After the buyer used the weapon to kill his estranged wife and two others, the site successfully invoked Section 230 to avoid liability. More recently, Grindr, a dating app, took cover behind Section 230 when Matthew Herrick, an actor in New York, sued the site as a result of false profiles that were created by an ex-boyfriend. The profiles, which included Herrick's home and work addresses, suggested that Herrick had rape fantasies, and that any resistance he put up was part of the fantasy. As a consequence, hundreds of men showed up at his apartment door or at his workplace, at all hours, month after month, forcibly demanding sex. "You look at that law, and it seems very narrow," Herrick's lawyer, Carrie Goldberg, told me.

Yang Liu, an Alexa AI principal scientist, and Ruhi Sarikaya, director of applied science, Alexa AI, have recently been named IEEE Fellows. The designation takes effect January 1, 2021. Liu is being honored for her "contributions to speech understanding and language-learning technology", while Sarikaya is being recognized for his "leadership in spoken language processing, and conversational understanding systems". Both currently lead research initiatives focused on making Alexa more natural and conversational, perceptive and context aware, and capable of self learning. The IEEE Fellow designation is conferred by the IEEE board of directors upon individuals with outstanding records of accomplishment in any of the IEEE fields of interest.

Artificial Intelligence (AI) has been a top trend in many industries lately, attracting massive media attention and investments. Over the last decade, this complex area of research has rapidly progressed from being a "resurrected cool technology from the past" to a full-blown driver of nothing less than a new industrial revolution -- a digital one. As of today, AI is widely commercialized in such applications as manufacturing robots, smart assistants (e.g. Siri), automated financial investing systems, virtual travel booking agents, social media monitoring tools, conversational bots, surveillance systems, online security systems, language translators, self-driving cars, and much more. In some industries, AI (including its many technologies and sub-disciplines, such as deep learning, recommender systems, and natural language processing), is becoming a standardized component rather than a cutting-edge innovation it once was. This rapid progress in AI adoption is also seen in the pharmaceutical industry -- not without caveats, however. Unlike "mainstream" use cases, like image recognition or spam email filtering, drug discovery research appears to be a much harder case for several reasons.

A version of the OS currently available to Insider program beta testers includes the ability to find files on your PC simply by asking for them. Enterprise users with OneDrive or SharePoint can also locate files in network drives that they have access to, but the key here is that it can find information without needing every specific detail. According to the blog post, just the part of a file name or name of an author can be enough for the AI to figure out which document you need. "Hey Cortana, opening marketing deck," and "Hey Cortana, open budget Excel from Anthony" are two examples, along with'find recent files" to pop up the last few items you've accessed. Build 20270 is now available for you in the Dev Channel.

Cortana may not be the personal assistant she once was, but a new update as part of the Windows Insider Dev Channel means that her capabilities to find files have improved. The new "File Skill" in the Cortana app appears in the new Windows 10 Insider Preview Build 20270, part of the Dev Channel. In this experimental app, Cortana's skills have been honed to the point where she can find files in the cloud, with a better understanding of what you're looking for. If your PC is enrolled in the Windows 10 May 2020 Update or later, some of these capabilities will already be available. As long as you have speech recognition enabled on your PC, you can ask Cortana "find my recent files," and it should unearth the last two or three files you've used on your PC.

Poor customer experiences are the primary cause for low conversion rates, lost leads and customer churn. In fact, a customer who suffered a third-rate experience will tell five times as many people about what happened than if they had a great experience. These bad impressions tend to stick with people and influence their future decisions for months or even years later. As a result, frustrated Sales Managers and Marketers often blame these sour interactions on substandard or poorly deployed software. Similarly, many marketers prefer today to keep things as hands-on as possible.

Gift giving has become a bit onerous. In the past, a recipient could keep a present or return it, but the gifts didn't raise significant questions or concerns. Even electronic gifts like a DVD player or remote-controlled car were inconsequential. Giving tech-focused products now requires a different mindset going in. A $20 or $30 Amazon Echo Dot might be the right price and readily available with free two-day shipping, but it raises some legitimate privacy concerns that you, as the giver, should be able to answer for.

In this article, we will have a detailed listing at AI Services supported and provided by Microsoft. Artificial Intelligence is a branch of computer science that aims to create intelligent machines, in simple terms, Human Intelligence processed by Machines, specifically computers. AI can be categorized as either weak and strong. Weak AI is AI systems which are specifically designed and trained for a particular task. A good example is personal assistants, such as Apple's Siri, Google Assistance, Microsoft Cortana, Amazon's Alexa are a form of Weak AI.

Due to researchers'aim to study personalized recommendations for different business fields, the summary of recommendation methods in specific fields is of practical significance. News recommendation systems were the earliest research field regarding recommendation systems, and were also the earliest recommendation field to apply the collaborative filtering method. In addition, news is real-time and rich in content, which makes news recommendation methods more challenging than in other fields. Thus, this paper summarizes the research progress regarding news recommendation methods. From 2018 to 2020, developed news recommendation methods were mainly deep learning-based, attention-based, and knowledge graphs-based. As of 2020, there are many news recommendation methods that combine attention mechanisms and knowledge graphs. However, these methods were all developed based on basic methods (the collaborative filtering method, the content-based recommendation method, and a mixed recommendation method combining the two). In order to allow researchers to have a detailed understanding of the development process of news recommendation methods, the news recommendation methods surveyed in this paper, which cover nearly 10 years, are divided into three categories according to the abovementioned basic methods. Firstly, the paper introduces the basic ideas of each category of methods and then summarizes the recommendation methods that are combined with other methods based on each category of methods and according to the time sequence of research results. Finally, this paper also summarizes the challenges confronting news recommendation systems.

Next destination recommendation is an important task in the transportation domain of taxi and ride-hailing services, where users are recommended with personalized destinations given their current origin location. However, recent recommendation works do not satisfy this origin-awareness property, and only consider learning from historical destination locations, without origin information. Thus, the resulting approaches are unable to learn and predict origin-aware recommendations based on the user's current location, leading to sub-optimal performance and poor real-world practicality. Hence, in this work, we study the origin-aware next destination recommendation task. We propose the Spatial-Temporal Origin-Destination Personalized Preference Attention (STOD-PPA) encoder-decoder model to learn origin-origin (OO), destination-destination (DD), and origin-destination (OD) relationships by first encoding both origin and destination sequences with spatial and temporal factors in local and global views, then decoding them through personalized preference attention to predict the next destination. Experimental results on seven real-world user trajectory taxi datasets show that our model significantly outperforms baseline and state-of-the-art methods.