Dear Abby: I am a man in my late 40s who has been looking for love all my life. One factor that has made it difficult is my height. What makes finding someone nearly impossible is that the online dating site profiles always ask for my height. Unfortunately, being extremely short in stature isn't a characteristic women are looking for, so even though I can spend upward of an hour filling out all that profile information, the system invariably returns a no-match for me. Do you think I should lie about my height, and when I meet the person, hopefully she can give me a chance?

When we hear the word Artificial Intelligence (AI), our minds automatically create images of robots. Not all AI is in the form of robots, it can also be in the form of voice detectors, image processors, smart assistants, etc. As much as we claim to know what AI is, we don't have a full grasp of how our lives are steadily becoming dependent on it nowadays. From relying on Netflix to suggest your next movie to watch, to asking Siri to play your favorite song, and using Google Map to get to your location, you'll agree that AI makes living easier. Now let's take you through 5 Eye-opening Facts about AI to Help You Understand it Better… Sure you've found yourself in a situation where for example, you searched Google or an e-commerce website for the specification of a device, clothing, etc. with the intention of maybe buying it.

Ever-increasing smartphone-generated video content demands intelligent techniques to edit and enhance videos on power-constrained devices. Most of the best performing algorithms for video understanding tasks like action recognition, localization, etc., rely heavily on rich spatio-temporal representations to make accurate predictions. For effective learning of the spatio-temporal representation, it is crucial to understand the underlying object motion patterns present in the video. In this paper, we propose a novel approach for understanding object motions via motion type classification. The proposed motion type classifier predicts a motion type for the video based on the trajectories of the objects present. Our classifier assigns a motion type for the given video from the following five primitive motion classes: linear, projectile, oscillatory, local and random. We demonstrate that the representations learned from the motion type classification generalizes well for the challenging downstream task of video retrieval. Further, we proposed a recommendation system for video playback style based on the motion type classifier predictions.

Research interest in Conversational AI has experienced a massive growth over the last few years and several recent advancements have enabled systems to produce rich and varied turns in conversations similar to humans. However, this apparent creativity is also creating a real challenge in the objective evaluation of such systems as authors are becoming reliant on crowd worker opinions as the primary measurement of success and, so far, few papers are reporting all that is necessary for others to compare against in their own crowd experiments. This challenge is not unique to ConvAI, but demonstrates as AI systems mature in more "human" tasks that involve creativity and variation, evaluation strategies need to mature with them. Conversational AI, or ConvAI as it has been abbreviated, is a sub-field of artificial intelligence (AI) where the goal is to build an autonomous agent that is capable of maintaining natural discourse with a human over some interface such as text or speech. The purpose may be to help humans perform tasks as a virtual/digital assistant, provide a natural language interface to another system as in information retrieval or navigation systems, or simply to converse like one would with an open domain chatbot.

Amazon's Alexa, Apple's Siri, Microsoft's Cortana, and Samsung's Bixby may be the flag bearers of voice assistants (VAs) but the technology itself is no longer limited to megacorporations. Instead, it is finding its way to numerous enterprise-level applications. Voice assistants typically found on mobile devices like Siri and Google Assistant are examples of Voice User Interfaces (VUIs). Although VUIs have existed as early as the 1950s, greater technological challenges meant that modes of communication like typing took precedence in most business implementations. A study showed that even expert typists were not faster than modern VUIs at taking down messages.

If you've wanted to add to your Google Assistant home setup without spending too much money, Best Buy has a new way that you could do that. The retailer has the original Nest Hub smart display for $40, or $50 off its normal price. This gadget came out in 2018 and has since been replaced by the sleep-tracking, second-generation Nest Hub -- but if you're willing to skip a few new features, you can get a largely similar device for one of the best prices we've seen. We gave the original Nest Hub, formerly known as the Google Home Hub, a score of 87 when it first came out for its lovely 7-inch display, charming minimalist design and extra privacy thanks to a lack of a camera. It makes a good smart alarm clock, even if it is slightly larger than something like the Echo Show 5, but it also won't look out of place on your kitchen countertop.

With the release of Rajnikanth's widely acclaimed film Robot, there came a novel perception of the term Artificial Intelligence among the people of India. It got the audience thinking -- what is AI? Is AI a threat or an opportunity for improving society? How does Chitti's wig not fall off after all those stunts? There exists no set-in-stone definition, but one way to define AI would be the ability of a machine to demonstrate human-like intelligence. Problem-solving, reasoning, and learning are a few of the many human skills aimed to be simulated by AI machines.

Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right time. There are already a lot of Internet platforms and Apps providing both search and recommendation services, showing us the demand and opportunity to simultaneously handle both tasks. However, most platforms consider these two tasks independently -- they tend to train separate search model and recommendation model, without exploiting the relatedness and dependency between them. In this paper, we argue that jointly modeling these two tasks will benefit both of them and finally improve overall user satisfaction. We investigate the interactions between these two tasks in the specific information content service domain. We propose first integrating the user's behaviors in search and recommendation into a heterogeneous behavior sequence, then utilizing a joint model for handling both tasks based on the unified sequence. More specifically, we design the Unified Information Search and Recommendation model (USER), which mines user interests from the integrated sequence and accomplish the two tasks in a unified way.

We propose a novel combinatorial inference framework to conduct general uncertainty quantification in ranking problems. We consider the widely adopted Bradley-Terry-Luce (BTL) model, where each item is assigned a positive preference score that determines the Bernoulli distributions of pairwise comparisons' outcomes. Our proposed method aims to infer general ranking properties of the BTL model. The general ranking properties include the "local" properties such as if an item is preferred over another and the "global" properties such as if an item is among the top $K$-ranked items. We further generalize our inferential framework to multiple testing problems where we control the false discovery rate (FDR), and apply the method to infer the top-$K$ ranked items. We also derive the information-theoretic lower bound to justify the minimax optimality of the proposed method. We conduct extensive numerical studies using both synthetic and real datasets to back up our theory.

Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are "missing completely at random" (MCAR), i.e., each entry is revealed at random, independent of everything else, with uniform probability. This is likely unrealistic due to the presence of "latent confounders", i.e., unobserved factors that determine both the entries of the underlying matrix and the missingness pattern in the observed matrix. For example, in the context of movie recommender systems -- a canonical application for matrix completion -- a user who vehemently dislikes horror films is unlikely to ever watch horror films. In general, these confounders yield "missing not at random" (MNAR) data, which can severely impact any inference procedure that does not correct for this bias. We develop a formal causal model for matrix completion through the language of potential outcomes, and provide novel identification arguments for a variety of causal estimands of interest. We design a procedure, which we call "synthetic nearest neighbors" (SNN), to estimate these causal estimands. We prove finite-sample consistency and asymptotic normality of our estimator. Our analysis also leads to new theoretical results for the matrix completion literature. In particular, we establish entry-wise, i.e., max-norm, finite-sample consistency and asymptotic normality results for matrix completion with MNAR data. As a special case, this also provides entry-wise bounds for matrix completion with MCAR data. Across simulated and real data, we demonstrate the efficacy of our proposed estimator.