of making discoveries to improve and save lives.”  

Download the DreamLab app now on iOS from the App Store or on Android from Google Play to help fight cancer.

Disclaimer: Downloading DreamLab uses data. DreamLab can be used when your device is charging and has mobile network or WiFi connectivity. Mobile data to use DreamLab is free for Vodafone Australia customers on the Vodafone Australia network. Roaming incurs international rates. 

Runners in the Shanghai marathon are getting a gorgeous 3D data souvenir

Runners in the past weekend’s Shanghai marathon are taking home a unique personalised souvenir of their run.

The 30,000 runners have been invited to plug in their run data from the event into a platform that will produce a 3D visualisation of how they performed. 

The colourful chart can be panned in 360 degrees on a mobile phone with WebGL, and will reflect how fast each runner went over different stretches of the race.

The platform is the brainchild of ad agency Wieden+Kennedy Shanghai, who produced it for BMW. It will accept data from popular Chinese fitness apps CoDoon, JoyRun, and Rejoice. Here’s an example of what runners will get on their phones.

If you pan the graphic up, some additional stats will appear:

Image: bmw

BMW’s website says participants can expect their personalised charts next week, on Nov. 25.

Wieden+Kennedy just released this video explaining the project:

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This sure beats receiving a regular medal for participation.

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Primer helps governments and corporations monitor and understand the world’s information

When Google was founded in 1998, its goal was to organize the world’s information. And for the most part, mission accomplished — but in 19 years the goalpost has moved forward and indexing and usefully presenting information isn’t enough. As machine learning matures, it’s becoming feasible for the first time to actually summarize and contextualize the world’s information. With two and a half years of R&D under its belt, Primer aims to do just that for governments, corporations and financial institutions.

Primer has collected a total of $14.7 million across Seed and Series A investment rounds led by Data Collective. Lux Capital, Amplify Partners and, In-Q-Tel, an investment firm supporting the CIA, have also provided capital to Primer. The team of 36 employees has been able to close initial customers at In-Q-Tel, Walmart and Singapore’s sovereign wealth fund.

Using a mixture of supervised and unsupervised machine learning models, Primer can ingest unstructured data and produce insights — think scouring the web for news related to a specific company and then organizing it into key themes. The general concept gives us flashbacks to the early days of Palantir. But Sean Gourley, founder and CEO of Primer, is quick to point out the difference between the two ambitious companies.

“If you want to charge high amounts of money, you can solve valuable problems infrequently or you can solve problems everyone has to deal with on a daily basis and it feels like infrastructure,” Gourley asserted.

He’s of course pointing out the notion that much of Palantir’s business model has focused on higher cost consulting services to help corporations and governments meet incredibly ambitious goals. Much hullabaloo has been made over rumors that Palantir’s software played a role in tracking down bin Laden. But Primer is promising considerably less in hopes of an even higher payoff.

Primer’s software is being positioned to augment low-level analyst work that manifests itself most commonly in the intelligence community and at big banks. By monitoring large quantities of information semiautonomously, Primer can potentially speed up the process by which research is gathered and presented.

Because Primer is being targeted at the intelligence community, Gourley wasn’t able to go into specifics about its capabilities in a military context. However, I was able to watch a demo of Primer Science, a version of Primer adapted to help academics monitor the regular release of new papers.

The version I saw was able to identify key machine learning papers published on ArXiv and contextualize them alongside social media postings and news reports. The platform made it easy to identify work completed by key research groups and quickly locate papers focusing on specific sub-topics like machine translation.

Collecting information in a single place is really the only way to consider events from every angle. Unfortunately, piles of information can quickly overwhelm even the best human analysts and critical details can go unnoticed.

Gourley gave me the example of news coverage to make a similar point. Press in every country cover events differently and its advantageous for someone tasked with gathering intelligence to look for disparities. The graphic below (and an interactive visualization here) shows contrasts in the coverage of terror events by U.S. and Russian media.

Alastair Dant, Primer

For less high-stakes corporate and financial users, Primer can classify common events like regulatory changes, product launches and M&A transactions. More long tail concerns are still flagged as interesting and human analysts can provide feedback, further training models to provide better insights the next time a similar event happens.

Training models can be a challenge for companies that operate in industries where data is very tightly controlled. Gourley explained to me that most customers are ok with using at least some of their data to improve models but that things can get tricky when it comes to feedback into the significance of findings.

The hope is that Primer can some day assist in prediction making by looking for statistical correlations between events. Such an effort would undoubtedly require strong synergies between human analysts and Primer’s technology.

Featured Image: John Mannes, Philippe Intraligi, RedlineVector/Getty Images