Personalized, relevant, and timely marketing interactions are undoubtedly the most effective. In the past, marketers sent out blanket campaigns to their users irrespective of their behavior while interacting with their product, the stage they were at in the product lifecycle, or their customer LTV. Of course, if you send out a blanket campaign to your whole user base then the message will not resonate with each user equally, some will be engaged and others will disregard the interaction. This raises an issue for marketers – how can they ensure that they do not annoy their loyal users whilst communicating on a personal level with their users? Instead of trying to find one marketing campaign that will please you full user base, it is better to create multiple different campaigns for segments of users with different interests or preferences.

Microsoft's artificial intelligence (AI) technology can now recognize conversational speech slightly better than humans who do so professionally, according to recently-released research from the company. Microsoft recently got its AI's error rate in understanding speech down to about 5.9% from 6.3%, which puts it slightly below the human error rate, which is also close to 5.9% "We [improved] on our recently reported conversational speech recognition system by about 0.4%, and now exceed human performance by a small margin," the report stated. This news comes a mere month after Microsoft announced that it had reached an error rate of 6.3%, at the time setting a record among its peers. However, Microsoft's research also noted that error rates of human transcribers can vary between 4.1% to 9.6%, depending on how carefully they perform the transcription. SEE: AI experts weigh in on Microsoft CEO's 10 new rules for artificial intelligence Still, the closeness in quality to human transcription is impressive.

One of the major trends I have been researching recently, has been the shift in interest towards Artificial Intelligence (AI) in its multiple forms and guises, and the potential it has to analyse vast quantities of data and quickly derive actionable insights. As we all know, AI, Machine Learning and Deep Learning are not new. However, there has been huge investment in the space in recent years and the ability to automatically apply complex mathematical calculations to Big Data – over and over, faster and faster – is a recent development. With steady advances in digitisation and cheap computing power, no wonder people are excited about the possibilities. One of the areas of AI that gets the most attention is Deep Learning.

Welcome to the very first edition of The Morning After, Engadget's revamped newsletter. First, I'd like to congratulate you for subscribing to what is undoubtedly the greatest newsletter you'll ever read. Thanks are also in order for giving us some of your precious inbox real estate each day. You're hearing from me, Editor in Chief Michael Gorman, because this is the Weekend Edition -- in which I'll be putting context around the most interesting and important stories we published over the past week. Come Monday at 6 AM ET, and every weekday after, the daily version will hit your inbox with summaries of the biggest stories from the previous day, delivered with Engadget's trademark wit and insight.

We have lots of cutting-edge projects for you being discussed in the Gitter channels, ranging from mapping brain activity at scale, computer vision, neural networks, data visualization to natural language processing. Check out the list below and don't hesitate to join the conversation. Did we miss a particular community in this category, or do you want us to feature your channel? Drop us a line in the Gitter HQ and we will add it to the list.

ZDNet's Mary Jo Foley is reporting that Microsoft is working on a new suite of development tools called Open Mind Studio. On August 2, Microsoft Executive Vice President of Applications and Services Qi Lu reportedly showed slides which presented Open Mind as a collection of tools for machine learning development. It also described Open Mind Studio as the "Visual Studio" for machine learning. Microsoft did not comment on the story and has not said when Open Mind might become available. Recently, the company has been building machine learning capabilities into a number of its other products, including Bing, Outlook, Skype, HoloLens and Cortana.

Decision tree is a graph to represent choices and their results in form of a tree. The nodes in the graph represent an event or choice and the edges of the graph represent the decision rules or conditions. It is mostly used in Machine Learning and Data Mining applications using R. Examples of use of decision tress is predicting an email as spam or not spam, predicting of a tumor is cancerous or predicting a loan as a good or bad credit risk based on the factors in each of these. Generally, a model is created with observed data also called training data. Then a set of validation data is used to verify and improve the model.

Speech is a fundamental aspect of human behavior, yet it remains something that many of us struggle with. It's believed that around 1 in 14 adults in the United States have some kind of voice disorder, and our understanding of such disorders makes it difficult to both diagnose and treat. A team from MIT and the Massachusetts General Hospital believe that machine learning can play a part in better understanding speech disorders. In a recent paper, they describe using a wearable device to collect accelerometer data to detect differences in people with Muscle Tension Dysphonia (MTD) and a control group. After such individuals with MTD had received therapy for the condition, their behaviors appeared to converge with that of the control group.

In one of my previous articles I envisioned the state of Customer Success in the year 2041. The future looks bright for our community, but in fact the future is happening right now. Salesforce.com's annual Dreamforce conference is taking place in the San Francisco Bay Area even as I write these words, creating the perfect setting for a topic that underlies all my earlier predictions. You see, while the future of Customer Success will definitely include Automation, Virtual Reality (VR), and The Internet of Things (IoT), the thread linking all of these advancements together is Artificial Intelligence (AI). In this article I will discuss how AI can both help and hinder Customer Success. All new technologies present the possibility of a double-edged sword, and we have never been faced with a blade quite as sharp as AI.

At the core of IBM's Watson Analytics is a powerful bull's-eye tool; it employs a powerful user query as the basis of analysis. The Data Scientist or business user is free to play with the variables in any dataset and directly visualize how the changing user inputs impact the answers. The bull's-eye also helps narrow down the user query to help get a much clearer answer. This kind of focus can be especially useful in a large dataset, where the user is likely to get lost. IBM's design philosophy behind Watson was to keep the average user sharply focused on the query.