Didi Chuxing may be one of the most electrifying companies in the world right now. The Chinese ride-sharing service just received a $1 billion dollar investment from Apple, and is poised to grow significantly in the coming year. Which is why we are thrilled to partner with Didi on the global Di-Tech Algorithm Competition, which boasts a $100K prize for the winner, and perhaps more exciting, the opportunity to join the Didi Research Lab and work on innovations that will affect hundreds of millions of riders.
Excited to talk about Machine Learning, Data Science, and Artificial Intelligence? Great! Us too. Ready to discover the key influencers you need to follow to stay current on all the latest happenings in these fields? Excellent! We’ve got the resources you need.
You see, at Udacity, we talk a great deal about Machine Learning, Data Science, and Artificial Intelligence. We talk to each other, we talk to our students, and we talk to the world at large. If you’re engaging in these same kinds of conversations, then you know how incredibly exciting these spaces are—everywhere you turn, there are more amazing voices with more amazing insights to contribute.
But honestly, it can be a little overwhelming!
One of the most fascinating things about Machine Learning—and those who work in the field—is the remarkable scope of what’s so rapidly becoming possible because of this technology. The applications are almost limitless, and we witness this every time we talk to a Machine Learning specialist.
Lauren Edelson is a perfect example, as you’ll see from her responses to our questions below. Lauren was gracious enough to share a great deal of insight and experience with us, and her answers cover a wide range of subjects, including computer science, the relationship of bioinformatics to Machine Learning, and Machine Learning impacts on fraud detection. When queried specially about all the different ways Machine Learning informs our modern lives, she mentioned everything from healthcare and Uber, to Spotify and the tracking of presidential election swing votes!
If you have any lingering doubts as to whether Machine Learning is an exciting field, banish those doubts, and read on!
The excitement around our Machine Learning Nanodegree program has been amazing to witness, and the vitality and dynamism in the space right now is pretty incredible. There are so many fascinating storylines in the world of Machine Learning, it’s sometimes hard to even know what to focus on. But unquestionably, the people working in this field—those individuals at the cutting-edge of these new technologies—are a critical part of the Machine Learning narrative. One of the things I find personally really exciting is how many women are shaping the future of Machine Learning. My former colleague Katie Malone is a wonderful example of this, and I’m very grateful she was able to take some time recently to talk Machine Learning with us!
In our previous post 5 Skills You Need to Become a Machine Learning Engineer, we identified the key skills you need to succeed in this field. Now, we’re going to address one of the most common questions that comes up from students interested in Machine Learning: Which programming language(s) do I need to know?
The answer may surprise you. It doesn’t really matter!
As long as you’re familiar with the Machine Learning libraries and tools available in your chosen language, the language itself isn’t as important. A variety of Machine Learning libraries are available in different programming languages. Depending on your role within a company, and the task you’re trying to accomplish, certain languages, libraries and tools can be more effective than others.
Interested in Machine Learning? You are not alone! More people are getting interested in Machine Learning every day. In fact, you’d be hard pressed to find a field generating more buzz these days than this one. Machine Learning’s inroads into our collective consciousness have been both history making (as when AlphaGo won 4 of 5 Go matches against the world’s best Go player!) and hysterical (Machine Learning Algorithm Identifies Tweets Sent Under The Influence Of Alcohol), but regardless how you discovered it, one thing is clear: Machine Learning has arrived.
That said, it’s one thing to get interested in Machine Learning, it’s another thing altogether to actually start working in the field. This post will help you understand both the overall mindset and the specific skills you’ll need to start working as a Machine Learning engineer.
As we near what could be the deciding match in the history-making meeting between AlphaGo and Lee Se-dol, we pause to review what’s taken place so far, and what it all means.
AlphaGo, Google’s Go-playing Artificial Intelligence software, has done the unthinkable. It has taken a 2-0 lead against Lee Se-dol, the world’s best Go player, in a series of matches that have already rewritten human history as we know it. And there is still more to come!
This is no publicity stunt. The technology is real, the victory genuine, and the change monumental. We’ve woken to a new morning for humankind—a world where machines can learn. By utilizing Deep Learning techniques, DeepMind (Google’s AI lab) has essentially been able to “train” the software to teach itself how to play, to improve with every move, and to ultimately master one of the most complex games ever invented.