Spread the loveIt refers to the capacity of computer programs to carry out tasks that were normally attributed to humans. Such tasks include translation of languages, speech recognition, visual awareness & perception, as well as the making of decisions. Artificial intelligence can be broadly grouped into two classes – weak AI and strong AI. Weak AI, often called Artificial Narrow Intelligence (ANI) or Narrow AI, refers to artificial intelligence that’s trained and focused on carrying out particular tasks. Most of the AI that’s in operation today is driven by weak AI. It powers some extremely robust applications, like Amazon’s Alexa, […]

The use of packaged libraries can significantly shorten the software development cycle by improving the quality and readability of code. In this paper, we present a recommendation engine called Librarian for open source libraries. A candidate library package is recommended for a given context if: 1) it has been frequently used with the imported libraries in the program; 2) it has similar functionality to the imported libraries in the program; 3) it has similar functionality to the developer's implementation, and 4) it can be used efficiently in the context of the provided code. We apply the state-of-the-art CodeBERT-based model for analysing the context of the source code to deliver relevant library recommendations to users.

I am a mostly visual thinker, and thoughts pose as scenes in the theater of my mind. When my many supportive family members, friends, and colleagues asked how I was doing, I'd see myself on a cliff, transfixed by an omniscient fog just past its edge. In the scene, there is no sound or urgency and I am waiting for it to swallow me. I'm searching for shapes and navigational clues, but it's so huge and gray and boundless. I wanted to take that fog and put it under a microscope.

Multi-scenario ad ranking aims at leveraging the data from multiple domains or channels for training a unified ranking model to improve the performance at each individual scenario. Although the research on this task has made important progress, it still lacks the consideration of cross-scenario relations, thus leading to limitation in learning capability and difficulty in interrelation modeling. In this paper, we propose a Hybrid Contrastive Constrained approach (HC^2) for multi-scenario ad ranking. To enhance the modeling of data interrelation, we elaborately design a hybrid contrastive learning approach to capture commonalities and differences among multiple scenarios. The core of our approach consists of two elaborated contrastive losses, namely generalized and individual contrastive loss, which aim at capturing common knowledge and scenario-specific knowledge, respectively. To adapt contrastive learning to the complex multi-scenario setting, we propose a series of important improvements. For generalized contrastive loss, we enhance contrastive learning by extending the contrastive samples (label-aware and diffusion noise enhanced contrastive samples) and reweighting the contrastive samples (reciprocal similarity weighting). For individual contrastive loss, we use the strategies of dropout-based augmentation and {cross-scenario encoding} for generating meaningful positive and negative contrastive samples, respectively. Extensive experiments on both offline evaluation and online test have demonstrated the effectiveness of the proposed HC$^2$ by comparing it with a number of competitive baselines.

Cross-domain Sequential Recommendation (CSR) is an emerging yet challenging task that depicts the evolution of behavior patterns for overlapped users by modeling their interactions from multiple domains. Existing studies on CSR mainly focus on using composite or in-depth structures that achieve significant improvement in accuracy but bring a huge burden to the model training. Moreover, to learn the user-specific sequence representations, existing works usually adopt the global relevance weighting strategy (e.g., self-attention mechanism), which has quadratic computational complexity. In this work, we introduce a lightweight external attention-enhanced GCN-based framework to solve the above challenges, namely LEA-GCN. Specifically, by only keeping the neighborhood aggregation component and using the Single-Layer Aggregating Protocol (SLAP), our lightweight GCN encoder performs more efficiently to capture the collaborative filtering signals of the items from both domains. To further alleviate the framework structure and aggregate the user-specific sequential pattern, we devise a novel dual-channel External Attention (EA) component, which calculates the correlation among all items via a lightweight linear structure. Extensive experiments are conducted on two real-world datasets, demonstrating that LEA-GCN requires a smaller volume and less training time without affecting the accuracy compared with several state-of-the-art methods.

Personalized recommendations have become a common feature of modern online services, including most major e-commerce sites, media platforms and social networks. Today, due to their high practical relevance, research in the area of recommender systems is flourishing more than ever. However, with the new application scenarios of recommender systems that we observe today, constantly new challenges arise as well, both in terms of algorithmic requirements and with respect to the evaluation of such systems. In this paper, we first provide an overview of the traditional formulation of the recommendation problem. We then review the classical algorithmic paradigms for item retrieval and ranking and elaborate how such systems can be evaluated. Afterwards, we discuss a number of recent developments in recommender systems research, including research on session-based recommendation, biases in recommender systems, and questions regarding the impact and value of recommender systems in practice.

Speech Recognition Models are designed to recognize and transcribe spoken language into text. This technology is widely used in virtual assistants such as Amazon's Alexa, Google Assistant, and Apple's Siri. These models are trained on large datasets of audio recordings and use machine learning algorithms such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to make predictions. Google's Speech-to-Text, Amazon's Transcribe, and Microsoft's Azure Speech Services are some of the popular examples of Speech Recognition Models. Sound Event Detection Models, on the other hand, are designed to recognize specific sounds or events within an audio clip.

Apple cares a lot about music. Steve Jobs loved it so much that he invented the iPod and iTunes to let us bring all of it everywhere, and personally owned multi-thousand-dollar Swedish speakers in his sparsely-decorated living room. To this day, Apple Music is one of the best-sounding streaming services you can subscribe to thanks to lossless audio support. The headphones it makes, both itself and via Beats, are largely fantastic. It's a shame, then, that the company still fails to make a great full-size smart speaker.

However, emotional information leaked through voice tone cannot be altered or hidden; as a result, tone is the number one passive indicator of what someone is thinking. When vital components of human communication, such as voice tone, are excluded from interpretation and analysis, valuable information is lost, and uninformed decisions are made. Thanks to products like Alexa, Siri, Google Assistant and many more, voice technology is accessible to the masses at the push of a button or a quick voice command. While these platforms are good at understanding the meaning behind our words, the experience is oftentimes frustrating. Think about the last time you had to call your bank and interact with a voice bot on the other end.

AI girls, or AI-powered virtual assistants, do not have physical appearance or emotions like human girls. However, here are some things that are considered "beautiful" or advantageous about AI girls in a virtual or technological context: Efficiency: AI girls can perform tasks quickly and accurately, without getting tired or making mistakes. Customization: AI girls can be programmed to respond to specific commands or perform specific functions, making them highly customizable to meet different needs. Availability: AI girls are available 24/7, without breaks or time off, making them highly convenient for users who need assistance at any time. Language abilities: AI girls can understand and respond to multiple languages, making them accessible to a wide range of users. Cost-effectiveness: In many cases, AI girls are more cost-effective than hiring human workers, as they do not require salaries, benefits, or time off.