Meta, Alphabet, and Microsoft might have an intense rivalry, but all three tech giants share one thing in common: repeating the words “artificial intelligence” over and over again during their respective earnings calls.
Meta was once “all in” on the metaverse, throwing billions of dollars at augmented reality (AR) and virtual reality (VR) hardware and software. Those metaverse bets continue (albeit in a more limited way), but now Meta CEO Mark Zuckerberg is emphasizing his company’s work with A.I. and machine learning.
Meanwhile, Microsoft’s collaboration with A.I. company OpenAI, which resulted in an A.I. chatbot integrated with the Bing search engine, seems to be paying off; the company now wants to imbue more services with A.I., including its Azure cloud infrastructure. Microsoft CEO Satya Nadella is betting big that A.I. may even allow his company to claw crucial cloud market-share away from dominant Amazon Web Services (AWS).
And then there’s Alphabet, Google’s umbrella company, which spent years (and billions) researching A.I. in a slow and cautious way. Given its rivals’ A.I. aspirations, however, it seems that Alphabet is jamming down the accelerator on its own A.I. efforts and products—with mixed results.
Of course, other tech companies large and small are embracing the A.I. hype in different ways. For tech professionals everywhere, that means at least some knowledge of A.I. may become necessary if you want to land a job and/or climb the ranks of your current organization. Here are some top-level learning resources for beginning your A.I. journey.
Starting Off with A.I.
Totally new to all things A.I.? That’s totally fine—everyone needs to start somewhere. Although it’s old at this point, this article by Hacker Noon gives a great overview of A.I., including terms such as deep learning, machine learning, and more. On a more practical level, Udacity’s Introduction to Artificial Intelligence breaks down A.I. fundamentals and applications.
If you’re seeking a job in A.I./machine learning and need a quick course, check out Google’s Machine Learning Crash Course, which features lessons, exercises, and real-world case studies. Microsoft offers a similar high-level overview of A.I. (yes, there’s an emphasis here on Microsoft products, but there’s also quite a bit on “universal” A.I. aspects).
Continuing Your A.I. Journey
Once you understand the fundamentals of A.I., you can decide whether you should take classes to expand your knowledge. Stanford Online offers a variety of courses in everything from natural language processing to machine learning models and reinforcement learning. Although these courses cost money, you may be able to persuade your current employer to pay for them—after all, you’re learning intensely valuable skills for the future.
Udemy and Coursera also offer practical A.I. courses. Udacy has advanced courses in computer vision, natural language processing, deep reinforcement learning, and much more, including some industry specialization (such as A.I. in healthcare). At this point, of course, you’re making a more sizable time and resource commitment to learning; make sure you can carve out time during your week to actually engage in the coursework.
More A.I. Learning
No A.I. learning track is complete without a visit to OpenAI, the kinda-nonprofit foundation (it’s complicated and getting more complicated by the month) that started out with the mission of creating an ethical framework for A.I. development, created the ChatGPT chatbot, and helped kick off the current A.I. “arms race.”
OpenAI has a very extensive tutorial in deep reinforcement learning (i.e., deep RL) as well as its own API and tools. As long as OpenAI’s products form the backbone of so much current A.I. work, it’ll be important to know how these things work.
If you consider yourself super-advanced in everything related to A.I. and machine learning, and want to build out your knowledge still further, check out Bloomberg’s Foundations of Machine Learning, a free online course. For experts, it offers a very deep dive into machine learning and statistical analysis.