Startups are key to solving today's biggest challenges and a huge driver of innovation -- and artificial intelligence is one of their sharpest tools. Virtual assistants, customized content, traffic apps, spell check, mobile check deposit and live captioning constitute just a small fraction of the everyday solutions using AI -- and many of these technologies were first developed by startups. AI learns from those who build it, so it is critical to have people of all backgrounds helping shape the technology to ensure its effectiveness, reduce bias and create better solutions for everyone. As Director of Product Inclusion and Equity at Google, I love to see Black founders tap into the power of our Google AI tech to help their communities and transform the way our products work and operate. In honor of Black History Month in the U.S., I asked four Google for Startups Black Founders Fund recipients from around the world and across different industries how they're using Google AI technology to address societal challenges.

Your smartphone is not listening to you around the clock -- but it's collecting so much information that it does not even need to. It has long been speculated that Apple, Google, Samsung and other popular phone makers are recording users 24/7 to collect information for advertising purposes. Most of us have seemingly randomly been promoted an advert for a product that we could have sworn was only talked about in private. To test this, we set up a freshly-factory-reset Samsung phone, using a new Google account on the Android device. We created a fictitious person named Robin, 22, and made a fake a Facebook account for him to use.

Artificial intelligence (AI) is changing the way we live and work, and its impact is only going to grow in the coming years. Here are 10 ways in which AI is transforming our lives: Personal Assistants: AI-powered personal assistants like Siri and Alexa are already popular, and their capabilities are expanding rapidly. These assistants can help us with everything from setting reminders to ordering groceries. Healthcare: AI is being used in healthcare to improve diagnosis, predict outcomes, and develop new treatments. AI-powered medical imaging can detect tumors and other abnormalities, and AI algorithms can predict patient outcomes and identify high-risk patients.

The use of pretrained embeddings has become widespread in modern e-commerce machine learning (ML) systems. In practice, however, we have encountered several key issues when using pretrained embedding in a real-world production system, many of which cannot be fully explained by current knowledge. Unfortunately, we find that there is a lack of a thorough understanding of how pre-trained embeddings work, especially their intrinsic properties and interactions with downstream tasks. Consequently, it becomes challenging to make interactive and scalable decisions regarding the use of pre-trained embeddings in practice. Our investigation leads to two significant discoveries about using pretrained embeddings in e-commerce applications. Firstly, we find that the design of the pretraining and downstream models, particularly how they encode and decode information via embedding vectors, can have a profound impact. Secondly, we establish a principled perspective of pre-trained embeddings via the lens of kernel analysis, which can be used to evaluate their predictability, interactively and scalably. These findings help to address the practical challenges we faced and offer valuable guidance for successful adoption of pretrained embeddings in real-world production. Our conclusions are backed by solid theoretical reasoning, benchmark experiments, as well as online testings.

AI (Artificial Intelligence) won't just start appearing one day like an all-knowing computer Genie in a lamp-shaped cloud, but you'll be surprised and amazed at how it is currently and will continue to surface in subtle ways that will change many things including entire industries and how you buy their products and services. Some things we have to purchase to survive in the modern world take research, study, and comparison and are generally hard to get good, accurate, and relevant information on so we end up picking arbitrarily or by copying what people we know did. I'm looking at your auto insurance, cell service, automobiles, and computers to name a few. AI won't be one big thing in our lives, it will be thousands of little things. They won't usually manifest themselves in an all-powerful central role like Alexa or Siri, they will be an invisible army of nameless extras hardly noticeable in the background and yet essential to almost every scene of our lives.

AI systems for music tagging have been around for quite a while. Ever since the mid-2010s, music streaming services have been competing for the most innovative music recommendation system using sophisticated tagging AI in the background. Slowly, production music libraries and music labels have caught on to tagging AIs, using it to categorize, filter, and query their huge music databases. Today, even artists are using auto-tagging systems to gain objective insights into their music to find the right audience for it. Although widespread, little is known about the inner workings of auto-tagging systems.

Remember Rosie from The Jetsons or Robot B-9 from Lost in Space? For decades, humans have longed for real robots that could perform tasks like the ones in our favorite cartoons and sci-fi shows. After all, who wouldn't want a robot to take care of all the things you don't want to do? While we might not have our very own bots to fold laundry or scope out danger (yet), the future of AI is here, and an AI assistant like ChatGPT can help us organize and optimize our lives. These super-smart computer programs use artificial intelligence to have conversations with humans and basically act like personal assistants.

We define very large multi-objective optimization problems to be multiobjective optimization problems in which the number of decision variables is greater than 100,000 dimensions. This is an important class of problems as many real-world problems require optimizing hundreds of thousands of variables. Existing evolutionary optimization methods fall short of such requirements when dealing with problems at this very large scale. Inspired by the success of existing recommender systems to handle very large-scale items with limited historical interactions, in this paper we propose a method termed Very large-scale Multiobjective Optimization through Recommender Systems (VMORS). The idea of the proposed method is to transform the defined such very large-scale problems into a problem that can be tackled by a recommender system. In the framework, the solutions are regarded as users, and the different evolution directions are items waiting for the recommendation. We use Thompson sampling to recommend the most suitable items (evolutionary directions) for different users (solutions), in order to locate the optimal solution to a multiobjective optimization problem in a very large search space within acceptable time. We test our proposed method on different problems from 100,000 to 500,000 dimensions, and experimental results show that our method not only shows good performance but also significant improvement over existing methods.

Mastering Time Management: A Step-by-Step Guide to Building a Virtual Assistant for Scheduling and Reminders with Machine Learning (Python + Google Calendar) In today’s fast-paced world, managing time and staying organized is crucial. Virtual assistants have become increasingly popular for handling scheduling, reminders, and other day-to-day tasks. In this tutorial, we will walk you through the process of developing a virtual assistant for scheduling and reminders using machine learning. We will cover the necessary steps, including data preparation, model selection, implementation, and deployment. Prerequisites: Basic understanding of Python programming Familiarity with machine learning concepts Access to a Python development environment (e.g., Jupyter Notebook, PyCharm, or Visual Studio Code) Section 1: Overview of Virtual Assistant Functionality Before diving into the implementation, let’s discuss the core functionalities of our virtual assistant. Our virtual assistant will: Understand natural language input for scheduling tasks and setting reminders Interact with users through a text-based interface Integrate with calendar applications for scheduling Send notifications for reminders Section 2: Data Preparation and Preprocessing To create a machine learning model capable of understanding natural language input, we first need to gather and preprocess the data. We will need a dataset containing text data with user queries related to […]

Last Friday afternoon, Twitter posted the source code of its recommendation algorithm to GitHub. Twitter said it was "open sourcing" its algorithm, something I would typically be in favor of. Recommendation algorithms and open source code are major focuses of my work as a researcher and advocate for corporate accountability in the tech industry. My research has demonstrated why and how companies like YouTube should be more transparent about the inner workings of their recommendation algorithms--and I've run campaigns pressuring them to do so. Mozilla, the nonprofit where I am a senior fellow, famously open-sourced the Netscape browser code and invited a community of developers around the world to contribute to it in 1998, and it has continued to push for an open internet since.