Archive for the ‘General’ Category

Solar Power: Pulling Clean Water from the Driest Deserts

Posted on: April 25th, 2017 by Dickson No Comments

Access to potable water remains one of the world’s biggest challenges. In an effort to help that challenge, researchers have created a solar-powered device that pulls drinking water from even the driest desert air. We talked about a company doing moisture farming in Israel late in 2015 and discussed the science necessary to make it work. Now, researchers from UC Berkeley and MIT are getting in on the fun.  

This kind of technology may provide fresh water for all climates, but monitoring humidity could help provide larger yield ‘harvests’ in the future. The higher the moisture content in the air, the more water that can be provided over time with efficiency. You can read more about the new technology here.

Dickson provides a number of products for continuous humidity monitoring, including a line of DicksonOne loggers that make real time monitoring easy and reliable.

Machine Learning: The Curiosity of Data

Posted on: April 20th, 2017 by Jeff Renoe No Comments

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There are a lot of things we ask of those interested in working at Dickson, but there is one quality we’re always on the lookout for. It’s curiosity. The desire to learn is fundamental to one’s success. That’s true in life as much as it is here at our company.

In today’s society, that trait is becoming increasingly important for our machines. You may not realize it, but our machines are learning. Not only are they smarter than ever before, but they’re adapting to the data they’re given to optimize their procedures in order to better their efficiency. It’s a type of artificial intelligence that limits the need for programming to help learn.

A simple example of this is often on display by IBM’s Watson. By asking people a few simple questions, Watson recommends a beer to the answerer. Each answer that the system receives is an additional data point that helps the machine provide better recommendations. Below is a video that details the process at work.

 

 

This is a very simplistic example of a very bold idea. The ability for a machine to almost immediately calculate data points and come to a conclusion can mean a tremendous amount for those scattered across the globe. Its potential is incredible. IBM believes that the investment in the practice will lead to a better future, and not just one that helps recommend you your next craft beer. It could cure disease and even positively impact world hunger.

The practice isn’t perfect though and is directly impacted by the quality of the data delivered. Microsoft learned this the hard way when it launched Tay, an AI twitter bot, that used machine learning to communicate on social media. The bots data points were driven through communications with others on the networks. Thanks to the types of language and comments directed to the bot from the community, Tay had to be taken down within 24 hours of launch for tweeting out offensive comments it had learned to the world.

The curiousness of the entire project means that attentive humans and machines can, and will, both learn from each other. While the potential for downfall is concerning, the potential benefits are mind blowing. Eventually, we at Dickson even hope to collect enough data to practice machine learning ourselves. Imagine being able to alert you before you experience an excursion because of trends our system has recognized in the past. That’s the potential we see in the future. Now we just have to maintain our curiosity until we learn how.

 

Gaming the System | How Machine Learning Could be what Eliminates Cancer

Posted on: April 13th, 2017 by Jeff Renoe No Comments

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To an untrained eye, data can look like a lot of 1’s and 0’s with no discernible pattern. To an experienced observer, the data turns into a picture. It shows us things we’d have never realized before its compilation. Machine learning is able to look even deeper and help turn a picture into a Picasso.

The idea is extremely potent and has wide ranging application. Take, for example, an article in TechCrunch that uses games as an example of the power behind the potential.

“Last year, Google’s artificial intelligence platform, AlphaGo, deployed techniques in deep learning to beat South Korea Grand Master Lee Sedol in the immensely complex game of Go, which has more moves than there are stars in the universe. Those same techniques of machine learning and AI can be brought to bear in the massive scientific puzzle of cancer.”

This same idea could be leveraged to help solve medicine’s ultimate puzzle:  The cure for cancer. Unfortunately, data needs to be readily available for machine learning to really do its thing. While decades of data exists regarding cancer treatment and results, much of it is unavailable because of one of two reasons. The most obvious is personal data security. HIPAA, the Health Insurance Portability and Accountability Act of 1996, guarantees user privacy and data security regarding personal information. It’s important to be sure, but, more often than not, pundits seem to agree that it’s become outdated.

The other issue involves a lack of innovation in healthcare. This is something we’ve spoken about often here and has cost the industry billions of dollars. This, albeit, is partially why HIPAA is so outdated. The world has changed since 1996 and technological advancement is a major reason why.

Consider this:  HIPAA was finalized 11 years before smartphones were even a thing. Today we have social media, where personal information can be shared at the push of a button in 140 characters or less, and now offer SaaS based systems in the Cloud. Yet, even today, you have to provide HIPAA direction for every single provider you meet with, and it can limit your care. Need to visit a new doctor? Better sign some paperwork. Need to be reviewed for a second opinion? Better resign the same paperwork. It’s a system of inefficiency, not only for patients but for researchers as well. TechCrunch touched on this fact briefly in their article.

“Many data sets, including medical records, genetic tests and mammograms, for example, are locked up and out of reach of our best scientific minds and our best learning algorithms.”

Now, we aren’t advocating for publishing everyone’s personal information and data for the purpose of research, far from it in fact. However, there would seem to be a way to provide data access without sacrificing an individual’s privacy. In 2015, there was even a Bill that made it through Congress that would have paved the way for such opportunities. The bill was ultimately reworked to remove the provisions that would have altered HIPAA laws, but the conversation was a start. Just because the right answer has yet to be found, doesn’t mean that it eventually won’t. There is some good news in all of this. Even without flushed out and updated regulations, some programs have the flexibility to provide some data to get the research kick started. TechCrunch discussed a few.

“A number of large-scale, government-led sequencing initiatives are moving forward. Those include the U.S. Department of Veteran Affairs’ Million Veteran Program; the 100,000 Genomes Project in the U.K.; and the NIH’s The Cancer Genome Atlas, which holds data from more than 11,000 patients and is open to researchers everywhere to analyze via the cloud. According to a recent study, as many as 2 billion human genomes could be sequenced by 2025.”

While the information currently available lacks the breadth that exists behind locked cabinet drawers it still shows promise. The ultimate eventuality of data sharing and machine learning could lead to early recognition of illnesses and accelerate the development of new drugs for things like cancer. If the opinion of TechCrunch is an accurate one, then we’re three steps from seeing it all happen.

  1. It should be easy for patients to share their data including records, imaging and testing results. It would be easy and legal for this kind of sharing to occur if a common consent form was mandated and in place in the field.
  2. There’s a lot of funding in the medical world, but more is needed when it comes to technology and machine learning and data science. “Just as the Chan Zuckerberg Foundation is funding new tool development for medicine, new AI techniques need to be funded for medical applications.”
  3. Data and research needs to be more wide reaching across gender and racial diversity. According to a research study by the University of California San Francisco, clinical studies still miss nearly 40 percent of the U.S. population. If we achieve results in machine learning, it is important that they are applicable to all.

Once the items above are in place, machine learning can do it’s job and work to reach the maximum of its potential to paint its proverbial Picasso. Like his art, even if the average patients can’t decipher the data, they’ll still be able to appreciate the results.

 

Freezer Malfunction Melts Arctic Ice Samples

Posted on: April 11th, 2017 by Dickson No Comments

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A freezer malfunction at the University of Alberta has melted some of the world’s largest collection of Canadian Arctic ice core samples. The university says about 12 percent of the collection was damaged when temperature in the storage freezer soared to 40 C over the weekend.

If your freezer malfunctioned over night would you be prepared, or would you be at risk of losing product and resources?

With DicksonOne cloud-based monitoring, you won’t have to worry. Featuring real time alerts, infinite data storage, and digital reporting you can be sure that your inventory is protected.


Read More about the University of Alberta Freezer Malfunction here.

 

[Webinar] Alarm Escalation and DicksonOne

Posted on: March 30th, 2017 by Matt M No Comments

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Yesterday, we at Dickson held our first formal webinar of the year where we discussed our newest feature: Alarm Escalation. We have heard that there were a few issues with logging into the webinar by some of our registrants. For that reason, we wanted to make sure a recording of the presentation was available for all. It’s been embedded below for your convenience. Be on the lookout for our next webinar that we are currently scheduling for Q2 on an exciting soon to be announced feature–just check out page 20 the April issue of Dickson Insights (our catalog) for details!

Additional information on Alarm Escalation can be found at any one of the following web pages.

You can also click here to look back on our previous webinar involving the creation of Alarm Templates (though, at the time this webinar was released, Alarm Escalation did not exist and was not covered. However, today you can use our template feature with escalating alarms).

If you’re interested in learning more about our DicksonOne cloud solution, send a message to support@dicksonone.com and we will follow up to address any questions you may have.

We hope you enjoyed the presentation. If you have any additional feedback, please feel free to email us at webinars@dicksondata.com. We’ll work whatever feedback we get into our next webinar. Be on the lookout for more information in the coming months.