Every time you search for something in Google, artificial intelligence is working behind the scenes to generate responses to your query. A deep learning system called RankBrain has changed the way the search engine functions. In many cases, RankBrain handles search queries better than traditional algorithmic rules that were hand-coded by human engineers, and Google realized a long time ago that AI is the future of their search platform. AI will try to understand exactly what we are searching for and then deliver personalized results to us, based on what it knows about us. You may not realize it, but AI is already deeply integrated into many of the Google products you are using today.

Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate with search systems. Evaluating such systems is very challenging since search results are presented in the format of natural language sentences. Given the unlimited number of possible responses, collecting relevance assessments for all the possible responses is infeasible. In this paper, we propose POSSCORE, a simple yet effective automatic evaluation method for conversational search. The proposed embedding-based metric takes the influence of part of speech (POS) of the terms in the response into account. To the best knowledge, our work is the first to systematically demonstrate the importance of incorporating syntactic information, such as POS labels, for conversational search evaluation. Experimental results demonstrate that our metrics can correlate with human preference, achieving significant improvements over state-of-the-art baseline metrics.

This is the final part of our 4-part series! In the previous 3, we covered the theory of collaborative filtering, how to build a Flask API, and how to deploy the API on AWS ECS. In this post, we'll build a simple Vue.js frontend that aims to simplify the movie recommendations as much as possible. Hence, we'll only ask the user to enter their favourite movie and recommend movies that are similar to it. Key functionalities of this project are the auto-search function for finding movie titles from MovieLens dataset and leveraging open-source scrapped movie posters to display recommended movies by the backend API.

To alleviate data sparsity and cold-start problems of traditional recommender systems (RSs), incorporating knowledge graphs (KGs) to supplement auxiliary information has attracted considerable attention recently. However, simply integrating KGs in current KG-based RS models is not necessarily a guarantee to improve the recommendation performance, which may even weaken the holistic model capability. This is because the construction of these KGs is independent of the collection of historical user-item interactions; hence, information in these KGs may not always be helpful for recommendation to all users. In this paper, we propose attentive Knowledge-aware Graph convolutional networks with Collaborative Guidance for personalized Recommendation (CG-KGR). CG-KGR is a novel knowledge-aware recommendation model that enables ample and coherent learning of KGs and user-item interactions, via our proposed Collaborative Guidance Mechanism. Specifically, CG-KGR first encapsulates historical interactions to interactive information summarization. Then CG-KGR utilizes it as guidance to extract information out of KGs, which eventually provides more precise personalized recommendation. We conduct extensive experiments on four real-world datasets over two recommendation tasks, i.e., Top-K recommendation and Click-Through rate (CTR) prediction. The experimental results show that the CG-KGR model significantly outperforms recent state-of-the-art models by 4.0-53.2% and 0.4-3.2%, in terms of Recall metric on Top-K recommendation and AUC on CTR prediction, respectively.

Bob Smith sits upright on the sofa as his grandson, Louis Brow, prepares to quiz him on youth slang. We are sitting in the living room of Louis's family home in Ilkley, West Yorkshire; Bob has travelled over from Walton-le-Dale, Lancashire, in his Nissan Micra. "Would you know what flexing is?" Louis begins. "If I was to flex on the Gram?" "You're bending, you're a contortionist," suggests Bob, gamely. "Nowadays it's someone showing off," Louis explains. I might say to my mate, that's a big flex, you're flexing, you're looking good." "Why not say the correct word?" Bob might not know the terminology, but he has had a major flex on social media recently. Louis is the Yorkshire Challenge belt (69kg) boxing champion and credits his success to the outdoor boxing gym, nicknamed the Dojo, that Grandad Bob helped him build in the back yard of the family home. A TikTok video of Louis using a tyre as a punchbag while his grandfather eggs him on went viral this year. Today, Louis confidently reels off his social media wins to Bob. He just hit 1m likes on TikTok; their video garnered 2.4m views. "Well, people like watching things," Bob says, sagely. Talk turns to another modern phenomenon: dating apps. Bob explains what dating was like in his youth. "There would be these dos in the church with disco dancing.

How data is represented and operationalized is critical for building computational solutions that are both effective and efficient. A common approach is to represent data objects as binary vectors, denoted \textit{hash codes}, which require little storage and enable efficient similarity search through direct indexing into a hash table or through similarity computations in an appropriate space. Due to the limited expressibility of hash codes, compared to real-valued representations, a core open challenge is how to generate hash codes that well capture semantic content or latent properties using a small number of bits, while ensuring that the hash codes are distributed in a way that does not reduce their search efficiency. State of the art methods use representation learning for generating such hash codes, focusing on neural autoencoder architectures where semantics are encoded into the hash codes by learning to reconstruct the original inputs of the hash codes. This thesis addresses the above challenge and makes a number of contributions to representation learning that (i) improve effectiveness of hash codes through more expressive representations and a more effective similarity measure than the current state of the art, namely the Hamming distance, and (ii) improve efficiency of hash codes by learning representations that are especially suited to the choice of search method. The contributions are empirically validated on several tasks related to similarity search and recommendation.

If you've seen Sci-Fi movies, you very well "know" what AI is! Machines and computers hell-bent on taking over the world and wiping out the human race! Well, they're just fictional movies, so no need to take them that seriously! On a more serious note, AI simply stands for Artificial Intelligence. It's the ability of a computer system, or a machine, to carry out functions that would usually have required human intelligence. To help you better understand the world of AI, we discuss how AI is being used today to help make our day-to-day lives that much easier and simpler.

While the name may sound familiar thanks to Carl Pei's new audio brand Nothing that just launched a pair of true wireless earbuds, Omthing is looking to bring affordable luxury audio products to the country. The company is eyeing a 5% share of India's smart wearables market in 3 yrs and is also looking to set up an R&D centre in India. All three products come with a premium design and an IPX4 rating for dust and water resistance. The Omthing AirFree Lace Neckband is available in black colour option and priced at Rs 1,499 is the cheapest of the three. Omthing AirFree TWS earbuds will retail at Rs 2,499 and are also available in black colour only.

Large-scale conversational assistants like Alexa, Siri, Cortana and Google Assistant process every utterance using multiple models for domain, intent and named entity recognition. Given the decoupled nature of model development and large traffic volumes, it is extremely difficult to identify utterances processed erroneously by such systems. We address this challenge to detect domain classification errors using offline Transformer models. We combine utterance encodings from a RoBERTa model with the Nbest hypothesis produced by the production system. We then fine-tune end-to-end in a multitask setting using a small dataset of humanannotated utterances with domain classification errors. We tested our approach for detecting misclassifications from one domain that accounts for <0.5% of the traffic in a large-scale conversational AI system. Our approach achieves an F1 score of 30% outperforming a bi- LSTM baseline by 16.9% and a standalone RoBERTa model by 4.8%. We improve this further by 2.2% to 32.2% by ensembling multiple models.

The judge said that most of the lawsuit could move forward, despite Apple's request to have it thrown out. Judge Jeffrey S. White, of federal district court in Oakland, did dismiss one piece involving users' economic harm. But he ruled that the plaintiffs, who are trying to make the suit a class action case, could continue pursuing claims that Siri turned on unprompted and recorded conversations that it shouldn't have and passed the data along to third parties, therefore violating user privacy.