ON : 26 December 2023
Companies hire AI developers to stay in the race and AI tools are a blessing to these developers. Otherwise, they’d have to work ten times more to do what’s already incredibly complex and time-consuming.
For instance, AI tools assist developers in writing, testing, and debugging code. They can automate repetitive tasks, maintain productivity, and understand the context by analyzing millions of programming codes in different languages.
Staying updated with the latest AI tools is the need of the hour. Only then AI developers can build more intelligent and sophisticated applications at never-before-imagined speeds.
This article aims to highlight some key AI technologies and tools that every AI developer should know to stay ahead of the curve.
Explore deeper insights on AI development by delving into our related blog: “Diversity and Inclusion in AI Development: Why It Matters”
Foundational AI development tools streamline the development process, automate repetitive tasks, and enhance the efficiency of AI algorithms.
Python, a multipurpose language known for its simplicity and readability, is a popular choice for AI development.
Its extensive libraries and strong community support empower developers to focus on core algorithms rather than language complexities.
Python’s user-friendly syntax and rich ecosystem make it ideal for tasks such as data collection, web scraping with APIs, and developing machine learning models.
R is a language that specifically caters to statistical computing and graphics. This makes it highly popular in AI development. It excels in statistical modeling and data analysis, making it a popular choice for developing and examining machine learning models.
R’s intuitive language and a vast library of functions enable developers to build complex AI systems, from predictive analytics to machine learning.
Machine Learning Frameworks
Machine Learning Frameworks are essential tools for developing, training and implementing machine learning models.
They provide pre-built functions and structures, reducing the complexity of creating custom algorithms.
Google Brain Team created TensorFlow as an open-source library known for its robustness and flexibility.
TensorFlow provisions an extensive range of neural network architectures.
It provides tools for both execution and graph computation, enabling efficient model development and deployment.
PyTorch, developed by Facebook’s AI Research lab, offers dynamic computational graphing.
It provides excellent flexibility and speed, making it suitable for research prototyping and production deployment.
PyTorch also supports distributed training, making it a good choice for large-scale machine-learning tasks.
Scikit-learn is a Python library known for its simplicity and practicality. It offers a wide variety of administered and unsupervised learning algorithms.
From regression, classification, and clustering to dimensionality reduction, Scikit-learn is a versatile tool for data mining and data analysis.
4. NumPy and Pandas
NumPy and pandas are fundamental libraries for data manipulation in Python.
NumPy provides support for large multi-dimensional arrays, matrices, and mathematical functions.
Pandas, on the other hand, provide you data assemblies and processes for working with numerical tables and time series.
Keras is a user-friendly neural network library in Python. It offers an expedient way to outline and train most deep-learning models.
Keras was created for speedy experimentation with deep neural networks.
Theano is an open-source project that lets you define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays.
It is integrated with Numpy and has strong GPU support, making it a powerful tool for deep learning and other compute-intensive tasks.
MXNet is a deep learning framework that allows users to define, train, and deploy deep neural networks across various types of devices.
It’s highly scalable and supports a flexible programming model and multiple languages, making it a popular choice for both research and production.
Deep Learning Tools
Deep Learning Tools are software applications or libraries used for designing, training, and deploying deep learning models.
They are essential for any AI developer as they simplify the process of creating complex neural networks.
1. Jupyter Notebooks
This is an open-source web application that lets you create and share documents.
They support live code, equations, visualizations, and narrative text.
Ideal for data cleaning, statistical modeling, and machine learning, they are a popular tool among data scientists and AI developers.
JetBrains developed PyCharm, a Python IDE that provides smart code completion, on-the-fly error checking, easy project navigation, and much more.
PyCharm supports web development frameworks and scientific tools and integrates with Jupyter Notebook, making it a comprehensive tool for AI development.
3. VS Code/ Visual Studio
Visual Studio Code is lightweight but is packed with an extraordinarily powerful source code editor.
Equipped with powerful features, it offers support for various programming languages and boasts a diverse ecosystem of extensions catering to different languages and runtimes.
AI developers often favor it for its integrated Git commands and debugging capabilities.
Natural Language Processing (NLP) Tools
Natural Language Processing (NLP) tools are software applications or libraries that facilitate the interaction between computers and human language.
They are crucial for AI developers working on tasks like text analysis, language translation, and sentiment analysis.
The Natural Language Toolkit (NLTK) holds a prominent position as a platform for developing Python applications focused on human language data.
Featuring interfaces to more than 50 corpora and lexical resources, NLTK also encompasses text-processing libraries for tasks such as classification, tokenization, stemming, tagging, parsing, and semantic reasoning.
SpaCy is a Python-based open-source library designed for advanced natural language processing.
It’s designed for industrial-strength NLP and excels at large-scale information extraction tasks.
SpaCy supports over 73 languages and offers a flexible and modular design for data science, scientific computing, and machine learning.
3. Hugging Face
Hugging Face is an AI community building the future.
It provides a platform where the machine-learning community collaborates on models, datasets, and applications.
Hugging Face also offers hundreds of models that are customized for a single purpose, making it a versatile tool for AI developers.
OpenAI stands as an AI research and deployment organization with a mission to ensure the widespread benefits of artificial general intelligence for humanity.
The GPT-4 model developed by OpenAI is a deep learning model capable of generating, editing, and iterating on both creative and technical writing tasks.
This positions it as a potent tool for AI developers.
5. Version Control
Version control, also identified as source control, involves the systematic tracking and management of changes made to software code.
This practice enhances the efficiency and intelligence of software teams, particularly advantageous for DevOps teams, by minimizing development time and elevating the success rate of deployments.
6. Git and GitHub
Git serves as a version control system, facilitating the management and tracking of source code history.
In contrast, GitHub functions as a cloud-based hosting service designed for the administration of Git repositories.
For those with open-source projects utilizing Git, GitHub proves instrumental in effective project management.
Docker emerged as an open platform geared towards the development, shipping, and operation of applications.
By allowing the separation of applications from infrastructure, Docker empowers the rapid delivery of software.
This approach enables the unified management of both applications and infrastructure, streamlining the software development and deployment processes.
8. Cloud (AWS, Azure, GCP)
Amazon Web Services (AWS) dominates infrastructure, including scalable storage, networking, server, mobile development, and cybersecurity solutions.
Microsoft Azure provides some of the most scalable and efficient software solutions.
Google Cloud Platform (GCP) offers high-end big data analytics solutions and allows easy interaction with other vendor products.
Want to learn about remote hiring? Explore our blog post: “Remote Hiring: Best Practices for Virtual AI Developer Recruitment“
Emerging technologies in AI are transforming the way we interact with the world.
These technologies are not only enhancing our daily lives but also revolutionizing various industries with their potential to solve complex problems.
1. Reinforcement Learning
Reinforcement learning is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take action in a dynamic environment in order to maximize the cumulative reward.
2. Federated Learning
Federated learning constitutes a machine learning methodology that educates an algorithm through numerous independent sessions, each employing its distinct dataset.
This differs from conventional centralized machine learning methods, where local datasets are amalgamated into a single training session.
3. Generative AI
Generative artificial intelligence (AI) is a category of AI that produces images, text, videos, and other media based on provided prompts.
Widely recognized AI generators like ChatGPT and DALL-E2 are gaining global popularity.
4. Explainable AI (XAI)
Explainable artificial intelligence (XAI) encompasses a set of procedures and techniques facilitating developers in comprehending the outcomes and outputs generated by machine learning algorithms.
It serves to elucidate an AI model, its anticipated impact, and potential biases.
5. Responsible AI
Responsible Artificial Intelligence (Responsible AI) is an approach to the development, evaluation, and deployment of AI systems in a secure, reliable, and ethical manner.
AI systems result from numerous decisions made by those involved in their development and deployment.
There are many essential AI developer skills that these professionals still need even after using these tools. Here’s a final thought to ponder over:
Instead of just being tool users, how can we become holistic AI developers?
This shift in perspective requires us to:
- Focus on the why, not just the how.
- Embrace the human-machine partnership.
- Prioritize explainability and transparency.
- Continuously learn and adapt.
Frequently Asked Questions
Q1. What is the best AI tool for software development?
The best AI tool varies based on specific needs, but some widely used ones include TensorFlow, PyTorch, and Scikit-learn.
Q2. What is the most popular way to develop AI tools?
Developing AI tools often involves programming languages such as Python and R. Additionally, frameworks like TensorFlow and PyTorch are commonly used for building AI models.
Q3. What should an AI developer know?
AI developers should be familiar with programming languages (e.g., Python, R, Java), AI frameworks (e.g., TensorFlow, PyTorch), machine learning algorithms, data structures, statistics, and mathematical concepts like linear algebra and calculus.
Q4. What are AI tools for developers?
Top tools for Artificial Intelligence development include TensorFlow, PyTorch, Scikit-learn, Keras, OpenCV, and NLTK (Natural Language Toolkit), among others.
Q5. Which is the best AI tool for coding?
AI tools tailored for coding assist developers in writing, debugging, and optimizing code. Some popular ones are Kite, Tabnine, and DeepCode.