Tag Archives: AI

AI and the FCC

In July, the White House released Winning the Race, America’s AI Plan, that described the administration’s view of the role that government will have in the future of AI. Under the section titled Recommended Policy Actions, the White House envisions the following role for the FCC:

Led by the Federal Communications Commission (FCC), evaluate whether state AI regulations interfere with the agency’s ability to carry out its obligations and authorities under the Communications Act of 1934.

I’ve been thinking about this directive since the report was released, trying to envision exactly what it means, particularly the reference to the Communications Act of 1934. There are already a handful of ways that the FCC has gotten involved with AI:

  • Addressing AI-generated Robocalls: The FCC has issued rulings clarifying that AI-generated voice calls fall under existing robocall restrictions, requiring prior express consent from the recipient. The agency has also levied fines against companies using AI-generated robocalls.
  • Political Advertising and Transparency: The FCC has proposed rules to mandate disclosures when AI is used to generate content in political advertisements broadcast on radio and television.
  • Spectrum Management and Innovation: The FCC is investigating how AI can be leveraged for better spectrum utilization and management. This includes exploring using AI to analyze spectrum usage data, potentially leading to more efficient allocation and sharing of wireless frequencies.

All of these efforts seem to fall within the FCC’s regulatory authority, which stems back to the Communications Act of 1934. But it’s hard to envision an FCC role beyond issues that it already regulates.

Subsequent to the announced AI plan, the Administration announced that the FCC is also being tasked to review State regulation of AI, and Chairman Brendan Carr announced on July 24 that it’s possible the FCC would preempt state AI regulations. This announcement perplexes me, because I can’t think of any authority that would allow the FCC to preempt State AI regulations.

The only way for the FCC to preempt state regulation of AI is if the FCC asserts federal regulation over AI. That would be contrary to the overall philosophy of the current FCC, which changed the tenor of broadband regulation starting when the FCC under Chairman Ajit Pai made it clear that the FCC has no role in regulating broadband and made it clear that broadband is not a telecommunications service. The FCC even went so far as to hand off some remaining broadband regulations to the FTC. Chairman Carr has openly agreed with this interpretation of the FCC’s authority over broadband.

Since the FCC has elected to not regulate broadband, I don’t see how it can regulate AI. The only tie between AI and the FCC is that AI rides fiber connections to get to and from data centers and customers. As such, AI is a service that uses broadband, but is clearly not broadband. The best analogy to AI from a regulatory perspective is that it is essentially the same as cloud services like Amazon AWS that ride fiber paths. I can’t recall any discussion ever of FCC authority to regulate cloud services.

To further confound the issue, the long-standing arrangement between States and the federal government is that States are free to regulate anything that the federal government chooses not to regulate. The recent AI report makes it clear that the Administration doesn’t think AI should be heavily regulated, even though some in Congress have some different ideas on the question.

The Chairman’s announcement makes it clear that the FCC is going to try to preempt state AI regulations that the federal government doesn’t like. But it seems certain that any attempt by the FCC to do so will end up in protracted legal battles. The National Conference of State Legislatures reported recently that all 50 states, Puerto Rico, the Virgin Islands, and Washington D.C. have passed or introduced AI legislation – so the FCC will have a tall task, and a lot of states willing to push back on any FCC attempt to preempt state legislatures.

The Human Touch

Recently, Verizon Consumer CEO Sowmyanarayan Sampath wrote to customers saying that Verizon customer service has “taken a different path” and the company is raising the bar on the customer service experience. This sounds a lot like communications with customers I’ve seen over the years from all of the big ISPs – I can think of dozens of company messages telling customers that a big ISP cares about customer service.

What’s different about Mr. Sampath’s email is that he also included an email address where customers can contact him directly if they are having a problem that is not getting resolved. I have to assume this will use a different email address from the one he uses for normal emails, because it seems likely that his inbox will quickly fill with customer complaints.

This reminded me of an experience I had back in the early 1980s when I worked at Southwestern Bell. The company had an executive telephone hotline that was supposedly a direct line to the President for customers who knew the special number. Calls to this number were recorded and landed on the desk of somebody who happened to sit close to me. I would often overhear some of the complaints that came to the executive line, and they were the normal things you would expect – overbilling, botched installations, etc. Employees around the company responded quickly to every referral from the executive hotline.

I have to think that Mr. Sampath is doing something similar and has recreated the executive hotline using an email address. If Verizon customer service is indeed getting better, I assume anybody who makes a valid claim to that email will get some attention from elsewhere in Verizon. If that doesn’t happen, this will quickly be chalked up as another big company public relations ploy rather than an actual aid for frustrated customers.

I have to wonder how well this idea will work with such a gigantic company with coast-to-coast customers. I know at Southwestern Bell that no employee wanted to get the internal message from the executive suite that they had messed up. Will that work for a much bigger company?

People who run smaller ISPs, or other small businesses that deal with the public, will laugh at this article, because fielding customer issues is a daily part of every executive’s work day. It’s something that nobody loves doing, but it comes with operating a business. Every ISP hopes that employees can satisfy every customer so that the top guys never hear about problems. But the folks at Southwestern Bell many years ago figured out that there had to be a way for customers who aren’t satisfied with the routine solution to have an outlet to be heard.

This story has me thinking about how important the human touch is with customers – having a real person to talk to who can solve a problem. That question was prompted for me when I noticed that Verizon is touting that it has incorporated AI into the customer service process. I have to wonder if AI will be used to tackle problems sent to Mr Sampath’s email.

While big companies can pretend otherwise, we have not yet reached the time when an AI can provide the same quality response as a real person. My gut tells me that it will be a huge mistake for the big ISPs and carriers to take the human touch out of customer interactions. If so, that’s good news for the smaller companies that compete with big ISPs. I foresee that small ISP advertising will emphasize that customers can always talk to a real person.

 

AI Hype Begins

It didn’t take long after the widespread introduction of AI into the business environment for a carrier to claim it is using AI better than the competition. Masha Abarinova wrote an article in Fierce Networks that quotes Comcast as saying it is using AI more effectively than its fiber competitors.

The article covers a discussion with Elad Nafshi, the chief network officer for Comcast, who brags on the ways Comcast is already using AI more effectively than fiber-based ISPs. She quotes Nashi as claiming that Comcast has embedded AI that is “literally feet away from a customer” with real-time pattern detection capabilities that give Comcast the ability to pinpoint interference in the network.

I can already anticipate the fiber ISP retort to this claim, with fiber ISPs saying they don’t need a last-foot AI capability because fiber doesn’t have any interference since it has the same quality of service from end-to-end in the network.

I’ve been waiting for this first shot across the bow and suspect that Comcast’s claim will set off a chain of industry players claiming their flavor of AI is better than the competition. These claims are mostly hype and are aimed at Wall Street analysts and not at the general public. The biggest companies in the industry never miss a chance to claim they have an advantage. It’s easy at this early stage of AI to make this kind of claim since nobody can tell how much of such a claim is hype versus reality. Throw around enough buzzwords, and nobody can challenge such a claim.

A more interesting observation in the article quotes Nafshi as saying that general AI use among customers has not resulted in increased network traffic. He noted that while customers are using ChatGPT and OpenAI, the interactions between customers and the clouds are mostly passing text, which is not data intensive.

This differs a lot from what other industry players have been claiming about the future of AI. The article cites AT&T’s prediction that its network traffic will double by 2028 due to AI. Zayo cited an expected huge growth in network traffic as the justification to buy the fiber networks from Crown Castle.

I’ve been scratching my head for several months trying to figure out how AI might create the predicted explosive growth. I’ve yet to see anybody describe the specific AI traffic or functions that could double the traffic for a company like AT&T.

Network traffic is growing for other reasons. Ericsson recently predicted a 16% annual growth in cellular traffic. Numerous predictions for home and business broadband have predicted growth rates of 10-12% annually. Something drastic and new would be needed to double overall traffic on AT&T by 2028.

What’s the Future of Keyboards?

My consulting firm does surveys and I want to highlight the results from a recent survey. This was a random survey with statistically valid results surveyed a cross section of the community, so the results are reasonably believable.

We asked survey respondents the number of hours per day they use a cell phone and computer or tablet. The following chart shows the response by age:These results are not as accurate as studies that require people to keep a usage log, and the above numbers tabulate the number of hours people think they use devices. Note that these statistics come from just one survey.

These results reinforce a  few things I’ve been reading from various studies. Those over 65 are still using devices for fewer hours per day than younger age groups. 18 to 34 years old are using devices more than older folks, on average.

The response that I want to highlight is the big shift in usage of those under 34 to using cell phones instead of computers. Just a few years ago, our surveys showed an even split of device use for this age group. Going back a few more years and usage would have been weighted to using computers.

Unfortunately, our surveys don’t reach those under 18, but everything I’ve been reading says that teens and younger kids have migrated to cell phones to a greater degree than shown by this survey for 18-34 year olds. Kids are not just using cell phones – they are talking to them and rarely use a phone’s keyboard.

That’s the phenomenon that makes me ask if we are seeing the beginning of the end for typing into computers. I’ve been reading science fiction my whole life, and a constant prediction of the future has always been communicating with computers by voice.

The recent advent of AI is likely to increase this trend away from typing. I’ve been promised a good computer assistant since I used Ask Jeeves in 1996. No good software has ever come along that isn’t more work than doing things myself, but with AI that is likely to change soon.

There is no denying that younger folks are already making the transition away from typing and now prefer the smart phone. Friends of mine with younger kids say they complain loudly about having to use a keyboard. I’m clearly old school. I spend four hours or more a day writing and a lot of time working on complicated spreadsheets. My brain is completely trained on using a keyboard for those functions, and I’m not sure I’d want to try to transition to talking. But I love voice-to-text on my phone and I see the appeal to use voice for other functions.

It’s not hard to envision a reasonably near future where people will transition more from keyboards to talking. The future choice will not be between computer and cell phone, but a choice of the best screen to use for various functions. Unless we finally get functional glasses or holograms that can display anywhere we go. Give me the whole package and maybe I’m ready to talk.

AI in Telecom

NVIDIA recently issued its third annual State of AI in Telecommunications report. The company manufacturers many of the cards used in AI data centers, so the company is clearly focused on AI adoption. NVIDIA issues similar reports for other industries.

The 2025 report is the result of a survey that NVIDIA administered to 450 telecom professionals across the globe. The respondents represented a global mix of telecommunications companies, including network operators, system integrators, internet service providers, network equipment providers, independent software vendors, and more. Sixty percent of respondents were executives and directors of companies. The report doesn’t tell us anything about the companies included in the survey but does note that NVIDIA has mostly been concentrating on cellular carriers.

The reason that is important is that I found this survey when somebody online mentioned this report when saying that most telcos have adopted or will soon be adopting AI. The NVIDIA report is fairly careful to say the results represent the views of the 450 respondents, but even NVIDIA occasionally slips up with global statements such as, “97% of telcos are adopting AI. Nearly half are already deploying it”.

Here is how NVIDIA claims AI is being used in telecom:

  • 37% are using AI in network operations and planning.
  • 44% are using AI to optimize the customer experience.
  • 58% are using AI to increase employee productivity.
  • A smaller percentage of respondents are using AI for improved security, customer retention, energy conservation, regulatory compliance, marketing optimization, retail operations, and logistics.
  • 21% say AI has helped them increase revenues by more than 10%, with another 24% saying revenues have increased 5-10%.
  • 11% claim that AI has helped them reduce costs by more than 10%, while another 26% say costs are down 5-10%.
  • 43% say that finding AI experts has been the key obstacle to further AI adoption.

All of this may be true for the largest carriers around the world, but I can’t imagine it is true for the large number of smaller ISPs I know.

I’m curious how many smaller ISPs have done anything serious with AI. It’s hard for anybody to claim they don’t use AI since it is now seemingly built into every online tool in some manner. I’ve talked to a number of folks at ISPs who use AI to help write emails, memos, and reports.

I’d love to hear from folks at small ISPs. Is your ISP using AI for anything like what is described by NVIDIA? That might include things like using AI to chat directly with customers online or using AI to monitor network performance. Everybody I’ve talked to is curious about the potential of AI, but almost universally they aren’t ready to trust it in real-time applications. I’m curious if there is a quiet AI revolution going on at smaller ISPs that I haven’t heard about.

In a few years small ISPs might not have a choice. I have to think that in a few years that many of the software packages that come with network gear will include AI application to monitor performance or troubleshoot problems, but I haven’t heard any claims about that yet from vendors.

AI Impact on Power and Broadband

AI technology seems to be a hot topic in every industry, and broadband is no exception. It seems inevitable that AI will be used to help monitor and control complex broadband networks. It looks like the biggest ISPs are already phasing AI into the customer service process.

The big question that nobody seems to be able to answer is if AI will change the amount of broadband the average household uses. It’s not an easy question to answer. It’s a reasonable question to ask because I seem to read weekly how AI is going to affect the way we communicate, and that seems likely something that will involve broadband.

An easier question to answer is AI’s impact on U.S. power consumption. It’s clear, at least for now, that AI and cryptocurrencies are fueling the construction of a lot of new data centers. The International Energy Agency’s (IEA) report for 2024 predicts a big uptick in worldwide power demand coming from data centers. IEA estimates that worldwide data centers in 2022 used about 460 Terawatt Hours (TWh) of power and predicts that by 2026 demand from data centers will grow to between 620 and 1,050 TWh. That would be the equivalent of adding as much energy used annually by Sweden at the low end of the estimate or Germany at the top end. IEA says that data centers in the U.S. will grow from using 4% of generated powerin 2022 to 6% in 2026.

The impact on broadband usage is harder to pin down. AI will impact broadband usage is several ways. There is the middle-mile impact of supporting the many new AI data centers. There are two types of data going to and from data centers. There is traffic sent to and from users asking AI to respond to queries. That means a public-facing AI data center should be equally as busy as a data center that responds to Google searches today. The second big use of broadband comes from feeding the public-facing data centers with the massive amount of data needed to ’train’ the AI. A public AI data center imports piles of data scraped from websites and other sources. An AI data center will create a busy node on the Internet that will draw a lot of traffic.

The biggest uses of future AI will probably not be in the big public AI data centers, but from data centers dedicated to big corporate users. Big corporations like Bank of America are not going to use the big public AI data centers, but will create their own AI data center to crunch their own data. One can picture a large new stream of Internet traffic coming from the many branches of Bank of America to feed the company’s own AI data center.

Both kinds of data centers will create new demand for long-haul and middle-mile fiber networks. Companies are likely to place AI data centers close to the existing long-haul networks that carry traffic from city to city. Companies that operate fiber transport networks are expecting a lot of AI-related traffic. I remember seeing that one of the justifications for the recent upgrade in Zayo long-haul networks was to prepare for AI.

The impact on home broadband is harder to predict. It’s possible that AI will decrease the amount of bandwidth used at home. If I research a topic today to write a blog, I do a Google search and perhaps visit a half dozen websites looking for background information. If I instead ask an AI search engine to find what I need, I’m going to look at fewer web sites if I’m satisfied with the AI answer to my questions. A recent article in Scientific American suggests that Google might use 30 times more energy to answer my question using AI instead of its traditional search engine. But at my home computer, I will likely use less bandwidth to get the condensed response from the Google AI. Most of my interfaces with AI involve transmitting short questions to the AI cloud, and receiving relatively short responses.

This doesn’t mean that there won’t eventually be more data-intensive uses for AI in the home. It may be possible to use AI to create a truly smart home that takes care of our needs automatically. I’m still waiting for the big virtual input screen that floats in the air in front of me like I’ve been seeing in futuristic movies. But for now, for most users, it’s hard to think that AI will increase bandwidth usage at home.

ISPs and AI

One of the most common questions I’ve been asked lately is what I think the impact AI will have on the broadband industry.

All of the big ISPs in the industry have actively been pursuing the use of AI. For example, AT&T Labs says it is investigating the use of AI to optimize the customer experience and auto-heal the network. Comcast says that it is using AI to help process petabytes of data every day. Comcast also worked with Broadcom to develop the first broadband chip for nodes, amps, and modems that bring AI into the network. Verizon is working on an AI solution to improve the customer experience in its IVR systems for customers calling the company. Charter is working AI into its customer interface. It’s also using AI to help customers generate commercials for advertising on the cable network.

Before talking about those uses, a basic primer on AI is needed. Most people are familiar with public AI platforms like Chat GPT or the Google Cloud Platform. No big corporations are using the open public versions of AI. Any data dumped into those systems is available to other users. Instead, corporations are buying and implementing private versions of AI that they train using their own data. One of the common issues with public AI platforms is that AI will hallucinate and invent an answer to a question. However, hallucination can be controlled in private networks where the user strictly controls the data.

All of the big ISPs, and seemingly most companies that field a lot of calls from customers, want to use AI to improve the customer experience. There are different approaches to using AI. One of the primary uses of AI is to eliminate customer menus where customers are asked to wade through a menu to choose who they want to talk to. AI can be used to interpret a customer request and direct the call to the appropriate place. AI can also be used to quickly pull all information about a caller to put it at the fingertips of a customer service rep. Maybe the most important feature of AI is that a customer conversation can carry across different customer service reps, meaning that a caller doesn’t have to repeat basic information every time they are transferred.

There are companies in the country that have completely automated AI to fully handle the customer interface, but it’s not likely that any big ISPs have gotten that bold yet. All of the feedback I’ve heard is that it’s still far too easy for an AI system to badly misinterpret what a customer wants. The same goes for attempts to fully automate an online chatbot. It doesn’t seem like anybody has come close to perfecting this yet, and doing it clumsily is frustrating for customers. But who knows, maybe in the future, most customer interfaces could be entirely handled by an AI representative.

Big ISPs are all investigating the use of AI in the network. The most obvious uses of AI is to interpret real-time network data to detect problems and analyze network quality. For many years, networks have used alarms to identify problems. One of the issues with an alarm system is that ISPs get constantly hit with minor alarms, and it’s not always easy to pick out the ones that matter. One of the hopes with AI is to look deeper at the performance of network equipment to identify problems long before an alarm is triggered.

ISPs are also starting to use AI for load balancing. It’s easy to think of broadband usage on a network as a steady state, but the reality is that usage spikes and dives erratically from second to second. AI can be used to examine usage on all segments of a network. For example, there are numerous paths from the network core in a fiber or cable network, and AI can examine all of them in real-time, as well as understand how usage spikes from neighborhoods can overwhelm other parts of the network.

The big temptation is to let AI take an active role in fixing problems. That idea makes a lot of network engineers nervous because AI is still nothing more than a series of algorithms created by programmers. It’s incredibly challenging for any programmer to create perfect programming, and the fear is having a network get out of control in a way that humans will have a hard time regaining control without shutting the network down. It’s not hard to envision an automated AI repeatedly magnifying and compounding a network problem.

The last use of AI by ISPs is to automate functions done by people. None of the big ISPs are talking about this because doing so sparks a lot of anxiety in the workforce. AI seems to be efficient at processing repetitive data or generating routine reports for management. It’s becoming obvious that other industries like banking and insurance companies have already been able to reduce some staff due to AI efficiencies. It’s likely that ISPs are already quietly reducing some clerical and middle-management staff due to AI. This is the part of AI that makes workers nervous. AI is more likely to replace white-collar workers and middle management than hands-on technicians. But this is going to be done quietly, at least until one of the big ISP CEO spills the beans on an investor call.

It’s going to be a while until any of these benefits move downhill to smaller companies. AI hardware and software is prohibitively expensive and smaller ISPs will have to wait until there are generic solutions offered by AI vendors.

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.