Recent developments in artificial intelligence (AI) have ushered in an era of intelligent machines that are capable of performing complex tasks like speech recognition, computer vision, and natural language processing. These so-called AI systems are built using machine learning algorithms which require large amounts of data to function.
By incorporating these algorithms into computers or digital devices, they can learn how to perform specific tasks on their own. Some of the most well known applications of AI include chatbots, robots, and autonomous vehicles.
While there is no doubt that advanced AI will continue to grow in popularity, there are also concerns about whether this technology is beyond the control of humans. After all, even if we’re not directly programming the system, we still influence it through the information given to it.
As such, there is now a growing movement towards creating “friendly” AI. Systems with programmed rules that strive to promote human welfare instead of exploiting us for resources. And though this may seem like a futuristic dream, some companies already have software that fits this description.
In this article, you’ll find out more about this concept as well as tips and tricks for developing your own friendly AI.
Get a good computer
A deep learning AI will need a powerful machine to run effectively. You can’t use an old laptop for this!
A deep neural network requires lots of computational power to train. This includes running each layer, back-to-back. Because these layers are connected, every node in one layer is dependent on all nodes in the next layer, and so forth.
That means that as more data sets get loaded into the system, the number of calculations needed also increases. If left alone, the computer would spend the majority of its time just keeping up with all the work it has to do, making the app take longer than expected to complete.
To prevent this, computers have internal features called GPUs (graphics processing units). These have many parallel processors which speed up computations at a cost of some extra electricity used to keep them cool.
You don’t necessarily need a fancy GPU, however – any decent CPU will do the trick. Just make sure it has enough RAM to store the growing datasets.
Get a good internet connection
Having a good internet connection is one of the most important things you can do as a beginner trying to train an AI. This article will talk about some reasons why!
Having a fast, reliable internet connection is very crucial for two main reasons. The first is that it allows you to access the vast amount of resources available online through sites such as YouTube, Reddit, or Wikipedia.
By diving into this content, you’ll be able to gain knowledge and insights on how to program computer software. You may even stumble upon something useful like someone sharing their source code!
The second reason is that many times when people say “deep learning” they mean “transfer learning.” Transfer learning means using what we have already learned to help us learn new skills.
For example, if you are familiar with how to use Photoshop then you could apply that knowledge by teaching your deep neural network to recognize images in programs such as Instagram or Google Photos.
On top of all of these benefits, there is also a significant benefit to having a good internet connection. That being said, let’s take a look at some ways to achieve that!
1.
Buy a good pinhole camera
A pinhole camera is simply an invention that allows you to take pictures using no lens, only a hole as the aperture. The term “pinhole” comes from early photography when people would use pins to block out parts of the image while exposing the film.
The technology behind pinholes was improved upon in the 19th century by photographers who replaced the pin with a piece of glass or metal that acts as the aperture. Modern pinhole cameras are usually made of thin sheets of plastic or cardboard, though some are still made of glass or other materials.
With most modern day pinhole cameras, you can cut away part of the front to make the aperture and then cover it up with your hand or another object to create depth in the picture. This way, you can experiment with different apertures and shutter speeds to get interesting results!
If you want to learn more about pinhole photography, there are many great resources available online.
Get some proper lighting
Lighting is one of the most important things you can get for your ai project. Just like with natural life ais, good lighting makes it possible to identify different features on their shape or structure.
Good quality light sources are an active participant in shaping how well your ai learns. This includes both continuous light sources such as lamps and flashlights, and discrete ones such as LEDs and computer screens.
The intensity, color, and angle of each light source should be stable and consistent when training your ai. Technically speaking, this means no power outages! 🙂
And don’t forget about ambient light either, all around us comes light which helps illuminate objects, even creating our perception of colors.
General tips: use a lot of shadows, drop offs, etcetera make it harder for the ai to figure out what part of the image isn’t defined by its contours and shapes. These effects help emphasize certain parts of the picture, giving you more information than just looking at the whole thing.
Buy some proper software
A lot of people get stuck when it comes to creating an AI that can do things like talk, recognize objects, and even perform tasks!
The first thing needed to create AI is strong natural language processing (NLP). NLP uses logic and rules to connect individual words or phrases into larger statements and questions.
Research the topic of interest
Recent developments in artificial intelligence (AI) have ushered in an era where computers can perform complex tasks that require learning, such as talking, understanding natural language, and even creating images and videos!
This is referred to as deep learning. Neural networks are computational structures that work by having several layers that communicate with each other.
Each layer performs simple calculations or processing on its input before passing it onto the next layer. The layers themselves are trained using examples of data.
For instance, if you wanted to teach your computer how to recognize cats, then it would need a lot of examples of pictures or videos of cats. Once it has enough training material, the AI will be able to identify more instances of what a cat looks like.
By repeating this process many times, the system will learn how to combine different features of a cat into determining whether something is a cat or not.
There are some great resources available free online for anyone to use to start off their research. You don’t have to go beyond tutorials written in English, but it is helpful to know some basic vocabulary so you understand what people are saying.
Once you have done that, there are lots of sites and apps that offer either paid or free accounts which allow you to access the full resource.
Read up on how to set up a deep learning program
When it comes down to it, creating an intelligent chatbot requires three main components: natural language processing (NLP), neural networks, and knowledge representation.
Natural language processing is the field of computer science that deals with understanding written and spoken languages. NLP algorithms are used to take in content and break it into discrete elements or tokens which can be manipulated and combined using rules and patterns.
Neural networks are computational models inspired by neurons in our brains. Just like our brain cells connect together to form circuits, artificial neural networks have nodes or connections that shift strength depending on what they’re exposed to.
Knowledge representation is the process of organizing information so you can use it effectively. For example, when we learn about physics, we understand concepts such as mass, gravity, momentum, and energy. These complex ideas can be organized and applied to other areas.
Practice making simple pictures
A lot of people make the assumption that because you can use AI to do some pretty amazing things like speak, write, and organize information; therefore, you just need to teach it how to make still images or videos and it will know what to do with that data!
This is not true though. The more fundamental level of learning that most research in deep learning focuses on are called image recognition tasks.
Practice doing something simple for very few iterations before moving onto harder challenges. Taking up a new skill should be done this way to ensure your brain gets used to the process and learns the basics well.
So, if you’re looking to start experimenting with AI, look no further than creating, editing, and organizing photos using software such as Photoshop, Gimp, or Lightroom!
These applications all have easy modes that get users started quickly while also giving them chance to practice their hand-eye coordination and creativity.
