Ask Sendai

Google’s AI tool for developers won’t add gender labels to images anymore, saying a person’s gender can’t be determined just by appearance. The company emailed developers about the change to its Cloud Vision API tool, which developers use to analyze images and identify faces, landmarks, explicit content, and other recognizable features. Source: theverge.com

A new study co-authored by an MIT economist shows that improved translation software can significantly boost international trade online — a notable case of machine learning having a clear impact on economic activity. The research finds that after eBay improved its automatic translation program in 2014, commerce shot up by 10.9 percent among pairs of countries where people could use the new system. To put the results in perspective, he adds, consider that physical distance is, by itself, also a significant barrier to global commerce.

We are standardizing OpenAI’s deep learning framework on PyTorch. In the past, we implemented projects in many frameworks depending on their relative strengths. We’ve now chosen to standardize to make it easier for our team to create and share optimized implementations of our models. As part of this move, we’ve just released a PyTorch-enabled version of Spinning Up in Deep RL, an open-source educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning. We are also in the process of writing PyTorch bindings for our highly-optimized blocksparse kernels, and will open-source those bindings in upcoming months. The main reason we’ve chosen PyTorch is to increase our research productivity at scale on GPUs.

Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world’s most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models. The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. In their new study, the researchers also identified several other promising antibiotic candidates, which they plan to test further. They believe the model could also be used to design new drugs, based on what it has learned about chemical structures that enable drugs to kill bacteria.

Anomalies, often referred to as outliers, are data points or patterns in data that do not conform to a notion of normal behavior. Anomaly detection, then, is the task of finding those patterns in data that do not adhere to expected norms. The capability to recognize or detect anomalous behavior can provide highly useful insights across industries. Flagging or enacting a planned response when these unusual cases occur can save businesses time, money, and customers. Automatically detecting and correctly classifying something unseen as anomalous is a challenging problem that has been tackled in many different manners over the years. Traditional machine learning approaches are sub-optimal when it comes to high dimensional data, because they fail to capture the complex structure in the data.

Synced spotlights 10 artificial intelligence papers that garnered extraordinary attention and accolades in 2019. The volume of peer-reviewed AI research papers has grown by more than 300 percent over the past three decades (Stanford AI Index 2019), and the top AI conferences in 2019 saw a deluge of paper. CVPR submissions spiked to 5,165, a 56 percent increase over 2018; ICLR received 1,591 main conference paper submissions, up 60 percent over last year; ACL reported a record-breaking 2,906 submissions, almost doubling last year’s 1,544; and ICCV 2019 received 4,303 submissions, more than twice the 2017 total.

The AI Index 2019 Report takes an interdisciplinary approach by design, analyzing and distilling patterns about AI’s broad global impact on everything from national economies to job growth, research and public perception. We’re excited to release the AI Index 2019 Report, one of the most comprehensive studies about AI to date. Because AI touches so many aspects of society, the Index takes an interdisciplinary approach by design, analyzing and distilling patterns about AI’s broad global impact on everything from national economies to job growth, research and public perception.

Machine learning is at the heart of everything we do at Spotify. Especially on Spotify Home, where it enables us to personalize the user experience and provide billions of fans the opportunity to enjoy and be inspired by the artists on our platform. This is what makes Spotify unique. Across our engineering community, we are working to unite autonomous teams and empower them to be more efficient by establishing best practices for tools and methods. Our recent adoption of a standardized machine learning infrastructure provides our engineers with the environment and tools that enable them to quickly create and iterate on models. We call it our ‘Paved Road’ approach, which includes utilizing services from TensorFlow, Kubeflow, and the Google Cloud Platform.

Document understanding is the practice of using AI and machine learning to extract data and insights from text and paper sources such as emails, PDFs, scanned documents, and more. In the past, capturing this unstructured or “dark data” has been an expensive, time-consuming, and error-prone process requiring manual data entry. Today, AI and machine learning have made great advances towards automating this process, enabling businesses to derive insights from and take advantage of this data that had been previously untapped.

Google recently released research about creating a tool for searching Similar Medical Images Like Yours (SMILY). The research uses embeddings for image-based search and allows users to influence the search through the interactive refinement of concepts. Source: infoq.com