If speed dating mixed with blind dating sounds like your idea of hell, look away now. Ten years since dating app Tinder first encouraged users to swipe through potential partners based largely on their looks, some singles are doing away with profile photos altogether. In the absence of Cilla and "our Graham", those looking for love are turning instead to a new cohort of "blind dating apps" in the hope of making more meaningful connections. "I'm already on Tinder, Badoo, Bumble, Hinge – all of them!" says Victoria Brown, a 26-year-old client success manager from Upminster, east London. "A blind dating app seemed like a good idea because usually you think: 'Oh, he's really good-looking' but then, when you start talking, the chat's not that good. Not seeing what someone looks like, at least at first, gives it a bit of a twist – although I was nervous about the reveal."

Apple is working on a big change to how its Siri voice assistant works. While you currently have to say "Hey Siri" to activate the assistant hands-free, that may not be the case for much longer. Bloomberg reports today that Apple engineers are working to drop the "Hey" part of the phrase, so you'd only have to say "Siri" followed by a command to activate the assistant… In the latest edition of his Power On newsletter, Bloomberg's Mark Gurman says that this is "a technical challenge that requires a significant amount of AI training and underlying engineering work." Apple has reportedly been working on this change for the last several months and hopes to roll it out either next year or in 2024 depending on the progress of development and testing. As it stands today, Apple is testing this change with employees and is collecting the necessary training data as part of that process.

With most technical fields, there exists a delay between fundamental academic research and practical industrial uptake. Whilst some sciences have robust and well-established processes for commercialisation, such as the pharmaceutical practice of regimented drug trials, other fields face transitory periods in which fundamental academic advancements diffuse gradually into the space of commerce and industry. For the still relatively young field of Automated/Autonomous Machine Learning (AutoML/AutonoML), that transitory period is under way, spurred on by a burgeoning interest from broader society. Yet, to date, little research has been undertaken to assess the current state of this dissemination and its uptake. Thus, this review makes two primary contributions to knowledge around this topic. Firstly, it provides the most up-to-date and comprehensive survey of existing AutoML tools, both open-source and commercial. Secondly, it motivates and outlines a framework for assessing whether an AutoML solution designed for real-world application is 'performant'; this framework extends beyond the limitations of typical academic criteria, considering a variety of stakeholder needs and the human-computer interactions required to service them. Thus, additionally supported by an extensive assessment and comparison of academic and commercial case-studies, this review evaluates mainstream engagement with AutoML in the early 2020s, identifying obstacles and opportunities for accelerating future uptake.

Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically train a collaborative filtering algorithm to extract user/item embedding vectors and therefore, a time-series of embedding vectors can be naturally defined. We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e.g., every month, iii) trains our time-series forecasting model with the extracted time-series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time-series data, i.e., neural controlled differential equations (NCDEs). Our experiments with four real-world benchmark datasets show that the proposed time-series forecasting-based upgrade kit can significantly enhance existing popular collaborative filtering algorithms.

Finding claims that researchers have made considerable progress in artificial intelligence over the last several decades is easy. However, our everyday interactions with cognitive systems (e.g., Siri, Alexa, DALL-E) quickly move from intriguing to frustrating. One cause of those frustrations rests in a mismatch between the expectations we have due to our inherent, folk-psychological theories and the real limitations we experience with existing computer programs. The software does not understand that people have goals, beliefs about how to achieve those goals, and intentions to act accordingly. One way to align cognitive systems with our expectations is to imbue them with mental states that mirror those we use to predict and explain human behavior. This paper discusses these concerns and illustrates the challenge of following this route by analyzing the mental state 'intention.' That analysis is joined with high-level methodological suggestions that support progress in this endeavor.

In natural language understanding (NLU) production systems, users' evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigation of this incremental symbol learning scenario. Our analysis reveals a troubling quirk in building broad-coverage NLU systems: as the training dataset grows, performance on the new symbol often decreases if we do not accordingly increase its training data. This suggests that it becomes more difficult to learn new symbols with a larger training dataset. We show that this trend holds for multiple mainstream models on two common NLU tasks: intent recognition and semantic parsing. Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows. Selectively dropping training examples to prevent dilution often reverses the trend, showing the over-reliance of mainstream neural NLU models on simple lexical cues. Code, models, and data are available at https://aka.ms/nlu-incremental-symbol-learning

Better still, the Echo Buds work with the Alexa app to stream music and play podcasts or Audible audiobook, all you have to do is ask.

Better still, the Echo Buds work with the Alexa app to stream music and play podcasts or Audible audiobook, all you have to do is ask.

From his perch at Google, Shodjai said that in some of those cases, children already understand that they shouldn't talk to, say, their parents in the same way as they would a product. Since launch, the company has also added features meant to reinforce good etiquette -- in late 2018, it updated the Assistant with a new "pretty please" mode, where requests that include a "please" or "thank you" would garner a grateful response.

In this series we'll build our own AI Assistant, like Siri, Alexa, or even Jarvis from Ironman. It will listen to spoken commands, perform actions from an expandable set of skill and talk back to us. I created a popular video series on YouTube, which goes over each of the steps below. The playlist is featured below. This series has 7 parts and 2 bonus video, 3 parts have been released so far, with more parts will be added soon, so check back!