Tag Archives: AI

ChatGPT and Creating For Yourself

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I’m sure you’ve been inundated by posts about ChatGPT over the past couple of weeks. If you managed to avoid it the short version is that there is a new model from OpenAI that can write articles, create poetry, and basically answer your homework. Lots of people are testing it out for things as mundane as writing Amazon reviews or creating configurations for routers.

It’s not a universal hit though. Stack Overflow banned ChatGPT code answers because they’re almost always wrong. My own limited tests show that it can create a lot of words from a prompt that seem to sound correct but feel hollow. Many others have accused the algorithm of scraping content from others on the Internet and sampling it into answers to make it sound accurate but not the best answer to the question.

Are we ready for AI to do our writing for us? Is the era of the novelist or technical writer finished? Should we just hang up our keyboards and call it a day?

Byte-Sized Content

When I was deciding what I wanted to do with my life after college I took the GMAT to see if I could get into grad school for an MBA. I scored well on the exam but not quite to the magical level to get a scholarship. However, one area that I did do surprising well in for myself was the essay writing section. I bought a prep book that had advice for the major sections but spent a lot of time with the writing portion because it was relatively new at the time and many people were having issues with how to write an essay. The real secret is that the essay was graded by a computer, so you just had to follow a formula to succeed:

  1. Write an opening paragraph covering what you’re going to say with three points of discussion.
  2. Write a paragraph about point 1 and provide details to support it.
  3. Report for points 2 & 3
  4. Write a summary paragraph restating what you said in the opening.

That’s it. That’s the formula to win the GMAT writing portion. The computer isn’t looking for insightful poetry or groundbreaking sci-fi world building. It’s been trained to look for structure. Main idea statements, supporting evidence, and conclusions all tick boxes that provide points to pass the section.

If all that sounds terribly boring and formulaic you’re absolutely right. Passing a test of competence isn’t the same and pushing the boundaries of the craft. A poet like e e cummings would have failed because his work has no structure and contains capitalization errors compared to the standards of grammar. Yet no one would deny that he is a master of his craft. Likewise, always following the standards is only important when you want to create things that already exist.

Free Thinking

Tech writing is structured but often involves new ideas that aren’t commonplace. How can you train an algorithm to write about Zero Trust Network Architecture or VR surgery if no examples of that exist yet? Can you successfully tell ChatGPT to write about space exploration through augmented reality if no one has built it yet? Even if you asked would you know what sounded correct from the reply.

Part of the issue comes from content consumption. We read things and assume they are correct. Words were written so they must have been researched and confirmed before being committed to the screen. Therefore we tend to read content in a passive form. We’re not reacting to what we’re seeing but instead internalizing it for future use. That’s fine if we’re reading for fun or not thinking critically about a subject. But for technical skills it is imperative that we’re constantly challenging what’s written to ensure that it’s accurate and useful.

If we only consumed content passively we’d never explore new ideas or create new ways to achieve outcomes. Likewise, if the only content we have is created by algorithm based on existing training and thought patterns we will never evolve past the point we are today. We can’t hope that a machine will have the insight to look beyond the limitations imposed upon it by the bounds of the program. I talked about this over six years ago where I said that machine learning would always give you great answers but true AI would be able to find them where they don’t exist.

That’s my real issue with ChatGPT. It’s great at producing content that is well within the standard deviation of what is expected. It can find answers. It can’t create them. If you ask it how to enter lunar orbit it can tell you. But if you ask it how to create a spacecraft to get to a moon in a different star system it’s going to be stumped. Because that hasn’t been created yet. It can only tell you what it’s seen. We won’t evolve as a species unless we remember that our machines are only as good as the programming we impart to them.

Tom’s Take

ChatGPT and programs like Stable Diffusion are fun. They show how far our technology has come. But they also illustrate the importance that we as creative beings can still have. Programs can only create within their bounds. Real intelligence can break out of the mold and go places that machines can’t dream of. We’ve spent billions of dollars and millions of hours trying to train software to think like a human and we’ve barely scratched the surface. What we need to realize is that while we can write software that can approximate how a human can think we can never replace the ability to create something from nothing.

AI is a Promotion

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When I worked at IBM as an intern, part of my job was writing a deployment script to help make our lives easier when installing new ThinkPads. In order to change an MTU setting on the token ring PCMCIA cards (long story), I had to write a script that iterated through all the possible combinations of adapters in the registry to find the one I was looking for and change the value.

Now, I was 22 at the time and green behind the ears, especially when it came to programming. I finally figured out that the most efficient way to do this in the language that I was using was a very deep nested if statement. It wasn’t my best work but it operated properly. I mentioned this to my mentors on my team with a remark of how hard it was to understand the logic at first. My comment was “You know, if it’s hard to read for anyone else then I never have to worry about gettin fired.”

To which the response was, “Yes, but you can never be promoted either.”

That sage wisdom brings me to the modern world and how AI can fix that issue.

Eternal Support

One of the first things you learn in IT is not to volunteer. If you know how to implement a technology or troubleshoot a specific issue then you become the go-to person for that problem. If you add a skill to the team like debugging Rust or configuring voice mail dial peers then you are the new owner for that particular skill.

This becomes annoying when you finally move past that skillset and you’re on to new and better things. If you are the person that knows how to extend database schemas or change auto attendant functions you are always going to be called upon to do those things. Rather than moving on to new challenges you will be called back to work on the things that no one else bothered to learn or couldn’t pawn off on someone else.

When you think about it, that’s part of the reason why so many entry-level positions start with some basics like VLAN programming or LUN configuration. Sure, you’re learning the basics and understanding how systems operate. But you’re also repeating the things that the junior person before you did. Why? Because they now have someone to do that work for them. And when the new person gets hired as a junior-level technician, you’re going to pass along those tasks to someone so they can learn and so you can move on. It’s the Circle of IT Life.

The reinforcement of knowledge is only so important to the organization. Yes, you need someone to keep the lights on and do the tasks regarded as menial by the senior teams. However, miring people in those tasks makes them feel undervalued after a while because no one wants to take them on. And, as above, if it’s something that no one else can be bothered to learn or take over you’re going to be stuck doing it until you leave or until the system in question is eventually replaced. And even then you’ll probably be asked to learn how to do the new thing because “this is your system”.

Learning, Not Thinking

The reason why I’m excited for applied AI in infrastructure and operations is because it will learn these menial tasks and do them as instructed every time without complaint or mistake. Yes, that means you need to train the algorithm correctly. Yes, that means you need to be on the lookout for changes and update accordingly when hardware changes or when procedure changes. Given those constraints AI will work for you, not against you.

When you move on to a new role you expect to face new challenges. If you move from a junior role to a senior role you want to work on harder things or solve new problems. You don’t want to get a new title with the same old work. If you change teams you want to experience new systems and implement new technology. You don’t want to be the cloud person that still deploys campus switches in wiring closets.

In a way, having AI take over the basic tasks of your job functions doesn’t remove your job. It promotes you to a new role. It could be that the first promotion is running the system that you implemented to do the work. A “train the trainer” kind of role, if you will. As soon as you can ensure that it is working correctly and that the rest of the team is aware of how to keep the engine running you can decide where you want to go next. You essentially have the freedom to learn and do and not have to be concerned about going back to doing the old things.

Is AI the Enemy?

AI doesn’t take away jobs. It takes away tasks. It streamlines processes and provides consistent, repeatable outcomes. If your job is a collection of tasks that need to be done then it is worth asking why it’s so easy for it to be replaced by an AI system. I’ve said on a number of occasions that there will always been room for jobs that AI can’t do, such as those that involve physical labor, as well as advanced thinking and creative problem solving.

Where AI does succeed is in the repeatable task department. If a role is comprised of repeatable tasks then the question becomes “why does this role exist?” If an algorithm can determine the best way to implement VLANs or offload clients to other devices then the job that was doing those roles needs to be examined. Can that person be encourage to do something else for the company? Can that role be reduced and given to someone with less experience? If those questions make you or someone on your team bristle, is that the fault of the AI? Or the fact of the job being less than ideal for the business?

Tom’s Take

AI can take away some of the unimportant and forgettable parts of your job and give you the freedom to do things that are more important to the business, possibly in a different role. Isn’t that the textbook definition of a promotion? Change makes people uncomfortable. It makes sense that no one wants to have their job taken away. But if you ask anyone in IT right now if they could get rid of 10-20% of the tasks in their job that they don’t like to do they’d agree in a heartbeat. AI isn’t going to remove your job. It’s going to give you the freedom to do something different. It’s also going to ensure you don’t have to keep getting called back to do that thing over and over again because no one else wants to learn it. I think it’s time we let new technologies like AI give everyone on the team the promotion they deserve.

AI and Trivia

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Photo by Pixabay on Pexels.com

I didn’t get a chance to attend Networking Field Day Exclusive at Juniper NXTWORK 2019 this year but I did get to catch some of the great live videos that were recorded and posted here. Mist, now a Juniper Company, did a great job of talking about how they’re going to be extending their AI-driven networking into the realm of wired networking. They’ve been using their AI virtual assistant, named “Marvis”, for quite a while now to solve basic wireless issues for admins and engineers. With the technology moving toward the copper side of the house, I wanted to talk a bit about why this is important for the sanity of people everywhere.

Finding the Answer

Network and wireless engineers are walking storehouses of useless trivia knowledge. I know this because I am one. I remember the hello and dead timers for OSPF on NBMA networks. I remember how long it takes BGP to converge or what the default spanning tree bridge priority is for a switch. Where some of my friends can remember the batting average for all first basemen in the league in 1971, I can instead tell you all about LSA types and the magical EIGRP equation.

Why do we memorize this stuff? We live in a world with instant search at our fingertips. We can find anything we might need thanks to the omnipotent Google Search Box. As long as we can avoid sponsored results and ads we can find the answer to our question relatively quickly. So why do we require people to memorize esoteric trivia? Is it so we can win free drinks at the bar after we’re done troubleshooting?

The problem isn’t that we have to know the answer. It’s that we need to know the answer in order to ask the right question. More often than now we find ourselves stuck in the initial phase of figuring out the problem. The results are almost always the same – things aren’t working. Finding the cause isn’t always easy though. We have to find some nugget of information to latch onto in order to start the process.

One of my old favorites was trying to figure out why a network I was working with had a segmented spanning tree. One side of the network was working just fine but there were three switches daisy chained together that didn’t. Investigations turned up very little. Google searches were failing me. It wasn’t until I keyed in on a couple of differences that I found out that I had improperly used a BPDU filtering command because of a scoping issue. Sure, it only took me two hours of searching to find it after I discovered the problem. But if I hadn’t memorized the BDPU filtering and guard commands and their behavior I wouldn’t have even known to ask about them. So it’s super important to know how every minutia of every protocol works, right?

Presenting the Right Questions

Not exactly. We, as human computers, memorize the answers to more efficiently search through our database to find the right answers. If the problem takes 5 minutes to present we can eliminate a bunch of causes. If it’s happening in layer 3 and not layer 2 we can toss out a bunch of other stuff. Our knowledge is allowing us to discard useless possibilities and focus on the right result.

And it’s horribly inefficient. I can attest to that given my various attempts to learn OSPF hello and dead timers through osmosis of falling asleep in my big CCNP Routing book. The answers don’t crawl off the page and into your brain no matter how loudly you snore into it. So I spent hours learning something that I might use two or three times in my career. There has to be a better way.

Not coincidentally, that’s where the AI-driven systems from Mist, and now Juniper, come into play. Marvis is wonderful at looking at symptoms and finding potential causes. It’s what we do as humans. Except Marvis has no inherent biases. It also doesn’t misremember the values for a given protocol or get confused about whether or not OSPF point-to-point networks are broadcast or not. Marvis just knows what it was programmed with. But it does learn.

Learning is the key to how these AI and machine learning (ML) driven systems have to operate. People tend to discount solutions because they think there’s no way it could be that solution this time. For example, a haiku:

It’s not DNS.
Could it be DNS?
It was DNS.

DNS is often the cause of our problems even if we usually discount it out of hand in the first five minutes of troubleshooting. Even if it was only DNS 50% of the time we would still toss DNS as the root cause within the first five minutes because we’ve “trained” our brains to know what a DNS problem looks like without realizing how many things DNS can really affect.

But AI and ML don’t make these false correlations. Instead, they learn every time what the cause was. They can look at the network and see the failure state, present options based on the symptoms, and even if you don’t check in your changes they can analyze the network and figure out what change caused everything to start working again. Now, the next time the problem crops up, a system like Marvis can present you with a list of potential solutions with confidence levels. If DNS is at the top of the list, you might want to look into DNS first.

AI is going to make us all better troubleshooters because it’s going to make us all less reliant on poor memory. Instead of misremembering how a protocol should be configure, AI and ML will tell us how it should look. If something is causing routing loops or if layer 2 issues are happening because of duplex mismatches we’ll be able to see that quickly and have confidence it’s the right answer instead of just guessing and throwing things at the wall until they stick. Just like Google has supplanted the Cliff Claven people at the bar that are storehouses of useless knowledge, so too will AI and ML reduce our dependence on know-it-alls that may not have all the answers.

Tom’s Take

I’m ready to be forgetful. I’m tired of playing “stump the chump” in troubleshooting with the network playing the part of the stumper and me playing the chump. I’ve memorized more useless knowledge than I ever care to recall in my life. But I don’t want to have to do the work any longer. Instead, I want to apply my gifts to training algorithms with more processing power than me to do all the heavy lifting. I’m more than happy to look at DNS and EIGRP timers than try to remember if MTU and reliability are part of the K-values for this network.

Don’t Build Big Data With Bad Data

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I was at Pure Accelerate 2017 this week and I saw some very interesting things around big data and the impact that high speed flash storage is going to have. Storage vendors serving that market are starting to include analytics capabilities on the box in an effort to provide extra value. But what happens when these advances cause issues in the training of algorithms?

Garbage In, Garbage Out

One story that came out of a conversation was about training a system to recognize people. In the process of training the system, the users imported a large number of faces in order to help the system start the process of differentiating individuals. The data set they started with? A collection of male headshots from the Screen Actors Guild. By the time the users caught the mistake, the algorithm had already proven that it had issues telling the difference between test subjects of particular ethnicities. After scrapping the data set and using some different diverse data sources, the system started performing much better.

This started me thinking about the quality of the data that we are importing into machine learning and artificial intelligence systems. The old computer adage of “garbage in, garbage out” is never more apt today than it has been in history. Before, bad inputs caused data to be suspect when extracted. Now, inputting bad data into a system designed to make decisions can have even more far-reaching consequences.

Look at all the systems that we’re programming today to be more AI-like. We’ve got self-driving cars that need massive data inputs to help navigate roads at speed. We have network monitoring systems that take analytics data and use it to predict things like component failures. We even have these systems running the background of popular apps that provide us news and other crucial information.

What if the inputs into the system cause it to become corrupted or somehow compromised? You’ve probably heard the story about how importing UrbanDictionary into Watson caused it to start cursing constantly. These kinds of stories highlight how important the quality of data being used for the basis of AI/ML systems can be.

Think of a future when self-driving cars are being programmed with failsafes to avoid living things in the roadway. Suppose that the car has been programmed to avoid humans and other large animals like horses and cows. But, during the import of the small animal data set, the table for dogs isn’t imported for some reason. Now, what would happen if the car encountered a dog in the road? Would it make the right decision to avoid the animal? Would the outline of the dog trigger a subroutine that helped it make the right decision? Or would the car not be able to tell what a dog was and do something horrible?

Do You See What I See?

After some chatting with my friend Ryan Adzima, he taught me a bit about how facial recognition systems work. I had always assumed that these systems could differentiate on things like colors. So it could tell a blond woman from a brunette, for instance. But Ryan told me that it’s actually very difficult for a system to tell fine colors apart.

Instead, systems try to create contrast in the colors of the picture so that certain features stand out. Those features have a grid overlaid on them and then those grids are compared and contrasted. That’s the fastest way for a system to discern between individuals. It makes sense considering how CPU-bound things are today and the lack of high definition cameras to capture information for the system.

But, we also must realize that we have to improve data collection for our AI/ML systems in order to ensure that the systems are receiving good data to make decisions. We need to build validation models into our systems and checks to make sure the data looks and sounds sane at the point of input. These are the kinds of things that take time and careful consideration when planning to ensure they don’t become a hinderance to the system. If the very safeguards we put in place to keep data correct end up causing problems, we’re going to create a system that falls apart before it can do what it was designed to do.

Tom’s Take

I thought the story about the AI training was a bit humorous, but it does belie a huge issue with computer systems going forward. We need to be absolutely sure of the veracity of our data as we begin using it to train systems to think for themselves. Sure, teaching a Jeopardy-winning system to curse is one thing. But if we teach a system to be racist or murderous because of what information we give it to make decisions, we will have programmed a new life form to exhibit the worst of us instead of the best.

AI, Machine Learning, and The Hitchhiker’s Guide

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I had a great conversation with Ed Horley (@EHorley) and Patrick Hubbard (@FerventGeek) last night around new technologies. We were waxing intellectual about all things related to advances in analytics and intelligence. There’s been more than a few questions here at VMworld 2016 about the roles that machine learning and artificial intelligence will play in the future of IT. But during the conversation with Ed and Patrick, I finally hit on the perfect analogy for machine learning and artificial intelligence (AI). It’s pretty easy to follow along, so don’t panic.

The Answer

Machine learning is an amazing technology. It can extrapolate patterns in large data sets and provide insight from seemingly random things. It can also teach machines to think about problems and find solutions. Rather than go back to the tired Target big data example, I much prefer this example of a computer learning to play Super Mario World:

You can see how the algorithms learn how to play the game and find newer, better paths throughout the level. One of the things that’s always struck me about the computer’s decision skills is how early it learned that spin jumps provide more benefit than regular jumps for a given input. You can see the point in the video when this is figured out by the system, whereafter all jumps become spinning for maximum effect.

Machine learning appears to be insightful and amazing. But the weakness of machine learning is be exemplified by Deep Thought from The Hitchhiker’s Guide to the Galaxy. Deep Thought was created to find the answer to the ultimate question of life, the universe, and everything. It was programmed with an enormous dataset – literally every piece of knowledge in the known universe. After seven million years, it finally produces The Answer (42, if you’re curious). Which leads to the plot of the book and other hijinks.

Machine learning is capable of great leaps of logic, but it operates on a fundamental truth: all inputs are a bounded data set. Whether you are performing a simple test on a small data set or combing all the information in the universe for answers you are still operating on finite information. Machine learning can do things very fast and find pieces of data that are not immediately obvious. But it can’t operate outside the bounds of the data set without additional input. Even the largest analytics clusters won’t produce additional output without more data being ingested. Machine learning is capable of doing amazing things. But it won’t ever create new information outside of what it is operating on.

The Question

Artificial Intelligence (AI), on the other hand, is more like the question in The Hitchhiker’s Guide. Deep Thought admonishes the users of the system that rather than looking for the answer to Life, The Universe, and Everything, they should have been looking for The Question instead. That involves creating a completely different computer to find the Question that matches the Answer that Deep Thought has already provided.

AI can provide insight above and beyond a given data set input. It can provide context where none exists. It can make leaps of logic similarly to those that humans are capable of doing. AI doesn’t simply stop when it faces an incomplete data set. Even though we are seeing AI in infancy today, the most advanced systems are capable of “filling in the blanks” to cover missing information. As the algorithms learn more and more how to extrapolate they’ll become better at making incomplete decisions.

The reason why computers are so good at making quick decisions is because they don’t operate outside the bounds of the possible. If the entire universe for a decision is a data set, they won’t try to look around that. That ability to look beyond and try to create new data where none exists is the hallmark of intelligence. Using tools to create is a uniquely biologic function. Computers can create subsets of data with tools but they can’t do a complete transformation.

AI is pushing those boundaries. Given enough time and the proper input, AI can make the leaps outside of bounds to come up with new ideas. Today it’s all about context. Tomorrow may find AI providing true creativity. AI will eventually pass a Turing Test because it can look outside the script and provide the pseudorandom type of conversation that people are capable of.

Tom’s Take

Computers are smart. They think faster than we do. They can do math better than we can. They can produce results in a fraction of the time at a scale that boggles the mind. But machine learning is still working from a known set. No matter how much work we pour into that aspect of things, we are still going to hit a limit of the ability of the system to break out of it’s bounds.

True AI will behave like we do. It will look for answers when their are none. It will imagine when confronted with the impossible. It will learn from mistakes and create solutions to them. That’s the power of intelligence versus learning. Even the most power computer in the universe couldn’t break out of its programming. It needed something more to question the answers it created. Just like us, it needed to sit back and let its mind wander.