All you need to know about Artificial Intelligence

Since 2019 the market growth of AI has been increasing in a 39% uphill trend till today and it keeps growing! If you don't know what AI is, we are going to explain it to you in very simple words, A.I. stands for Artificial Intelligence and **the main goal of AI is to enable machines to perform activities and analysis near to (or even better than) human-level intelligence.

There are 4 types of AI:

1. Reactive Machines: reactive machines do not store memories or use past experiences to determine future actions. They just perceive the world and react to it. A popular example is: IBM's Deep Blue, a machine that beat chess Grandmaster Garry Kasparov in 1997.
2. Limited Memory: it’s the most common type of AI used today, and this AI learns from past experiences and builds experiential knowledge by observing action and data. For example, self-driving cars are limited memory AI, they make immediate decisions using data from the recent past.
3. Theory of mind: refers to a human’s ability to represent the mental states of others, such as their desires, beliefs, intentions, and so on. For example, “I’m hungry so I am going to grab that apple” the AI would understand “she must be hungry”.
4. Self-awareness: these are machines that are aware of themselves and know their internal states, most likely are able to empathize with human states of being and emotions. For example, the AI of the “Her” movie, where the main character develops a love bond with its AI.

In the words of Scott Bonneau -“AI covers a vast range of use cases, from controlling the characters in your favorite video game to powering self-driving cars, and everything in between”

The progress of AI market value according to Statista:

“Revenues from the AI software market from 2018 - 2025” Statista (In Billions of U.S. dollars)

During the COVID-19 pandemic, our market was forced to increase automation to maintain human isolation, many jobs were left to computers and machines, and the businesses that were already advanced into automation seized the opportunity to gain leverage over this scenario to massively grow within new demands and opportunities.

So, what are the big goals of this technology in today’s market? What are the plans of big companies' team labs? How will all of this impact us?

These are crucial questions, but let’s start with the basic, projects of tech companies in AI:

• IBM: this company is investing in technologies of rapid growth like the cloud, data, and artificial intelligence. Similar to Microsoft, IBM sees a high potential for AI in health care, developing products that aim to individualize care, and also the market of finance has been stimulated by the creation of products that will help with subjects such as compliance and customer experiences. IBM today’s annual revenue is estimated at $70 billion USD.
• Microsoft: the company cloud computing service, Azure, is home to a broad ecosystem of AI-driven tools for medicine, language, robotics, medical imaging, and many other areas! Microsoft’s annual revenue is estimated at $61 billion USD.
• Amazon: AI has been the constant point of growth for Amazon, starting with its golden AI product, Alexa, the leading product in customer speech recognition. Amazon’s AI success is also a proven case in the improvement of the core of its business: search relevance due to user preferences, products filter, and customer experience are key to their success and its estimated annual revenue is $221 billion USD.
• Alphabet Google: voice search, digital AD pricing, email spam filters, and relevant search results all use internally developed AI tools and deep learning techniques to drive the world’s most dominant search engine. Alphabet Google’s annual revenue is estimated at $183 billion USD.
• Apple: the hardware-software ecosystem that made Apple a technology empire is also planning to sharpen them with AI to make the ecosystem even more competitive. With its new team member Samy Bengio, a prominent Google AI expert, Apple is investing more and more in a new campus at the Research Area of North Carolina, the campus will house jobs in machine learning, AI, and Software engineering, generating at least 3,000 job openings! Apple’s annual revenue is estimated at $274 billion USD.

Most of these companies are investing big time in AI because they want to provide a more personalized experience of their products, and generate a deeper bond with their customers to forecast outcomes, make predictions and boost their revenues.

The hiring growth for AI jobs has been increasing 32% since 2019 according to LinkedIn, this percentage % can be translated to 97 million new job vacancies by 2025, according to The World Economic Forum.

So, what to do with all of this information? Don’t worry, we saved the best for the last (; here is a CNBC list of the top demanded jobs in AI along with its annual salary average, which one calls your attention the most?

1. Data Scientist - $110.000 USD
2. Senior Software Engineer - $120.000 USD
3. Machine Learning Engineer - $125.000 USD
4. Data Engineer - $122.050 USD
5. Software Engineer - $100.000 USD
6. Software Developer - $95.000 USD
7. Software Architect - $135,107 USD
8. Senior Data Scientist - $127,500 USD
9. Full Stack Developer - $108,730 USD
10. Principal Software Engineer - $155,000 USD

One thing we are certain of, the future will be all about AI, Software Devs, Cloud, and Data.

We at 4Geeks Academy are fully aware of these trends and have designed the most complete and robust AI course ever brought so that you can effectively and quickly upskill into the future of work and pursue a career in AI.

Our AI/ML program is a first-of-its-kind education curriculum that focuses on the practical skills and strategies needed to lead a successful career in AI development, management, and leadership. It will help you develop the necessary tools to upskill for the future of AI work.

If you are considering diving into the AI/ML engineering world, click here to get to know more about our flexible Bootcamp programs.

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How Can I Learn AI? Your Path to AI Fluency

Learning AI involves understanding its core concepts, practical applications, and the tools used to build AI systems. While formal education offers structured learning, numerous free resources can help you grasp the fundamentals and even develop advanced skills. The key is to combine theoretical knowledge with hands-on practice to build a strong portfolio.

Free AI Learning Resources: Where to Begin?

Accessing high-quality AI education doesn't require significant financial investment, as many reputable institutions and platforms offer free courses and materials. These resources range from introductory concepts to advanced machine learning techniques, providing a flexible learning path for anyone interested in the field.

Online Courses and Tutorials

Several platforms provide free, self-paced courses that cover various aspects of AI, from foundational principles to specialized topics. These courses often include video lectures, readings, quizzes, and coding exercises to reinforce learning.

1. Google AI Education: Offers a comprehensive collection of free courses, guides, and tools, including Machine Learning Crash Course, which is excellent for beginners with some programming experience. Google AI Education
2. IBM AI Education: Provides free courses on Coursera and edX, covering topics like AI fundamentals, machine learning, deep learning, and natural language processing. Many of these courses offer free audit options. IBM SkillsBuild
3. Hugging Face Courses: Focuses on practical applications of natural language processing (NLP) and transformers, offering free courses that teach how to use their libraries for various AI tasks. Hugging Face Courses
4. fast.ai: Known for its "Practical Deep Learning for Coders" course, fast.ai emphasizes a "top-down" approach, teaching practical applications first and then delving into theory. All course materials are free and publicly available. fast.ai
5. MIT OpenCourseWare: Offers free access to course materials from MIT, including subjects related to AI, machine learning, and data science. While not always interactive, these provide deep academic insights. MIT OpenCourseWare

Books and Documentation

Beyond structured courses, many free books and extensive documentation provide in-depth knowledge and practical guidance. These resources are invaluable for understanding theoretical underpinnings and mastering specific tools.

• "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: This foundational textbook is available for free online and is considered a definitive resource for deep learning theory. Deep Learning Book
• Scikit-learn Documentation: An essential resource for machine learning in Python, providing detailed explanations, examples, and API references for a wide range of algorithms. Scikit-learn Documentation
• TensorFlow and PyTorch Documentation: Comprehensive guides for the two most popular deep learning frameworks, offering tutorials, API references, and best practices. TensorFlow Docs and PyTorch Docs

Community and Open-Source Projects

Engaging with the AI community and contributing to open-source projects can significantly accelerate your learning. Platforms like GitHub host countless AI projects, allowing you to examine code, learn from others, and even contribute.

• Kaggle: A platform for data science and machine learning competitions, Kaggle provides datasets, code notebooks, and a vibrant community where you can learn from top practitioners. Kaggle
• GitHub: Explore repositories related to AI, machine learning, and deep learning. Many researchers and developers share their code, models, and research papers openly.

A 30-Day Roadmap to Learning AI for Free

A structured approach can make learning AI more manageable and effective. This 30-day roadmap focuses on building foundational knowledge and practical skills using free resources.

Week 1: AI Fundamentals & Python Basics

• Days 1-3: Introduction to AI: Start with an introductory course like Google AI's "What is AI?" or IBM's "Introduction to AI" on edX (audit mode). Focus on understanding what AI is, its history, and key concepts like machine learning, deep learning, and neural networks.
• Days 4-7: Python for AI: If you're new to Python, complete a free Python crash course (e.g., Codecademy's "Learn Python 3" or Google's Python Class). Python is the dominant language in AI, so strong fundamentals are crucial. Practice basic data structures, functions, and control flow.

Week 2: Machine Learning Core Concepts

• Days 8-10: Supervised Learning: Dive into supervised learning. Explore linear regression, logistic regression, and decision trees. Use resources like the Machine Learning Crash Course by Google or fast.ai's introductory lessons.
• Days 11-13: Unsupervised Learning: Learn about clustering algorithms (K-means) and dimensionality reduction (PCA). Understand when and why to use these techniques.
• Days 14: Model Evaluation: Understand metrics like accuracy, precision, recall, F1-score, and confusion matrices. Learn about overfitting and underfitting and techniques like cross-validation.

Week 3: Deep Learning Introduction

• Days 15-18: Neural Networks: Begin with the basics of neural networks: neurons, activation functions, forward propagation, and backpropagation. Andrew Ng's "Neural Networks and Deep Learning" course on Coursera (audit mode) is an excellent resource.
• Days 19-21: Convolutional Neural Networks (CNNs): Explore CNNs for image recognition. Understand convolutions, pooling layers, and common architectures. Practice with a simple image classification task using TensorFlow or PyTorch tutorials.

Week 4: Natural Language Processing (NLP) & Project Work

• Days 22-24: NLP Fundamentals: Learn about text preprocessing, tokenization, word embeddings (Word2Vec, GloVe), and basic NLP tasks like sentiment analysis. Hugging Face's NLP course is a great starting point.
• Days 25-27: Recurrent Neural Networks (RNNs) & Transformers: Get an introduction to RNNs for sequential data and the revolutionary Transformer architecture.
• Days 28-30: Mini-Project & Next Steps: Apply your knowledge by completing a small project. This could be a simple image classifier, a sentiment analysis tool, or a text generator. Kaggle's beginner-friendly competitions are ideal for this. Reflect on your learning and identify areas for deeper exploration.

What's the Best Way to Learn AI?

The "best" way to learn AI is highly individual, but a combination of theoretical understanding, practical application, and continuous learning proves most effective.

1. Start with a Strong Foundation: Master Python programming and essential mathematical concepts (linear algebra, calculus, statistics).
2. Hands-on Practice: Theory alone is insufficient. Work on projects, participate in coding challenges, and experiment with different datasets. This builds intuition and problem-solving skills.
3. Leverage Free Resources: Utilize the wealth of free online courses, documentation, and open-source tools available from leading institutions and tech companies.
4. Join a Community: Engage with other learners and professionals through forums, meetups, or online communities. This provides support, networking opportunities, and exposure to diverse perspectives.
5. Specialize Gradually: Once you have a broad understanding, consider specializing in an area that interests you, such as computer vision, NLP, reinforcement learning, or AI ethics.
6. Stay Updated: The field of AI evolves rapidly. Continuously read research papers, follow AI news, and experiment with new technologies.

For those seeking a more intensive and structured learning environment, an AI bootcamp can accelerate your journey from beginner to job-ready professional. Programs like 4Geeks Academy's AI/ML program provide a comprehensive curriculum, mentorship, and career support, focusing on practical skills demanded by the industry.

Want a structured path with someone to guide you? The AI program comparison shows what each 4Geeks program includes at a glance.