Many companies use the term artificial intelligence, or AI, as a way to generate excitement for their products and to present themselves as on the cutting edge of tech development. But what exactly is artificial intelligence? And how will it help the development of future generations? Find out the answers to these questions and more in AI 101, a brand new FREE report from BI Intelligence, Business Insider's premium research service, that describes how AI works and looks at its present and potential future applications.

How to learn complex concepts in Machine Learning? In today's ocean of information about Machine Learning and Artificial Intelligence, it is easy to feel lost, and to label those fields as impossible to learn. That's why I decided to share my personal experience and guide you with some simple techniques that can boost your creativity and effectiveness. After applying them, your process of learning will become much faster and more pleasant. Those steps led myself to the success.

The major reason R is growing rapidly and is such a huge success, is because of its strong community. At the center of this is R's package ecosystem. These packages can be downloaded from the Comprehensive R Archive Network, or from bioconductor, github and bitbucket. At Rdocumentation you can easily search packages from CRAN, github and bioconductor that will fit your needs for the task at hand. Next to the package ecosystem R, you can also easily find help and feedback on your R endeavours. First of all there is R's built-in help system which you can access via the command?

For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies -- a category that includes the steam engine, electricity, and the internal combustion engine. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models. The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) -- that is, the machine's ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it's given. Within just the past few years machine learning has become far more effective and widely available. We can now build systems that learn how to perform tasks on their own. Why is this such a big deal? First, we humans know more than we can tell: We can't explain exactly how we're able to do a lot of things -- from recognizing a face to making a smart move in the ancient Asian strategy game of Go. Prior to ML, this inability to articulate our own knowledge meant that we couldn't automate many tasks. Second, ML systems are often excellent learners.

According to the 1960s pop hit, it was the year 2525 when we all started to notice dramatic changes to the world we live in. A conference held to debate the future of England's exam system heard that – in exam halls – the time might be a touch nearer. That, according to one projection put forward at the conference organised by the Westminster Education Forum, is the date by which humans will be removed from the marking of the exam system and artificial intelligence will take over. The projection is extreme, but the system is already on course for computers to have a far bigger role in the marking of the system. It throws up some interesting dilemmas.

Recently, I was wondering how I could escape the web development bubble for a while. The last year was all about those subjects, so I needed a side project to escape it for a while and to get into a zen mode of learning again. How did I get to machine learning? A couple of months ago, I started to listen to the Machine Learning Guide podcast. I found out about it by chance and highly recommend it to get you an introduction for machine learning. Tyler Renelle is doing an amazing job to get you excited about the topic. I almost feel like I am following him on the same path to learn about machine learning now. Even though I didn't actively plan about learning ML, it was interesting to hear about all those foreign concepts. There it was again; this excitement when everything is unexplored. I felt like a whole new world opened up in front of me. It was the same feeling when I finally got the foot into web development. As I read about a couple of machine learning articles, the course on Machine Learning by Andrew Ng was the by far most recommended to get started in machine learning. I have never taken an online course from start to end before, even though I actively give these online courses myself, but I decided to give it a shot this time. Fortunately, the course had started one week ago. So I enrolled in it and by now finished it.

Sentences handed down by artificial intelligence would be fairer, more efficient, transparent and accurate than those of sitting judges, according to Swinburne researchers. Dean of Swinburne Law School, Professor Dan Hunter, and Swinburne researcher Professor Mirko Bagaric say artificial intelligence (AI) could improve sentencing procedures by removing emotional bias and human error. In a paper for the Criminal Law Journal, Professors Bagaric and Hunter argue that AI sentencing would better identify, sort and calibrate all the variables associated with sentencing, including criminal history, education, drug/alcohol use, emotional motivations and employment. The pair argue that sentencing decisions are often influenced by more than 200 considerations, many of which are variables which have been established prior to court hearings. Professor Bagaric says subconscious bias plays a large part in sentencing in which judges or magistrates hand down harder penalties to offenders of a particular race or background.

Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through time, and are influenced by actions of an agent. As the representation learned captures the variation in the environment generated by agents, this kind of representation is particularly suitable for robotics and control scenarios. In particular, the low dimension helps to overcome the curse of dimensionality, provides easier interpretation and utilization by humans and can help improve performance and speed in policy learning algorithms such as reinforcement learning. This survey aims at covering the state-of-the-art on state representation learning in the most recent years. It reviews different SRL methods that involve interaction with the environment, their implementations and their applications in robotics control tasks (simulated or real). In particular, it highlights how generic learning objectives are differently exploited in the reviewed algorithms. Finally, it discusses evaluation methods to assess the representation learned and summarizes current and future lines of research.

We study and empirically optimize contextual bandit learning, exploration, and problem encodings across 500+ datasets, creating a reference for practitioners and discovering or reinforcing a number of natural open problems for researchers. Across these experiments we show that minimizing the amount of exploration is a key design goal for practical performance. Remarkably, many problems can be solved purely via the implicit exploration imposed by the diversity of contexts. For practitioners, we introduce a number of practical improvements to common exploration algorithms including Bootstrap Thompson sampling, Online Cover, and $\epsilon$-greedy. We also detail a new form of reduction to regression for learning from exploration data. Overall, this is a thorough study and review of contextual bandit methodology.

With this article, we, OpenDataScience, launch an open Machine Learning course. This is not aimed at developing another comprehensive introductory course on machine learning or data analysis (so this is not a substitute for fundamental education or online/offline courses/specializations and books). The purpose of this series of articles is to quickly refresh your knowledge and help you find topics for further advancement. Our approach is similar to that of the authors of Deep Learning book, which starts off with a review of mathematics and basics of machine learning -- short, concise, and with many references to other resources. The course is designed to perfectly balance theory and practice; therefore, each topic is followed by an assignment with a deadline in a week. You can also take part in several Kaggle Inclass competitions held during the course.