AI and Machine Learning

Power AI and Machine Learning with TigerGraph

57.6 B
$
AI and ML spend in 2021
75
%
AI implementation in 3 years
2.9 T
$
AI business value
Business Challenge
Solutions
Feature Engineering
Explainable AI
Business Challenge
Artificial Intelligence (AI) and Machine Learning (ML) are moving from bleeding edge technology to an expected competency for every organization. By 2021, businesses are expected to spend $57.6 billion on AI and ML and to reap $2.9 trillion in business value. AI is behind the headline-grabbing applications such as self- driving cars and virtual personal assistants (Siri, Alexa, etc.), but it's also providing better performance and cost savings for universal tasks such as online chat and customer support, product recommendations, design support, and fraud detection. For enterprises, the decision has become not whether to use ML, but where to use and how to do it well.
Traditional Solutions Are Missing the Mark
Machine Learning uses analytical and statistical techniques to uncover patterns in data and thus draw conclusions -- and recognizing patterns is the basis of intelligence. And therein lies two limitations: how good is your data and how good are your analytics?
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You can't detect a pattern in data if the pattern isn't there or if the pattern is very weak. Remember the 3 V's of Big Data: Volume, Variety, and Velocity. To succeed, your data needs to consist of millions of records, to cover a variety of cases, and hopefully to draw from multiple sources. In our increasingly digitized world, harvesting raw data is much less of a problem. Several challenges remain, however. Feature selection: Do I have the right data? Data integration: How do I bring my data from multiple sources together into one unified data model? Analytical performance: With so much data, can I afford the computational effort? Traditional solutions often fail due to a lack of variety of features that have a high correlation to the outcome and low volume of the training data, resulting in poor accuracy for ML solutions.
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Why TigerGraph, a Native Parallel Graph Database for Powering AI and Machine Learning?
Feature Engineering with Real-Time Deep Link Analytics
Graph databases offer solutions to many of these ML data challenges. Graphs are built on the idea of connecting and traversing links, so they are the natural and performant choice for data integration for ML. Graphs can also enrich the raw data. In traditional tabular data, each column is one "feature" that the ML system can use. In a graph, each type of connection is an additional feature. Moreover, small graph patterns, such as causal chains, loops, and forks can themselves be considered features. TigerGraph's native parallel graph architecture with deep link analytics means it can process the datasets with terabytes of data, and traverse millions of connections in a fraction of a second computing new graph-based features. Fraudsters get away with billions of dollars of fraud each year. The measure of an anti-fraud detection and prevention system is not whether it can catch fraud, but how much. China Mobile is using TigerGraph to check each of its hundreds of millions of daily calls in real time to see if it looks to be from a spammer or phone-based fraudster.
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China Mobile gathers over 118 graph features for each of its hundreds of millions of subscriber phones to feed its ML fraud detection engine, builds a detection model, and then again extracts the 118 features for each phone with every call in real-time. This generates training dataset with billions of new features, resulting in improved accuracy of the fraud detection.
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Explainable AI
One of the biggest obstacles to widespread AI adoption in the government as well as the enterprises such as banks, insurance, and telecom companies is a lack of transparency in how the AI system arrived at a particular decision. Consider a welfare claim that was rejected, as the AI solution computed a high probability of potential fraud based on the history of previous claims from the recipient.
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The welfare recipient deserves to know why his or her claim was rejected and the government organization needs to make sure that it wasn’t due to a bias against a specific race, religion, gender or culture. Consider another example where the AI system computes and offers a mortgage with a higher rate of interest or an insurance policy with a higher premium to an applicant. Again, it’s important for the bank or insurance company to explain the higher rate of interest for the loan or higher premium for a policy especially in case of the litigations related to a race, ethnicity, culture or gender bias. Graph- based features clearly show how the AI/ML solution arrived at a particular decision based on the combination of the computed feature values. TigerGraph also offers GraphStudio, a powerful GUI for visualizing, exploring and analyzing relationships to show how the features were computed and what led to a particular welfare claim being rejected or a higher mortgage interest rate for an applicant or a higher premium insurance policy. TigerGraph’s native parallel graph architecture ensures that the explainable AI can be rolled out to all the users within the enterprise as well as external parties such as the welfare recipients, mortgage and insurance policy applicants with real-time visualization, exploration, and analytics of interconnected data.
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Getting started with TigerGraph