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Local AI Inferencing Will Become Standard In Edge Applications In 2018
Edge-based inferencing will become a foundation of all AI-infused applications in the Internet of Things and People and the majority of new IoT&P application-development projects will involve building the AI-driven smarts for deployment to edge devices for various levels of local sensor-driven inferencing.
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Wrapping Our Primate Brains Around AI’s Next Grand Challenge
AI has moved beyond the Turing Test and is literally “moving” towards new directions. We argue that the new AI grand challenge is to allow intelligence to become more embodied and animal-like.
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7 Ways to Get High-Quality Labeled Training Data at Low Cost
Having labeled training data is needed for machine learning, but getting such data is not simple or cheap. We review 7 approaches including repurposing, harvesting free sources, retrain models on progressively higher quality data, and more.
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Putting Together A Full-Blooded AI Maturity Model
Here is a proposed “7A” model that is useful enough to capture of the core of what AI offers without falsely implying there is a static body of best practices in this area.
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Homebrewed Deep Learning and Do-It-Yourself Robotics
Learn how to experiment with embodied robotic cognition with IBM Project Intu, a platform that extends Deep Learning and other cognitive services to new devices with minimum coding.
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Cooperative Trust Among Neural Networks Drives Deeper Learning
Machine learning developers need to model a growing range of multi-partner scenarios where many learning agents and data sources interact under varying degrees of trustworthiness. This IBM site helps to take next step towards continuous intelligence.
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Predictions for Deep Learning in 2017
The first hugely successful consumer application of deep learning will come to market, a dominant open-source deep-learning tool and library will take the developer community by storm, and more Deep Learning predictions.
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Kobielus Predictions for Data Science in 2017
IBM Data Evangelist James Kobielus predictions for 2017, including key role of data scientists in survival of their companies. Join industry experts for a live #MakeDataSimple Crowdchat on Thursday December 8 at 1:00pm EST.
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The Experience of Being a High-Performing Data Scientist
Now in open beta, IBM Data Science Experience (DSX) delivers Machine Learning, Collaboration, and Creative capabilities in an open and integrated environment for team data science, including many productivity features for next-generation data science,
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Driving Data Science Productivity Without Compromising Quality
How will data science teams maintain quality standards in the face of advancing automation? Attend the IBM DataFirst Launch Event on Sep 27 in NYC and learn how to drive greater productivity from your data science teams without compromising the quality of the mission-critical business assets they produce.
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James Kobielus is SiliconANGLE Wikibon Lead Analyst for Data Science, Deep Learning, and Application Development. Previously he was an IBM Big Data Evangelist.
