How to Code an Artificial Intelligence Fast and Easy?

This article is about how to program an artificial intelligence fast and easy. This is a guide for beginners, with simple step-by-step instructions that will have you creating your own AI in no time!

First, let’s start by looking at the definition of an artificial intelligence (AI). An AI is a machine that imitates human thought and actions in some way. It can be used to solve complex problems, such as those encountered by real people. They are giving us what we humans cannot do on our own! A successful AI would be able to think and learn like a human but without the need for constant supervision or oversight. This means it could work independently from its creator but still communicate with them through text or voice commands. It would also be able to have a sense of self-preservation, and wouldn’t become a danger to anyone around it. There are many different types of AI that fall into one of the four categories below.

1-General Intelligence (as in knowledge) – the main method of intelligence is problem-solving, planning, reasoning and learning. There are various classifications for this type of intelligence:
a)Knowledge Automation – uses rules/knowledge to perform tasks where humans would need too much time to think about which action to take or which device or system they are using.
b)Human Virtual Assistants (Human VAs) – uses machine learning and knowledge automation to perform tasks where humans would need too much time to think about which action to take or which device or system they are using.
c)Mobile Assistants (MAIs) – uses machine learning and knowledge automation to assist humans.
d)Generalization Intelligence (as in abstract understanding of things and their relationships, as well as abstract reasoning). The problem-solving component is not used for solving tasks, but for creating new tasks
2-Verbal Intelligence (as in understanding other people’s words)- is the ability to understand language, both spoken and written, in context with the specific situation.
3-Olfactory Intelligence (as in smelling) – is the ability to detect odors in a specific context. This includes scent recognition, smell detection and perception.
4-Nominal Intelligence (as in concrete thinking and knowledge of things) – is the main method of intelligence used for problem solving, planning, reasoning and learning. It also has several sub-categories:
a)Knowledge Automation (using machine learning to think like a human).
b)Robotic Assistants (using machine learning to act like a human).
c)Mobile Assistants (using machine learning to work on various tasks like a human).

These are the different types of Artificial Intelligence.

Now that you know what AI is, here are some more important things to remember when programming one. First off, you’ll need two basic things: knowledge and instructions. Both of these can be broken down into two other parts: subroutines and variables. The subroutine contains the logic needed to solve a particular problem, while the variable holds information that will be passed back and forth between the subroutine and AI mainframe (or CPU). Together, variables and subroutines make up an AI’s memory. If you think of an AI as a human brain or computer system then these two elements are like your short-term memory and long-term memory respectively. So, it’s important to learn how to use them, but first you must have a place for the AI to store information (memory). If you’ve never programmed before then this will seem daunting at first but don’t worry! This guide will walk you through step-by-step.

First things first, you’ll need a programming language. There are many different programming languages out there and they can be broken down into two categories: high level and low level (representing ones that are easy to understand and ones that require more work). The language we’ll be using for this example is called Python.

If you’re interested in learning Python itself (which is recommended) the following link: Learn Python Programming Fast:

Once you’ve learnt the basics of Python you can use it to make a simple AI that can recognize colors! Now, let’s get rolling!

Creating the Memory for the Artificial Intelligence

To store information in your AI, create an empty string of text called a variable. Let’s call this one “color”. To remember or assign a color to something, type in “pink”. There we go! Your AI can now remember that “pink” is the color of strawberries, but it doesn’t know the meaning of the word. You can tell your AI to use this new ability or get more information from your parents, grandparents, sibling or friend.

Saving and Retrieving Information from AI

To save a piece of information (like a color) into your AI’s memory you just need to type in whatever you want it to be called, but instead put a space between each word and use caps lock. Variable names must be valid Python identifiers (e.g. numbers, letters and special characters only).
For example, if you want your AI to remember the word “color” because it has been placed in its memory then type in “color”. To retrieve this information later you just need to give it the variable name followed by a colon and then the string of information that you want to if from. For example, if you wanted your AI to remember a particular color use “color:pink”. To get more information about the color/name just type “color:pink”.

Getting More Information

There are two ways to get more information from other people. The first is through natural language processing (NLP). NLP allows you to use special commands like:
“What’s the color of an apple?”
“Who’s the fastest person in the world?”
“How many planets are there?”
In order to use these commands just type them between “ask:” and “:” and your AI will get more information for you. It will also store these questions in its memory so that they’re easy to access. NLP commands are also case insensitive, meaning that it doesn’t matter if you capitalize or not. This is especially useful if you want your AI to remember a particular word, such as someone’s name.

The second way to get more information is through natural language understanding (NLU). NLU allows you to query your AI about what it has learned or generated. For example, if you want to get general information about strawberries then type “What is the name of my favorite strawberry?”. To get a list of all the things your AI has learned in this way, type “What did I learn today?”

Natural language processing is one of the most interesting challenges in artificial intelligence because it’s harder than other areas like vision and robotics. It’s also challenging because it relies heavily on fuzzy logic and machine learning. NLU also has a very long and successful history that’s being used in many fields of research today. A common problem in NLU is that your AI doesn’t understand when it’s not talking to a person. This is also known as the Turing test because it was proposed by Alan Turing way back in the 1940s. If you think about it, this is an extremely hard problem to solve because it requires that someone be able to fool you into thinking that they’re actually human and not just a computer.

The last type of AI that’s very important to consider is natural language generation. When you think about most computer programs, they’re basically just doing math or calculations. Even though they’re programs, they don’t really act like a human being would as a person. This lack of human intelligence is often referred to as “wicked problems” because it’s not like real people would handle them correctly or efficiently. We humans are trained to handle problems that are easy and fast and we have pretty good intuition about how they should be solved. Computers, on the other hand, only have logic and numbers and don’t have any real understanding of how things work. They’re not able to think about things like instinct. They don’t even know what they’re doing when they try to communicate with you or how to really understand what you’re asking them.

Now imagine that we are able to create an AI that has human level intelligence just like a person. It has a natural language generation engine that allows it to produce hundreds of thousands, maybe millions of different responses to whatever questions you ask it. This is a common feature in these types of programs because they can be trained in such a way so that they can understand your sentences and react with good information in response. In the future, it is also likely that AIs can be trained to do more complicated things like make investments or suggest restaurants for you instead of providing instant answers.

Example Python Code for Natural Language Processing

You can edit and try-out this code at it is blazing fast industrial-strength natural language processing Python library designed to be fast and production ready.

pip install -U spacy
 python -m spacy download en_core_web_sm
 import spacy
 Load English tokenizer, tagger, parser and NER
 nlp = spacy.load("en_core_web_sm")
 Process whole documents
 text = ("When Sebastian Thrun started working on self-driving cars at "
         "Google in 2007, few people outside of the company took him "
         "seriously. “I can tell you very senior CEOs of major American "
         "car companies would shake my hand and turn away because I wasn’t "
         "worth talking to,” said Thrun, in an interview with Recode earlier "
         "this week.")
 doc = nlp(text)
 Analyze syntax
 print("Noun phrases:", [chunk.text for chunk in doc.noun_chunks])
 print("Verbs:", [token.lemma_ for token in doc if token.pos_ == "VERB"])
 Find named entities, phrases and concepts
 for entity in doc.ents:
     print(entity.text, entity.label_)

Are there Ethical Considerations surrounding these AIs?

It is important to note that no AI is perfect. There are always going to be inputs and outputs attached to the actions of these systems, and it becomes impossible to predict which ones will have a negative impact on society. For example, it is very possible that an AI could get trained in a way that makes its output appear contextually inappropriate.

It can be hard to even know which of these outputs are being caused by the AI and which are not. However, many experts feel that we should still listen to what an AI has to say, even if it doesn’t seem that there is a link between its input and output. It is important to remember that it is impossible to anticipate every possible negative outcome of an action, so it’s best to continue with caution until we have more information on the topic.

What do you think? Do you agree or disagree with some of these points? If you have any questions please let us know in the comments section below! 🙂 For more info, read PC Ocular magazine.

Benkő Attila is a Hungarian senior software developer, independent researcher and author of many computer science related papers.

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