SHANGHAI/HONG, KONG – A Chinese researcher's claim that he successfully modified the genes of human embryos may force the country to make a stark choice about the future of its burgeoning biotechnology industry -- one with significant implications for an emerging technology arms race with the West. He Jiankui, an American-educated scientist based in Shenzhen, announced on Monday that he'd used Crispr, a powerful gene-editing tool, to make recently born twin girls resistant to HIV. He's statement, which was not backed by peer-reviewed data and hasn't been verified, prompted widespread condemnation from scientists in China and elsewhere, with many calling it an irresponsible use of a technology whose long-term effects are still poorly understood. Yet whatever the veracity of He's claims, it's likely that China, with its aggressively entrepreneurial start-ups and less stringent regulation, will be the country where researchers most rapidly test the currently accepted boundaries of genetic manipulation. That presents its leaders with a dilemma: Whether to follow the U.S. and Europe in strictly regulating its application, or take a more hands-off approach, catalyzing rapid innovation in a strategic industry at the cost of what could be serious risks to patients.

CHIBA – A 75-year-old man operating a Japanese language school has been found dead at his apartment in Chiba Prefecture, prompting police to launch a murder investigation. The body of Jiro Iwai, who managed the school as well as other companies, was found around 1:30 p.m. Sunday when a female employee visited his home to find him dead and bleeding from a wound to the head. Investigative sources said Iwai, who lived alone in an apartment in Sakura, Chiba Prefecture, was likely struck multiple times in the killing. An autopsy showed he may have died of damage to his spinal cord. But despite the violent death, police said there were no signs of a struggle and that a light in the room where he was killed had been left on.

We introduce a novel harmonic analysis for functions defined on the vertices of a strongly connected directed graph of which the random walk operator is the cornerstone. As a first step, we consider the set of eigenvectors of the random walk operator as a non-orthogonal Fourier-type basis for functions over directed graphs. We found a frequency interpretation by linking the variation of the eigenvectors of the random walk operator obtained from their Dirichlet energy to the real part of their associated eigenvalues. From this Fourier basis, we can proceed further and build multi-scale analyses on directed graphs. We propose both a redundant wavelet transform and a decimated wavelet transform by extending the diffusion wavelets framework by Coifman and Maggioni for directed graphs. The development of our harmonic analysis on directed graphs thus leads us to consider both semi-supervised learning problems and signal modeling problems on graphs applied to directed graphs highlighting the efficiency of our framework.

Different neural networks trained on the same dataset often learn similar input-output mappings with very different weights. Is there some correspondence between these neural network solutions? For linear networks, it has been shown that different instances of the same network architecture encode the same representational similarity matrix, and their neural activity patterns are connected by orthogonal transformations. However, it is unclear if this holds for non-linear networks. Using a shared response model, we show that different neural networks encode the same input examples as different orthogonal transformations of an underlying shared representation. We test this claim using both standard convolutional neural networks and residual networks on CIFAR10 and CIFAR100.

Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexity challenges the paradigm of considering fairness to be a formal property of supervised learning with respect to protected personal attributes. Racial identity is not simply a personal subjective quality. For people labeled "Black" it is an ascribed political category that has consequences for social differentiation embedded in systemic patterns of social inequality achieved through both social and spatial segregation. In the United States, racial classification can best be understood as a system of inherently unequal status categories that places whites as the most privileged category while signifying the Negro/black category as stigmatized. Social stigma is reinforced through the unequal distribution of societal rewards and goods along racial lines that is reinforced by state, corporate, and civic institutions and practices. This creates a dilemma for society and designers: be blind to racial group disparities and thereby reify racialized social inequality by no longer measuring systemic inequality, or be conscious of racial categories in a way that itself reifies race. We propose a third option. By preceding group fairness interventions with unsupervised learning to dynamically detect patterns of segregation, machine learning systems can mitigate the root cause of social disparities, social segregation and stratification, without further anchoring status categories of disadvantage.

This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and interventions. We implement two components of the Cycle-of-Learning for Autonomous Systems, which is our framework for combining multiple modalities of human interaction. The current effort employs human demonstrations to teach a desired behavior via imitation learning, then leverages intervention data to correct for undesired behaviors produced by the imitation learner to teach novel tasks to an autonomous agent safely, after only minutes of training. We demonstrate this method in an autonomous perching task using a quadrotor with continuous roll, pitch, yaw, and throttle commands and imagery captured from a downward-facing camera in a high-fidelity simulated environment. Our method improves task completion performance for the same amount of human interaction when compared to learning from demonstrations alone, while also requiring on average 32% less data to achieve that performance. This provides evidence that combining multiple modes of human interaction can increase both the training speed and overall performance of policies for autonomous systems.

More than 90 Google employees have joined a petition protesting the company's plans to build a search engine that complies with China's online censorship regime. An employee-led backlash against the project has been churning for months at the company, but Tuesday's petition marks the first time workers at Google have used their names in a public document objecting to the plans. The existence of the project, code-named Dragonfly, was confirmed by chief executive Sundar Pichai last month. While China has long blocked search queries for what it has deemed politically sensitive material, Pichai said Google could still help Chinese Internet users find other information, such as health treatments, or steer them away from scams. But the project has drawn critics, who question Google's corporate values and have raised concerns about the consequences of tech companies cooperating with authoritarian governments.

A security guard stands in front of Google's booth at the China International Import Expo earlier this month in Shanghai. A security guard stands in front of Google's booth at the China International Import Expo earlier this month in Shanghai. Several Google employees have gone public with their opposition to the tech giant's plans for building a search engine tailored to China's censorship demands. The project, code-named Dragonfly, would block certain websites and search terms determined by the Chinese government -- a move that, according to a growing number of workers at Google, is tantamount to enabling "state surveillance." "We are among thousands of employees who have raised our voices for months. International human rights organizations and investigative reporters have also sounded the alarm, emphasizing serious human rights concerns and repeatedly calling on Google to cancel the project," said the letter's signatories, whose group initially numbered nine employees but has ballooned since its publication on Medium.

Cargill calls the robotic cattle driver "a first in the industry," and hopes that it will improve worker safety. Cargill calls the robotic cattle driver "a first in the industry," and hopes that it will improve worker safety. Brad Churchill, a slaughter operations manager at Cargill Meat Solutions, has worked in the cattle industry for more than 30 years -- and has seen many employee injuries caused by livestock. "A young man did nothing to provoke this 1,600-pound Angus steer who turned on him in an instant," Churchill said of one incident last year. The man crawled through an escape hatch, and ended up with a dislocated shoulder and few fractured ribs. Working with cattle is dangerous: The Bureau of Labor Statistics reports that in 2017, cattle injured 1,360 workers.

A group of Google employees published an open letter on Tuesday calling on their employer to cancel its plans to build a censored search engine for China, the latest expression of worker unrest at a company that earlier this month saw thousands stage walkouts over its handling of sexual misconduct cases. Google's plan for returning to China, which is known as Project Dragonfly and would reportedly allow the Chinese government to blacklist certain search terms and control air quality data, has garnered significant backlash internally since it was first reported on in August. More than 1,400 Google employees signed an internal petition criticizing the lack of transparency around the project, and at least one employee resigned in protest. But Tuesday's letter, which was initially signed by nine current Google employees, is a bold step for employees of a company that prizes internal transparency but considers leaking information to be not "Googley". Organizers of the letter said they would continuously update the letter as more employees signed on; by midday there were more than 50 signers.