Sign up for internet service with Comcast's Xfinity, and the company will get you in for $19.95 for a relatively slow 25 megabits per second, or $49.99 for "faster speeds" like 200 Mbps. But if you're having trouble with your video calls dropping out, buffering when watching Netflix or waiting for websites to load on your computer, getting faster internet speed may not be the answer. That's the admittedly biased opinion of Nick Weaver, the founder of Eero, a device that connects to your home internet and spreads Wi-Fi signals more evenly throughout the various rooms. "You're welcome to pay Comcast pay more money monthly if you like, but it won't solve the problem," says Weaver. You will get faster internet if using a wired connection, "but not in the places of the home where you need it," as in devices that depend upon Wi-Fi like laptops, smart TVs, connected speakers like Amazon Echo and more.

Energy, Oil and gas wealth is considered as one of the most valued commodities across the globe. As the revenue in the sector spikes, the security risks it faces also increases with more cyber and physical attacks taking place in the recent years. Unlike other industries, energy, oil & gas sector get a big bang on its whole working system every time a security breach happens. Ultimately, video analytics combined with artificial intelligence (AI) is shoving hackers the exit door with its extended technological influence. According to a report, global energy was valued at US$1.7 trillion, which is a 2.2% of the global GDP in 2016.

Technology is taking our day to day life and businesses to the next level. Yes, the days of doing everything manually is no more. Imagine if someone from the 1950s traveled through time to 2019, they would marvel at the way we use our handheld devices to navigate around the town, the way assistants such as Alexa, Cortana, and Siri are responding to our queries, and would be stunned by our addiction towards social media platforms. Artificial Intelligence may be considered a central tenet for the disruptive changes of the 4th Industrial Revolution by offering more transformative experiences than any other industrial revolution before. It is the core concept where computers and machines exhibiting intelligence like humans.

If your family is anything like mine, smart displays like the Nest Hub tend to get a lot of use by both adults and kids. The conversational capabilities of Google Assistant make that almost a certainty. So it makes sense that for one of its last major updates of 2020, Google is focusing on families with a suite of new features for Assistant-enabled smart displays and speakers. To start, Google is adding a new Family Notes feature that will let your clan append digital sticky notes to the Nest Hub and other smart displays. You can use them to leave messages and keep track of shared to-dos.

The feature allows you to carry out common actions on several smart devices simultaneously with a single custom Alexa command. For instance, you may already switch on your bedroom light and activate your coffee machine as part of your wake-up routine, and perhaps you'll want to turn on your TV to your favorite news network as well. Amazon suggests you could use routines to pause what's playing on your TV and turn on certain lights when you want to get up and grab a snack. Along with pausing and switching your TV on or off, you can use routines to start playing certain content on Fire TV or open a specific app. Alexa Routines are supported on all Fire TV devices worldwide.

Despite recent advances in many application-specific domains, we do not know how to build a human-level artificial intelligence (HLAI). We conjecture that learning from others' experience with the language is the essential characteristic that differentiates human intelligence from the rest. Humans can update the action-value function only with the verbal description as if they experience states, actions, and corresponding rewards sequences first hand. In this paper, we present our ongoing effort to build an environment to facilitate the research for models of this capability. In this environment, there are no explicit definitions of tasks or rewards given when accomplishing those tasks. Rather the models experience the experience of the human infants from fetus to 12 months. The agent should learn to speak the first words as a human child does. We expect the environment will contribute to the research for HLAI.

Compared with CrossWOZ (Chinese) and MultiWOZ (English) dataset which have coarse-grained information, there is no dataset which handle fine-grained and hierarchical level information properly. In this paper, we publish a first Cantonese knowledge-driven Dialogue Dataset for REStaurant (KddRES) in Hong Kong, which grounds the information in multi-turn conversations to one specific restaurant. Our corpus contains 0.8k conversations which derive from 10 restaurants with various styles in different regions. In addition to that, we designed fine-grained slots and intents to better capture semantic information. The benchmark experiments and data statistic analysis show the diversity and rich annotations of our dataset. We believe the publish of KddRES can be a necessary supplement of current dialogue datasets and more suitable and valuable for small and middle enterprises (SMEs) of society, such as build a customized dialogue system for each restaurant. The corpus and benchmark models are publicly available.

The COVID-19 pandemic has elevated the role of technology in the workplace, and more employers are relying on artificial intelligence, machine learning and virtual reality to save money and limit in-person contact. These technologies can be effective tools for hiring, training and assessing employee performance, as well as creating meaningful interactions during a time of isolation. However, employers must ensure that their use of technology doesn't run afoul of employment and labor laws. "It's incredibly important for HR organizations and hiring managers to understand the nuances of the technology that they're using if it is making decisions on their behalf," said Marc Goldberg, chief technology officer at the Society for Human Resource Management (SHRM) in Alexandria, Va. He was speaking during a panel discussion at the American Bar Association's 14th Annual Labor and Employment Law Conference, which was held virtually.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves, i know that sounds a little bit confuse but will be clear at the end. At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video. The key difference from traditional computer software is that a human developer hasn't written code that instructs the system how to tell the difference between the banana and the apple.

Personalization and recommendations are now accepted as core competencies in just about every online setting, ranging from media platforms to e-commerce to social networks. While the challenge of estimating user preferences has garnered significant attention, the operational problem of using such preferences to construct personalized offer sets to users is still a challenge, particularly in modern settings where a massive number of items and a millisecond response time requirement mean that even enumerating all of the items is impossible. Faced with such settings, existing techniques are either (a) entirely heuristic with no principled justification, or (b) theoretically sound, but simply too slow to work. Thus motivated, we propose an algorithm for personalized offer set optimization that runs in time sub-linear in the number of items while enjoying a uniform performance guarantee. Our algorithm works for an extremely general class of problems and models of user choice that includes the mixed multinomial logit model as a special case. We achieve a sub-linear runtime by leveraging the dimensionality reduction from learning an accurate latent factor model, along with existing sub-linear time approximate near neighbor algorithms. Our algorithm can be entirely data-driven, relying on samples of the user, where a `sample' refers to the user interaction data typically collected by firms. We evaluate our approach on a massive content discovery dataset from Outbrain that includes millions of advertisements. Results show that our implementation indeed runs fast and with increased performance relative to existing fast heuristics.