Amazon wants to make it safer and easier to use your phone while you're in the car. The feature turns your phone into a "driver-friendly" display with large touch targets and easy-to-read visuals. Auto Mode keeps things simple with four screens: Home, Navigation, Communicate and Play. The home screen includes shortcuts to pause or play your media source, navigate to home or work and make a call. The Navigation screen lets you create shortcuts for favorite locations, or if you ask Alexa to find someplace new, Auto Mode will display a simplified results list with only the most relevant info.

As part of the Next Generation Internet Policy Summit, Amsterdam and Helsinki launched beta AI registries that show how the government in each locale uses algorithms to provide services. Currently, the Amsterdam registry includes a small number of algorithms, but it will be expanded after feedback is gathered at the summit, which was organised by the City of Amsterdam and the European Commission. The algorithms in the registry come with a description of how they are used, what humans do with the information they provide and how it they are analysed for possible risks and biases. Citizens of Amsterdam can offer feedback and contact information for the individual deploying each algorithm is available. Amsterdam is well on its way to becoming a global AI hub.

Google's machine learning algorithm will convert that tune into a digital signal, and then you can convert it into a tune with Flute, Saxophone, Violin, …

MLOps, short for machine learning operations, is the equivalent of DevOps for ML models: taking them from development to production, and managing …

This extensively researched report presentation on global Machine Learning Courses market is designed to appropriately address a slew of vital market …

So far, few deepfakes have been used this political season. It's not because they aren't a potential threat, but because simpler deceptive tactics are still effective at spreading misinformation. So far, few deepfakes have been used this political season. It's not because they aren't a potential threat, but because simpler deceptive tactics are still effective at spreading misinformation. Despite people's fears, sophisticated, deceptive videos known as "deepfakes" haven't arrived this political season.

Film industries all over the world are producing several hundred movies rapidly and grabbing the attraction of people of all ages. Every movie producer is of keen interest in knowing which movies are either likely to hit or flop in the box office. So, the early prediction of the popularity of a movie is of the utmost importance to the film industry. In this study, we examine factors inside the hidden patterns which become movie popular. In past studies, machine learning techniques were implemented on blog articles, social networking, and social media to predict the success of a movie.

From Amazon to Netflix to Pinterest, recommendation systems are the cornerstone of a majority of the modern-day billion-dollar industries. However, building recommender systems is not a straightforward task. What if we can build them in a few lines? Dropping the nitty-gritty details and concentrating on implementing algorithms with more ease is what any data scientist would like to get their hands on. Abstraction is a common trait amongst popular machine learning libraries or frameworks like TensorFlow.

Artificial intelligence is leading to some drastic changes in the field of photography. Many photographers are discovering the profound benefits of machine learning and other AI capabilities. The market for artificial intelligence in photography was worth $10.7 billion in 2019. It is expected to reach over $29 billion by 2024. There have already been a lot of applications for machine learning with photos in marketing.

Most existing datasets for sound event recognition (SER) are relatively small and/or domain-specific, with the exception of AudioSet, based on a massive amount of audio tracks from YouTube videos and encompassing over 500 classes of everyday sounds. However, AudioSet is not an open dataset---its release consists of pre-computed audio features (instead of waveforms), which limits the adoption of some SER methods. Downloading the original audio tracks is also problematic due to constituent YouTube videos gradually disappearing and usage rights issues, which casts doubts over the suitability of this resource for systems' benchmarking. To provide an alternative benchmark dataset and thus foster SER research, we introduce FSD50K, an open dataset containing over 51k audio clips totalling over 100h of audio manually labeled using 200 classes drawn from the AudioSet Ontology. The audio clips are licensed under Creative Commons licenses, making the dataset freely distributable (including waveforms). We provide a detailed description of the FSD50K creation process, tailored to the particularities of Freesound data, including challenges encountered and solutions adopted. We include a comprehensive dataset characterization along with discussion of limitations and key factors to allow its audio-informed usage. Finally, we conduct sound event classification experiments to provide baseline systems as well as insight on the main factors to consider when splitting Freesound audio data for SER. Our goal is to develop a dataset to be widely adopted by the community as a new open benchmark for SER research.