The unstructured nature of data used in foundation model development is a challenge to systematic analyses for making data use and documentation decisions. From a Responsible AI perspective, these decisions often rely upon understanding how people are represented in data. We propose a framework designed to guide analysis of human representation in unstructured data and identify downstream risks. We apply the framework in two toy examples using the Common Crawl web text corpus (C4) and LAION-400M. We also propose a set of hypothetical action steps in service of dataset use, development, and documentation.

Construct theory in social psychology, developed by George Kelly are mental constructs to predict and anticipate events. Constructs are how humans interpret, curate, predict and validate data; information. AI today is biased because it is trained with a narrow construct as defined by the training data labels. Machine Learning algorithms for facial recognition discriminate against darker skin colors and in the ground breaking research papers (Buolamwini, Joy and Timnit Gebru. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. FAT (2018), the inclusion of phenotypic labeling is proposed as a viable solution. In Construct theory, phenotype is just one of the many subelements that make up the construct of a face. In this paper, we present 15 main elements of the construct of face, with 50 subelements and tested Google Cloud Vision API and Microsoft Cognitive Services API using FairFace dataset that currently has data for 7 races, genders and ages, and we retested against FairFace Plus dataset curated by us. Our results show exactly where they have gaps for inclusivity. Based on our experiment results, we propose that validated, inclusive constructs become industry standards for AI ML models going forward.

Google does a lot of stupid things. All giant corporations are the same in that regard. But it takes special effort to do something truly terrible. That's where Google's Project Nimbus comes in on the spectrum. Project Nimbus is a joint effort of Google, Amazon, and the Israeli government that provides futuristic surveillance capabilities through the use of advanced machine learning models.

"One of the things I get very concerned about is that, for so long, AI has been such a mystery. And in that blanket of mysteriousness, it's been built up as something magical. So much so that for the first number of years in this role, customers were coming to Cloud excited about AI as a technology, but not yet as a means to solve tactical business problems, as if any use of AI might be a magic wand," says Tracy Pizzo Frey, Senior Director, Outbound Product Management, Engagements & Responsible AI for Cloud AI & Industry Solutions at Google. The reality is, of course, very different. AI technology is not magic at all.

Google Cloud Vision API is a machine learning tool that can classify details from an image provided as an input into thousands of different categories with pre-trained API models. It offers these pre-trained models through an API and the categories are detected as individual objects within the image. In this tutorial, you are going to learn how to integrate Google Cloud Vision API in a React Native application and make use of real-time APIs. You can find the complete code inside this GitHub repo. If you are not familiar with Expo, this tutorial can be a good start.

This past May I worked with the Internet Archive's Television News Archive to apply Google's suite of cloud AI APIs to analyze a week of television news coverage to examine how AI "sees" television and what insights we might gain into the world of non-consumptive deep learning-powered video understanding. Using Google's video, image, speech and natural language APIs as lenses, more than 600GB of machine annotations trace how deep learning algorithms today understand video. What lessons can we learn about the state of AI today and how it can be applied in creative ways to catalog and explore the vast world of video? Working with the Internet Archive's Television News Archive, a week of television news was selected covering CNN, MSNBC and Fox News and the morning and evening broadcasts of San Francisco affiliates KGO (ABC), KPIX (CBS), KNTV (NBC) and KQED (PBS) from April 15 to April 22, 2019, totaling 812 hours of television news. This week was selected due to it having two major stories, one national (the Mueller report release on April 18th) and one international (the Notre Dame fire on April 15th).

Plate from Muybridge's Animal Locomotion series published in 1887. Deep learning has become the dominate lens through which machines understand video. Yet video files consume huge amounts of storage space and are extremely computationally demanding to analyze using deep learning. Certain use cases can benefit from converting videos to sequences of still images for analysis, enabling full data parallelism and vast reductions in data storage and computation. Representing video as still imagery also presents unique opportunities for non-consumptive analysis similar to the use of ngrams for text.

Google Cloud Platform (GCP) is considered to be one of the Big 3 cloud platforms among Microsoft Azure and AWS. GCP is widely used cloud solutions supporting AI capabilities to design and develop smart models to turn your data into insights at a cheap, affordable cost. The following excerpt is taken from the book'Cloud Analytics with Google Cloud Platform' authored by Sanket Thodge. GCP offers many machine learning APIs, among which we take a look at the 3 most popular APIs: A powerful API from GCP! This enables the user to convert speech to text by using a neural network model.

At Next '18 this past July, we announced a range of updates to our AI and machine learning offerings aimed at making AI more accessible to developers. With the excitement of Next behind us, we thought we'd share a little more on these updates and how they can help you quickly and easily inject AI into your applications. Cloud AutoML is a suite of machine learning products that leverages Google's state-of-the-art transfer learning and neural architecture search (NAS) technology so you can easily train high quality custom models, even if you have limited experience with machine learning. This delivers the best of both worlds: high model quality and ease of use. This new suite of products aligns with our mission to democratize AI, and make it easy, fast and useful for all developers and enterprises.

Tech giants love to tout how good their computers are at identifying what's depicted in a photograph. In 2015, deep learning algorithms designed by Google, Microsoft, and China's Baidu superseded humans at the task, at least initially. This week, Facebook announced that its facial-recognition technology is now smart enough to identify a photo of you, even if you're not tagged in it. But algorithms, unlike humans, are susceptible to a specific type of problem called an "adversarial example." These are specially designed optical illusions that fool computers into doing things like mistake a picture of a panda for one of a gibbon.