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Threat Landscape
Artificial Intelligence Cyber Security Machine learning Quantum Computers Threat Landscape

Quantum Computing Meets AI: A Lethal Combination

 

Quantum computers are getting closer to Q-day — the day when they will be able to crack existing encryption techniques — as we continue to assign more infrastructure functions to artificial intelligence (AI). This could jeopardise autonomous control systems that rely on AI and ML for decision-making, as well as the security of digital communications. 

As AI and quantum converge to reveal remarkable novel technologies, they will also combine to develop new attack vectors and quantum cryptanalysis.

How far off is this threat?

For major organisations and governments, the transition to post-quantum cryptography (PQC) will take at least ten years, if not much more. Since the last encryption standard upgrade, the size of networks and data has increased, enabling large language models (LLMs) and related specialised technologies. 

While generic versions are intriguing and even enjoyable, sophisticated AI will be taught on expertly picked data to do specialised tasks. This will quickly absorb all of the previous research and information created, providing profound insights and innovations at an increasing rate. This will complement, not replace, human brilliance, but there will be a disruptive phase for cybersecurity.

If a cryptographically relevant quantum computer becomes available before PQC is fully deployed, the repercussions are unknown in the AI era. Regular hacking, data loss, and even disinformation on social media will bring back memories of the good old days before AI driven by evil actors became the main supplier of cyber carcinogens.

When AI models are hijacked, the combined consequence of feeding live AI-controlled systems personalised data with malicious intent will become a global concern. The debate in Silicon Valley and political circles is already raging over whether AI should be allowed to carry out catastrophic military operations. Regardless of existing concerns, this is undoubtedly the future. 

However, most networks and economic activity require explicit and urgent defensive actions. To take on AI and quantum, critical infrastructure design and networks must advance swiftly and with significantly increased security. With so much at stake and new combined AI-quantum attacks unknown, one-size-fits-all upgrades to libraries such as TLS will not suffice. 

Internet 1.0 was built on old 1970s assumptions and limitations that predated modern cloud technology and its amazing redundancy. The next version must be exponentially better, anticipating the unknown while assuming that our current security estimations are incorrect. The AI version of Stuxnet should not surprise cybersecurity experts because the previous iteration had warning indications years ago.
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Technology
AI Model ChatGPT Machine learning OpenAI Technology

OpenAI's Latest AI Model Faces Diminishing Returns

 

OpenAI's latest AI model is yielding diminishing results while managing the demands of recent investments. 

The Information claims that OpenAI's upcoming AI model, codenamed Orion, is outperforming its predecessors in terms of performance gains. In staff testing, Orion reportedly achieved the GPT-4 performance level after only 20% of its training. 

However, the shift from GPT-4 to the upcoming GPT-5 is expected to result in fewer quality gains than the jump from GPT-3 to GPT-4.

“Some researchers at the company believe Orion isn’t reliably better than its predecessor in handling certain tasks,” noted employees in the report. “Orion performs better at language tasks but may not outperform previous models at tasks such as coding, according to an OpenAI employee.”

AI training often yields the biggest improvements in performance in the early stages and smaller gains in subsequent phases. As a result, the remaining 80% of training is unlikely to provide breakthroughs comparable to earlier generational improvements. This predicament with its latest AI model comes at a critical juncture for OpenAI, following a recent investment round that raised $6.6 billion.

With this financial backing, investors' expectations rise, as do technical hurdles that confound typical AI scaling approaches. If these early versions do not live up to expectations, OpenAI's future fundraising chances may not be as attractive. The report's limitations underscore a major difficulty for the entire AI industry: the decreasing availability of high-quality training data and the need to remain relevant in an increasingly competitive environment.

A June research (PDF) predicts that between 2026 and 2032, AI companies will exhaust the supply of publicly accessible human-generated text data. Developers have "largely squeezed as much out of" the data that has been utilised to enable the tremendous gains in AI that we have witnessed in recent years, according to The Information. OpenAI is fundamentally rethinking its approach to AI development in order to meet these challenges. 

“In response to the recent challenge to training-based scaling laws posed by slowing GPT improvements, the industry appears to be shifting its effort to improving models after their initial training, potentially yielding a different type of scaling law,” states The Information.
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Technology
AI backdoors AI Security backdoor vulnerability computational graph Cybersecurity HiddenLayer Machine learning model architecture neural network ShadowLogic Technology

HiddenLayer Unveils "ShadowLogic" Technique for Implanting Codeless Backdoors in AI Models

 

Manipulating an AI model's computational graph can allow attackers to insert codeless, persistent backdoors, reports AI security firm HiddenLayer. This vulnerability could lead to malicious use of machine learning (ML) models in a variety of applications, including supply chain attacks.

Known as ShadowLogic, the technique works by tampering with the computational graph structure of a model, triggering unwanted behavior in downstream tasks. This manipulation opens the door to potential security breaches across AI supply chains.

Traditional backdoors offer unauthorized system access, bypassing security layers. Similarly, AI models can be exploited to include backdoors or manipulated to yield malicious outcomes. However, any changes in the model could potentially affect these hidden pathways.

HiddenLayer explains that using ShadowLogic enables threat actors to embed codeless backdoors that persist through model fine-tuning, allowing highly targeted and stealthy attacks.

Building on prior research showing that backdoors can be implemented during the training phase, HiddenLayer investigated how to inject a backdoor into a model's computational graph post-training. This bypasses the need for training phase vulnerabilities.

A computational graph is a mathematical blueprint that controls a neural network's operations. These graphs represent data inputs, mathematical functions, and learning parameters, guiding the model’s forward and backward propagation.

According to HiddenLayer, this graph acts like compiled code in a program, with specific instructions for the model. By manipulating the graph, attackers can override normal model logic, triggering predefined behavior when the model processes specific input, such as an image pixel, keyword, or phrase.

ShadowLogic leverages the wide range of operations supported by computational graphs to embed triggers, which could include checksum-based activations or even entirely new models hidden within the original one. HiddenLayer demonstrated this method on models like ResNet, YOLO, and Phi-3 Mini.

These compromised models behave normally but respond differently when presented with specific triggers. They could, for example, fail to detect objects or generate controlled responses, demonstrating the subtlety and potential danger of ShadowLogic backdoors.

Such backdoors introduce new vulnerabilities in AI models that do not rely on traditional code exploits. Embedded within the model’s structure, these backdoors are harder to detect and can be injected across a variety of graph-based architectures.

ShadowLogic is format-agnostic and can be applied to any model that uses graph-based architectures, regardless of the domain, including autonomous navigation, financial predictions, healthcare diagnostics, and cybersecurity.

HiddenLayer warns that no AI system is safe from this type of attack, whether it involves simple classifiers or advanced large language models (LLMs), expanding the range of potential targets.






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Technology
Artificial Intelligence Llama 3.2 Machine learning Mera Technology

Meta Unveils its First Open AI Model That Can Process Images

 

Meta has released new versions of its renowned open source AI model Llama, including small and medium-sized models capable of running workloads on edge and mobile devices. 

Llama 3.2 models were showcased at the company's annual Meta Connect event. They can support multilingual text production and vision apps like image recognition. 

“This is our first open source, multimodal model, and it’s going to enable a lot of interesting applications that require visual understanding,” stated Mark Zuckerberg, CEO of Meta.

Llama 3.2 is based on the huge open source model Llama 3.1, which was released in late July. The previous Llama model was the largest open-source AI model in history, with 405 billion parameters (parameters are the adjustable variables within an AI model that help it learn patterns from data). The size shows the AI's ability to interpret and generate human-like text. 

The new Llama models presented at Meta Connect 2024 are significantly reduced in size. Meta explained that they choose to develop smaller models because not all researchers have the required computational resources and expertise to run a model as large as Llama 3.1.

In terms of performance, Meta's new Llama 3.2 models compete with industry-leading systems from Anthropic and OpenAI. The 3B model exceeds Google's Gemma 2 2.6B and Microsoft's Phi 3.5-mini in tasks such as instruction following and content summarisation. The 90B version, the largest of the models, surpasses both Claude 3-Haiku and GPT-4o-mini on a variety of benchmarks, including the widely used MMLU test, an industry-leading AI model evaluation tool. 

How to access Llama 3.2 models 

The new Llama 3.2 models are open source, so anyone can download and use them to power AI applications. The models can be downloaded straight from llama.com or Hugging Face, a popular open source repository platform. Llama 3.2 models are also available through a number of cloud providers, including Google Cloud, AWS, Nvidia, Microsoft Azure, and Grow, among others.

According to figures published in early September, demand for Meta's Llama models from cloud customers increased tenfold between January and July, and is expected to rise much more in the wake of the new 3.2 line of models. Meta partner Together AI is providing free access to the vision version of Llama 3.2 11B on its platform till the end of the year. 

Vipul Ved Prakash, founder and CEO of Together AI, stated that the new multimodal models will drive the adoption of open-source AI among developers and organisations. 

“We’re thrilled to partner with Meta to offer developers free access to the Llama 3.2 vision model and to be one of the first API providers for Llama Stack,” Prakash noted. “With Together AI's support for Llama models and Llama Stack, developers and enterprises can experiment, build, and scale multimodal applications with the best performance, accuracy, and cost.”
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Technology
AI Models data poisoning JFrog Security Machine learning Technology

Data Poisoning: The Hidden Threat to AI Models



As ongoing developments in the realms of artificial intelligence and machine learning take place at a dynamic rate, yet another new form of attack is emerging, one which can topple all those systems we use today without much ado: data poisoning. This type of attack involves tampering with data used by AI models in training to make them malfunction, often undetectably. The issue came to light when recently, more than 100 malicious models were uncovered on the popular repository for AI, Hugging Face, by a software management company, JFrog. 

What is Data Poisoning?

Data poisoning is an attack method on AI models by corrupting the data used for its training. In other words, the intent is to have the model make inappropriate predictions or choices. Besides, unlike traditional hacking, it doesn't require access to the system; therefore, data poisoning manipulates input data either before the deployment of an AI model or after the deployment of the AI model, and that makes it very difficult to detect.

One attack happens at the training phase when an attacker manages to inject malicious data into any AI model. Yet another attack happens post-deployment when poisoned data is fed to the AI; it yields wrong outputs. Both kinds of attacks remain hardly detectable and cause damage to the AI system in the long run.

According to research by JFrog, investigators found a number of suspicious models uploaded to Hugging Face, a community where users can share AI models. Those contained encoded malicious code, which the researchers believe hackers-those potentially coming from the KREOnet research network in Korea-might have embedded. The most worrying aspect, however, was the fact that these malicious models went undetected by masquerading as benign.

That's a serious threat because many AI systems today use a great amount of data from different sources, including the internet. In cases where attackers manage to change the data used in the training of a model, that could mean anything from misleading results to actual large-scale cyberattacks.

Why It's Hard to Detect

One of the major challenges with data poisoning is that AI models are built by using enormous data sets, which makes it difficult for researchers to always know what has gone into the model. A lack of clarity of this kind in turn creates ways in which attackers can sneak in poisoned data without being caught.

But it gets worse: AI systems that scrape data from the web continuously in order to update themselves could poison their own training data. This sets up the alarming possibility of an AI system's gradual breakdown, or "degenerative model collapse."

The Consequences of Ignoring the Threat

If left unmitigated, data poisoning could further allow attackers to inject stealth backdoors in AI software that enable them to conduct malicious actions or cause any AI system to behave in ways unexpected. Precisely, they can run malicious code, allow phishing, and rig AI predictions for various nefarious uses.

The cybersecurity industry must take this as a serious threat since more dependence occurs on generative AI linked together, alongside LLMs. If one fails to do so, widespread vulnerability across the complete digital ecosystem will result.

How to Defend Against Data Poisoning

The protection of AI models against data poisoning calls for vigilance throughout the process of the AI development cycle. Experts say that this may require oversight by organisations in using only data from sources they can trust for training the AI model. The Open Web Application Security Project, or OWASP, has provided a list of some best ways to avoid data poisoning; a few of these include frequent checks to find biases and abnormalities during the training of data.

Other recommendations come in the form of multiple AI algorithms that verify results against each other to locate inconsistency. If an AI model starts producing strange results, fallback mechanisms should be in place to prevent any harm.

This also encompasses simulated data poisoning attacks run by cybersecurity teams to test their AI systems for robustness. While it is hard to build an AI system that is 100% secure, frequent validation of predictive outputs goes a long way in detecting and preventing poisoning.

Creating a Secure Future for AI

While AI keeps evolving, there is a need to instil trust in such systems. This will only be possible when the entire ecosystem of AI, even the supply chains, forms part of the cybersecurity framework. This would be achievable through monitoring inputs and outputs against unusual or irregular AI systems. Therefore, organisations will build robust, and more trustworthy models of AI.

Ultimately, the future of AI hangs in the balance with our capability to race against emerging threats like data poisoning. In sum, the ability of businesses to proactively take steps toward the security of AI systems today protects them from one of the most serious challenges facing the digital world.

The bottom line is that AI security is not just about algorithms; it's about the integrity for the data powering those algorithms.


 

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threat mitigation
AI collaboration connected mindset Cybersecurity defense strategy Machine learning National Security public-private partnerships Technology threat mitigation

Adopting a Connected Mindset: A Strategic Imperative for National Security

 

In today's rapidly advancing technological landscape, connectivity goes beyond being just a buzzword—it has become a strategic necessity for both businesses and national defense. As security threats grow more sophisticated, an integrated approach that combines technology, strategic planning, and human expertise is essential. Embracing a connected mindset is crucial for national security, and here's how it can be effectively implemented.What is a Connected Mindset?A connected mindset involves understanding that security is not an isolated function but a comprehensive effort that spans multiple domains and disciplines. It requires seamless collaboration between government, private industry, and academia to address security challenges. This approach recognizes that modern threats are interconnected and complex, necessitating a comprehensive response.Over the past few decades, security threats have evolved significantly. While traditional threats like military aggression still exist, newer challenges such as cyber threats, economic espionage, and misinformation have emerged. Cybersecurity has become a major concern as both state and non-state actors develop new methods to exploit vulnerabilities in digital infrastructure. Attacks on critical systems can disrupt essential services, leading to widespread chaos and posing risks to public safety. The recent rise in ransomware attacks on healthcare, financial sectors, and government entities underscores the need for a comprehensive approach to these challenges.The Central Role of TechnologyAt the core of the connected mindset is technology. Advances in artificial intelligence (AI), machine learning, and big data analytics provide valuable tools for detecting and countering threats. However, these technologies need to be part of a broader strategy that includes human insight and collaborative efforts. AI can process large datasets to identify patterns and anomalies indicating potential threats, while machine learning algorithms can predict vulnerabilities and suggest proactive measures. Big data analytics enable real-time insights into emerging risks, facilitating faster and more effective responses.Despite the critical role of technology, human expertise remains indispensable. Cybersecurity professionals, intelligence analysts, and policymakers must collaborate to interpret data, evaluate risks, and devise strategies. Public-private partnerships are vital for fostering this cooperation, as the private sector often possesses cutting-edge technology and expertise, while the government has access to critical intelligence and regulatory frameworks. Together, they can build a more resilient security framework.To implement a connected mindset effectively, consider the following steps:
  • Promote Continuous Education and Training: Regular training programs are essential to keep professionals up-to-date with the latest threats and technologies. Cybersecurity certifications, workshops, and simulations can help enhance skills and preparedness.
  • Encourage Information Sharing: Establishing robust platforms and protocols for information sharing between public and private sectors can enhance threat detection and response times. Shared information must be timely, accurate, and actionable.
  • Invest in Advanced Technology: Governments and organizations should invest in AI, machine learning, and advanced cybersecurity tools to stay ahead of evolving threats, ensuring real-time threat analysis capabilities.
  • Foster Cross-Sector Collaboration: Cultivating a culture of collaboration is crucial. Regular meetings, joint exercises, and shared initiatives can build stronger partnerships and trust.
  • Develop Supportive Policies: Policies and regulations should encourage a connected mindset by promoting collaboration and innovation while protecting data privacy and supporting effective threat detection.
A connected mindset is not just a strategic advantage—it is essential for national security. As threats evolve, adopting a holistic approach that integrates technology, human insight, and cross-sector collaboration is crucial. By fostering this mindset, we can create a more resilient and secure future capable of addressing the complexities of modern security challenges. In a world where physical and digital threats increasingly overlap, a connected mindset paves the way for enhanced national security and a safer global community.

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Machine learning
Advanced Technology AI cybersecurity AI technology Cyber Security cybersecurity solutions Machine learning

How AI and Machine Learning Are Revolutionizing Cybersecurity

 

The landscape of cybersecurity has drastically evolved over the past decade, driven by increasingly sophisticated and costly cyberattacks. As more businesses shift online, they face growing threats, creating a higher demand for innovative cybersecurity solutions. The rise of AI and machine learning is reshaping the cybersecurity industry, offering powerful tools to combat these modern challenges. 

AI and machine learning, once seen as futuristic technologies, are now integral to cybersecurity. By processing vast amounts of data and identifying patterns at incredible speeds, these technologies surpass human capabilities, providing a new level of protection. Traditional cybersecurity methods relied heavily on human expertise and signature-based detection, which were effective in the past. However, with the increasing complexity of cybercrime, AI offers a significant advantage by enabling faster and more accurate threat detection and response. Machine learning is the engine driving AI-powered cybersecurity solutions. 

By feeding large datasets into algorithms, machine learning models can uncover hidden patterns and predict potential threats. This ability allows AI to detect unknown risks and anticipate future attacks, significantly enhancing the effectiveness of cybersecurity measures. AI-powered systems can mimic human thought processes to some extent, enabling them to learn from experience, adapt to new challenges, and make real-time decisions. These systems can block malicious traffic, quarantine files, and even take independent actions to counteract threats, all without human intervention. By analyzing vast amounts of data rapidly, AI can identify patterns and predict potential cyberattacks. This proactive approach allows security teams to defend against threats before they escalate, reducing the risk of damage. 

Additionally, AI can automate incident response, acting swiftly to detect breaches and contain damage, often faster than any human could. AI also plays a crucial role in hunting down zero-day threats, which are previously unknown vulnerabilities that attackers can exploit before they are patched. By analyzing data for anomalies, AI can identify these vulnerabilities early, allowing security teams to address them before they are exploited. 

Moreover, AI enhances cloud security by analyzing data to detect threats and vulnerabilities, ensuring that businesses can safely transition to cloud-based systems. The integration of AI in various cybersecurity tools, such as Security Orchestration, Automation, and Response (SOAR) platforms and endpoint protection solutions, is a testament to its potential. With AI’s ability to detect and respond to threats faster and more accurately than ever before, the future of cybersecurity looks promising.
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Vulnerability management
Data Centre Liquid Cooling Machine learning Technology Vulnerability management

Five Challenges to Adoption of Liquid Cooling in Data Centers

 

Data centre liquid cooling systems are becoming increasingly popular due to their greater heat management effectiveness when compared to traditional air cooling methods. However, as technology advances, new security issues emerge, such as cybersecurity and physical risks. 

These concerns are critical to industry professionals as they can result in data breaches, system disruptions, and considerable operational downtime. Understanding and minimising these risks ensures that a data centre is reliable and secure. This method emphasises the significance of a comprehensive approach to digital and physical security in the changing landscape of data centre cooling technology. 

But the transition from air to liquid is not easy. Here are some of the main challenges to the implementation of liquid cooling in data centres: 

Two cooling systems instead of one

It is rarely practical for an established data centre to switch to liquid cooling one rack at a time. The facilities personnel will have to operate two cooling systems rather than one, according to Lex Coors, chief data centre technology and engineering officer of Interxion, the European colocation behemoth. This makes liquid cooling a better option for new data centres or those in need of a major overhaul. 

No standards 

The lack of industry standards for liquid cooling is a significant barrier to widespread use of the technology. "The customer, first of all, has to come with their own IT equipment ready for liquid cooling," Coors stated. "And it's not very standardized -- we can't simply connect it and let it run.” Interxion does not currently have consumers using liquid cooling, but the company is prepared to support it if necessary, according to Coors. 

Corrosion

Corrosion is a challenge in liquid cooling, as it is in any system that uses water to flow through pipes. "Corrosion in those small pipes is a big issue, and this is one of the things we are trying to solve today," Mr. Coors added. Manufacturers are improving pipelines to reduce the possibility of leaks and to automatically close if one occurs. 

Physical security 

Physical tampering with data centre liquid cooling systems poses serious security threats since unauthorised modifications can disrupt operations and jeopardise system integrity. Malicious insiders, such as disgruntled or contractors, can use their physical access to change settings, introduce contaminants, or disable cooling devices. 

Such acts can cause overheating, device failures, and protracted downtime, compromising data centre performance and security. Insider threats highlight the importance of rigorous access controls, extensive background checks, and ongoing monitoring of personnel activities. These elements help to prevent and respond promptly to physical sabotage. 

Operational complexity 

The company that offers colocation and cloud computing services, Markley Group, plans to implement liquid cooling in a high-performance cloud data centre early next year. According to Jeff Flanagan, executive VP of Markley Group, the biggest risk could be increased operational complexity. 

"As a data center operator, we prefer simplicity," he said. "The more components you have, the more likely you are to have failure. When you have chip cooling, with water going to every CPU or GPU in a server, you're adding a lot of components to the process, which increases the potential likelihood of failure.”
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Virtual Reality
Artificial Intelligence Education Machine learning Technology Virtual Reality

Here's How Technology is Enhancing the Immersive Learning Experience

 

In the ever-changing environment of education, a seismic shift is taking place, with technology emerging as a change agent and disrupting conventional approaches to learning. Technology bridges the gap between theoretical knowledge and practical application, especially in the transformative realm of immersive technologies such as virtual reality (VR) and augmented reality (AR). These technologies give educators unparalleled possibilities for expanding learning experiences beyond the constraints of traditional textbooks and lectures. 

VR: A pathway to boundless exploration

Virtual reality (VR), previously considered a futuristic concept, is now a powerful force in education. It immerses students in computer-generated settings, promoting deep engagement and comprehension. VR integration in education is more than just a change; it is a revolution. From simulated field trips to historical recreations, the many applications of virtual reality in education allow students to delve into subjects like never before, discovering the world without leaving their seats. VR and AR can accommodate different learning styles. 

According to Deloitte, students who use immersive technologies are 30 times more likely to complete their schoolwork than their traditional counterparts. The rise of augmented reality (AR), virtual reality (VR), and other cutting-edge technology has considerably improved problem-solving abilities across multiple sectors. These immersive technologies promote inventive problem-solving approaches, allowing students to visualise complex situations and devise effective solutions in sectors such as engineering and design. 

A research published in the International Journal of Human-Computer Interaction states that incorporating immersive technologies into education improves critical thinking and problem-solving skills by giving students hands-on experience in simulated environments. 

The convergence of immersive technology goes beyond VR and AR. The introduction of these technologies aligns with a larger trend in industry recognition. According to Forbes, companies that use AR/VR see improved decision-making processes as a result of greater data visualisation and collaborative issue solving. In summary, the emergence of AR/VR, along with other sophisticated technologies, demonstrates its critical role in catalysing novel problem-solving approaches across varied sectors, hence increasing efficiency and understanding. 

Multiple sectors have begun to recognise the importance of AR and VR skills. The demand for employees with experience in these technologies is expanding. According to research by Burning Glass Technologies, job postings requiring VR skills surged by over 800% between 2014 and 2019. As we embrace the present, we must also consider the future. Predicting trends helps us plan for what comes next, keeping education at the forefront of technology breakthroughs.
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smart speakers
Advanced Technology Cyber Security Facial Recognition home security system Machine learning natural language processing smart cameras smart locks smart speakers

Enhancing Home Security with Advanced Technology

 

With global tensions on the rise, ensuring your home security system is up to par is a wise decision. Advances in science and technology have provided a variety of effective options, with even more innovations on the horizon.

Smart Speakers

Smart speakers like Amazon Echo, Google Nest, and Apple HomePod utilize advanced natural language processing (NLP) to understand and process human language. They also employ machine learning algorithms to recognize occupants and detect potential intruders. This voice recognition feature reduces the likelihood of system tampering.

Smart Cameras
Smart cameras offer an even higher level of security. These devices use facial recognition technology to control access to your home and can detect suspicious activities on your property. In response to threats, they can automatically lock doors and alert authorities. These advancements are driven by ongoing research in neural networks and artificial intelligence, which continue to evolve.

Smart Locks
Smart locks, such as those by Schlage, employ advanced encryption methods to prevent unauthorized entry while enhancing convenience for homeowners. These locks can be operated via smartphone and support multiple access codes for family members. The field of cryptography ensures that digital keys and communications between the lock and smartphone remain secure, with rapid advancements in this area.

Future Trends in Smart Home Security Technology

Biometric Security
Biometric technologies, including facial recognition and fingerprint identification, are expected to gain popularity as their accuracy improves. These methods provide a higher level of security compared to traditional keys or passcodes.

Blockchain for Security
Blockchain technology is gaining traction for its potential to enhance the security of smart devices. By decentralizing control and creating immutable records of all interactions, blockchain can prevent unauthorized access and tampering.

Edge Computing
Edge computing processes data locally, at the source, which significantly boosts speed and scalability. This approach makes it more challenging for hackers to steal data and is also more environmentally friendly.

By integrating these advanced technologies, you can significantly enhance the security and convenience of your home, ensuring a safer environment amid uncertain times.
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Technology
Artifcial Intelligence Generative AI Machine learning Predictive AI Technology

Predictive AI: What Do We Need to Understand?


We all are no strangers to artificial intelligence (AI) expanding over our lives, but Predictive AI stands out as uncharted waters. What exactly fuels its predictive prowess, and how does it operate? Let's take a detailed exploration of Predictive AI, unravelling its intricate workings and practical applications.

What Is Predictive AI?

Predictive AI operates on the foundational principle of statistical analysis, using historical data to forecast future events and behaviours. Unlike its creative counterpart, Generative AI, Predictive AI relies on vast datasets and advanced algorithms to draw insights and make predictions. It essentially sifts through heaps of data points, identifying patterns and trends to inform decision-making processes.

At its core, Predictive AI thrives on "big data," leveraging extensive datasets to refine its predictions. Through the iterative process of machine learning, Predictive AI autonomously processes complex data sets, continuously refining its algorithms based on new information. By discerning patterns within the data, Predictive AI offers invaluable insights into future trends and behaviours.


How Does It Work?

The operational framework of Predictive AI revolves around three key mechanisms:

1. Big Data Analysis: Predictive AI relies on access to vast quantities of data, often referred to as "big data." The more data available, the more accurate the analysis becomes. It sifts through this data goldmine, extracting relevant information and discerning meaningful patterns.

2. Machine Learning Algorithms: Machine learning serves as the backbone of Predictive AI, enabling computers to learn from data without explicit programming. Through algorithms that iteratively learn from data, Predictive AI can autonomously improve its accuracy and predictive capabilities over time.

3. Pattern Recognition: Predictive AI excels at identifying patterns within the data, enabling it to anticipate future trends and behaviours. By analysing historical data points, it can discern recurring patterns and extrapolate insights into potential future outcomes.


Applications of Predictive AI

The practical applications of Predictive AI span a number of industries, revolutionising processes and decision-making frameworks. From cybersecurity to finance, weather forecasting to personalised recommendations, Predictive AI is omnipresent, driving innovation and enhancing operational efficiency.


Predictive AI vs Generative AI

While Predictive AI focuses on forecasting future events based on historical data, Generative AI takes a different approach by creating new content or solutions. Predictive AI uses machine learning algorithms to analyse past data and identify patterns for predicting future outcomes. In contrast, Generative AI generates new content or solutions by learning from existing data patterns but doesn't necessarily focus on predicting future events. Essentially, Predictive AI aims to anticipate trends and behaviours, guiding decision-making processes, while Generative AI fosters creativity and innovation, generating novel ideas and solutions. This distinction highlights the complementary roles of both AI approaches in driving progress and innovation across various domains.

Predictive AI acts as a proactive defence system in cybersecurity, spotting and stopping potential threats before they strike. It looks at how users behave and any unusual activities in systems to make digital security stronger, protecting against cyber attacks.

Additionally, Predictive AI helps create personalised recommendations and content on consumer platforms. Studying what users like and how they interact provides customised experiences, making users happier and more engaged.

The bottom line is its ability to forecast future events and behaviours based on historical data heralds a new era of data-driven decision-making and innovation. 




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Technology
AI technology Algorithm Applications Machine learning Neural Networks Technology

Deciphering the Impact of Neural Networks on Artificial Intelligence Evolution

 

Artificial intelligence (AI) has long been a frontier of innovation, pushing the boundaries of what machines can achieve. At the heart of AI's evolution lies the fascinating realm of neural networks, sophisticated systems inspired by the complex workings of the human brain. 

In this comprehensive exploration, we delve into the multifaceted landscape of neural networks, uncovering their pivotal role in shaping the future of artificial intelligence. Neural networks have emerged as the cornerstone of AI advancement, revolutionizing the way machines learn, adapt, and make decisions. 

Unlike traditional AI models constrained by rigid programming, neural networks possess the remarkable ability to glean insights from vast datasets through adaptive learning mechanisms. This paradigm shift has ushered in a new era of AI characterized by flexibility, intelligence, and innovation. 

At their core, neural networks mimic the interconnected neurons of the human brain, with layers of artificial nodes orchestrating information processing and decision-making. These networks come in various forms, from Feedforward Neural Networks (FNN) for basic tasks to complex architectures like Convolutional Neural Networks (CNN) for image recognition and Generative Adversarial Networks (GAN) for creative tasks. 

Each type offers unique capabilities, allowing AI systems to excel in diverse applications. One of the defining features of neural networks is their ability to adapt and learn from data patterns. Through techniques such as machine learning and deep learning, these systems can analyze complex datasets, identify intricate patterns, and make intelligent judgments without explicit programming. This adaptive learning capability empowers AI systems to continuously evolve and improve their performance over time, paving the way for unprecedented levels of sophistication. 

Despite their transformative potential, neural networks are not without challenges and ethical dilemmas. Issues such as algorithmic bias, opacity in decision-making processes, and data privacy concerns loom large, underscoring the need for responsible development and governance frameworks. By addressing these challenges head-on, we can ensure that AI advances in a manner that aligns with ethical principles and societal values. 

As we embark on this journey of exploration and innovation, it is essential to recognize the immense potential of neural networks to shape the future of artificial intelligence. By fostering a culture of responsible development, collaboration, and ethical stewardship, we can harness the full power of neural networks to tackle complex challenges, drive innovation, and enrich the human experience. 

The evolution of artificial intelligence is intricately intertwined with the transformative capabilities of neural networks. As these systems continue to evolve and mature, they hold the promise of unlocking new frontiers of innovation and discovery. By embracing responsible development practices and ethical guidelines, we can ensure that neural networks serve as catalysts for positive change, empowering AI to fulfill its potential as a force for good in the world.
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Technology
AI CISO cyber threat Data protection LLM Machine learning Ransomware Technology

Enterprise AI Adoption Raises Cybersecurity Concerns

 




Enterprises are rapidly embracing Artificial Intelligence (AI) and Machine Learning (ML) tools, with transactions skyrocketing by almost 600% in less than a year, according to a recent report by Zscaler. The surge, from 521 million transactions in April 2023 to 3.1 billion monthly by January 2024, underscores a growing reliance on these technologies. However, heightened security concerns have led to a 577% increase in blocked AI/ML transactions, as organisations grapple with emerging cyber threats.

The report highlights the developing tactics of cyber attackers, who now exploit AI tools like Language Model-based Machine Learning (LLMs) to infiltrate organisations covertly. Adversarial AI, a form of AI designed to bypass traditional security measures, poses a particularly stealthy threat.

Concerns about data protection and privacy loom large as enterprises integrate AI/ML tools into their operations. Industries such as healthcare, finance, insurance, services, technology, and manufacturing are at risk, with manufacturing leading in AI traffic generation.

To mitigate risks, many Chief Information Security Officers (CISOs) opt to block a record number of AI/ML transactions, although this approach is seen as a short-term solution. The most commonly blocked AI tools include ChatGPT and OpenAI, while domains like Bing.com and Drift.com are among the most frequently blocked.

However, blocking transactions alone may not suffice in the face of evolving cyber threats. Leading cybersecurity vendors are exploring novel approaches to threat detection, leveraging telemetry data and AI capabilities to identify and respond to potential risks more effectively.

CISOs and security teams face a daunting task in defending against AI-driven attacks, necessitating a comprehensive cybersecurity strategy. Balancing productivity and security is crucial, as evidenced by recent incidents like vishing and smishing attacks targeting high-profile executives.

Attackers increasingly leverage AI in ransomware attacks, automating various stages of the attack chain for faster and more targeted strikes. Generative AI, in particular, enables attackers to identify vulnerabilities and exploit them with greater efficiency, posing significant challenges to enterprise security.

Taking into account these advancements, enterprises must prioritise risk management and enhance their cybersecurity posture to combat the dynamic AI threat landscape. Educating board members and implementing robust security measures are essential in safeguarding against AI-driven cyberattacks.

As institutions deal with the complexities of AI adoption, ensuring data privacy, protecting intellectual property, and mitigating the risks associated with AI tools become paramount. By staying vigilant and adopting proactive security measures, enterprises can better defend against the growing threat posed by these cyberattacks.

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Technology
Algorithm Bias Artificial Intelligence Fairness Machine learning Technology

Fairness is a Critical And Challenging Feature of AI

 

 

Artificial intelligence's ability to process and analyse massive volumes of data has transformed decision-making processes, making operations in health care, banking, criminal justice, and other sectors of society more efficient and, in many cases, effective. 

This transformational power, however, carries a tremendous responsibility: ensuring that these technologies are created and implemented in an equitable and just manner. In short, AI must be fair.

The goal of fairness in AI is not only an ethical imperative, but also a requirement for building trust, inclusion, and responsible technological growth. However, ensuring that AI is fair presents a significant challenge. 

Importance of fairness

Fairness in AI has arisen as a major concern for researchers, developers, and regulators. It goes beyond technological achievement and addresses the ethical, social, and legal elements of technology. Fairness is a critical component of establishing trust and acceptance of AI systems.

People must trust that AI decisions that influence their life, such as employment algorithms, are done fairly. Socially, AI systems that reflect justice can assist address and alleviate past prejudices, such as those against women and minorities, thereby promoting inclusivity. Legally, including fairness into AI systems helps to match those systems with anti-discrimination laws and regulations around the world. 

Unfairness can come from two sources: the primary data and the algorithms. Research has revealed that input data can perpetuate bias in a variety of societal contexts. 

For example, in employment, algorithms that process data that mirror society preconceptions or lack diversity may perpetuate "like me" biases. These biases favour candidates who are similar to decision-makers or existing employees in an organisation. When biassed data is used to train a machine learning algorithm to assist a decision-maker, the programme could propagate and even increase these biases. 

Fairness challenges 

Fairness is essentially subjective, impacted by cultural, social and personal perceptions. In the context of AI, academics, developers, and policymakers frequently define fairness as the premise that machines should neither perpetuate or exacerbate existing prejudices or inequities.

However, measuring and incorporating fairness into AI systems is plagued with subjective decisions and technical challenges. Researchers and policymakers have advocated many definitions of fairness, such as demographic parity, equality of opportunity and individual fairness. 

In addition, fairness cannot be limited to a single statistic or guideline. It covers a wide range of issues, including, but not limited to, equality of opportunity, treatment, and impact.

The path forward 

Making AI fair is not easy, and there are no one-size-fits-all solutions. It necessitates a process of ongoing learning, adaptation, and collaboration. Given the prevalence of bias in society, I believe that those working in artificial intelligence should recognise that absolute fairness is impossible and instead strive for constant development. 

This task requires a dedication to serious research, thoughtful policymaking, and ethical behaviour. To make it work, researchers, developers, and AI users must guarantee that fairness is considered along the AI pipeline, from conception to data collecting to algorithm design to deployment and beyond.
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Technology
AI Model AI Risks Generative AI Machine learning Technology

Here's How to Choose the Right AI Model for Your Requirements

 

When kicking off a new generative AI project, one of the most vital choices you'll make is selecting an ideal AI foundation model. This is not a small decision; it will have a substantial impact on the project's success. The model you choose must not only fulfil your specific requirements, but also be within your budget and align with your organisation's risk management strategies. 

To begin, you must first determine a clear goal for your AI project. Whether you want to create lifelike graphics, text, or synthetic speech, the nature of your assignment will help you choose the proper model. Consider the task's complexity as well as the level of quality you expect from the outcome. Having a specific aim in mind is the first step towards making an informed decision.

After you've defined your use case, the following step is to look into the various AI foundation models accessible. These models come in a variety of sizes and are intended to handle a wide range of tasks. Some are designed for specific uses, while others are more adaptable. It is critical to include models that have proven successful in tasks comparable to yours in your consideration list. 

Identifying correct AI model 

Choosing the proper AI foundation model is a complicated process that includes understanding your project's specific demands, comparing the capabilities of several models, and taking into account the operational context in which the model will be implemented. This guide synthesises the available reference material and incorporates extra insights to provide an organised method to choosing an AI base model. 

Identify your project targets and use cases

The first step in choosing an AI foundation model is to determine what you want to achieve with your project. Whether your goal is to generate text, graphics, or synthetic speech, the nature of your task will have a considerable impact on the type of model that is most suitable for your needs. Consider the task's complexity and the desired level of output quality. A well defined goal will serve as an indicator throughout the selecting process. 

Figure out model options 

Begin by researching the various AI foundation models available, giving special attention to models that have proven successful in jobs comparable to yours. Foundation models differ widely in size, specialisation, and versatility. Some models are meant to specialise on specific functions, while others have broader capabilities. This exploratory phase should involve a study of model documentation, such as model cards, which include critical information about the model's training data, architecture, and planned use cases. 

Conduct practical testing 

Testing the models with your specific data and operating context is critical. This stage ensures that the chosen model integrates easily with your existing systems and operations. During testing, assess the model's correctness, dependability, and processing speed. These indicators are critical for establishing the model's effectiveness in your specific use case. 

Deployment concerns 

Make the deployment technique choice that works best for your project. While on-premise implementation offers more control over security and data privacy, cloud services offer scalability and accessibility. The decision you make here will mostly depend on the type of application you're using, particularly if it handles sensitive data. In order to handle future expansion or requirements modifications, take into account the deployment option's scalability and flexibility as well. 

Employ a multi-model strategy 

For organisations with a variety of use cases, a single model may not be sufficient. In such cases, a multi-model approach can be useful. This technique enables you to combine the strengths of numerous models for different tasks, resulting in a more flexible and durable solution. 

Choosing a suitable AI foundation model is a complex process that necessitates a rigorous understanding of your project's requirements as well as a thorough examination of the various models' characteristics and performance. 

By using a structured approach, you can choose a model that not only satisfies your current needs but also positions you for future advancements in the rapidly expanding field of generative AI. This decision is about more than just solving a current issue; it is also about positioning your project for long-term success in an area that is rapidly growing and changing.
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Technology
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Transforming the Creative Sphere With Generative AI

 

Generative AI, a trailblazing branch of artificial intelligence, is transforming the creative landscape and opening up new avenues for businesses worldwide. This article delves into how generative AI transforms creative work, including its benefits, obstacles, and tactics for incorporating this technology into your brand's workflow. 

 Power of generative AI

Generative AI uses advanced machine learning algorithms and natural language processing models to generate material and imagery that resembles human expressions. While others doubt its potential to recreate the full range of human creativity, Generative AI has indisputably transformed many parts of the creative process.

Generative AI systems, such as GPT-4, excel at producing human-like writing, making them critical for content creation in marketing and communication applications. Brands can use this technology to: 

  • Create highly personalised and persuasive content. 
  • Increase efficiency by automating the creation of repetitive material like descriptions of goods and customer communications. 
  • Provide a personalised user experience to increase user engagement and conversion rates.
  • Stand out in competitive marketplaces by creating distinctive and interesting content with AI. 

Challenges and ethical considerations 

Despite its potential, integrating Generative AI into the creative sector results in significant ethical concerns: 

Bias in AI: AI systems may unintentionally perpetuate biases in training data. Brands must actively address this issue by curating training data, reviewing AI outputs for bias, and applying fairness and bias mitigation strategies.

Transparency and Explainability: AI algorithms can be complex, making it difficult for consumers to comprehend how decisions are made. Brands should prioritise transparency by offering explicit explanations for AI-powered methods. 

Data Privacy: Generative AI is based on data, and misusing user data can result in privacy breaches. Brands must follow data protection standards, gain informed consent, and implement strong security measures. 

Future of generative AI in creativity

As Generative AI evolves, the future promises exciting potential for further transforming the creative sphere: 

Artistic Collaboration: Artists may work more closely with AI systems to create hybrid works that combine human and AI innovation. 

Personalised Art Experiences: Generative AI will provide highly personalised art experiences by dynamically altering artworks to individual preferences and feelings. 

AI in Art Education: Artificial intelligence (AI) will play an important role in art education by providing tools and resources to help students express their creativity. 

Ethical AI in Art: The art sector will place a greater emphasis on ethical AI practices, including legislation and guidelines to ensure responsible AI use.

The future of Generative AI in creativity is full of possibilities, including breaking down barriers, encouraging new forms of artistic expression, and developing a global community of artists and innovators. As this journey progresses, "Generative AI revolutionising art" will be synonymous with innovation, creativity, and endless possibilities.
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Zero Trust Security
AI Model AI/ML vulnerabilities Cyber Security Machine learning Microsoft Ransomware Attacks. Supply Chain Attack Zero Trust Security

Microsoft's Cybersecurity Report 2023

Microsoft recently issued its Digital Defense Report 2023, which offers important insights into the state of cyber threats today and suggests ways to improve defenses against digital attacks. These five key insights illuminate the opportunities and difficulties in the field of cybersecurity and are drawn from the report.

  • Ransomware Emerges as a Pervasive Threat: The report highlights the escalating menace of ransomware attacks, which have become more sophisticated and targeted. The prevalence of these attacks underscores the importance of robust cybersecurity measures. As Microsoft notes, "Defending against ransomware requires a multi-layered approach that includes advanced threat protection, regular data backups, and user education."
  • Supply Chain Vulnerabilities Demand Attention: The digital defense landscape is interconnected, and supply chain vulnerabilities pose a significant risk. The report emphasizes the need for organizations to scrutinize their supply chains for potential weaknesses. Microsoft advises, "Organizations should conduct thorough risk assessments of their supply chains and implement measures such as secure coding practices and software integrity verification."
  • Zero Trust Architecture Gains Prominence: Zero Trust, a security framework that assumes no trust, even within an organization's network, is gaining momentum. The report encourages the adoption of Zero Trust Architecture to bolster defenses against evolving cyber threats. "Implementing Zero Trust principles helps organizations build a more resilient security posture by continuously verifying the identity and security posture of devices, users, and applications," Microsoft suggests
  • AI and Machine Learning Enhance Threat Detection: Leveraging artificial intelligence (AI) and machine learning (ML) is crucial in the fight against cyber threats. The report underscores the effectiveness of these technologies in identifying and mitigating potential risks. Microsoft recommends organizations "leverage AI and ML capabilities to enhance threat detection, response, and recovery efforts."
  • Employee Training as a Cybersecurity Imperative: Human error remains a significant factor in cyber incidents. The report stresses the importance of continuous employee training to bolster the human element of cybersecurity. Microsoft asserts, "Investing in comprehensive cybersecurity awareness programs can empower employees to recognize and respond effectively to potential threats."

Microsoft says, "A resilient cybersecurity strategy is not a destination but a journey that requires continuous adaptation and improvement."An ideal place to start for a firm looking to improve its cybersecurity posture is the Microsoft Digital Defense Report 2023. It is necessary to stay up to date on the current threats to digital assets and take precautionary measures to secure them.






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Technology
Business AI Generative AI Machine learning New Innovation Technology

Here's How to Implement Generative AI for Improved Efficiency and Innovation in Business Processes

 

Global business practices are being revolutionised by generative artificial intelligence (AI). With the use of this technology, businesses can find inefficiencies, analyse patterns and trends in huge databases, and create unique solutions to challenges. In the business world of today, generative AI technologies are becoming more and more significant as organisations search for methods to boost productivity, simplify workflows, and maintain their competitiveness in the global market. 

Generative AI is a branch of deep learning that allows machines to generate new original content based on previously learned patterns, also known as large learning models. This technology has the potential to transform the way businesses operate by providing previously unavailable insights and ideas. It is predicted by Gartner, Inc. that by 2026, over 80% of businesses will have implemented GenAI-enabled applications in production settings and/or used generative artificial intelligence (GenAI) models or APIs. This represents an increase from less than 5% in 2023. 

One way for businesses to use generative AI is to automate complex work processes. This technology can be utilised for generating reports or analyse large amounts of data in real time, greatly streamlining business workflows. The finance industry is one of many that will benefit from generative AI. Banks can use AI-powered chatbots to automate customer service and respond to customer inquiries more quickly. Overall, generative AI can aid the finance industry in the analysis of customer data, the identification of trends and insights, the prediction of market trends, and the detection of fraud. It can also be used to automate back-office processes, which reduces the possibility of errors and increases operational efficiency. 

Generative AI can also help businesses improve their innovation by generating new ideas based on data patterns. Companies can use generative AI to create new advertising slogans, logos, and other branding materials. AI algorithms can be trained to create appealing product designs and packaging, increasing product sales. Aside from content generation, Gen AI can impact audience segmentation, improve SEO and search rankings, and enable hyper-personalized marketing.

Furthermore, generative AI can be used to enhance product design. Echo3, Get3D, 3DFY.ai, and other next-generation AI tools can simulate various designs and materials and generate 3D models that can be evaluated and refined before production. Generative AI can also be used to forecast customer behaviour and preferences, allowing businesses to make better decisions. 

Generative AI has the potential to transform patient care in the healthcare industry. It can recognise patterns and make accurate predictions, allowing for faster and more accurate diagnosis. It can then create customised treatment plans for patients based on their specific medical history and risk factors.

By analysing data from sensors and other sources, manufacturing companies can use generative AI to optimise production processes. It can predict equipment failures and reduce downtime and maintenance costs. It can also assist businesses in developing new products and enhancing existing ones by replicating different designs and analysing them virtually. 

Investing in reliable infrastructure, collaborating with professional AI partners, and providing staff training can help organisations address the obstacles associated with implementing generative AI. Businesses can increase their success and competitiveness in the marketplace by implementing generative AI.
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