"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Olist is the largest eCommerce website in Brazil. It connects small retailers from all over the country to sell directly to customers. The business has generously shared a large dataset containing 110k orders on its site from 2016 to 2018. The SQL-style relational database includes customers and their orders in the site, which contains around 100k unique orders and 73 categories. It also includes item prices, timestamps, reviews, and gelocation associated with the order.
One of the most popular ways to build ensembles is to use the same algorithm multiple times but on the different subsets of the training dataset. Techniques that are used for this are called bagging and pasting. The only difference in these techniques is that while building subsets bagging allows training instances to be sampled several times for the same predictor, while pasting is not allowing that. When all algorithms are trained, the ensemble makes a prediction by aggregating the predictions of all algorithms. In the classification case that is usually the hard-voting process, while for the regression average result is taken.
This article was originally published by Industry Today on March 3, 2021, and is reproduced below in full with permission. With rapid changes, pressure to innovate, and acceleration of implementation of advanced technology across all stages of the supply chain over the past year, there are important intellectual property (IP) considerations that companies need to make to protect their inventions. Leading edge tech like Augmented and Virtual Reality, machine learning and Artificial Intelligence, and 3D printing have become integral to business success yet continue to cause confusion around how the technology should be patented. This article explores some of the nuances as they relate to the art of protecting the software that fuels the base technology of these advanced innovations and important considerations that need to be made in the current environment. Most machine learning (ML) and artificial intelligence (AI) innovations are generally based in computer software. While courts and the U.S. Patent and Trademark Office ("U.S. PTO") have established limits on the ability to patent computer software, it is still possible to obtain meaningful, broad, and valuable patent protection on computer software.
Last year alone, the ed-tech market raked in more than US$10 billion in venture capital investment globally in 2020, on the back of heavy adoption when schools and higher education centers shuttered because of the pandemic. Statistics however suggest that education is still grossly under digitized, with less than 4% of global expenditure on tech, presenting a serious challenge given the scale of what's to come. The knowledge economy and future skills require massive digital transformation, and, while accelerated through Covid-19 there is still far to go.
I worry that when writing these columns, I sometimes start by meandering my way off into the weeds, cogitating and ruminating on "this and that" before eventually bringing the story back home. So, on the basis that "a change is as good as a rest," as the old English proverb goes, let's do things a little differently this time. Take a look at the image below. What do you see in addition to the penny piece? What I see is a Mantis AI-in-Sensor (AIS) System-on-Chip (SoC), where the "AI" portion of this moniker stands for "artificial intelligence."
WHEN IT comes to using artificial intelligence (AI), intelligence agencies have been at it longer than most. In the cold war America's National Security Agency (NSA) and Britain's Government Communications Headquarters (GCHQ) explored early AI to help transcribe and translate the enormous volumes of Soviet phone-intercepts they began hoovering up in the 1960s and 1970s. Your browser does not support the audio element. Yet the technology was immature. One former European intelligence officer says his service did not use automatic transcription or translation in Afghanistan in the 2000s, relying on native speakers instead.
Google has released TensorFlow 3D, a library that adds 3D deep-learning capabilities to the TensorFlow machine-learning framework. The new library brings tools and resources that allow researchers to develop and deploy 3D scene understanding models. TensorFlow 3D contains state-of-the-art models for 3D deep learning with GPU acceleration. These models have a wide range of applications from 3D object detection (e.g. For instance, 3D object detection is a hard problem using point cloud data due to high sparsity.
In this post, I will show you how easy it is to use other state-of-the-art algorithms with PyCaret thanks to tune-sklearn, a drop-in replacement for scikit-learn's model selection module with cutting edge hyperparameter tuning techniques. I'll also report results from a series of benchmarks, showing how tune-sklearn is able to easily improve classification model performance. Hyperparameter optimization algorithms can vary greatly in efficiency. Random search has been a machine learning staple and for a good reason: it's easy to implement, understand and gives good results in reasonable time. However, as the name implies, it is completely random -- a lot of time can be spent on evaluating bad configurations.
In this issue: we look at Neural Architecture Search (NAS) and how it relates to AutoML; we explain the research paper “A Survey on Neural Architecture Search” and how it helps to understand NAS; we speak about Uber’s Ludwig toolbox that lowers the entry point for developers by enabling the training and testing of ML models that can be done without writing code.
Deepfakes have started to appear everywhere – from viral celebrity face swaps to impersonations of political leaders. Millions got their first taste of the technology when they saw former US president Barack Obama using an expletive to describe then-president Donald Trump, or actor Bill Hader shape shifting on a late-night talk show. Earlier this week, social media went into a frenzy after deepfakes surfaced of actor Tom Cruise in a series of TikTok videos that appear to show him doing a magic trick and playing golf, all with a smoothness that was unsettlingly realistic. This isn't even a super high quality deepfake and I'm willing to bet that it could fool most people. Now imagine the quality of deepfake a government agency could produce.https://t.co/wMFMarEtAi