Artificial intelligence is being embedded into products to save people time and money. Experts in many domains have already begun to see the results of this, from medicine to education to navigation. But these products are built using an army of data scientists and machine learning experts, and the rate at which these human experts can deliver results is far lower than the current demand. My lab at MIT, called Data to AI, wanted to change this. Recognizing the human bottleneck in creating these systems, a few years ago we launched an ambitious project: we decided “to teach a computer how to be a data scientist." Our goal was to create automated systems that can ask questions of data, come up with analytic queries that could answer those questions, and use machine learning to solve them—in other words, all the things that human data scientists do. After much research and experimentation, the systems we have developed now allow us to build end-to-end AI products that can solve a new problem in one day. In this talk, I will cover what these new technologies are, how we are using them to accelerate the design and development of AI products, and how you can take advantage of them to actually build AI products faster and cheaper.
This talk is focused on the methods and technologies to answer the question ‘Why does it take a long time to process, analyze and derive insights from the data?’ Dr. Veeramachaneni is leading the ‘Human Data Interaction’ Project to develop methods that are at the intersection of data science, machine learning, and large scale interactive systems. With significant achievements in storage , processing, retrieval, and analytics, the answer to this question now lies in developing technologies that are based on intricately understanding the complexities in how scientists, researchers, analysts interact with data to analyze, interpret, and derive models from it. In this talk, Dr. Veeramachaneni will present how his team is building systems to transform this interaction for the signals domain using an example of physiological signals. Prediction studies on physiological signals are time-consuming: a typical study, even with a modest number of patients, usually takes from 6 to 12 months.
In this talk, he will describe a large-scale machine learning and analytics framework, BeatDB, to scale and speed up mining predictive models from these waveforms. BeatDB radically shrinks the time an investigation takes by: (a) supporting fast, flexible investigations by offering a multi-level parameterization, (b) allowing the user to define the condition to predict, the features, and many other investigation parameters (c) pre-computing beat-level features that are likely to be frequently used while computing on-the-fly less used features and statistical aggregates.
2016 MIT Digital Health Conference
In recent years, great strides have been made to scale and automate Big Data collection, storage, and processing, but deriving real insight through relational and semantic data analysis still requires time-consuming guesswork and human intuition. Now, novel approaches designed across domains (education, medicine, energy, and others) have helped identify foundational issues in general data analysis, providing the basis for developing a “Data Science Machine,” an automated system for generating predictive models from raw data.
2016 MIT Information and Communication Technologies Conference