Tag Archives: AI

The Future of Broadband Maps

I read that an AI expert at a workshop hosted by the FCC and the U.S. National Science Foundation suggested that AI could be used to produce better broadband maps. I had to chuckle at that idea.

The primary reason for my amusement is that FCC maps are created from self-reported broadband coverage and speeds by the many ISPs in the country. ISPs have a variety of motivations for how and why they report data to the FCC. Some ISPs try to report accurate speeds and coverage. People may be surprised by this, but some of the biggest telcos, like CenturyLink and Frontier, seem to have gotten better at reporting DSL speeds – in some markets, you can find DSL capability being reported at a dozen different speeds to reflect that DSL speeds vary by the distance from the central office.

Other ISPs take the exact opposite approach and report marketing speeds that are far in excess of the capability of the technology being deployed. It’s not hard to find WISPs claiming 100 Mbps to 300 Mbps download capability when they are delivering speeds in the 10 Mbps to 30 Mbps range. My guess is that some of these ISPs are using the FCC maps as an advertisement to get customers to call them after looking at the FCC map. Some ISPs have already been accused of over-reporting speeds to try to block grant money from overbuilding them.

There are also endless examples of ISPs reporting coverage that doesn’t exist. The FCC mapping rules say that only locations that can be served within ten business days should be included in broadband coverage areas, and many ISPs are claiming much larger areas than they can serve quickly. Even worse, some ISPs claim coverage in areas that they can’t serve, such as when WISPs claim coverage of homes that are blocked from line-of-sight by hills or other impediments.

The only way that AI could be used to improve the maps is if the FCC gets serious about mapping and changes some rules, and enforces others. The FCC would have to eliminate the ability of ISPs to claim marketing speeds, which provides easy cover for overstating capabilities. The FCC would also have to get serious about enforcing coverage to meet the 10-day installation rule. If those two changes were made and enforced, the FCC might be able to use AI to improve the maps. AI could match claimed ISP coverage to speed test data and also reference and compare coverage to complaints and challenges from consumers. I don’t see the FCC ever being willing to get that aggressive with ISPs – because this process would be extremely contentious.

I don’t believe any of this will ever happen because after the wave of BEAD funding is finally spent, the FCC and everybody else is going to lose interest in the broadband maps. Nobody will care if some ISP overstates capabilities in an area as long as the BEAD winner is going to bring faster broadband.

There are already a number of State Broadband offices that are saying that the BEAD allocations are not going to be enough to fix broadband everywhere. My prediction is that states that care about fixing the remaining places will create their own broadband maps and will go back to ignoring the FCC maps.

The FCC won’t care. At the point where they can say with a straight face that 95% of homes will be be able to buy broadband that meets the FCC’s definition of broadband, the FCC is going to declare job done. For the last decade, the FCC has issued annual broadband reports to Congress that have said that the state of broadband is good and improving – all based upon maps that everybody knew were grossly overstated in both broadband speeds and coverage. I can’t see the FCC putting extra effort into proving that there are still homes left without good broadband.

There is No Artificial Intelligence

It seems like most new technology today comes with a lot of hype. Just a few years ago, the press was full of predictions that we’d be awash with Internet of Thing sensors that would transform the way we live. We’ve heard similar claims for technologies like virtual reality, block chain, and self-driving cars. I’ve written a lot about the massive hype surrounding 5G – in my way of measuring things, there isn’t any 5G in the world yet, but the cellular carriers are loudly proclaiming its everywhere.

The other technology with a hype that nearly equals 5G is artificial intelligence. I see articles every day talking about the ways that artificial intelligence is already changing our world, with predictions about the big changes on the horizon due to AI. A majority of large corporations claim to now be using AI. Unfortunately, this is all hype and there is no artificial intelligence today, just like there is not yet any 5G.

It’s easy to understand what real 5G will be like – it will include the many innovations embedded in the 5G specifications like frequency slicing and dynamic spectrum sharing. We’ll finally have 5G when a half dozen new 5G technologies are on my phone. Defining artificial intelligence is harder because there is no specification for AI. Artificial intelligence will be here when a computer can solve problems in much the way that humans do. Our brains evaluate available data on hand to see if we know enough to solve a problem. If not, we seek the additional data we need. Our brains can consider data from disparate and unrelated sources to solve problems. There is no computer today that is within a light-year of that ability – there are not yet any computers that can ask for specific additional data needed to solve a problem. An AI computer doesn’t need to be self-aware – it just has to be able to ask the questions and seek the right data needed to solve a given problem.

We use computer tools today that get labeled as artificial intelligence such as complex algorithms, machine learning, and deep learning. We’ve paired these techniques with faster and larger computers (such as in data centers) to quickly process vast amounts of data.

One of the techniques we think of artificial intelligence is nothing more than using brute force to process large amounts of data. This is how IBM’s Deep Blue works. It can produce impressive results and shocked the world in 1997 when the computer was able to beat Garry Kasparov, the world chess champion. Since then, the IBM Watson system has beat the best Jeopardy players and is being used to diagnose illnesses. These computers achieve their results through processing vast amounts of data quickly. A chess computer can consider huge numbers of possible moves and put a value on the ones with the best outcome. The Jeopardy computer had massive databases of human knowledge available like Wikipedia and Google search – it looks up the answer to a question faster than a human mind can pull it out of memory.

Much of what is thought of as AI today uses machine learning. Perhaps the easiest way to describe machine learning is with an example. Machine learning uses complex algorithms to analyze and rank data. Netflix uses machine learning to suggest shows that it thinks a given customer will like. Netflix knows what a viewer has already watched. Netflix also knows what millions of others who watch the same shows seem to like, and it looks at what those millions of others watched to make a recommendation. The algorithm is far from perfect because the data set of what any individual viewer has watched is small. I know in my case, I look at the shows recommended for my wife and see all sorts of shows that interest me, but which I am not offered. This highlights one of the problems of machine learning – it can easily be biased and draw wrong conclusions instead of right ones. Netflix’s suggestion algorithm can become a self-fulfilling prophecy unless a viewer makes the effort to look outside of the recommended shows – the more a viewer watches what is suggested, the more they are pigeonholed into a specific type of content.

Deep learning is a form of machine learning that can produce better results by passing data through multiple algorithms. For example, there are numerous forms of English spoken around the world. A customer service bot can begin each conversation in standard English, and then use layered algorithms to analyze the speaker’s dialect to switch to more closely match a given speaker.

I’m not implying that today’s techniques are not worthwhile. They are being used to create numerous automated applications that could not be done otherwise. However, almost every algorithm-based technique in use today will become instantly obsolete when a real AI is created.

I’ve read several experts that predict that we are only a few years away from an AI desert – meaning that we will have milked about all that can be had out of machine learning and deep learning. Developments with those techniques are not leading towards a breakthrough to real AI – machine learning is not part of the evolutionary path to AI. At least for today, both AI and 5G are largely non-existent, and the things passed off as these two technologies are pale versions of the real thing.

AI, Machine Learning and Deep Learning

Data CenterIt’s getting hard to read tech articles any more that don’t mention artificial intelligence, machine learning or deep learning. It’s also obvious to me that many casual writers of technology articles don’t understand the differences and they frequently interchange the terms. So today I’ll take a shot at explaining the three terms.

Artificial intelligence (AI) is the overall field of working to create machines that carry out tasks in a way that humans think of as smart. The field has been around for a long time and twenty years ago I had an office on a floor shared by one of the early companies that was looking at AI.

AI has been in the press a lot in the last decade. For example, IBM used its Deep Blue supercomputer to beat the world’s chess champion. It really didn’t do this with anything we would classify as intelligence. It instead used the speed of a supercomputer to look forward a dozen moves and was able to rank options by looking for moves that produced the lowest number of possible ‘bad’ outcomes. But the program was not all that different than chess software that ran on PCs – it was just a lot faster and used the brute force of computing power to simulate intelligence.

Machine learning is a subset of AI that provides computers with the ability to learn without programming them for a specific task. The Deep Blue computer used a complex algorithm that told it exactly how to rank chess moves. But with machine language the goal is to write code that allows computers to interpret data and to learn from their errors to improve whatever task they are doing.

Machine learning is enabled by the use of neural network software. This is a set of algorithms that are loosely modeled after the human brain and that are designed to recognize patterns. Recognizing patterns is one of the most important ways that people interact with the world. We learn early in life what a ‘table’ is, and over time we can recognize a whole lot of different objects that also can be called tables, and we can do this quickly.

What makes machine learning so useful is that feedback can be used to inform the computer when it makes a mistake, and the pattern recognition software can incorporate that feedback into future tasks. It is this feedback capability that lets computers learn complex tasks quickly and to constantly improve performance.

One of the earliest examples of machine language I can recall is the music classification system used by Pandora. With Pandora you can create a radio station to play music that is similar to a given artist, but even more interestingly you can create a radio station that plays music similar to a given song. The Pandora algorithm, which they call the Music Genome Project, ‘listens’ to music and identifies patterns in the music in terms of 450 musical attributes like melody, harmony, rhythm, composition, etc. It can then quickly find songs that have the most similar genome.

Deep learning is the newest field of artificial intelligence and is best described as the cutting-edge subset of machine learning. Deep learning applies big data techniques to machine learning to enable software to analyze huge databases. Deep learning can help make sense out of immense amounts of data. For example, Google might use machine learning to interpret and classify all of the pictures its search engine finds on the web. This enables Google to be able to show you a huge number of pictures of tables or any other object upon request.

Pattern recognition doesn’t have to just be visual. It can include video, written words, speech, or raw data of any kind. I just read about a good example of deep learning last week. A computer was provided with huge library of videos of people talking along with the soundtracks and was asked to learn what people were saying just by how people moved their lips. The computer would make its best guess and then compare its guess to the soundtrack. With this feedback the computer quickly mastered lip reading and is now outperforming experienced human lip readers. The computer that can do this is still not ‘smart’ but it can become incredibly proficient at certain tasks and people interpret this as intelligence.

Most of the promises from AI are now coming from deep learning. It’s the basis for self-driving cars that learn to get better all of the time. It’s the basis of the computer I read about a few months ago that is developing new medicines on its own. It’s the underlying basis for the big cloud-based personal assistants like Apple’s Siri and Amazon’s Alexa. It’s going to be the underlying technology for computer programs that start tackling white collar work functions now done by people.