Digest #2019.10.14 – The Ultimate Learning Machine

14 October 2019

The Ultimate Learning Machine – Babies… This article dives into how machines are trained and their motivation for learning compared to a human baby. Guess what… babies learn faster, require less data and have a power that machines don’t – curiosity! The AI lab at UC Berkeley is trying to innovate with AI to mimic a child’s curiosity and active learning. “AIs that are motivated by curiosity are more robust and resilient learners than those that are just motivated by immediate rewards.” The test program is cleverly called MESS, short for Model-Building, Exploratory, Social Learning System.

What’s your ML framework? – TensorFlow? PyTorch? Both? An interesting comparison between two of the most popular machine learning heavyweights. The article suggests PyTorch being more prevalent in research and TensorFlow dominating the industry side of Machine Learning. While the reality is not as black and white, the article with the visual data graphs is a good read.

PyTorch 1.3 is here! – And, named tensors, better mobile performance and quantization experiments are live! “The 1.3 release of PyTorch brings significant new features, including experimental support for mobile device deployment, eager mode quantization at 8-bit integer, and the ability to name tensors.” Check out the official announcement for all the exciting details.

Machine Learning for Autonomous Vehicles – Intriguing article about opensource machine learning for autonomous vehicles using Kubeflow. Interesting points on making vehicles autonomous, deep learning services, edge computing and training in the cloud. Ahh, hands-free driving, we’re getting there – fingers crossed!

Digest #2019.10.07 – Machine Learning in Space? Agriculture?

7 October 2019

How NASA uses Machine Learning – If you think the Earth is the only planet with Machine Learning you were wrong. The Mars Rover learning its path and environment at Mars? The healthcare needs for future astronauts? Planet exploration and discovery? Robotic astronaut!? All of these are questions NASA is experimenting and researching with Machine Learning. Check out the intriguing full article for details.

TensorFlow 2.0 is here! – For those of you who haven’t already heard, TensorFlow 2.0 is official! And, with TF 2.0 Keras is also official [Now the official high-level API for TensorFlow]. TensorFlow can now be run like Python code, yes, you can see the values of variables with print()! The APIs have been refined and there’s an upgrade script for a seamless transition from 1.x to 2.0. Oh, and did I mention backwards compatibility? Check out the official announcement for all exciting changes.

The benefits of AI and ML – With all the skepticism and fear of AI and ML, here’s a refreshing newspaper article that tells people not to be afraid. The robots aren’t coming for us. Not necessarily, not yet… 

Agriculture as a tough Machine Learning problem – You know Machine Learning is legit when a centuries-old tractor manufacturer starts classifying(no pun intended) itself as a software company. John Deere Labs believes it is solving one of the toughest Machine Learning problems, classification of grains! The article explores the possibility of classifying good and bad grains of corn and making harvesting adjustments accordingly. There’s a lot of deep learning and classification challenges to solve that can revolutionize the harvest. How exciting!

Digest #2019.09.30 – Making the Most of Machine Learning

30 September 2019

Understanding Fairness in Machine Learning – This article is a great reminder and defense for the statement, “the data speaks for itself.” Biases in training models affect the results of analyses. It is essential to understand how our models make decisions to tackle this bias by adding more balanced training data. Knowledge of biases in our training models also help in adjusting our training loss function or adjusting prediction thresholds to account for the type of fairness we want to work towards.  

Ways in which AI and ML are improving endpoint security – Take a look at the predicted billions of dollars that will be pouring in to improve cybersecurity. “Cloud platforms are enabling AI and machine learning-based endpoint security control applications to be more adaptive to the proliferating types of endpoints and corresponding threats.” The article sheds interesting light on how AI and ML can revolutionize the field of cybersecurity and improve endpoint security.

AI gears up for data analysis – The article explores how the use of Machine Learning and AI can help scientific research with the potential to improve data analyses speeds and accuracy of results dramatically. Machine learning can change scientific experiments, improve results, and significantly reduce production failures in different industries. “By looking at the long-term patterns [in the signals], you can actually spot imminent failures. One example could be a gradual increase in motor operating temperature, which may indicate that an actuation unit is on its way to overheating.” While investing in AI and ML and gathering data is resource-consuming, the returns and rewards prove to be well worth it.

Digest #2019.09.23 – Machine Learning is the word…

23 September 2019

MLOps, Rise of the Term: Most of us by now have heard this word frequenting around; MLOps, or Machine Learning Operations.. This is an interesting article on the rise of the term and the challenges actually faced by teams working cross-functionally and dealing with Machine Learning. It talks about the limitation of managed solutions for Machine Learning and hits close to home for Kubeflow. While working on Kubeflow and being in the community is exciting, we’ve only scratched the surface still have a long way to go. There’s also this funny info-graphic below about data scientists emailing Jupyter Notebooks to Developers for production – For real!

Artificial Intelligence to Improve Enterprise Storage?: This is an interesting article that explores the use of AI to improve storage operations in the enterprise. With data prevailing everywhere, petabytes and zettabytes of 0s and 1s, the lives of storage systems admins are becoming increasingly difficult. “When I’m trying to understand the IO pattern of an application or a workflow, one technique that I use is to capture the strace of the application, focusing on IO functions. For one recent application I examined, the strace output had more than 9,000,000 lines, of which a bit more than 8,000,000 lines were involved in IO. Trying to extract an IO pattern from more than 8,000,000 lines is a bit difficult.” – Jeff Layton, Nvidia Solutions Architect. Now, these tasks seem impossible for our brains but this where the mighty AI comes in. Machines champion taking large volumes of data and synthesizing it to useful chunks. Leveraging AI and ML can truly change the way we deal with, understand and use Enterprise storage. 

Apple is Building ML to Rule Them All: With every tech giant and pretty much every other business getting in on Machine Learning, it’s established that ML is the next big thing in the industry. Apple is coming in heavy on Machine Learning with something called Overton. “Apple claims to have a first-of-its kind solution with Overton – it aims to enable much of the personalization of ML models to be administered by the machine, not the human.” In general terms they are creating a Machine Learning management system; the system itself learns from the environment, responds to external factors and heals and corrects the models automatically when needed. Privacy concerns aside, our Siri responses, animojis and pet portraits are going to get snazzy – among other things…

Digest #2019.09.16 – The State of AI and ML

16 September 2019

• 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 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: 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: 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 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 to learn how to configure spatial partitioning properly for your applications.