Pattern recognition and machine learning

Working with pattern recognition and machine learning platforms allows organizations to extract information from big data. This core AI technology can be tricky, but extremely beneficial for enterprises. Read about machine learning best practices and techniques to uncover valuable predictive insights.

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Pattern recognition and machine learning News

August 17, 2020 17 Aug'20
Does technology increase the problem of racism and discrimination?

According to a publication from the MIT Technology Review, technology promotes racism. Most facial recognition algorithms discriminate against the Black population. And even certain concepts or technological terminology tend to be offensive, ...
January 06, 2020 06 Jan'20
U.S. restricts exports of geospatial AI software

The Trump administration's move to limit export of AI-based geospatial imaging software extends U.S. restrictions on technology that foreign countries could use to develop weapons systems.
October 30, 2019 30 Oct'19
The importance of AI for fraud prevention

As fraudsters become increasingly more professional and technologically advanced, financial organizations need to rely on products that use AI for to prevent fraud.
September 25, 2019 25 Sep'19
Cambridge Consultants demos show uses of AI

At a demo event, Cambridge Consultants highlighted some of the applications of AI with a system that can count TB cells in a sputum sample and a system that can fix image distortion.

Pattern recognition and machine learning Get Started

Bring yourself up to speed with our introductory content

deepfake AI (deep fake)

Deep fake (also spelled deepfake) is a type of artificial intelligence used to create convincing images, audio and video hoaxes. Continue Reading
unsupervised learning

Unsupervised learning refers to the use of artificial intelligence (AI) algorithms to identify patterns in data sets containing data points that are neither classified nor labeled. Continue Reading
supervised learning

Supervised learning is an approach to creating artificial intelligence, where the program is given labeled input data and the expected output results. Continue Reading

Evaluate Pattern recognition and machine learning Vendors & Products

Weigh the pros and cons of technologies, products and projects you are considering.

Bias in machine learning examples: Policing, banking, COVID-19

Human bias, missing data, data selection, data confirmation, hidden variables and unexpected crises can contribute to distorted machine learning models, outcomes and insights. Continue Reading
Autoencoders' example uses augment data for machine learning

Autoencoders are neural networks that serve machine learning models -- from denoising to dimensionality reduction. Seven use cases explore the practical application of autoencoder technology. Continue Reading
14 best machine learning platforms for 2020

Turn ever-growing volumes of data into enterprise insights with the right platform for machine learning. Learn more about the vendors and products in this cutting-edge market. Continue Reading

Manage Pattern recognition and machine learning

Learn to apply best practices and optimize your operations.

Machine learning and bias concerns weigh on data scientists

Data scientists are forever vigilant in their desire to identify and eliminate the many forms of bias that can compromise the credibility of machine learning models. Continue Reading
Responsible AI champions human-centric machine learning

Encompassing ethics, transparency and human centricity, responsible AI is an effective approach to deploying machine learning models and achieving actionable insights. Continue Reading
AI in tax preparation gets a boost from classification tech

Tax filers and tax collectors are using AI tools to make the process of paying and collecting taxes simpler. The data-rich, complex processes of tax collection are an ideal use case for AI. Continue Reading

Problem Solve Pattern recognition and machine learning Issues

We’ve gathered up expert advice and tips from professionals like you so that the answers you need are always available.

Data science's ongoing battle to quell bias in machine learning

Machine learning expert Ben Cox of H2O.ai discusses the problem of bias in predictive models that confronts data scientists daily and his techniques to identify and neutralize it. Continue Reading
Brands must allay worries for AI in transportation to take hold

The personal mobility market is turning to emotional analysis and AI to negate fear and trepidation around emerging vehicle technology and the future of transportation. Continue Reading
Collaborative robots' safety stalls enterprise implementation

Cobots are promising big gains, especially in enterprises utilizing manual labor. However, due to a number of safety concerns, human workers are still at risk. Continue Reading