For years, YouTube, the world's most popular video network, has been battling issues with "bad actors" wreaking havoc with the system. The Google-owned property wants to be a safe haven for advertisers to reach young viewers, primarily, with its mix of original videos and a library with virtually anything ever recorded on video. Yet once again, YouTube found itself under scrutiny this week for more abuses. Seemingly innocent videos of young girls doing gymnastics were hijacked by adult viewers commenting with time stamps and links to child pornography videos elsewhere on the web. So after being outed by YouTuber Matt Watson expressing his rage and losing top advertisers like Disney, AT&T, Epic Games and others in response, YouTube said it would change its ways, and disable commenting on any video involving children.

As artificial intelligence brings nations high speed facial recognition capabilities, surveillance societies are rising across nations: blurring the lines between privacy and security. This is due to the enormous scale of changes that are enabled by significant advancements in technology: digital imaging, high speed processing, skin texture analysis, thermal cameras, machine learning, 3D sensors, speech recognition, mood recognition and more. These advancements break technical barriers allowing the extensive collection, recording, storage, analysis and application of digital data and information. Moreover, the explosion in processing power allows powerful computers utilizing artificial intelligence and machine learning to execute facial recognition capabilities with almost perfect accuracy. Today, facial recognition technology can be used to not only identify individuals but also uncover additional personally identifiable information (PII), such as photos, blog posts, social networking profiles and internet behavior all through facial features alone. This is just the tip of the iceberg.

More and more businesses are beginning to appreciate chatbots. The ability of AI chatbots to mimic human conversations and provide instant digital connection has paved their way to success. Modern chatbots handle online requests 24/7, create brand engagement, reduce human errors, and save costs of customer service. Apart from that, chatbots particularly appeal to "speedy" Millennials by keeping them constantly "updated." Business giants such as Sephora and Marriott already see the benefits of using chatbots.

Looking to leverage AI in your organization? Don't miss the Business Summit at the AI Conference in New York, April 15–18, 2019. Register before March 1 to save with Early Price. In a recent post, we described what it would take to build a sustainable machine learning practice. By "sustainable," we mean projects that aren't just proofs of concepts or experiments. A sustainable practice means projects that are integral to an organization's mission: projects by which an organization lives or dies. These projects are built and supported by a stable team of engineers, and supported by a management team that understands what machine learning is, why it's important, and what it's capable of accomplishing. Finally, sustainable machine learning means that as many aspects of product development as possible are automated: not just building models, but cleaning data, building and managing data pipelines, testing, and much more. Machine learning will penetrate our organizations so deeply that it won't be possible for humans to manage them unassisted. Organizations throughout the world are waking up to the fact that security is essential to their software projects. Nobody wants to be the next Sony, the next Anthem, or the next Equifax.

When a news article revealed that Clarifai was working with the Pentagon and some employees questioned the ethics of building artificial intelligence that analyzed video captured by drones, the company said the project would save the lives of civilians and soldiers. "Clarifai's mission is to accelerate the progress of humanity with continually improving A.I.," read a blog post from Matt Zeiler, the company's founder and chief executive, and a prominent A.I. researcher. Later, in a news media interview, Mr. Zeiler announced a new management position that would ensure all company projects were ethically sound. As activists, researchers, and journalists voice concerns over the rise of artificial intelligence, warning against biased, deceptive and malicious applications, the companies building this technology are responding. From tech giants like Google and Microsoft to scrappy A.I. start-ups, many are creating corporate principles meant to ensure their systems are designed and deployed in an ethical way.

Article 22 regulating "automated individual decision-making" processes 28 29.

Artificial Intelligence (AI) researchers may look back on 2018 as the year that human rights became crucial to advancing the technology. Over the last six months of the year, a slew of reports focused on "artificial intelligence and human rights" were published by a variety of well-respected entities, including the most recent report of the UN Special Rapporteur on freedom of opinion and expression, Berkman Klein's report on "Artificial Intelligence & Human Rights: Opportunities & Risks", Access Now's "Human Rights in the Age of Artificial Intelligence" report, The Council of Europe's Draft Recommendation of the Committee of Ministers to member States on human rights impacts of algorithmic systems, and Business for Social Responsibility's, "Artificial Intelligence: A Rights-Based Blueprint for Business" series. Earlier in the year, I was asked to help kick off a workshop organized by Data & Society on the same topic and I wrote this post based on the remarks I prepared for that conference, supplemented by a few takeaways from recent reports. I come to this issue as a trained lawyer who spent the last decade working on human rights, with a special focus on the issues of "business and human rights" and human rights online. I have seen how the international human rights (IHR) framework can enable better understanding and contestation of human rights norms, monitor and mitigate the risk of human rights abuses, generate input and output legitimacy, and facilitate trust and coalition-building.

To talk about artificial intelligence today usually involves talking about automation, lost jobs, and whether your Uber or Lyft will be a self-driving vehicle in 5 years time. Taking a step back from the apps in our phones, however, and what begins to emerge is a larger fight between the US and China, AI superpowers whose contest will likely decide the course of human civilization. In January 2019, the UN's World Intellectual Property Organization (WIPO), whose role is to help spread ideas and innovation around the world, released a report that highlighted the rapid advance of AI patent applications across the world. According to the report, the United States and China's AI-related patent applications outstripped every other nation's by far. "The U.S. and China obviously have stolen a lead. They're out in front in this area, in terms of numbers of applications, and in scientific publications," said Francis Gurry, WIPO Director-General, at a news conference last month announcing the report.

Two divisions of the Chinese networking giant Huawei pleaded not guilty Thursday to charges that it stole trade secrets from America's third-largest wireless carrier, T-Mobile, in a bid to copy its technology. In federal court in Seattle, Huawei -- one of the world's biggest wireless equipment makers -- said it was not guilty of committing trade secret theft, nor of conspiring to hide such a plan. The case involves Huawei Device Co., Ltd. and Huawei Device USA. A jury trial has been set for March 2, 2020, before Chief Judge Ricardo S. Martinez of the U.S. District Court for the Western District of Washington. The pleas follow a 10-count indictment unsealed last month alleging in part that the Huawei divisions tried to collect information about a robotic arm that T-Mobile used to simulate human touch on its smartphones.

The behavior of self-driving cars must be compatible with an enormous set of conflicting and ambiguous objectives, from law, from ethics, from the local culture, and so on. This paper describes a new way to conveniently define the desired behavior for autonomous agents, which we use on the self-driving cars developed at nuTonomy. We define a "rulebook" as a pre-ordered set of "rules", each akin to a violation metric on the possible outcomes ("realizations"). The rules are partially ordered by priority. The semantics of a rulebook imposes a pre-order on the set of realizations. We study the compositional properties of the rulebooks, and we derive which operations we can allow on the rulebooks to preserve previously-introduced constraints. While we demonstrate the application of these techniques in the self-driving domain, the methods are domain-independent.