--- title: 'Digest #2019.09.16 – The State of AI and ML' description: 'Machine Learning and AI in 2019: A recent survey conducted by Dresner Advisory Services shows Machine Learning and AI to rank as highest priority for enterprises. R&D, Marketing, Sales, Insurance, Fintech, Telco, Retail and Healthcare enterprise rank machine learning as their biggest bet and believe it is critical to their success. “2019 […]' url: https://canonical-com-2950.demos.haus/blog/digest-2019-09-16-the-state-of-ai-and-ml?format=md --- 1. [Blog](https://canonical-com-2950.demos.haus/blog) 2. Article --- [anaqvi](https://canonical-com-2950.demos.haus/blog/author/anaqvi "More about anaqvi") 16 September 2019 # Digest #2019.09.16 – The State of AI and ML [AI](https://canonical-com-2950.demos.haus/blog/tag/ai) [Kubeflow News](https://canonical-com-2950.demos.haus/blog/tag/kubeflow-news) [machine learning](https://canonical-com-2950.demos.haus/blog/tag/machine-learning) --- Share the article * [Machine Learning and AI in 2019](https://www.forbes.com/sites/louiscolumbus/2019/09/08/state-of-ai-and-machine-learning-in-2019/#4d0bd38f1a8d): A recent survey conducted by Dresner Advisory Services shows Machine Learning and AI to rank as highest priority for enterprises. R&D, Marketing, Sales, Insurance, Fintech, Telco, Retail and Healthcare enterprise rank machine learning as their biggest bet and believe it is critical to their success. “2019 is a record year for enterprises’ interest in data science, AI, and machine learning features they perceive as the most needed to achieve their business strategies and goals.” * [Using Machine Learning in health-tech](http://news.mit.edu/2019/using-machine-learning-estimate-risk-cardiovascular-death-0912): With humans becoming increasingly health conscious and risk-averse, we’re seeing a boom in health-tech. Machine Learning is staying on top of the game here as well; researchers at MIT have invented a cardiovascular risk identifier. With heart disease being the most common cause of death in the world, the system called ‘CardioRisk’ uses a patient’s raw electrocardiogram (ECG). Using Machine Learning techniques the ECG is analysed against datasets and the system produces a risk score that places the patient in a relative risk category. “The intersection of machine learning and healthcare is replete with combinations like this — a compelling computer science problem with potential real-world impact.” * [Training on Large Images Using Spatial Partitioning on Cloud TPUs](https://cloud.google.com/blog/products/ai-machine-learning/train-ml-models-on-large-images-and-3d-volumes-with-spatial-partitioning-on-cloud-tpus): The pain of training your Machine Learning models without enough space on a single chip can now be put to ease – You can now leverage a new [spatial partitioning capability](https://github.com/tensorflow/estimator/blob/master/tensorflow_estimator/python/estimator/tpu/spatial_partitioning_api.md) on cloud TPUs. This makes it possible to split up a single model across several TPU chips allowing processing of much larger input data sizes. Use this [guide](https://github.com/tensorflow/estimator/blob/master/tensorflow_estimator/python/estimator/tpu/spatial_partitioning_api.md) to learn how to configure spatial partitioning properly for your applications. Visual Guide to spatial partitioning ## Sign up for our newsletter Get the latest Canonical news and updates in your inbox. Work email: \*I agree to receive information about Canonical's products and services. By submitting this form, I confirm that I have read and agree to [Canonical's Privacy Policy](https://canonical-com-2950.demos.haus/legal/dataprivacy). Sign up ## Share on --- ## Related posts [### From raw data to intelligent actions: inside our next-gen AI analytics data lake stack](https://canonical-com-2950.demos.haus/blog/inside-our-next-gen-ai-analytics-data-lake-stack) At Canonical, we believe organisations should be able to unlock the full value of their data without giving up control. That principle is at the heart of our next-generation... 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