Tag: Machine Learning

Artificial intelligence (AI) is a relatively new field that has rapidly evolved into a major influence on the strategic direction of organizations. Its significance extends far beyond automation, enhancing complex decision-making processes. AI is both a risk and a tool for managing risk—a paradox that organizations must confront as they navigate the landscape of 2024 and beyond.

AI as a Catalyst for Transformation

While AI is often associated with task automation, it also plays a critical role in improving decision-making. AI empowers change across various domains, from social to informational, by automating time-consuming processes and driving efficiency. Additionally, AI offers deeper insights to management teams than ever before.

In finance, for example, AI models outperform traditional methods by evaluating a broader set of factors to assess credit risk, predict market trends, detect fraud, and identify optimal investments. Similarly, in healthcare, AI enables early diagnosis and increases diagnostic accuracy, transforming how medical treatments are managed. These examples demonstrate that AI not only mitigates risks but also reshapes operational behavior, opening new avenues for efficiency and effectiveness.

Machine Learning’s Role in Enterprise Risk Management

Machine learning—one of the most crucial AI fields—plays a vital role in Enterprise Risk Management (ERM). By learning from data and detecting patterns beyond human observation, machine learning is particularly useful in industries like cybersecurity, where threats are constantly evolving. AI systems also monitor network activities in real-time, providing alerts for suspicious events to prevent breaches.

According to Gartner’s 2024 report, companies leveraging AI-based risk management tools saw a 30% reduction in data breach incidents. This statistic emphasizes AI’s ability to prevent risk events. Moreover, as data protection laws become stricter, AI helps organizations maintain compliance through precise monitoring and reporting mechanisms.

The dual role of AI as both a risk and a risk management tool defines the modern business landscape. Organizations that recognize AI’s strategic value and incorporate it into their planning will be well-positioned to thrive. The improvements in decision-making, efficiency, and risk forecasting that AI offers are too significant to ignore.

However, these opportunities come with responsibility. Companies must adopt ethical AI practices and ensure robust data protection to avoid negative societal impacts. Failure to address these issues could have serious consequences, not only for businesses but also for society as a whole.

Ultimately, the question is not whether to adopt AI but how to implement it sustainably and responsibly. Leaders with a vision for ethical AI usage will not only mitigate risks but also unlock new opportunities previously beyond reach. As business environments continue to evolve rapidly in 2024 and beyond, organizations that fail to adapt will fall behind. Integrating AI as both a tool and a mandate is essential for any innovative organization looking to succeed.

To Know More, Read Full Article @ https://ai-techpark.com/ai-is-both-a-risk-and-a-tool/

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The rise of artificial intelligence (AI) has transformed many sectors across the business landscape, reshaping how organizations operate. However, the convenience of AI introduces environmental challenges, such as increased energy consumption and hardware waste. These unintended consequences call for thoughtful strategies from chief information officers (CIOs), who must balance technological advancements with sustainability goals.

According to a Gartner survey, environmental issues are now a top priority for tech companies, and CIOs are facing pressure from executives, stakeholders, and regulators to implement sustainability initiatives. The convergence of AI and environmental responsibility requires proactive measures that can drive sustainable transformation.

This article offers a framework for adopting green algorithms—energy-efficient AI solutions—to help CIOs build sustainable IT organizations.

AI Supporting Environmental Sustainability

To integrate sustainability into IT operations, CIOs must establish clear mandates and requirements to track sustainability metrics, such as energy consumption and carbon footprint. The effectiveness of these efforts depends on embedding sustainability KPIs into the organization's digital infrastructure.

A practical example lies in modern data centers. Advanced optical networks, which use fiber cables, are significantly more energy-efficient than copper-based networks. Fiber cables require fewer raw materials and less manufacturing energy, reducing the environmental impact compared to the extraction and refinement processes for copper. In fact, IT companies implementing modern networking technologies have reported up to fourfold reductions in their environmental footprint.

While AI can introduce environmental challenges, it holds tremendous potential to advance sustainability initiatives when used thoughtfully. CIOs and project managers play a pivotal role in designing and implementing AI-driven solutions that align with sustainable development goals.

By focusing AI efforts on the right use cases, businesses can mitigate environmental impacts, enhance operational efficiency, and reduce unnecessary costs. AI has the potential to become a powerful ally, fostering both innovation and environmental responsibility in the IT industry. With proactive strategies, the convergence of AI and sustainability can transform the future of business.

Know More, Read Full Article @ https://ai-techpark.com/the-convergence-of-ai-and-sustainability-in-the-it-industry/

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Yashin, to kick things off, could you share what inspired you to transition from a career in academia and engineering to founding Pvotal Technologies?

Growing up, I thought a lack of proper education was the root of many societal issues and inefficiencies.

Idealistically, I entered academia thinking I could become a professor who would nurture the issues leading to a wavering generation of talent, innovation, and development. Unfortunately, I quickly realized how some processes were limiting, stifling, and stuck in an antiquated age.

I could not build or address problems I saw in my niche field due to software issues, data breaches, the high cost of licensing fees for some critical tools, and the poor integration of tools. These issues led me to lose thousands of hours in frustration fixing technical problems rather than focusing on my growth, thesis, and research. The tools I used became a greater source of frustration than my research, constantly distracting me from my objectives.

My skills and resolve were too limited to reform academia from within, so I decided to focus on the issues within the software industry to limit the problems that more talented academics faced. I co-founded Pvotal with Ashley to build a new generation of solutions that helped customers focus on the value they bring to customers rather than get stuck in an iterative cycle of integrating code and debugging updates.

Pvotal emphasizes creating “Infinite Enterprises.” Could you explain what this concept entails and how it aligns with your overall mission?

While many industries have adopted different interpretations of the ideal Infinite Enterprise, we believe the “infinite enterprise” is any company that has achieved an infinitely scalable, independent, resilient, and secure infrastructure. Once these criteria are met, we observed that it allows businesses to truly innovate, improve, and elevate their value proposition to customers.

The age-old adage of teens or some fresh graduates going into “founder mode” can build the next generation of software in their proverbial garage, shared workspace, or dorm room is simply no longer possible.

The rise of hyperspecialization, wanton integration of third-party code or vendors, and the unmanaged accumulation of technical debt has led most software companies to become antiquated, vulnerable, and overbloated pieces of code that can no longer efficiently protect their customers’ data, provide a competitive edge to their users, and have a reasonable cost/utilization footprint.

Most modern enterprise software has at least 17 paid or free SaaS, PaaS, and third-party code powering its operation or development. With a tough economy, inflation, and squeezed supply chains, these different services are forced to raise prices continually, thus shifting the burden on the end consumer. In addition to the increasing costs, these software are often abandoned or introduce vulnerabilities to the enterprise supply chains, which is why we have experienced a record-breaking number of successful cyberattacks, ransomware, and fraud every year for the past decade.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-yashin-manraj/

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As the world navigates through 2024, cybersecurity gets more unpredictable and dangerous. With increased sophisticated cyberattacks like ransomware, phishing, and APTs, there has never been a higher demand for cybersecurity professionals. But after the rising tide, the industry stands at a significant skills gap, presenting organizations with vulnerabilities to breaches and data theft.

Following a report by ISC², there is a lack of more than 3.4 million cybersecurity workers across the world. The gap becomes an important threat to diverse organizations, majorly finance, healthcare, and technology-related businesses that are more vulnerable to cyber attacks. As we head to the future, B2B businesses need to invest in the next generation of cybersecurity talent and equip them with crucial knowledge and skills to offer sensitive data and systems protection.

This is your roadmap for the business to educate professionals in cybersecurity across all types of skills with education and strategies to create the best plan of workforce development.

The Growing Need for Cybersecurity Talent in 2024 and Beyond

Current Workforce Shortage

Business operations in almost every corner of the globe are being adversely affected by a severe challenge: an acute shortage of cybersecurity professionals. According to Cybersecurity Ventures, by 2025, cybercrime will reach $10.5 trillion in annual damages worldwide, representing an even greater need for experts in that field. There are not enough trained professionals to fill such a high demand. This presents a challenge to businesses, particularly those sectors dealing with sensitive data, such as healthcare, government, and finance.

Evolving Threats

The 2024 threat landscape is not only about malware and phishing schemes. Organizations are becoming increasingly subjected to more advanced attacks, like ransomware-as-a-service (RaaS), attacks by nation-state entities, and supply chain attacks—all of which make much greater technical capability demands than traditional IT knowledge requires. It is thus crucial to train professionals who can predict the magnitude of new threats.

The Role of New Technologies

Emerging technologies such as artificial intelligence (AI), machine learning (ML), and cloud computing are impacting the cybersecurity sector with new vulnerabilities; therefore, professionals have to become adept at securing these systems; thus, the urgency for cybersecurity knowledge coupled with cutting-edge tech becomes highly integral in the future workforce.

To Know More, Read Full Article @ https://ai-techpark.com/preparing-the-next-generation-of-cybersecurity-professionals/

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We live in an age where personalization is key to our experiences, from music and web series to the products and services we use, all tailored to us based on data collected by websites and apps. This capability helps us better understand our needs and improve our overall quality of life.

Similarly, in the healthcare sector, technologies like artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) allow us to monitor our health and receive personalized treatments. Often referred to as AI in healthcare, this technological collaboration is transforming traditional patient care by introducing futuristic clinical and administrative solutions. Doctors, researchers, and healthcare providers are using these advanced tools to enhance healthcare delivery in areas such as preventive care, disease diagnosis and prediction, treatment plans, and administrative tasks.

AI in healthcare is also making strides in recruitment, allowing companies to contribute more effectively to consumer health. The growing use of AI in wearable devices and medical tools is particularly valuable for detecting early-stage heart diseases. These AI-powered sensors and devices enable healthcare professionals to monitor and identify life-threatening conditions at an early stage.

While there are many applications for AI in healthcare, this article will focus on how AI is currently being implemented and what the future holds for healthcare policies in this sector. The concept of patient-centric care is a driving force behind AI-powered prescription medicine, which enhances personal treatment by empowering patients and providing real-time, visual care.

Key Areas of AI in Healthcare

The integration of AI in healthcare is transforming modern healthcare systems, enabling them to diagnose and treat diseases with greater speed and accuracy. These advancements are improving care quality and creating more patient-centered healthcare processes. AI's key focus areas include improving care delivery, strengthening disease surveillance, and accelerating drug discovery.

The future of AI in healthcare holds vast potential to shape public and private health policies. By prioritizing education and training and adopting AI responsibly, leaders in the health tech industry can unlock the full potential of AI, creating innovative, long-lasting solutions to the complex challenges facing healthcare today.To Know More, Read Full Article @ https://ai-techpark.com/ai-in-healthcare/ 

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As cyberattacks advance in their sophistication and frequency, traditional cybersecurity defenders-the firewalls, antivirus software, even intrusion detection systems-are no longer sufficient in protecting companies. Organizations are bound to face advanced persistent threats (APTs), ransomware, as well as insider attacks in 2024 that often go undetected by automated detection tools. This makes proactive cybersecurity a dire necessity.

According to new research findings, the average amount of time taken before it is possible to detect a breach stands at more than 200 days, which is a very long window for cyberthieves to siphon sensitive data and cripple business operations.

This mainly occurs in B2B organizations operating within the finance, healthcare, and technology sectors, as these sectors are mainly characterized by sophisticated attackers seeking high-value data. However, the only solution is in cyber threat hunting-a proactive security approach aimed at detecting threats before they trigger damage.

In the guide here, we will cover the most important steps to implement a robust cyber threat hunting strategy tailored for 2024-overview of all the skills, processes, and technologies that will help in keeping your business safe.

What is Cyber Threat Hunting?

Cyber threat hunting is one of the proactive cyber security practice wherein the trained and well-equipped security analysts proactively search for hidden or undetected threats within an organization’s network.  While the traditional monitoring systems passively wait for alerts, the threat hunters search for malicious activity or a weakness that can be exploited.

Why It Matters in 2024

Today, the threat landscape for cyber defence is no longer passive but active detection. Attackers are continually evolving by attempting to evade detection with tactics like lateral movement, credential dumping, and fileless malware. Threat hunting becomes very critical in this approach since it looks beyond waiting for automated tools to flag an anomaly and instead hunts for and discovers sophisticated attacks made to evade traditional defenses.

Common Cyber Threats in 2024

Some of the prominent threats businesses will face in 2024 include the following:

Advanced Persistent Threats (APTs): Organized cyberattacks that siphon off data for long periods of time without being detected.

Ransomware: A ransomware attack encrypts a victim’s data and demands payment in lieu of providing decryption keys.

Insider Threats: It is an employee or contractor who intends to do evil or shows malacious carelessness in doing his duty that might lead to security breaches.

Zero-Day Exploits: In this case, attacks exploit vulnerabilities that have not been patched yet.

To Know More, Read Full Article @ https://ai-techpark.com/implementing-cybersecurity-threat-hunting/

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There is growing concern over deepfakes, which are videos and audios that are highly realistic yet fake across various industries, but perhaps more pertinent in the B2B context. These synthetic media can mislead society and create negative impacts on reputation and financial aspects. However, it is evident that social media platforms have an essential role in addressing the fake problem and enhancing the credibility of online interactions as enterprises operate in this challenging environment. This article looks at the rise of deep fakes and also explores how popular social media companies are responding to this problem.

Understanding Deepfakes

Deepfakes are a form of synthetic media that apply artificial intelligence and machine learning to generate hyper-realistic fake audiovisual data. This technology relies on neural networks, and particularly on generative adversarial networks (GANs), to create realistic modifications of existing media.

The first step involves the accumulation of massive data sets that include images, videos, and even voice clips of the targeted person. These datasets enable AI to capture the details of the person’s gestures, voice, and even their tone. For example, GANs are composed of two neural networks, including a generator and a discriminator. The generator thus generates fake content, and the discriminator compares it with real media. This process is carried out in a cycle where the generator generates outputs until the results are as real as the original content being emulated.

Deepfakes can accommodate a range of manipulations based on simple swaps of facial images in videos to advanced ways of forgery where a person looks and acts like doing something they never did. It can also be applied where someone’s voice is changed to say sentences he has never said. This level of realism presents some problems in differentiating between real media and fakes, which could perpetuate skepticism and distrust of digital media.

Social media platforms are at the forefront of the fight against deepfakes, serving as essential gatekeepers to maintain the integrity of online communication. As the sophistication of deepfake technology rapidly evolves, these platforms face the growing challenge of detecting and mitigating manipulated content before it spreads. Their role is critical, not just in protecting users from deception but also in preserving trust across digital spaces where businesses interact with clients, stakeholders, and the public.

For companies, the stakes are equally high. Deepfakes can significantly damage brand reputation and sow confusion, eroding the trust that is central to B2B relationships. Businesses must be vigilant, ensuring they remain informed about the latest developments in deepfake technology and taking proactive steps to defend against its potential harms. By adopting a strategy that includes close collaboration with social media platforms, regular updates to security protocols, and internal training on identifying manipulated content, companies can safeguard their reputation and maintain the trust of their audience.

To Know More, Read Full Article @ https://ai-techpark.com/role-of-social-media-platforms-in-combating-deepfakes/

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The first fundamental of Artificial Intelligence is data, with the Machine Learning models that feed on the continuously growing collections of data of different types. However, as far as it is a very significant source of information, it can be fraught with problems such as privacy limitations, biases, and data scarcity. This is beneficial in removing the mentioned above hurdles to bring synthetic data as a revolutionary solution in the world of AI.

What is Synthetic Data?

Synthetic data can be defined as data that is not acquired through actual occurrences or interactions but rather created fake data. It is specifically intended to mimic the characteristics, behaviors and organizations of actual data without copying them from actual observations. Although there exist a myriad of approaches to generating synthetic data, its generation might use simple rule-based systems or even more complicated methods, such as Machine Learning based on GANs. It is aimed at creating datasets which are as close as possible to real data, yet not causing the problems connected with using actual data.

In addition to being affordable, synthetic data is flexible and can, therefore, be applied at any scale. It enables organizations to produce significant amounts of data for developing or modeling systems or to train artificial intelligence especially when actual data is scarce, expensive or difficult to source. In addition, it is stated that synthetic data can effectively eliminate privacy related issues in fields like health and finance, as it is not based on any real information, thus may be considered as a powerful tool for data-related projects. It also helps increase the model’s ability to handle various situations since the machine learning model encounters many different situations.

Why is Synthetic Data a Game-Changer?

Synthetic data calls for the alteration of traditional methods used in industries to undertake data-driven projects due to the various advantages that the use of synthetic data avails. With an increasing number of big, diverse, and high-quality datasets needed, synthetic data becomes one of the solutions to the real-world data gathering process, which can be costly, time-consuming, or/and unethical.  This artificial data is created in a closed environment and means that data scientists and organisations have the possibility to construct datasets which correspond to their needs.

Synthetic data is an extremely valuable data product for any organization that wants to adapt to the changing landscape of data usage. It not only address practical problems like data unavailability and affordability but also flexibility, conforming to ethical standards, and model resilience. With a rising pace of technology advancements, there is a possibility of synthetic data becoming integral to building better, efficient, and responsible AI & ML models.

To Know More, Read Full Article @ https://ai-techpark.com/synthetic-data-in-machine-learning/

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AI-powered e-commerce platforms scale SMB operations by providing sophisticated pricing analysis and inventory management. Encryption and blockchain applications significantly mitigate concerns about data security and privacy by enhancing data protection and ensuring the integrity and confidentiality of information.

A 2024 survey of 530 small and medium-sized businesses (SMBs) reveals that AI adoption remains modest, with only 39% leveraging this technology. Content creation seems to be the main use case, with 58% of these businesses leveraging AI to support content marketing and 49% to write social media prompts.

Despite reported satisfaction with AI’s time and cost-saving benefits, the predominant use of ChatGPT or Google Gemini mentioned in the survey suggests that these SMBs have been barely scratching the surface of AI’s full potential. Indeed, AI offers far more advanced capabilities, namely pricing analysis and inventory management. Businesses willing to embrace these tools stand to gain an immense first-mover advantage.

However, privacy and security concerns raised by many SMBs regarding deeper AI integration merit attention. The counterargument suggests that the e-commerce platforms offering smart pricing and inventory management solutions would also provide encryption and blockchain applications to mitigate risks.

Regressions and trees: AI under the hood

Every SMB knows that setting optimal product or service prices and effectively managing inventory are crucial for growth. Price too low to beat competitors, and profits suffer. Over-order raw materials, and capital gets tied up unnecessarily. But what some businesses fail to realize is that AI-powered e-commerce platforms can perform all these tasks in real time without the risks associated with human error.

At the center is machine learning, which iteratively refines algorithms and statistical models based on input data to determine optimal prices and forecast inventory demand. The types of machine learning models employed vary across industries, but two stand out in the context of pricing and inventory management.

Regression analysis has been the gold standard in determining prices. This method involves predicting the relationship between the combined effects of multiple explanatory variables and an outcome within a multidimensional space. It achieves this by plotting a “best-fit” hyperplane through the data points in a way that minimizes the differences between the actual and predicted values. In the context of pricing, the model may consider how factors like region, market conditions, seasonality, and demand collectively impact the historical sales data of a given product or service. The resulting best-fit hyperplane would denote the most precise price point for every single permutation or change in the predictors.

To Know More, Read Full Article @ https://ai-techpark.com/ai-integration-and-data-security-in-e-commerce/

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Large Language Models (LLMs) are considered to be an AI revolution, altering how users interact with technology and the world around us. Especially with deep learning algorithms in the picture data, professionals can now train huge datasets that will be able to recognize, summarize, translate, predict, and generate text and other types of content.

As LLMs become an increasingly important part of our digital lives, advancements in natural language processing (NLP) applications such as translation, chatbots, and AI assistants are revolutionizing the healthcare, software development, and financial industries.

However, despite LLMs’ impressive capabilities, the technology has a few limitations that often lead to generating misinformation and ethical concerns.

Therefore, to get a closer view of the challenges, we will discuss the four limitations of LLMs devise a decision to eliminate those limitations, and focus on the benefits of LLMs.

Limitations of LLMs in the Digital World

We know that LLMs are impressive technology, but they are not without flaws. Users often face issues such as contextual understanding, generating misinformation, ethical concerns, and bias. These limitations not only challenge the fundamentals of natural language processing and machine learning but also recall the broader concerns in the field of AI. Therefore, addressing these constraints is critical for the secure and efficient use of LLMs.

Let’s look at some of the limitations:

Contextual Understanding

LLMs are conditioned on vast amounts of data and can generate human-like text, but they sometimes struggle to understand the context. While humans can link with previous sentences or read between the lines, these models battle to differentiate between any two similar word meanings to truly understand a context like that. For instance, the word “bark” has two different meanings; one “bark” refers to the sound a dog makes, whereas the other “bark” refers to the outer covering of a tree. If the model isn’t trained properly, it will provide incorrect or absurd responses, creating misinformation.

Misinformation

Even though LLM’s primary objective is to create phrases that feel genuine to humans; however, at times these phrases are not necessarily to be truthful. LLMs generate responses based on their training data, which can sometimes create incorrect or misleading information. It was discovered that LLMs such as ChatGPT or Gemini often “hallucinate” and provide convincing text that contains false information, and the problematic part is that these models point their responses with full confidence, making it hard for users to distinguish between fact and fiction.

To Know More, Read Full Article @ https://ai-techpark.com/limitations-of-large-language-models/

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