Amy manages the Neo4j graph analytics programs and marketing. She loves seeing how our ecosystem uses graph analytics to reveal structures within real-world networks and infer dynamic behavior.
In her career, Amy has consistently helped teams break into new markets at startups and large companies including EDS, Microsoft and Hewlett-Packard (HP). She most recently comes from Cray Inc., where she was the analytics and artificial intelligence market manager.
Amy has a love for science and art with an extreme fascination for complexity science and graph theory. When the weather is good, you’re likely to find her cycling the passes in beautiful Eastern Washington.
Financial fraud is growing more sophisticated every day – and fraudsters are not slowing down their efforts. To detect and prevent costly fraud schemes, you need a better understanding of your data. This infographic breaks down how graph data science unleashes the power of your existing data,... read more
A few weeks ago, we released Neo4j Bloom version 1.4 with new features to help you get started investigating graphs visually and providing new ways of viewing different types of graphs. We focused on these areas because, although people perform various types of activities in Bloom, it’s... read more
We're very delighted to talk with Dr. Alessandro Negro, the Chief Scientist of GraphAware, who authored the recently published book, Graph-Powered Machine Learning. Dr. Negro has been a long-time member of the graph community, and was the main author of the very first recommendation engine... read more
The Neo4j Graph Data Science (GDS) Library is an enterprise-ready analytics workspace and repository of graph algorithms. With the GDS Library, data scientists can leverage analytics-specific data structures optimized for global traversals and aggregation, and flexibly subset and reshape your... read more
Financial fraud is growing and it is a costly problem, estimated at 6% of the Global Domestic Product, more than $5 trillion in 2019. Despite using increasingly sophisticated fraud detection tools – often tapping into AI and machine learning – businesses lose more and more money to... read more
Financial fraud is growing and it is a costly problem, McKinsey, the fastest-growing type of first-party fraud is synthetic identity fraud. In synthetic identity fraud, the fraudster usually combines fake and real information to establish a credit record under a new, synthetic identity. This type... read more
Financial fraud is growing and it is a costly problem, estimated at 6% of the Global Domestic Product, more than $5 trillion in 2019. Despite using increasingly sophisticated fraud detection tools – often tapping into AI and machine learning – businesses lose more and more money to... read more
Financial fraud is growing and it is a costly problem, fastest-growing types of fraud in the U.S. – synthetic identity theft. Fraudsters meld various false and authentic elements (such as addresses, phone numbers, emails, employers and more) into a synthetic identity, which they then use for... read more
Why is it that we naturally talk about graphs as if they were context? That's because graphs were built to understand relationships – in fact, it's how graph theory started. Graphs were not only built to understand relationships, they were built with relationships. And that's... read more
This was not on my radar 12 months ago. My good friend and smart colleague, Leena Bengani, suggested we look a little more closely into AI standards and tools. Part of the impetus was a White House request of the National Institute for Standards and Tools to engage with the AI community at... read more
Editor’s Note: This presentation was given by Amy Hodler at GraphTour San Francisco in May 2019. Presentation Summary In an everlasting race to achieve technological advancement, many companies are looking at artificial intelligence (AI) and machine learning (ML) as the next big step toward... read more
This week we reach the final blog in our five-part series on AI and graph technology. Our focus here is on how graphs offer a way to provide transparency into the way AI makes decisions. This area is called AI explainability. One challenge in AI adoption is understanding how an AI solution made... read more
In last week's blog in our five-part series on AI and graph technology, we looked at how graphs offer greater efficiency of processing, and how graph-accelerated machine learning uses graphs to optimize models and speed up processes. This week, as we continue giving you a glimpse into how... read more
Last week in our five-part series on AI and graph technology, we examined knowledge graphs, which offer context for decision support. This week we continue giving you a glimpse into how a graph technology platform like Neo4j enhances AI with context with a look at how graphs offer greater... read more
Last week in the first installment of our five-part blog series on AI and graph technology, we gave an overview of four ways graphs add context for artificial intelligence: context for decisions with knowledge graphs, context for efficiency with graph accelerated ML, context for accuracy with... read more
The idea of artificial intelligence (AI) has a long history. Loosely defined, AI is a solution or set of tools to solve problems in ways that mimic human intelligence. Usually its most practical goal is to make predictions – either classifying things (such as adding a label) or predicting a value... read more
In part three of our series Toward AI Solutions, we explain how and why context makes AI more robust to manage difficult problems and help make the most informed decisions. Trustworthiness is a critical topic for AI standards efforts and called out in the NIST draft engagement plan for U.S.... read more
Last week, in part two of our series Toward AI Solutions, we explore graph technology as the fabric for AI context: "More recently, graph technologies have been increasingly integrated with machine learning and artificial intelligence solutions. These applications include using connections to... read more
Last week, part one of our "Toward AI Standards" blog series set the stage for defining what responsible artificial intelligence is and how graph technology provides the necessary context to make deeply informed decisions based on intelligent data. In this week's installment, we further break... read more
Last week, Neo4j CEO Emil Eifrem published the blog, "Towards AI Standards, Why Context Is Critical for Artificial Intelligence" that spoke about our recent response to the request for information from the U.S. The National Institute of Standards and Technology (NIST). The NIST is looking to... read more
Retail opportunities are as big and dynamic as the industry has ever seen, and yet, knowing what to do or how to attack said opportunities is a bit more difficult to ascertain. U.S. retail sales per year are in the trillions and the impact of social media influencers are turning in billions.... read more
Successful companies are embracing next-generation service assurance that leverages a comprehensive, real-time view of services and infrastructure with an eye on end-user experiences, new service creation and predictive modeling. But to compete in today’s market, communication service... read more
Service complexity is exploding. Communication Service Providers (CSPs) need a complete view of their network and its myriad interdependencies to drive real-time decisions and predict the impact of changes on the user experience. A native graph approach makes sense of complex networks and... read more
Service assurance is the way organizations optimize various services offered over networks (from phone calls and email to video and applications) to deliver a better end-user experience. For decades, service assurance practices used fragmented views of the network and services that are... read more
Graph analytics have value only if you have the skills to use them and if they can quickly provide the insights you need. Therefore, the best graph algorithms are easy to use, fast to execute and produce powerful results. Neo4j includes a growing, open library of high-performance graph... read more
