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Regular newsletter readers know that I am beyond excited about machine learning (ML) at the edge. Running algorithms on gateways or even on sensors instead of sending data to the cloud to be analyzed can save time, bandwidth costs, and energy, and can protect peoples privacy.

So far, ML at the edge has only involved inference, the process of running incoming data against an existing model to see if it matches. Training the algorithm still takes place in the cloud. But Qualcomm has been researching ways to make the training of ML algorithms at the edge less energy-intensive, which means it could happen at the edge.

Bringing ML to edge devices means user data stays on the device, which boosts privacy; italso reduces the energy and costs associated with moving data around. It can also lead to highly personalized services. These are all good things. So what has Qualcomm discovered?

In an interview with me, Qualcomms Joseph Soriaga, senior director of technology, broke down the companys research into four different categories. But first, lets talk about what it takes to train an ML model.

Training usually happens in the cloud because it requires a computer to analyze a lot of data and hold much of that data in memory while performing probabilities to assess if the data matches whatever goal the algorithm is trying to meet.So to train a model to identify cats, you have to give it a lot of pictures of cats; the computer then tries to figure out what makes a cat. As it refines its understanding, it will produce calculations that a data scientist can assess and refine further by weighting different elements of the assessment more heavily in favor of elements that make something look like a cat.

It requires a lot of computational heft, memory, and bandwidth to build a good model. The edge doesnt historically have a lot of computing power or memory available, which is why edge devices perform inference and dont learn while in operation. Soriaga and his team have come up with methods that can enable personalization and adaptation of existing models at the edge, which is a step in the right direction.

One method is called few-shot learning, which is designed for situations where a researcher wants to tweak an algorithm to better meet the needs of outliers. Soriaga offered up an example involving wake word detection. For customers who have an accent or a hard time saying a wake word, using this method to improve accuracy can boost detection rates by 30%. Because there is a limited and clear data set, and labels, its possible to train existing models without consuming much power or computing resources.

Another method for training at the edge is continuous learning with unlabeled data. Here, an existing model gets updated with new data coming into the edge device over time. But because the data is unlabeled and the edge data may be over-personalized a data scientist has to be aware of those limits when trying to adapt the model.

My favorite research topic is federated device learning, where you might use the prior two methods to tweak algorithms locally and then send the tweaked models back to the cloud or share them with other edge devices. Qualcomm, for example, has explored how to identify people based on biometrics. Recognizing someone based on their face, fingerprint, or voice could involve sending all of those data points to the cloud, but it would be far more secure to have an algorithm that can be trained locally for each user.

So the trained algorithm built in the cloud might recognize how to differentiate a face, but locally, it would have to match with an individual face. That individual face data would stay private but the features that make it a face would get sent back to help adjust the initial algorithm. Then that tweaked version of the algorithm would get sent back to the edge devices where some noise would get added to the face data to ensure privacy, but also to ensure that over time the cloud-based algorithm gets better without sharing that persons data.

This approach provides large sets of face or voice data without having to scrape it from social media or photo sites without permission. Federating the learning over many devices also means data scientists can get a lot of inputs but that the raw data doesnt ever leave the device.

Finally, we also need ways to reduce the computational complexity associated with building algorithms from scratch. Im not going to get too into depth here, because theres a lot of math, butheres where you can find more information. Broadly speaking, the solution to traditional training in the cloud is to make training on less compute-heavy devices easier.

Qualcomm researchers have decided that one way to do that is to avoid using backpropagationto figure out how to weigh certain elements when building a model. Instead, data scientists can use quantized training to reduce the complexity associated with backpropagationand use more efficient models. Qualcomms researchers came up with something called in-hindsight range estimation, to efficiently adapt models for edge devices. If you are keen on understanding this, then click through to the research paper. But the money statement is that using this method was as accurate as traditional training methods and resulted in a 79% reduction in memory transfer. That reduction computes to needing less memory and compute power.

This research is very exciting because training at the edge has long been the dream, but a dream that has been so hard to turn into reality. As regulations promote more privacy and security for the IoT, all while demanding reduced energy consumption, edge-based training is moving from a wish-we-had-it option to a need-to-have it option. Im hoping R&D keeps up.

Related

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Qualcomm is researching machine learning at the edge - Stacey on IoT

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Image: Chinnawat Ngamsom, Getty Images/iStockphoto

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Turn your tech skills into machine learning expertise with this book and class bundle - TechRepublic

CAMBRIDGE, Mass. If your Uber driver takes a shortcut, you might get to your destination faster. But if a machine learning model takes a shortcut, it might fail in unexpected ways.

In machine learning, a shortcut solution occurs when the model relies on a simple characteristic of a dataset to make a decision, rather than learning the true essence of the data, which can lead to inaccurate predictions. For example, a model might learn to identify images of cows by focusing on the green grass that appears in the photos, rather than the more complex shapes and patterns of the cows.

A new study by researchers at MIT explores the problem of shortcuts in a popular machine-learning method and proposes a solution that can prevent shortcuts by forcing the model to use more data in its decision-making.

By removing the simpler characteristics the model is focusing on, the researchers force it to focus on more complex features of the data that it hadnt been considering. Then, by asking the model to solve the same task two ways once using those simpler features, and then also using the complex features it has now learned to identify they reduce the tendency for shortcut solutions and boost the performance of the model.

One potential application of this work is to enhance the effectiveness of machine learning models that are used to identify disease in medical images. Shortcut solutions in this context could lead to false diagnoses and have dangerous implications for patients.

It is still difficult to tell why deep networks make the decisions that they do, and in particular, which parts of the data these networks choose to focus upon when making a decision. If we can understand how shortcuts work in further detail, we can go even farther to answer some of the fundamental but very practical questions that are really important to people who are trying to deploy these networks, says Joshua Robinson, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author of the paper.

Robinson wrote thepaperwith his advisors, senior author Suvrit Sra, the Esther and Harold E. Edgerton Career Development Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) and a core member of the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems; and Stefanie Jegelka, the X-Consortium Career Development Associate Professor in EECS and a member of CSAIL and IDSS; as well as University of Pittsburgh assistant professor Kayhan Batmanghelich and PhD students Li Sun and Ke Yu. The research will be presented at the Conference on Neural Information Processing Systems in December.

The long road to Understanding Shortcuts

The researchers focused their study on contrastive learning, which is a powerful form of self-supervised machine learning. In self-supervised machine learning, a model is trained using raw data that do not have label descriptions from humans. It can therefore be used successfully for a larger variety of data.

A self-supervised learning model learns useful representations of data, which are used as inputs for different tasks, like image classification. But if the model takes shortcuts and fails to capture important information, these tasks wont be able to use that information either.

For example, if a self-supervised learning model is trained to classify pneumonia in X-rays from a number of hospitals, but it learns to make predictions based on a tag that identifies the hospital the scan came from (because some hospitals have more pneumonia cases than others), the model wont perform well when it is given data from a new hospital.

For contrastive learning models, an encoder algorithm is trained to discriminate between pairs of similar inputs and pairs of dissimilar inputs. This process encodes rich and complex data, like images, in a way that the contrastive learning model can interpret.

The researchers tested contrastive learning encoders with a series of images and found that, during this training procedure, they also fall prey to shortcut solutions. The encoders tend to focus on the simplest features of an image to decide which pairs of inputs are similar and which are dissimilar. Ideally, the encoder should focus on all the useful characteristics of the data when making a decision, Jegelka says.

So, the team made it harder to tell the difference between the similar and dissimilar pairs, and found that this changes which features the encoder will look at to make a decision.

If you make the task of discriminating between similar and dissimilar items harder and harder, then your system is forced to learn more meaningful information in the data, because without learning that it cannot solve the task, she says.

But increasing this difficulty resulted in a tradeoff the encoder got better at focusing on some features of the data but became worse at focusing on others. It almost seemed to forget the simpler features, Robinson says.

To avoid this tradeoff, the researchers asked the encoder to discriminate between the pairs the same way it had originally, using the simpler features, and also after the researchers removed the information it had already learned. Solving the task both ways simultaneously caused the encoder to improve across all features.

Their method, called implicit feature modification, adaptively modifies samples to remove the simpler features the encoder is using to discriminate between the pairs. The technique does not rely on human input, which is important because real-world data sets can have hundreds of different features that could combine in complex ways, Sra explains.

From Cars to COPD

The researchers ran one test of this method using images of vehicles. They used implicit feature modification to adjust the color, orientation, and vehicle type to make it harder for the encoder to discriminate between similar and dissimilar pairs of images. The encoder improved its accuracy across all three features texture, shape, and color simultaneously.

To see if the method would stand up to more complex data, the researchers also tested it with samples from a medical image database of chronic obstructive pulmonary disease (COPD). Again, the method led to simultaneous improvements across all features they evaluated.

While this work takes some important steps forward in understanding the causes of shortcut solutions and working to solve them, the researchers say that continuing to refine these methods and applying them to other types of self-supervised learning will be key to future advancements.

This ties into some of the biggest questions about deep learning systems, like Why do they fail? and Can we know in advance the situations where your model will fail? There is still a lot farther to go if you want to understand shortcut learning in its full generality, Robinson says.

This research is supported by the National Science Foundation, National Institutes of Health, and the Pennsylvania Department of Healths SAP SE Commonwealth Universal Research Enhancement (CURE) program.

Written by Adam Zewe, MIT News Office

Paper: Can contrastive learning avoid shortcut solutions?

Original post:
MIT: Forcing ML Models to Avoid Shortcuts (and Use More Data) for Better Predictions - insideHPC

Gazelli Art House to present Code of Arms a group exhibition investigating the history of artificial intelligence (AI) and machine learning in art. The exploration of implementing code and AI in art in the 1970s 80s comes at a time of rapid change in our understanding and appreciation of computer art.

The exhibition brings together pioneer artists in computer and generative art such as George Nees (b.1926), Frieder Nake (b.1938), Manfred Mohr (b.1938) and Vera Molnar (b.1924), and iconic artists employing AI in their practice such as Harold Cohen (b.1928), Lynn Hershman Leeson (b.1941), and Mario Klingemann (b.1970).

Code of Arms follows the evolution of the medium through the works of exhibited artists. Harold Cohens painting, Aspect (1964), a work shown at the Whitechapel Gallery in 1965 (Harold Cohen: Paintings 1960-1965), marks the artists earliest point of enquiry unfolding his scientific and artistic genius. Cohen, who was most famous for creating the computer program AARON, a predecessor of contemporary AI technologies, implemented the program in his work from 80s onwards as seen by the drawings from this period in the exhibition.

Much of the early computer artworks explored geometric forms and structure employing the technology which was still in the infant stage. Plotter drawings carried out by flatbed precision plotter and early printouts by Manfred Mohr, Georg Nees, Frieder Nake and Vera Molnar from the mid-1960s through the 1980s are an excellent representation of that period: the artists focused on the visual forms rather than addressing the underlying meaning and ethics of using computers in their art. The artists saw machines as an external force that would allow them to explore the visual aspect of the works and experiment with the form in an objective manner. Coming from different backgrounds, they worked alongside each other and made an immense contribution to early computer art.

Initially working as an abstract expressionist artist, Manfred Mohr (b. 1938) was inspired by Max Benses information aesthetics which defined his approach to the creative process from the 1960s onwards. Encouraged by the computer music composer Pierre Barbaud whom he met in 1967, Mohr programmed his first computer drawings in 1969. On display are Mohrs plotter drawings of the 70s and 80s alongside a generative software piece from 2015.

Georg Nees (1926-2016) was a German academic who showed one of the worlds first computer graphics created with a digital computer in 1965. In 1970, at the 35th Venice Biennale, he presented his sculptures and architectural designs, which he continued to work on through the 1980s as seen through his drawings in this exhibition.

Frieder Nake (b. 1938) was actively pursuing computer art in the 1960s. With over 300 works produced and shown at various exhibitions (including Cybernetic Serendipity at ICA, London in 1968), Nake brought his background in computer science and mathematics into his art practice. At the The Great Temptation exhibition at the ZKM in 2005, Nees said: ?There it was, the great temptation for me, for once not to represent something technical with this machine but rather something useless geometrical patterns.? Alongside his iconic 60s plotter drawings, Nakes recent body of work (Sets of Straight Lines, 2018) will be on view as a reminder of the artists ability to transform and move away from the geometric abstraction.

Vera Molnar (b. 1924) is a Hungarian-French artist who is considered a pioneer of computer and generative art. Having created combinational images since 1959, Molnars first non-representational images (abstract geometric and systematic paintings) were produced in 1946. Her plotter drawings from the 80s are displayed alongside her later canvas and work on paper (Double Signe Sans Signification, 2005; Deux Angles Droits, 2006) demonstrating the artists consistency and dedication to the process over three decades.

The exhibition moves on to explore relationships between digital technologies and humans through works by Lynn Hershman Leeson (b.1941), an American artist and filmmaker working in moving image, collage, drawing and new media. The artist, who has recently been the focus of a solo exhibition at the New Museum, New York, will show a series of her rare drawings from the 60s and 70s, as well as her seminal work; Agent Ruby, commissioned by SFMoMA (2001), which is an algorithmic work that interacts with online users through a website, shaping the AIs memory, knowledge and moods. Leeson is known for the first interactive piece using Videodisc (Lorna (1983)), and Deep Contact (1984), the first artwork to incorporate a touch screen.

Mario Klingemann brings neural networks, code and algorithms into the contemporary context. The artist investigates systems of todays society employing deep learning, generative and evolutionary art, glitch art, data classification. The exhibition features his recent digital artwork Memories of Passersby I (Solitaire Version), 2018, and prints Morgan le Fay and Cobalamime from 2017.

Mark Westall

Mark Westall is the Founder and Editor of FAD magazine Founder and co-publisher of Art of Conversation and founder of the platform @worldoffad

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New exhibition to investigate the history of AI & machine learning in art. - FAD magazine