Remember movies that told stories about intelligent technologies enslaving humanity?
Terminators or Matrix, you name it. Fortunately, we are far from those scenarios since AI and machine learning (ML) are serving us and benefiting our businesses. It is no wonder that the market of intelligent technologies is snowballing: according to the Markets and Markets report, it will increase to $309.6 billion by 2026 (compared to $58.3 billion in 2021).
The IntelliSoft team is long enough in AI and machine learning development to talk about intelligent technologies. Once, we developed an AI-based fuel consumption forecasting model for one of our clients. We also empowered our solutions with IoT devices that send notifications when the fuel runs off and automatically order fuel and create invoices. Thus, we want to tell you more about AI and machine learning.
In this article, we’ve gathered the most common languages for AI and machine learning algorithms development. We’ll also highlight the main areas of intelligent technology applications.
Before we talk about the AI tech stack, let’s explore how intelligent technologies appeared.
Table of Contents
What Is AI: Historical Facts, Kinds, and Areas of Use
This broad concept appeared in 1956. It stands for a machine or system that can imitate human behavior to perform assigned tasks. The first research started in the 50s and was associated with symbolic calculations. Later, in the 60s, this area attracted the attention of the US Department of Defense, when the military began to train machines to think like a person. For example, DARPA managed to create virtual street maps (the 70s) and develop smart personal assistants (2003).
The work done at that time laid the foundation for automation principles and formal logic used in our PCs. There are computer systems based on them, which help make decisions in complex and ambiguous conditions. In general, there are 3 groups of AI.
AI Groups:
- Narrow. Such a “mind” is limited to specific tasks. Examples: Siri recognizes speech and understands its meaning, and Google drones see road signs and markings or build routes. A notable case is connected with this kind of intelligence: in 2016, a computer beat Lee Sedol, the outstanding Go player (moreover, the world champion), with a score of 4:1. It proved that a machine could be more intelligent than a human.
- AGI. The technology has not yet been implemented, but according to Mckinsey, it could happen with a 25% probability by 2023. Such AI is similar to the human mind and identifies itself. It independently performs various tasks, plans, assesses situations, and makes decisions.
- Superintelligence. Such a mind is theoretical. In terms of its cognitive abilities, it surpasses humanity in all areas, understands all processes, and realizes them.
Modern people use the possibilities of AI in everyday life without even realizing it: driving a self-driving car and following directions from the navigator, requesting “virtual assistant” on any issue, or setting smart email reminders. It is a small part of the functions we deal with daily. And now imagine the value it brings to the business.
AI solves the problems of different industries:
- Logistics: route planning, trip planning, and transportation distribution.
- Finance: personalization of customer experience, compliance with regulations, fraud detection, and identity identification.
- Production: equipment failure prediction, quality control, and manual labor automation.
- Healthcare: improving diagnostic accuracy, predicting risks, and helping with clinical trials.
- Retail: customer behavior analysis, experience personalization, demand forecasting, and chatbot communication.
- Education: analytics, personalized courses, and progress tracking.
- Marketing: research of the market, competitors, and audience or forecasting marketing campaigns.
As we can see, AI simplifies everyday life and brings businesses to a new level. It turns manual labor into automatic mode, analyzes unstructured data, identifies patterns, builds forecasts, and warns of violations in equipment and infrastructure.
According to Statista, machine learning was one of the top three most in-demand technologies in 2020. Let’s explore it in detail.
Related readings:
- Top 10 Data Warehouse Software Tools for Your Business
- Cloud Computing Scalability: What Is It and Why It’s Important?
- Best Tech Stack for Optical Character Recognition Automation
- What Are the Security Risks of Cloud Computing? Threats & Solutions
- Artificial Intelligence (AI) in the Law Industry: Key Trends, Examples, & Usages
Machine Learning as a Prominent Area of AI
The terms AI and ML are often used interchangeably. It is not entirely correct. ML is one of the ways to use AI in computer technology when working with data. This term was introduced in 1959 by Arthur Samuel. He created the first checkers program that could learn independently. According to him, after such training, computers demonstrate behavior that they were not programmed for.
The principle of operation is that the machine receives data and learns from them. It does not just simulate the behavior of people but imitates their learning. ML has come a long way in the development, and it resulted in the fact that it was used in most software products in 2020. We can call it the most promising tool for businesses in this area.
The technology is applied in various ways. We can single out 2 essential and large areas:
1. Machine vision:
- Face recognition: personal identification for secure access, the introduction of age restrictions, the search for criminals;
- medical diagnostics: recognition of diseases by photographs, X-rays, and MRI;
- car driving: identification of objects on the road, calculation of traffic speed;
- air monitoring: drones that detect military equipment and find violations in the territories of industrial facilities.
2. NLP (Natural Language Processing):
- speech recognition: voice commands to smart devices, typing, and voice search;
- automatic translation: recognition of languages and symbols, including images;
- sentiment analysis: determining the emotional attitude of the text’s author to the subject;
- auto-generation of text: the creation of texts of different styles that are almost indistinguishable from those written by a person.
Businesses use this technology to solve complex problems or automate forecasting and data processing. It is indispensable in digital marketing when it is necessary to personalize the customer experience and study their behavior. It is also used in healthcare to process big data, diagnose diseases, determine risks, and adjust treatment. Predicting the mortality of COVID-19 patients with this technology is 92% accurate.
As you can see, smart solutions are trendy today. But who creates them? We invite you to study this issue and consider what IT knowledge is needed to work on such projects.
How Much Programming Knowledge is Required to Learn AI and ML?
The work of specialists in these areas is aimed at the same result, but the methods of achieving it are different. To work with AI, you need to master Java, Python, and C++. In the case of ML, you must know everything related to data processing, for example, the TensorFlow library. Many companies hire people knowledgeable in both areas.
General Skills:
1. Technical.
- Languages. Mandatory “trinity” includes Python, C ++, and Java (we will talk about them in the next paragraph, where we will answer whether Python is the best language for AI in terms of convenience and simplicity).
- Linear algebra. It includes knowledge of vectors, matrices, derivatives, integrals, etc.
- Statistics. It is desirable to understand statistical concepts, including averages and deviations, and probability theory.
- Signal processing. Such knowledge is helpful in feature extraction.
- Applied Mathematics. It is important to understand how mathematical algorithms are applied in practice.
- Neural networks. When it comes to deep learning, knowledge in this area is vital.
- Working with data. Knowledge in data processing, analysis, visualization, ETL, SQL, NoSQL, and Big Data.
2. Non-technical:
- Subject area. The expert must be familiar with the related industry and understand business benefits.
- Creating experimental models. To demonstrate solutions to customers quickly, you must know how to work with prototypes.
- Communication skills to work in a team with other specialists, as well as marketing and sales departments.
3. Additional (for ML):
- Processing audio, video, and speech. To expand the capabilities of a product, a specialist must be proficient in NLTK, Gensim, Summarization, word2vec, etc.
- Computer vision. To work with the most complex systems, you may need to use CV and ML.
As promised, now let’s move on to languages a specialist should know. We will briefly describe the 8 languages in demand, and the first will be that “trinity” from the list above.
8 Best Programming Languages for AI and ML Development
1. Python
We are often asked 2 questions: Is Python good for AI? Do I need to learn Python for AI? We answer yes to everything. Python is a universal language with easy-to-learn syntax, libraries, and structures. The language is popular among students because of its simplicity. With its help, you can create various products: from cloud services to neural networks. It supports integrations with other languages such as C, C++, Java, or Cobra. You can include various libraries, for example, NumPy or SciPy.
2. Java
Is Java or C++ better for AI? Let’s put it this way: Java definitely ranks second. It is widespread in mobile products. Since they are often based on AI, Java is a powerful tool in this area. The strengths include efficient work with search algorithms, broad functionality for large projects, and easy code debugging. It is supported by a large community and has many additional libraries. Another advantage is versatility. For example, Java is suitable for robotic solutions and sensors.
3. C++
Is C++ good for AI? Of course! It deserves to be on the list of the most flexible languages. In the context of working with search engines, it reduces response time and improves rankings. Neural networks are often built on C++. Programmers appreciate it for performing fast calculations, which is essential for AI. It stands out among other languages, providing high control and efficiency.
4. R
It is another easy-to-learn language used for analysis, Big Data modeling, visualization, and forecasting. R is good at working with large numbers and graphs. It has many extensions, for example, TM is designed for text analysis. Combined with other powerful tools, R helps build comprehensive products and increase productivity.
5. Scala
It is a scalable language that can handle large amounts of data. Its notable feature is concise code, more readable and easier to write than other languages like Java. It is valued among AI developers for its speed and efficiency. The language integrates with Java and JavaScript and provides error-free coding with easy debugging, ensuring a fast and convenient development process.
6. Lisp
The language appeared in the 60s and has long been one of the main tools of AI researchers. In the 80s, it was used in solving applied problems. Its creator, John McCarthy, was a central person in the AI area. The structure of Lisp is simple and consistent, which allows writing readable and well-ordered code. It helps you build prototypes, create dynamic objects, and expand the possibilities of character processing. An example of a successful project is Grammarly.
7. Julia
This language is the first to mention when it comes to complex calculations in the scientific and technical fields. It is widely used in analytics as well as numerical and technical calculations. The processing speed is similar to Java and C++. The technology provides helpful features related to code introspection, metaprogramming, and debugging. It is a great choice for any ML project as there are multiple packages for various tasks.
Prolog
This logic language is not similar to classical ones. It was created specifically for AI, so it has useful industry-specific features like pattern matching or automatic return. It is an excellent option for establishing connections between objects and goals concisely. It supports different platforms, which increases the speed of work.
It is not a complete list of options for creating smart products. For example, Haskell is suitable for implementing AI algorithms, Matlab for matrices, and Smalltalk for GUIs. When choosing, start from the tasks and scale of your project.
AI is a high-tech field where the maximum level of knowledge is required. After all, creating products that think and act like humans is not an easy task. We recommend that you select contractors with industry-specific skills and experience working with the best technologies.
