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Data systems that learn to be better | MIT News

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Big data has gotten really, really big: By 2025, all the world’s data will add up to an estimated 175 trillion gigabytes. For a visual, if you stored that amount of data on DVDs, it would stack up tall enough to circle the Earth 222 times. 

One of the biggest challenges in computing is handling this onslaught of information while still being able to efficiently store and process it. A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) believe that the answer rests with something called “instance-optimized systems.”  

Traditional storage and database systems are designed to work for a wide range of applications because of how long it can take to build them — months or, often, several years. As a result, for any given workload such systems provide performance that is good, but usually not the best. Even worse, they sometimes require administrators to painstakingly tune the system by hand to provide even reasonable performance. 

In contrast, the goal of instance-optimized systems is to build systems that optimize and partially re-organize themselves for the data they store and the workload they serve. 

“It’s like building a database system for every application from scratch, which is not economically feasible with traditional system designs,” says MIT Professor Tim Kraska. 

As a first step toward this vision, Kraska and colleagues developed Tsunami and Bao. Tsunami uses machine learning to automatically re-organize a dataset’s storage layout based on the types of queries that its users make. Tests show that it can run queries up to 10 times faster than state-of-the-art systems. What’s more, its datasets can be organized via a series of “learned indexes” that are up to 100 times smaller than the indexes used in traditional systems. 

Kraska has been exploring the topic of learned indexes for several years, going back to his influential work with colleagues at Google in 2017. 

Harvard University Professor Stratos Idreos, who was not involved in the Tsunami project, says that a unique advantage of learned indexes is their small size, which, in addition to space savings, brings substantial performance improvements.

“I think this line of work is a paradigm shift that’s going to impact system design long-term,” says Idreos. “I expect approaches based on models will be one of the core components at the heart of a new wave of adaptive systems.”

Bao, meanwhile, focuses on improving the efficiency of query optimization through machine learning. A query optimizer rewrites a high-level declarative query to a query plan, which can actually be executed over the data to compute the result to the query. However, often there exists more than one query plan to answer any query; picking the wrong one can cause a query to take days to compute the answer, rather than seconds. 

Traditional query optimizers take years to build, are very hard to maintain, and, most importantly, do not learn from their mistakes. Bao is the first learning-based approach to query optimization that has been fully integrated into the popular database management system PostgreSQL. Lead author Ryan Marcus, a postdoc in Kraska’s group, says that Bao produces query plans that run up to 50 percent faster than those created by the PostgreSQL optimizer, meaning that it could help to significantly reduce the cost of cloud services, like Amazon’s Redshift, that are based on PostgreSQL.

By fusing the two systems together, Kraska hopes to build the first instance-optimized database system that can provide the best possible performance for each individual application without any manual tuning. 

The goal is to not only relieve developers from the daunting and laborious process of tuning database systems, but to also provide performance and cost benefits that are not possible with traditional systems.

Traditionally, the systems we use to store data are limited to only a few storage options and, because of it, they cannot provide the best possible performance for a given application. What Tsunami can do is dynamically change the structure of the data storage based on the kinds of queries that it receives and create new ways to store data, which are not feasible with more traditional approaches.

Johannes Gehrke, a managing director at Microsoft Research who also heads up machine learning efforts for Microsoft Teams, says that his work opens up many interesting applications, such as doing so-called “multidimensional queries” in main-memory data warehouses. Harvard’s Idreos also expects the project to spur further work on how to maintain the good performance of such systems when new data and new kinds of queries arrive.

Bao is short for “bandit optimizer,” a play on words related to the so-called “multi-armed bandit” analogy where a gambler tries to maximize their winnings at multiple slot machines that have different rates of return. The multi-armed bandit problem is commonly found in any situation that has tradeoffs between exploring multiple different options, versus exploiting a single option — from risk optimization to A/B testing.

“Query optimizers have been around for years, but they often make mistakes, and usually they don’t learn from them,” says Kraska. “That’s where we feel that our system can make key breakthroughs, as it can quickly learn for the given data and workload what query plans to use and which ones to avoid.”

Kraska says that in contrast to other learning-based approaches to query optimization, Bao learns much faster and can outperform open-source and commercial optimizers with as little as one hour of training time.In the future, his team aims to integrate Bao into cloud systems to improve resource utilization in environments where disk, RAM, and CPU time are scarce resources.

“Our hope is that a system like this will enable much faster query times, and that people will be able to answer questions they hadn’t been able to answer before,” says Kraska.

A related paper about Tsunami was co-written by Kraska, PhD students Jialin Ding and Vikram Nathan, and MIT Professor Mohammad Alizadeh. A paper about Bao was co-written by Kraska, Marcus, PhD students Parimarjan Negi and Hongzi Mao, visiting scientist Nesime Tatbul, and Alizadeh.

The work was done as part of the Data System and AI Lab (DSAIL@CSAIL), which is sponsored by Intel, Google, Microsoft, and the U.S. National Science Foundation. 

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Getting AI to Learn Like a Baby is Goal of Self-Supervised Learning 

Scientists are studying how to create AI systems that learn from self-supervision, akin to how babies learn from observing their environment. (Credit: Getty Images) 

By AI Trends Staff  

Scientists are working on creating better AI that learns through self-supervision, with the pinnacle being AI that could learn like a baby, based on observation of its environment and interaction with people.  

This would be an important advance because AI has limitations based on the volume of data required to train machine learning algorithms, and the brittleness of the algorithms when it comes to adjusting to changing circumstances. 

Yann LeCun, chief AI scientist at Facebook

“This is the single most important problem to solve in AI today,” stated Yann LeCun, chief AI scientist at Facebook, in an account in the Wall Street Journal. Some early success with self-supervised learning has been seen in the natural language processing used in mobile phones, smart speakers, and customer service bots.   

Training AI today is time-consuming and expensive. The promise of self-supervised learning is for AI to train itself without the need for external labels attached to the data. Dr. LeCun is now focused on applying self-supervised learning to computer vision, a more complex problem in which computers interpret images such as a person’s face.  

The next phase, which he thinks is possible in the next decade or two, is to create a machine that can “learn how the world works by watching video , listening to audio, and reading text,” he stated. 

More than one approach is being tried to help AI learn by itself. One is the neuro-symbolic approach, which combines deep learning and symbolic AI, which represents human knowledge explicitly as facts and rules. IBM is experimenting with this approach in its development of a bot that works alongside human engineers, reading computer logs to look for system failure, understand why a system crashed and offer a remedy. This could increase the pace of scientific discovery, with its ability to spot patterns not otherwise evident, according to Dario Gil, director of IBM Research. “It would help us address huge problems, such as climate change and developing vaccines,” he stated. 

Child Psychologists Working with Computer Scientists on MESS  

DARPA is working with the University of California at Berkeley on a research project, Machine Common Sense, funding collaborations between child psychologists and computer scientists. The system is called MESS, for Model-Building, Exploratory, Social Learning System.   

Alison Gopnik, Professor of Psychology, University of California, Berkeley and the author of “The Philosophical Baby”

“Human babies are the best learners in the universe. How do they do it? And could we get an AI to do the same?,” queried Alison Gopnik, a professor of psychology at Berkeley and the author of “The Philosophical Baby” and “The Scientist in the Crib,” among other books, in a recent article she wrote for the Wall Street Journal.  

“Even with a lot of supervised data, AIs can’t make the same kinds of generalizations that human children can,” Gopnik said. “Their knowledge is much narrower and more limited, and they are easily fooled. Current AIs are like children with super-helicopter-tiger moms—programs that hover over the learner dictating whether it is right or wrong at every step. The helicoptered AI children can be very good at learning to do specific things well, but they fall apart when it comes to resilience and creativity. A small change in the learning problem means that they have to start all over again.” 

The scientists are also experimenting with AI that is motivated by curiosity, which leads to a more resilient learning style, called “active learning” and is a frontier in AI research.  

The challenge of the DARPA Machine Common Sense program is to design an AI that understands the basic features of the world as well as an 18-month-old. “Some computer scientists are trying to build common sense models into the AIs, though this isn’t easy. But it is even harder to design an AI that can actually learn those models the way that children do,” Dr. Gopnik wrote. “Hybrid systems that combine models with machine learning are one of the most exciting developments at the cutting edge of current AI.” 

Training AI models on labeled datasets is likely to play a diminished role as self-supervised learning comes into wider use, LeCun said during a session at the virtual International Conference on Learning Representation (ICLR) 2020, which also included Turing Award winner and Canadian computer scientist Yoshua Bengio.  

The way that self-supervised learning algorithms generate labels from data by exposing relationships between the data’s parts is an advantage.   

“Most of what we learn as humans and most of what animals learn is in a self-supervised mode, not a reinforcement mode. It’s basically observing the world and interacting with it a little bit, mostly by observation in a test-independent way,” stated LeCun, in an account from VentureBeat.  “This is the type of [learning] that we don’t know how to reproduce with machines.” 

Bengio was optimistic about the potential for AI to gain from the field of neuroscience, in particular for its explorations of consciousness and conscious processing. Bengio predicted that new studies will clarify the way high-level semantic variables connect with how the brain processes information, including visual information. These variables that humans communicate using language could lead to an entirely new generation of deep learning models, he suggested. 

“There’s a lot of progress that could be achieved by bringing together things like grounded language learning, where we’re jointly trying to understand a model of the world and how high-level concepts are related to each other,” said Bengio. “Human conscious processing is exploiting assumptions about how the world might change, which can be conveniently implemented as a high-level representation.”  

Bengio Delivered NeurIPS 2019 Talk on System 2 Self-Supervised Models 

At the 2019 Conference on Neural Information Processing Systems (NeurIPS 2019), Bengio spoke on this topic in a keynote speech entitled,  “From System 1 Deep Learning to System 2 Deep Learning,” with System 2 referring to self-supervised models.  

“We want to have machines that understand the world, that build good world models, that understand cause and effect, and can act in the world to acquire knowledge,” he said in an account in TechTalks.  

The intelligent systems should be able to generalize to different distributions in data, just as children learn to adapt as the environment changes around them. “We need systems that can handle those changes and do continual learning, lifelong learning, and so on,” Bengio stated. “This is a long-standing goal for machine learning, but we haven’t yet built a solution to this.”  

Read the source articles in the Wall Street Journal, Alison for the Wall Street Journal, in VentureBeat and in TechTalks. 

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