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Article - Machine Learning, Big Data, and Your Family History

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Article - Machine Learning, Big Data, and Your Family History

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Data science brings people together.

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How can artificial intelligence, machine learning, and deep learning benefit your family? These technologies are moving into every field, industry, and hobby, including what some say is the United State's second most popular hobby, family history. Today, it's so much easier to trace your roots back to find out more about your progenitors. Tyler Folkman, senior manager at Ancestry, the leading family history company, describes to us how he and his team use convolutional neural networks, LSTMs, conditional random fields, and the like to more easily piece together the puzzle of your family tree.

Ginette: Today we peek into an area rich in data that has lots of interesting AI and machine learning problems.

Curtis: The second most popular hobby in the United States, some claim, is family history research. And whether that's true or not, it's has had a lot of growth recently. Personal DNA testing products have exploded in popular over the past three years, but beyond this popular product, lots of people go a step further and start tracing their roots back to piece together the puzzle of their family tree. Today we're going to dive into the data side of this hobby with the leading family history research company.

Ginette: And you are listening to Data Crunch.

Curtis: A podcast about how data and prediction shape our world.

Ginette: A Vault Analytics production.

Ginette: This episode of Data Crunch is supported by Lightpost Analytics, a company helping bridge the last mile of AI: making data and algorithms understandable and actionable for a non-technical person, like the CEO of your company.

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Tyler: My name's Tyler Folkman.

Curtis: Who is a Senior manager of data science at Ancestry.

Tyler: As I look across Ancestry and family history, we almost have, like, every kind of machine learning problem you might want, I mean, probably not every kind, but we have genetically based machine learning problems on the DNA science side. We have search optimization because people need to search our databases. We have recommendation problems because we want to hint the best resources out to people or provide them. For example, if we have a hundred things we think might be relevant to a person, what order do we showed them? So we use recommendation algorithms for that. We have a lot of computer vision problems because people upload pictures and a lot of our documents, if they're not like digitized yet, meaning that they've extracted the text, they might just be raw photos, or even just the things that our pictures uploaded, we want to understand what's in them, so is this a picture of a graveyard is it a family portrait? Is it an old photo? And so tons of computers vision stuff, natural language processing. On the business side, we have marketing problems just like any other business, like how do you optimize marketing spend? How do you optimize customer experience, customer flow? And so it's really a cool place because you really can get exposed to almost any type of problem you might be interested in.

Curtis: So back in the 80s, before you could go easily find information on the Internet, genealogists had to spend a ton of time trekking around to libraries to try to find information on their ancestors. Ancestry saw a business opportunity and started selling floppy disks, and eventually CDs, full of genealogical resources for genealogists to easily access in their home.

Tyler: And then they grew up through the Internet age and moved out online, and so they started digitizing a lot of the records , like the Census records, birth records, marriage records . A lot of people could build trees on their sites, so one of the core products they offer is you can come in, tell us who you are, and tell us about your ancestry, so like who are your parents? Who are your grandparents? And as you start telling us stuff about you, we try to provide you with the resources to learn more, so maybe you don't know who your great grandparents are, but we can give you a marriage record for them that will point you in the right direction to make some connections to find out who those people are, and that's the main offering Ancestry had and then recently, as DNA has become a thing, and I guess recently is relative; it's been around for a while now, they've offered this ability to know yourself from a genetic side around ethnicity, so you can take a DNA test, and it will tell you your ethnic breakdown from your DNA , and you can find out some really other cool things like communities is a project we have, or a product, that allows people to see they fit into these different kind of genetic communities over time.

Ginette: There are a lot of incredible stories out there coming from DNA products like Ancestry's, like discovering genetic communities you belong to that you didn't know about. I've also heard some crazy stories, like people finding out their fathers aren't their fathers or discovering surprising new details about grandparents' lives as a result of getting to know newly identified relatives. So the DNA test is one particular aspect of Ancestry's offerings, but what else is the company working on right now?

Tyler: Two big things we're working on right now: one is around this idea of how do we use all the data in our databases. So for example, when you build a tree, you enter in information about you and your family, and then you might also have your mom, who uses Ancestry too, or a relative, and she enters in information and there's clearly overlap because you're related.

Ginette: One really nice thing about Ancestry, unlike some other models out there, is they keep your work separate from anyone else's so no one can change the information you've put on your tree, but this approach often means there will be lots of duplicate records of the same person, introducing a problem that machine learning can help solve.

Tyler: What we like to do is connect all of these pieces of information, which is this problem of entity resolution, into clusters of true people, so we have an algorithm using machine learning that looks at all of our billions of records, our billions of records and trees, I should say, and tries to sort out when a record or a tree are referring to the same person in real life and then connect them or link them, and that allows our users to make really interesting discoveries that they don't have in their own trees because we've linked things for them. What we might call them new person hint, which is a pretty recent thing we're testing right now, if you're in one of our beta tests as a user, you'd get new person hints, which yeah, we would actually recommend to you a new person in your tree based on the inferences we made from connecting everyone.

We call those hints, if you've used the product, there's a little green leaf that pops up and kind of shakes, like for example when I put my mom's note in, I put her name in, but I didn't put in the marriage date because I didn't know it. About 10 seconds after I put her note in, I got a hint for her marriage record. Someone else had found it, and we, using our machine learning on the back end, had been able to make that connection for people and hit that up to them.

Curtis: This is a great example of a clever UI element that makes a powerful machine learning algorithm useful to an end user. They went the full mile from algorithm development all the way to the end-user experience, which is often something that's overlooked. Now, with all the duplicate information in Ancestry's backend, it takes a solid system to handle this and some sophisticated methods. So how exactly do they approach it?

Tyler: Much smarter engineers than I built a pretty impressive system that at real time processes data, so as you make a change on Ancestry, we real-time try to find where you fit with that new information or any changes that might happen to you. It's a pretty large system; I frankly don't know all the details of it, but it involves a lot of optimizations around the graphs, so our in-house graph database, which is very fast, optimizations on how to do what we call blocking, which is a pretty standard entity resolution thing for machine learning, which is kind of this idea where we have billions of things we need to compare to and a billion squared is a really, really large number, so you can't do that in real time, so what we do is we do what's called blocking, which is we try to find just the pieces of information that are relevant that might match, so if there's no overlapping things, a very simple blocking algorithm might say, "you have no overlapping information, thus I don't even need to compare you," and so it just moves on, right? And we use some pretty sophisticated blocking methods to quickly narrow down the set of candidates that we have to compare with to keep it fast.

Tyler has a great way of explaining blocking in simpler terms using a popular cartoon book known as or Ginette: Where's Waldo? Where's Wally?, depending on which country you live in . It has pages packed full of hundreds of cartoon characters doing a lot of different activities, and your task is to find the tiny man in red and white on a visually rich and often chaotic two-page display.

If you look at a big Tyler: Where's Waldo, it's really hard to find Waldo, right? You look around and you're trying to find it, but if someone came in and drew a box around Waldo that was not exactly on Waldo but within some radius of Waldo, if it were a circle, that makes it a lot easier, right? It narrows the scope you have to search in. That's what blocking does for us, and we use some genetic algorithms to find the best circle to draw around Waldo basically, where our Waldo is a set of people we think might be relevant to this person or records.

There's a lot of interesting literature on blocking. Traditionally, it's been about information overlap so for example you might implement some heuristics around blocking which says like, "if you are born 100 years plus or minus this thing, you should not be compared because that's just not possible." What we have done, we actually use some genetic algorithms to tune our search mechanism for blocking to be able to find the appropriate things for high recall, meaning that blocking you really want to be performant, so within some performant constraint, you want to have really high to recall, meaning it doesn't miss things it should get, and then you want your your record-linking algorithm to be high precision, so the blocking thing gives you all the things that could possibly be right within some time constraint, and then the compare algorithm or the entity linker is really, really good at getting precise decisions on the things that the blocking gives it.

Curtis: Ancestry has some intricate custom machine learning models at play here, and I wanted to get Tyler's perspective on a recent trend towards using more turnkey models, as opposed to building something out yourself, such as Google's AutoML or other tools that are coming out on the market that make process data easier. Now depending on what type of machine learning you're doing or the type of model you have, this might make sense or it might not. From Tyler's perspective, there could be some great turnkey tools out there, but there are some really important questions you have to ask yourself before deciding to use them.

Tyler: I think that the modeling side, I don't want to say it's easy, but for some problems, it's a little bit more standardized, so for example, if you're trying to do like a customer churn type of model and you have a lot of the same data that other people have on customer churn and then you just kind of want to plug it into a model, I think it's very easy these days for an engineer or even maybe a nonengineer to kind of go find these tools, plug-in the data, and get something up. I think where it's a little bit trickier is understanding how to debug that thing. So for example, maybe you get the answer you wanted. It's really, really good, but do you understand? Do you know where it's not working well? Do you know where it might bite you? Do you know what assumptions you may be were valid/not valid so that you can kind of know when you go to production is this going to work how it worked in my test offline? I think a lot of that takes some understanding of how that algorithm is working, and I know there's work on trying to explain machine learning too, but I think a lot of that's hard to know without actually knowing how the models are working on some level. Do you need be able to write that from scratch yourself? Probably not, but you need to know enough to understand how it's working and where it's working, and if it's not working, what do you do next? I mean that's one of the challenges when I work with deep learning is you might run a perfectly valid model, and this happens to me all the time when I play with reinforcement learning. Like, "oh, this thing should work. I think this is exactly the thing that they made," but for some reason it doesn't. And so what do you do? Like how do you fix it? It's, like, not trivial, and even before you get to the modeling, how do you get your data in a state that it's going to be modelable, like removing outliers, getting the right features. I think those are sometimes are the harder problems that we think about that really influence the modeling side that we don't have very good automated ways, so like for example, for obituaries, the size of the text can matter for extracting names, because sometimes the name is the first word, and it's bigger, and so one of the things we looked at was including the size of the name as a feature in our model, which isn't a standard thing because most people don't have that data, right? They just take the raw, the raw NLP data, the raw text, and so just a turnkey NLP thing would never take that into account, but we knew enough about how it works to plug it in.

Tyler brings up a key point here: understanding, at least on a basic level, machine learning algorithms and data science processes is really, really important in order to successfully create, deploy, and adapt models, and often the best way to understand them is through active learning. Many of you have reached out to us saying that you'd love to start working with machine learning even though it isn't what you originally studied. One place you can look is Ginette: Brilliant.org where you can learn in bite-sized pieces at your own pace. Their courses are entertaining, challenging, and educational, and they go beyond lectures to help you actively learn. It's a great resource. They offer courses in computer-science algorithms, artificial neural networks, and machine learning that will help you better understand algorithms.

If you'd like to deeply understand machine learning and data science, give them a try by going to brilliant.org/DataCrunch. They were good enough to sponsor this episode, and using this link lets them know that you came from us, and you can sign up for free, preview courses, and start learning! Also, the first 200 people that go to that link will get 20% off the annual premium subscription. Once again, that's brilliant.org/DataCrunch to understand machine learning!

Ginette: Now let's talk about another interesting machine learning project called "Newspapers." Ancestry has the largest historical newspaper collection available online.

Tyler: So that was the major thing I did when I first started. As I've started taking on more of a lead roll, someone else on my team, Drew Pearson, has kind of taken that on with someone named Maria Morley (Fabiano), and then there's another set of people who I work closely with, I guess if I'm saying names, Carol Anderson and Phil Crone who are full-time here are working on newspapers. So newspapers is exactly what it sounds like. It's a collection of newspapers in the US. I'm pretty sure there's over hundreds of millions of pages of newspapers, and what they are working on and doing a great job with, is trying to use natural language processing and deep learning to understand, at this point, obituaries, so even to this day people write obituaries in newspapers when someone passes away.

And you can find them online a lot now on, like, the online version of newspapers, and they're a really, really great source for genealogists because they're often written by family members, so they're usually very good information, like the death date is going to be right. You can usually trust it. Information on people's names are usually going to be right. The problem with newspapers' data is that they are unstructured text, right? Like no one's gone through and said "oh, here's the birthday or the death day. Here's the name. Here's the spouse's name," etc., and so what we've been working on is how do we understand using machine learning a raw obituary and extract all the entities. In machine learning, this would be named entity recognition is what it would most commonly be called, but for us it's named entity recognition specifically for obituaries, and so we've leveraged deep learning specifically algorithms around like convolutional neural networks, LSTMs, conditional random fields, to basically take in all this raw data and tag the appropriate people and places and dates with pretty high accuracy, so we can say this is this person who died. This is the day they died. This is the place they died. And once we can do that, we can then start connecting you in to our entity linker to link you to the right person so now that person gets relevant information from newspapers, which they might not otherwise get.

Curtis: To prepare for a project like this, it takes a training set, which often requires people to do labeling by hand so the computer can learn what is correct, and this is a problem that many companies come across and often leads them to build out an in-house team or outsourcing the work to a third party. Building out that team can take explaining and convincing the higher-ups that developing a labelling team is necessary and valuable.

Tyler: This project spawned an in-house labeling team, so we now have a team, which looks like will exist for the near future, whose sole job, or not sole job but a lot of their job is labeling machine learning data, so what we actually did is we used this tool that's open source called Brat, and Brat what it will do is you can give it a sentence and it will make it easy for people to tag things, so it's basically a labeling tool, and we spun up a team here, trained them, which isn't trivial, a lot of iteration back and forth on figure out like how exactly like do we want these things labeled, what are the special cases, and they went in and labeled all the entities, the places, the relationship connections so that we can learn from it, which was super important and really unlocked a lot of value for us, because otherwise getting off the ground would have been extremely hard, and it's been something we've leveraged moving forward as we've had other projects that need labels, cause we already have this team that has that capability, we've been able to move a lot of other projects forward a lot faster. Getting the training data was definitely challenging, especially since we didn't have a team, like getting the team spun up and convincing that to happen took some time.

Ginette: Tyler also points out the prominence of open source tools for machine learning. Gone are the days when you would have to spend a lot of money on software to do modeling and understanding data-today, the state of the art is in open source, making it available to anyone, and in fact we'll have one of the top thinkers in open-source python coming up on a future episode.

Tyler: Interesting thing about machine learning is a lot of the like, especially in deep learning, a lot of the open source stuff is the state-of-the-art. Deep learning especially is an interesting field because there's so many different hyperparameters or architectures you could try and if each attempt at a model say takes 10 hours on one GPU, it's not cheap to try different architectures, right? So what you'll see often in industry is that people leverage the great open source community, where Google, Facebook, and companies that have the resources to run these types of things, openAI, will make available things that they found that works, and so we started from what they've done, so like a pretty standard model is to use LSTMs with text data, right? If you have SQL models, you might drop a conditional random field on the end which helps order things out at the end, and starting from there, it actually got us pretty far, and then from there, we can make the tweaks necessary to kind of adapt it to our problem and take into account things that are different for us, but really the great open source community around machine learning right now, it really leverages us to get to a pretty good starting place pretty quickly, even tools like SciKit Learn for traditional machine learning are just great ways to get a baseline type model in like a day.

Curtis: Ancestry and many other companies are benefiting a lot from the open source community and all the great tools that have been developed there. And this is only the tip of the iceberg for Ancestry. We talk to Tyler a little bit about what machine learning might bring genealogy in the future.

Tyler: Machine learning is really going to help leverage a few core things for genealogists. I think (1) it's going to help the expert, make their job easier, like how do we help the expert genealogist, who a lot of our users are, find those next discoveries that aren't trivial. Like, I'm not an expert in my genealogy, so my discoveries are a little bit easier to serve up to me, but how do you really use machine learning to dive deep in that data and find that needle in the haystack that they're looking for in a way that they could probably do by themselves but that we could help them do faster, and the other side is how do we use the data we have and hopefully machine learning to make the experience better for a novice or a potentially interested user. like we have a lot of people who come to our site and try things out, and expert genealogy is a lot harder than maybe hobbyist genealogy, meaning you want to know a little bit more about your family and understand some things but you're maybe not committing tons and tons of time to really digging deep into the search system, right? And so how do we get them to things that they need to to get the answers they're looking for about their family in a way that's easily consumable in a way that's really nice for a user that's not going to spend you hours and hours every day working on that kind of stuff, and I think in general that's an interesting field around how do you merge machine learning and user experience in a way that's just nice, and I think we're still trying to figure that out and a lot of companies are looking into this, and how do we provide algorithms' suggestions in a way that people can use them and also give us feedback cuz I think that's an interesting UX problem.

Ginette: And a huge thank you to Ancestry and Tyler Folkman for being on our podcast. He also shared with us a lot of great advice on how to break into the machine learning industry, so if you are thinking about getting into the space, stay tuned for a future episode.

And as mentioned in our podcast, if you want some freebies from Lightpost Analytics, a sponsor of today's episode, go to lightpostanalytics.com/datacrunch. They'll teach you how to find insights and share them effectively, creating improvements for your company and greater success for you.

And as always, for the transcript and links for this podcast, you can go to datacrunchpodcst.com, and you'll find the links at the bottom of the show transcript. If you like what you're learning here with us, please share our podcast with your coworkers and friends and go to iTunes or your favorite podcast playing platform and leave us a review.

Links

lightpostanalytics.com/datacrunch

brilliant.org/DataCrunch

Attributions

Music

"Loopster" Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 3.0 License
http://creativecommons.org/licenses/by/3.0/

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Photo by Roman Kraft on Unsplash

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Machine Learning, Big Data, and Your Family History

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Podcast: Play in new window | Download (Duration: 21:10 - 24.2MB) | Embed How can artificial intelligence, machine learning, and deep learning benefit your family? These technologies are moving into every field, industry, and hobby, including what some say is the United State's second most popular hobby, family history.

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Article - Why rural areas can't catch a break on speedy broadband 

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Article - Why rural areas can't catch a break on speedy broadband 

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Data science brings people together.

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When Galen Manners was offered a promotion at his job, he didn't want to relocate to the big city from his family in rural Kansas, where he helped his two brothers run their 2,000-acre farm in his spare time.

Shara Tibken/CNET

His employer gave him the option to work from home. But there was one problem. There was no high-speed internet access to remotely connect him to the company's headquarters, more than 300 miles away in Little Rock, Arkansas. So Manners, who had worked for a small wireless provider, built his own broadband network.

"I networked cell towers for a living, so I figured I could do the same thing for myself," he said.

Manners cobbled together his network using a dedicated connection from a phone company in the closest "city": Parsons, Kansas, population 9,000. He rented rooftop space on an eight-story building, one of the tallest in town, and engineered a private wireless network using a cell tower he had erected on his family's farm.

Soon his cousin down the road asked if he could be connected to the network. Then a neighbor on the next farm wanted connectivity. By the time he connected 20 relatives and neighbors, a business was born. Manners started Wave Wireless in 2000. Since then, the small provider has grown to serve 2,500 customers, including the parents of Federal Communications Commission Chairman Ajit Pai, who were customers before moving away from the Parsons area.

Manners' small wireless internet service provider isn't uncommon. It's estimated there are about 2,000 companies providing fixed wireless broadband access across all 50 states, serving an average of 1,200 customers each, according to Wireless Internet Service Providers Association, or WISPA.

They're part of a solution to a critical problem facing the US -- delivering broadband services to millions of rural Americans who don't have access in their homes, farms and businesses. With broadband having become as essential as running water and electricity to improving people's daily lives and providing a standard of living equal to that of urban and suburban parts of the country, policy makers are working together to close the connectivity gap.

Pai said that what gives people hope in rural America is the "promise of digital opportunity."

Broadband is a game changer for rural America.

"Broadband is a game changer for rural America," he said in an interview. Pai, who has traveled to 41 states since taking office in January 2017, said federal and state authorities need to have the same sense of mission they had back when they made a priority of providing electricity to every American.

"I see it is as an echo of the rural electrification efforts we saw in the 1930s, almost 100 years ago," he said.

At stake isn't just whether someone in rural Arkansas can watch cat videos on YouTube or check Facebook. It's about whether rural communities can survive.

In previous generations, communities thrived based on their proximity to infrastructure like roads, railways, airports and rivers to distribute goods. Today, it's about having access to reliable, affordable high-speed internet. Communities without access will simply wither and die, says Jonathan Chambers, a former FCC official and partner at the Washington-based consulting firm Conexon, which works with electric co-ops looking to deliver rural broadband service.

"People will vote with their feet and move away from places that do not provide high-speed internet access," he said. "They will leave, and that community will not survive."

The problem

In spite of the billions of dollars in private investment and government subsidies over multiple decades, the numbers still paint a disturbing picture. Roughly 39 percent of rural Americans lack access to high-speed broadband, compared with just 4 percent of urban Americans, according to a report from the FCC using 2016 figures.

The internet that rural Americans can access is slower and more expensive than it is for their urban counterparts. And to add insult to injury, the rural population generally earns less than those in urban areas.

Building networks in rural America is incredibly expensive, and in some places it's nearly impossible. The terrain can be a problem in areas like West Virginia, nestled among the Appalachian, Allegheny and Cumberland mountain ranges. In Alaska or Minnesota, the ground could be frozen for more than half the year, making it nearly impossible to install fiber or other infrastructure.

But the biggest barrier to getting broadband in certain areas of the country is low population density. Broadband providers simply won't offer service if they can't get enough customers to pay for it. In sparsely populated areas like Cass County, Iowa, where my CNET colleague Shara Tibken was born and raised, residents may see internet speeds of 5 megabits per second, a far cry from the 25 Mbps speeds the FCC defines as broadband.

"The challenge in rural areas is it's just not economical for private parties to invest there," said Doug Brake, director of broadband and spectrum policy at the Information Technology and Innovation Foundation.

Even as progress is made to get rural America connected, the disparity between the haves and have-nots in terms of speed and network quality is likely to get worse. Big broadband providers will focus on the more densely populated and profitable areas of the country, delivering gigabit speed broadband -- often from not just one provider but from multiple companies. The advent of 5G wireless, which promises to bring increased speeds and network responsiveness, is also unlikely to reach rural communities.

"Market forces are what will drive the deployment of 5G," said Blair Levin, who oversaw the FCC's National Broadband report in 2010 and who served as chief of staff to Clinton-era FCC Chairman Reed Hundt. "The 5G economics are very different than they are for 4G. And cities, because of their density, are in a much better position to drive 5G deployment than rural communities."

The politics of rural broadband

The digital divide is one of the few issues that Republicans and Democrats can agree on. Since President Obama pledged in his 2011 State of the Union address that high-speed wireless would be available to 98 percent of Americans, there have been fervent discussions about how to get broadband into rural communities.

The issue has also been taken up by President Trump, who has gotten much of his political support in areas where broadband is hard to come by. His infrastructure proposal this year called for $50 billion in rural spending, including on broadband, but so far neither his budget nor the Republican Congress have allocated additional funds for rural broadband deployment.

At a Senate Commerce Committee hearing in early October, Democrats and Republicans were critical of cuts the FCC has made to a program funded through its Universal Service Fund, which reimburses some of the costs for phone companies offering broadband in rural communities. Committee Chairman John Thune, a Republican from South Dakota, called the FCC's cuts to this funding "simply unacceptable."

"The FCC's failure to ensure sufficient and predictable funding jeopardizes the vitality of America's rural communities," he said. Thune said Pai had committed to conducting a "thorough economic analysis on the impact of ... funding cuts on broadband deployment in rural areas." But he said that no analysis has occurred and that cuts had increased by almost 25 percent.

Sen. Amy Klobuchar, a Democrat from Minnesota, echoed Thune's frustration, saying cuts to the program were "preventing new broadband deployment and driving up prices for our customers."

In response to the hearing, Pai has blamed the previous FCC, which was led by Democrats, for some of these issues. And he said he hopes to resolve the concerns about the budget cuts by the end of the year.

Then there are the maps, which are supposed to identify where broadband and wireless service does and doesn't exist so the FCC can ensure it doesn't subsidize network build-outs in areas that already have coverage.

At an FCC oversight Senate hearing in August, Democrats and Republicans slammed the FCC for producing inaccurate maps. Sen. Jon Tester, a Democrat from Montana, was the most succinct.

"They stink," he said.

Cutting red tape

So what has the FCC been doing? The Republican-led agency has taken a three-pronged approach to the issue: cutting regulation to spur investment, encouraging cheaper technology alternatives and finding more efficient ways to allocate subsidies.

But experts like Brake say the agency has leaned more heavily on cutting regulatory red tape, which has drawn criticism.

One example includes easing the path for carriers to deploy new radios for 5G service by removing local regulatory hurdles.

Pai argues that if the FCC limits the fees that big cities can charge wireless companies to deploy their 5G infrastructure, these carriers will be more willing to plow that money into rural broadband. But critics like Levin called this a false premise and said the FCC's move does nothing more than strip local communities of negotiating power.

"Saving money in New York of Los Angeles has zero impact on investment in Montana," he said. "If it worked that way, we'd have already solved the rural broadband deployment problem."

Pai also says his agency's repeal of the Obama-era net neutrality rules is making it more attractive for broadband companies of all sizes to invest in expanding their networks, which he says will spur investment in rural communities.

"Absolutely, we've seen some very positive initial evidence that this is working," he said in an interview.

He cites VTel Wireless, a mobile carrier serving rural Vermont and parts of New Hampshire, as an example. The company sent a letter to the FCC saying that the repeal of the net neutrality rules gave it the confidence to invest $4 million in upgrading its 4G LTE network.

Last week, he pointed to an industry report from USTelecom, which had opposed the 2015 net neutrality rules and is suing the state of California over its own proposed net neutrality protections, saying that investment is up over the past year as a result of Pai's deregulatory policies.

But his Democratic colleague on the FCC, Commissioner Jessica Rosenworcel, disagrees with these changes.

"The argument is that we will see more deployment in rural locations," she said in an interview. "But I don't believe that we have evidence that suggests that's happening. Instead, what we have are more companies with more rights to block and censor content online, and that's not good for any of us."

Analysts say that cutting regulatory red tape will only take things so far.

"Even if you make it cheaper to deploy and invest in the network, if you can't sustain a business because the population density is too low, it doesn't really matter," Brake said.

Tried and true technology

For decades, much of the work the FCC has done in getting broadband to rural America has centered on providing subsidies and incentives to traditional phone companies. Most Americans, including those living in rural parts of the country, rely on various flavors of fixed broadband infrastructures, such as DSL or cable modem service. The lucky few are able to get access to fiber, which is considered the gold standard in broadband because it provides near limitless capacity.

But pushing fiber closer to customers or directly to their door is expensive. An FCC report authored by Paul de Sa, former chief of the FCC's Office of Strategic Planning and Policy Analysis under then-FCC Chairman Tom Wheeler, estimated it would take $40 billion to expand internet access to cover 98 percent of Americans and another $40 billion to deliver broadband to 100 percent of the US population.

Cheaper alternatives

While fiber is the ideal, it's unlikely Congress will come up with an additional $40 billion to $80 billion to pay for it. That's where wireless comes in.

Wireless 4G LTE and even 5G could replace broadband in some places. But wireless technologies aren't without issues. 5G, which needs hundreds of radios to cover relatively short distances, is likely prohibitively expensive to make sense for rural areas. And wireless still needs fiber connections to offload traffic.

The most useful forms of wireless for rural are fixed wireless links, like the ones offered by companies such as Manners' Wave Wireless. These links generally use unlicensed spectrum at high-frequency ranges to transmit signals from one point to the next. There are technical restrictions for these links, such as the need for radios to have line of sight to each other, and interference issues like trees or mountains.

There's also the use of unlicensed TV broadcast spectrum called white spaces. Microsoft, which holds several royalty-free technology patents for using this spectrum, announced a program in July 2017 to connect 2 million people in rural America by 2022 through partnerships with telecom companies. The company promised to have 12 projects up and running in 12 states in the next 12 months.

The FCC has set rules for the use of white space spectrum and established an administrator of a national database to identify channels that can be used by devices accessing the shared spectrum. But there have been problems with the database's accuracy, and there's not yet an ecosystem of devices, which means it could be a while before the technology is widely used by consumers.

And then there's satellite. While satellite broadband access can blanket large swaths of the globe, the technology is expensive and the connection is slow, largely due to the lag time that results from its trip from space.

But low Earth orbit satellites could be changing this. These satellites operate significantly closer to users, which reduces the lag time, known in the industry as latency. They're also less expensive to deploy. Companies such as Google, Facebook, Boeing, SpaceX and Virgin have experimented with this technology.

The FCC has been trying to free up spectrum for satellite services and modify its rules to allow for more low orbiting satellites to be launched.

Where to invest

The FCC distributes roughly $8 billion a year in subsidies as part of its Universal Service Fund to help offset the cost of deploying broadband in rural America.

Traditionally, much of this money has gone to phone companies to upgrade their existing copper lines for broadband. But as part of its effort to modernize its universal service programs, the FCC conducted its first-ever reverse auction to allocate funds in a technology-agnostic way. While the reverse auction accounted for only a small fraction of the total funds available -- just $1.5 billion -- many considered it a step in the right direction in terms of matching government funds with broadband providers who would actually deliver service. Pai noted it saved the agency $500 million.

"We are trying to make sure these dollars, which are scarce, are stretched as far as possible," Pai said.

He also noted that the auction was designed to encourage new players to bid. Indeed, the biggest winners were wireless ISPs, and rural electric companies, many of which are using their existing utility infrastructure to deploy fiber, were the second largest winners in the auction.

"The fact that there are a variety of different winners speaks to the fact that we were technologically neutral," he said.

Spectrum policy

One of the FCC's most powerful weapons is its ability to allocate public airwaves, or the spectrum that's critical to any wireless network. But how it allocates the spectrum can have huge consequences.

Take for example, rules for the upcoming auction of spectrum in the 3.5 GHz band known as Citizens Broadband Radio Service (CBRS) spectrum. Rural providers of fixed wireless internet, like Wave Wireless, are eying the spectrum because it can cover wider areas and offer higher speeds.

In 2015, rules were established that allocate licenses in smaller census tracts, making it possible for smaller players to bid on licenses.

But after two years of developing the technical standards for the use of the spectrum, Pai's FCC reopened the rules last October and proposed increasing the size of the licenses, making them more attractive to large wireless companies like AT&T, Verizon and T-Mobile.

The proposed changes have pitted small wireless ISPs that want to use the spectrum to expand their coverage in rural areas against large mobile carriers that say they need the spectrum to deliver 5G service. But those larger players are more concerned with urban and suburban areas.

The FCC is expected to vote to accept the changes at its Oct. 23 meeting.

Claude Aiken, president and CEO of WISPA and a former FCC official, said he's concerned the FCC's actions will slow wireless broadband in rural areas. He said the CBRS band - and other midband airwaves - are "the best opportunities we will see in a generation to address" the rural broadband access problem.

Wave Wireless' Manners said the licensed midband spectrum is necessary to allow his business to expand service to more customers and to deliver better speeds. In the rolling hills of the southeastern corner of Kansas near Branson, Missouri, where he operates his service, the terrain isn't always conducive to the higher-frequency, unlicensed spectrum he's traditionally used.

"We need access to good midband spectrum that we can afford," he said. "Otherwise the big players are going to gobble it up for mobile use, it'll sit underutilized, and rural America won't be able to get access to any of it."
CNET's Shara Tibken contributed to this report.
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Rural America needs better internet, here's why it doesn't have it

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When Galen Manners was offered a promotion at his job, he didn't want to relocate to the big city from his family in rural Kansas, where he helped his two brothers run their 2,000-acre farm in his spare time. His employer gave him the option to work from home.

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Global System for Mobile Communications (GSM) - Data science brings people together.

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