will.i.am Quotes

Algorithms don't do a good job of detecting their own flaws.

Forget artificial intelligence - in the brave new world of big data, it's artificial idiocy we should be looking out for.

People think 'big data' avoids the problem of discrimination because you are dealing with big data sets, but, in fact, big data is being used for more and more precise forms of discrimination - a form of data redlining.

After an afternoon of interviewing Siri it turns out there are millions of questions that it can't or won't answer: How did you get my phone number? How many Siris are there? Did you have a Christmas party? Who is playing the tiny xylophone before and after each interaction? Are you spying on us, plotting the downfall of our species?

Error-prone or biased artificial-intelligence systems have the potential to taint our social ecosystem in ways that are initially hard to detect, harmful in the long term, and expensive - or even impossible - to reverse.

I worry about Google's data ethics and about the idea of handing over the corpus of my life, but I can't deny that it is exceptional at making sense of my ever-growing photo library.

Machine learning is looking for patterns in data. If you start with racist data, you will end up with even more racist models. This is a real problem.

Genomics, Artificial Intelligence, and Deep Machine learning technologies are helping practitioners deliver better diagnosis and actually freeing up time for patient interaction.

I sit on the board of Cloudera, a big vantage point of big data.

Increasingly, our decisions will be made by the algorithms that surround us. Whenever there is a big dilemma, you just ask Google what to do. And what kind of life is that?

We see incredible opportunity to solve some of the biggest social challenges we have by combining high-performance computing and AI - such as climate change and more.

Sexism, racism, and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many 'intelligent' systems that shape how we are categorized and advertised to.

As AI becomes the new infrastructure, flowing invisibly through our daily lives like the water in our faucets, we must understand its short- and long-term effects and know that it is safe for all to use.

Data drives success. That is how we began our success with eSpeed. It was always based on the data.

I believe in human-centered AI to benefit people in positive and benevolent ways.

Networks love data. They love to be able to look at numbers and try to predict what they think will work.

The world is looking for big data scientists, and there just aren't enough to go around.

There are very few industries that I know of - I mean, there are companies in fashion, in cosmetics. They're developing AI models and training them in the cloud in the beginning. If they're successful, they build their own datacenters and develop the software in their own datacenter, like Uber does.

YouTube's algorithm doesn't know what's going on in the world.

People are building apps that are doing super-crazy things, and there's a lot of talk about modeling and microtargeting. Facebook can predict when people are going to break up, and Target is able to predict if a woman is pregnant before she knows just based on the type of lotion she bought.

To learn something from your data, the forming of a hypothesis lies with the human being, which turns into a query, which becomes a result. The problem is that there are too many queries to make, too many questions to ask.

For my own part, I believe that there is social and psychological justification for significant inequalities of incomes and wealth.

First you use machines, then you wear machines, and then ...? Then you serve machines.

if they take half the buildings that they use to praise god and give it to the motherfuckers who need god we'd be 'aight

Hillary Clinton, income inequality, it's richest damn woman next to the Kennedy family, and you're trying to tell me she cares about income inequality?