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Letter decrying predictive criminality AI research paper passes 1,000 signatures

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The Coalition for Critical Technology (CCT) penned a letter opposing the publication of research called “A Deep Neural Network Model to Predict Criminality Using Image Processing.” At the time of publication the letter has more than 1,000 signatures from researchers, practitioners, academics, and others. According to a press release from Harrisburg University, the paper is slated for publication in a book series from Springer Publishing, and the letter urges readers to demand that Springer pull the paper and condemn the use of criminal justice statistics to predict criminality.

The use of algorithms in predictive policing is a fraught subject. As the CCT letter elaborates, criminal justice data is notoriously flawed. “Countless studies have shown that people of color are treated more harshly than similarly situated white people at every stage of the legal system, which results in serious distortions in the data,” the letter reads. One of the primary researchers on the Harrisburg University paper, Jonathan Korn, is a former NYPD officer.

According to Harrisburg University’s press release, the paper promises, “With 80 percent accuracy and with no racial bias, the software can predict if someone is a criminal based solely on a picture of their face. The software is intended to help law enforcement prevent crime.”

The notion of determining criminality from a person’s face is fundamentally racist and has deep roots in historical pseudoscience; using facial recognition technology is just a modern means of attempting to answer the same flawed question. The CCT’s letter begins by condemning the very idea of the researcher’s question: “Such claims are based on unsound scientific premises, research, and methods, which numerous studies spanning our respective disciplines have debunked over the years.”

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This is not the first time the paper in question has appeared. In early May, Harrisburg University pushed out the same press release, only to take it down after harsh criticism. Motherboard kept an archived version of the release and reported that one of the researchers, Nathaniel J.S. Ashby, said in an email, “The post/tweet was taken down until we have time to draft a release with details about the research which will address the concerns raised.”

But it’s unclear what has changed since May. The “new” press release is identical to the first one, and Ashby declined to respond to VentureBeat’s questions about how they’ve changed the paper. VentureBeat asked Springer if it will accede to the CCT’s demands, but the publisher has not responded at this time. (Update, June 23, 12:09pm PT: Although Springer has not responded to VentureBeat, the company confirmed on Twitter that it will not publish the article. The company did not expound on its plans for any of the group’s other demands.)

An overarching issue is the inability of technology to determine things that are fundamentally socially constructed and defined. “Machine learning does not have a built-in mechanism for investigating or discussing the social and political merits of its outputs,” the letter asserts. This reflects Dr. Ruha Benjamin’s statement from a talk earlier this year in which she explained that “computational depth without historic or sociological depth is superficial learning.” Researcher Abeba Birhane further unpacks this notion in her award-winning paper “Algorithmic Injustices: Towards a Relational Ethics.”

The CCT’s letter is a rich resource for prior research on the topics of criminality, predictive policing, facial recognition, and related issues.

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/vGTbl1pJ2fg/

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Using embedded analytics in software applications can drive your business forward

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Analytics in your tools can help users gain insights that can help move your clients and the organization to the next level.

People interacting with charts and analyzing statistics. Data visualization concept. 3d vector illustration. People work

Image: Mykyta Dolmatov, Getty Images/iStockphoto

More about Big Data

More than two years ago, Edsby, which provides a learning management system for educational institutions, began embedding analytics into its software that enabled teachers and administrators to detect student learning trends, assess test scores across student populations, and more, all in the spirit of improving education results. 

The Edsby example is not an isolated event. Increasingly, commercial and company in-house software developers are being asked to deliver more value with their applications. In other words, don’t just write applications that process transactions; tell us about the trends and insights transactions reveal by embedding analytics as part of the application.

“Software teams are responsible for building applications with embedded analytics that help their end users make better decisions,” said Steve Schneider, CEO of Logi Analytics, which provides embedded analytics tools for software developers.” This is the idea of providing high-level analytics in the context of an application that people use every day.”

SEE: Microservices: A cheat sheet (free PDF) (TechRepublic)

Schneider said what users want is transactional apps with built-in analytics capabilities that can provide insights to a variety of users with different interests and skill sets. “These are highly sophisticated analytics that must be accessible right from the application,” he said. 

With the help of pick-and-click tools, transaction application developers are spared the time of having to learn how to embed analytics from the ground up in their apps. Instead, they can choose to embed an analytics dashboard into their application, or they can quickly orchestrate an API call to another application without a need to custom develop all of the code.

“You can just click on the Embed command, and the tool will give you a Java script,” Schneider said. “In some cases, you have to do a little configuration for security, but it makes it much easier to get analytics-enriched apps to your user market faster.”

Getting apps to market faster

Here’s how an embedded analytics tool can speed apps to market.

A marketing person is tasked with buying ads and organizing campaigns. He or she gathers information and feeds it to IT, which periodically issues reports that show the results of ad placements and campaigns.

SEE: How to overcome business continuity challenges (free PDF) (TechRepublic)

Now with an application that contains embedded analytics, the marketing person can directly drill down into the reporting information embedded in the app without having to contact IT. This can be done through a self-service interface in real time.

“In one case, a manufacturer was trying to improve operational performance through the use of an application and set of stated metrics,” Schneider said. “Everyone had to log in to the application to record their metrics, but the overall goal of improving performance remained elusive. The manufacturer decided to augment the original application with an embedded analytics dashboard that displayed the key metrics and each team’s performance. This provided visibility to everyone. This quickly evolved into a friendly competition between different groups of employees to see who could achieve the best scores, and the overall corporate metrics performance improved.” 

For most developers, embedding analytics in applications is still in early stages—but embedded analytics in apps is an area that is poised to expand, and that at some point will be able to incorporate both structured and unstructured data in in-app visualizations.

Best practices for embedded analytics

Companies and commercial enterprises interested in using embedded analytics in transactional applications should consider these two best practices:

  1. Think about the users of your application and the problems that they’re trying to solve

This begins with asking users what information they need in order to be successful. “Application developers can also benefit if they think more like product managers,” Schneider said. In other words, what can I do with embedded analytics in my application to truly delight my customer—even if it is the user next door in accounting who I see every day?

2. Start simple

If you haven’t used embedded analytics in applications before, choose a relatively easy-to-achieve objective for your first app and work with a cooperative user. By building a series of successful and high usable apps from the start, you instill confidence in this new style of application. At the same time, you can be defining and standardizing your embedded app development methodology in IT.

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Source: https://www.techrepublic.com/article/using-embedded-analytics-in-software-applications-can-drive-your-business-forward/#ftag=RSS56d97e7

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China and AI: What the World Can Learn and What It Should Be Wary of

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China announced in 2017 its ambition to become the world leader in artificial intelligence (AI) by 2030. While the US still leads in absolute terms, China appears to be making more rapid progress than either the US or the EU, and central and local government spending on AI in China is estimated to be in the tens of billions of dollars.

The move has ledat least in the Westto warnings of a global AI arms race and concerns about the growing reach of China’s authoritarian surveillance state. But treating China as a “villain” in this way is both overly simplistic and potentially costly. While there are undoubtedly aspects of the Chinese government’s approach to AI that are highly concerning and rightly should be condemned, it’s important that this does not cloud all analysis of China’s AI innovation.

The world needs to engage seriously with China’s AI development and take a closer look at what’s really going on. The story is complex and it’s important to highlight where China is making promising advances in useful AI applications and to challenge common misconceptions, as well as to caution against problematic uses.

Nesta has explored the broad spectrum of AI activity in Chinathe good, the bad, and the unexpected.

The Good

China’s approach to AI development and implementation is fast-paced and pragmatic, oriented towards finding applications which can help solve real-world problems. Rapid progress is being made in the field of healthcare, for example, as China grapples with providing easy access to affordable and high-quality services for its aging population.

Applications include “AI doctor” chatbots, which help to connect communities in remote areas with experienced consultants via telemedicine; machine learning to speed up pharmaceutical research; and the use of deep learning for medical image processing, which can help with the early detection of cancer and other diseases.

Since the outbreak of Covid-19, medical AI applications have surged as Chinese researchers and tech companies have rushed to try and combat the virus by speeding up screening, diagnosis, and new drug development. AI tools used in Wuhan, China, to tackle Covid-19 by helping accelerate CT scan diagnosis are now being used in Italy and have been also offered to the NHS in the UK.

The Bad

But there are also elements of China’s use of AI that are seriously concerning. Positive advances in practical AI applications that are benefiting citizens and society don’t detract from the fact that China’s authoritarian government is also using AI and citizens’ data in ways that violate privacy and civil liberties.

Most disturbingly, reports and leaked documents have revealed the government’s use of facial recognition technologies to enable the surveillance and detention of Muslim ethnic minorities in China’s Xinjiang province.

The emergence of opaque social governance systems that lack accountability mechanisms are also a cause for concern.

In Shanghai’s “smart court” system, for example, AI-generated assessments are used to help with sentencing decisions. But it is difficult for defendants to assess the tool’s potential biases, the quality of the data, and the soundness of the algorithm, making it hard for them to challenge the decisions made.

China’s experience reminds us of the need for transparency and accountability when it comes to AI in public services. Systems must be designed and implemented in ways that are inclusive and protect citizens’ digital rights.

The Unexpected

Commentators have often interpreted the State Council’s 2017 Artificial Intelligence Development Plan as an indication that China’s AI mobilization is a top-down, centrally planned strategy.

But a closer look at the dynamics of China’s AI development reveals the importance of local government in implementing innovation policy. Municipal and provincial governments across China are establishing cross-sector partnerships with research institutions and tech companies to create local AI innovation ecosystems and drive rapid research and development.

Beyond the thriving major cities of Beijing, Shanghai, and Shenzhen, efforts to develop successful innovation hubs are also underway in other regions. A promising example is the city of Hangzhou, in Zhejiang Province, which has established an “AI Town,” clustering together the tech company Alibaba, Zhejiang University, and local businesses to work collaboratively on AI development. China’s local ecosystem approach could offer interesting insights to policymakers in the UK aiming to boost research and innovation outside the capital and tackle longstanding regional economic imbalances.

China’s accelerating AI innovation deserves the world’s full attention, but it is unhelpful to reduce all the many developments into a simplistic narrative about China as a threat or a villain. Observers outside China need to engage seriously with the debate and make more of an effort to understandand learn fromthe nuances of what’s really happening.The Conversation

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Dominik Vanyi on Unsplash

Source: https://singularityhub.com/2020/07/03/china-and-ai-what-the-world-can-learn-and-what-it-should-be-wary-of/

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Building a Discord Bot for ChatOps , Pentesting or Server Automation (Part 5)

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Coding and debugging with Visual Studio Code

Open Visual Studio Code and press CTRL+Shift+P to enter the input window. Write “ssh” and select “Remote-SSH: Add New SSH Host…” for adding our server. It will ask you IP Address and the user of our Digital Ocean server

The app will show us the success message allowing us to connect directly

Once again press CTRL+Shift+P and enter “Remote-SSH: Connect to Host…” and select the connection

Now we will use the knowledge of the previous steps. Create the “.env” file with your secret constants, the “requirements.txt” file with the dependencies and the “bot.py” file with your existing bot’s code

To test it quickly we need a “.env” file with the “DISCORD_TOKEN” constant

A “requirements.txt” file like this one

And for the simplest bot code write this in the “bot.py” file

In summary

Go back to the terminal or use the integrated terminal in Visual Studio Code and install the requirements with the command

To test the bot write the command

You should see the “<Your bots name and id> is connected” message in the terminal and in Discord you should see the bot status as online

If you like to debug in Visual Studio Code to fix some bugs or to understand the logic, press F5 key in the IDE and select “Python File”

The IDE will enter debug mode allowing you to breakpoint the code and see the content of the variables

We are all set for this step.

If you encounter typos or something doesn’t work no more write me a comment and I will keep this guide updated. Last update June 28 2020.

Source: https://chatbotslife.com/building-a-discord-bot-for-chatops-pentesting-or-server-automation-part-5-feea1c09b2de?source=rss—-a49517e4c30b—4

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