Almost every HR vendor I talk with claims to have artificial intelligence (AI)-based solutions, predictive analytics, chatbots or some other form of algorithmic solution to make HR better. As I've learned about all these products and started to see them in action, let me give you tips on what to look for. In the recruitment market, data is really driving our future. Thanks to the ubiquitous nature of social networks and dozens of intelligent sourcing and assessment tools, our research shows, AI is creating significant value. As you search for new recruiting tools (sourcing, candidate assessment, intelligent chatbots and mobile recruiting platforms), ask the vendor to show you how its AI works.

In the past decade, terms like machine learning, artificial intelligence, and data mining are becoming greater buzzwords as computing power, APIs, and the massively increased availability of data enable new technologies like self-driving cars. However, we've been using methodologies like machine learning in psychometrics for decades. So much of the hype is just hype. Unfortunately, there is no widely agreed-upon definition, and as Wikipedia notes, machine learning is often conflated with data mining. A broad definition from Wikipedia is that machine learning explores the study and construction of algorithms that can learn from and make predictions on data.

In the 18th century, those operating at the highest levels of society, from London to Moscow, needed to be able to speak French, then the language of status, the nobility, politics, intellectual life and modernisation. A hundred years later, British advances in industry, science and engineering meant that English succeeded French: a tongue with West Germanic origins replaced a romance language as the means of conducting business and diplomacy on the international stage. Today, even in some parts of China, English is still used as the global lingua franca, a leveller that enables deals to get done and the wheels of commerce and technology to spin. Around a decade ago, another type of language – one that was written rather than spoken – was held up as a deterministic factor for those seeking to gain influence or advantage in the digital age: coding. Its champions proselytised that proficiency in programming would determine employability and access to a thrusting, energetic entrepreneurial future.

Welcome to the first entry in our "Innovations for Educators" series, spotlighting interesting technologies that have the potential to amplify and complement the work done by educators. Artificial intelligence (AI) is all around us. From self-driving cars to voice and facial recognition technologies to computers that can compose music, AI stands to offer unprecedented convenience in our personal lives. At the same time, AI is also transforming the world of work. From helping lawyers scan hundreds of documents and predicting which are the most useful to a case, to helping doctors analyze massive amounts of data to develop treatment plans for patients, AI can perform in seconds tasks that would normally take hours of human effort.

Auto ML is a powerful concept for the next generation of AI tools. It's part of a general movement to extend AI-based automation to data science. Just within the past several years, the possibilities created by machine learning and deep learning have exploded across many industries. Unfortunately, machine learning is difficult and tedious, and there aren't enough qualified practitioners. Although many companies are envisioning a future of ubiquitous AI, a lack of data scientists experienced with machine learning will prevent them from making that vision a reality.

We present a framework and analysis of consistent binary classification for complex and non-decomposable performance metrics such as the F-measure and the Jaccard measure. The proposed framework is general, as it applies to both batch and online learning, and to both linear and non-linear models. Our work follows recent results showing that the Bayes optimal classifier for many complex metrics is given by a thresholding of the conditional probability of the positive class. This manuscript extends this thresholding characterization -- showing that the utility is strictly locally quasi-concave with respect to the threshold for a wide range of models and performance metrics. This, in turn, motivates simple normalized gradient ascent updates for threshold estimation. We present a finite-sample regret analysis for the resulting procedure. In particular, the risk for the batch case converges to the Bayes risk at the same rate as that of the underlying conditional probability estimation, and the risk of proposed online algorithm converges at a rate that depends on the conditional probability estimation risk. For instance, in the special case where the conditional probability model is logistic regression, our procedure achieves $O(\frac{1}{\sqrt{n}})$ sample complexity, both for batch and online training. Empirical evaluation shows that the proposed algorithms out-perform alternatives in practice, with comparable or better prediction performance and reduced run time for various metrics and datasets.

Programming artificial intelligence is an exciting prospect. Building AI apps from the ground up is an arduous endeavor, but thankfully with the development of advanced AI frameworks, building programs with AI capabilities is easier than ever before. Before getting started with AI, you'll need to decide which programming language you'd like to use to undertake your project. Which language you choose will depend on a multitude of factors, such as your level of programming knowledge, the programming languages you are familiar with, and the open-source frameworks you want to take advantage of. The most popular programming languages for AI applications is, according to InfoWorld, Python, Java, Lisp, Prolog, and C .

About this course: This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience.

Google, Microsoft, IBM and AWS are just some of the tech behemoths taking on machine learning, creating APIs and developing a number of sophisticated deep learning frameworks. As new areas of technology are exploited and pulled into the mainstream, the demand for skilled workers begins to rise. This is our guide to getting started with machine learning. First coined in 1959 by Arthur Samuel - a computer scientist at IBM at the time - "Machine Learning" essentially enables computers to learn without being directly programmed. Machine learning (ML) is fundamentally the application of AI that we recognise today, for example, machines performing'smart' tasks.

WalkMe Ltd., maker of a platform for understanding and improving user experience, has added predictive analytics capabilities to its intelligent assistant technology that interprets user behavior to predict next actions and provide context-sensitive responses. The company primarily targets its technology at e-commerce scenarios in which abandonment is a common problem, as well as at internal uses such as helping employees fill out forms or complete online training courses. "We saw that most users don't ask for help, so our engagement engine understands their problems and gives guidance automatically," said Rephael Sweary, WalkMe's co-founder and president. WalkMe AI Predictive Analytics works with any enterprise software or mobile application to observe user interactions and determine the statistical likelihood that a person will abandon a process because of confusion or complexity. The software collects hundreds of data points per second in real-time, including information that isn't personally identifiable such as browser type and time of day.