AI and People Analytics have taken off. As I've written about in the past, the workplace has become a highly instrumented place. Companies use surveys and feedback tools to get our opinions, new tools monitor emails and our network of communications (ONA), we capture data on travel, location, and mobility, and organizations now have data on our wellbeing, fitness, and health. And added to this is a new stream of data which includes video (every video conference can be recorded and more than 40% of job interviews are recorded), audio (tools that record meetings can sense mood), and image recognition that recognizes faces wherever we are. In the early days of HR analytics, companies captured employee data to measure span of control, the distribution of performance ratings, succession pipeline, and other talent-related topics. Today, with all this new information entering the workplace (virtually everywhere you click at work is stored somewhere), the domain of people analytics is getting very personal. While I know HR professionals take the job of ethics and safety seriously, I'd like to point out some ethical issues we need to consider.

San Francisco supervisors approved a ban on police using facial recognition technology, making it the first city in the U.S. with such a restriction. SAN FRANCISCO – San Francisco supervisors voted Tuesday to ban the use of facial recognition software by police and other city departments, becoming the first U.S. city to outlaw a rapidly developing technology that has alarmed privacy and civil liberties advocates. The ban is part of broader legislation that requires city departments to establish use policies and obtain board approval for surveillance technology they want to purchase or are using at present. Several other local governments require departments to disclose and seek approval for surveillance technology. "This is really about saying: 'We can have security without being a security state. We can have good policing without being a police state.' And part of that is building trust with the community based on good community information, not on Big Brother technology," said Supervisor Aaron Peskin, who championed the legislation.

In this Oct. 31 photo, a man has his face painted to represent efforts to defeat facial recognition. It was during a protest at Amazon headquarters over the company's facial recognition system. In this Oct. 31 photo, a man has his face painted to represent efforts to defeat facial recognition. It was during a protest at Amazon headquarters over the company's facial recognition system. San Francisco has become the first U.S. city to ban the use of facial recognition technology by police and city agencies.

A Navy scientist has re-engineered the software that allows long-endurance drones to powerlessly climb into the sky on bubbles of warm air. In a U.S. patent application published on May 2, Aaron Kahn, an engineer working on the Autonomous Locator of Thermals (ALOFT) project at the Naval Research Laboratory, reported that he has extensively tested the new software that detects and estimates the position of thermals, i.e., rising columns of warm air that birds use to stay aloft without flapping their wings. Unlike birds, soaring drones need the benefits of thermal detection and position estimation software as the warm air tends to drift relative to the ground due to winds. Prior systems relied on batch estimation processes that "require storing large arrays of data, which is not ideal for operation on small micro-controllers with limited memory resources." Kahn's new soaring software uses extended Kalman filtering, a kind of algorithm already used by the Navy for navigating submarines and cruise missiles. Now it can help orbit drones like the tiny CICADA glider or long-endurance solar-soaring UAVs that might also have photovoltaic or fuel cells feeding battery-powered propellers.

The deep learning algorithms of artificial intelligence can identify patterns that help inventors think laterally, make connections between nonobvious ideas, pinpoint hidden invention features, and exploit new science and technology-based opportunities. "To invent, you need a good imagination and a pile of junk." So said Thomas Edison, America's most prolific inventor. Yet the march of technology is now changing the great man's inventive equation: powerful algorithmic advisory systems are now giving inventors far more fertile imaginations, even if they don't have very much of one themselves. After being fed vast datasets of information on a field of inventive endeavor, deep learning algorithms identify patterns that help inventors think laterally, make connections between nonobvious ideas, pinpoint hidden invention features that rivals have missed, and exploit new science and technology-based opportunities from, say, patents and journals.

Argument mining is a rising area of Natural Language Pro- cessing (NLP) concerned with the automatic recognition and interpretation of argument components and their relations. Neural models are by now mature technologies to be ex- ploited for automating the argument mining tasks, despite the issue of data sparseness. This could ease much of the man- ual effort involved in these tasks, taking into account hetero- geneous types of texts and topics. In this work, we evaluate different attention mechanisms applied over a state-of-the-art architecture for sequence labeling. We assess the impact of different flavors of attention in the task of argument compo- nent detection over two datasets: essays and legal domain. We show that attention not models the problem better but also supports interpretability.

In this paper we adapt the semi-supervised deep learning architecture known as Convolutional Ladder Networks, from the domain of computer vision, and explore how well it works for a semi-supervised Named Entity Recognition and Classification task with legal data. The idea of exploring a semi-supervised technique is to asses the impact of large amounts of unsupervised data (cheap to obtain) in specific tasks that have little annotated data, in order to develop robust models that are less prone to overfitting. In order to achieve this, first we must check the impact on a task that is easier to measure. We are presenting some preliminary results, however, the experiments carried out show some very interesting insights that foster further research in the topic.

However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles--the'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)--rather than on practices, the'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Therefore, our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers'apply ethics' at each stage of the pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

A technician works in a lab at GeseDNA Technology in Beijing. To counter China, the U.S. plans to impose new export restrictions on "emerging and foundational technology" that researchers say could affect the way they share genetic materials with international labs. A technician works in a lab at GeseDNA Technology in Beijing. To counter China, the U.S. plans to impose new export restrictions on "emerging and foundational technology" that researchers say could affect the way they share genetic materials with international labs. For the last 15 years, Addgene has dedicated itself to accelerating medical research.

In this work we focus on fine-tuning a pre-trained BERT model and applying it to patent classification. When applied to large datasets of over two millions patents, our approach outperforms the state of the art by an approach using CNN with word embeddings. In addition, we focus on patent claims without other parts in patent documents. Our contributions include: (1) a new state-of-the-art method based on pre-trained BERT model and fine-tuning for patent classification, (2) a large dataset USPTO-3M at the CPC subclass level with SQL statements that can be used by future researchers, (3) showing that patent claims alone are sufficient for classification task, in contrast to conventional wisdom.