Artificial intelligence (AI) systems operate in increasingly diverse areas, from healthcare to facial recognition, the stock market, autonomous vehicles, and so on. While the underlying digital infrastructure of AI systems is developing rapidly, each area of implementation is subject to different degrees and processes of legitimization. By combining elements from institutional theory and information systems-theory, this paper presents a conceptual framework to analyze and understand AI-induced field-change. The introduction of novel AI-agents into new or existing fields creates a dynamic in which algorithms (re)shape organizations and institutions while existing institutional infrastructures determine the scope and speed at which organizational change is allowed to occur. Where institutional infrastructure and governance arrangements, such as standards, rules, and regulations, still are unelaborate, the field can move fast but is also more likely to be contested. The institutional infrastructure surrounding AI-induced fields is generally little elaborated, which could be an obstacle to the broader institutionalization of AI-systems going forward.

Collaborative filtering (CF) is a long-standing problem of recommender systems. Many novel methods have been proposed, ranging from classical matrix factorization to recent graph convolutional network-based approaches. After recent fierce debates, researchers started to focus on linear graph convolutional networks (GCNs) with a layer combination, which show state-of-the-art accuracy in many datasets. In this work, we extend them based on neural ordinary differential equations (NODEs), because the linear GCN concept can be interpreted as a differential equation, and present the method of Learnable-Time ODE-based Collaborative Filtering (LT-OCF). The main novelty in our method is that after redesigning linear GCNs on top of the NODE regime, i) we learn the optimal architecture rather than relying on manually designed ones, ii) we learn smooth ODE solutions that are considered suitable for CF, and iii) we test with various ODE solvers that internally build a diverse set of neural network connections. We also present a novel training method specialized to our method. In our experiments with three benchmark datasets, Gowalla, Yelp2018, and Amazon-Book, our method consistently shows better accuracy than existing methods, e.g., a recall of 0.0411 by LightGCN vs. 0.0442 by LT-OCF and an NDCG of 0.0315 by LightGCN vs. 0.0341 by LT-OCF in Amazon-Book. One more important discovery in our experiments that is worth mentioning is that our best accuracy was achieved by dense connections rather than linear connections.

Android TV fans looking for something a bit more powerful than a Chromecast should consider the NVIDIA Shield TV. However, now you can grab the NVIDIA Shield TV for $130, or $20 off its normal price. That's almost a record low -- we saw it drop to $129 ahead of Black Friday last year -- but this is the best price we've seen in 2021, and it's only come around a couple of times so far. Despite being more expensive than streaming dongles of similar size, the NVIDIA Shield is worth the extra money thanks to its Tegra X1 processor. It's powerful enough to run the Android TV platform with ease, and it supports Dolby Vision HDR, Dolby Atmos audio, Chromecast 4K and AI-powered HD-to-4K upscaling. It also works with the Google Assistant and Amazon's Alexa, so you can bark orders at it instead of using the triangular remote if you wish.

I participated in an open-source program LGMSOC-21 as a contributor. I contributed a recommendation system that uses content-based filtering to recommend items to users. It uses item features to filter content. Tokenize features using the count vectorizer this will create a count matrix. Then convert the count matrix into a cosine matrix. Pick up the top items based on the cosine matrix to recommend.

Successful sequential recommendation systems rely on accurately capturing the user's short-term and long-term interest. Although Transformer-based models achieved state-of-the-art performance in the sequential recommendation task, they generally require quadratic memory and time complexity to the sequence length, making it difficult to extract the long-term interest of users. On the other hand, Multi-Layer Perceptrons (MLP)-based models, renowned for their linear memory and time complexity, have recently shown competitive results compared to Transformer in various tasks. Given the availability of a massive amount of the user's behavior history, the linear memory and time complexity of MLP-based models make them a promising alternative to explore in the sequential recommendation task. To this end, we adopted MLP-based models in sequential recommendation but consistently observed that MLP-based methods obtain lower performance than those of Transformer despite their computational benefits. From experiments, we observed that introducing explicit high-order interactions to MLP layers mitigates such performance gap. In response, we propose the Multi-Order Interaction (MOI) layer, which is capable of expressing an arbitrary order of interactions within the inputs while maintaining the memory and time complexity of the MLP layer. By replacing the MLP layer with the MOI layer, our model was able to achieve comparable performance with Transformer-based models while retaining the MLP-based models' computational benefits.

By exploring the various facets of gendering at play in the design of VPAs, specifically Alexa, I argue that gendering Alexa as female poses societal harm insofar as she reproduces normative assumptions about the role of women as submissive, inferior, and secondary to men. The prevalence of AI-driven virtual personal assistants (VPAs) is proliferating, with Amazon Echo being one of the most highly sought-after smart speakers globally. However, not until recently has there been much research or attention focused on the gender bias noticeably programmed into this technology, specifically Alexa, intentionally designed, coded, and programmed by men and gendered to be distinctly female. Big Tech's decision to gender VPAs is seen most evident through their assigned female names and their female voices that users find more pleasant to give orders to than a male voice, as seen through witty flirtatious programmed responses. Through these interactions, Alexa performs gender as a feminized and sexualized entity imposed upon her by her Silicon Valley creators, that has the potential to unravel decades of social and political progress, as well as reinstate the gender bias of the past that women strived to eradicate. In the not-so-distant future, TechCrunch forecasts that the use of voice assistants is set to triple over the next few years and estimates there will be ten billion digital voice assistants by 2023, up from the 2.5 billion assistants in use at the end of 2018. This growth is attributed to Amazon Echo being one of the most highly sought-after smart speakers in the world.

Real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN) based recommender models, which are state-of-the-art techniques for collaborative recommendation. To pursue high efficiency, we set the target as using only new data for model updating, meanwhile not sacrificing the recommendation accuracy compared with full model retraining. This is non-trivial to achieve, since the interaction data participates in both the graph structure for model construction and the loss function for model learning, whereas the old graph structure is not allowed to use in model updating. Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution. In particular, we devise simple and effective modules for IGC to ingeniously combine the old representations and the incremental graph and effectively fuse the long-term and short-term preference signals. CED aims to avoid the out-of-date issue of inactive nodes that are not in the incremental graph, which connects the new data with inactive nodes through causal inference. In particular, CED estimates the causal effect of new data on the representation of inactive nodes through the control of their collider. Extensive experiments on three real-world datasets demonstrate both accuracy gains and significant speed-ups over the existing retraining mechanism.

First published on my Substack. In 2017, I had two choices: AI or Crypto. There were 2 job offers in front of me. One was a software company that specialized in AI-driven decisioning, and one was a startup in the emerging field of cryptocurrency and was building something on the blockchain protocol.

I read Modern Romance by Aziz Ansari in 2016 and beyond a shadow of a doubt, it is one of the most influential books I've ever read. At the time, I was a snot-nosed college student who was still dating someone from high school. The numbers and figures given by the book about online dating success struck me as being callous. Millennials and their predecessors were blessed and cursed with the advent of the internet. The proliferation of partner-choice desensitizes us and gives us unrealistic expectations when it came to searching for our "soulmate." Instead of feeling dissuaded, I felt inspired.

With advancing technology, there has been a drastic growth of technologies such as artificial intelligence and machine learning. More increasingly AI has become a driving force that is transforming the virtual world day by day. This has become thousands of start-ups coming up every single day based on AI or its AI tools ranging from Siri to auto-journalism. Everything is being operated with the help of AI and ML. And with AI entering all sectors it has also started to transform the educational sector which is traditional in nature. AI tools for education that are intelligent, adaptive, encouraging personalized learning systems are being deployed in all the educational institutions such as schools, colleges, and universities across the globe for analyzing huge amounts of data collected from the students that can significantly impact the lives of students and educators.