Matt Bencke is CEO of Spare5. Previously, he worked at Getty Images, Microsoft and Boeing. Artificial intelligence is changing at a breathtaking pace the way we interact with our devices, friends and data. It promises to revolutionize how we work, shop, socialize, date, bank, heal, navigate… how we live. Yet here I am in the engine room of the revolution, getting increasingly frustrated with the prevalent AI conversations and memes.

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Recently I posted an article by Jeff Selingo to my LinkedIn feed entitled "What happens when millions of jobs are lost to automation." Predictably, those who actually read the article decided it was about education -- specifically, how a lot of people are being educated to do things that no one will be doing after a few years. Meanwhile, people who read only the title commented that even more jobs will be created in our hyperautomated future. The issue is more complicated. When we defund education and fail to address long-standing social issues, we simply don't prepare future generations (or even much of the current one) for this future.

An agreement to send Canadian authorities passenger name record (PNR) data for flights from the European Union cannot be entered into in its current form, a top European Union judge has said. That's because parts of the draft agreement are incompatible with EU citizens' fundamental privacy rights, according to Paolo Mengozzi, Advocate General of the Court of Justice of the EU, in a legal opinion issued Thursday. His opinion, on a case brought by the European Parliament, is only advisory, and it still remains for the CJEU to make a final ruling on the matter. But if the court follows his advice, it could disrupt the European Commission's plans for a new directive on the sharing of PNR data among EU member states and with other countries. The agreement, which the EU and Canada began negotiating in 2010, concerns the transfer of PNR data to Canadian authorities for the purpose of combatting terrorism and other serious transnational crime. The passenger name records concerned contain 19 categories of information, covering the passenger's identity, nationality, address, contact details of the person making the reservation, payment information such as the number of the credit card used to reserve the flight, luggage details, and additional services requested concerning health problems, mobility, or dietary requirements.

Three military veterans once involved in the U.S. drone program have thrown their support behind a Yemeni man's legal fight to obtain details about why his family members were killed in a 2012 strike. The former soldiers' unusual decision to publicly endorse the lawsuit against President Obama and other U.S. officials adds another twist to Faisal bin Ali Jaber's four-year quest for accountability in the deaths of his brother-in-law and nephew, who he believes needlessly fell victim to one of the most lethal covert programs in U.S. history. The former enlisted service members told the U.S. Court of Appeals for the District of Columbia Circuit in a recent filing that they believe the 2012 drone strike serves as a case study of how mistakes frequently occur in the nation's targeted-killing program, where life-or-death decisions are based upon top-secret evidence. The veterans say they "witnessed a secret, global system without regard for borders, conducting widespread surveillance with the ability to conduct deadly targeted killing operations." Though the veterans did not disclose any personal knowledge of the strike that is alleged to have killed Jaber's relatives, they claim the military frequently labels the deaths of unknown victims as "enemy kills."

A few years ago, Scott Fortmann-Roe wrote a great essay titled "Understanding the Bias-Variance Tradeoff." As data science morphs into an accepted profession with its own set of tools, procedures, workflows, etc., there often seems to be less of a focus on statistical processes in favor of the more exciting aspects (see here and here for a pair of example discussions). While this will serve as an overview of Scott's essay, which you can read for further detail and mathematical insights, we will start by with Fortmann-Roe's verbatim definitions which are central to the piece: Error due to Bias: The error due to bias is taken as the difference between the expected (or average) prediction of our model and the correct value which we are trying to predict. Of course you only have one model so talking about expected or average prediction values might seem a little strange. However, imagine you could repeat the whole model building process more than once: each time you gather new data and run a new analysis creating a new model.

SYSPRO is unveiling the new capabilities to 24 leading enterprise software analysts from 12 different global firms during a two week roadshow. It has labeled its tour, "Going for the Gold in Best of Class ERP" and is showcasing sophisticated leading-edge technology advances, such as bots, predictive search, and product integrations to the Internet of Things (IoT), plus a host of powerful new, role-based data access for users. Industry analysts have been predicting for the last 12 months or more that such technologies will erase some of the key competitive barriers that might otherwise impede newer or mid-sized manufacturing firms from competing as effectively as they'd like. "To compete effectively today in a global market, manufacturers and distributors of all sizes need to leverage state-of-the-art technologies that can optimize profit margins while delivering a better customer experience," said Predrag Jakovlevic, Principal Analyst, Enterprise Applications, for Technology Evaluation Centers. "While giant ERP companies often dominate the news, companies like SYSPRO are now announcing delivery of some of the hottest new IT capabilities embedded within their software, like bots and predictive search, and have made commitments to bringing machine learning, artificial intelligence and IoT to market in 2017. The point is, very affordable ERP solutions like SYSPRO can give even small and mid-sized manufacturers the ability to be very successful against much larger market competitors."

Gradient boosting (GB) is a machine learning algorithm developed in the late '90s that is still very popular. It produces state-of-the-art results for many commercial (and academic) applications. This page explains how the gradient boosting algorithm works using several interactive visualizations. We take a 2-dimensional regression problem and investigate how a tree is able to reconstruct the function \( y f(\vx) f(x_1, x_2) \). Play with the tree depth, then look at the tree-building process from above!

That's what disruptive technology is about. Olli is built by Local Motors, the start-up famous for creating the first 3D printed car. However, the brains for Olli were provided by IBM, which contributed its Watson artificial intelligence technology to the vehicle. Together, IBM and Local Motors have produced a vehicle that does far more than simply drive a predetermined route. Instead, Olli combines the capabilities of a chauffeur, a tour guide and a technology expert to communicate with passengers using spoken conversational language.

Businesses have recently realized how effectively Chatbot Strategy can be used for their Brands. Chatbots are now helping people in reducing work load, reducing staff and performing redundant daily tasks. All these helps in reducing human error, efforts and ultimately reducing costs for the organizations. Automating repetitive & mundane work will definitely increase the productivity, creativity, and efficiency of the employees. Bots have an edge over Apps.