Like a true Midwesterner, my dad has been feuding with the squirrels in his backyard for years. Every few months, he comes home with a new "squirrel-proof" bird feeder, each more expensive than the previous, each one promising to finally do the trick. My mom rolls her eyes at the pile of hardware-store receipts and discarded feeders. I shake my head watching this all play out--knowing full well those feeders never stood a chance. Walk down the birdseed aisle in any hardware store and you'll find an entire product category promising "squirrel-proof" solutions.

In 2018, Silicon Valley, like Hamlet's engineer, was hoist with its own petard. Citizens were panicking about data privacy, researchers were sounding alarms about artificial intelligence, and even industry stakeholders rebelled against app addiction. Policymakers, meanwhile, seemed to take a renewed interest in breaking up big tech, as a string of congressional hearings put CEOs in the hot seat over the products they made. Everywhere, techies were grasping for answers to the unintended consequences of their own creations. So the Omidyar Network--a "philanthropic investment firm" created by eBay founder Pierre Omidyar--set out to provide them.

Data scientists naturally use a lot of machine learning algorithms, which work well for detecting patterns, automating simple tasks, generalizing responses and other data heavy tasks. As a subfield of computer science, machine learning evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Over time, machine learning has borrowed from many other fields, including statistics. Most of today's algorithms have a history in various mathematical subfields. Many of these subfields overlap but I've taken a stab at categorizing some popular algorithms.

Most of us can position ourselves somewhere on The AI Fear Continuum. For nearly 100 years, science fiction writers have prepared us for the worst as they explored scenarios from Hollywood 1927's Metropolis to adaptations of Isaac Asimov's I, Robot and Philip K Dick's Do Androids Dream Electric Sheep (famously the basis for Blade Runner). We're ready to be tricked (Alien), enslaved (The Matrix), killed (West World) or even have our whole species wiped out (Terminator). Conversely, news stories about AI feel overwhelmingly positive, informing us that AI is saving the day in sectors as diverse as health care, law and climate change. Marketing professionals have two key points to consider; claims that AI can help to make marketing more creative and effective, and claims that it will make many of our job roles surplus to requirements.

Summary: The data science press is so dominated by articles on AI and Deep Learning that it has led some folks to wonder whether Deep Learning has made traditional machine learning irrelevant. Here we explore both sides of that argument. On Quora the other day I saw a question from an aspiring data scientist that asked – since all the competitions on Kaggle these days are being won by Deep Learning algorithms, does it even make sense to bother studying traditional machine learning methods? Has Deep Learning made traditional machine learning irrelevant? I can understand on a couple of levels why he asked the question.

Summary: The data science press is so dominated by articles on AI and Deep Learning that it has led some folks to wonder whether Deep Learning has made traditional machine learning irrelevant. Here we explore both sides of that argument. On Quora the other day I saw a question from an aspiring data scientist that asked – since all the competitions on Kaggle these days are being won by Deep Learning algorithms, does it even make sense to bother studying traditional machine learning methods? Has Deep Learning made traditional machine learning irrelevant? I can understand on a couple of levels why he asked the question.

No longer was it an esoteric discipline commanded by the few, the proud, the data scientists. Now it was, in theory, everyone's business. Machine learning's power and promise, and all that surrounded and supported it, moved more firmly into the enterprise development mainstream. GET A 15% DISCOUNT through Jan.15, 2017: Use code 8TIISZ4Z. Cut to the key news in technology trends and IT breakthroughs with the InfoWorld Daily newsletter, our summary of the top tech happenings.

No longer was it an esoteric discipline commanded by the few, the proud, the data scientists. Now it was, in theory, everyone's business. Machine learning's power and promise, and all that surrounded and supported it, moved more firmly into the enterprise development mainstream. That movement revolved around three trends: new and improved tool kits for machine learning, better hardware (and easier access to it), and more cloud-hosted, as-a-service variants of machine learning that provided both open source and proprietary tools. Once upon a time, if you wanted to implement machine learning in an app, you had to roll the algorithms yourself.

No longer was it an esoteric discipline commanded by the few, the proud, the data scientists. Now it was, in theory, everyone's business. Machine learning's power and promise, and all that surrounded and supported it, moved more firmly into the enterprise development mainstream. That movement revolved around three trends: new and improved tool kits for machine learning, better hardware (and easier access to it), and more cloud-hosted, as-a-service variants of machine learning that provided both open source and proprietary tools. Once upon a time, if you wanted to implement machine learning in an app, you had to roll the algorithms yourself.

On Quora the other day I saw a question from an aspiring data scientist that asked – since all the competitions on Kaggle these days are being won by Deep Learning algorithms, does it even make sense to bother studying traditional machine learning methods? Has Deep Learning made traditional machine learning irrelevant? I can understand on a couple of levels why he asked the question. First if you look at the recently completed Kaggle problems it's easy to draw the conclusion that deep learning is the only way to win. Second, if you follow the data science literature we are being bombarded by information about advancements in deep learning, especially as it's implemented in AI, with very little new coming out about all the other algorithms that make up our tool kit.