Imbalanced classes put "accuracy" out of business. This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class. Standard accuracy no longer reliably measures performance, which makes model training much trickier. In this guide, we'll explore 5 effective ways to handle imbalanced classes. Let's say your client is a leading research hospitals, and they've asked you to train a model for detecting a disease based on biological inputs collected from patients.

Among the many reasons humans are bizarre among mammals (the dearth of body hair, the bipedalism, the fact that someone invented the turducken) is a sad shortcoming: You and I don't have sensory whiskers. Cats, dogs, raccoons, sea lions--you name a mammal and it's probably got special hairs sprouting out of its face. After all, whiskers are immensely useful. Rats use them to navigate the darkness, for instance, while a seal's whiskers detect the movements of fishy prey. Whiskers are all the rage in nature, so why not give them to robots?

When I was writing books on networking and programming topics in the early 2000s, the web was a good, but an incomplete resource. Blogging had started to take off, but YouTube wasn't around yet, nor was Quora, Twitter, or podcasts. Over ten years later as I've been diving into AI and machine learning, it is a completely different ballgame. There are so many resources -- it's difficult to know where to start (and stop)! To save you some of the effort I went through in researching all the different nooks and crannies of the web to find the best content; I've organized them into a big collection here.

More marketers are showing interest in artificial intelligence (AI), but it might be some time before it has a real impact on their business. According to an April 2017 survey of marketing leaders worldwide from CRM technology provider Salesforce, respondents expect to see improvements in efficiency and advancements in personalization over the next five years. Indeed, nearly 60% of respondents said they expect AI will improve the efficiency of campaign analytics, digital asset management and the collection of business insights across data and systems. More than 60% of marketers also envision leveraging AI to create dynamic landing pages and websites. And the same percentage expect the technology to have an impact on programmatic advertising and media buying.

To gain insights on how to program swarms of tiny robots, scientists are studying one of nature's most cohesive species--fire ants. When the insects work together, they're a force to be reckoned with. The small creatures are capable of using their bodies to create towering structures of more than 30 stacked ants and buoying themselves into a raft so buoyant it stays afloat even when a human hand forces it under water. Researchers at the Georgia Institute of Technology have been working for years to analyze how ants socially and physically form such elaborate globs without a leader or a discernable overall plan. In a study recently published in the journal Royal Society Open Science, high-speed cameras show ants banding together to form a tower around a slippery rod.

Engineers are studying human behaviour in great detail in order to make robots that not only look like us, but can also understand us and interact with us in socially acceptable ways. These studies are teaching us many things about our own human nature, as my recent paper explains. The robots in films like Blade Runner are very humanlike, with thoughts and feelings, motives and desires. But making robots that are just like us is a huge challenge. Technical limitations make it currently impossible to make robots identical to humans, although Hiroshi Ishiguru has made a geminoid (a humanlike robot that looks like himself), and David Hanson has made a number of impressive android heads.

A time series is a sequentially indexed representation of your historical data that can be used to solve classification and segmentation problems, in addition to forecasting future values of numerical properties, for example, air pollution level in Madrid for the last two days. This is a very versatile method often used for predicting stock prices, sales forecasting, website traffic, production and inventory analysis, or weather forecasting, among many other use cases. Soon, BigML will have time series as a new resource. Following our mission of democratizing machine learning and making it easy for everyone, we will provide new learning material for you to start with time series from scratch and become a power user over time. We start by publishing a series of six blog posts that will progressively dive deeper into the technical and practical aspects of time series with an emphasis on time series models for forecasting.

Machine learning is a hot topic for developers, but where can one learn about how to use the technology? A lot depends on your current background and your long-term goals. I have already written about the basic differences between machine-learning techniques, but this was done at a relatively high level. Getting into the details can range from learning about machine-learning methodologies at an abstract level to examining deep-learning frameworks used to develop applications. Here, we'll take a more detailed look at some of the online resources available to you, and include links to websites with much more information about machine-learning classes, frameworks, and resources.

Most economists agree that advances in robotics and AI over the next few decades are likely to lead to significant job losses. But what's less often considered is how these changes could also impact social mobility. A new report from UK charity Sutton Trust explains the danger, noting that unless governments take action, the next wave of automation will dramatically increase inequality within societies, further entrenching the divide between rich and poor. The are a number of reasons for this, say the report's authors, including the ability of richer individuals to re-train for new jobs; the rising importance of "soft skills" like communication and confidence; and the reduction in the number of jobs used as "stepping stones" into professional industries. For example, the demand for paralegals and similar professions is likely to be reduced over the coming years as artificial intelligence is trained to handle more administrative tasks.

A quick, hassle-free way to stay on top of robotics news, our robotics digest is released on the first Monday of every month. Sign up to get it in your inbox. Let's kick off our June review by looking at some great new robotics research and development in action: Inspired by arthropod insects and spiders, Harvard Professor George Whitesides and Alex Nemiroski--a former postdoctoral fellow in Whitesides' Harvard lab--have created a type of semi-soft robot capable of standing and walking. The team also created a robotic water strider capable of pushing itself along the liquid surface. The robots are described in a recently published paper in the journal Soft Robotics.