Is Data Science Dead? Long Live Business Science
11 days ago
What Are The Most Popular Programming Languages for AI development?
AI is a huge technology. That's why a lot of developers simply don't know how to get started and choose the best programming language. Also, personally, I've met a bunch of people who have no coding background whatsoever, yet they want to learn artificial intelligence.
- Less coding required. AI has a lot of algorithms. Testing all of them can make into a hard work. That's where Python usually comes in handy. The language has "check as you code" methodology that eases the process of testing.
- Built-in libraries. They proved to be convenient for AI developers. To name but a few, you can use Pybrain for machine learning, Numpy for scientific computation, and Scipy for advanced computing.
- Flexibility and independence. A good thing about Python is that you can get your project running on different OS with but a few changes in the code. That saves time as you don't have to test the algorithm on every OS separately.
- Support. Python community is among the reasons why you cannot pass the language by when there's an AI project at stakes. The community of Python's users is very active - you can find a more experienced developer to help you with your trouble.
- Popularity. Millennials love the language. Its popularity grows day-to-day, and it's only likely to remain so in the future. There are a lot of courses, open source projects, and comprehensive articles that'll help you master Python in no time.
- Lisp allows you to write self-modifying code rather easily;
- You can extend the language in a way that fits better for a particular domain thus creating a domain specific language;
- A solid choice for recursive algorithms.
- It has impressive flexibility for data security. With GDPR regulation and overall concerns about data protection, being able to ensure of client's data security is crucial. Java provides the most flexibility in creating different client environments, therefore protecting one's personal information.
- It is loved for a robust ecosystem. A lot of open sources projects are written using Java. The language accelerates development a great deal comparing to its alternatives.
- Low cost of streamlining.
- Impressive community. There are a lot of experienced developers and experts in Java who are open to sharing their knowledge and expertise. Also, there's but a ton of open source projects and libraries you can use to learn AI development.
- You can declare facts and create rules based on those facts. That allows a developer to answer and reason different queries.
- Prolog is a straightforward language that for a problem-solution kind of development.
- Another good news is that Prolog supports backtracking so the overall algorithm management will be easier.
- Oz - allows an image to manipulate another one;
- Moose - an impressive tool for code analysis and visualization;
- Amber (with Pharo as the reference language) is a tool for front-end programming.
- creating clean datasets;
- split a big data set into a few training sets and test sets;
- use data analysis to create predictions for the new data;
- the language can be easily ported to Big Data environments.
- Haskell is great at creating domain specific languages.
- Using Haskell, you can separate pure actions from the I/O. That enables developers to write algorithms like alpha/beta search.
- There are a few very good libraries - take matrix for an example.