"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
The Azure Machine Learning service speeds up the process of identifying useful algorithms and machine learning pipelines, which automates model selection and tuning. This can cut development time from days to hours, said Bharat Sandhu, director of product marketing, big data and analytics at Microsoft. It also provides DevOps capabilities, via integrated CI/CD tooling, to enable experiment tracking and manage machine learning models deployed in the cloud and on the edge, said Venky Veeraraghavan, group program manager for Microsoft Azure, in a blog post. The Azure Machine Learning service supports popular open source frameworks, including PyTorch, TensorFlow and scikit-learn, so developers and data scientists can use familiar tools. Developers can use Visual Studio Code, Visual Studio, PyCharm, Azure Databricks notebooks or Jupyter notebooks to build apps that use the service.
Authors: Tero Karras (NVIDIA) Samuli Laine (NVIDIA) Timo Aila (NVIDIA) Abstract: We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.
Artificial intelligence related efforts are on the rise, in seemingly all industries. In financial services, AI is being commissioned with increasingly critical accountabilities in making data-driven decisions on what in and when to invest but also how much to invest. The marketing industry is making targeted communications more relevant than ever, with tools and automation that rely increasingly on AI. And the healthcare industry is developing AI tools that could ultimately save lives by speeding up detection and reducing human error. The insurance industry too will be impacted by advancements in AI.
A/B or split testing has been the standard way to optimize marketing campaigns for years. Google first ran an A/B test in 2000 to identify the optimum number of searches to display on its result pages. Today A/B testing is common practice in many different digital marketing channels including display ads, landing pages, email marketing, and pretty much anywhere that copy, images, or placement can be adjusted. A basic example of A/B testing would be splitting visitors to a website into two groups (A and B) and showing each group a slightly different version of the homepage. Everything else might be the same on the page apart from the header image.
Using the 3.37 million images, the computer model was used to assess some 375,000 animal images. The processing of these was rapid, at rate of about 2,000 images per minute. The program was run on a standard laptop computer. When compared with the identifications made by scientists, the artificial intelligence achieved 97.6 percent accuracy and at a pace far faster than any human could hope to match. The program therefore serves a practical use for wildlife image classification.
You're probably used to the presence of facial recognition cameras at airports and other transport hubs, but what about at concerts? That's the step Taylor Swift's team took at her May 18th show at the Rose Bowl, in a bid to identify her stalkers. According to Rolling Stone, the camera was hidden inside a display kiosk at the event, and sent images of anyone who stopped to look at the display to a "command post" in Nashville, where they were cross-referenced with other photos of the star's known stalkers. As the target of numerous death and rape threats, Swift arguably has a valid motivation for leveraging such technology. However, it's unclear who has ownership of the photos of her concertgoers, or how long they will remain on file.
Google Assistant hasn't been traveling, but it has picked up some new accents. The voice assistant now has the ability to speak in an Australian or English accent (though Google calls it British). The feature is available across all devices including Android phones and Google Home speakers, but only for English speakers in the US for the time being. In order to produce the accents in an accurate way, Google is tapping into the artificial intelligence of DeepMind. Google Assistant uses WaveNet, the AI company's speech synthesis model powered by deep neural networks, to generate natural sounding voices.
Today, Google shared information about some of the AI work it's doing in Asia, but in a blog post about the work, it also made a pretty clear statement about how its facial recognition technology will and won't be used for the time being. The company noted that while facial recognition systems stand to be quite useful in a variety of situations, from assistive technologies to locating missing people, they also comes with risks. "Like many technologies with multiple uses, facial recognition merits careful consideration to ensure its use is aligned with our principles and values, and avoids abuse and harmful outcomes," Google said. "We continue to work with many organizations to identify and address these challenges, and unlike some other companies, Google Cloud has chosen not to offer general-purpose facial recognition APIs before working through important technology and policy questions." Facial recognition technology has come under the spotlight in recent years, with everyone from local law enforcement to Taylor Swift employing it in some way.