Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.

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.