Biases can creep into any data set, and these can cause trouble when this data is used to train an AI model
Productive use of AI requires the application of existing models to new applications through a process called transfer learning
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Moving AI To the Edge with BrainChip
BrainChip is developing a novel ultra low power “neuromorphic” AI processor that can be embedded in literally any electronic device, rather than centralizing learning in high performance processors
AI is impacting IT operations more quickly than expected, and companies like Splunk are leveraging it to augment staff capabilities
Ken Grohe of Weka discusses various business use cases for AI-enabled applications with Chris Grundemann and Stephen Foskett
Per Nyberg of Stradigi AI discusses “blue collar” AI applications with Stephen Foskett. What problems can businesses solve with AI technology? Machine learning can find anomalies and outliers in manufacturing and finance, look for relationships in data, and cutting through the complexity of multi-disciplinary data
There are many “last mile” items on the enterprise checklist, and companies are struggling to connect everything together
AI will be part of everything we do in the future, not replacing us but augmenting our work, and this is especially true in information security
Just as data analytics transformed business intelligence so is artificial intelligence transforming data science
In this episode, we ask Red Hat about the platform requirements for AI applications in production