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The Role of IoT and Blockchain in the Print Industry

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The Role of IoT and Blockchain in the Print Industry

The combination of blockchain and IoT in the print industry has the potential to create innovative use cases.

Due to the need for added security and connectivity, the use of blockchain in the print industry in conjunction with IoT is gradually gaining pace.

Despite the rapid digitization of businesses, especially in media, the print industry is seeing continuous growth. Now, the seamless connectivity and network penetration of IoT is combining with the secure and massive record-keeping ability of blockchain in the print industry. This union is already grabbing the attention of industry leaders like Toppan printing, who along with the IoT company Wisekey, are working to create security tags that enable physical objects to be authenticated online. Similarly, there are several other cases where the pain points of the print industry, such as security and intellectual property protection, are being addressed by the application of IoT and blockchain together.

Potential Applications of IoT and blockchain in The Print Industry

Different types of printing have different functional needs and different priorities regarding improvement requirements. Here is how the synergy of blockchain and IoT can benefit different sub-sectors in the print industry.

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1. Publication

Publication organizations that usually deal with printing books, magazines, and newspapers have content related issues, like identifying and verifying sources, protection of intellectual property, and piracy. Blockchain technology, combined with IoT, can minimize or eradicate content related issues using a number of methods. An example of this is the collaboration of Waltonchain and Loci to create a blockchain and IoT based system that collects and maps all IP data to prevent deliberate, as well as accidental IP infringement. Piracy of physical print material can be prevented by using a blockchain based registry and an IoT based tracking and authentication system to disable and detect illegal sales.

2. Security Printing

Security printing involves the:

  • printing of exclusive, discreetly identifiable, high authenticity documents and other printed material such as banknotes,

  • identification documents like passports, smart cards and special product authentication tags, and

  • smart, tracking-enabled packaging.

This type of printing involves embedding uniquely traceable security features into physical documents to prevent illegal duplication and forgery. For instance Arcis, a digital security company, announced a digital security breakthrough that embeds an authenticating image inside a physical document, which can be used to detect duplication. Such technological applications will enhance the tracking, security, and verifiability of highly sensitive documents, such as high value contracts and research papers. Creating smart security tags for physical objects, such as those made by Toppan, is also an example to follow while innovating IoT and blockchain enabled security printing solutions.

3. Commercial printing

Commercial printing is a broad and diverse subsector of the printing industry. It involves a wide range of printing services, from forms and brochures, to stationery, and even magazines and newsletters. Due to the variety of projects and the possible enormity of volume, commercial printers need efficient, connected, and smart printing solutions. This problem can be solved by using smart, multifunctional printers that are connected to the design sources, as well as to each other, for co-ordinating efficient printing sequences. This will save a lot of time and resources and also minimize the need for human intervention in routine tasks, allowing the human resources to be focused on the non-routine, creative functions.

In addition to these industry-specific applications, there are these general performance benefits that can be obtained by using IoT, enhanced with blockchain. This includes a productive and efficient work process, optimally managed resources, effectively utilized assets, and improved security and reliability. Realization of the potential of IoT in blockchain and the creative application of the IoT-blockchain combination will lead to many more industry specific use cases, which is why CIOs need to constantly performa pulse-check on the growth of these technologies.


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TECHNOLOGY

NLP & Computer Vision in Cybersecurity

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NLP & Computer Vision in Cybersecurity

Natural language processing (NLP) and computer vision are two branches of artificial intelligence (AI) that are disrupting cybersecurity.

NLP is the ability of computers to understand and process human language, including speech and text. In cybersecurity, NLP can be used for fraud detection by analyzing large amounts of text data, such as emails and chat logs, to identify patterns of malicious activity. NLP can also be used for threat intelligence by analyzing data from various sources, such as news articles and social media, to identify potential security threats.

Computer vision, on the other hand, refers to the ability of computers to interpret and understand images and videos. In cybersecurity, computer vision can be used for password cracking by analyzing images and videos that contain passwords or other sensitive information. It can also be used for facial recognition, which verifies the identity of individuals who access sensitive information or systems.

Cybersecurity is a critical issue in our increasingly connected world, and artificial intelligence (AI) is playing an increasingly important role in helping to keep sensitive information and systems secure. In particular, natural language processing (NLP) and computer vision are two areas of AI that are having a major impact on cybersecurity.

NLP_in_Cybersecurity.png

Source: Masernet

NLP and computer vision have the potential to revolutionize the way organizations approach cybersecurity by allowing them to analyze large amounts of data, identify patterns of malicious activity, and respond to security threats more quickly and effectively. However, it’s important to be aware that AI itself presents new security risks, such as the potential for AI systems to be hacked or misused. As a result, organizations must adopt a comprehensive and well-informed approach to cybersecurity that takes into account the full range of risks and benefits associated with AI technologies. Here are 4 ways NLP & computer vision are useful in cybersecurity.

1. Detecting Fraud

NLP can be used to analyze large amounts of text data, such as emails and chat logs, to identify patterns of fraud and other types of malicious activity. This can help organizations to detect and prevent fraud before it causes significant harm.

2. Analyzing Threats

NLP can also be used to analyze large amounts of text data from a variety of sources, such as news articles and social media, to identify potential security threats. This type of “big data” analysis can help organizations to respond to security threats more quickly and effectively.

3. Preventing Password Cracking

Computer vision can be used to crack passwords by analyzing images and videos that contain passwords or other sensitive information. This type of technology can help organizations to better protect their sensitive information by making it more difficult for attackers to obtain passwords through visual means.

4. Improving Facial Recognition

Computer vision can also be used for facial recognition, which can help organizations to improve their security by verifying the identity of individuals who access sensitive information or systems.

Conclusion

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Source: Visua

AI technologies like NLP and computer vision are playing an increasingly important role in helping to keep sensitive information and systems secure. These technologies have the potential to revolutionize the way that organizations approach cybersecurity by allowing them to analyze large amounts of data, identify patterns of malicious activity, and respond to security threats more quickly and effectively. However, it’s also important to recognize that AI itself presents new security risks, such as the potential for AI systems to be hacked or misused. As a result, organizations must take a holistic and well-informed approach to cybersecurity that takes into account the full range of risks and benefits associated with these powerful new technologies.

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What’s Wrong with the Algorithms?

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What's Wrong with the Algorithms?

Social media algorithms have become a source of concern due to the spread of misinformation, echo chambers, and political polarization.

The main purpose of social media algorithms is to personalize and optimize user experience on platforms such as Facebook, Twitter, and YouTube.

Most social media algorithms sort, filter, and prioritize content based on a user’s individual preferences and behaviors. Social media algorithms have come under scrutiny in recent years for contributing to the spread of misinformation, echo chambers, and political polarization.

Facebook’s news feed algorithm has been criticized for spreading misinformation, creating echo chambers, and reinforcing political polarization. In 2016, the algorithm was found to have played a role in the spread of false information related to the U.S. Presidential election, including the promotion of fake news stories and propaganda. Facebook has since made changes to its algorithm to reduce the spread of misinformation, but concerns about bias and polarization persist.

Twitter’s trending topics algorithm has also been criticized for perpetuating bias and spreading misinformation. In 2016, it was revealed that the algorithm was prioritizing trending topics based on popularity, rather than accuracy or relevance. This led to the promotion of false and misleading information, including conspiracy theories and propaganda. Twitter has since made changes to its algorithm to reduce the spread of misinformation and improve the quality of public discourse.

YouTube’s recommendation algorithm has been criticized for spreading conspiracy theories and promoting extremist content. In 2019, it was revealed that the algorithm was recommending conspiracy theory videos related to the moon landing, 9/11, and other historical events. Additionally, the algorithm was found to be promoting extremist content, including white nationalist propaganda and hate speech. YouTube has since made changes to its algorithm to reduce the spread of misinformation and extremist content, but concerns about bias and polarization persist.

In this article, we’ll examine the problem with social media algorithms including the impact they’re having on society as well as some possible solutions.

1. Spread of Misinformation

Spread_of_Information.jpg

Source: Scientific American

One of the biggest problems with social media algorithms is their tendency to spread misinformation. This can occur when algorithms prioritize sensational or controversial content, regardless of its accuracy, in order to keep users engaged and on the platform longer. This can lead to the spread of false or misleading information, which can have serious consequences for public health, national security, and democracy.

2. Echo Chambers and Political Polarization

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Source: PEW Research Center

Another issue with social media algorithms is that they can create echo chambers and reinforce political polarization. This happens when algorithms only show users content that aligns with their existing beliefs and values, and filter out information that challenges those beliefs. As a result, users can become trapped in a self-reinforcing bubble of misinformation and propaganda, leading to a further division of society and a decline in the quality of public discourse.

3. Bias in Algorithm Design and Data Collection

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Source: Springer Link

There are also concerns about bias in the design and implementation of social media algorithms. The data used to train these algorithms is often collected from users in a biased manner, which can perpetuate existing inequalities and reinforce existing power structures. Additionally, the designers and developers of these algorithms may hold their own biases, which can be reflected in the algorithms they create. This can result in discriminatory outcomes and perpetuate social injustices.

4. Democracy in Retreat

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Source: Freedom House

Social media algorithms are vulnerable to manipulation and can spread false or misleading information, which can be used to manipulate public opinion and undermine democratic institutions. The dominance of a few large social media companies has led to a concentration of power in the hands of a small number of organizations, which can undermine the diversity and competitiveness of the marketplace of ideas, a key principle of democratic societies.

How to Improve Social Media Algorithms?

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Source: Tech Xplore

Governments and regulatory bodies have a role to play in holding technology companies accountable for the algorithms they create and their impact on society. This could involve enforcing laws and regulations to prevent the spread of misinformation and extremist content, and holding companies responsible for their algorithms’ biases.

There are several possible solutions that can be implemented to improve social media algorithms and reduce their impact on democracy. Some of these solutions include:

  • Increased transparency and accountability: Social media companies should be more transparent about their algorithms and data practices, and they should be held accountable for the impact of their algorithms on society. This can include regular audits and public reporting on algorithmic biases and their impact on society.

  • Regulation and standards: Governments can play a role in ensuring that social media algorithms are designed and operated in a way that is consistent with democratic values and principles. This can include setting standards for algorithmic transparency, accountability, and fairness, and enforcing penalties for violations of these standards.

  • Diversification of ownership: Encouraging a more diverse and competitive landscape of social media companies can reduce the concentration of power in the hands of a few dominant players and promote innovation and diversity in the marketplace of ideas.

  • User education and awareness: Social media users can be educated and empowered to make informed decisions about their usage of social media, including recognizing and avoiding disinformation and biased content.

  • Encouragement of responsible content creation: Social media companies can work to encourage the creation of high-quality and responsible content by prioritizing accurate information and rewarding creators who produce this content.

  • Collaboration between industry, government, and civil society: Addressing the challenges posed by social media algorithms will require collaboration between social media companies, governments, and civil society organizations. This collaboration can involve the sharing of data and best practices, the development of common standards and regulations, and the implementation of public education and awareness programs.

Conclusion

Social media companies have the power to censor and suppress speech, which can undermine the right to free expression and the democratic principle of an open and inclusive public discourse. It is crucial for technology companies and policymakers to address these issues and work to reduce the potential for harm from these algorithms. Social media platforms need to actively encourage and facilitate community participation in the development and improvement of their algorithms. This would involve setting up forums for discussion and collaboration, providing documentation and support for developers, and engaging with the community to address their concerns and ideas. In order to ensure that the algorithms are fair and unbiased, tech companies need to be transparent about the data they collect and use to train their algorithms. This would involve releasing the data sets used to train the algorithms, along with information about how the data was collected, what it represents, and any limitations or biases it may contain.

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TECHNOLOGY

Daasity builds ELT+ for Commerce on the Snowflake Data Cloud

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Cloud Computing News

Modular data platform Daasity has launched ELT+ for Commerce, Powered by Snowflake.

It is thought ELT+ for Commerce will benefit customers by enabling consumer brands selling via eCommerce, Amazon, retail, and/or wholesale to implement a full or partial data and analytics stack. 

Dan LeBlanc, Daasity co-founder and CEO, said: “Brands using Daasity and Snowflake can rapidly implement a customisable data stack that benefits from Snowflake’s dynamic workload scaling and Secure Data Sharing features.

“Additionally, customers can leverage Daasity features such as the Test Warehouse, which enables merchants to create a duplicate warehouse in one click and test code in a non-production environment. Our goal is to make brands, particularly those at the enterprise level, truly data-driven organisations.”

Building its solution on Snowflake has allowed Daasity to leverage Snowflake’s single, integrated platform to help joint customers extract, load, transform, analyse, and operationalise their data. With Daasity, brands only need one platform that includes Snowflake to manage their entire data environment.

Scott Schilling, senior director of global partner development at Snowflake, said: “Daasity’s ELT+ for Commerce, Powered by Snowflake, will offer our joint customers a way to build a single source of truth around their data, which is transformative for businesses pursuing innovation.

“As Snowflake continues to make strides in mobilising the world’s data, partners like Daasity give our customers flexibility around how they build data solutions and leverage data across the organisation.” 

Daasity enables omnichannel consumer brands to be data-driven. Built by analysts and engineers, the Daasity platform supports the varied data architecture, analytics, and reporting needs of consumer brands selling via eCommerce, Amazon, retail, and wholesale. Using Daasity, teams across the organisation get a centralised and normalised view of all their data, regardless of the tools in their tech stack and how their future data needs may change. 

ELT stands for Extract, Load, Transform, meaning customers can extract data from various sources, load the data into Snowflake, and transform the data into actions that marketers can pursue. For more information about Daasity, our 60+ integrations, and how the platform drives more profitable growth for 1600+ brands, visit us at Daasity.com.

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