In October 2020, the Belarusian company VIPIT launched on Product Hunt with the Good Style app. Now users can virtually try on clothes before ordering in the online store. VIPIT launched the app in the US market in partnership with Shein. Now the startup has entered the CIS market. Moreover, VIPIT works in collaboration with Belarusian brands in the Belarusian market.
A hundred years have passed since the Bipedal Event of 2065... An international ban on unofficial use of super artificial intelligence is enacted as the Earth adjusts to life with non-human races (now called bipeds despite humans sharing the classification). One day, while sifting through an abandoned government warehouse in space, Winston, a punk canine biped, finds Grant, a programmer from New York who's been cryogenically frozen since the 2030s. Together, they hang out in Winston's spaceship and eat donuts. There may also be some nunchaku wielding mechs, quantum encrypted black holes, and a little occult stuff sprinkled in.
Do you ever wonder what goes behind bringing various levels of autonomy to vehicles? What the vehicle sees (perception) and how the vehicle predicts the actions of different agents in the scene (behavior prediction) are the first two steps in autonomous systems. In order for these steps to be successful, large-scale driving datasets are key. Driving datasets typically comprise of data captured using multiple sensors such as cameras, LIDARs, radars, and GPS, in a variety of traffic scenarios during different times of the day under varied weather conditions and locations. The Amazon Machine Learning Solutions Lab is collaborating with the Laboratory of Intelligent and Safe Automobiles (LISA Lab) at the University of California, San Diego (UCSD) to build a large, richly annotated, real-world driving dataset with fine-grained vehicle, pedestrian, and scene attributes. This post describes the dataset label taxonomy and labeling architecture for 2D bounding boxes using Amazon SageMaker Ground Truth. Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning (ML) workflows.
Participants sit a Blue Origin space simulator during a conference on robotics and artificial intelligence in Las Vegas on June 5, 2019. On Saturday, Blue Origin announced that an unidentified bidder will pay $28 million for a suborbital flight on the company's New Shepard vehicle. Participants sit a Blue Origin space simulator during a conference on robotics and artificial intelligence in Las Vegas on June 5, 2019. On Saturday, Blue Origin announced that an unidentified bidder will pay $28 million for a suborbital flight on the company's New Shepard vehicle. Amazon billionaire Jeff Bezos is going into space on July 20 on a reusable rocket made by his space exploration company, Blue Origin.
The voice of Alexa, the virtual assistant developed by Amazon, is provided by Nina Rolle, a Colorado-based voiceover artist, according to a new book. Amazon has never revealed who provides the default female voice that responds to commands and questions given to Alexa, but the author Brad Stone said he identified the voice as Rolle's after "canvasing the professional voiceover community" for his new book, Amazon Unbound: Jeff Bezos and the Invention of a Global Empire. Rolle, who is based in Boulder, has conducted voiceover work for clients including Honda, Jenny Craig and Chase bank. According to Stone's book, she was selected after Amazon spent months assessing various candidates, with the final choice signed off by Jeff Bezos, the company's founder. Stone writes that Rolle said she was unable to talk about the role when he contacted her in February.
When making an online purchase, it becomes important for the customer to read the product reviews carefully and make a decision based on that. However, reviews can be lengthy, may contain repeated, or sometimes irrelevant information that does not help in decision making. In this paper, we introduce MRCBert, a novel unsupervised method to generate summaries from product reviews. We leverage Machine Reading Comprehension, i.e. MRC, approach to extract relevant opinions and generate both rating-wise and aspect-wise summaries from reviews. Through MRCBert we show that we can obtain reasonable performance using existing models and transfer learning, which can be useful for learning under limited or low resource scenarios. We demonstrated our results on reviews of a product from the Electronics category in the Amazon Reviews dataset. Our approach is unsupervised as it does not require any domain-specific dataset, such as the product review dataset, for training or fine-tuning. Instead, we have used SQuAD v1.1 dataset only to fine-tune BERT for the MRC task. Since MRCBert does not require a task-specific dataset, it can be easily adapted and used in other domains.
"We believe that we will be our best selves the more that we are together," he said. As more tech companies leverage the promise of flexible work arrangements as a competitive advantage, some are going the opposite route, betting that a strong office culture is what will help them recruit and retain the best talent. Proponents of fully in-office work cite a range of benefits, from the collaboration that can result from happenstance interactions to easier communication. Plus, they add, plenty of people enjoy working in offices, especially after months spent, for some, in makeshift arrangements. Given the tech industry's status as a bellwether for workplace trends, professionals in many industries are watching to see where it lands.
"Mitchell knows what she's talking about. Artificial Intelligence has significantly improved my knowledge when it comes to automation technology, [but] the greater benefit is that it has also enhanced my appreciation for the complexity and ineffability of human cognition."―John Warner, Chicago Tribune "Without shying away from technical details, this survey provides an accessible course in neural networks, computer vision, and natural-language processing, and asks whether the quest to produce an abstracted, general intelligence is worrisome . . . Mitchell's view is a reassuring one." AI isn't for the faint of heart, and neither is this book for nonscientists . . .
Chris Fregly is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is also the founder of the Advanced Spark, TensorFlow, and KubeFlow Meetup Series based in San Francisco. Chris regularly speaks at AI and Machine Learning conferences across the world including the O'Reilly AI, Strata, and Velocity Conferences. Previously, Chris was Founder at PipelineAI where he worked with many AI-first startups and enterprises to continuously deploy ML/AI Pipelines using Apache Spark ML, Kubernetes, TensorFlow, Kubeflow, Amazon EKS, and Amazon SageMaker. He is also the author of the O'Reilly Online Training Series "High Performance TensorFlow in Production with GPUs" Antje Barth is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in Düsseldorf, Germany.
The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings. We study the inductive biases of linearizations of neural networks, which we show to be surprisingly good summaries of the full network functions. Inspired by this finding, we propose a technique for embedding these inductive biases into Gaussian processes through a kernel designed from the Jacobian of the network. In this setting, domain adaptation takes the form of interpretable posterior inference, with accompanying uncertainty estimation. This inference is analytic and free of local optima issues found in standard techniques such as fine-tuning neural network weights to a new task. We develop significant computational speed-ups based on matrix multiplies, including a novel implementation for scalable Fisher vector products. Our experiments on both image classification and regression demonstrate the promise and convenience of this framework for transfer learning, compared to neural network fine-tuning. Code is available at https://github.com/amzn/xfer/tree/master/finite_ntk.