Google Maps is getting an "Immersive View" that will offer users digitally rendered looks at major US cityscapes, Alphabet CEO Sundar Pichai told the audience at Google's I/O 2022 keynote on Wednesday. The new feature uses computer vision and AI to blend Maps' existing Street View function with aerial photography to create high-resolution models of the various buildings and urban features of a given location. "With our new immersive view, you'll be able to experience what a neighborhood, landmark, restaurant or popular venue is like -- and even feel like you're right there before you ever set foot inside," wrote Miriam Daniel, VP of Google Maps, in a blog post. What's more, Maps' other tools and features can be applied to the view as well, enabling users to see what the area looks like at different times of the day and varying weather conditions. Immersive View will first be available for Los Angeles, London, New York, San Francisco and Tokyo later this year, with more cities to follow.
Meta, Facebook's parent company, is facing another lawsuit filed by one of is former content moderators. According to The Washington Post, this one is filed by Daniel Motaung, who's accusing the company and San Francisco subcontractor Sama of human trafficking Africans to work in exploitative and unsafe working conditions in Kenya. The lawsuit alleges that Sama targets poor people across the region, including those from Kenya, South Africa, Ethiopia, Somalia and Uganda, with misleading job ads. They were reportedly never told that they'd be working as Facebook moderators and would have to view disturbing content as part of the job. Motaung said the first video he watched was of someone being beheaded and that he was fired after six months on the job for trying to spearhead workers' unionization efforts.
Machine-learning algorithms that generate fluent language from vast amounts of text could change how science is done -- but not necessarily for the better, says Shobita Parthasarathy, a specialist in the governance of emerging technologies at the University of Michigan in Ann Arbor. In a report published on 27 April, Parthasarathy and other researchers try to anticipate societal impacts of emerging artificial-intelligence (AI) technologies called large language models (LLMs). These can churn out astonishingly convincing prose, translate between languages, answer questions and even produce code. The corporations building them -- including Google, Facebook and Microsoft -- aim to use them in chatbots and search engines, and to summarize documents. They sometimes parrot errors or problematic stereotypes in the millions or billions of documents they're trained on.
So declared tech insider Mariya Yao in a recent Forbes column that enumerated the various ways machine learning can (and should) be employed to improve marketing efforts in today's data-glutted world. "In the new economy," Yao added, "a marketing unit without machine learning mastery operates at a serious handicap." As a Technology Review article put it, machine learning (which uses an algorithm to identify and learn from data patterns) helps marketers "radically rethink" their campaigns by anticipating future customer moves and more accurately assessing needs through the scouring of data, the identification of patterns and the creation of "predictive models." Here are 20 examples of how machine learning is revolutionizing marketing. How it's using machine learning in marketing: Combining machine learning with natural language processing, OneSpot aims to increase brand engagement and content consumption via algorithms that automatically analyze a brand's content assets.
Twilio powers real-time business communications and data solutions that help companies and developers worldwide build better applications and customer experiences. Although we're headquartered in San Francisco, we have presence throughout South America, Europe, Asia and Australia. We're on a journey to becoming a globally anti-racist, anti-oppressive, anti-bias company that actively opposes racism and all forms of oppression and bias. At Twilio, we support diversity, equity & inclusion wherever we do business. We employ thousands of Twilions worldwide, and we're looking for more builders, creators, and visionaries to help fuel our growth momentum.
Glassdoor estimates the average salary for a Machine Learning Engineer at $131,001 USD. Indeed lists 2091 openings with an averMachine Learning Engineer age nationwide salary of $131,276 USD. The San Francisco Bay Area is the high-end of the salary range at $193,485 with Eden Prairie, Minnesota at $106,780. ZipRecruiter calculates the average US Machine Learning Engineer salary at $130,530. Our first pick is the Machine Learning Engineer -- learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker, Deep Learning Topics within Computer Vision and NLP, Developing Your First ML Workflow, Operationalizing Machine Learning Projects, and a Capstone Project -- Inventory Monitoring at Distribution Centers, Second, the Machine Learning with PyTorch Open Source Torch Library -- machine learning, and for deep learning specifically, are presented with an eye toward their comparison to PyTorch, scikit-learn library, similarity between PyTorch tensors and the arrays in NumPy or other vectorized numeric libraries,clustering with PyTorch, image classifiers, And third, AWS Certified Machine Learning -- AWS Machine Learning-Specialty (ML-S) Certification exam, AWS Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services, Machine Learning Modeling.
MARSEILLE, France and PROVIDENCE, R.I., April 27, 2022 (GLOBE NEWSWIRE) -- Volta Medical, a pioneering medtech startup advancing novel artificial intelligence (AI) algorithms to treat cardiac arrhythmias, today announced it will participate at Heart Rhythm 2022, where Volta VX1 digital AI companion technology will be featured in several venues, including a poster session, podium presentation, Rhythm Theater program and the Volta exhibit booth. VX1 is a machine and deep learning-based algorithm designed to assist operators in the real-time manual annotation of 3D anatomical and electrical maps of the human atria during atrial fibrillation (AF) or atrial tachycardia. It is the first FDA cleared AI-based tool in interventional cardiac electrophysiology (EP). On Friday, April 29, VX1 will be highlighted in two scientific sessions: session DH-202, "Machine Learning Applications for Arrhythmia Detection and Treatment" from 10:30-11:30 a.m. Volta's Rhythm Theater presentation, "Can AI Solve the Persistent AF Paradigm?," will be held Saturday, April 30 from 10:00-11:00 a.m.
Shobita Parthasarathy says that LLMs could help to advance research, but their use should be regulated. Machine-learning algorithms that generate fluent language from vast amounts of text could change how science is done -- but not necessarily for the better, says Shobita Parthasarathy, a specialist in the governance of emerging technologies at the University of Michigan in Ann Arbor. In a report published on 27 April, Parthasarathy and other researchers try to anticipate societal impacts of emerging artificial-intelligence (AI) technologies called large language models (LLMs). These can churn out astonishingly convincing prose, translate between languages, answer questions and even produce code. The corporations building them -- including Google, Facebook and Microsoft -- aim to use them in chatbots and search engines, and to summarize documents. They sometimes parrot errors or problematic stereotypes in the millions or billions of documents they're trained on.
Dr. Barry Po is the EVP & Chief Marketing Officer at mCloud, using AI to unlock the untapped potential of energy-intensive assets. A San Francisco auditorium is spellbound. The audience is catching a 90-minute glimpse into the future of work. Teams edit documents together seamlessly in real time. They collaborate virtually over video around the world with a single click as they effortlessly navigate the reams of data and information needed to make important decisions.
For over 23 years, Larry Collins worked as a toll collector on the Carquinez Bridge in San Francisco. He loved his job -- every day, he would come to work and greet drivers, provide directions, answer questions, and collect toll fees. Over the years, although the toll price had changed tremendously, his job was always in a stable condition. But, this all changed during March of 2020. In the midst of the coronavirus pandemic, Collins was suddenly informed that his tollbooth was getting shut down and replaced by an artificial intelligence-based toll collector machine. Collins was not the lone victim of industrial automation unemployment, just in the Northern California region, 185 other toll booths were also shut down and replaced by technological alternatives (Semuels). As the 21st-century technological advances continue, applications of artificial intelligence are expected to expand exponentially. Slowly but surely, artificial intelligence is automating a multitude of manual jobs, causing widespread unemployment around the world (Peterson). There is clear uncertainty about the future of artificial intelligence. A recent report from the conference on Computers, Privacy, and Data Protection suggested that the European Commission (EU), is strongly "considering the possibility of legislating for Artificial Intelligence". This legislation would explore a number of nuances that come with future artificial intelligence job automation and will consider the implementation of a novel regulatory framework (MacCarthy). On the other hand, organizations such as Deltec, an international financial research institute, are in support of artificial intelligence automation and don't want regulation as it would hinder humanity's ability to research and solve problems in an efficient manner (Trehan). Currently, there has been no clear conclusion to this ongoing debate -- experts have varying opinions but agree that a full-proof solution is direly needed.