If you have ever bought something from an online store, chances are the store's used your email address with wanton disregard, bombarding you with email after email about its products and sales until you reach for the sweet oblivion of unsubscription. Stores and brands do this to keep customers engaged--but they don't know how many emails are too many. Adobe previewed a tool today with the promise to help alleviate what they call "customer fatigue." By using machine learning algorithms to crunch the numbers of how often emails are opened and clicked on, marketers can see whether customers are tired of getting their emails. The algorithms will allow Adobe to actually calculate how fatigued every customer might be, and only send a certain number of emails based on that score.

The procedure used to carry out the learning process in a neural network is called the training algorithm. There are many different training algorithms, whith different characteristics and performance. The learning problem in neural networks is formulated in terms of the minimization of a loss function, f. This function is in general, composed of an error and a regularization terms. The error term evaluates how a neural network fits the data set. On the other hand, the regularization term is used to prevent overfitting, by controlling the effective complexity of the neural network.

Trying to discern drug smugglers passing through customs presents exactly the same problem as trying to discern security threats passing through our networks. Machine learning has been applied to both with varying degrees of success, but ultimately the technology reaches the same limitations. Machine learning has two basic elements: feature vectors and classification exemplars -- the data that is gathered and the corresponding classification examples. In the case of drug smugglers, we might observe number of travelers, point of origin, point of destination, number of bags, length of stay and weight of the bags. We might also flag any traveler or pair of travelers with two or more bags whose combined weight is greater than 150 pounds, whose stay is less than a week and who originated from a climate conducive to poppies.

We've already started to see effective use of emerging technologies, such as Industrial IoT (IIoT)-enabled remote condition monitoring and Big Data analytics for predictive maintenance and similar offline applications; but process engineers are interested in knowing if and how emerging technologies can be used to improve the actual production process and product quality. In a recent pilot program, Mitsui Chemicals, Inc. and NTT Communications Corporation (NTT Com), the industrial control technology (ICT) solutions and international communications business within NTT Group, have successfully created a Deep Learning technique that accurately predicts the quality of gas products during production; 20 minutes before the final product is created. As we learned in a recent press release from NTT Com, the technique is based on modeling the relationship between the different data sets sourced from raw materials feeding into the reactor; reactor conditions; and the trace gas impurities that represent gas product quality, expressed here as "X-gas." The goal of this joint project between NTT Com and Mitsui Chemicals is to improve the accuracy of detecting abnormalities in product quality to improve operational efficiencies and product quality. The two companies initiated the pilot project at one of Mitsui Chemicals' gas production plants in 2015.

Machines have never replaced humans before, and they probably aren't doing so right now, argues Noah Smith Of all the economic questions being debated today, the most frightening one is, "Will the robots take our jobs?" This nightmare scenario comes in several flavors. The extreme version is that automation simply makes human workers obsolete, just as cars made horses redundant. A less apocalyptic possibility is what economists call "skill-biased technological change" -- people who are technically savvy, mentally flexible and educated will reap greater and greater rewards, while everyone else sees their wages decline. These two scenarios might look different on paper, but the net result is largely the same -- a very big portion of humanity would be either be impoverished or reduced to living off of the government dole.

On this week's Tech Tent - your weekly status update on the technology business - we have three stories reflecting our current anxieties about the nature of our digital world. We'll discuss the biggest cybersecurity threat of the moment, the use of bots in the fight to get the upper hand on social media during the US elections and the ongoing debate about the risks of artificial intelligence. This week, producer Jat Gill and I attended a demo by security firm Sophos which left us both rather scared. James Lyne from the firm played a bad guy launching an attack on a company, while two colleagues played the increasingly confused system administrators trying to work out what was going on as chaos engulfed the network. We then heard that the principle weapon in the attackers' armoury was now ransomware.

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"We need you to get out there and -- for once in your life -- focus your indiscriminate rage in a useful direction." Oliver's urging of viewers to exercise their civic rights came during the Federal Communication Commission's public comment period for the pending Net Neutrality rules in 2014, which would have a profound impact on the future development of the Internet. Within hours of the TV show host's rant, the FCC's capacity to accept public comments online crashed, highlighting just how ill-prepared the system was to handle any meaningful level of civic participation. The FCC's comment consideration schedule for Net Neutrality rules consequently was seriously delayed. Six hundred staff lawyers at the Commission subsequently spent nearly nine months to gather, divvy up, read, and categorize what eventually totaled 4 million public responses to the proposed rules, costing taxpayers an estimated 4 million.

Anki is probably better known for its OVERDRIVE set of AI-powered racing car robots. But a few months ago, the company revealed a different, less competitive, and more educational product: Cozmo. Now the robot truck with a personality is being made available for everyone to purchase, whether for fun, for learning, or simply for mucking around with advanced technologies in a playful form. Because more than just a toy, the Cozmo robot is artificial intelligence, computer vision, and development platform rolled into one, made friendly, and made more affordable. Cozmo is part child s toy and part tinkerer s toy, with both audiences being served well.

If you have questions about these projects, let us know and we'll try to get more details from the researchers. As a reminder, we'll return to normal Video Friday next week, so if you have video suggestions for us, send us an email or reach us on Twitter or Facebook.