“If we don’t get women and people of color at the table — real technologists doing the real work — we will bias systems. Trying to reverse that a decade or two from now will be so much more difficult, if not close to impossible. This is the time to get women and diverse voices in so that we build it properly, right? And it can be great. It’s going to be ubiquitous. It’s going to be awesome. But we have to have people at the table.” — Fei-Fei Li
Elon Musk has signed his name alongside more than 100 others to ask the UN to regulate the use of autonomous weapons systems. The group of concerned engineers, many of whom are respected in the field of AI, is asking the global body to “protect civilians” from “misuse” of AI-driven weapons. They believe that smart, self-guided kill bots would become the tool of choice for despots and tyrants.
‘How much should we let algorithms shape our lives?’ is the question at the heart of Ed Finn’s recent book “What Algorithms Want: Imagination in the Age of Computing”. Scanning Silicon Valley, computer science, and the cultural sphere alike it offers a smart and accessible reading of our current moment.
In 10 years, because AI will make so much money for humanity, we will enter the Age of Plenty, making strides to eradicate poverty and hunger, and giving all of us more spare time and freedom to do what we love.
In 10 years, because AI will replace half of human jobs, we will enter the Age of Confusion, and many people will become depressed as they lose the jobs and the corresponding self-actualization. And many of you will become parents concerned with how to improve education in order to prevent your children from being replaced by AI.
Welcome to the Apple Machine Learning Journal. Here, you can read posts written by Apple engineers about their work using machine learning technologies to help build innovative products for millions of people around the world.
Source: Apple Machine Learning Journal
Courts, banks, and other institutions are using automated data analysis systems to make decisions about your life. Let’s not leave it up to the algorithm makers to decide whether they’re doing it appropriately.
Today, we’ll be looking at the current-state-of-the-AI in three creative domains: music, writing and video/movies.
There are four major ways to train deep learning networks: supervised, unsupervised, semi-supervised, and reinforcement learning. We’ll explain the intuitions behind each of the these methods. Along the way, we’ll share terms you’ll read in the literature in parentheses and point to more resources for the mathematically inclined. By the way, these categories span both traditional machine learning algorithms and the newer, fancier deep learning algorithms.
Originally published in Andreessen Horowitz’s AI Playbook.
Steven Hawking recently commented that artificial intelligence (AI) would be “either the best thing or the worst thing ever to happen to humanity”. He was referring to the opportunity that AI offers to improve mankind’s situation, set alongside the risks that it also presents. These same competing possibilities apply no less when AI is considered in the context of smart cities and the planet’s growing urbanization. With smart cities, though, this is not just some abstract balance: there is a genuine choice of path to be made as smart cities and AI evolve together. This article explores the choice.
“I am a big fan of Fufby and Fuzzable and Snifkin, partially because they’re so quintessentially guinea pig,” Shane said. “The neural network really picked up the spirit of the guinea pig names.”