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A team of University of Iowa researchers received $800,000 from Amazon and the National Science Foundation to limit the discriminatory effects of machine learning algorithms.

Larry Phan

University of Iowa researcher Tianbao Yang seats at his desk where he works on AI research on Friday, Aril 8, 2022.

University of Iowa researchers are examining discriminative qualities of artificial intelligence and machine learning models, which are likely to be unfair against ones race, gender, or other characteristics based on patterns of data.

A University of Iowa research team received an $800,000 grant funded jointly by the National Science Foundation and Amazon to decrease the possibility of discrimination through machine learning algorithms.

The three-year grant is split between the UI and Louisiana State University.

According to Microsoft, machine learning models are files trained to recognize specific types of patterns.

Qihang Lin, a UI associate professor in the department of business analytics and grant co-investigator, said his team wants to make machine learning models fairer without sacrificing an algorithms accuracy.

RELATED: UI professor uses machine learning to indicate a body shape-income relationship

People nowadays in [the] academic field ladder, if you want to enforce fairness in your machine learning outcome, you have to sacrifice the accuracy, Lin said. We somehow agree with that, but we want to come up with an approach that [does] trade-off more efficiently.

Lin said discrimination created by machine learning algorithms is seen disproportionately predicting rates of recidivism a convicted criminals tendency to re-offend for different social groups.

For instance, lets say we look at in U.S. courts, they use a software to predict what is the chance of recidivism of a convicted criminal and they realize that that software, that tool they use, is biased because they predicted a higher risk of recidivism of African Americans compared to their actual risk of recidivism, Lin said.

Tianbao Yang, a UI associate professor of computer science and grant principal investigator, said the team proposed a collaboration with Netflix to encourage fairness in the process of recommending shows or films to users.

Here we also want to be fair in terms of, for example, users gender, users race, we want to be fair, Yang said. Were also collaborating with them to use our developed solutions.

Another instance of machine learning algorithm unfairness comes in determining what neighborhoods to allocate medical resources, Lin said.

RELATED: UI College of Engineering uses artificial-intelligence to solve problems across campus

In this process, Lin said the health of a neighborhood is determined by examining household spending on medical expenses. Healthy neighborhoods are allocated more resources, creating a bias against lower income neighborhoods that may spend less on medical resources, Lin said.

Theres a bad cycle that kind of reinforces the knowledge the machines mistakenly have about the relationship between the income, medical expense in the house, and the health, Lin said.

Yao Yao, UI third-year doctoral candidate in the department of mathematics, is conducting various experiments for the research team.

She said the importance of the groups focus is that they are researching more than simply reducing errors in machine learning algorithm predictions.

Previously, people only focus on how to minimize the error but most time we know that the machine learning, the AI will cause some discrimination, Yao said. So, its very important because we focus on fairness.

Read more:
Amazon awards grant to UI researchers to decrease discrimination in AI algorithms - UI The Daily Iowan

Now landing your favorite ML job at Tesla might be closer than you think! Read to learn more.

If working at Tesla is one of your dream jobs then heres your chance. Tesla is hiring artificial intelligence and machine learning engineers and if you think you have done exceptional work in software, hardware, or AI then you may directly apply with your CV by visiting the official Tesla website. While you may be wondering what qualifies to be called exceptional work then the CEO, Elon Musk himself has provided a tip. In one of his tweets, Musk said, As always, Tesla is looking for hardcore AI engineers who care about solving problems that directly affect peoples lives in a major way.

Tesla has scope for machine learning engineers who are interested in working in developing Full Self-Driving (FSD) chips, Dojo systems, Neural Networks, Autonomy Algorithms, and coding. If given a chance you may be part of the Tesla Bot project as well. In another tweet, Elon Musk clarified that he is fine with hiring people without AI background too. A background in AI is not needed, just exceptional skill in software or computer design, he said. On the other hand, the Tesla website job posting has described that the company is looking for mechanical, electrical, controls, and software engineers to help leverage its AI expertise beyond the vehicle fleet.

For hardcore coders, Tesla wants engineers who can build Autopilot software foundations up from the lowest levels of the stack, tightly integrating with Teslas custom hardware. The company aims to Implement super-reliable bootloaders with support for over-the-air updates and bring up customized Linux kernels. It wants the ML engineers to write fast, memory-efficient low-level code to capture high-frequency, high-volume data from the sensors, and to share it with multiple consumer processes without impacting central memory access latency or starving critical functional code from CPU cycles.

Another special job related to machine learning in Tesla is Autopilot Simulation Deep Learning Engineers. Autopilot is of critical importance to Teslas mission. It is safer, makes driving more enjoyable, and will ultimately deliver on the promise of self-driving cars. As a member of Teslas Autopilot Simulation team, you will be in a unique position to accelerate the pace at which Autopilot improves over time. The Simulation team realizes these goals through generating synthetic datasets for neural network training, building tools that enable Autopilot software developers to perform virtual test drives instead of real ones, and through testing all Autopilot code changes and software releases for regressive behavior.

As an Autopilot Simulation Deep Learning Engineer, you will contribute to the development of Autopilot simulation by enabling and accelerating the creation of photorealistic 3D scenes through neural rendering, neural animation, and scene/object reconstruction. You will be at the cutting edge of deep learning applications and will train neural networks on a cluster in large-scale distributed settings as Tesla has one of the biggest training clusters in the world. You will have tremendous reach and autonomy, with an exciting opportunity to impact Teslas goal of full self-driving and its mission to accelerate the worlds transition to sustainable energy. As the machine learning engineer, you will be a creative self-starter with an ability to self-assign projects across different areas of the Autopilot team. You will also have the opportunity to identify gaps between real and simulated environments, and then you will get to close those gaps through state-of-the-art machine learning/deep learning methods.

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Want to Land Your Dream ML Job at Tesla? Here's How? - Analytics Insight

Swiss Re, in collaboration with MachineHack, successfully completed the Machine Learning Hackathon held from March 11th to 28th for data scientists and ML professionals to predict accident risk scores for unique postcodes. The end goal? To build a machine learning model to improve auto insurance pricing.

The hackathon saw over 1100+ registrations and 300+ participants from interested candidates. Out of those, the top five were asked to participate in a solution showcase held on the 6th of April. The top five entries were judged by Amit Kalra, Managing Director, Swiss Re and Jerry Gupta, Senior Vice President, Swiss Re who engaged with the top participants, understood their solutions and presentations and provided their comments and scores. From that emerged the top three winners!

Lets take a look at the winners who impressed the judges with their analytics skills and took home highly coveted cash prizes and goodies.

Pednekar comes with over 19 years of work experience in IT, project management, software development, application support, software system design, and requirement study. He is passionate about new technologies, especially data science, AI and machine learning.

My expertise lies in creating data visualisations to tell my datas story & using feature engineering to add new features to give a human touch in the world of machine learning algorithms, said Pednekar.

Pednekars approach consisted of seven steps:

For EDA, Pednekar has analysed the dataset to find out the relationship between:

Image: Rahul Pednekar

Image: Rahul Pednekar

Here, Pednekar merged Population & Road Network datasets with train using left join. He created Latitude and Longitude columns by extracting data from the WKT columns in Roads_network.

He proceeded to

And added new features:

Pednekar completed the following steps:

Image: Rahul Pednekar

Image: Rahul Pednekar

Pednekar has thoroughly enjoyed participating in this hackathon. He said, MachineHack team and the platform is amazing, and I would like to highly recommend the same to all data science practitioners. I would like to thank Machinehack for providing me with the opportunity to participate in various data science problem-solving challenges.

Check the code here.

Yadavs data science journey started a couple of years back, and since then, he has been an active participant in hackathons conducted on different platforms. Learning from fellow competitors and absorbing their ideas is the best part of any data science competition as it just widens the thinking scope for yourself and makes you better after each and every competition, says Yadav.

MachineHack competitions are unique and have a different business case in each of their hackathons. It gives a field wherein we can practice and learn new skills by applying them to a particular domain case. It builds confidence as to what would work and what would not in certain cases. I appreciate the hard work the team is putting in to host such competitions, adds Yadav.

Check the code here.

Rank 03: Prudhvi Badri

Badri entered the data science field while pursuing a masters in computer science at Utah State University in 2014 and had taken classes related to statistics, Python programming and AI, and wrote a research paper to predict malicious users in online social networks.

After my education, I started to work as a data scientist for a fintech startup company and built models to predict loan default risk for customers. I am currently working as a senior data scientist for a website security company. In my role, I focus on building ML models to predict malicious internet traffic and block attacks on websites. I also mentor data scientists and help them build cool projects in this field, said Badri.

Badri mainly focused on feature engineering to solve this problem. He created aggregated features such as min, max, median, sum, etc., by grouping a few categorical columns such as Day_of_Week, Road_Type, etc. He built features from population data such as sex_ratio, male_ratio, female_ratio, etc.

He adds, I have not used the roads dataset that has been provided as supplemental data. I created a total of 241 features and used ten-fold cross-validation to validate the model. Finally, for modelling, I used a weighted ensemble model of LightGBM and XGBoost.

Badri has been a member of MachineHack since 2020. I am excited to participate in the competitions as they are unique and always help me learn about a new domain and let me try new approaches. I appreciate the transparency of the platform sharing the approaches of the top participants once the hackathon is finished. I learned a lot of new techniques and approaches from other members. I look forward to participating in more hackathons in the future on the MachineHack platform and encourage my friends and colleagues to participate too, concluded Badri.

Check the code here.

The Swiss Re Machine Learning Hackathon, in collaboration with MachineHack, ended with a bang, with participants presenting out-of-the-box solutions to solve the problem in front of them. Such a high display of skills made the hackathon intensely competitive and fun and surely made the challenge a huge success!

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Meet the winners of the Machine Learning Hackathon by Swiss Re & MachineHack - Analytics India Magazine

AI, Artificial Intelligence concept,3d rendering,conceptual image.

This week IBM announced its latest Z-series mainframe and MLCommons released its latest benchmark series. The two announcements had something in common Machine Learning (ML) acceleration which is becoming pervasive everywhere from financial fraud detection in mainframes to detecting wake words in home appliances.

While these two announcements were not directly related, but they are part of a trend, showing how pervasive ML has become.

MLCommons Brings Standards to ML Benchmarking

ML benchmarking is important because we often hear about ML performance in terms of TOPS trillions of operations per second. Like MIPS (Millions of Instructions per Second or Meaningless Indication of Processor Speed depending on your perspective), TOPS is a theoretical number calculated from the architecture, not a measured rating based on running workloads. As such, TOPS can be a deceiving number because it does not include the impact of the software stack., Software is the most critical aspect of implementing ML and the efficiency varies widely, which Nvidia clearly demonstrated by improving the performance of its A100 platform by 50% in MLCommons benchmarks over the years.

The industry organization MLCommons was created by a consortium of companies to build a standardized set of benchmarks along with a standardized test methodology that allows different machine learning systems to be compared. The MLPerf benchmark suites from MLCommons include different benchmarks that cover many popular ML workloads and scenarios. The MLPerf benchmarks addresses everything from the tiny microcontrollers used in consumer and IoT devices, to mobile devices like smartphones and PCs, to edge servers, to data center-class server configuration. Supporters of MLCommons include Amazon, Arm, Baidu, Dell Technologies, Facebook, Google, Harvard, Intel, Lenovo, Microsoft, Nvidia, Stanford and the University of Toronto.

MLCommons releases benchmark results in batches and has different publishing schedules for inference and for training. The latest announcement was for version 2.0 of the MLPerf Inference suite for data center and edge servers, version 2.0 for MLPerf Mobile, and version 0.7 for MLPerf Tiny for IoT devices.

To date, the company that has had the most consistent set of submissions, producing results every iteration, in every benchmark test, and by multiple partners, has been Nvidia. Nvidia and its partners appear to have invested enormous resources in running and publishing every relevant MLCommons benchmark. No other vendor can match that claim. The recent batch of inference benchmark submissions include Nvidia Jetson Orin SoCs for edge servers and the Ampere-based A100 GPUs for data centers. Nvidias Hopper H100 data center GPU, which was announced at Spring 2022 GTC, arrived too late to be included in the latest MLCommons announcement, but we fully expect to see Nvidia H100 results in the next round.

Recently, Qualcomm and its partners have been posting more data center MLPerf benchmarks for the companys Cloud AI 100 platform and more mobile MLPerf benchmarks for Snapdragon processors. Qualcomms latest silicon has proved to be very power efficient in data center ML tests, which may give it an edge on power-constrained edge server applications.

Many of the submitters are system vendors using processors and accelerators from silicon vendors like AMD, Andes, Ampere, Intel, Nvidia, Qualcomm, and Samsung. But many of the AI startups have been absent. As one consulting company, Krai, put it: Potential submitters, especially ML hardware startups, are understandably wary of committing precious engineering resources to optimizing industry benchmarks instead of actual customer workloads. But then Krai countered their own objection with MLPerf is the Olympics of ML optimization and benchmarking. Still, many startups have not invested in producing MLCommons results for various reasons and that is disappointing. Theres also not enough FPGA vendors participating in this round.

The MLPerf Tiny benchmark is designed for very low power applications such as keyword spotting, visual wake words, image classification, and anomaly detection. In this case we see results from a mix of small companies like Andes, Plumeria, and Syntiant, as well as established companies like Alibaba, Renesas, Silicon Labs, and STMicroeletronics.

IBM z16 Mainframe

IBM Adds AI Acceleration Into Every Transaction

While IBM didnt participate in MLCommons benchmarks, the company takes ML seriously. With its latest Z-Series mainframe computer, the z16, IBM has added accelerators for ML inference and quantum-safe secure boot and cryptography. But mainframe systems have different customer requirements. With roughly 70% of banking transactions (on a value basis) running on IBM mainframes, the company is anticipating the needs of financial institutes for extreme reliable and transaction processing protection. In addition, by adding ML acceleration into its CPU, IBM can offer per-transaction ML intelligence to help detect fraudulent transactions.

In an article I wrote in 2018, I said: In fact, the future hybrid cloud compute model will likely include classic computing, AI processing, and quantum computing. When it comes to understanding all three of those technologies, few companies can match IBMs level of commitment and expertise. And the latest developments in IBMs quantum computing roadmap and the ML acceleration in the z16, show IBM is a leader in both.

Summary

Machine Learning is important from tiny devices up to mainframe computers. Accelerating this workload can be done on CPUs, GPUs, FPGAs, ASICs, and even MCUs and is now a part of all computing going forward. These are two examples of how ML is changing and improving over time.

Tirias Research tracks and consults for companies throughout the electronics ecosystem from semiconductors to systems and sensors to the cloud. Members of the Tirias Research team have consulted for IBM, Nvidia, Qualcomm, and other companies throughout the AI ecosystems.

Read the original:
IBM And MLCommons Show How Pervasive Machine Learning Has Become - Forbes

Unsupported sleeper detection

From the machine model development for detecting unsupported sleepers, the accuracy of each model is shown in Table 4.

From the table, it can be seen that each model performs well. The accuracy of each model is higher than 90% when the data processing is appropriate. CNN performs the best based on its accuracies. When CNN is applied with FFT and padding, the accuracies are the first and second highest compared to other models. For RNN and ResNet, the accuracies are higher than 90% when specific data processing is used. However, the accuracies become 80% approximately when another data processing technique is used. For FCN, data processing is not needed. The FCN model can achieve an accuracy of 95%. From the table, the models with the highest accuracy are CNN, RNN, FCN, and ResNet respectively. The complicated architecture of ResNet does not guarantee the highest accuracy. Moreover, the training time of ResNet (46s/epoch) is the longest followed by RNN (6s/epoch), FCN (2s/epoch), and CNN (1s/epoch) respectively. It can be concluded that the CNN model is the best model to detect supported sleepers in this study because it provides the highest accuracy or 100% while the training time is the lowest. At the same time, easy data processing likes padding is good enough to provide a good result. It is better than FFT in the CNN model which requires longer data processing. The accuracy of testing data of each model is shown in Fig.8.

Accuracies of testing data on unsupported sleeper detection.

The tuned hyperparameters of the CNN model with padding data are shown in Table 5.

Compared to the previous study, Sysyn et al.1 applied statistical methods and KNN which provided the best detection accuracy of 65%. The accuracy of the CNN model developed in this study is significantly higher. It can be assumed that the machine learning techniques used in this study are more powerful than the ones used in the previous study. Moreover, CNN is proven that it is suitable for pattern recognition.

For the unsupported sleeper severity classification, the performance of each model is shown in Table 6.

From the table, it can be seen that the CNN model still performs the best with an accuracy of 92.89% and provides good results with both data processing. However, the accuracies of RNN and ResNet significantly drop when unsuitable data processing is conducted. For example, the accuracy of the RNN model with padding drops to 33.89%. The best performance that RNN can achieve is 71.56% which is the lowest compared to other models. This is because of the limitation of RNN that vanishing gradient occurs when time-series data is too long. In this study, the number of data points for padding data is 1181 which can result in the issue. Therefore, RNN does not perform well. ResNet performs well with an accuracy of 92.42% close to CNN while the accuracy of FCN is fairly well. For the training time, CNN is the fastest model with the training time of 1s/epoch followed by FCN (2s/epoch), RNN (5s/epoch), and ResNet (32s/epoch) respectively. From these, it can be concluded that the CNN model is the best model for unsupported sleeper severity classification in this study. Moreover, it can be concluded that CNN and ResNet are suitable with padding data while RNN is suitable with FFT data. The accuracy of testing data of each model is shown in Fig.9.

Accuracies of testing data on unsupported sleeper severity classification.

The confusion matrix of the CNN model is shown in Table 7.

To clearly demonstrate the performance of each model, precision and recall are shown in Table 8.

From the table, the precisions and recalls of CNN and ResNet are fairly good with values higher than 80% while RNN is the worst. Some precisions of RNN are lower than 60% which cannot be used in realistic situations. CNN seems to be the better model than ResNet because all precisions are higher than 90%. Although some precisions of ResNet are higher than CNN, the precision of class 2 is about 80%. Therefore, the use of the CNN model is better.

For hyperparameter tuning, the tuned hyperparameters of CNN are shown in Table 9.

Originally posted here:
Prognostics of unsupported railway sleepers and their severity diagnostics using machine learning | Scientific Reports - Nature.com