The Basics of Artificial Intelligence

Image by LJ

Artificial Intelligence is the development of computer systems to perform tasks that usually require a human being. These tasks include learning, reasoning, problem-solving, perception, speech recognition, and language translation. AI systems aim to mimic human cognitive functions, enabling machines to adapt and improve their performance over time. Let’s explore the different types of AI, the practical applications and overall concerns.

Narrow AI or Weak AI is designed and trained for a specific task. Unlike General AI, which would possess human-like intelligence across a broad range of activities, Narrow AI excels within a well-defined domain. This specialization makes it highly efficient and effective for particular applications.

One of the defining features of Narrow AI is its focused expertise. It is tailored to perform a specific function or solve a particular problem, whether it’s image recognition, natural language processing, or playing board games. Narrow AI operates within a limited scope, meaning it does not possess the versatility and adaptability associated with human intelligence. 

Training is a critical aspect of Narrow AI development. These systems rely on large datasets specific to their intended application to learn patterns and make predictions or decisions. The quality and diversity of the training data significantly impact the system’s performance.

Virtual personal assistants like Siri, Alexa, and Google Assistant are classic examples of Narrow AI. They excel in voice recognition, language understanding, providing information or executing specific commands. Applications that can identify objects in images or transcribe spoken words leverage Narrow AI. This technology is widely used in photo categorization, facial recognition, and voice-to-text conversion. Many online platforms use Narrow AI algorithms to analyze user behavior and preferences, offering personalized recommendations for content, products, or services. This includes streaming services, e-commerce websites, and social media platforms. The AI systems in autonomous vehicles are another example of Narrow AI. These systems are specialized in tasks like identifying obstacles, interpreting traffic signs, and making real-time decisions based on sensor data.

Strong AI, or General AI, represents the theoretical concept of artificial intelligence possessing the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to human intelligence. Unlike Narrow AI, which specializes in specific domains, Strong AI aims to exhibit a broad spectrum of cognitive abilities, allowing it to perform any intellectual task that a human can. The hallmark of Strong AI would be versatility. It would not be confined to a specific domain or set of tasks; instead, it could comprehend and adapt to diverse situations, displaying human-like cognitive flexibility. Strong AI would have the capacity to learn from experience and reason across various domains. It could apply knowledge gained in one area to understand and solve problems in entirely different contexts. In the realm of Strong AI, the idea of self-awareness and consciousness is considered. This implies that the AI system not only processes information and performs tasks but also has an awareness of its own existence and the ability to reflect on its experiences.

Despite the potential benefits and the intriguing concept of Strong AI, several significant challenges stand in the way of its realization. Replicating human-like intelligence requires a deep understanding of the complexities of human cognition, including perception, memory, reasoning, and consciousness. This understanding is still limited. As AI approaches human-level intelligence, ethical and philosophical questions arise. Issues related to consciousness, moral reasoning, and the ethical treatment of AI entities become crucial considerations. Achieving Strong AI requires immense computational power and sophisticated algorithms. The complexity of emulating human intelligence poses a substantial technological challenge that for now remains firmly in the realm of science fiction.

           Machine Learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming. The core idea behind machine learning is to empower machines to learn from data, identify patterns, and make decisions or predictions based on that learning. It plays a pivotal role in various applications, ranging from recommendation systems to image recognition and natural language processing.

Data is the lifeblood of machine learning. ML algorithms learn from historical data, which is used to train the model. The quality, quantity, and relevance of the data directly impact the performance of the machine learning system. In supervised learning, a common type of machine learning, the algorithm learns from labeled data. The software uses input variables called features, and the goal is for the code to learn the mapping from the features and create outputs, which are known as labels. Machine learning algorithms are mathematical models that learn patterns from data. These algorithms can be categorized into various types, including linear regression, decision trees, support vector machines, and neural networks. The choice of algorithm depends on the nature of the problem and the characteristics of the data. During the training phase, the machine learning model is exposed to labeled data, and it adjusts its internal parameters to minimize the difference between its predictions and the actual labels. This process continues until the model achieves a satisfactory level of accuracy. Once trained, the model is tested on new, unseen data to evaluate its performance. 

In supervised learning, the model is trained on a labeled dataset, meaning it learns from input-output pairs. The goal is to predict the output for new, unseen inputs accurately. Unsupervised learning involves training models on unlabeled data. The algorithm discovers patterns or structures within the data without explicit guidance on the output. Reinforcement learning is centered around agents that learn to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties, guiding it toward optimal decision-making.

Companies like Netflix and Amazon use machine learning algorithms to analyze user preferences and recommend movies, products, or content tailored to individual tastes. Machine learning powers facial recognition systems, image classification, and speech recognition technologies. Applications range from security systems to virtual assistants. In healthcare, machine learning is used for disease prediction, medical image analysis, and personalized treatment recommendations based on patient data. Machine learning algorithms analyze financial data to make predictions about market trends, stock prices, and investment strategies. Natural Language Processing applications use machine learning to understand and generate human language. This includes chatbots, language translation, and sentiment analysis.

Machine learning models are only as good as the data they are trained on. Biases in the data can lead to biased predictions, and insufficient or low-quality data may result in inaccurate models. Some machine learning models, especially complex ones like deep neural networks, are often considered as “black boxes” because understanding how they arrive at specific decisions can be challenging. Balancing the complexity of a model is crucial. Overfitting occurs when a model learns the training data too well but struggles to generalize to new data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data. Machine learning systems can inadvertently exacerbate existing biases in the training data. 

Deep Learning is a specialized subset of machine learning that revolves around the concept of artificial neural networks. It aims to emulate the human brain’s structure and functionality, allowing machines to learn and make decisions in a manner similar to how humans do. The term “deep” in deep learning refers to the use of deep neural networks with multiple layers, enabling the system to automatically learn hierarchical representations of data.

At the core of deep learning are neural networks, which are composed of layers of interconnected nodes or artificial neurons. These networks are inspired by the structure and functioning of the human brain, with each layer extracting progressively more abstract features from the input data. Deep neural networks consist of an input layer, one or more hidden layers, and an output layer. Each layer contains nodes, also known as neurons or units, which process and transform the input data. The hidden layers allow the network to learn intricate patterns and representations. Activation functions introduce non-linearities to the neural network, enabling it to learn complex relationships within the data. The connections between nodes in a neural network are defined by weights and biases. During the training process, these parameters are adjusted to minimize the difference between the predicted output and the actual output, allowing the network to learn from data. Deep learning models undergo a training process where they learn to map input data to the desired output. This involves feeding the model with labeled data, adjusting the weights and biases, and optimizing the model to make accurate predictions. 

The simplest form of deep learning architecture is Feedforward Neural Networks, where information travels from the input layer through the hidden layers to the output layer. Convolutional Neural Networks are optimized for image processing, and use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images. Just to clarify; a convolutional layer is a special layer in a computer program that looks at different parts of a picture at a time, finding specific patterns like edges or shapes to help understand what’s in the picture. Recurrent Neural Networks are more suitable for sequence data, and have connections that form directed cycles, allowing them to retain information from previous inputs in the sequence.

 Deep learning has significantly improved the accuracy of image recognition tasks, allowing systems to identify and classify objects in images. It has also revolutionized speech recognition technologies. In Natural Language Processing deep learning models are used for tasks such as language translation, sentiment analysis, and chatbots. Deep learning is applied to medical image analysis, diagnosis prediction, and drug discovery. It helps detect abnormalities in medical images and assists in personalized treatment plans. Deep learning powers perception systems in autonomous vehicles, enabling them to recognize and respond to the surrounding environment. Deep learning is employed in gaming for character animation, object recognition, and procedural content generation. It enhances the immersive experience in virtual reality environments.

Deep learning models often require large amounts of labeled data for training, and obtaining such datasets can be challenging in certain domains. Training deep neural networks can be computationally intensive, requiring powerful hardware. Understanding how deep learning models arrive at specific decisions, especially in complex architectures, remains a challenge.  

Deep Learning has emerged as a revolutionary force in the realm of artificial intelligence, enabling machines to learn intricate patterns and representations from data. As the field continues to evolve, the applications of deep learning are expanding across diverse domains, pushing the boundaries of what machines can achieve in terms of perception, understanding, and decision-making. The ongoing research and advancements in deep learning hold the promise of transforming industries and shaping the future of intelligent systems.

While AI overall brings about numerous benefits, it also poses challenges, including ethical concerns, bias in algorithms, job displacement, and the potential for misuse. As AI continues to advance, it’s crucial to address these challenges and ensure responsible development and deployment. Artificial Intelligence is a transformative force reshaping industries and societies. Understanding its basics empowers us to appreciate the positive and negative aspects of this technology. As we navigate the evolving landscape of AI, staying informed and engaged will be key to harnessing its benefits responsibly.

Why Unreal Engine

Image by Craig Adderley

So why am I trying to learn Unreal Engine? With so many great game engines out there, why this one in particular? Well, the big reason of course is that I can download it and play with it completely for free, which suits my rather slim budget quite well. Another reason is that I have some old friends in similar situations as myself who are also learning UE, which means I’m not alone. The game engine also has an impressive array of capabilities that will allow me to do just about anything I’ve ever imagined.

Nothing beats free software with a huge range of support behind it. I don’t have the time or extra cash to go to a school where I have access to expensive software. Plus, I may spend a year learning it and then decide I want to try something else. Free means there is no pressure to make an income out of this if I don’t want to. For now it’s still fun and engaging, but I’m still a newb. This is a similar line of thinking that directed my interest toward learning Blender and not Maya. 

 Unreal Engine is software developed by Epic Games, offering a myriad of capabilities including robust graphics and advanced rendering features, including high-quality visual effects, realistic lighting, and detailed textures. This enables developers to create beautiful, lifelike environments and characters. UE is a preferred choice for creating AAA titles and cutting-edge virtual reality applications. It’s relatively user-friendly and offers a wide range of built-in tools for various aspects of game development, such as level design, animation, physics simulation, and visual scripting through Blueprint. These intuitive tools streamline the development process, empowering both seasoned professionals and newcomers to bring their creative visions to life.

Unreal Engine also supports multiple platforms, including PC, consoles, mobile devices, and VR headsets. This cross-platform compatibility allows developers to create games for diverse audiences without needing a lot of extra software or resources.

Another benefit of Unreal Engine is the community support and extensive documentation. The engine boasts a large and active community of developers, fostering collaboration, sharing of knowledge, and access to numerous resources, tutorials, and forums. This support network is invaluable for developers seeking assistance, troubleshooting, or guidance during their game development journey.

Now we are back to UE’s attractive pricing model. While it used to have a royalty-based system for revenue generated from games, Epic Games now has a more friendly approach, offering royalty-free access to the engine’s full suite of tools, making it even more accessible to developers of all sizes. Of course if you make an awesome game and get a ton of money you need to pay your dues, which is fine. If I somehow manage to publish something with this engine that makes more than a million dollars, Epic is welcome to their cut.

In summary, Unreal Engine’s graphical capabilities, user-friendly interface, versatility,  support, and price point, make it an easy favorite for people like me who are starting out and exploring our options.

Happiness Part One

Image by Chris F

I find myself in a bit of an unusual position as I delve into this topic. The ideas surrounding this form of self-improvement have often struck me as cheesy and uncomfortable. However, as I’ve matured over the years, my perspective has shifted, leading me to a profound realization: happiness stands as the single most overlooked aspect of my personal development journey. I admit, I’m a bit hesitant to explore the realm of self-improvement in my writing. It’s not that I lack knowledge—I’ve delved into everything from Napoleon Hill to Tim Ferris, devouring hundreds of books on the subject. However, I’m not exactly the stereotypical ‘self-help guru.’ I don’t rise at 4 am, run 10 miles, meditate for 30 minutes, and then consume a bio-hacked breakfast. I’ve attempted that lifestyle before, but it simply doesn’t align with who I am. My approach is decidedly more moderate. Having said that, I’ll begin by sharing what I consider to be the most valuable insights from my own experiences.

It’s easy to forget that the pursuit of happiness is something we have to constantly work at. If we neglect it for too long we can slide into a spiral of negativity. Sometimes the change is so subtle we don’t even realize it’s happening until our emotions surface or someone points it out to us. The desire for true happiness is a common theme for humanity, but understanding how to cultivate and sustain it remains a challenge for many, including myself. Fortunately, there are numerous practical approaches, insights, and lifestyle changes that can contribute to nurturing our well-being.

Mindfulness, in the context of self-awareness, is often associated with meditation. This can be a powerful tool that can bring real benefits to our daily lives. I honestly think that learning this practice can bring relief from a host of psychological disorders. By practicing mindfulness, you can obtain a heightened awareness of the present moment, enabling you to better manage stress, anxiety, and negative thoughts. Exercises like focused breathing or mindful walking can gradually train the mind to stay present, fostering a sense of calm and contentment. It took me a while to take mindfulness seriously. I was in my 30’s when I read “The Power of Now” by Eckhart Tolle and it was a serious game changer. Granted I have to take a lot of what he says with a grain of salt, but the techniques and the ideas work. You should also understand that this is not as easy as it sounds. It takes time, discipline and repetition.

Cultivating gratitude is another somewhat surprising path to happiness. Taking time each day to acknowledge and appreciate the positive aspects of our lives can have a similar effect on our mental well-being. We can do this through interactions with friends and family or as part of our daily meditation by reflecting on what we are grateful for. This practice helps us appreciate what we already have. This one is still really hard for me. I think clawing my way up from the bottom has jaded my sense of what being grateful means. Like so many other things though, this is something I’m working on because I can see the value in it.

Human beings are generally social creatures, and meaningful relationships play a crucial role in our happiness. Spending quality time with friends and family, or contributing to our community can increase our happiness by improving our feeling of  belonging. As an introvert I have some difficulty with making and keeping friends, so I have relatively few really good friends. I still find joy in the creation and participation in large gatherings of familiar people. There is something about surrounding myself with positive interactions that creates a feeling of peace and satisfaction. I usually do this by feeding people. I invite them over for a barbecue, and watch them mingle and create new connections. I also recommend Meetup groups if you are single and have free time.

Another way to stay happy is by keeping engaged in challenging and inspiring activities. For me hiking is a big part of this. There is something about spending all day in the wilderness, the freedom, the independence, that keeps me mellow. The sense of accomplishment and boost to self esteem doesn’t hurt either. My other common go-to activity is video games. There is something about immersion in a sprawling fantasy world that triggers my sense of wonder and creativity. 

I believe physical health and mental well-being are thoroughly linked. Exercise, diet and sleep all combine to affect our mood and mental resilience. Engaging in physical activity releases “feel-good” hormones, and a healthy diet and proper rest contribute to a more balanced and energized state of mind. It’s been my experience that you don’t have to be a crazy work out nut to gain some substantial positive effects. A short 20-minute walk outside is enough to settle my thoughts in most cases. Controlling your diet can be tough, it takes time and money to eat well in most cases and it’s easy to cut corners and eat poor quality food. Sleep can also be challenging for some people. I used to be a terrible insomniac, only sleeping 4 or 5 hours a night. There are so many issues that contribute to poor sleeping, it’s hard to narrow down what one particular person’s issues are, and not every cure works the same for everybody. My issues were mainly a combination of future anxiety and back problems. It eventually took a combination of meditation, melatonin, a chiropractor and a tempurpedic mattress to get me mostly sorted out.

Success in happiness is a combination of a variety of factors. These factors are different for everybody. The first step is understanding that there are tried and true changes that can affect your well-being in a positive manner. By incorporating these elements into our daily lives, we can incrementally improve day by day. Learn to not look at happiness as something transient like a drug induced-high. It is a state of mind and being that can be cultivated and sustained. Start small, be patient and stick with a plan. The journey may be difficult at first, but it gets easier and I guarantee it’s worth it.

Sampling

Image by pixabay

Through the course of research and learning I am slowly discovering more of what I need to know. I can see why a background in computer science is required by many potential employers. Since I have neither the time or money to get a computer science degree at this time I am figuring out how to gain as much knowledge as possible using free or less expensive resources.

I have listened to Cyber security for Dummies and Artificial Intelligence for Dummies and I’m almost finished with Data Science for Dummies. All of these books are free on Audible. Cyber security quite frankly scares the crap out of me. It was stressful just listening to it. I think a job in that field would be too high-liability for my tastes. I’m already stuck in an industry like that and not interested in taking my chances on another. Artificial intelligence is interesting and I definitely want to learn more about it. In the end, however, I’m more interested to see how this technology can make my games better, than being on the cutting edge of development. Data science is interesting and I feel like I need to know more about it. Even though I feel pretty strong in math, a lot of the terminology they were using went over my head. It’s also such a broad topic, I feel the need to dive into some specifics.

I have also started taking some free classes including the CS50 intro to computer science from Harvard through edX. The main instructor is very engaging and easy to understand. I’m not crazy about the edX user interface or the way they keep prompting me to upgrade to an accredited version of the course. It’s still a free class with lots of good info and professional instruction so I feel like you can’t go wrong.

I started a Coursera class on machine learning and realized I was in over my head from the beginning. I have put that class on hold until I can get a little more background. My latest Udemy class on Unreal Engine RPG building is a little more C++ intensive than I had originally thought, so I may have to put it on hold as well until I can practice with visual studio some more.  This class is also a wake up call on how much space you need on your hard drive for Unreal Engine. The first part of the class had me do a modest download of 20 gigs to get started with some terrain and trees and such off Quixel Bridge. The next part of the lesson asked me to download Valley of the Ancients, which is over 200 gigs, to use some of its geologic features.  On a somewhat related note, I recently found out my internet service provider only allows so much free data per month before they start charging me extra.

I feel like I have a little more direction after listening to some audio books and dipping my toes in a few classes. As I rework my goals I will rework my blog to accommodate as needed. I am feeling energized as retirement draws closer and the new world of employment opens up. I know that my goals of a stay at home game development job are far off but I’m not discouraged yet, and believe me, there are a lot of naysayers. I’m also a pragmatic realist and have lots of backup options. An upgrade, however, is doing something that makes me happy, and that is still a goal worth shooting for.

What is an Upgrade?

What is an upgrade? This is one of the questions I have wrestled with throughout my life and one I will address periodically in my writing. One of the first things people may think of is improving the technology you’re working with, like getting more RAM for your computer or maybe a better graphics card. Others may immediately consider trading in the old wife for a newer younger model. I would suggest that upgrading is not quite so simple. If you calculate the cost of upgrading individual computer components including the time spent and the frustration of conflicting software issues or the continuous nickel and dime effect, is it really worth not buying a whole new computer? Don’t get me started on doing a wife trade in. I think books have been written on the subject. Lets just say younger is not equivalent to better.

      I believe an upgrade should add value, reliability, and an overall improvement to your life. Value can be monetary or emotional, but it should retain its value. A vacation to the south of France may seem like an upgrade over last year’s vacation to Orlando, but that extra 10-20k is retained only as memories, pictures and bragging rights. Reliability means the ability to provide value continuously or at least multiple times without a major decrease in effectiveness. That ice cream sundae might make you feel better right now, but it only lasts a few minutes. Upgrading to a tub of ice cream that will last for weeks, might seem like sustainable happiness, but when we look at value and long term health, it is still a very questionable choice. 

      Clarifying what constitutes an overall improvement is even more subjective and complex. Investing in transient items like food and clothing might not seem worthwhile unless we factor in long term health and confidence benefits. A healthier diet will likely cost more in both money and time, but if you can live an extra decade the impact is surely a net positive. Yet, how can we know if we will actually live longer? I think most of us subconsciously mistrust the data and assume we could be hit by a car and die any day. You might disagree, but how many of you can consistently deny that unhealthy treat? How many times have you postponed that rigorous exercise routine?

How much should you spend on a wardrobe improvement? This is not something I generally worry about, as I usually only buy a couple of new clothing items a year to replace worn items. Recently, however, I’ve lost a bunch of weight and I’m looking to upgrade. A new set of clothes can give us confidence and help with networking or nailing a job interview. It’s easy to be cheap and get low quality brands, but people notice. I live in southern California, the land of image critique. I know business owners who respect a well dressed potential employee along with the ability to use an iron and a lint brush, or pay for a dry cleaner. All that being said, it’s easy to go overboard and blow money on a closet full of stuff you’ll never wear. I wouldn’t know but apparently clothes shopping can be addicting. I know someone who has  50k in their closet from when they used to work in an office and now it just sits there while she works from home in her pajamas. 

I would propose that a way to solve or at least mitigate some of the ambiguity of what constitutes positive or negative change is to invest in education and discipline. These skills can provide long term value and have the bonus of being virtually free to obtain. The education portion gives you a better idea of what will improve your life and what will not. Improving discipline incorporates the ability to make more positive changes and be consistent. Those who have spent a lot of time in the books know that education can also be a trap. It is a business after all. People invest thousands of dollars on education sold to them by professional sales teams all the time. No, I’m not just talking about college students, but they tend to be the most prevalent  victims. Look up any skill from chess to dancing and you can find dozens of books or personal instructors willing to take your money and educate you. So knowing what to learn and how to learn efficiently is also important. I suppose you could say we need to learn what to learn, independent of the agenda of others.

The acquisition of discipline is a whole other beast. This skill is mysterious and difficult to maintain, it waxes and wanes, it punishes and gives. Discipline can also seem like a catch 22, you need it to win, but too much losing can destroy your confidence and this becomes the downward spiral that saps discipline as well. So how do you get it? Is it something some people have and others do not? You can literally fill a library with books on the subject, and I’ve read a lot of them. The fact is, however, reading and doing are very different things. 

I believe discipline works differently for different people and is affected by a variety of environmental and genetic factors. Personally, I always had a chip on my shoulder from growing up relatively poor, but much of that went away when I finally achieved a comfortable living. I have evolved and motivation has become much more situational for me. If you are new to needing or wanting discipline, start by thinking about what motivates you or gets you psyched up, like before the big game. Think about your goals and what you are passionate about. Imagine that you can actually brainwash yourself into being excited about something through self-talk. I plan to address discipline in future posts, but if you are interested and new or out of practice with self improvement, I suggest you pick up Napoleon Hill’s classic “Think and Grow Rich” for some light inspiration.

Inspiration and Distraction

Image by: cottonbro-studio

I’m working on a class through Udemy that is going to teach me how to make a role playing game. It looks like it will be an action adventure type RPG like Skyrim or The Witcher. For the moment I’m working on the landscaping tutorials, learning how to sculpt and erode terrain. I noticed that I can drag and drop different materials onto the panels of my landscape, but I’m a little disappointed I can’t just drag grass and detritus and stuff directly onto my brush and paint the terrain with it. The tutorial has me creating a custom landscape blueprint with six different materials from Quixel Bridge and merging them. More on that when I get through it. 

It’s been hard to find time lately since I’ve been sucked back into Elder Scrolls Online. Those who have played know what I’m talking about. This is a game my wife and I probably have a thousand hours in. ESO is the type of game I would like to learn to create.

So what happened was, we were checking out the preview videos for Starfield and lamenting about how it wasn’t on Playstation and it was not an MMO. My wife loves sci-fi games so I thought she might like to try it on PC. However, after watching one Youtube video she declared decisively: “It’s basically Mass Effect, and we can’t even play together.” So that decided it. Gaming is a family event in our house and if we can’t interact in the same world we don’t spend much time on it. Then we got to talking and comparing it to other games, and of course ESO came up and we watched a video on the Necrom DLC and the next thing you know we were questing.

So why ESO? First of all its on console, my wife, son and I all game, we all have our own PS4s and our own TVs. This setup costs way less than if we were all computer gamers. Second, its an MMO which means we can all play together and the game is massive. There is so much to do, there is something for everybody. I can be the merchant, my wife can do pvp and my son can explore and collect the flora and fauna. Most importantly, we can spend quality family time slaying monsters and bad guys together.

ESO isn’t perfect, I would like a classless progression system, flight, skyships, underwater cities and a million other things. I think of all the wonderful options the game provides and imagine what could be better. This is part of the inspiration, not only do I want to know how to create everything on ESO with Unreal Engine, I want to know how to make it bigger and better. 

Anyway, hopefully I can break away before I dump another thousand hours in and learn some more Unreal Engine.

My first completed class

My first step on improving my future career outlook was to take an Unreal Engine course on Udemy.com. It is a 10-hour course that teaches how to create a basic game involving fire damage, platforming, collecting some orbs and opening an end door.

If you are unfamiliar with Udemy you should check it out. They have classes on just about everything. Some of them seem a little overpriced or outdated but they have sales fairly often and I like that you can go at your own pace jumping back and forth as needed. The learning is better structured and the instructors seem to be more knowledgeable and the classes more organized than many YouTube videos I have tried.

I’ll start by saying that this 10 hour course actually took me closer to 30 hours. I needed to slow things down to see what the instructor was doing and go back and re-watch several portions. There were also fairly large gaps of time between some of the lectures due to my schedule and I needed to go back and refresh my mind when restarting.

One of the issues I ran into occurred when creating the timing for the moving platforms and other animations. We used a timeline node and created a cycle with keys where we input the x and y values given by the instructor. For some reason whenever I compiled and saved the timelines and went the play the game, the animations did not work. When I went back in and re-entered the keys a second time and compiled it suddenly worked. This happened every time I did a timeline, I would have to go in and redo the coordinates for it to work. I still haven’t figured out why.

The other issue is somewhat embarrassing but apparently something similar happened to a friend of mine. There is a door at the end of the level that I thought was supposed to open when I approached the collision barrier around it. Needless to say, it did not work and I spent an uncomfortable amount of time trying to figure out why. Well it turns out it had about 2 weeks of time between when I created the door and when I finally tried to use it in game. It turns out that the door was set to open when the ‘F’ key was pressed on the keyboard and I had totally forgotten until I went back through the lesson. So I am making an effort to go through classes more consistently with less of a gap between lessons. 

This class was relatively easy to follow with a good walk through of basic level editing functions and a tutorial on actors in the beginning. I feel like it improved my confidence with the software somewhat and I learned a lot of the terminology involved with Unreal Engine and basic blueprint programming.

My first game build