The created computer worm targets Gen AI-powered applications and demonstrates it against Gen AI-powered email assistants. It has already been demonstrated against GenAI-powered email assistants to steal personal data and launch spamming campaigns.

A group of researchers, Ben Nassi from Cornell Tech, Stav Cohen from the Israel Institute of Technology, and Ron Bitton from Intuit created Morris II, a first-generation AI worm that can steal data, spread malware, spam others through an email client, and spread through multiple systems.

This worm was developed and successfully functions in test environments using popular LLMs. The team has published a paper titled “ ComPromptMized: Unleashing Zero-click Worms that Target GenAI-Powered Applications” and created a video showing how they used two methods to steal data and affect other email clients.

Naming the AI worm after Morris, the first computer worm that rippled worldwide attention online in 1988, this worm targets AI apps and AI-enabled email assistants that generate text and images using models like Gemini Pro, ChatGPT 4.0, and LLaVA.

The researchers warned that the worm represented a new breed of “zero-click malware”, where the user does not need to click on anything to trigger the malicious activity or even propagate it. Instead, it is carried out by the automatic action of the generative AI tool. They further added, “The study demonstrates that attackers can insert such prompts into inputs that, when processed by GenAI models, prompt the model to replicate the input as output (replication) and engage in malicious activities (payload)”. Additionally, Morris II successfully mined confidential information such as social security numbers and credit card details during the research.

Conclusion

With developing ideas of using AI in cyber security, further tests and attention to such details must be prioritized before embedding AI to secure data and information.

The post Researchers created an AI worm that steals data and infects ChatGPT and Gemini first appeared on AITechTrend.

Data engineers use their technical expertise to ensure the systems they build are secure, scalable, and reliable—meaning they can handle vast amounts of data and provide it in real time. Data engineering is a rapidly growing field with many lucrative job opportunities. In today’s fast-paced business landscape, the ability to efficiently design, build, and manage data pipelines is crucial for enterprises aiming to extract valuable insights and make data-driven decisions. Due to its instrumental role in transforming raw data into actionable intelligence, Data Engineering has emerged as a high-demand job. They are expected to know about big data frameworks, databases, building data infrastructure, containers, and more. It is also important that they have hands-on exposure to tools such as Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig and many more. 

Key Responsibilities of a Data Engineer are: 

1. Obtain data from third-party providers with the help of robust API integrations. 

2. Build, Design, and maintain data architectures using a systematic approach that satisfies business needs. 

3. Create high-grade data products by coordinating with engineering, product, data scientists, and business teams. 

4. Develop optimized data pipelines and make sure they are executed with high performance. 

5. Track the latest developments in the domain of data infrastructure and analytical tools. 

6. Perform research to handle any problems faced while meeting the business objectives. 

7. Use the data efficiently and identify tasks that can be automated. 

8. Implement different methods to enhance data quality and reliability. 

Here is a list of the important skills for data engineers that one should possess to build a successful career in big data: 

1. SQL 

Data engineers use SQL for performing ETL tasks within a relational database. SQL is ideal for use when the destination and data source are the same type of database. Today, more and more cloud-based systems add SQL-like interfaces that allow you to use SQL. ETL is central to getting your data where you need it. Relational database management systems (RDBMS) remain the key to data discovery and reporting, regardless of their location. Traditional data transformation tools are still relevant today, while next-generation Kafka, cloud-based tools, and SQL are on the rise for 2024. Strong SQL skills allow using databases to construct data warehouses, integrating them with other tools, and analyzing that data for business purposes. There are several SQL types that data engineers might focus exclusively on at some point (Advanced Modelling, Big Data, etc.), but getting there requires learning the basics of this technology. 

2.  Machine Learning and AI 

A big data engineer should be familiar with Python’s libraries SciPy, NumPy, sci-kit learn, pandas, etc. They should also be familiar with the terminology and algorithms. Machine Learning is a big data analytics skill that is used to predict or process data through algorithms like Clustering, Classification, Regression, or Natural language processing. A big data engineer must understand the basic concept of machine learning. Machine learning is a subset of artificial intelligence. Data engineers typically require a functional knowledge of machine learning, which involves data modeling and statistical analysis.  

Applying this skill can help you better understand data scientists’ requirements and create relevant and usable solutions for them. 

3. Multi-Cloud computing 

A data engineer needs to have a thorough understanding of the underlying technologies that make up cloud computing. They would need to know their way around IaaS, PaaS, and SaaS implementation. Cloud computing refers to the provision of computing services over the Internet. These services include servers, storage, databases, networking, software, analytics, and intelligence, to help businesses innovate faster and more efficiently. Companies worldwide increasingly depend on the cloud for their computing power and data storage needs.  

As a result, they often require the services of data engineers who can use various cloud computing solutions on an organizational scale, such as SaaS, PaaS, and IaaS. Data engineering is all about designing, programming, and testing software, which is required for modern database solutions. This can be easier when you are using existing cloud services. The trend is to participate in multi-cloud over cloud technology and have a good understanding of the underlying technologies that make up cloud computing. Concepts of IaaS, PaaS, and SaaS are the trend, and big companies expect data engineers to have the relevant knowledge. 

4. NoSQL 

A data engineer should know how to work with key-value pairs and object formats like Avro, JSON, or Parquet in the open-source Apache-based or MongoDB and Cassandra. Big resources still manage file data hierarchically using Hadoop’s open-source ecosystem. The cloud could also be full of semi-structured or unstructured data with more than 225 no SQL schema data stores, which makes it one of the most important skills to be thorough with. Knowing how to work with key-value pairs and object formats is still necessary. NoSQL is a type of database management system (DBMS) that is designed to handle and store large volumes of unstructured and semi-structured data. Unlike traditional relational databases that use tables with pre-defined schemas to store data, NoSQL databases use flexible data models that can adapt to changes in data structures and are capable of scaling horizontally to handle growing amounts of data. NoSQL databases are often used in applications where there is a high volume of data that needs to be processed and analyzed in real-time, such as social media analytics, e-commerce, and gaming. They can also be used for other applications, such as content management systems, document management, and customer relationship management. Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages, lack of ability to perform ad hoc joins across tables, lack of standardized interfaces, and huge previous investments in existing relational databases. Most NoSQL stores lack true ACID transactions, although a few databases have made them central to their designs. Examples of NoSQL include Apache River, BaseX, Ignite, Hazelcast, Coherence, and many more others.  

5 . Hyper Automation 

Hyperautomation focuses on improving the quality of work, increasing decision-making agility, and accelerating business processes. They require skills to run value-added tasks. Hyper automation is the concept of automating everything in an organization that can be automated. Organizations that adopt hyper automation aim to streamline processes across their business using artificial intelligence (AI), robotic process automation (RPA), and other technologies to run without human intervention.  

In addition to these technical skills, having a good understanding of data governance, and data security, and the ability to work in cross-functional teams will be invaluable for future data engineers. Continuously updating your knowledge and staying abreast of emerging technologies and trends is also vital to remain competitive in the rapidly evolving field of data engineering. The technical skills that are most in-demand for data engineers are constantly evolving, and it’s important to stay up-to-date and continually develop your skills in this exciting and rapidly growing field. The world is full of data, which is why the demand for data engineers is at an ever-increasing high. Society and industries of every kind depend on data to make critical decisions. A leading expert in the field can become a champion in the industry after acquiring relevant skills for data engineer and gaining hands-on experience. 

The post 5 Data Engineering Skills to Transform Your Career in 2024  first appeared on AITechTrend.

Learning new software or skills for the betterment of your career has now become an essential process. This is for either gaining an edge over others or dealing with a new generation of team members. Cooperates require their employee to bring everything they have in their platter so that they know what they can do with their skills. They also require them to master new skills in no time so that can attain benefits from it. But, mastering a skill requires time and also correct guidance and approach towards it. There are numerous software available after offices have shifted to computers. Softwares that make work easier. To learn these software an employee has to be certified or go under on-the-job training. One such software is Tableau. Tableau is used by cooperates to scan large numbers of data and determine valuable information from it. Tableau has been in the market for decades and has clients like Amazon, Walmart, Adobe, and Cisco. It also has products like Desktop, Prep and Server that have helped its clients to decode data. To master such software takes time and luckily here is a list of five books that an analyst can read to achieve mastery in Tableau. So, let’s take a look at these books.

5 Must Read Books to Master Tableau

There are various books that claim to teach and guide analysts on how to use Tableau and decode even the most complex data structure in minutes. But, we have picked five of these books that are very good and have easy-to-understand language that may help an analyst to up their skill and also learn some new features of this amazing software. These books are best sellers and are widely read by analysts to understand the workings of Tableau. Let’s not waste much time and see these books.

Tableau Best Practices10.0 by Jenny Zhang

Source: Amazon

If you have used Tableau before then this book by Zhang is a good read as it has ample real-life problems that can help you learn new things about this software. This book helps if you spend most of your time data analyzing and visualizing. It also guides you on how to connect to a ton of variety of data from cloud or local servers and blend this data in a fast and efficient way and also perform complex calculations like LOD and Table calculations. The problems mentioned in the book also have a step-by-step guide given by Tableau experts. This book is very helpful for analysts who want to upgrade their skills in data analytics and also for data enthusiasts.

Learning Tableau 10 Second Edition by Joshua N. Milligan

Source:Amazon

This book by Joshua N. Milligan is also a good book for analysts. In this book, the author has made sure that he has written everything he knows about this software and also mentioned instructions related to the features. It has a dedicated guide from scratch that is how to make a pie chart, bar chart, and tree maps and also an installation guide to various tools that the software has to offer to its users. It also has detailed information on different techniques used to tackle different challenges. The book also deals with how to effectively use data for storytelling and also how to get insights from data that can help the business to flourish. This book is very helpful to learn how to manage data and also derive insightful information that can help make crucial decisions for business growth. This book is good for beginners and also advanced-level data analysts.

Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master by Ryan Sleeper

Source: Amazon

Ryan Sleeper is one of the most qualified Tableau consultants. In this book, he has given instructions about how Tableau works and has given numerous ways to derive insights from a large pile of data. This book is a good guide to understanding and working on Tableau. This book is as good as a manual for Tableau as it has everything an analyst should know while using Tableau and enjoy the full features of this software. It also has a step-by-step guide for every feature that is offered by Tableau for data analysis. This book also is a good read for people who want to become data analysts and want to learn this software and use it in the future.

Mastering Tableau by David Baldwin

Source: Amazon

David Baldwin is also a prolific writer who has written many books that have helped employees enhance their skills in business intelligence for almost 17 years. In this book, he has shared his experience while using Tableau. For this software, he has focused on Tableau training by shedding light on developing, BI solutions, Project management, technical writing, and web and graphic design. He has also written a detailed guide on the new features introduced by Tableau in its new version. i.e. 10.0. The features that are introduced in this version consist of creative use of different types of calculations like row-level, and aggregate-level, and how this software is able to solve complex data visualization challenges put to it. He also guides the reader about the tools offered by Tableau and helps them understand the tools of this software. The book has a systematic approach to training its reader to use Tableau as it starts from basic level training of features and then slowly moves towards advanced tools that include calculations, R integration parameters and sets and also data blending techniques.

Tableau 10: Business Intelligence Cookbook by Donabel Santos

Source: Amazon

This book is also a good pick for analysts and people who want to pursue a career in data analysis. This book also covers all practical cases but with a different approach. It has arranged cases from basic level to advanced level cases to make the readers understand each and every tool in Tableau and also ensure that the readers are getting practical experience too. The book also involves a step-by-step guide to creating basic and advanced charts and also an attempt to make the Tableau interface familiar to its readers. It also guides the readers on how to create effective dashboards and many other wonders about this software. As Santos itself is a data geek and has spent a lot of time around data she has tried to answer all the questions about Tableau in this book. She has also focused on the ratings of this book as the better the rating more it sells so this book is packed with some valuable tips and tricks that an analyst of any level can use and master this software. This book is very helpful to up your skills and learn new things about Tableau.

These are the top five books that are recommended to master Tableau in no time. But, reading and keeping it aside will not help as to master skills one needs to practice whatever they have learned and hone that skill with time. These books will give you information that you require but mastering Tableau is ultimately in your hands. If you keep practicing the tips and tricks given by these experts then you can master it and also get appreciation from your seniors and also have an edge over your peers. As one says perfect practice makes a man perfect. 

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DENVER, Jan. 31, 2024 /PRNewswire/ — Generative AI is driving one of the largest booms in productivity since the industrial revolution. However, enterprises operating in regulated environments are acutely aware of the risks associated with generative AI concerning data security, privacy and sovereignty. Fresh on the mind of executives are the high-profile leaks of proprietary information, leading to cascading bans on employees using this technology.

A recent survey by Salesforce reveals these bans may not be having the desired effect, with over half of the users who reported utilizing this technology indicating they are doing so without consent from their organizations. Further, a separate study conducted by Liminal showed that 63% of employees would be comfortable sharing personal or proprietary corporate data with generative AI tools, regardless of company policy.

A team of prior executives from Amazon Web Services and FIS joined forces to build Liminal, a unique security platform allowing regulated enterprises to safely use generative AI, across every use case. Their model-agnostic, horizontal approach ensures organizations have complete control over the data submitted to these tools in every interaction – whether through direct engagements, through the consumption of off-the-shelf software with generative AI capabilities, or via generative AI-enabled applications built in-house.

“We want every organization to have the ability to say yes to generative AI,” said Steven Walchek, Liminal’s Founder and CEO. “With Liminal, CIOs and CISOs can securely administer generative AI while protecting their most sensitive data across every use case, regardless of the model(s) they want to use.”

“Generative AI will continue to proliferate and become increasingly specialized. We have strong conviction in Liminal’s approach to solving for the critical barrier to organizational adoption,” said High Alpha Partner Eric Tobias. “We’ve been excited to invest in Steve, and the work of the Liminal founding team, since the first day we met. Their successes to date are empowering a new era of value creation driven by unlocking generative AI for enterprises in regulated industries.”

About Liminal

Liminal empowers regulated enterprises to securely deploy and leverage generative AI across all use cases. With Liminal, organizations have complete control over the data submitted to large language models (LLMs). Whether that be through direct interactions, through the consumption of off-the-shelf software with generative AI capabilities, or via the generative AI-enabled applications built in-house, Liminal’s unique horizontal platform helps ensure protection against regulatory compliance risk, data security risk, and reputational risk. Across every model, in every application you use, and in every application you’re building. For more information, visit liminal.ai or follow Liminal on LinkedIn.

Note to editors: Please visit Liminal’s Leaders Bio Page for additional founder backgrounds.

SOURCE Liminal

https://www.prnewswire.com/news-releases/generative-ai-safely-unlocked-for-regulated-industries-with-public-release-of-liminal-302046861.html

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NEW YORK, Jan. 2, 2024 /PRNewswire/ — Wavestone has published the results of its 12^th annual Data and AI Leadership Executive Survey of Fortune 1000 and Global data leadership.  This year, 95.3% of survey participants held a C-suite title or were their company’s corporate head of data and AI responsibilities, with 89.8% holding the title of Chief Data Officer (CDO) or Chief Data and Analytics Officer (CDAO) within their organization.  These executives held their positions during 2023 at over 100 Fortune 1000 and Global data leadership organizations.

This represents the 12^th annual edition of the Wavestone survey, which was first published in 2012 by NewVantage Partners (acquired by Wavestone in 2021) at the behest of a group of Fortune 1000 CIOs and data leaders who were looking to understand whether it was time to expand and accelerate data and analytics initiatives and investments.  The Data and AI Executive Leadership Survey has evolved over the past dozen years and is now widely recognized as the longest running survey of Fortune 1000 and global data, analytics, and AI leaders. 

Wavestone has published its 12th annual Data & AI Leadership Executive Survey of Fortune 1000 & Global data leaders.Post this

In the Foreword to this year’s survey, Randy Bean, Innovation Fellow at Wavestone and Founder of NewVantage Partners, and Thomas H. Davenport, author of the landmark study Competing on Analytics, write “The past year has been an extraordinary one in many respects, not the least of which is the amazing rise of Generative AI. That overshadows any other development in the data and technology domain, and in this 12^th annual survey from Wavestone (formerly NewVantage Partners), Generative AI has a strong influence. Generative AI seems to have catalyzed more positive change in organizations’ data and analytical cultures than in any time since the inception of this survey.”

Major findings of the 2024 Data and AI Leadership Executive Survey are: 

1. Leading companies continue investments in data and analytics with the expectation of delivering business value.
2. Companies see Generative AI as potentially the most transformative technology in a generation.
3. Companies believe the Chief Data Officer/Chief Data and Analytics Officer (CDO/CDAO) role is necessary, although turnover has been high and tenures short.
4. Companies recognize that integrating data and AI into traditional business processes and changing organizational culture requires time and commitment.
5. Companies believe data and AI safeguards and governance are essential, but much more needs to be done.

Among noteworthy results of the survey are:

• 87.9% of participants reported that investments in data and analytics are a top organizational priority.
  
• 62.3% of participants reported that investments in Generative AI are a top organizational priority.
  
• 89.6% of participants reported that investment in Generative AI is increasing within their organization.
  
• 79.4% of participants stated that Generative AI should be part of the Chief Data Officer/Chief Data and Analytics Officer (CDO/CDAO) function.
  
• 15.9% of participants stated that the industry has done enough to address data and AI ethics.

About Wavestone

Wavestone, a leading independent consultancy headquartered in France, and Q_PERIOR, a consulting leader in the Germany-Switzerland-Austria region, joined forces in 2023 to become the most trusted partner for critical transformations. Drawing on more than 5,500 employees across Europe, North America and Asia, the firm combines seamlessly first-class sector expertise with a 360° transformation portfolio of high-value consulting services.

SOURCE Wavestone

https://www.prnewswire.com/news-releases/wavestone-releases-2024-data-and-ai-leadership-executive-survey-302024534.html

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Mage Data

The Software and Technology Awards, hosted by New World Report, annually recognizes the very best companies at the forefront of technological innovation across various industries. Now in its eighth year, the awards continue to acknowledge and celebrate the continued efforts of pace-setters and disruptors in the modern technology arena, as well as those who have sustained excellence and exhibited long term dedication to their commitment to development and advancement.

“We are thrilled and deeply honored to be recognized as the Best Data Security Platform in the 2023 Software and Technology Awards” said Padma Vemuri, Senior VP of Product & Design at Mage Data. “This recognition reaffirms our team’s relentless efforts in delivering cutting-edge solutions that address the evolving challenges of data security and privacy faced by enterprises around the world.”

Mage Data’s Data Security Platform offers unparalleled protection and reliability with its comprehensive suite of data security and privacy solutions designed to ensure compliance with stringent data protection regulations worldwide. Its sophisticated approach to sensitive data discovery and advanced anonymization capabilities has garnered widespread acclaim from both industry experts and customers alike.

“We are immensely proud and honored to receive the Best Data Security Platform 2023 award from New World Report” said Dinesh Kumar, Director of Marketing & Analytics at Mage Data. “This accolade reaffirms our resolve to continue innovating and setting new benchmarks in the realm of data security and privacy.”

About Mage Data:

Mage Data is the leading solutions provider of data security and data privacy software for global enterprises. Built upon a patented and award-winning solution, the Mage platform enables organizations to stay on top of privacy regulations while ensuring security and privacy of data. Top Swiss Banks, Fortune 10 organizations, Ivy League Universities, and Industry Leaders in the financial and healthcare businesses protect their sensitive data with the Mage platform for Data Privacy and Security. Deploying state-of-the-art privacy enhancing technologies for securing data, Mage Data delivers robust data security while ensuring privacy of individuals.

Visit www.magedata.ai to explore the brand’s new website and check out the company’s solutions.

About New World Report:

New World Report gives business leaders and managers insight into the most recent technological advancements by some of the world’s most innovative companies. It also informs those companies about the advice, guidance, and services that are available to them that could really support their business. Senior managers are also kept informed of any recent transactions, regulatory changes, and the most recent news and advice from all over the world. New World Report is owned by AI Global Media, runs a number of award programmes and produces a specialized monthly newsletter.

Visit https://www.thenewworldreport.com/ to read more.

SOURCE Mage Data

https://www.prnewswire.com/news-releases/mage-data-named-as-the-best-data-security-platform-2023-302021945.html

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When a router or host encounters an issue processing an IP data packet, it will send an ICMP message to the source of the data. These messages include information like the original datagram and an error code.

Flow Control

One of ICMP’s primary uses is to report network communications errors. Suppose something happens during data transmission between two devices, such as a packet getting lost or an invalid header checksum. In that case, the sending device receives an ICMP message informing it of the issue.

Routers, the unsung heroes directing traffic across the internet, have a symbiotic relationship with ICMP. They use the protocol to communicate errors back to the sender, ensuring transparency in network communication and preventing problems that could lead to an outage in a large corporate environment. Furthermore, the ICMP protocol, vital for network diagnostics, facilitates the exchange of control messages and error notifications between network devices, ensuring efficient communication across interconnected systems.

For example, when a packet has an invalid header or is too big for its intended destination, ICMP will send a Destination Unreachable message to the sending device. Similarly, when a router deems a packet too old (its time-to-live field has expired), it will notify the source of the discarded box by sending a Time Exceeded message.

ICMP also supports the ping utility, one of the most popular ways to test a network for connectivity and latency. The mechanism also underlies another diagnostic heavyweight, ‘traceroute,’ which traces the path packets take from their origin to their ultimate destinations by analyzing intermediary routers’ “Time Exceeded” messages. This enables administrators to pinpoint potential points of failure and make adjustments accordingly.

Error Reporting

The IP protocol enables data transmission between devices but does not perform error reporting or exception handling. ICMP fills this role by providing communication between devices and the upper layers of the network when packet transmission experiences an error. This allows higher-layer protocols to handle error conditions more efficiently and ensures that the necessary components for network communication are delivered correctly.

ICMP messages are encapsulated within IP packets, so they are transmitted over the Internet and can be received by any device with an IP connection. These messages do not have a priority value, meaning they are not given special treatment by network devices and can sometimes be interrupted. This is a good design feature because it prevents a single ICMP message from creating a chain of error messages, which could overwhelm the network and lead to unnecessary slowdowns.

In addition to error-reporting messages, ICMP also delivers query messages that facilitate network diagnostics. For example, the ping and tracert programs use ICMP to determine how long data travels between routers. These devices are called hops in the ping and tracert programs, and the information revealed by these messages can be used to locate and troubleshoot issues with a network.

Other ICMP message types include the destination unreachable error, sent when a device detects a packet has not been delivered to its destination host. In addition, a source quench message can be sent to the message sender when the routers along the route experience congestion and cannot deliver packets.

Network Diagnostics

ICMP provides error reporting and diagnostic functions in addition to its core functionality as a protocol for Internet packet delivery. It enables network devices to communicate issues they encounter while forwarding IP data packets to upper-layer protocols such as DNS and SMTP.

For example, when a device receives an ICMP Echo Request message from another device, it responds with an ICMP Echo Reply to verify that the other device is functioning correctly. This exchange verifies device connectivity and provides valuable information about network latency and other factors.

However, ICMP can also be weaponized for malicious purposes such as Distributed Denial of Service attacks. This is accomplished by directing many ICMP messages toward a target system, which overwhelms and exhausts the system’s resources, leaving it unresponsive to users.

Error-reporting messages are at the heart of ICMP’s functionality, and some are particularly useful in troubleshooting issues in complex network infrastructure. For instance, the ICMP Destination Unreachable message sends a packet back to the packet’s source when routers or intermediate hosts determine that it cannot reach the destination device. Similarly, the ICMP source quench message notifies the packet’s source that its transmission rate should be reduced to prevent congestion. This is an excellent tool for reducing unnecessary traffic and lowering the probability of data packet loss.

Security

ICMP works at the network layer, integrating feedback and error reporting functions with IP operations. It informs upper-layer protocols about errors or exceptions during packet transmission, which helps them improve error control and flow control.

For example, if data packets of a particular size are too large for the router to handle, the router will discard them and send an ICMP message back to the originating device that explains what happened. This helps the sender take corrective action to avoid the issue in the future.

All ICMP messages are sent as datagrams, self-contained chunks of information holding the ICMP header and ICMP data part. The ICMP header contains a type and code, which determines the specific types of ICMP messages that can be sent. Each ICMP message also has a checksum to ensure that the ICMP data portion has not been corrupted during transmission.

Examples of ICMP messages include destination unreachable (Type 3), which indicates the router cannot forward the datagram to the intended destination host; source quench (Type 4), which informs the sending device that it is sending too fast and should decrease its speed; and time exceeded (Type 5) that tells the transmitting machine that the datagram’s time-to-live field is about to expire and that it needs to resend it soon. ICMP messages also let the network administrator know when the routers in the network are experiencing problems.

The post How ICMP Protocol Facilitates Error Reporting and Network Diagnostics first appeared on AITechTrend.

Overview of Data Privacy Issues in Online Gambling

Online gambling platforms, like many other digital services, require users to provide personal and financial details. This could range from basic information like names and addresses to more sensitive data like bank account numbers or credit card details. While most reputable online casinos employ advanced encryption technologies to protect this data, there are still inherent risks. Data breaches, unauthorised access, and even internal misuse can lead to personal information being compromised.

Tips for Protecting Personal & Financial Information While Gambling Online

1. Use Strong, Unique Passwords: Avoid using easily guessable passwords. Incorporate a mix of letters, numbers, and symbols. Also, refrain from using the same password across multiple platforms.
2. Enable Two-Factor Authentication: Almost all reputable online casinos now offer two-factor authentication (2FA) for added security. This requires a second form of verification, often a code sent to your mobile, before granting access.
3. Regularly Monitor Account Activity: Frequently check your gambling accounts for any unfamiliar activity. If you spot something unusual, contact the platform’s customer service immediately.
4. Avoid Saving Payment Details: While it might be convenient, avoid saving credit card or bank details on the platform. Entering details manually each time provides an added layer of security.

Regulations & Industry Standards for Data Privacy in Online Gambling

The online gambling industry in the UK is regulated by the UK Gambling Commission (UKGC). The UKGC mandates that all licensed online casinos must adhere to strict data protection standards in line with the Data Protection Act and the General Data Protection Regulation (GDPR). 

These regulations ensure that casinos collect, store, and use player data in a manner that safeguards player privacy. Any breach can lead to hefty fines and even the revocation of the casino’s license.

Risks of Using Unsecured Networks or Devices for Online Gambling

Gambling on the go might be tempting, but using public Wi-Fi networks can expose your data to significant risks. Public networks are often less secure, making it easier for hackers to intercept data. Similarly, using shared or unsecured devices can lead to your login details or other sensitive information being accessed by others. Always ensure you’re using a secure, private network and your personal device when accessing online gambling platforms.

Importance of Choosing Reputable Online Casinos

Not all online casinos are created equal. While many are legitimate and prioritise user security, others might be lax in their data protection measures. It’s crucial to choose the best well-licensed online casinos approved by authorities like the UKGC. Reputable casinos will employ advanced encryption technologies, conduct regular security audits, and have clear data protection policies in place.

Final Word on Gambling SecurityThe pull of online gambling is undeniable, but it’s essential to approach it with a keen awareness of data privacy. By understanding the potential risks and adopting measures to protect your personal and financial information, you can enjoy a secure online gambling experience. Always prioritise using reputable, licensed platforms and stay updated on the latest in data protection regulations and best practices. Upon entering the world of online gambling, being informed and vigilant is your best defence against potential data breaches.

The post Online Gambling and Data Privacy: How to Protect Yourself first appeared on AITechTrend.

Deep learning and big data are two terms that have gained significant attention in recent years, both in the tech community and beyond. While they are often mentioned separately, the reality is that deep learning and big data are deeply intertwined, and understanding their relationship is crucial for anyone looking to explore the full potential of these technologies.

What is Deep Learning?

To understand deep learning, we need to first delve into the field of artificial intelligence (AI). AI refers to the development of computer systems that can perform tasks that typically require human intelligence. Deep learning is a subfield of AI that focuses on training artificial neural networks to learn from vast amounts of data and make intelligent decisions.

Deep learning is inspired by the structure and function of the human brain. Neural networks consist of interconnected layers of artificial neurons that process and analyze data. As these networks learn, they can recognize complex patterns, understand speech, perform natural language processing, classify images, and even generate creative content.

What is Big Data?

Big data, on the other hand, refers to the vast amounts of structured and unstructured data that are generated in our digital world. This includes everything from text documents and images to social media posts and sensor data. The key characteristics of big data are commonly referred to as the three Vs: volume, velocity, and variety.

Volume refers to the sheer size of the data, often measured in terabytes or petabytes. Velocity refers to the speed at which the data is generated and needs to be processed, often in real-time. Variety refers to the different types of data, such as structured data in databases or unstructured data in text documents or images.

The Relationship Between Deep Learning and Big Data

The Need for Big Data in Deep Learning

Deep learning models thrive on large amounts of data. The more data they have access to, the better they can learn and make accurate predictions or decisions. Big data provides the fuel that powers deep learning algorithms, allowing them to train on vast amounts of information and extract meaningful insights.

Without big data, it would be challenging to train deep learning models effectively. Small datasets may lead to overfitting, where the model fails to generalize and performs poorly on new, unseen data. In contrast, big data enables deep learning algorithms to learn complex patterns and generalize their knowledge to new situations.

The Role of Deep Learning in Extracting Value from Big Data

While big data provides the raw material for deep learning, deep learning, in turn, can help extract value from big data. The sheer volume and complexity of big data make it challenging to derive meaningful insights using traditional analytics approaches. Deep learning algorithms, with their ability to handle unstructured data and recognize patterns, offer a way to unlock the hidden potential of big data.

For example, deep learning can be used for image recognition tasks, enabling machines to automatically analyze and categorize images at scale. Deep learning can also be applied to natural language processing, allowing for sentiment analysis of customer reviews or automated translation of text. In both cases, deep learning enables businesses to extract valuable information from vast amounts of unstructured data.

The Applications of Deep Learning and Big Data

Healthcare

The healthcare industry stands to benefit greatly from the combination of deep learning and big data. By analyzing large medical datasets, deep learning algorithms can aid in diagnosing diseases, predicting patient outcomes, and providing personalized treatment recommendations. The integration of big data and deep learning has the potential to revolutionize healthcare, improving patient care and saving lives.

Finance

In the financial sector, deep learning and big data are already changing the game. Deep learning algorithms can analyze vast amounts of financial data to identify patterns and make predictions about market trends. This helps financial institutions make data-driven decisions, manage risks, and improve investment strategies. The use of deep learning in finance is expected to continue to grow in the coming years.

Transportation

Deep learning and big data are also making waves in the transportation industry. With the help of big data, deep learning algorithms can analyze traffic patterns, predict demand, and optimize route planning. This can lead to more efficient transportation systems, reduced congestion, and improved sustainability.

The Future of Deep Learning and Big Data

The potential of deep learning and big data is vast and ever-expanding. As more data is generated and advancements in deep learning techniques continue, we can expect to see further breakthroughs and applications in various fields.

One area that holds great promise is the combination of deep learning and the Internet of Things (IoT). As billions of devices generate data, deep learning algorithms can analyze and interpret this information to improve efficiency, optimize processes, and enhance decision-making.

Additionally, the application of deep learning and big data in fields such as cybersecurity, marketing, and manufacturing is also expected to grow in the coming years. The possibilities are endless, and the only limit is our imagination.

The Future is Bright for Deep Learning and Big Data

Deep learning and big data are revolutionizing industries and unlocking new possibilities. From healthcare and finance to transportation and cybersecurity, the potential applications are vast. As we continue to generate and gather more data, the role of deep learning in extracting value from big data will only become more crucial. Together, deep learning and big data have the power to reshape our world and drive innovation.

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Quantum computing has emerged as a groundbreaking technology that holds immense potential to transform industries across the globe. With its ability to process vast amounts of data at unprecedented speeds, quantum computing is opening up new possibilities and solving complex problems that were previously deemed unsolvable. As we approach 2023, it’s essential to take a closer look at the top ten quantum computing trends that will shape the technological landscape in the coming year.

1. Quantum Supremacy

One of the most significant trends in quantum computing is the pursuit of quantum supremacy. Quantum supremacy refers to the point at which a quantum computer can perform calculations that surpass the capabilities of classical computers. In 2023, we can expect significant advancements towards achieving quantum supremacy, leading to groundbreaking discoveries and technological breakthroughs.

2. Quantum Computing in Finance

The financial industry stands to benefit greatly from the advancements in quantum computing. With its ability to process large datasets and perform complex calculations, quantum computers can revolutionize portfolio optimization, risk management, fraud detection, and algorithmic trading. In 2023, we can expect increased adoption of quantum computing solutions in the finance sector, enabling more efficient and accurate financial analysis.

3. Quantum Machine Learning

Machine learning algorithms play a crucial role in various industries, and quantum computing can significantly enhance their capabilities. Quantum machine learning combines the power of quantum computers with classical machine learning techniques to solve complex problems more efficiently. In 2023, we can expect advancements in quantum machine learning algorithms that enable faster and more accurate predictions across sectors such as healthcare, logistics, and cybersecurity.

4. Quantum Cryptography

Cybersecurity is a growing concern in the digital age, and quantum computing provides a potential solution with its advanced cryptography techniques. Quantum cryptography utilizes the principles of quantum mechanics to generate unbreakable encryption keys, ensuring secure communication channels. In 2023, we can anticipate the development of more robust quantum cryptographic systems that safeguard sensitive data and protect against cyber threats.

5. Quantum Sensing

Quantum sensing utilizes the unique properties of quantum systems to measure physical quantities with extreme precision. Quantum sensors have the potential to revolutionize various industries, including healthcare, environmental monitoring, and navigation. In 2023, we can expect advancements in quantum sensing technology, leading to more accurate and efficient measurement devices with applications in areas such as early disease detection and precise navigation systems.

6. Scalable Quantum Computers

One of the key challenges in quantum computing is scaling up the number of qubits, the basic units of quantum information processing. In 2023, we can expect significant progress in developing scalable quantum computers that can reliably handle larger and more complex calculations. This advancement will pave the way for more practical applications of quantum computing across industries.

7. Quantum Internet

Quantum internet aims to create a network that enables secure communication using quantum protocols. In 2023, we can expect advancements towards building a quantum internet infrastructure that allows for the transmission of quantum information across vast distances. This development will unlock new possibilities for secure quantum communication, quantum teleportation, and distributed quantum computing.

8. Quantum Simulations

Quantum simulations allow researchers to model and explore complex quantum systems that would be impossible to replicate in a traditional laboratory setting. In 2023, we can anticipate the utilization of quantum simulations to solve fundamental scientific problems, accelerate drug discovery, and optimize materials design.

9. Quantum AI

Combining the power of quantum computing with artificial intelligence (AI) has the potential to revolutionize various industries. Quantum AI algorithms can process vast amounts of data and identify patterns with incredible speed and accuracy. In 2023, we can expect advancements in quantum AI, leading to smarter and more efficient AI-powered systems that can outperform classical AI models.

10. Quantum Computing as a Service

Quantum computing as a service (QCaaS) allows organizations to access quantum computing resources through cloud platforms. In 2023, we can expect the development of more user-friendly QCaaS platforms that make quantum computing accessible to a wider range of businesses and researchers. This trend will accelerate the adoption of quantum computing solutions and drive further innovations in the field.

Conclusion

As we look towards 2023, the future of quantum computing appears promising and full of opportunities. The top ten trends discussed in this article highlight the immense potential of quantum computing to transform industries, solve complex problems, and drive technological advancements. As researchers and innovators continue to push the boundaries of quantum computing, we can anticipate a quantum-powered future that revolutionizes the way we approach computation and problem-solving.

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