Showing posts with label Artificial Intelligence. Show all posts
Showing posts with label Artificial Intelligence. Show all posts

Tuesday, March 2, 2021

How AI protects your purchases

Artificial Intelligence has the power to transform experiences. It helps make our daily lives easier, more convenient, and tailored to our likes and dislikes. AI helps you shop for the exact item you had in mind or inspires you with a song or movie that suits your taste.

It catches your typos when you’re searching for the right ride for your road trip and helps to keep you protected from fraud. Pioneered in 1993, Visa’s AI has helped keep your payments safe and secure getting smarter and smarter as time goes by.

Because that’s the thing about AI: The longer it’s been around, the more it knows and the better it becomes. Let’s take another look. When you apply for a loan or a credit card, Visa’s AI assigns a risk score to the application. This score can help your bank or credit union decide whether to accept the application helping reduce new account fraud due to identity theft.

When you make a purchase, Visa’s AI uses sophisticated algorithms and more than 500 data factors to score the riskiness of every transaction separating good ones from bad ones. This helps prevent fraud while approving the purchase you want to make even if you’re a new or infrequent shopper.

And if your bank or credit union experiences an unexpected service outage? Visa’s AI can step in on behalf of your bank and predict how your bank might have responded, which can help prevent your transaction from being declined so you can be on your way. Oh, and Visa’s AI can also help shorten the time between your card being authorized at the checkout, and the funds actually leaving your account meaning fewer “pending transactions” on your statement and greater clarity about your account balance.

Unfortunately, in the age of cyber-crime, you may not be the only one trying to track your money. Across the world, hackers are always innovating and using advanced tools to exploit and compromise consumer data such as account numbers and security codes. But Visa’s security and AI capabilities help protect you.

Visa’s network is the most trusted network in the world. More than 204 billion transactions fuel it with information, keeping payments that flow through it safe and secure.


By identifying sophisticated attack patterns and alerting financial institutions and merchants before hackers even get a chance to make fraudulent transactions. So whether you're setting off on a dream road trip or just want to order a new jacket, you can shop with peace of mind knowing that Visa’s AI has your back. Today, and tomorrow.

Exploring Ocean Using AI

I'm excited to share with you Exploring our ocean using artificial intelligence. This is a project that involves a number of individuals from NOAA, National Geographic Society, CVision AI, MIT Media Lab, as well as MBARI. Funding has been graciously provided by the National Geographic Society, NOAA, as well as the National Science Foundation.

This project really centers around one question: How do we explore a realm as vast and ever-changing as the ocean. With the advent of modern robotics, persistent and distributed observations of processes and life in our ocean are now on the horizon. Implementing artificial intelligence, or AI, has been touted as a crucial pathway to enable rapid processing of ocean data, and this is really required for us to be able to scale our observations to the entire ocean. 

Adoption of AI in the ocean is limited though by the availability of curated data, particularly underwater imagery and video, in order to train these algorithms. Our Ocean Shot is to use AI to automate processing of underwater imagery and video to fully explore and discover our ocean. We want to realize a vision of ocean exploration and discovery that involves the use of distributed observation platforms conducting measurements at unprecedented spatiotemporal scales.

Now artificial intelligence can help but in order to train algorithms that can automate the detection and classification of concepts in underwater imagery, we really need labeled data. Labeled data requires localization (or bounding boxes) and identification (or annotation) of concepts in every training image.

But the bottleneck for deploying AI in the ocean is the availability of labeled data, which we seeks to address. We're able to do this by first leveraging existing data like MBARI's Video Annotation and Reference System (VARS), which is a 30-year annotated database from ROV-collected video.

If you do a search for a concept like Aegina in the VARS database, you can return a number of images like the two images that you see here: the top image shows Aegina clearly as the only object or concept in the image, whereas the image below shows Aegina with a number of other objects. So while some underwater image data may contain annotations with concept names, in more cluttered fields like the image on the bottom,  the locations of all concepts within an image are critical for training machine learning algorithms.

Our solution which is a publicly available database for training machine learning algorithms on underwater imagery. By leveraging existing data, and providing a repository for new data, we will construct a global data set that can be used to train algorithms for rapid and widespread exploration and discovery of our ocean. 

To date, FathomNet contains 771 concepts and more than 117,000 localizations from MBARI's Video Annotation and Reference System, with additional contributions from NOAA, National Geographic Society, and other partners planned in 2021. Training AI algorithms on FathomNet data have yielded promising results that can be used to achieve our Ocean Shot.

FathomNet can be found at, and the website will be released in the next few months. Users can either search for concepts from the website directly, or by using an API. Data exploration involves the use of concept trees, geographic locations, or a number of other filters, and contributions can also be verified by experts. There are also additional annotation tools that can enable users to augment  existing data, or make modifications to data that they contribute to the database.

Algorithms trained on FathomNet have been used to detect and classify animals observed by numerous deep-sea robotic systems at MBARI, NOAA, and NGS, and the videos that you see on the bottom are examples of footage that have had machine learning algorithms applied to them collected by these different robotic platforms including NOAA's ROV Deep Discoverer, National Geographic Society's Deep Sea Camera system, MBARI's MiniROV platform, and finally MBARI's i2MAP autonomous underwater vehicle, that is specifically designed for midwater transects.

What is Artificial Intelligence (AI)? - How AI works

Throughout this article we'll  aim to explain - first, what is artificial intelligence? Secondly, how does AI work? So, what is artificial intelligence you  ask? In 1956, John McCarthy the man who coined the term AI defined it to be the science  and engineering of making intelligent machines. 

Simply it's a science of making smart machines  that will eventually think like humans do. In movies, these are typically depicted  as robots who will destroy the world. Now, artificial intelligence is used in our  everyday lives and also is much broader than that.

Like when you asked your  phone to call your mother, or when youtube helped you find this  very video. Great choice by the way! To properly understand what artificial intelligence can achieve we probably   want to know what we even mean by human intelligence. There's a lot to human intelligence and we define it in five components that frame the goals we have as AI is developed.

First, the ability to learn and improve. Second, the ability to adapt in new environments and situations. Even though we may never have walked down a specific path we know how to adapt to it and walk through it without having experienced it before. Third, the ability to react to surroundings. For example, when we drive down a road and a person appears we know to stop the car. 

Fourth, the ability to solve problems. For example, figuring out the solution to a puzzle. Finally, the fifth component is understanding language. As humans we communicate through language and are also able to understand context. For example if someone says I ate a date today. We understand that we mean the fruit (date) was eaten not a person.

Machines can do some of these sometimes better than humans. For example, when AlphaGo beat the  world champion at the game of go. The game that has more combinations than atoms in the universe. But in terms of AI being used and developed right now - there's a large range going from what is called strong AI also known as artificial general intelligence (AGI) to weak AI. Strong AI is intelligence at its general meaning it can do many things like humans. Such as solve many different types of problems in many different situations.

Weak AI are machines and algorithms that can solve one specific problem very well but won't deal very well with being thrown a new problem in a new context. Currently artificial general intelligence has not yet been achieved but is a constant subject of research. So how does AI really work?

To be able to perform these many tasks and learn the artificial intelligence requires: information (inputs) as well as outputs such as predictions or decisions. Inputs can come from numbers, for example, age or height in a scientific experiment all collected in a spreadsheet or text in tweets or online reviews images and videos and audio. The analysis of text refers to the field of natural language processing in data science, images is computer vision and audio is speech recognition. 

We won't go into how these work specifically in this article however if you're interested check out the related articles in the links. Once we have inputs we want to be able to use them to result in outputs such as a prediction for example: What netflix shows should we recommend for this user? or decisions such as stopping a self-driving car when it sees a pedestrian crossing the road. One field that focuses on creating models to make predictions or decisions through AI is called machine learning.

Machine learning is the use of algorithms or models to analyze and learn from large amounts of data without explicitly being told how. Three key ways that we can teach a machine to learn are: supervised, unsupervised and reinforcement. Supervised learning is where you provide the data with the answers to the questions you're after to later make predictions on new or future data. For example you may want to predict whether it will or won't rain in the future. You've already collected information on temperature and humidity levels as well as whether it rained or not then, you let your machine learn from this and predict rain for future days.

In unsupervised learning you don't have the specific answer to your question in your data. Unlike in supervised learning this approach is used to group items together for example the items on amazon under what items do other customers buy after viewing this item or an anomaly detection for example finding abnormal bank transactions. The bank will not be provided information or whether the transaction was fraudulent or not. However, with the information they have they're able to
detect whether something seems unusual. 

Finally, there's reinforcement learning where you let the machine harness the power of trial and error you give it a reward if it does something correctly and a punishment when it doesn't.

To summarise AI is a way in which we can make machines intelligent. To enable these machines to understand the world they require inputs such as numbers audio video or other data. This enables machines to provide us with outputs such as decisions or predictions.