Microsoft leads the AI Patent Race and more from TWIML & AI

1024 683 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Bits & Bytes

  • Microsoft leads the AI patent race. As per EconSight research findings, Microsoft leads the AI patent race going into 2019 with 697 patents that the firm classifies as having a significant competitive impact as of November 2018. Out of the top 30 companies and research institutions as defined by EconSight in their recent analysis, Microsoft has created 20% of all patents in the global group of patent-producing companies and institutions.
  • AI hides data from its creators to cheat at its appointed task. Research from Stanford and Google found that the ML agent intended to transform aerial images into street maps and back was found to be hiding information it would need later.
  • Tech Mahindra launches GAiA for enterprises. GAiA is the first commercial version of the open source Acumos platform, explored in detail in my conversation with project sponsor Mazin Gilbert about a year ago.
  • Taiwan AI Labs and Microsoft launch AI platform to facilitate genetic analysis. The new AI platform “TaiGenomics” utilizes AI techniques to process, analyze, and draw inferences from vast amounts of medical and genetic data provided by patients and hospitals.
  • Google to open AI lab in Princeton. The AI lab will comprise a mix of faculty members and students. Elad Hazan and Yoram Singer, who both work at Google and Princeton and are co-developers of the AdaGrad algorithm, will lead the lab. The focus of the group is developing efficient methods for faster training.
  • IBM designs AI-enabled fingernail sensor to track diseases. This tiny, wearable fingernail sensor can track disease progression and share details on medication effectiveness for Parkinson’s disease and cardiovascular health.
  • ZestFinance and Microsoft collaborate on AI solution for credit underwriting. Financial institutions will be able to use the Zest Automated Machine Learning (ZAML) tools to build, deploy, and monitor credit models using the Microsoft Azure cloud and ML Server.

 

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