I wrote about recommender systems last week, but there is so much discussion around their effects right now in the mainstream tech press that they deserve a second issue. As a recap, I said that there were two things that made recommender systems super ineffective, and that YouTube, one of the premier companies tech using recommendations, suffers from both a lot of the first and a lot of the second. Recommender systems today have two huge problems that are leading companies (sometimes at enormous pressure from the public) to rethink how they're being used: technical bias, and business bias. The real problem is YouTube's business model. YouTube is THIRSTY for advertising money, at all times.

In a Silicon Valley startup, Fetch Robotics, about 50 employees, and 125 robots work cohesively. According to CEO Melonee Wise, the human employees see the robots as pets, even calling them "pups". This is becoming the new normal, with many companies employing robots to supplement their workforce as opposed to completely replacing it. Despite the fear that robots are predicted to take over most jobs, the future might, in fact, be more about working alongside robots rather than robots running the workforce completely. At Fetch Robotics, Wise states that "no one has ever lost a job because of our robots."

SDN, artificial intelligence (AI) and machine learning (ML) are all making a big difference in AT&T's network operations, but what has been the biggest game changer for the communication service provider is really a lot simpler than that -- learning how to share data. While the algorithms have greatly improved over the years, the real challenge of AI and ML -- technologies that underpin AT&T Inc. (NYSE: T)'s software-defined network -- was gaining access to the right data and having the ability to act on it, according to Chris Volinsky, AT&T's assistant vice president of Big Data Research. He says that necessitated a change in culture at AT&T to one that was powered by data and in which silos between divisions came down to allow for data sharing. "With the big-data revolution over the last five years, enterprises in AT&T realize the value of unlocking data and making it available for analysis," he says. "In my early career, I had to beat down doors for access to data. It'd take months of escalations. Now there's a real data-powered culture in the company whereby people realize the value in letting others have access to data and the benefit to the company's efficiency and customer experience."

As it has more than 12,500 patents, eight Nobel prizes, and a 140-year history of field-testing crazy ideas, it should surprise no one that AT&T would be an important player in artificial intelligence. "AT&T is a backbone of the internet," explains Nadia Morris, head of Innovation at the AT&T Connected Health Foundry. The company manages wireless, landline, and even private secure networks to power connectivity for both individuals and corporations. All these networks generate incredible volumes of data that is ripe for machine analysis. AT&T has built AI and machine learning systems for decades, using algorithms to automate operations such as common call center procedures and the analysis and correction of network outages.

The pervasive fear that artificial intelligence (AI) will take over human economic livelihood has been felt in places like the manufacturing sector, as large swaths of the industry automate labor formerly done by humans. However, proponents of machine learning say ultimately AI and robotics will improve the way we do virtually everything, and ultimately create new jobs. Still, nearly 40 percent of U.S. jobs were slated as a "high risk" for automation by the early 2030s in a March 2017 report by PricewaterhouseCoopers (PwC). While the PwC report acknowledges it's unlikely all those jobs will be automated for "a variety of economic, legal, and regulatory reasons," PwC also acknowledges that new tech typically means the creation of new jobs for human workers as well, conceding "the net impact of automation on total employment is therefore unclear." Many technologists purport that the new job creation will offset some of the pain of displacement; retraining programs and continuing education opportunities are key to bringing in displaced workers into the new high-tech fold.

With more than 12,500 patents, eight Nobel prizes, and a 140 year history of field-testing crazy ideas, no one should be surprised that AT&T would be an important player in artificial intelligence. "AT&T is a backbone of the internet," explains Nadia Morris, Head of Innovation at the AT&T Connected Health Foundry. The company manages wireless, landline, and even private secure networks to power connectivity for both individuals and corporations. All these networks generate incredible volumes of data ripe for machine analysis. AT&T has built AI and machine learning systems for decades, using algorithms to automate operations such as common call center procedures and the analysis and correction of network outages.

Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text. However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions.