Microsoft is committed to the responsible advancement of AI to enable every person and organization to achieve more. Over the last few months, we have talked about advancements in our Azure infrastructure, Azure Cognitive Services, and Azure Machine Learning to make Azure better at supporting the AI needs of all our customers, regardless of their scale. Meanwhile, we also work closely with some of the leading research organizations around the world to empower them to build great AI. Today, we're thrilled to announce an expansion of our ongoing collaboration with Meta: Meta has selected Azure as a strategic cloud provider to help accelerate AI research and development. As part of this deeper relationship, Meta will expand its use of Azure's supercomputing power to accelerate AI research and development for its Meta AI group.
May 11 (Reuters) - The science-fiction is harder to see in Google's second try at glasses with a built-in computer. A decade after the debut of Google Glass, a nubby, sci-fi-looking pair of specs that filmed what wearers saw but raised concerns about privacy and received low marks for design, the Alphabet Inc (GOOGL.O) unit on Wednesday previewed a yet-unnamed pair of standard-looking glasses that display translations of conversations in real time and showed no hint of a camera. The new augmented-reality pair of glasses was just one of several longer-term products Google unveiled at its annual Google I/O developer conference aimed at bridging the real world and the company's digital universe of search, Maps and other services using the latest advances in artificial intelligence. "What we're working on is technology that enables us to break down language barriers, taking years of research in Google Translate and bringing that to glasses," said Eddie Chung, a director of product management at Google, calling the capability "subtitles for the world." Selling more hardware could help Google increase profit by keeping users in its network of technology, where it does not have to split ad sales with device makers such as Apple Inc (AAPL.O)and Samsung Electronics CO (005930.KS)that help distribute its services.
When prepping for a job interview, the first place I go is Google. After all, the company's search engine is a launchpad to learn about your potential company, workshop possible questions, and walk away feeling knowledgable and prepared. Now, Google is stepping up its interview game even further--by implementing an interviewing tool powered by artificial intelligence. Before you call your parents for interview advice, check out Google's solution. This piece of artificial intelligence is called "Interview Warmup," a simple yet powerful program you can use to practice common interview questions for different professions.
There are different ways to define common sense machine learning. It could mean using simple models whenever possible, avoiding overfitting, correctly selecting features, or doing cross-validation the right way. Or it could mean not using any data set. The first definition may be the topic of a future article. Here I focus on the latter.
This book was designed around major building blocks of the Python ecosystem that are useful to machine learning projects. There are a lot of things you could learn about Python, from language mechanics to the various libraries. Our goal is to take you straight to developing an intuition for the elements you can use in Python projects with laser-focused tutorials. We designed the tutorials to focus on how to get things done with Python. They give you the tools to both rapidly understand and apply each technique or operation. Each tutorial is designed to take you about one hour to read through and complete, excluding the extensions and further reading. You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book is intended to be read and used, not to sit idle. I would recommend picking a schedule and sticking to it.
Building a data processing pipeline is one of the most common problem statements, for which you would have written small scripts or built a full-fledged scalable system based on the amount, and frequency of data. In this article, we will talk about the idea of event-driven scalability, the backbone that will be cost-optimized, and requires a minimum amount of development and operations. Why build an event-driven and scalable data processing pipeline? While working with startups or building a team project or a personal project, that requires a pipeline for data processing, there is always a constraint of cost. We will use a simple example for building a metadata extraction system for e-commerce products.
One of the most common challenges in an e-commerce business to build a well-performing product recommender and categorisation model. A product recommender is used to recommend similar products to users so that total time and money spent on platform per user will be increased. There is also a need to have a model to categorise products correctly since there might be some wrongly categorised products in those platforms especially where most of content is generated by users as in case of classified websites. A product categorisation model is used to catch those products and place them back into their right categories to improve overall user experience on the platform. This article has 2 main parts.
Is AI the face of the new Brick-and-Mortar market? Artificial Intelligence's business applications are undergoing unprecedented growth as a new evolving reality. Therefore, giving AI its due credit stands to reason on a two-fold basis. Firstly – AI helps your retail business improve the bottom line and increase productivity. Secondly, customers are actively looking for a value-added experience from physical stores apart from a high-quality product.
Microsoft and Meta are extending their ongoing AI partnership, with Meta selecting Azure as "a strategic cloud provider" to accelerate its own AI research and development. Microsoft officials shared more details about the latest on the Microsoft-Meta partnership on Day 2 of the Microsoft Build 2022 developers conference. Microsoft and Meta -- back when it was still known as Facebook -- announced the ONNX (Open Neural Network Exchange) format in 2017 in the name of enabling developers to move deep-learning models between different AI frameworks. Microsoft open sourced the ONNX Runtime, which is the inference engine for models in the ONNX format, in 2018. Today, Meta officials said they'll be using Azure to accelerate research and development across the Meta AI group.
Silicon Valley CEOs usually focus on the positives when announcing their company's next big thing. In 2007, Apple's Steve Jobs lauded the first iPhone's "revolutionary user interface" and "breakthrough software." Google CEO Sundar Pichai took a different tack at his company's annual conference Wednesday when he announced a beta test of Google's "most advanced conversational AI yet." Pichai said the chatbot, known as LaMDA 2, can converse on any topic and had performed well in tests with Google employees. He announced a forthcoming app called AI Test Kitchen that will make the bot available for outsiders to try.