It's been another banner year for home security and smart home at CES, and after scouring the show floor, we've rounded up the smart products that stood apart from the rest. Among our picks: a video doorbell and a smart deadbolt with their own screens (about time), a Matter-enabled smart lamp that can control other Matter devices without an internet connection, a smart pet door that opens when your pets approach from either side, and a smart toothbrush that promises to scour your teeth and gums in 20 seconds flat. Wouldn't it be nice to see who was at your doorstep without fishing out your smartphone--or, you know, opening the door? You can with help from the SwitchBot Video Doorbell, a wired- or battery-powered unit that comes with its own 4 x 3 display, which you can mount in the kitchen, your living room, or even right next to the front door. The display can be outfitted with 512GB of local storage--meaning no need for the cloud--while the doorbell itself supports 4K video and offers a 165-degree field of view.

Previous real-time MRI (rtMRI)-based speech synthesis models depend heavily on noisy ground-truth speech. Applying loss directly over ground truth mel-spectrograms entangles speech content with MRI noise, resulting in poor intelligibility. We introduce a novel approach that adapts the multi-modal self-supervised AV-HuBERT model for text prediction from rtMRI and incorporates a new flow-based duration predictor for speaker-specific alignment. The predicted text and durations are then used by a speech decoder to synthesize aligned speech in any novel voice. We conduct thorough experiments on two datasets and demonstrate our method's generalization ability to unseen speakers. We assess our framework's performance by masking parts of the rtMRI video to evaluate the impact of different articulators on text prediction. Our method achieves a $15.18\%$ Word Error Rate (WER) on the USC-TIMIT MRI corpus, marking a huge improvement over the current state-of-the-art. Speech samples are available at \url{https://mri2speech.github.io/MRI2Speech/}

Last week, Google put one of its engineers on administrative leave after he claimed to have encountered machine sentience on a dialogue agent named LaMDA. Because machine sentience is a staple of the movies, and because the dream of artificial personhood is as old as science itself, the story went viral, gathering far more attention than pretty much any story about natural-language processing (NLP) has ever received. The notion that LaMDA is sentient is nonsense: LaMDA is no more conscious than a pocket calculator. More importantly, the silly fantasy of machine sentience has once again been allowed to dominate the artificial-intelligence conversation when much stranger and richer, and more potentially dangerous and beautiful, developments are under way. The fact that LaMDA in particular has been the center of attention is, frankly, a little quaint.

This is an introductory article. I suggest you know the works behind gradient descent, some linear algebra, and basic DL knowledge . There is a magic in recurrent neural networks that I have always cherished. Within a few dozen minutes of training his model (with rather arbitrarily-chosen hyperparameters), it started to generate meaning that was on the edge of making sense. How is it possible to extract the essence of previous terms to guess future words?

We will build a deep neural network that functions as part of an end-to-end machine translation pipeline. The completed pipeline will accept English text as input and return the French translation. For our model, we will use an English and French sample of sentences. The data is located in data/small_vocab_en and data/small_vocab_fr. The small_vocab_en file contains English sentences with their French translations in the small_vocab_fr file.

Microsoft will add predictive typing, also known as text prediction, to Microsoft Word beginning in March. As Neowin noted, the feature has been added to the Microsoft 365 public roadmap and is due to arrive next month. Microsoft rolled out text predictions for Outlook for the web last November, and is in the process of rolling them out for Outlook for Android this month. This new feature does seem to be tied to Microsoft 365, though, and it's not clear whether it will be part of the newly announced Office 2021. Microsoft described how text prediction works last year.

The world of UI/UX is changing every month. What if you could use machine learning to help you keep up with all of the changes? Machine learning can help developers make more user-friendly web applications. Learn some background on machine learning and algorithms and see examples of where Brain.js The world of UI/UX is changing every month.

We aim to identify and control unintentional humor occurring in human-computer interaction, and recreate it intentionally. In this research we focus on text prediction systems, a type of interactive programs employed in mobile phones, search engines, and word processors. More specifically, we identified two design principles, inspired by humor and emotion theories, and implemented them in a proof-of-concept tool simulating a specific type of text prediction.