When Google Flu Trends was launched in 2009, Google’s chief economist, Hal Varian, explained that search trends could be used to “predict the present.” At the time, the notion that useful patterns and insights could be extracted from large-scale search query data made perfect sense. After all, many users’ digital journeys begin with a search query — including 8 out of 10 people seeking health-related information. So what could possibly go wrong? The answer is infamous in the business and data science communities. Google Flu was shut down in 2015 after the tool’s forecasts overestimated flu levels by nearly 100% relative to data provided by the Centers for Disease Control. Critics were quick to point to the project as the poster-child for big data hubris — the fallacy that inductive reasoning fueled by copious amounts of data can supplant traditional, deductive analysis guided by human hypotheses.
More recently, organizations have shifted towards amplifying predictive power by coupling big data with complex, automated machine learning (autoML). AutoML, which uses machine learning to generate better machine learning, is advertised as affording opportunities to “democratize machine learning” by allowing firms with limited data science expertise to develop analytical pipelines capable of solving sophisticated business problems. In a Kaggle prediction competition held just a few months back, an autoML engine pitted against some of the best data scientists in the world finished second after leading most of the way. However, these advancements have raised concerns about AI hubris. By commoditizing machine learning for process improvement, autoML once again raises questions about what the interplay between data, models, and human experts should look like. What does all this mean for managing in an AI-enabled world? Read More
Tag Archives: AutoML
Stop Experimenting With Machine Learning And Start Actually Using It
It turns out there’s a fatal flaw in most companies’ approach to machine learning, the analytical tool of the future: 87% of projects do not get past the experiment phase and so never make it into production.
Why do so many companies, presumably on the basis of rational decisions, limit themselves simply to exploring the potential of machine learning, and even after undertaking large investments, hiring data scientists and investing resources, time and money, fail to take things to the next level?
Quite simply, an inbuilt experimental mindset. Read More
AI for AI: IBM debuts AutoAI in Watson Studio
Today’s machine learning models are rapidly becoming highly complex, involving labor-intensive data preparation and feature engineering. As a result, enterprises are quickly deploying sophisticated neural network architectures with tens of millions of parameters. Consistent breakthroughs from researchers produce new machine learning methods and new architectures for neural networks designed to solve unique problems.
Faced with these complex challenges, your team’s process for getting the most from AI involves designing, optimizing, and governing models.
AI for AI makes it possible to automate the end-to-end data science and AI process, allowing your business to take the next steps in complementing human-led expertise and innovation with machine-generated insights. Read More
Automated Machine Learning (AML) Comes of Age – Almost
You know you’ve come of age when the major review publications like Gartner and Forrester publish a study on your segment. That’s what’s finally happened. Just released is “The Forrester New Wave™: Automation-Focused Machine Learning Solutions, Q2 2019”.
This is the first reasonably deep review of platforms and covers nine of what Forrester describes as ‘the most significant providers in the segment’. Those being Aible, Bell Integrator, Big Squid, DataRobot, DMway Analytics, dotData, EdgeVerve, H2O.ai, and Squark.
I’ve been following these automated machine learning (AML) platforms since they emerged. I wrote first about them in the spring of 2016 under the somewhat scary title “Data Scientists Automated and Unemployed by 2025!”.
Well we’ve still got six years to run and it hasn’t happened yet. On the other hand no-code data science is on the rise and AML platforms along with their partially automated platform brethren are what’s behind it. Read More
This AI Learns From Its Dreams
Off-Policy Classification – A New Reinforcement Learning Model Selection Method
Reinforcement learning (RL) is a framework that lets agents learn decision making from experience. One of the many variants of RL is off-policy RL, where an agent is trained using a combination of data collected by other agents (off-policy data) and data it collects itself to learn generalizable skills like robotic walking and grasping. In contrast, fully off-policy RL is a variant in which an agent learns entirely from older data, which is appealing because it enables model iteration without requiring a physical robot. With fully off-policy RL, one can train several models on the same fixed dataset collected by previous agents, then select the best one. However, fully off-policy RL comes with a catch: while training can occur without a real robot, evaluation of the models cannot. Furthermore, ground-truth evaluation with a physical robot is too inefficient to test promising approaches that require evaluating a large number of models, such as automated architecture search with AutoML. Read More
Off-Policy Evaluation via Off-Policy Classification
In this work, we consider the problem of model selection for deep reinforcement learning (RL) in real-world environments. Typically, the performance of deep RL algorithms is evaluated via on-policy interactions with the target environment. However, comparing models in a real-world environment for the purposes of early stopping or hyperparameter tuning is costly and often practically infeasible. This leads us to examine off-policy policy evaluation (OPE) in such settings. We focus on OPE for value-based methods, which are of particular interest in deep RL, with applications like robotics, where off-policy algorithms based on Q-function estimation can often attain better sample complexity than direct policy optimization. Existing OPE metrics either rely on a model of the environment, or the use of importance sampling (IS) to correct for the data being off-policy. However, for high-dimensional observations, such as images, models of the environment can be difficult to fit and value-based methods can make IS hard to use or even illconditioned, especially when dealing with continuous action spaces. In this paper, we focus on the specific case of MDPs with continuous action spaces and sparse binary rewards, which is representative of many important real-world applications. We propose an alternative metric that relies on neither models nor IS, by framing OPE as a positive-unlabeled (PU) classification problem with the Q-function as the decision function. We experimentally show that this metric outperforms baselines on a number of tasks. Most importantly, it can reliably predict the relative performance of different policies in a number of generalization scenarios, including the transfer to the real-world of policies trained in simulation for an image-based robotic manipulation task. Read More
What is Automated Machine Learning (AutoML)?
What is Automated Machine Learning? Quite simply, it is the means by which your business can optimize resources, encourage collaboration and rapidly and dependably distribute data across the enterprise and use that data to predict, plan and achieve revenue goals. Read More
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