Improving artificial intelligence (AI) technology and the proliferation of messaging apps -- which enable users and businesses to interact through a variety of mediums, including text, voice, image, video, and file sharing -- are fueling the popularity of chatbots. These software programs use messaging as an interface through which to carry out various tasks, like checking the weather or scheduling a meeting. Bots are still nascent and monetization models have yet to be established for the tech, but there are a number of existing strategies -- like "as-a-service" or affiliate marketing -- that will likely prove successful for bots used as a tool within messaging apps. Chatbots can also provide brands with value adds -- services that don't directly generate revenue, but help increase the ability of brands and businesses to better target and serve customers, and increase productivity. These include bots used for research, lead generation, and customer service.

Unless you've recently graduated from one of the new Data Science courses that have been popping up online and in various universities around the world, then becoming a Data Scientist was most likely slightly accidental and was more about the journey than the destination. I started out as a physicist and had a strong mathematical grounding, but I had a passion for medicine. After completing my bachelor's degree I took a master's degree in medical physics. This is where I gained an appreciation for the importance of image analysis and the role that data plays in medicine. I created a virtual model of a human torso by segmenting images from the Visible Human Project.

The material is arguably good, but the formatting is absolutely horrendous. The worst thing about this book is that it isn't written in Latex. It's probably written in MS Word, which most can agree has asinine handling of equations. Because of this, all of the equations and math syntax is offset from the rest of the text in each line, which makes it really distracting (and irritating) to read. Some of the tables have the header on one page and the data in the next -- again, a significant and egregious formatting issue that was just overlooked or flat-out ignored. Frankly I am surprised the author hasn't changed the formatting after so many different volumes.

In addition to targeting customers based on inferred wants and needs, a compelling facet of personalization is something Forrester Research identifies as "Operationalizing Emotion", which alludes to customers making purchasing decisions based as much (or more) on emotional experiences than on rational conclusions. Today's market is all the more risky for companies that don't provide a stellar customer experience from start to finish, and it's becoming more common for businesses to suffer longer-term revenue losses for a single negative experience, whether directly experienced by the customer or based on empathy for others' experiences. A quick and personalized response to any customer dissatisfaction seems almost an essential application for businesses that want to stay afloat for the long-haul.

Discussions about the ethics, safety, and societal impact of Artificial Intelligence seem to come back to the same cultural touch points found in AI stories that warn of worst-case scenarios. Whether in press coverage or in policy position papers, we keep going back to the same stories. We need to tell more diverse and realistic stories about AI if we want to understand how these technologies fit into our society today, and in the future. In Kubrick's HAL 9000, the calm assistant shuts Dave out of the system in 2001. In The Terminator, the AI defense system SkyNet becomes self-aware and initiates a nuclear holocaust to decimate the human race.

Humans on bicycles have a lot to gain from self-driving cars that move humans out of the driver's seat. Because drivers are judged to be at fault in the majority of cycling accidents that result in serious injury or death. Unfortunately, it's harder for an autonomously driven vehicle to avoid a bicycle than a car. A number of studies from different countries have found that drivers are solely responsible for between 60% and 80% of collisions between cars and adult cyclists. The numbers are similar for collisions that result in serious injury or death.

Baidu is currently considered to be pack leader amongst the Chinese internet giants as they race to develop and deploy machine and deep learning technology. Much like their US-based counterparts such as Google and Amazon, self-teaching, neural net technology is being integrated into both their core services and used to innovate in new ways. Cutting edge artificial intelligence (AI) methods such as machine learning and deep learning are being used to reap huge benefits across industries as diverse as finance and healthcare. The basic idea is that once we teach computers to learn in the same way we do, they can absorb and process Big Data at a tremendous rate, soon becoming at least, if not more, reliable than humans when it comes to making decisions. The work of the Chinese giants – most prominently Baidu but also online retailer Ali Baba and chat provider Tencent - in the AI field has received relatively little coverage in western media compared to that afforded to the US giants.

In this webinar we'll introduce you to a powerful tree-based machine learning algorithm called gradient boosting. Gradient boosting often outperforms linear regression, Random Forests, and CART. Boosted trees automatically handle variable selection, variable interactions, nonlinear relationships, outliers, and missing values. We'll see that CART decision trees are the foundation of gradient boosting and discuss some of the advantages of boosting versus a Random Forest. We will explore the gradient boosting algorithm and discuss the most important modeling parameters like the learning rate, number of terminal nodes, number of trees, loss functions, and more.

Last October, Uber had one of its self- driving trucks make a beer run, traveling 200 kilometers down the interstate to deliver a cargo of Budweiser from Fort Collins to Colorado Springs. A person rode in the truck but spent most of the trip in the sleeper berth, monitoring the automated system. The self-driving truck developed by Uber's recently acquired Otto unit reflects remarkable technological achievements. It also provides yet another indicator of a looming shift in the economy that could have deep political consequences. It is uncertain how long it will take for driverless trucks and cars to take over the roads.

Last year, it was reported that supercomputer IBM Watson diagnosed a rare form of leukaemia in a patient at a University of Tokyo-affiliated hospital whose case had baffled her medical team. The cloud-based, artificial intelligence-powered supercomputer is capable of cross-referencing and analysing data from tens of millions of oncology papers from research institutes all over the world. From vast volumes of data, it can instantly pull out the information it needs, much faster than humans can. The University of Tokyo reported that the 60-year-old Japanese woman was correctly diagnosed in just 10 minutes by Watson, after her genetic data was cross-referenced with the computer's own database. More and more, health technologies originally viewed as futuristic – like virtual avatars and chatbots – have become reality.