Surprise (stands for Simple Python RecommendatIon System Engine) is a Python library for building and analyzing recommender systems that deal with explicit rating data. It provides various ready-to-use prediction algorithms such as baseline algorithms, neighborhood methods, matrix factorization-based ( SVD, PMF, SVD, NMF), and many others. Also, various similarity measures (cosine, MSD, Pearson…) are built-in. In both, I will use collaborative filtering techniques and content-based techniques to filter items, and no worries I will explain the differences between them. I use here the MovieLens dataset.

Conversational AI amalgamates traditional software, such as chatbots or some form (voice or text) of interactive virtual assistants, with large volumes of data and machine learning algorithms to mimic human interactions. This imitation of human interactions is made possible by its underlying technologies -- machine learning, more specifically, Natural Language Processing (NLP). Conversational AI can recognize speech input and text input and translate the same across various languages to provide customer support using either a typed or spoken interface. A voice assistant or a chatbot empowered by conversational AI is not only a more intuitive software for the end user but is also capable of comprehensively understanding the nuances of a human query. Hence, conversational AI, in a sense, enables effective communication and interaction between computers and humans.

User-item interactions in recommendations can be naturally de-noted as a user-item bipartite graph. Given the success of graph neural networks (GNNs) in graph representation learning, GNN-based C methods have been proposed to advance recommender systems. These methods often make recommendations based on the learned user and item embeddings. However, we found that they do not perform well wit sparse user-item graphs which are quite common in real-world recommendations. Therefore, in this work, we introduce a novel perspective to build GNN-based CF methods for recommendations which leads to the proposed framework Localized Graph Collaborative Filtering (LGCF). One key advantage of LGCF is that it does not need to learn embeddings for each user and item, which is challenging in sparse scenarios. Alternatively, LGCF aims at encoding useful CF information into a localized graph and making recommendations based on such graph. Extensive experiments on various datasets validate the effectiveness of LGCF especially in sparse scenarios. Furthermore, empirical results demonstrate that LGCF provides complementary information to the embedding-based CF model which can be utilized to boost recommendation performance.

Static analysis remains one of the most popular approaches for detecting and correcting poor or vulnerable program code. It involves the examination of code listings, test results, or other documentation to identify errors, violations of development standards, or other problems, with the ultimate goal of fixing these errors so that systems and software are as secure as possible. There exists a plethora of static analysis tools, which makes it challenging for businesses and programmers to select a tool to analyze their program code. It is imperative to find ways to improve code analysis so that it can be employed by cyber defenders to mitigate security risks. In this research, we propose a method that employs the use of virtual assistants to work with programmers to ensure that software are as safe as possible in order to protect safety-critical systems from data breaches and other attacks. The pro- posed method employs a recommender system that uses various metrics to help programmers select the most appropriate code analysis tool for their project and guides them through the analysis process. The system further tracks the user's behavior regarding the adoption of the recommended practices.

Want a smart alarm clock to help you get back to work (or school) now that September is fast approaching? The internet giant is running a sale on Echo devices, and the latest Echo Show 5 is on sale for only $55, or an even $30 below its official price. You'll get a similar discount on the Echo Show 5 Kids model, which sells for $65 (down from $95) with a year of Amazon Kids service thrown in. Both prices are much lower than we saw just a few weeks ago. If you'd prefer something larger, the current-generation Echo Show 8 has dipped to $100, a $30 savings. And if you prefer Alexa on your face, the second-generation Echo Frames are down to an all-time low price of $180 (versus the usual $250).

Do you ever ask Siri if it's going to rain tomorrow? Or ask Alexa to play your favourite song? For millions of us, having conversational interactions with technology has quickly become second nature. This presents a real challenge for many financial institutions, whose traditional, interactive voice response (IVR) systems often fall far below the expectations set by the voice-enabled virtual assistants of their customers' smartphones and smart speakers. Even as banks race to deliver intelligent, conversational self-service over the telephone, they must also work to keep pace with the digital services of online banks and fintech pioneers.

It is a dynamic and threshold breaking time for advancing innovation across Conversational Artificial Intelligence, Computer Vision and Recommender Systems (RecSys), with NVIDIA accelerating new ground with the launch of TensorRT 8, alongside multiple RecSys competition successes - more on this news in depth shortly! But firstly, let's set the scene on exactly why this matters so much today. As we move into an Era of Convergence blending algorithm, engineering and culture alike, and reflecting both the level of integration and the increased pace of socio-technical change, it becomes imperative to manage the demands this inevitably creates - in order to optimise the vast opportunities. Deep learning is a case in point - applicable to a diverse and growing range of industries from medical devices through to conversational IVR and automated driving; and across a wide range of applications in production, including image and video analysis, natural language processing (NLP) and recommender systems. But as the number of applications increases so do the demands!

Between Facebook's core social network product and apps like Messenger and WhatsApp, Mark Zuckerberg's tech giant has undeniably changed the way people communicate. Could it next help change the way we communicate with robots? In 2021, the idea of being able to communicate with artificial intelligence through natural language is nowhere near as science fiction as it once was. Whether it's Amazon's Echo voice assistant or the voice bots we interact with when you phone your bank, A.I. today means that machines can do a pretty good job of understanding what humans are asking for, without the human in question having to do much to modify the way that they're speaking. Partly because most robots are still used in industrial or lab-based settings, where there's not quite the same requirement for them to be accessible to everyday users, robot interactions remain more opaque in their operation.

Even news of millions of so-called smart speakers being hacked right before 2017's holiday season didn't seem to slow down sales of Amazon's Echo and Google Home. But if you have either, you should definitely take precautions against hackers. If these speakers are hacked, they could divulge sensitive information such as when you'll be out of town or any upcoming doctor's appointments, along with your credit card and bank account info, shares NBC.com. Equally alarming, if it's connected to your home security system, a hacker could simply turn it off and walk right through your front door. To keep yourself and your home safe, limit how much info you connect through these types of speakers and unplug it when you go on vacation. Don't worry--there's no risk with these 15 hilarious things you can ask Alexa to do.

In this paper, we present our research on programming human-level artificial intelligence (HLAI), including 1) a definition of HLAI, 2) an environment to develop and test HLAI, and 3) a cognitive architecture for HLAI. The term AI is used in a broad meaning, and HLAI is not clearly defined. I claim that the essence of Human-Level Intelligence to be the capability to learn from others' experiences via language. The key is that the event described by language has the same effect as if the agent experiences it firsthand for the update of the behavior policy. To develop and test models with such a capability, we are developing a simulated environment called SEDRo. There is a 3D Home, and a mother character takes care of the baby (the learning agent) and teaches languages. The environment provides comparable experiences to that of a human baby from birth to one year. Finally, I propose a cognitive architecture of HLAI called Modulated Heterarchical Prediction Memory (mHPM). In mHPM, there are three components: a universal module that learns to predict the next vector given the sequence of vector signals, a heterarchical network of those modules, and a reward-based modulation of learning.