AI beyond the basics: a guide for tech-savvy readers

AI beyond the basics

If you're looking for more advanced info about AI but don't want to drown in a sea of jargon, you've come to the right place.

Let's get straight into it.

What is a neural network?


Neural networks are a subset of machine learning that form the core of deep learning algorithms. 


Neural networks are composed of several layers. 

  1. Input layer: receives the input.
  2. One or more hidden processing layers: these process the input using "neurons," a fancy way to say "special mathematical functions."
  3. Output layer: emits the output.


Each neuron in a layer applies a mathematical function, called an activation function, that calculates a result based on the inputs and certain weights. The neuron then forwards the result to the next layer.


Each layer has weights and thresholds (more accurately known as biases) derived from initial, labeled training data. While further training the model, weights, and thresholds are iteratively adjusted until the network provides more accurate answers more often.


Several different neural architectures exist, each serving specific tasks better than others.

The AI superset


AI refers to any system that performs an activity typically considered to be part of the realm of humans. In its strictest sense, an email spam filter that learns from past emails is a form of AI.

AI is the superset of numerous subset technologies, such as:

  • Computer vision
  • Robotics
  • Text-to-speech
  • Machine learning
  • Deep learning
  • Natural language processing (NLP)

Machine learning (ML)


Machine learning refers to the ability of machines to adjust and adapt based on input data, otherwise known as "learning." ML is being applied more frequently to massive datasets as computing power increases.


Unlike hardcoded solutions, self-improving algorithms power ML solutions. You don't need to change the code for the system to change how it operates because the algorithms are programmed to adjust based on changing data.

Deep learning


Deep learning is a subfield of machine learning that leverages multi-layered neural networks for deeper learning processes. Scientists don't always know how deep learning solutions produce their outputs because they operate in a hidden, black-box environment. This is a bug rather than a feature, and understanding that black box could open enormous possibilities for AI.

Natural language processing (NLP)


NLP is when computers process and understand natural language. A fundamental concept of NLP is to turn unstructured language into structured language.


NLP is used extensively in modern AI solutions. Some examples of NLP in AI include:

  • Translating texts
  • Analyzing sentiment
  • Summarizing text
  • Generating text


Python has extensive inbuilt support for NLP—such as scikit-learn, Natural Language Toolkit, PyNLPl, and other libraries—making it a popular programming language for developing AI applications.


Combining NLP AI tools with other software can prove especially powerful. Some of the tools you can create include:


  • Company search engine: You can combine AI and NLP features to your company's knowledge base and provide answers to users who search for company-specific data. To create the front end, you can find freelance developers to help you.
  • Chatbots: Similarly, you can create AI and NLP-powered chatbots that integrate into your company website.
  • Social media monitoring: Through AI and NLP, you can determine how people feel about your brand and write software to trigger alerts on sudden drops in favorability.

Computer vision (CV)


CV is a multidisciplinary field with broad applications in AI. One significant use of CV is to understand what images contain.


CV tools typically work on neural networks built using a different architecture to language model neural networks.


Machine learning and deep learning are crucial for advanced CV functionality.


Two popular tools based on CV are the generative AI tools for image generation: Midjourney and DALL-E 2. These tools combine NLP functionality with CV technology to generate spectacular AI images.


DALL-E offers a public API to integrate AI image generation into your product or service. If you're not a programmer, you can find qualified experts to to help you launch your AI Image Generation product.

Next stop: build your app


Extensive opportunities exist for using AI in combination with other technologies. The tools you can develop are limited only by skill and imagination. With all this advanced knowledge about AI, the next step is to get started on your app. Want help? Find an AI coding expert to help you fill the skills gap, so that all that's left is your imagination.

Back to hub