Graph representations for real-world social networks in the past have missed two important elements: the multiplexity of connections as well as representing time. To this end, in this paper, we present a new dynamic heterogeneous graph representation for social networks which includes time in every single component of the graph, i.e., nodes and edges, each of different types that captures heterogeneity. We illustrate the power of this representation by presenting four time-dependent queries and deep learning problems that cannot easily be handled in conventional homogeneous graph representations commonly used. As a proof of concept we present a detailed representation of a new social media platform (Steemit), which we use to illustrate both the dynamic querying capability as well as prediction tasks using graph neural networks (GNNs). The results illustrate the power of the dynamic heterogeneous graph representation to model social networks. Given that this is a relatively understudied area we also illustrate opportunities for future work in query optimization as well as new dynamic prediction tasks on heterogeneous graph structures.

Gone are those days where humans have to perform complex task, the world is gradually growing into a new system called the Artificial intelligence. With Artificial intelligence gradually growing into use, a time will come where you will walk into a banking hall and discover that all you have to do is to interract with an Artificial intelligence program and got your need sorted out swiftly. The need to solve complex issues so swiftly is the reason why the need for this system is needed. Artificial Intelligence (A.I), is simply the ability of a computer program or a machine to think and learn. This process helps to see your computer performing a very complex task and at same time, making it smart.

The combination of all 4 of these services allow for Sophia's identity to be tied to her face using computer vision. This is a walkthrough of the face detect, face landmark, face align, and face identity services on SingularityNET using the face of Sophia the robot. This is an example of multiple agents doing hand-offs between each other, each performing their own service, to perform on overall macro-service, which is to identify Sophia from any future pictures using the final raw vector of her face. Try out these services yourself at https://singularitynet.io Follow me on twitter: https://twitter.com/profit_ai

Here a sentiment analysis based on the tweets published from 7th of Novemember 2018 about BITCOIN, and saved on our database. The program analyzed 28254 Tweets, and labeled 21995 as SPAM or USELESS, and 6259 as decent QUALITY or USEFUL. Our Artificial Intelligence for sentiment analysis, marked 105 tweets as Angry, 24 as Fear, 5 as Bored, 3 as Sarcasm, 346 as Excited, 53 as Sad, and 493 as Happy. Is that increasing/consistent sell volume vs decreasing buy volume but price is increasing? Chinese left the property market because of tighter capital flight controls imposed by their government.

The reliable way of achieving consensus in blockchain is critical part for design of any system intended to support transparent and reliable financial ecosystem, unbiased content management and communication systems or trustable and efficient shared data storage - in all cases cheap, fast and secure at the same time. In the following, we will briefly discuss problems specific to different types of consensus algorithm, indicating problems associated with them. Further, we will introduce Proof-Of-Reputation (POR) idea and follow up with different variations and parameters of the algorithm and discuss possible architectures of its implementation. The key of the blockchain is its ability to solve so called "Byzantine General's Problem" of distributed coordination of multiple agents, with some of the agents being potentially unreliable or even alien. If probability of all agents in the system to be responsible for new block formation is the same, and there may be up to M unreliable or alien agents in the network, it is required to have at least 2*M 1 reliable and loyal agents.

SenseTime announced a partnership with Rockchip to produce an AI facial recognition chip, according to the company's official WeChat platform on August 15th. Based on the announcement, the AI unicorn's acial recognition SDK will be preloaded on the AI chip on Rochchip's affiliate chip production platform. SenseTime, founded in 2014, is one of the best-funded AI unicorns in China. Previously in June, SenseTime partnered with Xiaomi [1810:HK] to apply its facial recognition technology in Xiaomi's flagship smartphone Mi 8 Explorer. As early as in December 2017, powered by SenseTime's facial recognition technology, the Vivo X20 could be unlocked within 0.1 seconds, featuring the world's leading facial recognition technology.

Hi Steemians, today I just want to share to you about "EKKBAZ", what is EKKBAZ? It is like being "Henry Sy" of your own. Base on their website it says "Powered by blockchain, artificial intelligence, social and collaborative technologies to significantly simplify direct to store interactions, reduce middlemen and empower root-level store owners in any corner of the earth. In short through ekkbaz we can now take advantage the B2B set up of business, overcoming some constraint in doing the traditional way of business. The goal of EKKBAZ will be developing BAZ Protocol, a decentralized and intelligent network with purpose-built components for FMCG businesses.

I recently stumbled across the Kimera AGI project and saw new possibilities for our future... Presently, if we want to communicate with our friends and family on social media, we have to use intermediaries such as Facebook and Google. Imagine a world where our devices are intelligent, and when we want to announce something, we don't need to use a corporate middleman such as Facebook - because our devices already know who our friends and family members are - we simply give the information to our Artificial General Intelligence (AGI) Personal Assistant (PA) on our phone or tablet, and it sends the information directly to our friends' and family members' AGI PA on their devices. The information you send remains private - only your friends and family receive it. It doesn't end up in a third-party corporate database where personal information can be extracted, analysed and sold behind your back. Now what if you also got paid for using said AGI PA? Incentivised usage!

OTPPAY is a new modern platform that lets you pay for your purchases with any crypto currency. Platforms convert funds and pay for items already in the currency indicated by the seller. OTPPAY Plans to win the world's foremost in the conversion of all crypto-currency possibilities, as well as the sale of exchanges and payments for services and goods. Artificial intelligence that continually learns helps increase the speed of turning each round into an OTPPAY token platform that will be tied to the market and will be adjusted depending on its growth and fall. You can instantly convert all your crypto codes into tokens on a platform, protecting them from volatility in the crypto currency market.

Artificial intelligence is a world full of promises. This technology outbreak might not surprise us too much, as we've already seen a drastic change in our life mainly because of new innovations in tech since 1990. Up till now, AI is still developing, and one of the major problems that need to be solved for this tech to grow is the need to collects and labels data for machine learning. Until this day, data sets for machine learning are still collected and labeled hand by hand, which creates such task very tiring and require a lot of efforts if there is not enough manpower to do that. On the other hand, automating this process might create another problem as we have to create an app that literally is an AI itself to label data correctly.