feed on feeds - 2019/09/30 - items 0 to 15

At the start of 2018, Wag looked like tech's next Big Thing. From a report: In January, the founders of the dog-walking startup announced they had landed a $300 million investment from SoftBank's Vision Fund. The world's largest tech investor, SoftBank had $93 billion at its disposal and a network of global connections second to none. Unlike almost any other venture capital firm, it was capable of single-handedly supercharging businesses and shaking up entire industries. Launched in 2015 at the height of the on-demand boom, Wag was founded by brothers Joshua and Jonathan Viner, along with Jason Meltzer, who previously ran a traditional dog walking business. Together, they followed Uber's playbook: connect pet owners with Wag's network of dog walkers, who work as independent contractors. The startup attracted endorsements from celebrities including singer Mariah Carey and actress Olivia Munn, who is also an investor. By the time of the SoftBank deal, Wag had reached 100 US cities. With SoftBank's backing, and the appointment of a veteran CEO around the same time, Wag looked primed to become a global pet care services leader. More than a year and a half later, SoftBank and Wag have fallen short. Wag has gone through multiple rounds of layoffs, endured management changes, and shuttered its customer service hub in the Hollywood Hills, according to interviews with 17 former employees who've recently left Wag, some as part of layoffs. Most spoke with CNN Business on condition of anonymity, citing non-disclosure agreements or fears of retaliation. Some of the former employees claim that Hilary Schneider, a veteran tech executive who joined Wag as CEO in January 2018, has yet to get a handle on fundamental issues facing the business -- including growth, safety of pets, and customer service.

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As survivors of the Bosnia massacres are leaving this world, the threat of history repeating itself is bigger than ever.

House Democrats subpoena Rudy Giuliani for documents related to his communication with Ukrainian officials.

A U.S. online privacy bill is not likely to come before Congress this year, Reuters reported Monday, citing three sources, as lawmakers disagree over issues like whether the bill should preempt state rules, forcing companies to deal with much stricter legislation in California that goes into effect on Jan. 1. From a report: Without a federal law, technology companies, retailers, advertising firms and others dependent on collecting consumer data to track users and increase sales must adapt to the California law, potentially harming corporate profits over the long term. The delay is a setback for companies ranging from Amazon and Facebook to Alphabet's Google and retailers like Walmart, who either directly collect shopper information to run their websites, or provide free services and derive revenues from advertising that relies on online data collection. "This will be tremendously challenging... companies need to really focus on complying with California now because there is not going to be a life raft from a federal level," Gary Kibel, a partner specializing in technology and privacy at law firm Davis & Gilbert. While the sources, who are involved in the negotiations, still think it is possible at least one discussion draft of the bill could land before the year ends, congressional negotiators must still agree on whether it is adequate to simply ask consumers to consent to collection of personally identifiable information and give them the opportunity to opt out and how the new law would be enforced.

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While YouTube has publicly said that it's working on addressing problems that are making its website ever so addictive to users, a new paper from Google, which owns YouTube, seems to tell a different story. From a report: It proposes an update to the platform's algorithm that is meant to recommend even more targeted content to users in the interest of increasing engagement. Here's how YouTube's recommendation system currently works. To populate the recommended-videos sidebar, it first compiles a shortlist of several hundred videos by finding ones that match the topic and other features of the one you are watching. Then it ranks the list according to the user's preferences, which it learns by feeding all your clicks, likes, and other interactions into a machine-learning algorithm. Among the proposed updates, the researchers specifically target a problem they identify as "implicit bias." It refers to the way recommendations themselves can affect user behavior, making it hard to decipher whether you clicked on a video because you liked it or because it was highly recommended. The effect is that over time, the system can push users further and further away from the videos they actually want to watch. To reduce this bias, the researchers suggest a tweak to the algorithm: each time a user clicks on a video, it also factors in the video's rank in the recommendation sidebar. Videos that are near the top of the sidebar are given less weight when fed into the machine-learning algorithm; videos deep down in the ranking, which require a user to scroll, are given more. When the researchers tested the changes live on YouTube, they found significantly more user engagement. Though the paper doesn't say whether the new system will be deployed permanently, Guillaume Chaslot, an ex-YouTube engineer who now runs AlgoTransparency.org, said he was "pretty confident" that it would happen relatively quickly.