Do you want better traffic lights, or do you want autonomous, self-driving cars? That's the kind of dilemma the talent development function must face when it comes to applying artificial intelligence to helping people do their best work every day and enabling organizations to achieve business goals. The conversation around AI within TD is accelerating. Technology providers are introducing AI-enabled capabilities within their applications, and TD professionals are beginning to work with open-source AI tools, such as Google's TensorFlow and the Microsoft Cognitive Toolkit. However, the industry has more questions than answers, chief among them being "Can AI really make a positive impact on organizational learning?" To answer that question, TD professionals need to decide whether they will jump into finding a killer app (in other words, a high-impact, full-proof AI use case) or first take a breath and reflect on how AI compares to other technologies that have redefined our industry.

By the end of the course, you'll have the keys to the entire R Machine Learning Kingdom!

By the end of the course, you'll have the keys to the entire R Machine Learning Kingdom!

Hi, I'm Mike Brandt, Cofounder and CEO of 33A, an AI design firm helping companies apply AI. In the past I was a lecturer in higher education on emerging technology and business innovation, and led teams in organizations that developed new services for their company. This canvas is the summary of a book I've written, back then for the heavily disrupted marketing and advertising industry. Today digital technology and AI are changing all work and all our lives. For more people to see themselves able driving that change and creating opportunities.

Hi, I'm Mike Brandt, Cofounder and CEO of 33A, an AI design firm helping companies apply AI. In the past I was a lecturer in higher education on emerging technology and business innovation, and led teams in organizations that developed new services for their company. This canvas is the summary of a book I've written, back then for the heavily disrupted marketing and advertising industry. Today digital technology and AI are changing all work and all our lives. For more people to see themselves able driving that change and creating opportunities.

What can (and can't) be done with it? How to start creating AI methods? Join the Elements of AI course, online and free, created by Reaktor, an AI and tech partner for modern businesses and the University of Helsinki, the oldest institution of academic education in Finland. The course combines theory with practical exercises and can be completed at your own pace. At the end of the course you will also receive a certificate of participation.

Artificial intelligence and automation are continuing to drill deeper into our society. Yet, there seems to be a disconnect between the people designing and implementing these systems, and those who will be most affected by the outcome. The reported median annual salary for an AI programmer in the UK in 2019 is currently around £60,000. Meanwhile, the reported median annual salary for all workers in the UK is reportedly around £36,611. Automating routine operations presents a lot of benefits.

Editorial Note: This post is published with identical content on the Google DeepVariant blog. In this post, we formulate DNA sequencing error correction as a multiclass classification problem and propose two deep learning solutions. Our first approach corrects errors in a single read, whereas the second approach, shown in Figure 1, builds a consensus from several reads to predict the correct DNA sequence. Our Colab notebook tutorial implements the second approach using the Nucleus and TensorFlow libraries. Our goal is to show how Nucleus can be used alongside TensorFlow for solving machine learning problems in genomics.

Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn how to engineer features and build more powerful machine learning models. Who is this course for? So, you've made your first steps into data science, you know the most commonly used prediction models, you perhaps even built a linear regression or a classification tree model. At this stage you're probably starting to encounter some challenges - you realize that your data set is dirty, there are lots of values missing, some variables contain labels instead of numbers, others do not meet the assumptions of the models, and on top of everything you wonder whether this is the right way to code things up.

Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behavior and learning potential of the brain, and exploit them to undertake practical online learning tasks [1]. In Stream Learning (SL), aka data stream mining or machine learning for data streams, applications (such as mobile phones, sensor networks, industrial process controls and intelligent user interfaces, among others) generate huge amounts of data in the form of fast streams, acquiring special relevance with the advent of the Big Data and IoT era. In these scenarios, algorithms cannot explicitly access all historical data because the storage capacity needed for this purpose becomes unmanageable. Indeed, data streams are fast and large (potentially, infinite), so information must be extracted from them in real-time, being therefore necessary to learn in an online manner [2]. Besides, some of these scenarios produce non-stationary data streams which are becoming increasingly prevalent, and where the process generating the data may change over time, producing changes in the patterns to be modeled (concept drift).