The power of artificial intelligence (AI) continues to grow.
The website Fortunes Insights predicts that the AI market will grow from $387.45 billion in 2022 to $1.39 trillion in 2029. This is especially surprising given the complexity involved in implementing artificial intelligence.
AI is already changing the landscape of software development, with new tools constantly emerging to assist developers at every level of the software development stack, from web design to back-end software development.
List of AI tools for developers
Here is a list of AI tools for developers that will change the way software is developed in 2023.
- chatGPT – This AI chat bot can write code or explain how a particular code works.
- GitHub Copilot – This AI-based pair of programmers uses the OpenAI Codex to write methods, functions, and entire classes.
- Synk Code – Powered by the DeepCode engine, this static code analysis tool uses AI to detect bugs and security holes while reducing false positives.
- Stenography.dev – This Chrome extension examines classes and methods and automatically generates documentation.
- Krisp.ai – Noisy audio is the bane of multimedia designers. This AI tool removes echo, speech and background noise from sound files.
- Beatoven.ai – This AI tool generates royalty-free music tailored to the mood and energy that web designers want to express.
- Fireflies.ai – Currently in beta, this sentiment analysis tool listens to standup meetings and daily scrums to gauge the sentiment and mood of your development team.
- Jasper.ai – This copywriting tool helps users create blogs, but also allows you to create content for website mockups instead of the standard Lorem Ipsum entry.
- PaperCup – This AI language processing tool translates audio, creates audio files and dubbs them into video files.
- Midjourney – Currently in beta, this advanced AI tool creates sophisticated high-resolution artwork based on user prompts and keywords.
AI tools and platforms
So why are these systems smart?
AI systems inspect vast amounts of data to find patterns and themes that can be used to make decisions about different situations. This pattern determination is called modeling.
Suppose you give an AI program a billion pictures of different cats, and tell the program that each picture is an image of a cat. Next, an AI program creates a cat model. Once the model is created, you can pass any photo to the AI program and ask, “Is this a cat?” If the photo matches the cat model, the AI program will respond “yes”. If not, the answer is no.
The “smartness” of an AI program comes from its ability to learn from its inspection algorithms. Every input and output, each new photo and yes or no answer helps fine-tune the AI’s understanding of what cats are.
The key to creating AI is having a huge amount of data and a program that knows how to examine and learn from all that data.
AI tools are built from various data science components.
AI tools and data science
This is where data scientists come in. Behind all AI are a multitude of data scientists. Data scientists play a key role in creating the algorithms that drive the models that make AI smart.
No magic. AI and its capabilities are largely the result of the efforts of data scientists. Without the data scientist, AI is just his sci-fi fantasy.
A common concern is that artificial intelligence and automation will steal many careers, from programmers to taxi drivers. However, continuing AI-focused research and development will require data scientists creating both increasingly sophisticated models of AI technology and more powerful AI-powered applications. As a result, the world needs more data scientists, not fewer.
