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What is critical race theory?

Developed by the former Harvard Law professor Derrick Bell and other scholars in the 1970s and 80s, critical race theory, or CRT, examines the ways in which racism was embedded into American law and other modern institutions, maintaining the dominance of white people.

CRT argues that racism is not a matter of individual bigotry but a systemic issue that creates an uneven playing field for people of colour.

Kimberl Williams Crenshaw, a law professor widely credited with coining the term, told the New York Times: It is a way of seeing, attending to, accounting for, tracing and analyzing the ways that race is produced, the ways that racial inequality is facilitated, and the ways that our history has created these inequalities that now can be almost effortlessly reproduced unless we attend to the existence of these inequalities.

A year or so ago few people had heard of it, yet Republicans have whipped up a moral panic that CRT is being rammed down the throats of schoolchildren. They caricature it as teaching Black children to internalise victimhood and white children to self-identify as oppressors.

Is it taught in schools?

No, it is not a part of the secondary school curriculum. The National School Boards Association and other education leaders are adamant that CRT is not being taught in K-12 schools, which teach students from five to 18 years old.

But Rupert Murdochs Fox News and other rightwing media have turned it into a catch-all buzzword for any teaching in schools about race and American history. They loosely apply it to concepts such as equity and anti-bias training for teachers.

Patti Hidalgo Menders, president of the Loudoun County Republican Womens Club in Virginia, told the Guardian last week: They may not call it critical race theory, but theyre calling it equity, diversity, inclusion. They use culturally responsive training for their teachers. It is fundamentally CRT.

Its dividing our children into victims and oppressors and whats a child supposed to do with that?

Efforts to weaponise CRT were reinforced by former president Donald Trump and a rightwing ecosystem including influential thinktanks. Last year Christopher Rufo, a conservative scholar now at the Manhattan Institute, told the Fox News host Tucker Carlson that CRT was a form of cult indoctrination.

In January the Heritage Foundation hosted a panel discussion where the moderator, Angela Sailor, warned: Critical race theory is the complete rejection of the best ideas of the American founding. This is some dangerous, dangerous philosophical poisoning in the blood stream.

What role did CRT play in Virginias election?

Winning Republican candidate Glenn Youngkins signature issue was education. He hammered government schools on culture war issues such as race and transgender rights and falsely claimed that his Democratic opponent, Terry McAuliffe, called his friend, President Joe Biden, and asked the FBI to silence conservative parents.

Youngkin said he would ban the teaching of CRT in Virginia classrooms. At a campaign event in Glen Allen last month, the candidate said to applause: What we wont do is teach our children to view everything through the lens of race. On day one, I will ban critical race theory.

McAuliffe was forced on to the defensive and had to engage with the issue. He accused Republicans of using the Trump playbook of division and deceit, a message that did not cut through in the same way.

Why did the issue resonate with voters?

This can be seen as a rightwing backlash to last years Black Lives Matter protests and conversations about structural racism that followed the police murder of George Floyd, an African American man in Minneapolis. It also can be seen as a response to Americas changing demographics, specifically the increase in the minority population.

It also comes after lengthy school closures during the pandemic infuriated many parents. School board meetings in Virginia and elsewhere have turned ugly, even violent, and protest signs calling for bans on masks and CRT are sometimes almost interchangeable.

This week conservatives targeted school board elections nationwide over masking rules and teaching racial justice issues. In Virginia, 14% of voters listed education as a top issue, and about seven of 10 of those voted for Youngkin.

McAuliffe did not help himself when, during a debate, he said, I dont believe parents should be telling schools what they should teach a line that was constantly replayed in Youngkin attacks ads.

Youngkin also highlighted a high school bathroom sexual assault case in affluent Loudoun county, in northern Virginia, to argue against allowing transgender students into their chosen restrooms.

Is it just Virginia?

No. Officials in Republican-controlled states across America are proposing numerous laws to ban teachers from emphasizing the role of systemic racism. Legislation aiming to curb how teachers talk about race has been considered by at least 15 states, according to research by Education Week.

Ron DeSantis, the governor of Florida, has described CRT as state-sanctioned racism.

Brad Little, the governor of Idaho, signed into law a measure banning public schools from teaching CRT, which it claimed will exacerbate and inflame divisions on the basis of sex, race, ethnicity, religion, color, national origin, or other criteria in ways contrary to the unity of the nation and the wellbeing of the state of Idaho and its citizens.

Red states are also targeting the 1619 Project, a series by the New York Times which contends that modern American history began with the arrival of enslaved people four centuries ago and examines that legacy.

Republicans are expected to use the Youngkin formula to woo suburban voters in next years midterm elections for Congress.

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How did Republicans turn critical race theory into a winning electoral issue? - The Guardian

Indiana Republicans have dominated state politics over this past generation, holding all Statehouse constitutional offices, super majorities in the General Assembly and all but two seats of the congressional delegation for much of the past decade.

Aug. 14, 2001, became the fateful date when they began to turn things around after Democrat Govs. Evan Bayh and Frank OBannon had won the previous four gubernatorial elections. That was the day The Phoenix Group had its open house at the Klipsch Audio Technologies headquarters near the Indianapolis pyramids.

It was a fundraiser like no other the party had seen in years. The Phoenix Group had formed earlier that year by GOP financiers Jim Kittle, Bob Grand and Randall Tobias in an effort to reinvigorate the once thriving Indiana Republican machine that had been shut out of gubernatorial races since the rise of Evan Bayh in 1988.

Kittle and other Republican financiers had grown frustrated over what they saw as a four-year cycle of reinventing the wheel when it came to statewide races. It seems like we start from scratch every time, he told Howey Politics in 2001. And theres been growing frustration over high-level campaigns run by John Mutz, Stephen Goldsmith, David McIntosh and Indianapolis mayoral nominee Sue Anne Gilroy where a lack of money hasnt been a problem. What had hamstrung Indiana Republicans had been top-flight competition, poor strategy and execution, and a lack of competitive technology.

The Phoenix Group was essentially a shadow party. When Mike McDaniel stepped down, it created a race between Kittle and Grant Countys John Earnest (who had lost a chair race to Rex Early a decade before), with Kittle prevailing.

It was all seen as a precursor to Mitch Daniels leaving his White House budget director post to run for governor. That notion had been speculated months before. I remember meeting Chairman McDaniel earlier that year when he said the Republicans had a secret weapon. He then wrote the name Mitch Daniels on a piece of paper.

Daniels was an acolyte to legendary Marion County Chairman Keith Bulen, then rose through the ranks as a staffer to U.S. Sen. Richard Lugar, at one point leading the National Republican Senatorial Committee. After Dan Quayle was elected vice president in 1988, Gov. Robert Orr had offered Daniels the open Senate seat, which he turned down due to family considerations.

Kittle, Grand and Tobias essentially created The Phoenix Group to lure Daniels out of the White House and into the 2004 governors race.

Daniels would say after Kittle was elected chair, I would walk across hot coals for Jim Kittle and Ed Simcox.

As we all know, Daniels did return to Indiana full time (he didnt move the family to Washington when he worked for President George W. Bush) coming back to defeat Democrat Gov. Joe Kernan in 2004, setting in motion the second GOP dynasty that is still intact today.

Rex Early enunciated the central truth about the reality of a major party power: Being state chairman with a governor and being state chairman without a governor is the difference between ice cream and dog poop.

Since 1960, there have been 37 Republican and Democratic chairmen and for two years, Ann DeLaney ran the Indiana Democratic Party. This club of power is almost exclusively dominated by white males. In addition to DeLaney, the only minority chair was Robin Winston, who led Indiana Democrats under Gov. Frank OBannon.

There were the transformational chairs like Republicans James Neal, Jim Kittle, Eric Holcomb and now Kyle Hupfer, and Democrats like John Livengood and Joe Andrew, who helped pave the way for gubernatorial party switches and prolonged power maintenance.

There were placeholder chairs like McDaniel and Democrats Gordon St. Angelo, Dan Parker and John Zody, who attempted with varying degrees of success to stabilize their parties while lacking resources (i.e. the governor).

There were those who served at the pleasure of their governors: Thomas Milligan and Bruce Melchert under Gov. Doc Bowen; Gordon Durnil during Gov. Orrs two terms; Murray Clark and Holcomb under Gov. Daniels; Hupfer under Gov. Holcomb; Democrats John Livengood, Michael Pannos and DeLaney under Gov. Bayh, and Winston under Gov. OBannon; Joe Hogsett and Kip Tew under Gov. Kernan; Tim Berry and Jeff Cardwell under Gov. Mike Pence.

And there were the rescue chairs Early and current Democratic Chairman Mike Schmuhl who took the job when their parties were at a low ebb.

Schmuhl took the helm of Indiana Democrats in March. The party has been relegated to essentially Indianapolis, Lake County and the university cities. There are only two Democrats in the General Assembly south of Bloomington.

He is now leading Democrats on a Small Town Tour hoping to show voters, as former legislator Melanie Wright said last week, that they dont have horns sprouting from their heads. According to Schmuhl, the party has to show up even in the deepest red counties. This past week Schmuhl, former congresswoman Jill Long Thompson and others have appeared in LaGrange, Cicero and North Vernon to talk about President Bidens American Rescue Plan.

Told about Rex Earlys quote about being a chair without a governor, Schmuhl knew about it, saying, Thats a really good one. I had ice cream last night.

The columnist is publisher of Howey Politics Indiana at http://www.howeypolitics.com. Find Howey on Facebook and Twitter @hwypol.

Read more from the original source:
Brian Howey: How Indiana Republicans found their way to ice cream - Terre Haute Tribune Star

The Republican candidates in New Yorks competitive races differ from one another in tone, experience and the local issues that reflect their distinctive districts.

But all of those contests, party officials and strategists say, are shaped by the continued salience of public safety in the minds of voters, discussion of education matters like the gifted and talented program that Mayor Bill de Blasio wants to phase out, and intense feelings over vaccine mandates. Some Republicans even argue that the challenging national environment that Democrats appear to be facing may be evident in a handful of city races, too.

This has a lot of likenesses to 2009, when Obama came in on hope and change and then fell flat, said Nick Langworthy, the chairman of the New York Republican State Committee. In 2009 we had great gains at the local level, and then had a cataclysm in 2010. Are we facing that, or is there going to be flatness all the way around?

Whatever the turnout, Republicans are virtually certain to be shut out of citywide offices. Indeed, by nearly every metric, the Republican Party has been decimated in the nations largest city. They are vastly outnumbered in voter registration and have struggled to field credible candidates for major offices.

At the City Council level, Republican hopes boil down to a matter of margins.

The most optimistic Republican assessment, barring extraordinary developments, is that they could increase their presence to five from three on the 51-seat City Council, as they did in 2009. But even that would require a surprise outcome in a sleeper race and it is possible they retain only one seat (setting aside the candidates who are running on multiple party lines).

Officials on both sides of the aisle believe a more realistic target for the Republicans is three or four seats, a number that could still affect the brewing City Council speakers race and may indicate pockets of discontent with the direction of the city.

What to Know About the 2021 New York Election

The most high-profile of those contests is the last Republican-held seat in Queens.

Ms. Singh, a teacher who is endorsed by the left-wing Working Families Party, is running against Joann Ariola, the chairwoman of the Queens Republican Party. The race has stirred considerable interest from the left and the right and attracted spending from outside groups.

Read the rest here:
The New York City Council Races Where Republicans Still Stand a Chance - The New York Times

Artificial intelligence and machine learning (AI and ML) are key technologies that help organizations develop new ways to increase sales, reduce costs, streamline business processes, and understand their customers better. AWS helps customers accelerate their AI/ML adoption by delivering powerful compute, high-speed networking, and scalable high-performance storage options on demand for any machine learning project. This lowers the barrier to entry for organizations looking to adopt the cloud to scale their ML applications.

Developers and data scientists are pushing the boundaries of technology and increasingly adopting deep learning, which is a type of machine learning based on neural network algorithms. These deep learning models are larger and more sophisticated resulting in rising costs to run underlying infrastructure to train and deploy these models.

To enable customers to accelerate their AI/ML transformation, AWS is building high-performance and low-cost machine learning chips. AWS Inferentia is the first machine learning chip built from the ground up by AWS for the lowest cost machine learning inference in the cloud. In fact, Amazon EC2 Inf1 instances powered by Inferentia, deliver 2.3x higher performance and up to 70% lower cost for machine learning inference than current generation GPU-based EC2 instances. AWS Trainium is the second machine learning chip by AWS that is purpose-built for training deep learning models and will be available in late 2021.

Customers across industries have deployed their ML applications in production on Inferentia and seen significant performance improvements and cost savings. For example, AirBnBs customer support platform enables intelligent, scalable, and exceptional service experiences to its community of millions of hosts and guests across the globe. It used Inferentia-based EC2 Inf1 instances to deploy natural language processing (NLP) models that supported its chatbots. This led to a 2x improvement in performance out of the box over GPU-based instances.

With these innovations in silicon, AWS is enabling customers to train and execute their deep learning models in production easily with high performance and throughput at significantly lower costs.

Machine learning is an iterative process that requires teams to build, train, and deploy applications quickly, as well as train, retrain, and experiment frequently to increase the prediction accuracy of the models. When deploying trained models into their business applications, organizations need to also scale their applications to serve new users across the globe. They need to be able to serve multiple requests coming in at the same time with near real-time latency to ensure a superior user experience.

Emerging use cases such as object detection, natural language processing (NLP), image classification, conversational AI, and time series data rely on deep learning technology. Deep learning models are exponentially increasing in size and complexity, going from having millions of parameters to billions in a matter of a couple of years.

Training and deploying these complex and sophisticated models translates to significant infrastructure costs. Costs can quickly snowball to become prohibitively large as organizations scale their applications to deliver near real-time experiences to their users and customers.

This is where cloud-based machine learning infrastructure services can help. The cloud provides on-demand access to compute, high-performance networking, and large data storage, seamlessly combined with ML operations and higher level AI services, to enable organizations to get started immediately and scale their AI/ML initiatives.

AWS Inferentia and AWS Trainium aim to democratize machine learning and make it accessible to developers irrespective of experience and organization size. Inferentias design is optimized for high performance, throughput, and low latency, which makes it ideal for deploying ML inference at scale.

EachAWS Inferentiachip contains four NeuronCores that implement a high-performancesystolic arraymatrix multiply engine, which massively speeds up typical deep learning operations, such as convolution and transformers. NeuronCores are also equipped with a large on-chip cache, which helps to cut down on external memory accesses, reducing latency, and increasing throughput.

AWS Neuron, the software development kit for Inferentia, natively supports leading ML frameworks, likeTensorFlow andPyTorch. Developers can continue using the same frameworks and lifecycle developments tools they know and love. For many of their trained models, they can compile and deploy them on Inferentia by changing just a single line of code, with no additional application code changes.

The result is a high-performance inference deployment, that can easily scale while keeping costs under control.

Sprinklr, a software-as-a-service company, has an AI-driven unified customer experience management platform that enables companies to gather and translate real-time customer feedback across multiple channels into actionable insights. This results in proactive issue resolution, enhanced product development, improved content marketing, and better customer service. Sprinklr used Inferentia to deploy its NLP and some of its computer vision models and saw significant performance improvements.

Several Amazon services also deploy their machine learning models on Inferentia.

Amazon Prime Video uses computer vision ML models to analyze video quality of live events to ensure an optimal viewer experience for Prime Video members. It deployed its image classification ML models on EC2 Inf1 instances and saw a 4x improvement in performance and up to a 40% savings in cost as compared to GPU-based instances.

Another example is Amazon Alexas AI and ML-based intelligence, powered by Amazon Web Services, which is available on more than 100 million devices today. Alexas promise to customers is that it is always becoming smarter, more conversational, more proactive, and even more delightful. Delivering on that promise requires continuous improvements in response times and machine learning infrastructure costs. By deploying Alexas text-to-speech ML models on Inf1 instances, it was able to lower inference latency by 25% and cost-per-inference by 30% to enhance service experience for tens of millions of customers who use Alexa each month.

As companies race to future-proof their business by enabling the best digital products and services, no organization can fall behind on deploying sophisticated machine learning models to help innovate their customer experiences. Over the past few years, there has been an enormous increase in the applicability of machine learning for a variety of use cases, from personalization and churn prediction to fraud detection and supply chain forecasting.

Luckily, machine learning infrastructure in the cloud is unleashing new capabilities that were previously not possible, making it far more accessible to non-expert practitioners. Thats why AWS customers are already using Inferentia-powered Amazon EC2 Inf1 instances to provide the intelligence behind their recommendation engines and chatbots and to get actionable insights from customer feedback.

With AWS cloud-based machine learning infrastructure options suitable for various skill levels, its clear that any organization can accelerate innovation and embrace the entire machine learning lifecycle at scale. As machine learning continues to become more pervasive, organizations are now able to fundamentally transform the customer experienceand the way they do businesswith cost-effective, high-performance cloud-based machine learning infrastructure.

Learn more about how AWSs machine learning platform can help your company innovate here.

This content was produced by AWS. It was not written by MIT Technology Reviews editorial staff.

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High-performance, low-cost machine learning infrastructure is accelerating innovation in the cloud - MIT Technology Review

NEW YORK, Nov. 6, 2021 /PRNewswire/ -- Crowdsourcing has become an increasingly popular way to develop machine learning algorithms to address many clinical problems in a variety of illnesses. Today at the American College of Rheumatology (ACR) annual meeting, a multicenter team led by an investigator from Hospital for Special Surgery (HSS) presented the results from the RA2-DREAM Challenge, a crowdsourced effort focused on developing better methods to quantify joint damage in people with rheumatoid arthritis (RA).

Damage in the joints of people with RA is currently measured by visual inspection and detailed scoring on radiographic imagesof small joints in the hands, wrists and feet. This includes both joint space narrowing (which indicates cartilage loss) and bone erosions (which indicates damage from invasion of the inflamed joint lining). The scoring system requires specially trained experts and is time-consuming and expensive. Finding an automated way to measure joint damage is important for both clinical research and for care of patients, according to the study's senior author, S. Louis Bridges, Jr., MD, PhD, physician-in-chief and chair of the Department of Medicine at HSS.

"If a machine-learning approach could provide a quick, accurate quantitative score estimating the degree of joint damage in hands and feet, it would greatly help clinical research," he said. "For example, researchers could analyze data from electronic health records and from genetic and other research assays to find biomarkers associated with progressive damage. Having to score all the images by visual inspection ourselves would be tedious, and outsourcing it is cost prohibitive."

"This approach could also aid rheumatologists by quickly assessing whether there is progression of damage over time, which would prompt a change in treatment to prevent further damage," he added. "This is really important in geographic areas where expert musculoskeletal radiologists are not available."

For the challenge, Dr. Bridges and his collaborators partnered with Sage Bionetworks, a nonprofit organization that helps investigators create DREAM (Dialogue on Reverse Engineering Assessment and Methods) Challenges. These competitions are focused on the development of innovative artificial intelligence-based tools in the life sciences. The investigators sent out a call for submissions, with grant money providing prizes for the winning teams. Competitors were from a variety of fields, including computer scientists, computational biologists and physician-scientists; none were radiologists with expertise or training in reading radiographic images.

For the first part of the challenge, one set of images was provided to the teams, along with known scores that had been visually generated. These were used to train the algorithms. Additional sets of images were then provided so the competitors could test and refine the tools they had developed. In the final round, a third set of images was given without scores, and competitors estimated the amount of joint space narrowing and erosions. Submissions were judged according to which most closely replicated the gold-standard visually generated scores. There were 26 teams that submitted algorithms and 16 final submissions. In total, competitors were given 674 sets of images from 562 different RA patients, all of whom had participated in prior National Institutes of Health-funded research studies led by Dr. Bridges. In the end, four teams were named top performers.

For the DREAM Challenge organizers, it was important that any scoring system developed through the project be freely available rather than proprietary, so that it could be used by investigators and clinicians at no cost. "Part of the appeal of this collaboration was that everything is in the public domain," Dr. Bridges said.

Dr. Bridges explained that additional research and development of computational methods are needed before the tools can be broadly used, but the current research demonstrates that this type of approach is feasible. "We still need to refine the algorithms, but we're much closer to our goal than we were before the Challenge," he concluded.

About HSS

HSS is the world's leading academic medical center focused on musculoskeletal health. At its core is Hospital for Special Surgery, nationally ranked No. 1 in orthopedics (for the 12th consecutive year), No. 4 in rheumatology by U.S. News & World Report (2021-2022), and the best pediatric orthopedic hospital in NY, NJ and CT by U.S. News & World Report "Best Children's Hospitals" list (2021-2022). HSS is ranked world #1 in orthopedics by Newsweek (2021-2022). Founded in 1863, the Hospital has the lowest complication and readmission rates in the nation for orthopedics, and among the lowest infection rates. HSS was the first in New York State to receive Magnet Recognition for Excellence in Nursing Service from the American Nurses Credentialing Center five consecutive times. The global standard total knee replacement was developed at HSS in 1969. An affiliate of Weill Cornell Medical College, HSS has a main campus in New York City and facilities in New Jersey, Connecticut and in the Long Island and Westchester County regions of New York State, as well as in Florida. In addition to patient care, HSS leads the field in research, innovation and education. The HSS Research Institute comprises 20 laboratories and 300 staff members focused on leading the advancement of musculoskeletal health through prevention of degeneration, tissue repair and tissue regeneration. The HSS Global Innovation Institute was formed in 2016 to realize the potential of new drugs, therapeutics and devices. The HSS Education Institute is a trusted leader in advancing musculoskeletal knowledge and research for physicians, nurses, allied health professionals, academic trainees, and consumers in more than 130 countries. The institution is collaborating with medical centers and other organizations to advance the quality and value of musculoskeletal care and to make world-class HSS care more widely accessible nationally and internationally. http://www.hss.edu.

SOURCE Hospital for Special Surgery

http://www.hss.edu

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Researchers Present Global Effort to Develop Machine Learning Tools for Automated Assessment of Radiographic Damage in Rheumatoid Arthritis -...