In this section, we will talk about Artificial Intelligence, its history, applications, the different types of AI, and the programming languages that are used for AI. Note that I will not be talking about how to code AI but mainly focus on the various languages which support AI. No, don't close this tab!!! Ok fine, I'll start doing my job of explaining properly. "The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." In simple words, AI is the science of making machines that can think. It's a technique of getting machines to work and behave like humans which accomplishes this task by creating machines and robots.

Graph Convolution Network (GCN) has attracted significant attention and become the most popular method for learning graph representations. In recent years, many efforts have been focused on integrating GCN into the recommender tasks and have made remarkable progress. At its core is to explicitly capture high-order connectivities between the nodes in user-item bipartite graph. However, we theoretically and empirically find an inherent drawback existed in these GCN-based recommendation methods, where GCN is directly applied to aggregate neighboring nodes will introduce noise and information redundancy. Consequently, the these models' capability of capturing high-order connectivities among different nodes is limited, leading to suboptimal performance of the recommender tasks. The main reason is that the the nonlinear network layer inside GCN structure is not suitable for extracting non-sematic features(such as one-hot ID feature) in the collaborative filtering scenarios. In this work, we develop a new GCN-based Collaborative Filtering model, named Refined Graph convolution Collaborative Filtering(RGCF), where the construction of the embeddings of users (items) are delicately redesigned from several aspects during the aggregation on the graph. Compared to the state-of-the-art GCN-based recommendation, RGCF is more capable for capturing the implicit high-order connectivities inside the graph and the resultant vector representations are more expressive. We conduct extensive experiments on three public million-size datasets, demonstrating that our RGCF significantly outperforms state-of-the-art models. We release our code at https://github.com/hfutmars/RGCF.

Dating in 2020 is a roller coaster, from endless swiping to video chat dates, the worry that your quarantine-boo might be fake is all too real. "I've been on Tinder on-and-off for the past three years, but have been back on since March when the pandemic started. I have been seeing more bots than usual," said Carlos Zavala, 25, of his dating experience. Online dating in the U.S. has become the most popular way couples connect, a Stanford study published in 2019 found. That finding is being put to the test with the outbreak of the coronavirus in the U.S. since mid-March.

Dating in 2020 is a roller coaster, from endless swiping to video chat dates, the worry that your quarantine-boo might be fake is all too real. "I've been on Tinder on-and-off for the past three years, but have been back on since March when the pandemic started. I have been seeing more bots than usual," said Carlos Zavala, 25, of his dating experience. Online dating in the U.S. has become the most popular way couples connect, a Stanford study published in 2019 found. That finding is being put to the test with the outbreak of the coronavirus in the U.S. since mid-March.

A couple of Amazon Echo devices are on sale right now, but the best deal of the bunch is on the Echo Plus. The speaker with a built-in smart home hub is now $80, which is $70 off its typical price of $150. You can get the Echo Plus by itself for $80, or you can choose the bundle the includes a Philips Hue smart light bulb for free. That means you don't need to buy any extra connecting hubs when you purchase other IoT devices like light bulbs, door locks, security cameras and others. If it works with the Zigbee protocol (a majority of IoT devices do) or Amazon's own Works with Alexa platform, then it'll be able to connect to the hub inside of the Echo Plus.

The cat's out of the bag as far as Google's latest smart speaker goes, with the company essentially confirming the rumored Home successor on Thursday with a coyly worded email containing a snapshot and a video of the unannounced device. Many key details about the new Google smart speaker are still shrouded in mystery, but we can tease out a few facts by studying the official photo and Google's brief teaser video. Well, no duh, but given that Google gives so little away in its teaser video, we might as well notch this one as one of the few certainties. In the brief video, a man on a sofa says "Hey Google, play some music," and four telltale LEDs on the new speaker (speakers, actually, there two of them) light up as the music begins to play. So yes, Google Assistant confirmed.

Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on how machine learning is employed not only to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods, approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

Fashion is a fast-changing industry where designs are refreshed at large scale every season. Moreover, it faces huge challenge of unsold inventory as not all designs appeal to customers. This puts designers under significant pressure. Firstly, they need to create innumerous fresh designs. Secondly, they need to create designs that appeal to customers. Although we see advancements in approaches to help designers analyzing consumers, often such insights are too many. Creating all possible designs with those insights is time consuming. In this paper, we propose a system of AI assistants that assists designers in their design journey. The proposed system assists designers in analyzing different selling/trending attributes of apparels. We propose two design generation assistants namely Apparel-Style-Merge and Apparel-Style-Transfer. Apparel-Style-Merge generates new designs by combining high level components of apparels whereas Apparel-Style-Transfer generates multiple customization of apparels by applying different styles, colors and patterns. We compose a new dataset, named DeepAttributeStyle, with fine-grained annotation of landmarks of different apparel components such as neck, sleeve etc. The proposed system is evaluated on a user group consisting of people with and without design background. Our evaluation result demonstrates that our approach generates high quality designs that can be easily used in fabrication. Moreover, the suggested designs aid to the designers creativity.

Medical workers are our heroes as the COVID-19 outbreak continues with deaths and confirmed cases rising. A CNN survey found that healthcare systems are coming under strain because of the increasing number of patients infected by the coronavirus. Partners Healthcare is supporting patients in the COVID-19 outbreak by using AI to detect those infected and those showing signs of the virus. Healthcare workers at Partners Healthcare understand the current crisis because of high patient numbers experienced and are working round the clock. One question that comes to mind as COVID-19 pandemic continues is: How responsible is the current use of AI to combat the outbreak?

One of the best things about Amazon Alexa is that she can make life easier by helping you prepare for the day ahead. She read you the forecast, remind you of upcoming meetings, and much more. Now she can brew you a pot of coffee before you ever leave the bed. Like many people, coffee is essential for making my mornings run smoothly. While living in quarantine with my two young kids, I'm always looking for ways to automate my morning and make life less hectic, like letting Alexa brew me up a pot of the good stuff.