Tag: google

The Google Squeeze

1 answer, perhaps, lies in Google’s behavior itself: unlike traditional monopolies, it is hard to argue that Google’s product isn’t getting better. Sure, OTAs need to pay to play on the hotel module, but the hotel module is a genuine improvement over 10 blue links. The same can be said of the other areas where Google gives answers instead of options. I absolutely get the argument that this might be an unfair extension of Google’s search dominance, but the possibility of stifling innovation, both directly and also its incentives, are worth consideration.

Beachheads and Obstacles

Amazon, on the other hand, seems to have learned the right lessons from its mobile failures; what is notable about the company’s approach to Alexa is that it leverages and learns from the mobile era. Alexa benefits from Amazon’s investments in data centers and networking, interacts with both iOS and Android to the greatest extent possible, and is in line with Amazon’s overall business — making buying things that much more convenient. Alexa is an operating system for the home, and perhaps beyond.

Chrome Privacy Sandbox

we are announcing a new initiative to develop a set of open standards to fundamentally enhance privacy on the web. We’re calling this a Privacy Sandbox. we will work with the web community to develop new standards that advance privacy, while continuing to support free access to content. Over the last couple of weeks, we’ve started sharing our preliminary ideas for a Privacy Sandbox – a secure environment for personalization that also protects user privacy. Some ideas include new approaches to ensure that ads continue to be relevant for users, but user data shared with websites and advertisers would be minimized by anonymously aggregating user information, and keeping much more user information on-device only. Our goal is to create a set of standards that is more consistent with users’ expectations of privacy.

Photo Query Matching

Google Local Photos Match the User Query

What does this mean for your business? Well given Google’s penchant for wanting to answer a users’ query and their belief that they can pick a photo better than you, it means the same thing that it always has. It means you need to upload lots of great photos so that no matter which one Google chooses it is a good one.

But now it also means that you really need to be thinking about photos that reflect the broad range of products and services that you deliver and that users might be searching on. If you carry wedding bands and engagement rings and earrings and necklaces you will want to be sure that you have great photos of each. And that they are easily identified in the image.

Hal Varian

COWEN: But then you must think we’re not doing enough theory today. Or do you think it’s simply exhausted for a while? VARIAN: Well, one area of theory that I’ve found very exciting is algorithmic mechanism design. With algorithmic mechanism design, it’s a combination of computer science and economics. The idea is, you take the economic model, and you bring in computational costs, or show me an algorithm that actually solves that maximization problem. Then on the other side, the computer side, you build incentives into the algorithms. So if multiple people are using, let’s say, some communications protocol, you want them all to have the right incentives to have the efficient use of that protocol. So that’s a case where it really has very strong real-world applications to doing this — everything from telecommunications to AdWords auctions.

Solving Quake

We used a tournament-style evaluation to demonstrate that an agent can achieve human-level performance in a 3D multiplayer first-person video game, Quake III Arena in Capture the Flag mode, using only pixels and game points scored as input. We used a 2-tier optimization process in which a population of independent RL agents are trained concurrently from 1000s of parallel matches on randomly generated environments. Each agent learns its own internal reward signal and rich representation of the world. These results indicate the great potential of multiagent reinforcement learning for artificial intelligence research.

Google to Grab

As for me, I am personally privileged to be Head of Engineering for Data Insights: Our Ads business, Personalization and Segmentation Platforms, User Data Platform, and all of Grab’s online databases and operational data stores… which sounds pretty impressive until you realize that the Ads engineering department consists of Scott. Heya, Scott. I kid, I kid; there are others but you get the idea; we’re still a startup, and everyone is doing so much with so little. It keeps us from getting complacent. I’ve got teams in Ho Chi Minh City, Jakarta, Singapore and Seattle, and the company has placed so much faith in us that we move heaven and earth to get things done. It’s not easy. Grab definitely isn’t for everyone in the US. In Seattle we’re 16 timezones removed from Singapore, so their mornings are our evenings and we have to sacrifice a lot in order to be effective. Many of us basically live in Singapore time, and we’re often in conference calls until midnight to 2am. But it’s worth it. Being at Grab is a privilege. After meeting Siti I know that more than ever before. What is happening here is a phenomenal, generational, once-in-a-lifetime opportunity, and I am forever grateful and humbled to be a part of this incredible team, helping change the lives of 650 million wonderful people. It’s on us to make it happen.