James Gleick Quotes

When you use Google, do you get more than one answer? Of course you do. Well, that's a bug. We have more bugs per second in the world. We should be able to give you the right answer just once. We should know what you meant.

I'm a geek through and through. My last job at Microsoft was leading much of the search engine relevance work on Bing. There we got to play with huge amounts of data, with neural networks and other AI techniques, with massive server farms.

Google is all about information. So the notion of using and presenting information in the right point at the right time to users is what, in essence, describes Google.

Without your data, Google couldn't pursue the dream of trying to figure out what you're really thinking when you're asking a question, of trying to discern, from the imprecision of your language, the exact answer you're looking for.

We believe PulseRank will replace PageRank over time for the real-time Web.

Scientists need the infrastructure for scientific search to aid their research, and they need it to offer relevancy and ways to separate the wheat from the chaff - the useful from the noise - via AI-enabled algorithms. With AI, such an infrastructure would be able to identify the exact study a scientist needs from the tens of thousands on a topic.

Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the Web. It would understand exactly what you wanted, and it would give you the right thing. We're nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.

AI does not keep me up at night. Almost no one is working on conscious machines. Deep learning algorithms, or Google search, or Facebook personalization, or Siri or self driving cars or Watson, those have the same relationship to conscious machines as a toaster does to a chess-playing computer.

Computers ought to help people find their own best path through lots of textual information.

We want Google to be the third half of your brain.

The ultimate search engine would basically understand everything in the world, and it would always give you the right thing. And we're a long, long ways from that.

Search as a paradigm will continue to be probably even increasingly important because the information that's out there is only going to grow exponentially - and the only way to sort through all that is by some form of search.

Before Google, I spent the summer building a program that would look at what websites you would go to and what websites other people would go to - and built a collaborative filtering program that helped you find related sites to look at.

Data is going to be dynamic... and, combined with the performance of computing that allows for artificial intelligence to be applied, is going to bring all kinds of insights and additives to enhance everyday life.

A new, a vast, and a powerful language is developed for the future use of analysis, in which to wield its truths so that these may become of more speedy and accurate practical application for the purposes of mankind than the means hitherto in our possession have rendered possible.

The whole concept of data science is that the software becomes the expert, and you, as the average user, are able to understand what's going on.

We are going to completely change what it means to do advanced analytics with our data solutions. We have machine-learning stuff that is about really bringing advanced analytics and statistical machine learning into data-science departments everywhere.

Watson augments human decision-making because it isn't governed by human boundaries. It draws together all this information and forms hypotheses, millions of them, and then tests them with all the data it can find. It learns over time what data is reliable, and that's part of its learning process.

I came in as an engineer and worked on artificial intelligence at Google. I worked on related sites and matching advertising to queries with some of our earliest ads.

Deep learning allows you to create predictive models at a level of quality and sophistication that was previously out of reach. And so deep learning also enhances the product function of data science because it can generate new product opportunities.

Technologies like Ayasdi's exist now to automatically discover information from data without having someone making guesses up front.

The battle between Google and Apple has shifted from devices, operating systems, and apps to a new, amorphous idea called 'contextual computing.' We have become data-spewing factories, and the only way to make sense of it all is through context.

I love sophisticated algorithms that help consumers in a tangible way.

Google will fulfill its mission only when its search engine is AI-complete. You guys know what that means? That's artificial intelligence.

The French have launched their own version of Google, called Quaero. You just type in the subject you're interested in, and Quaero refuses to look it up for you.