Let's take a detailed look. This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.

Recommender systems typically suggest to users content similar to what they consumed in the past. If a user happens to be exposed to strongly polarized content, she might subsequently receive recommendations which may steer her towards more and more radicalized content, eventually being trapped in what we call a "radicalization pathway". In this paper, we study the problem of mitigating radicalization pathways using a graph-based approach. Specifically, we model the set of recommendations of a "what-to-watch-next" recommender as a d-regular directed graph where nodes correspond to content items, links to recommendations, and paths to possible user sessions. We measure the "segregation" score of a node representing radicalized content as the expected length of a random walk from that node to any node representing non-radicalized content. High segregation scores are associated to larger chances to get users trapped in radicalization pathways. Hence, we define the problem of reducing the prevalence of radicalization pathways by selecting a small number of edges to "rewire", so to minimize the maximum of segregation scores among all radicalized nodes, while maintaining the relevance of the recommendations. We prove that the problem of finding the optimal set of recommendations to rewire is NP-hard and NP-hard to approximate within any factor. Therefore, we turn our attention to heuristics, and propose an efficient yet effective greedy algorithm based on the absorbing random walk theory. Our experiments on real-world datasets in the context of video and news recommendations confirm the effectiveness of our proposal.

Thinking artificial intelligence could boost your portfolio right about now? But which AI stocks beyond the usual suspects: Alphabet Inc. (NASDAQ: GOOG), Apple Inc. (NASDAQ: AAPL), Twilio Inc. (NYSE: TWLO), ServiceNow, Inc. (NYSE: NOW), NVIDIA Corporation (NASDAQ: NVDA) and QUALCOMM Incorporated (NASDAQ: QCOM). Whether you believe some of the famous companies above are bearish and/or overvalued (another argument for another day) or are just looking to inject some new blood into your portfolio, let's go through some options you might consider. But first, in the interest of education, what are AI stocks and which ones should you consider right now? Let's find out. At its most basic level, artificial intelligence (AI) refers to an algorithm or dynamic machine that learns and interprets the data given to it.

Game day is right around the corner, which means it's one of the best times of the year to upgrade your TV. Aside from the holiday shopping season, the few weeks leading up to the Super Bowl have some of the best TV deals you'll find throughout the year. Sets from Samsung, LG, Hisense and others can be hundreds of dollars off and you can even find devices like soundbars and streamers for less, too. Just keep in mind that often the best discounts will be on more expensive TVs. But retailers like Amazon and Best Buy still have a bunch of TVs under $500 that are worth considering if you don't want to spend too much -- and it's possible to save money on those, too.

Clinicians are spending excessive time on EHR systems which leads to burnout and less satisfaction with their work. Physicians spend more than half of their working time on EHR-related tasks1. This averages to be 4.5 hours while in the clinic, and 1.5 hours during their personal time1. The excessive time doctors spend on EHR-related tasks is due to documentation and reporting requirements1. Unfortunately, clinicians end up spending less time seeing patients and communicating with other healthcare professionals because of these burdens. This can lead to medical errors for patients and increased frustration in the workplace.

Grindr is still facing trouble in China. Bloomberg reports the gay dating app has disappeared from Apple's App Store in the country, with researchers at Qimai estimating the software was removed on January 27th. There was no immediate explanation for the departure, but it came just days after China's Cyberspace Administration launched a campaign to purge illegal online material, porn and rumors ahead of the Winter Olympics. We've asked Apple and Grindr for comment. The app's departure came after weeks of glitches with Grindr's service, such as problems adding likes or sending messages.

Gay dating app Grindr has disappeared from multiple app stores in China as authorities tighten control of the country's already heavily policed internet and purge online behaviour the ruling Communist Party dislikes. The country's cyber authority is in the midst of a month-long campaign to root out illegal and sensitive content during the Lunar New Year holiday and February's Winter Olympics. Although the world's most populous nation decriminalised homosexuality in 1997, same-sex marriage is illegal and LGBTQ issues remain taboo. The LGBTQ community is under pressure as censorship of web content combines with a ban on depictions of gay romance in films. Data from mobile research firm Qimai shows that Grindr was removed from Apple's App Store in China on Thursday.

NLP is leveraged to improve search results by filtering and contextualizing them, making the results more relevant for shoppers. This can also be done by focusing on visual elements in a search. Thanks to machine learning, AI software tags, arranges and searches visually for content by categorizing image and/or video features. This technology also allows shoppers to not just find products that match, but those that complement them too. So to purchase an item, a customer need not search and browse endlessly, simply upload an image. It often happens that sales teams fail to follow up with marketing leads, allowing qualified potential customers to get lost in the system.

A 2019 study conducted by DataChildFutures found that 46% of participating Italian households had AI-powered speakers, while 40% of toys were connected to the internet. More recent research suggests that by 2023 more than 275 million intelligent voice assistants, such as Amazon Echo or Google Home, will be installed in homes worldwide. As younger generations grow up interacting with AI-enabled devices, more consideration should be given to the impact of this technology on children, their rights and wellbeing. AI-powered learning tools and approaches are often regarded as critical drivers of innovation in the education sector. Often recognized for its ability to improve the quality of learning and teaching, artificial intelligence is being used to monitor students' level of knowledge and learning habits, such as rereading and task prioritization, and ultimately to provide a personalized approach to learning. Knewton is one example of AI-enabled learning software that identifies knowledge gaps and curates education content in line with user needs.

Maximizing a monotone submodular function is a fundamental task in machine learning. In this paper, we study the deletion robust version of the problem under the classic matroids constraint. Here the goal is to extract a small size summary of the dataset that contains a high value independent set even after an adversary deleted some elements. We present constant-factor approximation algorithms, whose space complexity depends on the rank $k$ of the matroid and the number $d$ of deleted elements. In the centralized setting we present a $(3.582+O(\varepsilon))$-approximation algorithm with summary size $O(k + \frac{d \log k}{\varepsilon^2})$. In the streaming setting we provide a $(5.582+O(\varepsilon))$-approximation algorithm with summary size and memory $O(k + \frac{d \log k}{\varepsilon^2})$. We complement our theoretical results with an in-depth experimental analysis showing the effectiveness of our algorithms on real-world datasets.