In the last few years, machine learning has been heavily promoted by sales and marketing teams as being the "holy grail" and as a set of technologies that will solve everyone's problems. After you've finished reading this post, you'll be able to cast a critical eye over any machine learning literature in the future and arrive at your own conclusions. Download our Machine Learning Industry Guide to identify specific ways in which machine learning software and platforms can benefit your business with industry insight. Machine learning is everywhere at the moment, so, let's bust some the myths that have been getting circulated in the past few years. Machines are going to take over the world!

Artificial Intelligence (AI) is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition. Artificial Intelligence, often abbreviated as "AI", may connote robotics or futuristic scenes, AI goes well beyond the automatons of science fiction, into the non-fiction of modern day advanced computer science. Professor Pedro Domingos, a prominent researcher in this field, describes "five tribes" of machine learning, comprised of symbolists, with origins in logic and philosophy; connectionists, stemming from neuroscience; evolutionaries, relating to evolutionary biology; Bayesians, engaged with statistics and probability; and analogizers with origins in psychology. Recently, advances in the efficiency of statistical computation have led to Bayesians being successful at furthering the field in a number of areas, under the name "machine learning". Similarly, advances in network computation have led to connectionists furthering a subfield under the name "deep learning". Machine learning (ML) and deep learning (DL) are both computer science fields derived from the discipline of Artificial Intelligence.

In general, neural networks are not currently capable of learning tasks in a sequential fashion. When a novel, unrelated task is learnt by a neural network, it substantially forgets how to solve previously learnt tasks. One of the original solutions to this problem is pseudo-rehearsal, which involves learning the new task while rehearsing generated items representative of the previous task/s. This is very effective for simple tasks. However, pseudo-rehearsal has not yet been successfully applied to very complex tasks because in these tasks it is difficult to generate representative items. We accomplish pseudo-rehearsal by using a Generative Adversarial Network to generate items so that our deep network can learn to sequentially classify the CIFAR-10, SVHN and MNIST datasets. After training on all tasks, our network loses only 1.67% absolute accuracy on CIFAR-10 and gains 0.24% absolute accuracy on SVHN. Our model's performance is a substantial improvement compared to the current state of the art solution.

The goal of each machine is to find a good predictor for its own task, based on its own local data, as well as communicating with the other machines so as to leverage the similarity to other related tasks. Distributed multi-task learning lies between a homogeneous distributed learning setting (e.g. Shamir and Srebro, 2014), where all machines have data from the same source distribution, and inhomogeneous consensus problems (e.g. Ram et al., 2010; Boyd et al., 2011; Balcan et al., 2012), where each machine sees data from a different source, but the goal is to reach a single consensus predictor. In many distributed learning problems, different machines do indeed see different distributions.

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.