What is the difference between artificial intelligence and machine learning, and when would you use one over the other?

How can I tell if my machine learning model is working for me? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. Businesses must eventually sustain themselves beyond external funding sources by turning profits. What's talked about less than ML itself is how one can leverage machine learned models to generate profits. I've got a whole sub-section of the book that shows how to account for the revenues and costs of building a machine learning system, using traditional accounting concepts. Essentially, data learning loops bring about profit and create investment opportunities for the AI-First vendor: better predictions can lead to more automation, which lowers operating costs, which in turn means more gross profit that can be invested in research and development (models and data), leading to better predictions, and so on.

How are data and AI transforming transportation? While our growing dependencies on mobile phones stand to threaten road safety and increase rates of distracted driving, other technology innovations can work in safety's favor. Developments in 5G networks, autonomous vehicles, and artificial intelligence are poised to transform the way we drive and the safety of our roads. Today, road crews have to physically go on-site to inspect a problem and determine what next steps are required. But through new video and sensor data, road maintenance crews will receive alerts of life-threatening hazards faster than ever.

What makes AI/machine learning successful within an organization? Don't ask questions whose answers are not actionable. We see so many efforts in AI where deep learning tools are being used as a hammer looking for any type of nail. We see this a lot in healthcare - for example in identifying high risk patients in untreatable conditions. Great - so we can sort out which patients are at higher risk but unless there is an action they can take we just caused a tremendous amount of anxiety in a lot of people - and of course anxiety and stress are disease drivers - so net net this causes harm.

Is the term Machine Intelligence going to take over the terms Artificial Intelligence and Machine Learning? Machine intelligence, artificial intelligence and machine learning are not different terms for the same thing. They're related but distinct from each other. It's important for us all to recognize the nuanced differences among them, so we can do a much better job of figuring out which one, or which combination of them, is best for a specific undertaking. If, for example, you think you're using machine intelligence but are really just using automation, you're missing out on the potential MI has to offer. My States Title colleague Andy Mahdavi and I put together a list designed to make the distinctions as clear as possible.

What's the best way for businesses to leverage AI and data analytics in this software-driven market? With all the hand-waving around "AI," it's easy to get buzzword exhaustion. But it's really important that we don't lose sight of the reality: machine learning will fundamentally change the way we illuminate and understand engineering performance. Consider how things like sabermetrics have revolutionized the way GMs, coaches and players understand and unlock high performance[1]. Now multiply that impact by some big number, given how essential software engineering to the modern world, and how much sheer activity data (issues worked, code committed, scans run, deployments made, etc. etc.) is produced in the course of building software.

Quality control certification, checked guarantee of standard of company product. How is AI/ML reinventing existing software categories? I discussed this at length in a blog post. The following short answer is adapted from that TL;DR piece. Wherever you have large data sets, coupled with lots of human processing, there's an opportunity for algorithms to intervene.

Drone point of view of a Tractor spraying on a cultivated field. What does the future hold for machine learning/AI within the agricultural sector? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. From the food we eat to the clothing we wear and the gasoline in millions of cars, agriculture touches our daily existence like few other industries. This year alone, we've experienced several major supply shocks--including massive flooding in the American Midwest, a trade war, and the outbreak of serious crop and animal diseases in China--all of which highlight just how unpredictable the system is. Fortunately, we've also reached the point where there are nearly infinite amounts of data available to understand and forecast the complex interplay between global agricultural markets.

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).

What are the risks and benefits of artificial intelligence? It's a complicated topic, but I'll try to unpack a few key points here. Let's start with a quick definition: AI is the simulation of human intelligence by machines. Example of AI systems used regularly in developed countries include Amazon's Alexa, smart replies in Gmail, Chatbots, predictive searches in Google, and recommendations. At a baseline level, AI helps improve our everyday lives by solving pain points, streamlining processes, and advancing human knowledge.