#ai for cybersecurity

[enterprisetrainingexperts]

The Influence of Generative AI, on the Advancement of Cybersecurity Solutions

This study examines how artificial intelligence (AI) is revolutionizing cybersecurity. It provides an analysis of how AI technologiesre changing the way threats are detected defense strategies are developed and incident response is handled in the digital world. Explore the ways in which AI is improving cybersecurity measures to proactively address evolving cyber threats and strengthen security. Find out more about it here.

[professionalitcertification]

Next-Gen Protection: Exploring Generative AI in Cybersecurity

It delves into the transformative role of generative AI in reshaping the landscape of digital security. This exploration highlights how AI in cybersecurity isn't just a tool but a game-changer, offering unparalleled advancements in threat detection and response. By leveraging generative AI cybersecurity techniques, organizations are empowered to predict and neutralize potential cyber threats with greater accuracy and speed than ever before. The article focuses on the innovative applications of AI for cybersecurity, illustrating how generative models can simulate and anticipate complex cyberattacks, enabling proactive defense mechanisms. Read More.

[exposimetro]

Cyber Beach

[technoregression]

YAY!!!

(link to post)

YAY YAY YAY YAY YAYYYYYY

now do Microsoft and apple and closedAI and Samsung and nestle and Coca Cola and

[us-the-voices-and-more-voices-2]

everyone “the ai is going to take over the world!”

Me an intellectual “brain organoid computation is a man made horror”

ANYWAY HOW’S IT GOING GUYS!!!

I for one accept the LITERAL newborn babies-

(this isn’t even a joke they are literally the equivalent of newborns or pre-term babies with the ability to interact and move in digital or real worlds… which we can grow and develop… bro why did we do this to children)

-who are being used for computer computation, I am so fucking sorry for them holy shit.

As soon as they figure out how to make a circulatory system it is OVER, these poor babies LITERAL BABIES will be basically tortured in a torture nexus being used a biological CPU’s for their entire lives..

they have sentience they are LITERALLY BABIES holy shit.

so uh, yeah artificial intelligence being the hype????

(I knew it was never gonna happen lol, it's a bit too hard and annoying)

FUCK NO BRAIN ORGANOIDS!

anyway nothing particularly fancy I’ve literally expected this to happen my entire life now, and it’s here.

like.. clones, genetics, bioengineering, all this cool stuff it's happening!!!

and uh kinda horrifying I do hope they are treating the organoids okay. the military, and other cool governmental orgs are totally using these.

and that is HORRIFYING, but you know we persist!

anyway cyberpunk is here, be nice to the children (brain organoids) and uh go about your day's guys!

[applejuiceyjuice-art]

i’m replying to this on my personal blog because i do not want to address this on the cloutchase blog. normally, we would delete an ask like this, but it bothers me too much personally.

it sounds like your brother is subscribed to “dead internet theory” which is a fringe conspiracy theory and total doomer talk. it basically proposes that the internet "died" in 2016/2017, and is or will be only made of bots that are generating content for other bots to engage with eternally, and that bot content makes up a majority of the internet in order to manipulate people. it is hyperbolic, using language like "everything online is generated by a bot and you are alone."

idk where he got the idea that it started with discord users, and we aren’t just going to take this information as fact for the sake of making content for our silly website gijinkas. it would be incredibly depressing and irresponsible of us to spread potentially dangerous conspiracy information like this, even if we somehow twisted it into the form of a joke (which we can't). cloutchase is moreso a celebration of the unique environment of the internet. (REMINDER: these are just funny ocs, and while there are serious conversations to be had surrounding actual problems with these sites, cloutchase is NOT the space to be doing so. read the FAQ.)

people believe in this theory because it “feels” true, the internet now feels fake and algorithmically produced and that nobody here is a real person. that’s why many people have nostalgia for “old internet,” where we remember a time where everything was seemingly user run.

but really the main difference between old internet and new internet is commercialization. inevitably, websites grew into businesses and businesses must generate profit. algorithms, advertisements, and ai are much more prominent than ever, flagging algorithms with human bias censor actual people, and so on. seeing and experiencing these things makes it kinda feel like nothing matters or whatever.

what’s actually important to YOUR experience online is asking this: is the majority of content you engage with generated by bots? no!!! you make it your own! the internet is massive, but we still find each other!!! there are REAL people behind this project and receiving your messages. we read everything, you know. WE aren't bots.

bots have always been around the internet, it isn’t a new thing. sure, we didn’t have sophisticated ai generation back then, but spam bots have always existed. malware spread through bots, some ARE bots. in some far off circles of the internet now, there are content farms and viewbotting, but here we are, meeting on this tumblr blog.

the internet will only die when its users decide to stop using it. is this not unlike the y2k scare?

[123gracker]

Cybersecurity vs. SaaS Marketing: Why Selling Security Is a Whole Different Game

Selling cybersecurity isn't the same as selling SaaS. This post explores the unique challenges and strategies involved in marketing security solutions. Learn why trust, risk aversion, and compliance play a crucial role in cybersecurity sales, and how to tailor your approach for success.

In the world of B2B tech, marketing cybersecurity solutions is like playing chess while everyone else is playing checkers. It's more complex, higher stakes, and requires a completely different strategy. This article dives into why marketing cybersecurity products is so different from selling your average SaaS solution, and why it matters for your business.

Think about it: When you're marketing a typical SaaS product, you're selling efficiency, productivity, or cost savings. But with cybersecurity? You're selling peace of mind in a digital world full of threats. It's not just about making life easier—it's about keeping businesses safe from invisible dangers.

Here's what we'll uncover:

Why cybersecurity products are trickier to explain (and sell)

How building trust is your secret weapon in security marketing

The constant race against new threats (and how it affects your marketing)

Why your customers might not know they need you (until it's too late)

Navigating the maze of regulations and compliance

Whether you're a marketer looking to level up your skills, a business owner trying to protect your digital assets, or just curious about how the world of cybersecurity ticks, this article will shed light on why marketing in this field is a unique challenge—and an exciting opportunity.

The Need for Specialized Marketing Skills in Cybersecurity

Before we dive into the specific differences, it's crucial to understand why cybersecurity marketing requires a specialized skill set:

Technical Proficiency: Cybersecurity marketers need a deep understanding of complex technical concepts to effectively communicate product value.

Risk Communication: Balancing the need to convey urgency without resorting to fear-mongering requires a nuanced approach.

Regulatory Knowledge: Familiarity with various compliance standards and regulations is essential for credible marketing in this space.

Rapid Adaptation: The ever-evolving threat landscape demands marketers who can quickly pivot strategies and messaging.

Trust Building: In a field where skepticism is high, marketers must excel at building and maintaining trust through every interaction.

Now, let's explore the five critical areas that make cybersecurity marketing a different beast from its SaaS counterparts, and learn how savvy marketers are rising to meet these challenges head-on.

1. Complexity of the Product

Cybersecurity Marketing

Cybersecurity solutions often involve intricate technologies and specialized knowledge. The products are designed to protect against sophisticated threats and vulnerabilities, which can be difficult for non-experts to fully grasp. As a result, cybersecurity marketers face the challenge of:

Simplifying complex concepts without losing their technical essence

Educating potential customers on the importance and functionality of various security measures

Balancing technical accuracy with accessibility to appeal to both IT professionals and business decision-makers

Specialized Skill: The ability to translate highly technical concepts into clear, compelling narratives that resonate with both technical and non-technical audiences.

Traditional SaaS Marketing

While SaaS products can also be complex, they often focus on solving more straightforward business problems. Marketers of traditional SaaS products typically:

Highlight user-friendly interfaces and intuitive functionality

Focus on immediate business benefits and ROI

Use less technical jargon in their marketing materials

2. Emphasis on Trust and Credibility

Cybersecurity Marketing

Trust is paramount in cybersecurity. Organizations are essentially entrusting their digital assets and sensitive information to the cybersecurity solution provider. To build this trust, cybersecurity marketers must:

Demonstrate deep expertise in the field

Showcase a proven track record of protecting against threats

Utilize case studies and customer testimonials extensively

Produce thought leadership content to establish authority

Highlight certifications, compliance, and industry recognition

Specialized Skill: The ability to build and maintain trust through every marketing touchpoint, from content creation to customer interactions.

Traditional SaaS Marketing

While trust is important for all SaaS products, the stakes are generally lower. Traditional SaaS marketers focus on:

User reviews and ratings

Ease of use and customer support

Integration capabilities with other tools

Cost-effectiveness and scalability

3. Rapidly Evolving Threat Landscape

Cybersecurity Marketing

The cybersecurity field is in a constant state of flux, with new threats emerging regularly. This dynamic environment requires cybersecurity marketers to:

Stay informed about the latest threats and trends

Quickly adapt marketing messages to address emerging challenges

Demonstrate how their solutions evolve to counter new risks

Educate the market about new types of threats and vulnerabilities

Position their products as forward-thinking and proactive

Specialized Skill: The ability to rapidly assimilate new information about emerging threats and translate it into compelling marketing messages and strategies.

Traditional SaaS Marketing

While innovation is important in SaaS, the pace of change is typically slower. SaaS marketers often focus on:

Long-term value proposition and stability

Gradual feature improvements and updates

Industry trends rather than immediate threats

4. Target Audience's Risk Awareness

Cybersecurity Marketing

Many organizations take a reactive approach to cybersecurity, only prioritizing it after experiencing a threat. This creates unique challenges and opportunities for marketers:

Educating potential clients about the importance of proactive measures

Using fear-based marketing carefully to highlight risks without being alarmist

Demonstrating the cost of inaction through real-world examples

Targeting both technical (CISOs, IT managers) and non-technical (CEOs, CFOs) decision-makers

Specialized Skill: The ability to effectively communicate risk and urgency without resorting to fear-mongering, while also tailoring messages to different stakeholders within an organization.

SaaS Marketing

Traditional SaaS products often address known pain points or inefficiencies. Marketers typically focus on:

Highlighting productivity gains and cost savings

Showcasing how the product solves existing problems

Appealing to a more defined set of decision-makers within an organization

5. Regulatory and Compliance Considerations

Cybersecurity Marketing

Cybersecurity solutions must often adhere to specific regulatory standards, adding another layer of complexity to marketing efforts:

Communicating compliance capabilities effectively

Addressing concerns related to data protection laws (e.g., GDPR, CCPA)

Highlighting adherence to industry-specific regulations (e.g., HIPAA for healthcare)

Demonstrating how the solution helps clients meet their own compliance requirements

Specialized Skill: A deep understanding of various regulatory frameworks and the ability to articulate how cybersecurity solutions address compliance requirements.

General SaaS Marketing

While some SaaS products may need to address compliance, it's typically not as central to the marketing message:

Focus on general data security and privacy features

Highlight any relevant certifications (e.g., SOC 2)

Address compliance as a feature rather than a core selling point

The Learning Curve for B2B SaaS Marketers

For B2B SaaS marketers transitioning into cybersecurity, the learning curve can be steep and time-consuming. Here's why:

Technical Knowledge Acquisition: Understanding the intricacies of cybersecurity technology, threat landscapes, and defense mechanisms requires significant study and often hands-on experience.

Regulatory Comprehension: Grasping the nuances of various compliance standards and their implications for different industries takes time and continuous learning.

Risk Communication Skills: Developing the ability to effectively communicate about risks without causing panic or disengagement is a delicate skill that takes practice to master.

Trust-Building Expertise: Learning how to establish and maintain trust in a highly skeptical market requires time to build credibility and refine communication strategies.

Rapid Adaptation Abilities: Cultivating the agility to quickly understand and respond to new threats and market changes is an ongoing process that improves with experience.

Cross-Functional Collaboration: Developing the ability to work effectively with technical teams, compliance officers, and C-suite executives requires time to build relationships and understand diverse perspectives.

Industry-Specific Knowledge: Each industry (e.g., healthcare, finance, government) has unique cybersecurity needs and regulations, requiring marketers to develop sector-specific expertise.

Typically, it can take 2-3 years for a B2B SaaS marketer to become proficient in cybersecurity marketing, and 3-5 years to be considered an expert in the field. This timeline can vary based on the individual's background, the complexity of the cybersecurity solutions they're marketing, and the resources available for professional development.

Conclusion

Marketing cybersecurity solutions requires a unique approach that goes beyond traditional SaaS marketing strategies. The complexity of the products, the critical need for trust and credibility, the rapidly changing threat landscape, the varying levels of risk awareness among potential clients, and the intricate regulatory environment all contribute to making cybersecurity marketing a distinct challenge.

Successful cybersecurity marketers must balance technical expertise with clear communication, educate their audience while building trust, and stay agile in response to new threats and regulations. By understanding these key differences and investing in specialized skills, marketers can create more effective strategies that resonate with the specific needs and concerns of the cybersecurity market.

For B2B SaaS marketers looking to transition into this field, patience and dedication are key. The journey to becoming a proficient cybersecurity marketer is demanding but rewarding, offering the opportunity to play a crucial role in protecting organizations and individuals in our increasingly digital world.

Visit for more info: https://gracker.ai/

[xlsdesignt]

what u think, to much colour, or less?

https://sdesignt.threadless.com/

[ogxfuturetech]

The Comprehensive Guide to Web Development, Data Management, and More 

Introduction 

Everything today is technology driven in this digital world. There's a lot happening behind the scenes when you use your favorite apps, go to websites, and do other things with all of those zeroes and ones — or binary data. In this blog, I will be explaining what all these terminologies really means and other basics of web development, data management etc. We will be discussing them in the simplest way so that this becomes easy to understand for beginners or people who are even remotely interested about technology.  JOIN US

What is Web Development? 

Web development refers to the work and process of developing a website or web application that can run in a web browser. From laying out individual web page designs before we ever start coding, to how the layout will be implemented through HTML/CSS. There are two major fields of web development — front-end and back-end. 

Front-End Development 

Front-end development, also known as client-side development, is the part of web development that deals with what users see and interact with on their screens. It involves using languages like HTML, CSS, and JavaScript to create the visual elements of a website, such as buttons, forms, and images. JOIN US

HTML (HyperText Markup Language): 

HTML is the foundation of all website, it helps one to organize their content on web platform. It provides the default style to basic elements such as headings, paragraphs and links. 

CSS (Cascading Style Sheets):  

styles and formats HTML elements. It makes an attractive and user-friendly look of webpage as it controls the colors, fonts, layout. 

JavaScript :  

A language for adding interactivity to a website Users interact with items, like clicking a button to send in a form or viewing images within the slideshow. JOIN US

Back-End Development 

The difference while front-end development is all about what the user sees, back end involves everything that happens behind. The back-end consists of a server, database and application logic that runs on the web. 

Server: 

A server is a computer that holds website files and provides them to the user browser when they request it. Server-Side: These are populated by back-end developers who build and maintain servers using languages like Python, PHP or Ruby. 

Database:  

The place where a website keeps its data, from user details to content and settings The database is maintained with services like MySQL, PostgreSQL, or MongoDB. JOIN US

Application Logic —  

the code that links front-end and back-end It takes user input, gets data from the database and returns right informations to front-end area. 

Why Proper Data Management is Absolutely Critical 

Data management — Besides web development this is the most important a part of our Digital World. What Is Data Management? It includes practices, policies and procedures that are used to collect store secure data in controlled way. 

Data Storage –  

data after being collected needs to be stored securely such data can be stored in relational databases or cloud storage solutions. The most important aspect here is that the data should never be accessed by an unauthorized source or breached. JOIN US

Data processing:  

Right from storing the data, with Big Data you further move on to process it in order to make sense out of hordes of raw information. This includes cleansing the data (removing errors or redundancies), finding patterns among it, and producing ideas that could be useful for decision-making. 

Data Security:  

Another important part of data management is the security of it. It refers to defending data against unauthorized access, breaches or other potential vulnerabilities. You can do this with some basic security methods, mostly encryption and access controls as well as regular auditing of your systems. 

Other Critical Tech Landmarks 

There are a lot of disciplines in the tech world that go beyond web development and data management. Here are a few of them: 

Cloud Computing 

Leading by example, AWS had established cloud computing as the on-demand delivery of IT resources and applications via web services/Internet over a decade considering all layers to make it easy from servers up to top most layer. This will enable organizations to consume technology resources in the form of pay-as-you-go model without having to purchase, own and feed that infrastructure. JOIN US

Cloud Computing Advantages:  

Main advantages are cost savings, scalability, flexibility and disaster recovery. Resources can be scaled based on usage, which means companies only pay for what they are using and have the data backed up in case of an emergency. 

Examples of Cloud Services: 

Few popular cloud services are Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. These provide a plethora of services that helps to Develop and Manage App, Store Data etc. 

Cybersecurity 

As the world continues to rely more heavily on digital technologies, cybersecurity has never been a bigger issue. Protecting computer systems, networks and data from cyber attacks is called Cyber security. 

Phishing attacks, Malware, Ransomware and Data breaches: 

This is common cybersecurity threats. These threats can bear substantial ramifications, from financial damages to reputation harm for any corporation. 

Cybersecurity Best Practices:  

In order to safeguard against cybersecurity threats, it is necessary to follow best-practices including using strong passwords and two-factor authorization, updating software as required, training employees on security risks. 

Artificial Intelligence and Machine Learning 

Artificial Intelligence (AI) and Machine Learning (ML) represent the fastest-growing fields of creating systems that learn from data, identifying patterns in them. These are applied to several use-cases like self driving cars, personalization in Netflix. 

AI vs ML —  

AI is the broader concept of machines being able to carry out tasks in a way we would consider “smart”. Machine learning is a type of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. JOIN US

Applications of Artificial Intelligence and Machine Learning: some common applications include Image recognition, Speech to text, Natural language processing, Predictive analytics Robotics. 

Web Development meets Data Management etc. 

We need so many things like web development, data management and cloud computing plus cybersecurity etc.. but some of them are most important aspects i.e. AI/ML yet more fascinating is where these fields converge or play off each other. 

Web Development and Data Management 

Web Development and Data Management goes hand in hand. The large number of websites and web-based applications in the world generate enormous amounts of data — from user interactions, to transaction records. Being able to manage this data is key in providing a fantastic user experience and enabling you to make decisions based on the right kind of information. 

E.g. E-commerce Website, products data need to be saved on server also customers data should save in a database loosely coupled with orders and payments. This data is necessary for customization of the shopping experience as well as inventory management and fraud prevention. 

Cloud Computing and Web Development 

The development of the web has been revolutionized by cloud computing which gives developers a way to allocate, deploy and scale applications more or less without service friction. Developers now can host applications and data in cloud services instead of investing for physical servers. 

E.g. A start-up company can use cloud services to roll out the web application globally in order for all users worldwide could browse it without waiting due unavailability of geolocation prohibited access. 

The Future of Cybersecurity and Data Management 

Which makes Cybersecurity a very important part of the Data management. The more data collected and stored by an organization, the greater a target it becomes for cyber threats. It is important to secure this data using robust cybersecurity measures, so that sensitive information remains intact and customer trust does not weaken. JOIN US

Ex: A healthcare provider would have to protect patient data in order to be compliant with regulations such as HIPAA (Health Insurance Portability and Accountability Act) that is also responsible for ensuring a degree of confidentiality between a provider and their patients. 

Conclusion 

Well, in a nutshell web-developer or Data manager etc are some of the integral parts for digital world.

As a Business Owner, Tech Enthusiast or even if you are just planning to make your Career in tech — it is important that you understand these. With the progress of technology never slowing down, these intersections are perhaps only going to come together more strongly and develop into cornerstones that define how we live in a digital world tomorrow. 

With the fundamental knowledge of web development, data management, automation and ML you will manage to catch up with digital movements. Whether you have a site to build, ideas data to manage or simply interested in what’s hot these days, skills and knowledge around the above will stand good for changing tech world. JOIN US

[enterprisetrainingexperts]

The Evolution of Cybersecurity: Embracing Generative AI Technologies

It explores the transformative role of AI in cybersecurity. It delves into how generative AI cybersecurity strategies are reshaping defense mechanisms, offering advanced solutions to combat digital threats. Highlighting the integration of AI for cybersecurity, the piece underscores the synergy between artificial intelligence and digital protection, marking a new era in AI cybersecurity. Read More.

[professionalitcertification]

It explores the transformative role of AI in cybersecurity. It delves into how generative AI cybersecurity strategies are reshaping defense mechanisms, offering advanced solutions to combat digital threats. Highlighting the integration of AI for cybersecurity, the piece underscores the synergy between artificial intelligence and digital protection, marking a new era in AI cybersecurity. Read More.

[frank-olivier]

The AI Dilemma: Balancing Benefits and Risks

One of the main focuses of AI research is the development of Artificial General Intelligence (AGI), a hypothetical AI system that surpasses human intelligence in all areas. The AGI timeline, which outlines the expected time frame for the realization of AGI, is a crucial aspect of this research. While some experts predict that AGI will be achieved within the next few years or decades, others argue that it could take centuries or even millennia. Regardless of the time frame, the potential impact of AGI on human society and civilization is enormous and far-reaching.

Another important aspect of AI development is task specialization, where AI models are designed to excel at specific tasks, improving efficiency, productivity, and decision-making. Watermarking technology, which identifies the source of AI-generated content, is also an important part of AI development and addresses concerns about intellectual property and authorship. Google's SynthID technology, which detects and removes AI-generated content on the internet, is another significant development in this field.

However, AI development also brings challenges and concerns. Safety concerns, such as the potential for AI systems to cause harm or injury, must be addressed through robust safety protocols and risk management strategies. Testimonials from whistleblowers and insider perspectives can provide valuable insight into the challenges and successes of AI development and underscore the need for transparency and accountability. Board oversight and governance are also critical to ensure that AI development meets ethical and regulatory standards.

The impact of AI on different industries and aspects of society is also an important consideration. The potential of AI to transform industries such as healthcare, finance and education is enormous, but it also raises concerns about job losses, bias and inequality. The development of AI must be accompanied by a critical examination of its social and economic impacts to ensure that the benefits of AI are distributed fairly and the negative consequences are mitigated.

By recognizing the challenges and complexities of AI development, we can work toward creating a future where AI is developed and deployed in responsible, ethical and beneficial ways.

Ex-OpenAI Employee Reveals Terrifying Future of AI (Matthew Berman, June 2024)

Ex-OpenAI Employees Just Exposed The Truth About AGI (TheAIGRID, October 2024)

Anthropic CEO: AGI is Closer Than You Think [machines of loving grace] (TheAIGRID, October 2024)

AGI in 5 years? Ben Goertzel on Superintelligence (Machine Learning Street Talk, October 2024)

Generative AI and Geopolitical Disruption (Solaris Project, October 2024)

Monday, October 28, 2024

[thursdayisbetterthanfriday]

Atom: The Beginning & AI Cybersecurity

Atom: The Beginning is a manga about two researchers creating advanced robotic AI systems, such as unit A106. Their breakthrough is the Bewusstein (Translation: awareness) system, which aims to give robots a "heart", or a kind of empathy. In volume 2, A106, or Atom, manages to "beat" the highly advanced robot Mars in a fight using a highly abstracted machine language over WiFi to persuade it to stop.

This may be fiction, but it has parallels with current AI development in the use of specific commands to over-run safety guides. This has been demonstrated in GPT models, such as ChatGPT, where users are able to subvert models to get them to output "banned" information by "pretending" to be another AI system, or other means.

There are parallels to Atom, in a sense with users effectively "persuading" the system to empathise. In reality, this is the consequence of training Large Language Models (LLM's) on relatively un-sorted input data. Until recent guardrail placed by OpenAI there were no commands to "stop" the AI from pretending to be an AI from being a human who COULD perform these actions.

As one research paper put it:

"Such attacks can result in erroneous outputs, model-generated hate speech, and the exposure of users’ sensitive information." Branch, et al. 2022

There are, however, more deliberately malicious actions which AI developers can take to introduce backdoors.

In Atom, Volume 4, Atom faces off against Ivan - a Russian military robot. Ivan, however, has been programmed with data collected from the fight between Mars and Atom.

What the human researchers in the manga didn't realise, was the code transmissions were a kind of highly abstracted machine level conversation. Regardless, the "anti-viral" commands were implemented into Ivan and, as a result, Ivan parrots the words Atom used back to it, causing Atom to deliberately hold back.

In AI cybersecurity terms, this is effectively an AI-on-AI prompt injection attack. Attempting to use the words of the AI against itself to perform malicious acts. Not only can this occur, but AI creators can plant "backdoor commands" into AI systems on creation, where a specific set of inputs can activate functionality hidden to regular users.

This is a key security issue for any company training AI systems, and has led many to reconsider outsourcing AI training of potential high-risk AI systems. Researchers, such as Shafi Goldwasser at UC Berkley are at the cutting edge of this research, doing work compared to the key encryption standards and algorithms research of the 1950s and 60s which have led to today's modern world of highly secure online transactions and messaging services.

From returning database entries, to controlling applied hardware, it is key that these dangers are fully understood on a deep mathematical, logical, basis or else we face the dangerous prospect of future AI systems which can be turned against users.

As AI further develops as a field, these kinds of attacks will need to be prevented, or mitigated against, to ensure the safety of systems that people interact with.

References:

Twitter pranksters derail GPT-3 bot with newly discovered “prompt injection” hack - Ars Technica (16/09/2023)

EVALUATING THE SUSCEPTIBILITY OF PRE-TRAINED LANGUAGE MODELS VIA HANDCRAFTED ADVERSARIAL EXAMPLES - Hezekiah Branch et. al, 2022 Funded by Preamble

In Neural Networks, Unbreakable Locks Can Hide Invisible Doors - Quanta Magazine (02/03/2023)

Planting Undetectable Backdoors in Machine Learning Models - Shafi Goldwasser et.al, UC Berkeley, 2022

[jcmarchi]

Toward a code-breaking quantum computer

New Post has been published on https://thedigitalinsider.com/toward-a-code-breaking-quantum-computer/

Toward a code-breaking quantum computer

The most recent email you sent was likely encrypted using a tried-and-true method that relies on the idea that even the fastest computer would be unable to efficiently break a gigantic number into factors.

Quantum computers, on the other hand, promise to rapidly crack complex cryptographic systems that a classical computer might never be able to unravel. This promise is based on a quantum factoring algorithm proposed in 1994 by Peter Shor, who is now a professor at MIT.

But while researchers have taken great strides in the last 30 years, scientists have yet to build a quantum computer powerful enough to run Shor’s algorithm.

As some researchers work to build larger quantum computers, others have been trying to improve Shor’s algorithm so it could run on a smaller quantum circuit. About a year ago, New York University computer scientist Oded Regev proposed a major theoretical improvement. His algorithm could run faster, but the circuit would require more memory.

Building off those results, MIT researchers have proposed a best-of-both-worlds approach that combines the speed of Regev’s algorithm with the memory-efficiency of Shor’s. This new algorithm is as fast as Regev’s, requires fewer quantum building blocks known as qubits, and has a higher tolerance to quantum noise, which could make it more feasible to implement in practice.

In the long run, this new algorithm could inform the development of novel encryption methods that can withstand the code-breaking power of quantum computers.

“If large-scale quantum computers ever get built, then factoring is toast and we have to find something else to use for cryptography. But how real is this threat? Can we make quantum factoring practical? Our work could potentially bring us one step closer to a practical implementation,” says Vinod Vaikuntanathan, the Ford Foundation Professor of Engineering, a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and senior author of a paper describing the algorithm.

The paper’s lead author is Seyoon Ragavan, a graduate student in the MIT Department of Electrical Engineering and Computer Science. The research will be presented at the 2024 International Cryptology Conference.

Cracking cryptography

To securely transmit messages over the internet, service providers like email clients and messaging apps typically rely on RSA, an encryption scheme invented by MIT researchers Ron Rivest, Adi Shamir, and Leonard Adleman in the 1970s (hence the name “RSA”). The system is based on the idea that factoring a 2,048-bit integer (a number with 617 digits) is too hard for a computer to do in a reasonable amount of time.

That idea was flipped on its head in 1994 when Shor, then working at Bell Labs, introduced an algorithm which proved that a quantum computer could factor quickly enough to break RSA cryptography.

“That was a turning point. But in 1994, nobody knew how to build a large enough quantum computer. And we’re still pretty far from there. Some people wonder if they will ever be built,” says Vaikuntanathan.

It is estimated that a quantum computer would need about 20 million qubits to run Shor’s algorithm. Right now, the largest quantum computers have around 1,100 qubits.

A quantum computer performs computations using quantum circuits, just like a classical computer uses classical circuits. Each quantum circuit is composed of a series of operations known as quantum gates. These quantum gates utilize qubits, which are the smallest building blocks of a quantum computer, to perform calculations.

But quantum gates introduce noise, so having fewer gates would improve a machine’s performance. Researchers have been striving to enhance Shor’s algorithm so it could be run on a smaller circuit with fewer quantum gates.

That is precisely what Regev did with the circuit he proposed a year ago.

“That was big news because it was the first real improvement to Shor’s circuit from 1994,” Vaikuntanathan says.

The quantum circuit Shor proposed has a size proportional to the square of the number being factored. That means if one were to factor a 2,048-bit integer, the circuit would need millions of gates.

Regev’s circuit requires significantly fewer quantum gates, but it needs many more qubits to provide enough memory. This presents a new problem.

“In a sense, some types of qubits are like apples or oranges. If you keep them around, they decay over time. You want to minimize the number of qubits you need to keep around,” explains Vaikuntanathan.

He heard Regev speak about his results at a workshop last August. At the end of his talk, Regev posed a question: Could someone improve his circuit so it needs fewer qubits? Vaikuntanathan and Ragavan took up that question.

Quantum ping-pong

To factor a very large number, a quantum circuit would need to run many times, performing operations that involve computing powers, like 2 to the power of 100.

But computing such large powers is costly and difficult to perform on a quantum computer, since quantum computers can only perform reversible operations. Squaring a number is not a reversible operation, so each time a number is squared, more quantum memory must be added to compute the next square.

The MIT researchers found a clever way to compute exponents using a series of Fibonacci numbers that requires simple multiplication, which is reversible, rather than squaring. Their method needs just two quantum memory units to compute any exponent.

“It is kind of like a ping-pong game, where we start with a number and then bounce back and forth, multiplying between two quantum memory registers,” Vaikuntanathan adds.

They also tackled the challenge of error correction. The circuits proposed by Shor and Regev require every quantum operation to be correct for their algorithm to work, Vaikuntanathan says. But error-free quantum gates would be infeasible on a real machine.

They overcame this problem using a technique to filter out corrupt results and only process the right ones.

The end-result is a circuit that is significantly more memory-efficient. Plus, their error correction technique would make the algorithm more practical to deploy.

“The authors resolve the two most important bottlenecks in the earlier quantum factoring algorithm. Although still not immediately practical, their work brings quantum factoring algorithms closer to reality,” adds Regev.

In the future, the researchers hope to make their algorithm even more efficient and, someday, use it to test factoring on a real quantum circuit.

“The elephant-in-the-room question after this work is: Does it actually bring us closer to breaking RSA cryptography? That is not clear just yet; these improvements currently only kick in when the integers are much larger than 2,048 bits. Can we push this algorithm and make it more feasible than Shor’s even for 2,048-bit integers?” says Ragavan.

This work is funded by an Akamai Presidential Fellowship, the U.S. Defense Advanced Research Projects Agency, the National Science Foundation, the MIT-IBM Watson AI Lab, a Thornton Family Faculty Research Innovation Fellowship, and a Simons Investigator Award.

[nando161mando]

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