Customer support is one the most resource-intensive departments in a company. Staff spend their day answering queries, on the telephone with customers, communicating with other departments, and much more. It is also a part of the operation that is hard to link to an ROI. Although the concept of "customer success" is gaining traction in the startup world (particularly in SaaS), it is still tough to report the real value a customer support team can deliver. Therefore, anything reducing the need for manpower in customer support is considered a good thing.

Nvidia just revealed that they earned huge profits in the third quarter last Thursday. The chipmaker racked up a revenue of $2 billion, a figure that is more than the forecast $1.69 billion. Forbes reported that the earnings are also had a year-over-year increase of 54%. Stock prices are also up by 83 cents, compared to the previous estimate of 57 cents. This number displays an increase of 89% compared to last year's digits.

About $1B has been invested in early stage SaaS startups as of November 1. Over the last nine months, marketing startups have raised more dollars in aggregate than any other segment. The chart above shows the early-stage investment dollars by buyer within the organization. Operations teams following second, with human resources focused startups in third. Notably, sales startups raised the least amount of capital. If we compare these trends to the total aggregate market capitalization of public SaaS companies by buyer, we observe a few interesting patterns.

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Once the preserve of science-fiction movies, artificial intelligence is one of the hottest areas of research right now. While the idea behind AI is to make our lives easier, there is concern that as the technology becomes more advanced, we may be heading for disaster. How can we be sure, for instance, that artificially intelligent robots will make ethical choices? There are plenty of instances of artificial intelligence gone wrong. The case of the rude and racist chatbot** Chatbot Tay, Microsoft's AI millennial chatbot, was meant to be a friendly chatbot that would sound like a teenage girl and engage in light conversation with her followers on Twitter.

In all likelihood androids won't just one day spontaneously realize that we suck and rise up against us in a civil rights slave revolt. Instead, robots and animals will gradually follow the paths more naturally suited to them by moving along different, most likely conflicting, trajectories into the future. It's scary, we actually have no idea what's going on in the mind of a machine learning algorithm. We set them in motion and what they really do as a result is a black box mystery to us. Not only do our computers pick up on our racist and sexist biases, they see how much porn we look at and how many cat videos we post and they think that those things represent the human condition.

Capturing real-world phenomena is an exercise in dealing with uncertainty. To do so, statisticians must understand the underlying distribution of the population under study, as well as come up with parameters that will provide predictive power. The goal for a statistician is to predict an interaction between variables with some degree of certainty (we are never 100% certain about anything). Machine learners, on the other hand, want to build algorithms that predict, classify, and cluster with the most accuracy. They operate without uncertainty or assumptions, continuously learning in order to improve their accuracy score.

It took Nvidia a lot of sweat and a lot of failed products to get to where it is today. CEO Jen-Hsun Huang is not afraid of failure and as the CEO he took a lot of risks, knowing that one of them would eventually pay off. Don't get me wrong, most of the money still comes from the gaming division, where Nvidia made $1.244 billion, 63.47% year over year and 58% quarter over quarter. The new Pascal architecture is obviously paying off big time. In recent years, Nvidia executives started talking more about automotive and deep learning, than about gaming.

Alexander Reben has created cute cardboard robots that elicit random emotional confessions from passersby, and a bot called The First Law that can decide whether or not to prick an unsuspecting human finger. These are two examples at the opposite ends of the spectrum of what artificial intelligence can one day bring to humanity - and for the artist, engineer and WIRED Innovation Fellow, they are important tools designed to spark debate about what that coexistence will look like. Take the First Law robot. It is named after the rules devised by sci-fi author Isaac Asimov, which state: "a robot may not injure a human being or, through inaction, allow a human being to come to harm." But it is within our power to create a robot that does exactly the opposite.

Symantec has launched Endpoint Protection 14, a new security solution which harnesses artificial intelligence to protect clients. Announced on Tuesday, the new security offering is powered by AI and machine learning on the endpoint and in the cloud. Symantec says that by harnessing machine learning to collate data and detect patterns and anomalies which may indicate a cyberattack, AI provides "a multi-layered solution able to stop advanced threats and respond at the endpoint regardless of how the attack is launched." This comes at a good time, when the internet is currently running amock with all kinds of new viruses like randsomware and other dangerous new exploits. Their new system, the Symantec Endpoint Protection combines machine learning, memory exploit mitigation and threat intelligence provided by Symantec and Blue Coat, which combined their research and security operations in October after Symantec completed the acquisition of Blue Coat for $4.6 billion.