These AI systems create images based on instructions or directions. They will, if told to, create an image of a kiwi bird eating a kiwi fruit while sitting on a big padlock key. They can be used to create ads, fashion designs or movie production storyboards. DALL-E, Midjourney and Wombo Dream are examples of AI image generators. AI can also create three-dimensional spaces and objects, both real and digital. It can design buildings, rooms and even whole city plans, as well as virtual spaces for gameplay or metaverse-style collaboration.

We present a new method based on episodic Knowledge Graphs (eKGs) for evaluating (multimodal) conversational agents in open domains. This graph is generated by interpreting raw signals during conversation and is able to capture the accumulation of knowledge over time. We apply structural and semantic analysis of the resulting graphs and translate the properties into qualitative measures. We compare these measures with existing automatic and manual evaluation metrics commonly used for conversational agents. Our results show that our Knowledge-Graph-based evaluation provides more qualitative insights into interaction and the agent's behavior.

AI is taking the world by storm. While it was once a thing of sci-fi movies, it's no longer fiction. In fact, AI--tech that can think, learn, and make autonomous decisions--is now seeping its way into our lives. Think of self-driving cars (Tesla), navigation (Google Maps), or even virtual assistants (Siri): all of them rely heavily on AI. You might have also heard about the AI-powered robot from Boston Dynamics that can do a summersault, a handstand, or even a split leap that's now making a lot of noise online.

The need for improvement has been changing the world for thousands of years. Numerous inventions have filled gaps with intended and unintended uses in response to this desire for advancement. Take the wheel, for example. It was developed to assist potters with their clay back in Mesopotamia around 3500 B.C. (Gambino, 2009). Now, we get thousands of different uses out of the wheel.

Graph Convolutional Networks (GCNs) and their variants have achieved significant performances on various recommendation tasks. However, many existing GCN models tend to perform recursive aggregations among all related nodes, which can arise severe computational burden to hinder their application to large-scale recommendation tasks. To this end, this paper proposes the flattened GCN~(FlatGCN) model, which is able to achieve superior performance with remarkably less complexity compared with existing models. Our main contribution is three-fold. First, we propose a simplified but powerful GCN architecture which aggregates the neighborhood information using one flattened GCN layer, instead of recursively. The aggregation step in FlatGCN is parameter-free such that it can be pre-computed with parallel computation to save memory and computational cost. Second, we propose an informative neighbor-infomax sampling method to select the most valuable neighbors by measuring the correlation among neighboring nodes based on a principled metric. Third, we propose a layer ensemble technique which improves the expressiveness of the learned representations by assembling the layer-wise neighborhood representations at the final layer. Extensive experiments on three datasets verify that our proposed model outperforms existing GCN models considerably and yields up to a few orders of magnitude speedup in training efficiency.

Sparsity of the User-POI matrix is a well established problem for next POI recommendation, which hinders effective learning of user preferences. Focusing on a more granular extension of the problem, we propose a Joint Triplet Loss Learning (JTLL) module for the Next New ($N^2$) POI recommendation task, which is more challenging. Our JTLL module first computes additional training samples from the users' historical POI visit sequence, then, a designed triplet loss function is proposed to decrease and increase distances of POI and user embeddings based on their respective relations. Next, the JTLL module is jointly trained with recent approaches to additionally learn unvisited relations for the recommendation task. Experiments conducted on two known real-world LBSN datasets show that our joint training module was able to improve the performances of recent existing works.

Cold start is an essential and persistent problem in recommender systems. State-of-the-art solutions rely on training hybrid models for both cold-start and existing users/items, based on the auxiliary information. Such a hybrid model would compromise the performance of existing users/items, which might make these solutions not applicable in real-worlds recommender systems where the experience of existing users/items must be guaranteed. Meanwhile, graph neural networks (GNNs) have been demonstrated to perform effectively warm (non-cold-start) recommendations. However, they have never been applied to handle the cold-start problem in a user-item bipartite graph. This is a challenging but rewarding task since cold-start users/items do not have links. Besides, it is nontrivial to design an appropriate GNN to conduct cold-start recommendations while maintaining the performance for existing users/items. To bridge the gap, we propose a tailored GNN-based framework (GPatch) that contains two separate but correlated components. First, an efficient GNN architecture -- GWarmer, is designed to model the warm users/items. Second, we construct correlated Patching Networks to simulate and patch GWarmer by conducting cold-start recommendations. Experiments on benchmark and large-scale commercial datasets demonstrate that GPatch is significantly superior in providing recommendations for both existing and cold-start users/items.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Marketers know customers don't just crave personalized content -- they expect it in the business-to-consumer (B2C) and business-to-business (B2B) brands with which they interact. When trying to execute highly personalized email campaigns, however, marketers encounter many barriers. More than 40% of marketers cite a lack of resources -- time, people, and money -- as a significant challenge.

In the very near future, Amazon's famed voice assistant, Alexa, may sound quite different from the dutiful (and impersonal) voice you've grown accustomed to since it rolled out in 2014. At least, that's what Rohit Prasad, Amazon's senior vice president and head scientist for Alexa, announced at Amazon's re:MARS conference, a global artificial intelligence (AI) event that Amazon founder and executive chair Jeff Bezos hosted over the summer. With just a one-minute audio sample, the technology could bring a loved one's voice bounding through an Echo device's speakers. Prasad used a short presentation to show the audience how the new speech-synthesizer technology could help us forge lasting memories of our deceased relatives. "Alexa, can grandma finish reading me The Wizard of Oz?" A young boy asked a cute Echo speaker with big Panda eyes.

Google Home no longer needs to lean solely on smart home devices like thermostats to know whether or not you're around. Home's optional presence sensing feature can now use interactions with Nest speakers and smart displays to help detect activity in your abode, letting it perform automated actions. If you talk to your Nest Audio or tap your Nest Hub, for instance, Google may know to turn the lights on. Second-gen Nest Hubs can also use their Soli radar sensor to tell when you're close. You can enable presence sensing in the Google Home app for Android and iOS by visiting the Features section in the settings.