Right now, it seems like a good time to revisit my Corsair Carbide Series Air 740 high airflow ATX cube case purchase. Back on 1/30/2018 it seemed like a good idea to add a new computer case to my office. Over the years my Cooler Master Storm Stryker case has worked out well, but it was time to move to something new. That something new happened to be a Corsair computer case. The Air 740 is a pretty decent cube of a computer case. It features a glass door on one side that houses the motherboard and an enclosed side for the power supply and storage drives.
During the build I elected to create my first Storage Space via a 3 hard drive storage pool. That effectively takes 6 terabytes of storage and pools it down to a 2 terabyte storage pool. In Windows 10 you can navigate to the Control Panel –> System and Security –> Storage Space and setup or manage a storage pool. It has worked out well enough. My primary system runs on an M.2 drive attached to the motherboard and I have a few solid-state drives plugged into the system, but my primary storage is the storage pool. You can imagine that over the last few months I have considered getting larger drives for my storage pool. Editing those pesky 4K videos just eats up storage space.
That will end my meandering around the point for the day. Overall, I have been very happy with my purchase of a Corsair Carbide Series Air 740 high airflow ATX cube case. Keep in mind that it will need to sit under your desk and is wider than a regular ATX computer case footprint. It has worked great and was easy to cable. I thought that the lack of an external physical media drive (Blu-ray/DVD) would be a deal breaker, but I have not really missed it.
My goal for today is to engage in some almost live journal style blogging. You can expect a few updates to this post throughout the day. I am trying to disable my posts from sending out updates.
Today started with two shots of espresso and some hash browned potatoes from yesterday.
Things started off with a quick read of the weekend edition of Inside AI the newsletter from Rob May.
Cancelling my Pandora subscription seemed reasonable earlier this week. The free version of the product has commercials, but that is ok, I guess.
Today I’m writing using Microsoft Word Online. It only took a matter of moments before the simplified ribbon was turned off. Earlier today it seemed like a good idea to switch out from writing using Google Docs and swing back over to using Microsoft Word Online. We will see how long that swing lasts this time. My writing efforts tend to vacillate back and forth between the two different word processing platforms.
Well that plan to remove the linking for this post failed miserably. It turned out within the settings section a sharing section exists where I needed to remove links to Twitter, Google+, and Tumblr. That was easy enough and now these posts should be running on radio silence.
Just amount ago I typed the date at the top of this Google Doc. Outside of looking at the date on my phone and typing the actual date does not have all that much meaning anymore. Things start and things end from day to day. You could probably change the date and it would take me a fair amount of time to figure it out. Perhaps that is a sad commentary about the endless stream of things that are happening. Inside the digital world a greater and greater number of applications and systems are being setup based on the principles of artificial intelligence to create things. Taking a collection of tons of photographs and creating a set of rules based on the images to make a new image based on the patterns in the original stack is interesting. Taking this back to thinking about video games. We have been able to create randomized digital worlds that are highly complex and contain lot of customized elements.
This second paragraph was going to be pretty epic. I was going to write about the classes on Coursera that have had my attention recently. Unfortunately, I’m so tired at the moment that putting together some epic prose is just not happening at the moment. Sure that happens from time to time, but I was hoping to dig deep and write something insightful. Maybe tomorrow will be better. Just like the grass might be greener someplace else. Sometimes it seems like the grass might be greener anywhere that is not here. All of this relates back to how I’m thinking about AI at the moment. Most of the time when you are trying to chase down a solution to the very complex problem you are facing it might seem like a good idea to go after one of the shiny new technologies that appearing in AI implementations. Your solution is just an API away. That is the road that always looks the easy. Some of the more developed use cases that is probably the case. Unless you want to scan photographs to figure out if Peppercorn the dog is still a dog the technology might even seem to be getting fairly mature.
Tomorrow my efforts are going to be more targeted. I’m going to focus on the applied use of neural networks to solve problems. If that is something you are interested in understanding, then tomorrow just might be a highlight for you.
Yesterday was going to be pretty epic. It was the first day of trying to explain the world of artificial intelligence, machine learning, deep learning, and neural networks to people in a meaningful way. Yeah, I went back and read the prose from yesterday. It was a massive false start. In some ways that is sort of how artificial intelligence ended up changing things. Sometimes you end up trying to align the solution you have to real world problems. Other times you just face a really big challenge and figuring out how to do it requires thinking outside the box. A few of those examples might be pretty interesting to think about. Our friends at Google really wanted to organize information and figuring out how to crawl in internet was something that ended up being a definable and repeatable process. It is something that can be done and the steps are clear. Extending that to searching photographs, videos, and creating an accurate knowledge graph involves solving very different types of problems.
One of my favorite examples over the years has been how image classification systems including the one being used by Google Photos cannot tell if Peppercorn the Dog is in fact a dog or a cat. My guess is that most of the time if you had a reasonably good photograph you can successfully categorize the pet as a dog or a cat. A lot of my photographs are labeled Peppercorn the dog… that context clue should be enough to help classify my 13 year old Australian cattle dog as well a dog. I went through the trouble of helping the algorithm tag and classify Peppercorn as a pet in Google Photos. Strangely enough that did help the algorithm learn. Translating that categorization problem to understanding classifiers can help you figure out how hard it is to sort the world around us based on a given set of heuristics. Trying to setup a framework to figure out what are all the things in a photograph is really interesting. That is a challenge that a technology like Tensorflow could help with. Software like that helps by putting together a method to classify and identify things. Sure the way I go about that is based on the things I know and can figure out.
Solving really hard problems like setting a self driving car or sorting my 10,000 digital photographs into albums take a certain type of technology. Finding the right technology solutions to solve really hard problems is where the fields of artificial intelligence, machine learning, deep learning, and neural networks are getting really interesting. That is really and understatement. My interest in the convergence of technology and modernity has been growing for years. Sometimes I spend hours just thinking about the intersection of technology and modernity. Perhaps that is why these inquiries into artificial intelligence are so timely.
My big plan for the month of October is pretty simple. It all centers around a simple enough proposition. That proposition includes just one question. As a question it seems to be pretty decent on the surface. My question remains, “Would it be possible to explain the current state of artificial intelligence in 30 days by writing 500 words a day?” Sure some of the days will probably go a little bit over the 500 word guidepost, but that is really just an arbitrary setting in the march toward explaining things. Overall this exercise will help generate a roughly 15,000 word essay explaining the current state of artificial intelligence. Tomorrow should kick start things with an introductory essay on the subject and I will probably try to sketch out the first few topics.
Notes from Monday, October 1, 2018
Programming a computer opens the door to executing some definable and repeatable tasks. Programs are all about doing something. Some of that something often happens within a set of rules. A of programs have been built to do something based on a set of instructions. People have been building video games that help execute a set of rules within a definable and generally repeatable user experience for years. Any brief history of artificial intelligence could start with and end with understanding computer games. Figuring out how to develop a program that can compete and ultimately execute strategy is the basic framework I have used to describe artificial intelligence. For me it is about figuring out if a program can figure out what is next. Being able to make a move or execute a strategy within computer games has gotten more complex. Games in general have gotten a lot more complex. Self generating worlds full of highly complex storylines are a far cry from playing tic-tac-toe, checkers, chess, or go. We have entered a new age within the development of artificial intelligence. Computer programs can now be written that capable of playing through old Nintendo games.
For me any history of artificial intelligence is about what is possible. It could be something like the OpenAI bot beating competition level human Dota 2 players. Getting to a point where a piece of software could engage in more than simply completing a set of definable and repeatable tasks took some time. Building out the frameworks to accomplish that type of coding took some time to flush out and develop. Right now we are really watching the turning point in people executing ideas related to artificial intelligence. We really did initially see a world full of artificial intelligence drawn out in works of science fiction. That went on for some time until people figured out how write computer code that wrote computer code. For me that is the key element of the turning point we are seeing today. Getting to a point where a program can iterate or create something new based on the original code will push things forward very rapidly.
That start to writing a brief history of artificial intelligence was more or less a false start. I’m going to need to try again tomorrow and hopeful do a better job. Maybe the basic premise of using video games to tell the story of artificial intelligence was flawed. Digging into milestones and technical achievements might be a better way, but it just does not focus on and tell a cohesive story. I think somewhere within the history of video games is a story that also could be a brief history of artificial intelligence.