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Intelligent applications start with machine intelligence

The collection and analysis of data from transactions, sensors, and biometrics continues to grow at a prodigious rate, taxing the analytic capabilities of even the most sophisticated organizations. The quantity of possible insights in a given dataset is an exponential function of the number of data points. On top of this, aggregate data growth is an exponential function with time. Unfortunately, the world cannot train enough data scientists to meet this runaway, double-exponential demand curve for analytics.

This dynamic is driving computer scientists and mathematicians alike to examine new approaches to improve both the quality and the speed of their analytics platforms. Today’s hypothesis-driven analytics and manual machine learning algorithms and statistical tools will not suffice. High-performance computers and algorithms can examine big and complex data far faster and seek insights more comprehensively than any human is capable of.  As such, we need to find exponential improvements in analysis and modeling techniques to meet the growing demand.

Topology is a mathematical discipline that studies shape. Topological Data Analysis (TDA) refers to the adaptation of this discipline to analyzing big and highly complex data. It draws on the insight that all data has an underlying shape and that shape has meaning.

Ayasdi’s approach is to deliver an enterprise software platform that layers TDA on top of a broad range of machine learning, statistical, and geometric algorithms. The analysis creates a summary or compressed representation of your data to help rapidly uncover critical patterns and relationships hidden within. By identifying the geometric relationships that exist between data points, and clusters of data points, Ayasdi offers an extremely intuitive way of interrogating your data to understand the underlying properties that characterize inherent segments and sub-segments.

Once you have completed your analysis for a specific business problem, your developers can use the Ayasdi platform create intelligent applications that let your business people develop, validate and maintain new models on their own, and you can use the Ayasdi’s API to deploy operational applications that deploy your models in production.

Read about this breakthrough technology below, peruse our blog posts on the subject or check out our Resources section for academic papers on the subject.

Machine Intelligence

How TDA improves machine learning

TDA makes machine learning dramatically more effective.

Clustering

Clustering maps an input data point to a cluster.

Dimensionality
Reduction

Dimensionality Reduction maps an input data point to a lower dimensional data point.

Regression &
Classification

Supervised Learning algorithms map an input data point to a predicted value.

Ayasdi's platform uses TDA to bring together and automate a broad range of machine learning, statistical, and geometric algorithms. Ayasdi Workbench creates highly interactive visual networks that you can rapidly explore and manipulate to understand critical patterns and relationships in your data. TDA makes your machine learning techniques work better by ameliorating their shortcomings and by automating repetitive processes

Topological data analysis and machine learning: The core of machine intelligence

Topological Data Analysis is a powerful framework for advanced analytics on big data and on highly-dimensional, complex data sets. Machine learning is a class of algorithms that adjust and learn from your data to take or suggest actions in the future. TDA and machine learning strengthen each other. Together they form the core of Ayasdi's machine intelligence platform.

Topology is a mathematical discipline that studies shape. TDA refers to the adaptation of topology to analyze big data and highly complex data. It draws on the philosophy that all data has an underlying shape and that shape has meaning. Ayasdi’s approach to TDA layers it on top of a broad range of machine learning, statistical, and geometric algorithms. The analysis creates a summary or compressed representation of your data to help rapidly uncover critical patterns and relationships. By identifying the geometric relationships that exist between data points, Ayasdi’s approach to TDA offers an extremely simple way of interrogating your data to understand the underlying properties that characterize the segments and sub-segments that lie within data.

Machine learning is a class of algorithms that adjust and learn from data to take or suggest actions in the future. It promises to help companies achieve the following goals:

  • Effectively segment existing data
  • Identify the key attributes and features that drive segmentation
  • Find patterns and anomalies in the data
  • Precisely classify new data points as they arrive

There are two classes of machine learning techniques – unsupervised and supervised. Unsupervised learning helps with discovering the hidden structure in data. Supervised learning helps with the construction of predictive models. Innovations in these techniques promise to help drive new revenue streams, forge stronger customer relationships, predict risk, and prevent fraud.

Learn More about Ayasdi and Machine Intelligence

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