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Navigating the AI Landscape: From Machine Learning Foundations to Multimodal Advancements – Medium

January 11, 2024 | Author: admin

Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence.

These tasks include problem-solving, understanding natural language, speech recognition, visual perception, learning and decision-making. While Machine Learning is a subset of AI that focuses on developing algorithms and statistical models that enable computers to perform a task without explicit programming. Instead of being explicitly programmed, these systems learn from data and improve their performance over time.

Machine learning models rely on large amounts of data for training. The more diverse and extensive data, the better the AI model can learn pattern and make accurate predictions or decisions. These models learn from historical data to recognise patterns, relationships and trends. AI algorithms can be applied to uncover valuable patterns, correlations and make sense of the complex information present in big data.

Machine learning models are only as good as the data they are trained on

In programming, the relationship between input and output is explicitly defined by a set of rules coded by human programmer. The process follows a deterministic path where the function adheres strictly to the predefined logic to produce the desired output. In contrast, machine learning flips this paradigm by learning the rules directly from the input-output pairs without explicit programming.

This shift from explicit rule-based programming to learning from data characterises the power and flexibility of machine learning, enabling systems to adapt and improve their performance based on experience and vast array of examples. In XiMnets initial foray into AI technology, we embarked on the journey armed with over two decades of invaluable experience in design and technologies and a wealth of distinctive and exclusive data.

We have successfully navigated various AI projects, including tasks such as employing object recognition for intelligent image cropping, utilising BERT for question-answering and tracking users website browsing behaviour for personalised recommendations. Our most enjoyable AI project involves employing Image-to-Image Translation with Conditional Adversarial Networks (pix2pix) to extract dominants colours from an image and subsequently generate a vibrant 25-colour palette for website design.

Meanwhile, our most challenging endeavour is to leverage pix2pix for recommending the optimal webpage layout and design based on given content.

In the context of machine learning, prompts serve as the stimuli that guide machine learning models, shaping their comprehension and responses. The effectiveness of prompt engineering directly influences how well the AI model understands and responds to different queries or tasks. ChatGPT is as effective as the prompts get.

As users interact with ChatGPT, the better the prompts provided by users, the more proficiently ChatGPT can generate meaningful and contextually fitting output. Additionally, with OpenAI, the capabilities extend beyond chat completion; we also broadening the scope of our AI solutions with embeddings and fine-tuning.

Multimodal AI can integrate and make sense of information from one or more models including visual, audio, speech and text, instead of relying on a single modality. This enables the AI system to have a more comprehensive understand of the encountered data. Multimodal AI is a new technology that has the potential to reshape the way we interact with the world around us.

In conclusion, the field of AI is rapidly evolving. With over two decades of expertise in the design and technologies with the marketing mind, XiMnet possess a deep understanding of what works and what doesnt.

This extensive experience not only shapes our AI strategies but also provides us exclusive access to distinct datasets, contributing to more nuanced and impactful machine learning outcomes that distinguish our AI solutions. As AI technology advances, we anticipate even more innovative applications that significantly enhance various aspects of our lives.

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Navigating the AI Landscape: From Machine Learning Foundations to Multimodal Advancements - Medium

Beyond Algorithms: How AI is Learning Our Social Cues – DataDrivenInvestor

January 11, 2024 | Author: admin

The journey of artificial intelligence (AI) has been nothing short of remarkable. From its inception in the mid-20th century, AI was envisioned as a means to mimic human intelligence. This vision was rooted in the belief that machines could be programmed to perform tasks that typically require human cognition. The early years of AI were characterized by optimism and a focus on creating systems that could solve logical problems and perform specific, rule-based tasks.

Initially, AIs triumphs were in areas that demanded computational prowess rather than emotional intelligence. For instance, the world witnessed AIs potential when IBMs Deep Blue defeated chess grandmaster Garry Kasparov in 1997. These early achievements, though impressive, were confined to the realms of mathematics and logic. They demonstrated AIs ability to process and execute complex algorithms but did not venture into the nuances of human emotions or social behaviours.

As technology progressed, so did the capabilities of AI. The focus shifted from performing rudimentary, rule-based tasks to tackling more complex activities. This transition was marked by the advent of machine learning a branch of AI that learns from and makes decisions based on data.

Enabling AI to interpret social cues is fraught with challenges. The world of human emotion and social interaction is rich, complex, and often subjective. Teaching a machine to navigate this world involves not just technological hurdles but also ethical and cultural considerations.

Machine learning, along with natural language processing (NLP) and computer vision, became instrumental in evolving AI from a tool of computational logic to one capable of understanding and interacting with the human world in a more nuanced way.

Today, AI stands on the brink of a new frontier: social intelligence. This emerging domain represents a significant leap from traditional AI capabilities. Social intelligence in AI refers to the ability of machines to understand and appropriately respond to human social cues such as facial expressions, tone of voice, body language, and contextual subtleties. This development is not just a technological achievement but a bridge towards more empathetic and effective human-machine interactions.

Data Acquisition

AIs journey in understanding human interaction begins with data acquisition. This involves collecting a vast array of social data, such as text (from social media, emails, chat conversations), speech (voice recordings, call center data), visual cues (videos, images capturing facial expressions and body language), and even physiological signals (like heart rate or skin conductance). The quality and diversity of this data are crucial for the accuracy and comprehensiveness of social cue interpretation.

Alongside NLP, developments in computer vision, particularly in facial recognition, opened new avenues for AI in social understanding. AI systems began to recognize and interpret human facial expressions, a fundamental aspect of non-verbal communication. Emotion analysis algorithms were developed, allowing AI to infer emotions based on facial cues, a step closer to mimicking human empathy and understanding.

In this evolving landscape, optimism abounds. As AI ethicist Kate Darling remarks,

AI can unlock new possibilities we cannot yet envision.

With responsible research, development, and collaboration across disciplines, AI systems can gain social nuance and adaptability. The promise of a future where AI understands and augments our social interactions is within reach.

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NIST Identifies Types of Cyberattacks That Manipulate Behavior of AI Systems | NIST – NIST

January 11, 2024 | Author: admin

An AI system can malfunction if an adversary finds a way to confuse its decision making. In this example, errant markings on the road mislead a driverless car, potentially making it veer into oncoming traffic. This evasion attack is one of numerous adversarial tactics described in a new NIST publication intended to help outline the types of attacks we might expect along with approaches to mitigate them.

Credit: N. Hanacek/NIST

Adversaries can deliberately confuse or even poison artificial intelligence (AI) systems to make them malfunction and theres no foolproof defense that their developers can employ. Computer scientists from the National Institute of Standards and Technology (NIST) and their collaborators identify these and other vulnerabilities of AI and machine learning (ML) in a new publication.

Their work, titled Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST.AI.100-2), is part of NISTs broader effort to support the development of trustworthy AI, and it can help put NISTs AI Risk Management Framework into practice. The publication, a collaboration among government, academia and industry, is intended to help AI developers and users get a handle on the types of attacks they might expect along with approaches to mitigate them with the understanding that there is no silver bullet.

We are providing an overview of attack techniques and methodologies that consider all types of AI systems, said NIST computer scientist Apostol Vassilev, one of the publications authors. We also describe current mitigation strategies reported in the literature, but these available defenses currently lack robust assurances that they fully mitigate the risks. We are encouraging the community to come up with better defenses.

AI systems have permeated modern society, working in capacities ranging from driving vehicles to helping doctors diagnose illnesses to interacting with customers as online chatbots. To learn to perform these tasks, they are trained on vast quantities of data: An autonomous vehicle might be shown images of highways and streets with road signs, for example, while a chatbot based on a large language model (LLM) might be exposed to records of online conversations. This data helps the AI predict how to respond in a given situation.

One major issue is that the data itself may not be trustworthy. Its sources may be websites and interactions with the public. There are many opportunities for bad actors to corrupt this data both during an AI systems training period and afterward, while the AI continues to refine its behaviors by interacting with the physical world. This can cause the AI to perform in an undesirable manner. Chatbots, for example, might learn to respond with abusive or racist language when their guardrails get circumvented by carefully crafted malicious prompts.

For the most part, software developers need more people to use their product so it can get better with exposure, Vassilev said. But there is no guarantee the exposure will be good. A chatbot can spew out bad or toxic information when prompted with carefully designed language.

In part because the datasets used to train an AI are far too large for people to successfully monitor and filter, there is no foolproof way as yet to protect AI from misdirection. To assist the developer community, the new report offers an overview of the sorts of attacks its AI products might suffer and corresponding approaches to reduce the damage.

The report considers the four major types of attacks: evasion, poisoning, privacy and abuse attacks. It also classifies them according to multiple criteria such as the attackers goals and objectives, capabilities, and knowledge.

Evasion attacks, which occur after an AI system is deployed, attempt to alter an input to change how the system responds to it. Examples would include adding markings to stop signs to make an autonomous vehicle misinterpret them as speed limit signs or creating confusing lane markings to make the vehicle veer off the road.

Poisoning attacks occur in the training phase by introducing corrupted data. An example would be slipping numerous instances of inappropriate language into conversation records, so that a chatbot interprets these instances as common enough parlance to use in its own customer interactions.

Privacy attacks, which occur during deployment, are attempts to learn sensitive information about the AI or the data it was trained on in order to misuse it. An adversary can ask a chatbot numerous legitimate questions, and then use the answers to reverse engineer the model so as to find its weak spots or guess at its sources. Adding undesired examples to those online sources could make the AI behave inappropriately, and making the AI unlearn those specific undesired examples after the fact can be difficult.

Abuse attacks involve the insertion of incorrect information into a source, such as a webpage or online document, that an AI then absorbs. Unlike the aforementioned poisoning attacks, abuse attacks attempt to give the AI incorrect pieces of information from a legitimate but compromised source to repurpose the AI systems intended use.

Most of these attacks are fairly easy to mount and require minimum knowledge of the AI system and limited adversarial capabilities, said co-author Alina Oprea, a professor at Northeastern University. Poisoning attacks, for example, can be mounted by controlling a few dozen training samples, which would be a very small percentage of the entire training set.

The authors who also included Robust Intelligence Inc. researchers Alie Fordyce and Hyrum Anderson break down each of these classes of attacks into subcategories and add approaches for mitigating them, though the publication acknowledges that the defenses AI experts have devised for adversarial attacks thus far are incomplete at best. Awareness of these limitations is important for developers and organizations looking to deploy and use AI technology, Vassilev said.

Despite the significant progress AI and machine learning have made, these technologies are vulnerable to attacks that can cause spectacular failures with dire consequences, he said. There are theoretical problems with securing AI algorithms that simply havent been solved yet. If anyone says differently, they are selling snake oil.

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How helpful was artificial intelligence to online retailers in 2023? – Digital Commerce 360

January 11, 2024 | Author: admin

With more than a full year in the books for generative AI platforms like ChatGPT and DALL-E, retailers have had time to test the technology and see what it looks like when implemented into their businesses. But generative is just part of the equation. The larger, far more expansive field of artificial intelligence entered standard operating procedures in various forms for many online retailers in 2023.

Between artificial intelligence and machine learning, automation in retail is becoming increasingly common. Whereas artificial intelligence refers to technology that can mimic human intelligence, machine learning is different. Machine learning technology enables a program to perform specific tasks and provide accurate results by identifying patterns. And as online retailers continue to use both, the lines can sometimes get blurry, but the results are clear.

Salesforce said artificial intelligence accounted for $194 billion in online holiday sales, primarily through predictive recommendations. And thats just in November and December. The software provider said artificial intelligence influenced 17% of all online orders in the last two months of 2023.

Below, we recap some of Digital Commerce 360s most insightful coverage about artificial intelligence (including generative AI) and machine learning in online retail from the past year. These stories highlight meaningful AI/ML trends among online retailers in 2023. Most notably, they include use cases spanning from product design to chatbots and digital marketing, and much more.

Online pet retailer Finn invested in artificial intelligence to appeal to specific groups of customers quickly.

SodaStream invested in artificial intelligence to determine which ad campaigns would be most successful via email, SMS text and on social media.

Thousands of shoppers each month negotiate with Industry Wests artificial intelligence chatbot in hopes of reaching a deal for a product discount.

How SMBs are using AI

Small and medium-sized businesses like mens grooming retailer Huron are using AI to balance financials easily. The retailer is also balancing how it sells to customers shopping via Amazon versus its direct-to-consumer website. The brand is using plug-ins to upsell.

Googles AI dressing room technology could reduce ecommerce returns and give retailers data to better target consumers, experts say.

Tailored Brands Inc. invests in artificial intelligence to understand its retail and rental customers for digital and in-store shoppers.

Online music instrument and equipment retailer Sweetwater increases email open rates and online sales thanks to AI-generated email recommendations.

Machine learning software enables Mars Petcare to measure how appealing pet food images are to online consumers.

The My Skin Biome tool from Beekman 1802 and Perfect Corp. works directly from the website on a users mobile phone.

Generative AI has been a key discussion topic all year. Online retailers are already incorporating it into their design processes to come up with new products and variations of existing products.

Adding shoppable products on both English and Spanish blog posts which are AI-generated has helped the retailer more than triple its average order value.

Menswear retailer Otero attributes its low return rate to the accuracy of its online fitting tool using Perfitly.

Impressed with the sophistication of generative AI chatbots, ski and sporting goods brand Evo plans to launch a customer service chatbot in time for the holiday season.

The generative AI tool creates and publishes a summary of all of the reviews at the top of the customer reviews section on the product detail page.

Large tool manufacturer Stanley Black & Decker is looking for a generative AI tool to write product descriptions and speed up its product detail product optimization. But the technology is not there yet.

Generative AI systems like ChatGPT are the hottest thing in tech these days, and some retailers will be showing off the power of the technology during the upcoming holiday season.

Toothpaste CPG brand Colgate-Palmolive tests a generative AI chatbot to more efficiently gather analysis to create better-converting product detail pages.

Generative AI is a valuable tool for digital marketers looking to simplify tasks. The technology is a creative reservoir for good and bad and sometimes outright goofy ideas. But thats a good thing when trying to stand out from the competition, retailers say.

Major retailers and consumer brands including eBay, Colgate, Ghirardelli, Newegg and Stanley Black & Decker are using generative AI today to speed product detail page content creation or optimization. While some have AI-created content live today, others are still perfecting their tools before debuting them to the public.

The chocolate brands ecommerce content operations and development manager shares how the major chocolate brand is using AI to make decisions about product detail page images.

Queenly uses generative AI to populate product listings from a series of questions answered by online resellers.

Submit your data withthis quick surveyand well see where you fit in our next ranking update.

Stay on top of the latest developments in the ecommerce industry. Sign up for a complimentary subscription toDigital Commerce 360 Retail News.Follow us onLinkedIn,TwitterandFacebook. Be the first to know when Digital Commerce 360 publishes news content.

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Machine learning identifies promising antibacterial ruthenium-based drug candidates – Chemistry World

January 11, 2024 | Author: admin

A machine learning model has been created that can identify ruthenium-based antibiotic drug candidates. With a small training set of just 288 antibacterial organometallic compounds, the algorithm scanned millions of structures, selecting the most active against resistant bacteria. The most promising candidates were tested and showcased almost six times greater antibiotic activity than the training set.

Antibiotics have become a cornerstone of most modern medicine, as many hospital treatments rely on antibiotics as a measure to control infection, says lead author Angelo Frei from the University of Bern in Switzerland. However, growing bacterial resistance to these drugs has become a serious problem. Recently, researchers have recognised the potential of metal-based antimicrobials including ruthenium complexes. Compared with traditional organic carbon-based chemicals, metal compounds are 10 times more likely to be active against bacteria and are not necessarily more toxic to humans, explains Frei. They represent a vast compound class that has remained largely unexplored for its use in medicine, he adds. Ruthenium compounds are also simple to synthesise, making them easier drug candidates to explore.

Frei says that the team first used a combinatorial chemistry approach developed by co-author Wee Han Ang to create a library of 288 ruthenium compounds, which were then tested against methicillin-resistant Staphylococcus aureus (MRSA). We found a substantial amount to be active (9.4%), and used this data to train machine-learning models to predict the activity against MRSA, he adds. After these first steps, researchers built a virtual library of 77 million ruthenium complexes. The algorithm then identified two million potentially active structures. To verify the predictions, the team assembled a smaller sample of 54 structures and tested them in the lab against MRSA. 53.7% of these compounds were active, which represents a 5.7x higher hit rate than the initial screening, comments Frei.

Organometallic compounds often have distinct mechanisms of action compared to traditional organic antibiotics, which could be advantageous to overcome existing resistance mechanisms, explains Concepcin Gimeno, an expert in metallodrugs at the Institute of Chemical Synthesis and Homogeneous Catalysis in Zaragoza, Spain. Ruthenium complexes interesting properties include biocompatibility and a very low toxicity compared to other metal complexes, adds Gimeno. Ruthenium complexes are already being investigated in clinical trials for cancer.

Nils Metzler-Nolte, an expert in bioinorganic chemistry at Ruhr University Bochum, Germany, admires the versatility of the method. Building upon previous work in combinatorial chemistry by the Ang group a simple one-pot reaction gives over 250 compounds with vastly different 3D shapes and properties, he explains. This is quite unmatched when you consider the three-dimensional space mapped out with these compounds. This is an attractive aspect of organometallic complexes compounds with radically new structures and chemical properties [could offer] antibiotics with new and unprecedented modes of action, says Metzler-Nolte.

Although ruthenium is relatively expensive and scarce, the syntheses are only between one and three steps, which is very economical compared with commercially available drugs. Moreover, the cost of drug discovery and development is not dictated by the cost of the synthesis, but rather by the huge cost of clinical trials, Metzler-Nolte points out.

Follow-up studies will involve a series of experimental and computational validations to confirm and refine the predictions, then synthesis, characterisation, biological tests, iterative design and more, says Gimeno. Perhaps most importantly, molecular simulations will help understand the unusual antibiotic mode of action of these metal complexes, as well as any resistance observed. Some studies show a very low even non-existent development of resistance for metal-based compounds, but I think it would be foolish to underestimate bacteria, says Frei. Our first aim is to generate more data and larger libraries to cover more of the periodic table and predict more specific properties, such as the degree of activity and toxicity.

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Machine learning identifies promising antibacterial ruthenium-based drug candidates - Chemistry World

Conference to address internet security on the farm – Cedar Valley Daily Times

January 9, 2024 | Author: admin

AMES The first of its kind Cybersecurity on the Farm Conference, offered by Iowa State University Extension and Outreach, will be held at the Iowa State University Alumni Center in Ames on Jan. 11, from 8:30 a.m. to 3:30 p.m.

Registration is available through Jan. 10, cost applies. Refreshments and lunch are included. Register online at https://go.iastate.edu/BPGFN4.

This one-day conference is designed to address the unique intersection of todays agriculture and cybersecurity. The resource fair will be available during the lunch hour and throughout the day and features experts and service providers at the juncture of farming and cyber tech.

For farmers, this workshop offers insights into the ever-evolving world of digital lending in farming and the shift toward online agricultural marketplaces. There will be critical discussions on the potential cyber threats that emerge when working in the agricultural sector. By the end of the day, farmers will be better equipped to navigate farming on the internet while keeping an eye on safety and security.

Through panel discussions with industry experts and a resource fair with trusted service providers, this conference is designed to support farmers as they work to create a seamless integration of cybersecurity into existing systems.

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Conference to address internet security on the farm - Cedar Valley Daily Times

Unveiling the Significance of Ethical Hacking in Cloud Computing – EC-Council

January 9, 2024 | Author: admin

Cloud computing is gaining prominence across all industries because of its scalability, adaptability, and many other advantages. These benefits include cost reductions through efficient virtualization, enhanced peer collaborative capabilities, swift access to documents, transactions, and updates, and extensive scalability. However, as businesses increasingly rely on cloud hosting for storage and computational needs, the vulnerability of their cloud services to cyber attacks rises as well (Jayanti, 2022).

Inadequate security measures pose a financial threat to organizations and carry the potential for severe reputational harm when customer data is compromised, leading to a loss of trust and business opportunities. Consequently, while security experts diligently devise new strategies and policies to combat cyber threats and fortify applications, systems, and networks across their cloud infrastructure, ethical hacking emerges as a proactive means of ensuring security. This blog post delves into the significance of ethical hacking in cloud computing.

Ethical hackers need to understand the specific cloud-based vulnerabilities that require consistent identification, mitigation, and maintenance. This diligence is essential to prevent any potential breaches or related complications that could occur (James, 2023). Although security threats are often intertwined with discussions on vulnerabilities, the ethical hackers perspective on vulnerabilities is nuanced. From the standpoint of penetration testing, the following list encapsulates some of the vulnerabilities to be considered.

Misconfigurations represent a significant factor contributing to substantial data breaches in cloud environments. These misconfigurations include errors or oversights in the security protocols implemented, potentially exposing valuable data to vulnerabilities. Such lapses typically result from a lack of familiarity with best practices or the need for more peer review within the clients DevOps or infrastructure team. Misconfigurations within security groups on the service providers end can grant unauthorized access to the cloud platform and its data, culminating in data theft or loss.

The expansive nature of cloud architecture, spanning diverse environments, introduces intricate pathways for networking and data transit. Vulnerabilities in connection security and access management can result in critical data loss. Human errors, such as weak credentials, insufficient security awareness, susceptibility to phishing attacks, and improper data storage and sharing practices, can all contribute to data theft, putting the data and applications hosted on cloud servers at risk. Subsequently, malicious actions like data deletion, access denial, and data manipulation may contribute to data loss.

Inadequate coding practices have posed a significant challenge in cloud infrastructures for years. A single line of flawed code has the potential to expose many risks and vulnerabilities. Prominent among these vulnerabilities are SQL injections, cross-site request forgery (CSRF), and cross-site scripting (XSS), all of which provide opportunities for attackers to compromise cloud infrastructures due to the presence of insecure coding practices.

A prevalent vulnerability in cloud systems is the presence of insecure identity and access management (IAM). In essence, this occurs when a user or a service within your infrastructure gains access to resources that they should not or do not need to access. Recently, most software and cloud applications mandate robust security measures such as strong passwords, multi-factor authentication (MFA), and single sign-on (SSO). Cloud applications lacking these robust access management systems are susceptible to data breaches. Security experts strongly endorse implementing organization-wide policies like the principle of least privilege or the zero-trust model as effective measures against potential threats.

APIs serve as meticulously documented interfaces that cloud service providers furnish to their clientele, offering a straightforward means to access their services. In cloud computing, APIs are pivotal in efficiently managing data for the cloud infrastructure and the applications it hosts. However, when these interfaces lack proper security measures, they become a substantial vulnerability, potentially exposing systems to malware attacks. Insecure APIs pose a significant threat by creating avenues of communication that malicious actors can exploit to compromise the systems integrity (Jackson-Barnes, 2022).

This vulnerability arises when a specific data repository, such as an S3 bucket or, less commonly, an SQL database, becomes partially or entirely accessible to the public. Alternatively, it can occur when data is stored with a third-party service provider whose storage security standards are suboptimal. While data privacy is safeguarded by compliance and governance standards, navigating the complexities of cloud compliance can be challenging, especially when dealing with multiple cloud service providers. Therefore, businesses must select a cloud service provider equipped with the necessary security tools to ensure the protection and security of their data.

Lack of visibility in cloud assets and associated telemetries leads to challenges in detecting and identifying probable risks across the cloud infrastructure of an organization. With the expanding adoption of cloud services, the scale of an organizations infrastructure grows proportionally. Managing thousands of instances of cloud services can lead to confusion or oversight of certain active instances. This complexity is exacerbated when multiple service providers and hybrid cloud models are employed. Therefore, having effortless and readily accessible visibility in an organizations Infrastructure is essential to mitigate this risk effectively.

Unauthorized access transpires when an individual gains entry to a portion of your organizations cloud assets. As highlighted in the section about cloud misconfigurations, this can stem from overly permissive access rules or former employees retention of valid credentials. Malicious insiders can also infiltrate your cloud resources by exploiting account hijacking following a successful phishing attack or exploiting weak credential security. This vulnerability is especially dangerous, as it places data and intellectual property at risk of theft or tampering (Alvarenga, 2022).

Ethical hacking is a sanctioned and lawful procedure involving deliberate circumvention of an IT or network infrastructures security measures. Its purpose is to identify vulnerabilities and potential points of weakness that could lead to a security breach. The primary objective of ethical hacking is to enhance an organizations overall safety by pinpointing vulnerabilities within its network and identifying potential openings that could be exploited by cyber attacks, ultimately preventing data loss and security breaches. Ethical hacking professionals adopt the mindset and tactics of potential attackers to uncover all vulnerabilities within the organizations systems.

Before delving deeper, it is crucial to delve into service level agreements (SLAs) and shared responsibility models, as these significantly shape the landscape of cloud penetration testing. Ethical hacking in a cloud environment is intricately tied to these SLAs and shared security responsibilities.

Within the shared responsibility model framework, the cloud service provider allows for examining cloud security to the extent that the client is authorized. To illustrate, assessing vulnerabilities related to virtualization, network, and Infrastructure is typically outside the purview of the clients responsibilities. This results in ethical hacking capabilities being constrained to access data and applications, except for the infrastructure as a service (IaaS) model, wherein the operating systems security falls under the clients jurisdiction.

Here are various hacking and penetration testing methodologies tailored for the cloud environment (Varghese, 2023), ensuring a comprehensive and authentic assessment of critical aspects within the cloud platform and applications:

By leveraging these methodologies, ethical hackers can ensure their penetration tests are thorough, reflective of real-world scenarios, and equipped to uncover vulnerabilities across the cloud infrastructure and applications.

Fundamentally, the ethical hacking approach revolves around three key steps: identifying vulnerabilities, exploiting weaknesses, and proposing improvement solutions (Guide et al., 2021). In cloud environments, the testing scope encompasses the cloud perimeter, internal cloud systems, and the management, administration, and development infrastructure for on-premises cloud solutions.

Here are some best practices in ethical hacking that can help ensure the highest level of security for your organization:

By adhering to these ethical hacking best practices, organizations can enhance their security posture and be better prepared to defend against potential threats in the dynamic landscape of cloud computing.

ConclusionCloud computings reach is undeniable, attracting IT professionals, enterprises across industries, and cyber security experts. However, with great convenience comes great responsibility, and the increasing reliance on cloud services exposes organizations to heightened cyber threats. Ethical hacking emerges as a proactive and essential approach to safeguarding cloud environments. By thinking and acting like potential adversaries, ethical hackers identify vulnerabilities before malicious actors can exploit them, strengthening the defenses of cloud systems.

References

Alvarenga, G. (2022, June 28). Top 6 Cloud Vulnerabilities. Crowdstrike. https://www.crowdstrike.com/cybersecurity-101/cloud-security/cloud-vulnerabilities/

Guide Point Security. (2021, March 11). Cloud Penetration Testing. Retrieved from: https://www.guidepointsecurity.com/education-center/cloud-penetration-testing/

Jackson-Barnes, S. (2022, November 11). Cloud Computing: Common Vulnerabilities and How to Overcome Them. Orientsoftware. https://www.orientsoftware.com/blog/vulnerability-in-cloud-computing/

James, N. (2023, July 07). Cloud Vulnerability Management: The Detailed Guide. Getastra. https://www.getastra.com/blog/security-audit/cloud-vulnerability-management/

Jayanti. (2022, October 23). Everything you Need to Know about Cloud Hacking and its Methodologies. Analytics Insight. https://www.analyticsinsight.net/everything-you-need-to-know-about-cloud-hacking-and-its-methodologies/

Varghese, J. (2023, August 22). Cloud Penetration Testing: A Complete Guide. Getastra. https://www.getastra.com/blog/security-audit/cloud-penetration-testing/

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How Edge Computing Is Transforming Data Processing and Cloud Architectures – Medium

January 9, 2024 | Author: admin

Photo by Growtika on Unsplash

Edge computing is fundamentally transforming traditional cloud-based data frameworks. By enabling data processing and analysis closer to the source, edge paradigms allow for faster and more efficient architectures while expanding whats possible.

In this article, Ill cover:

Up until recently, most data pipelines relied on a centralized cloud model. Data from endpoints like mobile devices, autonomous vehicles and IoT sensors flowed upwards into cloud data centers for processing, analysis and storage before sending back results.

This model introduced latency since data had to traverse wide area networks to move back and forth from the cloud. It also led to exorbitant costs when huge numbers of devices simultaneously communicated with cloud servers.

Maintaining constant connectivity to the cloud from swarms of distributed endpoints proved challenging. Real-time responsiveness was difficult when it took data multiple seconds to do the round trip from endpoint to cloud and back again.

For innovations on the edge like self-driving cars and industrial automation, milliseconds matter. Complex analytics on massive datasets also pushed cloud infrastructure to its limits in both compute performance and expenditure.

Finally, consolidating sensitive data like medical records or proprietary telemetry data in centralized clouds also raised privacy and security issues.

Edge computing solutions have emerged to sidestep the limitations of cloud-centric models. Instead of routing all data and compute operations through centralized servers, edge

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How Edge Computing Is Transforming Data Processing and Cloud Architectures - Medium

Why Automakers Embrace Cloud Technology For Digital Transformation – Mobility Outlook

January 9, 2024 | Author: admin

In a paradigm shift for the automotive industry, dedicated automotive cloud platforms are taking centre stage, ushering in a phase of differentiated competition. As automakers increasingly migrate to cloud platforms, a foundation for digital transformation is laid, marking a pivotal moment in automotive technology. This analysis, based on the 'Automotive Cloud Service Platform Industry Report, 2023' from ResearchAndMarkets, delves into the evolving landscape, spotlighting key trends, innovations, and competitive strategies.

Cloud Capabilities Reshaping Automotive Ecosystem

The report highlights the escalating demand for cloud services, with China carving out a distinctive path in cloud service development. Original Equipment Manufacturers (OEMs) are driving changes in cloud service demand, with a particular focus on cloud applications and business models. The automotive cloud landscape is witnessing a transformative shift in computing architecture, especially in Electric/Electronic (E/E) architecture for vehicle cloud computing.

Data Lake & Cloud Native

Exploring the synergy between Data Lake and Cloud Native technologies, cloud platform companies are creating novel storage and computing systems. Notably, Data Lake Cloud Native architecture gains prominence, exemplified by real-world applications in autonomous driving data lakes from industry giants like AWS and Alibaba Cloud. This trend underscores the industry's commitment to leveraging cloud-native solutions for enhanced functionality and security.

From Single Cloud Adoption To Multi-Cloud Strategies

The industry is experiencing a transition from single cloud adoption to a multi-cloud approach, emphasising versatility and resilience. Distributed edge cloud applications are expanding, magnifying the role of telematics cloud control platforms and integrating cloud intelligence into automotive systems. In this dynamic landscape, cloud-native security undergoes evolution, ensuring robust protection for the connected vehicles' ecosystem.

Exponential Vehicle Data Fuelling Cloud Migration

The report underlines the inevitability of cloud migration, driven by the exponentially increasing volume of vehicle data. Companies aim to digitise the entire vehicle life cycle, from R&D and production to sale, operation, and after-sales service. Cloud migration becomes imperative as vehicle intelligence and connectivity surge, especially with the evolution of autonomous driving functions.

Market Insights & Competition Dynamics

In 2022, China's automotive cloud service market surpassed RMB 15 billion and is anticipated to sustain a growth rate of 30-40% over the next five years. The industry witnesses a fierce competition with major players like Baidu, Alibaba, Tencent, Huawei, and Douyin entering the market, each launching dedicated automotive cloud platforms. Differentiated competitive edges are crucial, focusing on basic resource layer services and upper-layer R&D tool chains.

Advancements In Basic Resource Layer Services

The report highlights the significance of supercomputing centres as a vital indicator of service capabilities. Alibaba and Baidu lead the deployment of supercomputing centres, enhancing their intelligent computing solutions. The emphasis on constructing intelligent computing centres underscores the industry's commitment to advanced computing infrastructure.

Innovations In R&D Tool Chains

Cloud service providers are committed to creating fully furnished service experiences for users, offering full-process and fully closed-loop services. Examples include Tencent's autonomous driving cloud platform, Huawei's autonomous driving cloud platform 'Octopus,' and Baidu's full-stack layout. These innovations aim to provide comprehensive solutions for AI software and hardware ecosystems.

Shift To Multi-Cloud Strategies

As OEMs transition to the cloud, the report identifies a shift in underlying cloud strategy logic from resource pursuit to efficiency. OEMs are adopting a multi-cloud strategy, placing different business types on different cloud platforms to integrate advantages, deploy business more precisely, and reduce costs. Challenges, however, include storage/computing power allocation, cross-cloud data synchronization, and the impact of costs and network delays, the report added.

Courtesy: ResearchAndMarkets. Photo is representational; courtesy: Continental.

VeriSilicon Unveils the New VC9800 IP for Next Generation Data Centers – AiThority

January 9, 2024 | Author: admin

Delivering exceptional throughput, AI-powered encoding, and superb image enhancement for data centers

VeriSiliconunveiled its latest VC9800 series Video Processor Unit (VPU) IP with enhanced video processing performance to strengthen its presence in the data center applications. The newly launched series IP caters to the advanced requirements of next generation data centers including video transcoding servers, AI servers, virtual cloud desktops, and cloud gaming.

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The VC9800 series of VPU IP boasts high performance, high throughput, and server-level multi-stream encoding and decoding capabilities. It can handle up to 256 streams and support all mainstream video formats, including the new advanced format VVC. Through Rapid Look Ahead encoding, the VC9800 series IP improves video quality significantly with low memory footprint and encoding latency. With capable of supporting 8K encoding and decoding, it offers enhanced video post-processing and multi-channel encoding at various resolutions, thus achieves an efficient transcoding solution.

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The VC9800 series of VPU IP can seamlessly interface with Neural Network Processor (NPU) IP, enabling a complete AI-video pipeline. When combined with VeriSilicons Graphics Processor Unit (GPU) IP, the subsystem solution is able to deliver enhanced gaming experiences. In addition, the hardware virtualization, super resolution image enhancement, and AI-enabled encoding functions of this series IP also offer effective solutions for virtual cloud desktops.

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VeriSilicons advanced video transcoding technology continues leading in Data Center domain. We are working closely with global leading customers to develop comprehensive video processing subsystem solutions to meet the requirements of the latest Data Centers, said Wei-Jin Dai, Executive VP and GM of IP Division of VeriSilicon. For AI computing, our video post-processing capabilities have been extended to smoothly interact with NPUs, ensuring OpenCV-level accuracy. Weve also introduced super resolution technology to the video processing subsystem, elevating image quality and ultimately enhancing user experiences for cloud computing and smart display.

[To share your insights with us, please write tosghosh@martechseries.com]

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VeriSilicon Unveils the New VC9800 IP for Next Generation Data Centers - AiThority

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