How is machine learning changing the world of education? This is a big question. I believe that the application of new technologies from the fields of Machine Learning (ML) and Artificial Intelligence (AI) have the capability of transforming education but that there will be more hype than results in the short run--much like the case of other transformative educational technologies in the past (I am old enough to remember when the filmstrip and, then, the VHS cassette were supposedly going to revolutionize the delivery of instruction in our classrooms). Perhaps the area of promise that has garnered the most attention is "personalized learning." This can be a slippery concept to define (for example, some observers consider Individualized Education Plans as a form of personalized learning--one that requires no particular technology) but I am using it here to mean the use of educational technology to permit students in the same classroom to learn different curricular content, tailored to their own pace and level of mastery (e.g. the Summit Learning platform).
How can startups compete in Deep Learning when the tech giants (Google, Amazon, Baidu, Microsoft, Apple) have so much more data? There is this narrative out there that it is "all about the data," "whoever has the most data wins…" I may have subscribed to that theory in the past but I now think it is largely wrong, and may have been perpetuated by the big companies to tout their advantage. Deep Learning tools and frameworks are so nascent, and the skill set so rare, that meaningfully better algorithms are possible, and make a huge difference. For example, I believe Blue Hexagon has those. When there are literally 100's of thousands of new malware variants PER DAY, it just makes sense that IF you could get neural networks to analyze the traffic, at line speed, it would be order of magnitude better than the current signature and sandbox based approaches.
Is Uber legitimately more evil than its competitors, or is it just getting worse press? Of these, IP infringement (or at least allegations of it) is commonplace in the tech industry. Many companies (Airbnb, Zenefits, Microsoft) have also played fast and loose with legal boundaries especially in their early growth days -- but not to a degree where they've built tools specifically to hide deviant practices. Tech CEOs losing their temper publicly is practically unheard of, even though we did have the CEO of Zenefits going ballistic on a prospective hire. Even Steve Jobs' outbursts happened behind closed doors, and were depicted in a manner that further lionized him.
Why is Machine Learning difficult to understand? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. I'm usually the first person to say something is hard, but I'm not going to here. Learning how to use machine learning isn't any harder than learning any other set of libraries for a programmer. The key is to focus on using it, not designing the algorithm. Look at it this way: if you need to sort data, you don't invent a sort algorithm, you pick an appropriate algorithm and use it right.