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Snowflake celebrates its largest-ever user conference with launch of new innovations

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Snowflake Summit 2022 at the Caesars Forum.

Data Cloud company Snowflake has concluded its fourth-annual Snowflake Summit conference, with nearly 9,000 in-person attendees and more than 11,500 registered to watch the virtual event, live in Las Vegas, Nevada in June.

Snowflake Summit 2022 is Snowflake’s largest in-person conference to-date, bringing customers, partners, technical experts, and executives together to explore the power of the Data Cloud. The four-day conference featured four marquee keynote addresses, 275+ total sessions spanning 10 different session tracks, 200+ ecosystem partners, 100+ customer speakers, 28 hands-on labs, an abundance of SnowPro™ certifications achieved by conference goers, and more.

Snowflake chairman and CEO, Frank Slootman, and Snowflake co-founder and president of products, Benoit Dageville, kicked off the event with seven key innovation pillars that position Snowflake at the forefront of technology and data innovation. A key theme throughout was that Snowflake first massively transformed analytics, then collaboration, and now application development with the Data Cloud.

Snowflake SVP of products, Christian Kleinerman, then revealed during his keynote address expanded capabilities and product innovations that advance the Data Cloud:
● Unistore Workload: Snowflake’s transformative Unistore workload, currently in private preview, will unlock new transactional use cases, and deliver a modern approach to working with transactional and analytical data together in a single platform.
● Native Application Framework: Snowflake’s Native Application Framework, currently in private preview, empowers developers to quickly and easily build, monetise, and deploy data-intensive applications in the Data Cloud.
● Cybersecurity Workload: Snowflake’s new Cybersecurity workload provides a unified, secure, and scalable data platform for helping security teams eliminate blind spots and respond to threats at cloud-scale.
● Data Programmability Updates: Snowflake’s data programmability enhancements, including Snowpark for Python, currently in public preview, and a native integration with Streamlit, currently in development, make Python’s rich ecosystem of open-source packages and libraries accessible for data scientists, data engineers, and application developers to streamline development, and build and share interactive applications.
● Expanded Data Access: Snowflake’s new innovations increase data access with enhancements for working with streaming data through Snowpipe Streaming, currently in private preview, and Materialised Tables, currently in development, alongside making data stored in open formats and on-premises available in the Data Cloud with Iceberg Tables, currently in development, and External Tables for On-Premises Storage, currently in private preview.
● Snowflake Marketplace Advancements: Snowflake Marketplace now goes beyond just data, expanding to offer applications that run natively in the Data Cloud so customers can get value faster, improve security and governance, and access cutting-edge technologies for extended collaboration efforts, while providers can reach more customers and use built-in monetisation through Snowflake’s Marketplace.

More than 100 customer sessions from organisations like Capital One, Geico, Novartis, Warner Bros. Discovery, and more shared best practices and strategies for driving data-driven outcomes. Additionally, AT&T was named the 2022 Data Drivers Awards winner for North America, the premier data awards that honour Snowflake customers leading their organisations and reimagining what’s possible with the Data Cloud.

Snowflake’s expansive partner ecosystem shared 80+ announcements around Snowflake Summit. Additionally, Snowflake shared record growth in its Powered by Snowflake program, with 425 partners, and over 165 Snowflake Marketplace partners (as of April 30, 2022). Snowflake also launched a new Snowflake Partner Network (SPN) Competency program, enabling partners to further validate and differentiate their Snowflake vertical expertise to customers, the Snowflake field teams, and the overall Data Cloud ecosystem.

Snowflake Summit also recognised early-stage companies that are leveraging Snowflake to build innovative applications and products in the 2022 Snowflake Startup Challenge. Hundreds of organisations entered the Startup Challenge to compete for a chance to win up to $1 million in funding from Snowflake Ventures, with Snowflake CMO, Denise Persson and Dageville announcing the winners live on-stage on June 16. A panel of Sequoia Capital, Snowflake executives, and Snowflake board members selected Houseware as the ultimate winner. Snowflake Ventures also announced recent investments in Domino Data Lab, Immuta, and Matillion.

Snowflake further doubled down on its number one value ‘put customers first,’ by unveiling the latest findings from its 2022 Customer Experience Survey at Snowflake Summit. The annual survey, conducted June 2022 and produced in partnership with Walker, assessed over 6,000 Snowflake customers across more than 50 countries to provide Snowflake with a Net Promoter (NPS) Score. This year’s NPS Score of 72 is more than three times the industry average of 19, based on the Qualtrics 2021 NPS Industry Benchmarking Report, and demonstrates Snowflake’s continued commitment to customer-centricity.

Denise Persson, CMO at Snowflake, said: “Snowflake Summit 2022 was all about the ‘World of Data Collaboration,’ and the energy, announcements, and attendance at the event was incredible to witness.

“Every year Snowflake Summit raises the bar, and this year was no exception with data leaders from around the globe coming together to share, learn, collaborate, and explore new ways to drive businesses forward with the Data Cloud.”

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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

Political_Polarization.jpg

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

Bias_in_Algorithm_Design.png

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

Derosion_of_Democracy.jpeg

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?

Boost_Social_Media_Posts.jpeg

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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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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