Google's Duplex, which calls businesses on your behalf and imitates a real human, ums and ahs included, has sparked a bit of controversy among privacy advocates. Doesn't Google recording a person's voice and sending it to a datacenter for analysis violate two-party consent law, which requires everyone in a conversation to agree to being recorded? Let's take California's law as the example, since that's the state where Google is based and where it used the system. Penal Code section 632 forbids recording any "confidential communication" (defined more or less as any non-public conversation) without the consent of all parties. Google has provided very little in the way of details about how Duplex actually works, so attempting to answer this question involves a certain amount of informed speculation.

"Existing patterns of structural discrimination may be reproduced and aggravated in situations that are particular to these technologies – for example, machine learning system goals that create self-fulfilling markers of success and reinforce patterns of inequality, or issues arising from using non-representative or "biased" datasets.

Conference Bot can manage details relating to a chatbot conference and automatically introduces people to one another, enhances inter-personal engagement and as a result also increases the sales margin. An HR bot can fill in lost minutes and can allow members of staff to ask questions and get immediate feedback round the clock. It can also enable an employee to resolve minute issues that they might have themselves as opposed to having them rely on another person's schedule. Chatbots can assist in the process of inventory management in an organization. It can be implemented along with Internet of Things (IoT) technology to notify workers at a warehouse in case a product runs out of stock or when new stock arrives.

Today we are starting a six-part series on Negative SEO. The series will be broken into three areas and will show how negative search engine optimization (SEO) has an effect on links, content and user signals. Positive SEO under this broader view would be any tactic performed with the intent to positively impact rankings for a uniform resource locator (URL), and possibly its host domain, by manipulating a variable within the links, content or user signals areas. Negative SEO would be any tactic performed with the intent to negatively impact rankings for a URL, and possibly its host domain, by manipulating a variable within the links, content or user signal buckets. If you can accidentally hurt your rankings by shifting a variable, then it would logically suggest that an external entity shifting that same variable associated with your site could result in a ranking decrease or outright deindexation.

Visitors enjoy an artificial intelligence robot's performance during the 2018 Exposition on China's Indigenous Brands at Shanghai Exhibition Center on May 10, 2018 in Shanghai. China, thanks to its 1.37 billion residents, leads the world on a number of counts. The smartphone user population totaled 663 million last year, more than anywhere else, and its internet penetration stood at 751 million. Now China ranks first in the world in the number of patent applications from two heavily watched, fast-growing areas of high tech: cryptocurrency and artificial intelligence, claims American startup ecosystem research organization Startup Genome in a 2018 report. Chalk that up to active government support, domestic demand for new technology and, per one study, a more relaxed patent regime.

As the 80's hit song by Timbuk 3 goes'The future's so bright we've got to wear shades' and in the advertising, marketing and communications industry one could easily believe that to be true. With the advent of IP delivered programmatic, dynamic creative, artificial intelligence, voice search, virtual reality, augmented reality etc. which provide the ability to personally target individuals at scale this should herald an epoch-making opportunity for the industry. However, far from it being seen as a time for confidence, it is actually proving to be a period of consternation for marketers and agencies alike. At the heart of this marketing revolution is the sheer volume and quality of data that we now have at our disposal -- as it has often been said, data is the new oil. But is it possible to have too much of a good thing?

Currently there is no standard way to identify how a dataset was created, and what characteristics, motivations, and potential skews it represents. To begin to address this issue, we propose the concept of a datasheet for datasets, a short document to accompany public datasets, commercial APIs, and pretrained models. The goal of this proposal is to enable better communication between dataset creators and users, and help the AI community move toward greater transparency and accountability. By analogy, in computer hardware, it has become industry standard to accompany everything from the simplest components (e.g., resistors), to the most complex microprocessor chips, with datasheets detailing standard operating characteristics, test results, recommended usage, and other information. We outline some of the questions a datasheet for datasets should answer. These questions focus on when, where, and how the training data was gathered, its recommended use cases, and, in the case of human-centric datasets, information regarding the subjects' demographics and consent as applicable. We develop prototypes of datasheets for two well-known datasets: Labeled Faces in The Wild~\cite{lfw} and the Pang \& Lee Polarity Dataset~\cite{polarity}.

Legislation follows innovation like hangovers follow red wine, and the impact can be extensive. In January this year, there were only rumblings of new cryptocurrency legislation in South Korea, and the value of Bitcoin fell by more than 15%, a drop from which it has not recovered. The 2018 General Data Protection Regulation (GDPR) come as a long overdue update of the 1998 Data Protection Act and the Privacy and Electronic Communications Regulations (PECR). The updates include conditions like the right to be removed from a given database, and the right to be notified of data sales to third parties. Now we are in the midst of the Cambridge Analytica data scandal, and the amount of personal data being collected has become a central matter of public debate.

Machine learning software has been touted as the next wave of innovation, promising to help governments and businesses make faster and more accurate decisions. But human rights activists and technology groups warned Wednesday that creating systems that discriminate should be treated as a violation of human rights. It came with the release at the RightsCon conference of the so-called Toronto Declaration on preventing machine learning from being used to support discrimination. Machine learning systems – sometimes called artificial intelligence – are more than pattern recognition software, say adherents of the declaration. Used wrongly – deliberately or inadvertently -- by data scientists and software developers, they can violate privacy, data protection, freedom of expression, participation in cultural life and equality before the law.

Today in Toronto, a coalition of human rights and technology groups released a new declaration on machine learning standards, calling on both governments and tech companies to ensure that algorithms respect basic principles of equality and non-discrimination. Called The Toronto Declaration, the document focuses on the obligation to prevent machine learning systems from discriminating, and in some cases violating, existing human rights law. The declaration was announced as part of the RightsCon conference, an annual gathering of digital and human rights groups. "We must keep our focus on how these technologies will affect individual human beings and human rights," the preamble reads. "In a world of machine learning systems, who will bear accountability for harming human rights?"