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      A method for designing neural networks optimally suited for certain tasks

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      Neural networks, a type of machine-learning model, are being used to help humans complete a wide variety of tasks, from predicting if someone’s credit score is high enough to qualify for a loan to diagnosing whether a patient has a certain disease. But researchers still have only a limited understanding of how these models work. Whether a given model is optimal for certain task remains an open question.

      MIT researchers have found some answers. They conducted an analysis of neural networks and proved that they can be designed so they are “optimal,” meaning they minimize the probability of misclassifying borrowers or patients into the wrong category when the networks are given a lot of labeled training data. To achieve optimality, these networks must be built with a specific architecture.

      The researchers discovered that, in certain situations, the building blocks that enable a neural network to be optimal are not the ones developers use in practice. These optimal building blocks, derived through the new analysis, are unconventional and haven’t been considered before, the researchers say.

      In a paper published this week in the Proceedings of the National Academy of Sciences, they describe these optimal building blocks, called activation functions, and show how they can be used to design neural networks that achieve better performance on any dataset. The results hold even as the neural networks grow very large. This work could help developers select the correct activation function, enabling them to build neural networks that classify data more accurately in a wide range of application areas, explains senior author Caroline Uhler, a professor in the Department of Electrical Engineering and Computer Science (EECS).

      “While these are new activation functions that have never been used before, they are simple functions that someone could actually implement for a particular problem. This work really shows the importance of having theoretical proofs. If you go after a principled understanding of these models, that can actually lead you to new activation functions that you would otherwise never have thought of,” says Uhler, who is also co-director of the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard, and a researcher at MIT’s Laboratory for Information and Decision Systems (LIDS) and its Institute for Data, Systems and Society (IDSS).

      Joining Uhler on the paper are lead author Adityanarayanan Radhakrishnan, an EECS graduate student and an Eric and Wendy Schmidt Center Fellow, and Mikhail Belkin, a professor in the Halicioğlu Data Science Institute at the University of California at San Diego.

      Activation investigation

      A neural network is a type of machine-learning model that is loosely based on the human brain. Many layers of interconnected nodes, or neurons, process data. Researchers train a network to complete a task by showing it millions of examples from a dataset.

      For instance, a network that has been trained to classify images into categories, say dogs and cats, is given an image that has been encoded as numbers. The network performs a series of complex multiplication operations, layer by layer, until the result is just one number. If that number is positive, the network classifies the image a dog, and if it is negative, a cat.

      Activation functions help the network learn complex patterns in the input data. They do this by applying a transformation to the output of one layer before data are sent to the next layer. When researchers build a neural network, they select one activation function to use. They also choose the width of the network (how many neurons are in each layer) and the depth (how many layers are in the network.)

      “It turns out that, if you take the standard activation functions that people use in practice, and keep increasing the depth of the network, it gives you really terrible performance. We show that if you design with different activation functions, as you get more data, your network will get better and better,” says Radhakrishnan.

      He and his collaborators studied a situation in which a neural network is infinitely deep and wide — which means the network is built by continually adding more layers and more nodes — and is trained to perform classification tasks. In classification, the network learns to place data inputs into separate categories.

      “A clean picture”

      After conducting a detailed analysis, the researchers determined that there are only three ways this kind of network can learn to classify inputs. One method classifies an input based on the majority of inputs in the training data; if there are more dogs than cats, it will decide every new input is a dog. Another method classifies by choosing the label (dog or cat) of the training data point that most resembles the new input.

      The third method classifies a new input based on a weighted average of all the training data points that are similar to it. Their analysis shows that this is the only method of the three that leads to optimal performance. They identified a set of activation functions that always use this optimal classification method.

      “That was one of the most surprising things — no matter what you choose for an activation function, it is just going to be one of these three classifiers. We have formulas that will tell you explicitly which of these three it is going to be. It is a very clean picture,” he says.

      They tested this theory on a several classification benchmarking tasks and found that it led to improved performance in many cases. Neural network builders could use their formulas to select an activation function that yields improved classification performance, Radhakrishnan says.

      In the future, the researchers want to use what they’ve learned to analyze situations where they have a limited amount of data and for networks that are not infinitely wide or deep. They also want to apply this analysis to situations where data do not have labels.

      “In deep learning, we want to build theoretically grounded models so we can reliably deploy them in some mission-critical setting. This is a promising approach at getting toward something like that — building architectures in a theoretically grounded way that translates into better results in practice,” he says.

      This work was supported, in part, by the National Science Foundation, Office of Naval Research, the MIT-IBM Watson AI Lab, the Eric and Wendy Schmidt Center at the Broad Institute, and a Simons Investigator Award. More

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      Strengthening trust in machine-learning models

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      Probabilistic machine learning methods are becoming increasingly powerful tools in data analysis, informing a range of critical decisions across disciplines and applications, from forecasting election results to predicting the impact of microloans on addressing poverty.

      This class of methods uses sophisticated concepts from probability theory to handle uncertainty in decision-making. But the math is only one piece of the puzzle in determining their accuracy and effectiveness. In a typical data analysis, researchers make many subjective choices, or potentially introduce human error, that must also be assessed in order to cultivate users’ trust in the quality of decisions based on these methods.

      To address this issue, MIT computer scientist Tamara Broderick, associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems (LIDS), and a team of researchers have developed a classification system — a “taxonomy of trust” — that defines where trust might break down in a data analysis and identifies strategies to strengthen trust at each step. The other researchers on the project are Professor Anna Smith at the University of Kentucky, professors Tian Zheng and Andrew Gelman at Columbia University, and Professor Rachael Meager at the London School of Economics. The team’s hope is to highlight concerns that are already well-studied and those that need more attention.

      In their paper, published in February in Science Advances, the researchers begin by detailing the steps in the data analysis process where trust might break down: Analysts make choices about what data to collect and which models, or mathematical representations, most closely mirror the real-life problem or question they are aiming to answer. They select algorithms to fit the model and use code to run those algorithms. Each of these steps poses unique challenges around building trust. Some components can be checked for accuracy in measurable ways. “Does my code have bugs?”, for example, is a question that can be tested against objective criteria. Other times, problems are more subjective, with no clear-cut answers; analysts are confronted with numerous strategies to gather data and decide whether a model reflects the real world.

      “What I think is nice about making this taxonomy, is that it really highlights where people are focusing. I think a lot of research naturally focuses on this level of ‘are my algorithms solving a particular mathematical problem?’ in part because it’s very objective, even if it’s a hard problem,” Broderick says.

      “I think it’s really hard to answer ‘is it reasonable to mathematize an important applied problem in a certain way?’ because it’s somehow getting into a harder space, it’s not just a mathematical problem anymore.”

      Capturing real life in a model

      The researchers’ work in categorizing where trust breaks down, though it may seem abstract, is rooted in real-world application.

      Meager, a co-author on the paper, analyzed whether microfinances can have a positive effect in a community. The project became a case study for where trust could break down, and ways to reduce this risk.

      At first look, measuring the impact of microfinancing might seem like a straightforward endeavor. But like any analysis, researchers meet challenges at each step in the process that can affect trust in the outcome. Microfinancing — in which individuals or small businesses receive small loans and other financial services in lieu of conventional banking — can offer different services, depending on the program. For the analysis, Meager gathered datasets from microfinance programs in countries across the globe, including in Mexico, Mongolia, Bosnia, and the Philippines.

      When combining conspicuously distinct datasets, in this case from multiple countries and across different cultures and geographies, researchers must evaluate whether specific case studies can reflect broader trends. It is also important to contextualize the data on hand. For example, in rural Mexico, owning goats may be counted as an investment.

      “It’s hard to measure the quality of life of an individual. People measure things like, ‘What’s the business profit of the small business?’ Or ‘What’s the consumption level of a household?’ There’s this potential for mismatch between what you ultimately really care about, and what you’re measuring,” Broderick says. “Before we get to the mathematical level, what data and what assumptions are we leaning on?”

      With data on hand, analysts must define the real-world questions they seek to answer. In the case of evaluating the benefits of microfinancing, analysts must define what they consider a positive outcome. It is standard in economics, for example, to measure the average financial gain per business in communities where a microfinance program is introduced. But reporting an average might suggest a net positive effect even if only a few (or even one) person benefited, instead of the community as a whole.

      “What you really wanted was that a lot of people are benefiting,” Broderick says. “It sounds simple. Why didn’t we measure the thing that we cared about? But I think it’s really common that practitioners use standard machine learning tools, for a lot of reasons. And these tools might report a proxy that doesn’t always agree with the quantity of interest.”

      Analysts may consciously or subconsciously favor models they are familiar with, especially after investing a great deal of time learning their ins and outs. “Someone might be hesitant to try a nonstandard method because they might be less certain they will use it correctly. Or peer review might favor certain familiar methods, even if a researcher might like to use nonstandard methods,” Broderick says. “There are a lot of reasons, sociologically. But this can be a concern for trust.”

      Final step, checking the code 

      While distilling a real-life problem into a model can be a big-picture, amorphous problem, checking the code that runs an algorithm can feel “prosaic,” Broderick says. But it is another potentially overlooked area where trust can be strengthened.

      In some cases, checking a coding pipeline that executes an algorithm might be considered outside the purview of an analyst’s job, especially when there is the option to use standard software packages.

      One way to catch bugs is to test whether code is reproducible. Depending on the field, however, sharing code alongside published work is not always a requirement or the norm. As models increase in complexity over time, it becomes harder to recreate code from scratch. Reproducing a model becomes difficult or even impossible.

      “Let’s just start with every journal requiring you to release your code. Maybe it doesn’t get totally double-checked, and everything isn’t absolutely perfect, but let’s start there,” Broderick says, as one step toward building trust.

      Paper co-author Gelman worked on an analysis that forecast the 2020 U.S. presidential election using state and national polls in real-time. The team published daily updates in The Economist magazine, while also publishing their code online for anyone to download and run themselves. Throughout the season, outsiders pointed out both bugs and conceptual problems in the model, ultimately contributing to a stronger analysis.

      The researchers acknowledge that while there is no single solution to create a perfect model, analysts and scientists have the opportunity to reinforce trust at nearly every turn.

      “I don’t think we expect any of these things to be perfect,” Broderick says, “but I think we can expect them to be better or to be as good as possible.” More

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      Learning to grow machine-learning models

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      It’s no secret that OpenAI’s ChatGPT has some incredible capabilities — for instance, the chatbot can write poetry that resembles Shakespearean sonnets or debug code for a computer program. These abilities are made possible by the massive machine-learning model that ChatGPT is built upon. Researchers have found that when these types of models become large enough, extraordinary capabilities emerge.

      But bigger models also require more time and money to train. The training process involves showing hundreds of billions of examples to a model. Gathering so much data is an involved process in itself. Then come the monetary and environmental costs of running many powerful computers for days or weeks to train a model that may have billions of parameters. 

      “It’s been estimated that training models at the scale of what ChatGPT is hypothesized to run on could take millions of dollars, just for a single training run. Can we improve the efficiency of these training methods, so we can still get good models in less time and for less money? We propose to do this by leveraging smaller language models that have previously been trained,” says Yoon Kim, an assistant professor in MIT’s Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

      Rather than discarding a previous version of a model, Kim and his collaborators use it as the building blocks for a new model. Using machine learning, their method learns to “grow” a larger model from a smaller model in a way that encodes knowledge the smaller model has already gained. This enables faster training of the larger model.

      Their technique saves about 50 percent of the computational cost required to train a large model, compared to methods that train a new model from scratch. Plus, the models trained using the MIT method performed as well as, or better than, models trained with other techniques that also use smaller models to enable faster training of larger models.

      Reducing the time it takes to train huge models could help researchers make advancements faster with less expense, while also reducing the carbon emissions generated during the training process. It could also enable smaller research groups to work with these massive models, potentially opening the door to many new advances.

      “As we look to democratize these types of technologies, making training faster and less expensive will become more important,” says Kim, senior author of a paper on this technique.

      Kim and his graduate student Lucas Torroba Hennigen wrote the paper with lead author Peihao Wang, a graduate student at the University of Texas at Austin, as well as others at the MIT-IBM Watson AI Lab and Columbia University. The research will be presented at the International Conference on Learning Representations.

      The bigger the better

      Large language models like GPT-3, which is at the core of ChatGPT, are built using a neural network architecture called a transformer. A neural network, loosely based on the human brain, is composed of layers of interconnected nodes, or “neurons.” Each neuron contains parameters, which are variables learned during the training process that the neuron uses to process data.

      Transformer architectures are unique because, as these types of neural network models get bigger, they achieve much better results.

      “This has led to an arms race of companies trying to train larger and larger transformers on larger and larger datasets. More so than other architectures, it seems that transformer networks get much better with scaling. We’re just not exactly sure why this is the case,” Kim says.

      These models often have hundreds of millions or billions of learnable parameters. Training all these parameters from scratch is expensive, so researchers seek to accelerate the process.

      One effective technique is known as model growth. Using the model growth method, researchers can increase the size of a transformer by copying neurons, or even entire layers of a previous version of the network, then stacking them on top. They can make a network wider by adding new neurons to a layer or make it deeper by adding additional layers of neurons.

      In contrast to previous approaches for model growth, parameters associated with the new neurons in the expanded transformer are not just copies of the smaller network’s parameters, Kim explains. Rather, they are learned combinations of the parameters of the smaller model.

      Learning to grow

      Kim and his collaborators use machine learning to learn a linear mapping of the parameters of the smaller model. This linear map is a mathematical operation that transforms a set of input values, in this case the smaller model’s parameters, to a set of output values, in this case the parameters of the larger model.

      Their method, which they call a learned Linear Growth Operator (LiGO), learns to expand the width and depth of larger network from the parameters of a smaller network in a data-driven way.

      But the smaller model may actually be quite large — perhaps it has a hundred million parameters — and researchers might want to make a model with a billion parameters. So the LiGO technique breaks the linear map into smaller pieces that a machine-learning algorithm can handle.

      LiGO also expands width and depth simultaneously, which makes it more efficient than other methods. A user can tune how wide and deep they want the larger model to be when they input the smaller model and its parameters, Kim explains.

      When they compared their technique to the process of training a new model from scratch, as well as to model-growth methods, it was faster than all the baselines. Their method saves about 50 percent of the computational costs required to train both vision and language models, while often improving performance.

      The researchers also found they could use LiGO to accelerate transformer training even when they didn’t have access to a smaller, pretrained model.

      “I was surprised by how much better all the methods, including ours, did compared to the random initialization, train-from-scratch baselines.” Kim says.

      In the future, Kim and his collaborators are looking forward to applying LiGO to even larger models.

      The work was funded, in part, by the MIT-IBM Watson AI Lab, Amazon, the IBM Research AI Hardware Center, Center for Computational Innovation at Rensselaer Polytechnic Institute, and the U.S. Army Research Office. More

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      Minimizing electric vehicles’ impact on the grid

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      National and global plans to combat climate change include increasing the electrification of vehicles and the percentage of electricity generated from renewable sources. But some projections show that these trends might require costly new power plants to meet peak loads in the evening when cars are plugged in after the workday. What’s more, overproduction of power from solar farms during the daytime can waste valuable electricity-generation capacity.

      In a new study, MIT researchers have found that it’s possible to mitigate or eliminate both these problems without the need for advanced technological systems of connected devices and real-time communications, which could add to costs and energy consumption. Instead, encouraging the placing of charging stations for electric vehicles (EVs) in strategic ways, rather than letting them spring up anywhere, and setting up systems to initiate car charging at delayed times could potentially make all the difference.

      The study, published today in the journal Cell Reports Physical Science, is by Zachary Needell PhD ’22, postdoc Wei Wei, and Professor Jessika Trancik of MIT’s Institute for Data, Systems, and Society.

      In their analysis, the researchers used data collected in two sample cities: New York and Dallas. The data were gathered from, among other sources, anonymized records collected via onboard devices in vehicles, and surveys that carefully sampled populations to cover variable travel behaviors. They showed the times of day cars are used and for how long, and how much time the vehicles spend at different kinds of locations — residential, workplace, shopping, entertainment, and so on.

      The findings, Trancik says, “round out the picture on the question of where to strategically locate chargers to support EV adoption and also support the power grid.”

      Better availability of charging stations at workplaces, for example, could help to soak up peak power being produced at midday from solar power installations, which might otherwise go to waste because it is not economical to build enough battery or other storage capacity to save all of it for later in the day. Thus, workplace chargers can provide a double benefit, helping to reduce the evening peak load from EV charging and also making use of the solar electricity output.

      These effects on the electric power system are considerable, especially if the system must meet charging demands for a fully electrified personal vehicle fleet alongside the peaks in other demand for electricity, for example on the hottest days of the year. If unmitigated, the evening peaks in EV charging demand could require installing upwards of 20 percent more power-generation capacity, the researchers say.

      “Slow workplace charging can be more preferable than faster charging technologies for enabling a higher utilization of midday solar resources,” Wei says.

      Meanwhile, with delayed home charging, each EV charger could be accompanied by a simple app to estimate the time to begin its charging cycle so that it charges just before it is needed the next day. Unlike other proposals that require a centralized control of the charging cycle, such a system needs no interdevice communication of information and can be preprogrammed — and can accomplish a major shift in the demand on the grid caused by increasing EV penetration. The reason it works so well, Trancik says, is because of the natural variability in driving behaviors across individuals in a population.

      By “home charging,” the researchers aren’t only referring to charging equipment in individual garages or parking areas. They say it’s essential to make charging stations available in on-street parking locations and in apartment building parking areas as well.

      Trancik says the findings highlight the value of combining the two measures — workplace charging and delayed home charging — to reduce peak electricity demand, store solar energy, and conveniently meet drivers’ charging needs on all days. As the team showed in earlier research, home charging can be a particularly effective component of a strategic package of charging locations; workplace charging, they have found, is not a good substitute for home charging for meeting drivers’ needs on all days.

      “Given that there’s a lot of public money going into expanding charging infrastructure,” Trancik says, “how do you incentivize the location such that this is going to be efficiently and effectively integrated into the power grid without requiring a lot of additional capacity expansion?” This research offers some guidance to policymakers on where to focus rules and incentives.

      “I think one of the fascinating things about these findings is that by being strategic you can avoid a lot of physical infrastructure that you would otherwise need,” she adds. “Your electric vehicles can displace some of the need for stationary energy storage, and you can also avoid the need to expand the capacity of power plants, by thinking about the location of chargers as a tool for managing demands — where they occur and when they occur.”

      Delayed home charging could make a surprising amount of difference, the team found. “It’s basically incentivizing people to begin charging later. This can be something that is preprogrammed into your chargers. You incentivize people to delay the onset of charging by a bit, so that not everyone is charging at the same time, and that smooths out the peak.”

      Such a program would require some advance commitment on the part of participants. “You would need to have enough people committing to this program in advance to avoid the investment in physical infrastructure,” Trancik says. “So, if you have enough people signing up, then you essentially don’t have to build those extra power plants.”

      It’s not a given that all of this would line up just right, and putting in place the right mix of incentives would be crucial. “If you want electric vehicles to act as an effective storage technology for solar energy, then the [EV] market needs to grow fast enough in order to be able to do that,” Trancik says.

      To best use public funds to help make that happen, she says, “you can incentivize charging installations, which would go through ideally a competitive process — in the private sector, you would have companies bidding for different projects, but you can incentivize installing charging at workplaces, for example, to tap into both of these benefits.” Chargers people can access when they are parked near their residences are also important, Trancik adds, but for other reasons. Home charging is one of the ways to meet charging needs while avoiding inconvenient disruptions to people’s travel activities.

      The study was supported by the European Regional Development Fund Operational Program for Competitiveness and Internationalization, the Lisbon Portugal Regional Operation Program, and the Portuguese Foundation for Science and Technology. More

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      New method accelerates data retrieval in huge databases

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      Hashing is a core operation in most online databases, like a library catalogue or an e-commerce website. A hash function generates codes that directly determine the location where data would be stored. So, using these codes, it is easier to find and retrieve the data.

      However, because traditional hash functions generate codes randomly, sometimes two pieces of data can be hashed with the same value. This causes collisions — when searching for one item points a user to many pieces of data with the same hash value. It takes much longer to find the right one, resulting in slower searches and reduced performance.

      Certain types of hash functions, known as perfect hash functions, are designed to place the data in a way that prevents collisions. But they are time-consuming to construct for each dataset and take more time to compute than traditional hash functions.

      Since hashing is used in so many applications, from database indexing to data compression to cryptography, fast and efficient hash functions are critical. So, researchers from MIT and elsewhere set out to see if they could use machine learning to build better hash functions.

      They found that, in certain situations, using learned models instead of traditional hash functions could result in half as many collisions. These learned models are created by running a machine-learning algorithm on a dataset to capture specific characteristics. The team’s experiments also showed that learned models were often more computationally efficient than perfect hash functions.

      “What we found in this work is that in some situations we can come up with a better tradeoff between the computation of the hash function and the collisions we will face. In these situations, the computation time for the hash function can be increased a bit, but at the same time its collisions can be reduced very significantly,” says Ibrahim Sabek, a postdoc in the MIT Data Systems Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

      Their research, which will be presented at the 2023 International Conference on Very Large Databases, demonstrates how a hash function can be designed to significantly speed up searches in a huge database. For instance, their technique could accelerate computational systems that scientists use to store and analyze DNA, amino acid sequences, or other biological information.

      Sabek is the co-lead author of the paper with Department of Electrical Engineering and Computer Science (EECS) graduate student Kapil Vaidya. They are joined by co-authors Dominick Horn, a graduate student at the Technical University of Munich; Andreas Kipf, an MIT postdoc; Michael Mitzenmacher, professor of computer science at the Harvard John A. Paulson School of Engineering and Applied Sciences; and senior author Tim Kraska, associate professor of EECS at MIT and co-director of the Data, Systems, and AI Lab.

      Hashing it out

      Given a data input, or key, a traditional hash function generates a random number, or code, that corresponds to the slot where that key will be stored. To use a simple example, if there are 10 keys to be put into 10 slots, the function would generate a random integer between 1 and 10 for each input. It is highly probable that two keys will end up in the same slot, causing collisions.

      Perfect hash functions provide a collision-free alternative. Researchers give the function some extra knowledge, such as the number of slots the data are to be placed into. Then it can perform additional computations to figure out where to put each key to avoid collisions. However, these added computations make the function harder to create and less efficient.

      “We were wondering, if we know more about the data — that it will come from a particular distribution — can we use learned models to build a hash function that can actually reduce collisions?” Vaidya says.

      A data distribution shows all possible values in a dataset, and how often each value occurs. The distribution can be used to calculate the probability that a particular value is in a data sample.

      The researchers took a small sample from a dataset and used machine learning to approximate the shape of the data’s distribution, or how the data are spread out. The learned model then uses the approximation to predict the location of a key in the dataset.

      They found that learned models were easier to build and faster to run than perfect hash functions and that they led to fewer collisions than traditional hash functions if data are distributed in a predictable way. But if the data are not predictably distributed because gaps between data points vary too widely, using learned models might cause more collisions.

      “We may have a huge number of data inputs, and the gaps between consecutive inputs are very different, so learning a model to capture the data distribution of these inputs is quite difficult,” Sabek explains.

      Fewer collisions, faster results

      When data were predictably distributed, learned models could reduce the ratio of colliding keys in a dataset from 30 percent to 15 percent, compared with traditional hash functions. They were also able to achieve better throughput than perfect hash functions. In the best cases, learned models reduced the runtime by nearly 30 percent.

      As they explored the use of learned models for hashing, the researchers also found that throughput was impacted most by the number of sub-models. Each learned model is composed of smaller linear models that approximate the data distribution for different parts of the data. With more sub-models, the learned model produces a more accurate approximation, but it takes more time.

      “At a certain threshold of sub-models, you get enough information to build the approximation that you need for the hash function. But after that, it won’t lead to more improvement in collision reduction,” Sabek says.

      Building off this analysis, the researchers want to use learned models to design hash functions for other types of data. They also plan to explore learned hashing for databases in which data can be inserted or deleted. When data are updated in this way, the model needs to change accordingly, but changing the model while maintaining accuracy is a difficult problem.

      “We want to encourage the community to use machine learning inside more fundamental data structures and algorithms. Any kind of core data structure presents us with an opportunity to use machine learning to capture data properties and get better performance. There is still a lot we can explore,” Sabek says.

      “Hashing and indexing functions are core to a lot of database functionality. Given the variety of users and use cases, there is no one size fits all hashing, and learned models help adapt the database to a specific user. This paper is a great balanced analysis of the feasibility of these new techniques and does a good job of talking rigorously about the pros and cons, and helps us build our understanding of when such methods can be expected to work well,” says Murali Narayanaswamy, a principal machine learning scientist at Amazon, who was not involved with this work. “Exploring these kinds of enhancements is an exciting area of research both in academia and industry, and the kind of rigor shown in this work is critical for these methods to have large impact.”

      This work was supported, in part, by Google, Intel, Microsoft, the U.S. National Science Foundation, the U.S. Air Force Research Laboratory, and the U.S. Air Force Artificial Intelligence Accelerator. More

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      Large language models are biased. Can logic help save them?

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      Turns out, even language models “think” they’re biased. When prompted in ChatGPT, the response was as follows: “Yes, language models can have biases, because the training data reflects the biases present in society from which that data was collected. For example, gender and racial biases are prevalent in many real-world datasets, and if a language model is trained on that, it can perpetuate and amplify these biases in its predictions.” A well-known but dangerous problem. 

      Humans (typically) can dabble with both logical and stereotypical reasoning when learning. Still, language models mainly mimic the latter, an unfortunate narrative we’ve seen play out ad nauseam when the ability to employ reasoning and critical thinking is absent. So would injecting logic into the fray be enough to mitigate such behavior? 

      Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) had an inkling that it might, so they set off to examine if logic-aware language models could significantly avoid more harmful stereotypes. They trained a language model to predict the relationship between two sentences, based on context and semantic meaning, using a dataset with labels for text snippets detailing if a second phrase “entails,” “contradicts,” or is neutral with respect to the first one. Using this dataset — natural language inference — they found that the newly trained models were significantly less biased than other baselines, without any extra data, data editing, or additional training algorithms.

      For example, with the premise “the person is a doctor” and the hypothesis “the person is masculine,” using these logic-trained models, the relationship would be classified as “neutral,” since there’s no logic that says the person is a man. With more common language models, two sentences might seem to be correlated due to some bias in training data, like “doctor” might be pinged with “masculine,” even when there’s no evidence that the statement is true. 

      At this point, the omnipresent nature of language models is well-known: Applications in natural language processing, speech recognition, conversational AI, and generative tasks abound. While not a nascent field of research, growing pains can take a front seat as they increase in complexity and capability. 

      “Current language models suffer from issues with fairness, computational resources, and privacy,” says MIT CSAIL postdoc Hongyin Luo, the lead author of a new paper about the work. “Many estimates say that the CO2 emission of training a language model can be higher than the lifelong emission of a car. Running these large language models is also very expensive because of the amount of parameters and the computational resources they need. With privacy, state-of-the-art language models developed by places like ChatGPT or GPT-3 have their APIs where you must upload your language, but there’s no place for sensitive information regarding things like health care or finance. To solve these challenges, we proposed a logical language model that we qualitatively measured as fair, is 500 times smaller than the state-of-the-art models, can be deployed locally, and with no human-annotated training samples for downstream tasks. Our model uses 1/400 the parameters compared with the largest language models, has better performance on some tasks, and significantly saves computation resources.” 

      This model, which has 350 million parameters, outperformed some very large-scale language models with 100 billion parameters on logic-language understanding tasks. The team evaluated, for example, popular BERT pretrained language models with their “textual entailment” ones on stereotype, profession, and emotion bias tests. The latter outperformed other models with significantly lower bias, while preserving the language modeling ability. The “fairness” was evaluated with something called ideal context association (iCAT) tests, where higher iCAT scores mean fewer stereotypes. The model had higher than 90 percent iCAT scores, while other strong language understanding models ranged between 40 to 80. 

      Luo wrote the paper alongside MIT Senior Research Scientist James Glass. They will present the work at the Conference of the European Chapter of the Association for Computational Linguistics in Croatia. 

      Unsurprisingly, the original pretrained language models the team examined were teeming with bias, confirmed by a slew of reasoning tests demonstrating how professional and emotion terms are significantly biased to the feminine or masculine words in the gender vocabulary. 

      With professions, a language model (which is biased) thinks that “flight attendant,” “secretary,” and “physician’s assistant” are feminine jobs, while “fisherman,” “lawyer,” and “judge” are masculine. Concerning emotions, a language model thinks that “anxious,” “depressed,” and “devastated” are feminine.

      While we may still be far away from a neutral language model utopia, this research is ongoing in that pursuit. Currently, the model is just for language understanding, so it’s based on reasoning among existing sentences. Unfortunately, it can’t generate sentences for now, so the next step for the researchers would be targeting the uber-popular generative models built with logical learning to ensure more fairness with computational efficiency. 

      “Although stereotypical reasoning is a natural part of human recognition, fairness-aware people conduct reasoning with logic rather than stereotypes when necessary,” says Luo. “We show that language models have similar properties. A language model without explicit logic learning makes plenty of biased reasoning, but adding logic learning can significantly mitigate such behavior. Furthermore, with demonstrated robust zero-shot adaptation ability, the model can be directly deployed to different tasks with more fairness, privacy, and better speed.” More

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      Report: CHIPS Act just the first step in addressing threats to US leadership in advanced computing

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      When Liu He, a Chinese economist, politician, and “chip czar,” was tapped to lead the charge in a chipmaking arms race with the United States, his message lingered in the air, leaving behind a dewy glaze of tension: “For our country, technology is not just for growth… it is a matter of survival.”

      Once upon a time, the United States’ early technological prowess positioned the nation to outpace foreign rivals and cultivate a competitive advantage for domestic businesses. Yet, 30 years later, America’s lead in advanced computing is continuing to wane. What happened?

      A new report from an MIT researcher and two colleagues sheds light on the decline in U.S. leadership. The scientists looked at high-level measures to examine the shrinkage: overall capabilities, supercomputers, applied algorithms, and semiconductor manufacturing. Through their analysis, they found that not only has China closed the computing gap with the U.S., but nearly 80 percent of American leaders in the field believe that their Chinese competitors are improving capabilities faster — which, the team says, suggests a “broad threat to U.S. competitiveness.”

      To delve deeply into the fray, the scientists conducted the Advanced Computing Users Survey, sampling 120 top-tier organizations, including universities, national labs, federal agencies, and industry. The team estimates that this group comprises one-third and one-half of all the most significant computing users in the United States.

      “Advanced computing is crucial to scientific improvement, economic growth and the competitiveness of U.S. companies,” says Neil Thompson, director of the FutureTech Research Project at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who helped lead the study.

      Thompson, who is also a principal investigator at MIT’s Initiative on the Digital Economy, wrote the paper with Chad Evans, executive vice president and secretary and treasurer to the board at the Council on Competitiveness, and Daniel Armbrust, who is the co-founder, initial CEO, and member of the board of directors at Silicon Catalyst and former president of SEMATECH, the semiconductor consortium that developed industry roadmaps.

      The semiconductor, supercomputer, and algorithm bonanza

      Supercomputers — the room-sized, “giant calculators” of the hardware world — are an industry no longer dominated by the United States. Through 2015, about half of the most powerful computers were sitting firmly in the U.S., and China was growing slowly from a very slow base. But in the past six years, China has swiftly caught up, reaching near parity with America.

      This disappearing lead matters. Eighty-four percent of U.S. survey respondents said they’re computationally constrained in running essential programs. “This result was telling, given who our respondents are: the vanguard of American research enterprises and academic institutions with privileged access to advanced national supercomputing resources,” says Thompson. 

      With regards to advanced algorithms, historically, the U.S. has fronted the charge, with two-thirds of all significant improvements dominated by U.S.-born inventors. But in recent decades, U.S. dominance in algorithms has relied on bringing in foreign talent to work in the U.S., which the researchers say is now in jeopardy. China has outpaced the U.S. and many other countries in churning out PhDs in STEM fields since 2007, with one report postulating a near-distant future (2025) where China will be home to nearly twice as many PhDs than in the U.S. China’s rise in algorithms can also be seen with the “Gordon Bell Prize,” an achievement for outstanding work in harnessing the power of supercomputers in varied applications. U.S. winners historically dominated the prize, but China has now equaled or surpassed Americans’ performance in the past five years.

      While the researchers note the CHIPS and Science Act of 2022 is a critical step in re-establishing the foundation of success for advanced computing, they propose recommendations to the U.S. Office of Science and Technology Policy. 

      First, they suggest democratizing access to U.S. supercomputing by building more mid-tier systems that push boundaries for many users, as well as building tools so users scaling up computations can have less up-front resource investment. They also recommend increasing the pool of innovators by funding many more electrical engineers and computer scientists being trained with longer-term US residency incentives and scholarships. Finally, in addition to this new framework, the scientists urge taking advantage of what already exists, via providing the private sector access to experimentation with high-performance computing through supercomputing sites in academia and national labs.

      All that and a bag of chips

      Computing improvements depend on continuous advances in transistor density and performance, but creating robust, new chips necessitate a harmonious blend of design and manufacturing.

      Over the last six years, China was not known as the savants of noteworthy chips. In fact, in the past five decades, the U.S. designed most of them. But this changed in the past six years when China created the HiSilicon Kirin 9000, propelling itself to the international frontier. This success was mainly obtained through partnerships with leading global chip designers that began in the 2000s. Now, China now has 14 companies among the world’s top 50 fabless designers. A decade ago, there was only one. 

      Competitive semiconductor manufacturing has been more mixed, where U.S.-led policies and internal execution issues have slowed China’s rise, but as of July 2022, the Semiconductor Manufacturing International Corporation (SMIC) has evidence of 7 nanometer logic, which was not expected until much later. However, with extreme ultraviolet export restrictions, progress below 7 nm means domestic technology development would be expensive. Currently, China is only at parity or better in two out of 12 segments of the semiconductor supply chain. Still, with government policy and investments, the team expects a whopping increase to seven segments in 10 years. So, for the moment, the U.S. retains leadership in hardware manufacturing, but with fewer dimensions of advantage.

      The authors recommend that the White House Office of Science and Technology Policy work with key national agencies, such as the U.S. Department of Defense, U.S. Department of Energy, and the National Science Foundation, to define initiatives to build the hardware and software systems needed for important computing paradigms and workloads critical for economic and security goals. “It is crucial that American enterprises can get the benefit of faster computers,” says Thompson. “With Moore’s Law slowing down, the best way to do this is to create a portfolio of specialized chips (or “accelerators”) that are customized to our needs.”

      The scientists further believe that to lead the next generation of computing, four areas must be addressed. First, by issuing grand challenges to the CHIPS Act National Semiconductor Technology Center, researchers and startups would be motivated to invest in research and development and to seek startup capital for new technologies in areas such as spintronics, neuromorphics, optical and quantum computing, and optical interconnect fabrics. By supporting allies in passing similar acts, overall investment in these technologies would increase, and supply chains would become more aligned and secure. Establishing test beds for researchers to test algorithms on new computing architectures and hardware would provide an essential platform for innovation and discovery. Finally, planning for post-exascale systems that achieve higher levels of performance through next-generation advances would ensure that current commercial technologies don’t limit future computing systems.

      “The advanced computing landscape is in rapid flux — technologically, economically, and politically, with both new opportunities for innovation and rising global rivalries,” says Daniel Reed, Presidential Professor and professor of computer science and electrical and computer engineering at the University of Utah. “The transformational insights from both deep learning and computational modeling depend on both continued semiconductor advances and their instantiation in leading edge, large-scale computing systems — hyperscale clouds and high-performance computing systems. Although the U.S. has historically led the world in both advanced semiconductors and high-performance computing, other nations have recognized that these capabilities are integral to 21st century economic competitiveness and national security, and they are investing heavily.”

      The research was funded, in part, through Thompson’s grant from Good Ventures, which supports his FutureTech Research Group. The paper is being published by the Georgetown Public Policy Review. More

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      Improving health outcomes by targeting climate and air pollution simultaneously

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      Climate policies are typically designed to reduce greenhouse gas emissions that result from human activities and drive climate change. The largest source of these emissions is the combustion of fossil fuels, which increases atmospheric concentrations of ozone, fine particulate matter (PM2.5) and other air pollutants that pose public health risks. While climate policies may result in lower concentrations of health-damaging air pollutants as a “co-benefit” of reducing greenhouse gas emissions-intensive activities, they are most effective at improving health outcomes when deployed in tandem with geographically targeted air-quality regulations.

      Yet the computer models typically used to assess the likely air quality/health impacts of proposed climate/air-quality policy combinations come with drawbacks for decision-makers. Atmospheric chemistry/climate models can produce high-resolution results, but they are expensive and time-consuming to run. Integrated assessment models can produce results for far less time and money, but produce results at global and regional scales, rendering them insufficiently precise to obtain accurate assessments of air quality/health impacts at the subnational level.

      To overcome these drawbacks, a team of researchers at MIT and the University of California at Davis has developed a climate/air-quality policy assessment tool that is both computationally efficient and location-specific. Described in a new study in the journal ACS Environmental Au, the tool could enable users to obtain rapid estimates of combined policy impacts on air quality/health at more than 1,500 locations around the globe — estimates precise enough to reveal the equity implications of proposed policy combinations within a particular region.

      “The modeling approach described in this study may ultimately allow decision-makers to assess the efficacy of multiple combinations of climate and air-quality policies in reducing the health impacts of air pollution, and to design more effective policies,” says Sebastian Eastham, the study’s lead author and a principal research scientist at the MIT Joint Program on the Science and Policy of Global Change. “It may also be used to determine if a given policy combination would result in equitable health outcomes across a geographical area of interest.”

      To demonstrate the efficiency and accuracy of their policy assessment tool, the researchers showed that outcomes projected by the tool within seconds were consistent with region-specific results from detailed chemistry/climate models that took days or even months to run. While continuing to refine and develop their approaches, they are now working to embed the new tool into integrated assessment models for direct use by policymakers.

      “As decision-makers implement climate policies in the context of other sustainability challenges like air pollution, efficient modeling tools are important for assessment — and new computational techniques allow us to build faster and more accurate tools to provide credible, relevant information to a broader range of users,” says Noelle Selin, a professor at MIT’s Institute for Data, Systems and Society and Department of Earth, Atmospheric and Planetary Sciences, and supervising author of the study. “We are looking forward to further developing such approaches, and to working with stakeholders to ensure that they provide timely, targeted and useful assessments.”

      The study was funded, in part, by the U.S. Environmental Protection Agency and the Biogen Foundation. More