In a relatively short time, artificial intelligence, or AI, has become inescapable.

Every time you shop online, search for information, or watch a show on a streaming service, you’re interacting with some form of artificial intelligence.

From factory workers to waitstaff to engineers, AI is altering jobs and careers across many industries. AI’s rapid evolution has the potential to shape the future of technology, business, and society itself.  

The AI market is projected to reach $407 billion by 2027. For those who pursue careers in AI, there are significant opportunities to make your mark in this transformational field.

What is Artificial Intelligence?

AI is a branch of computer science focused on creating systems to perform complex tasks that would normally require human interaction. These tasks include understanding speech, recognizing patterns, making decisions, and learning from experience. 

“Basically, we are trying to mimic human intelligence,” says Bruce Huang, director of information technology programs at Harvard Extension School. “We have been using machines to automate everything we do, and now we are talking about machines that can, in some way, make decisions, act, think, and react, mimicking humans.”

AI is a broad field with numerous subfields, each with its own objectives and specializations. It is an umbrella term that encompasses a of technologies, including machine learning, deep learning, and natural language processing (NLP).

General references to AI apply most broadly to technologies that power many of the digital services we use every day, like chatbots that provide customer service in real time, the voice of digital assistants that answer questions through Alexa or Siri, or apps that tell how long the wait time is at a restaurant.

Other everyday AI applications include:

  • Social media algorithms that personalize your feed based on your online activity.
  • Voice recognition software that transcribes speech into written text.
  • Recommendation engines used by streaming services to suggest shows or products based on your past purchases.

Some of the most common examples of AI in use today include:

  • ChatGPT, which uses large language models (LLMs) to generate text in response to questions or comments.
  • Google Translate, which uses deep learning algorithms to translate text from one language to another.
  •  Netflix, which uses machine learning algorithms to create personalized recommendation engines for users based on their previous viewing history.

What are the Different Types of AI?

AI is characterized into three categories:

  1.  Artificial narrow intelligence (ANI)
    The most common form of AI used today, ANI performs a single task, such as a digital voice assistant like Apple’s Siri, a search engine like Google, or a customer service chatbot.
  2. Artificial general intelligence (AGI)
    This AI can understand, learn, adapt, and implement knowledge across a wide range of tasks to perform at a level that a human can. You can see an example of this in ChatGPT.
  3. Artificial superintelligence
    This level refers to a future scenario where AI surpasses human intelligence. At present, this concept is mostly speculative.

What is the Difference Between Types of AI?

The main difference between traditional AI and generative AI lies in their capabilities and applications. 

Traditional AI

  • Primarily used to analyze data and make predictions
  • Excels at pattern recognition
  • Can analyze data and explain what it sees

Generative AI

  • Creates new data similar to its training data
  • Excels at pattern creation
  • Can use data to create something new based on what is available

Different types of AI are often confused, but the main point is this: While we have made leaps and bounds in narrow AI, general AI remains largely uncharted territory.

The following are are some important terms related to traditional AI you may also be familiar with:

  • Machine learning (ML) is a subset of AI where machines learn from data without being explicitly programmed.
  • Deep learning is a subset of Machine Learning, where neural networks mimic the human brain to find patterns and make decisions.
  • Data science incorporates AI and ML but also involves other disciplines to gather insights from data.
  • Reactive machines are the most basic type of artificial intelligence. These machines don’t possess any knowledge of previous events but instead “react” to what is before them in a given moment. They can perform only certain advanced tasks in a narrow scope, such as playing chess.
  • Limited memory machines possess a limited understanding of past events and can interact more with the world around them than reactive machines. For example, self-driving cars use limited memory to observe vehicles, turn, and adjust their speed.
  • Theory of mind machines, which don’t yet exist, can create representations of the world, and also understand other entities that exist within the world.
  • Self-aware machines are purely theoretical and possess an understanding of the world, others, and itself.

If you are considering an AI career, check out Harvard Extension School’s Data Analytics Graduate Certificate and Programming Graduate Certificate and Technology Graduate Courses and Programs, where you can learn computer programming and language fundamentals and deepen your analytics knowledge.

Why You Should Learn Artificial Intelligence

AI isn’t a far-off future technology — it is already transforming how we work, live, and interact with each other. 

The number of AI-related jobs has grown significantly over recent years. As industries continue to adopt AI technologies to streamline their operations and make better decisions, the demand for AI specialists will likely only increase.

Many fields — including education, healthcare, and finance — already use AI to create models and analyze data.

“We are in an important transition period,” Huang said. “We are transitioning from the digital age — the dotcoms and ecommerce — to the AI era. A lot of routine jobs will be done by AI, like it or not. AI is here and this is the new era.”

The AI field is positioned for rapid growth

AI is projected to create around 97 million new jobs in the coming years.

As industries continue to adopt AI technologies to streamline their operations and make better decisions, the demand for AI specialists will only increase. 

A few fast facts about the growing field of AI:

  • AI market size is expected to reach $407 billion by 2027.
  • AI will have an estimated 21 percent net increase on the United States’ GDP by 2030.
  • AI is expected to see an annual growth rate of 37.3 percent from 2023 to 2030.
  • ChatGPT had one million users within the first five days of its public availability.
  • One in 10 cars will be self-driving by 2030.
  • 64 percent of businesses expect AI to increase productivity.
  • Companies are turning to AI to address labor shortages.

Everything from supply chains to marketing, product production, research, analysis, and more are fields that will adopt artificial intelligence within their business structures.

Chatbots, image-generating AI, and mobile applications are all major AI trends that will emerge in the coming years.

Jobs in AI are High-Paying

Professionals working in AI can expect higher-than-average salaries for this in-demand skill set.

According to data from Glassdoor, as of November 2023, the average salary of an AI engineer in the United States is $153,719 per year, with the potential for bonuses and profit sharing. Machine learning engineers and data scientists see average salaries of $151,158 and $178,515 annually.

Average salaries for AI roles, as of February 2024:

  • AI engineer: $127,986
  • AI researcher: $108,932
  • Machine learning engineer: $126,397
  • Robotics engineer: $96,013
  • Software engineer: $118,761     
  • Data scientist: $129,640

We are transitioning from the digital age — the dotcoms and ecommerce — to the AI era. A lot of routine jobs will be done by AI, like it or not. AI is here and this is the new era.

Bruce Huang

How Long Does It Take to Learn Artificial Intelligence?

There are different ways to learn the skills needed for a career in AI; the time it takes will depend on the route you choose.

Ask yourself how you intend to use AI in your career. Will you be focusing on AI as the main part of your work, or will it be an extra tool in your skills toolbox? This can determine which approach is the best one for you.

The self-taught approach

If you opt for the self-taught route, your schedule of completion will depend on your current background and knowledge of computer science, mathematics, statistics, and other related subjects; the complexity of topics you want to master; and how much time you have to study.

A variety of self-paced online courses are available. It can take several months to a year or more to gain a solid understanding of AI concepts and programming languages.

Enroll in a university program

Pursuing a formal degree through a university typically involves studying computer science, data science, or related fields. A bachelor’s degree typically takes three to four years to complete and includes comprehensive training

If you are a business leader seeking to understand how AI can be applied to your company, a professional certificate, such as Harvard Extension’s new AI graduate certificate, can offer you the expertise you need.

Whichever route you choose, continuous learning, practical application, and staying up-to-date with advancements are essential for pursuing a career in AI.

How Can I Start Learning AI?

If you are already a software engineer with foundational skills, you might just need to learn more sophisticated program languages and skills specific to AI. If you are new to the field, you will need a strong foundation in skills that will be the backbone for understanding AI.

  • Mathematics
    A basic understanding of linear algebra, calculus, and probability is essential. Concepts such as matrices and linear transformations from linear algebra are frequently used in AI algorithms.
  • Statistics
    Concepts like statistical significance, distribution, regression, and likelihood play a significant role in different AI applications.
  • Programming
    Understanding and learning to code is critical in learning AI.
  • Data structures and algorithms
    Both play a crucial role in organizing and categorizing information.

The depth of understanding and mastery required in these prerequisite areas can vary by different types of AI.

Common AI tools and programming languages

You are likely already familiar with ChatGPT, the AI chatbot capable of generating unique, human-like responses to users’ questions. The popular AI tool uses a large language model trained on vast amounts of data from the internet to answer questions, compose essays, offer advice, and write code in fluent, natural language.

Other AI tools growing in popularity are:

  • Dall-E2
    Transforms text prompts into computer graphics
  • Lumen5
    A video-creation tool that enables users to create video content using a simple drag-and-drop interface
  • Soundraw
    An automated music generator

If you are interested in creating the next wave of AI tools, you will also need an understanding of top coding languages:

  • Python
    This is frequently the go-to language for AI. Python is a powerful yet beginner-friendly language that aids in implementing machine learning models.
  • R
    This is another coding language predominantly employed in data science that supports performing statistical functions and creating data visualizations. The learning curve is deeper than Python, but it will allow you to dig deeper into AI and data science.
  • Java or C++
    These languages provide efficient low-level machine control for building scalable machine learning or artificial intelligence models.

Master the essential prerequisite skills

Whether you want to earn AI credentials to build and use AI-related tools or are a business leader or manager seeking to understand how to use AI tools, embarking on the journey to learn AI can be daunting. Having a strategic approach can make the process more structured and manageable.

Here are steps to guide you as you think about what you want to learn about AI.

1. Choose your focus

Start by deciding where to focus your efforts based on your career goals.

If you are interested in becoming a data scientist or AI engineer, focus on skill sets such as programming, data science, and understanding various machine learning algorithms you will need to become proficient in using AI techniques to solve real-world problems. 

If you prefer a research role, you will need to understand the theory behind AI and machine learning. This requires a solid grasp of mathematics, statistics, and theoretical computer science.

2. Apply your skills to projects

Applying the skills you learn in real-world projects solidifies your understanding and offers practical experience. This could be as simple as creating a machine learning model to predict house prices or as complex as developing a deep learning model for image recognition.

3. Join a community

Participating in forums like Stack Overflow or GitHub, joining AI groups on LinkedIn, or attending AI meetups and conferences can offer valuable learning opportunities.

By joining communities, you can stay updated on the latest trends, get help when you’re stuck, and network with other AI enthusiasts.

4. Keep learning! 

AI is a rapidly evolving field. Once you get the basics, it’s important to keep learning and improving your skills. Follow AI blogs, read research papers, take advanced courses, and always be on the look out for new ways to challenge yourself.

Start Learning AI Today!

Now that you have the fundamentals of AI, it’s time to dive into learning. 

Harvard Division of Continuing Education offers multiple ways to learn through Harvard Extension School degrees and certificates, all designed to provide you with the skills you need to pivot into AI or dive deeper.

Many Harvard Extension School programs are taught by industry leaders.

“Our faculty includes esteemed and distinguished Harvard John A. Paulson School of Engineering and Applied Sciences faculty. They are subject-matter experts who bring their industry experience directly to us,” Huang said.

Current Extension School offerings include:

The journey to learning AI is challenging but immensely rewarding. Keep your end goal in mind, stay committed to your journey, and you will be well on your way to becoming an accomplished AI professional before you know it.