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A16Z AI Playbook

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Precisely defining artificial intelligence is tricky. John McCarthy proposed that AI is the simulation of human intelligence by machines for the inaugural summer research project in 1956. Others have defined AI as the study of intelligent agents, human or not, that can perceive their environments and take actions to maximize their chances of achieving some goal. Jerry Kaplan wrestles with the question for an entire chapter in his book Artificial Intelligence: What Everyone Needs To Know before giving up on a succinct definition. Rather than try to define AI precisely, we'll simply differentiate AI's goals and techniques: Some people use Artificial Intelligence and Machine Learning interchangeably.


AI Weekly: Why a slow movement for machine learning could be a good thing

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In 2019, the number of published papers related to AI and machine learning was nearly 25,000 in the U.S. alone, up from roughly 10,000 in 2015. And NeurIPS 2019, one of the world's largest machine learning and computational neuroscience conferences, featured close to 2,000 accepted papers from thousands of attendees. There's no question that the momentum reflects an uptick in publicity and funding -- and correspondingly, competition -- within the AI research community. But some academics suggest the relentless push for progress might be causing more harm than good. In a recent tweet, Zachary Lipton, an assistant professor at Carnegie Mellon University, jointly appointed in the Tepper School of Business and the machine learning department, proposed a one-year moratorium on papers for the entire community, which he said might encourage "thinking" without "sprinting/hustling/spamming" toward deadlines.


Google AI Won't Use 'Man' or 'Woman' to ID People, Says Business Insider

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An automated service will no longer label people as male or female, according to a report from Business Insider. Google's Cloud Vision API, a "computer vision" product that has the ability to "[a]ssign labels to images and quickly classify them into millions of predefined categories," might be making some changes to two specific labels. Business Insider claimed to have seen a Feb. 20 email from Google to developers, which stated that the company would avoid using gendered labels for its image tags. Business Insider claimed that this was a direct quote from the email: "Given that a person's gender cannot be inferred by appearance, we have decided to remove these labels in order to align with the Artificial Intelligence Principles at Google, specifically Principle #2: Avoid creating or reinforcing unfair bias." In the Artificial Intelligence Principles published by Google AI, Principle #2 states: "We will seek to avoid unjust impacts on people, particularly those related to sensitive characteristics such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious belief."


Why Golang and Not Python? Which Language is Perfect for AI?

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Golang is now becoming the mainstream programming language for machine learning and AI with millions of users worldwide. Python is awesome, but Golang is perfect for AI programming! Launched a decade back, November 2009, Golang recently turned ten. The language built by Google's developers is now making programmers more productive. These creators main goal was to create a language that would eliminate the so-called "extraneous garbage" of programming languages like C .


Can AI flag disease outbreaks faster than humans? Not quite

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Did an artificial-intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China? But what the humans lacked in sheer speed, they more than made up in finesse. Early warnings of disease outbreaks can help people and governments save lives. In the final days of 2019, an AI system in Boston sent out the first global alert about a new viral outbreak in China. But it took human intelligence to recognize the significance of the outbreak and then awaken response from the public health community.


Google AI tool will no longer use gendered labels like 'woman' or 'man' in photos of people

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An artificial intelligence tool Google provides to developers won't add gender labels to images anymore, saying a person's gender can't be determined just by how they look in a photo, Business Insider reports. The company emailed developers today about the change to its widely used Cloud Vision API tool, which uses AI to analyze images and identify faces, landmarks, explicit content, and other recognizable features. Instead of using "man" or "woman" to identify images, Google will tag such images with labels like "person," as part of its larger effort to avoid instilling AI algorithms with human bias. In the email to developers announcing the change, Google cited its own AI guidelines, Business Insider reports. "Given that a person's gender cannot be inferred by appearance, we have decided to remove these labels in order to align with the Artificial Intelligence Principles at Google, specifically Principle #2: Avoid creating or reinforcing unfair bias."


Google removes nearly 600 apps for spam-like ads after they were downloaded 4.5 BILLION times

Daily Mail - Science & tech

In a large scale effort to insulate users from disruptive spam and ads Google removed nearly 600 apps from its Play store that had been downloaded an astonishing 4.5 billion times. According to Google, the apps violated its rules on disruptive ad policies, meaning they show advertisements that were'displayed to users in unexpected ways, including impairing or interfering with the usability of device functions.' This can include plastering a device's screen with full-screen ads even when the application isn't being used or even when a user is trying to make a phone call. 'This is an invasive maneuver that results in poor user experiences that often disrupt key device functions and this approach can lead to unintentional ad clicks that waste advertiser spend,' wrote Google in a blog post. Advertisers scammed by those apps will be refunded according to Google.


Google's AI drops 'man' and 'woman' gender labels to avoid possible bias

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Google has announced that its image recognition AI will no longer identify people in images as a man or a woman, reports Business Insider. The change was revealed in an email to developers who use the company's Cloud Vision API that makes it easy for apps and services to identify objects in images. In the email, Google said it wasn't possible to detect a person's true gender based simply on the clothes they were wearing. But Google also said that they were dropping gender labels for another reason: they could create or reinforce biases. Given that a person's gender cannot be inferred by appearance, we have decided to remove these labels in order to align with the Artificial Intelligence Principles at Google, specifically Principle #2: Avoid creating or reinforcing unfair bias.


Can AI flag disease outbreaks faster than humans? Not quite

The Japan Times

BOSTON – Did an artificial-intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China? But what the humans lacked in sheer speed, they more than made up in finesse. Early warnings of disease outbreaks can help people and governments save lives. In the final days of 2019, an AI system in Boston sent out the first global alert about a new viral outbreak in China. But it took human intelligence to recognize the significance of the outbreak and then awaken response from the public health community.


Google used AI and human moderation to take down over 75 million Google Maps reviews

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Hundreds of millions of people contribute over 20 million reviews, ratings, and other pieces of content to Google Maps' over 200 million points of interest daily -- it's how the platform continues to grow so rapidly. But user contributions are intrinsically fraught. That's why increasingly, Google is using AI and machine learning to spot malicious contributions at submission time, ensuring they don't reach the over 1 billion users who regularly use Maps. In a blog post, Google said that it uses automated detection systems, including machine learning models, to scan millions of contributions to detect and remove policy-violating content. In the case of reviews, its systems audit every review before they're published to Maps, looking for signs of fake or misleading content.