Tag: Machine Learning

According to research from VMware Carbon Black, ransomware attacks surged by 148% during the onset of the Covid-19 pandemic, largely due to the rise in remote work. Key trends influencing the continuing upsurge in ransomware attacks include:

Exploitation of IT outsourcing services: Cybercriminals are targeting managed service providers (MSPs), compromising multiple clients through a single breach.

Vulnerable industries under attack: Healthcare, municipalities, and educational facilities are increasingly targeted due to pandemic-related vulnerabilities.

Evolving ransomware strains and defenses: Detection methods are adapting to new ransomware behaviors, employing improved heuristics and canary files, which serve as digital alarms, deliberately placed in a system or to entice hackers or unauthorized users.

Rise of ransomware-as-a-service (RaaS): This model enables widespread attacks, complicating efforts to counteract them. According to an independent survey by Sophos, average ransomware payouts have escalated from $812,380 in 2022 to $1,542,333 in 2023.

Preventing Ransomware Attacks

To effectively tackle the rising threat of ransomware, organizations are increasingly turning to comprehensive strategies that encompass various facets of cybersecurity. One key strategy is employee education, fostering a culture of heightened awareness regarding potential cyber threats. This involves recognizing phishing scams and educating staff to discern and dismiss suspicious links or emails, mitigating the risk of unwittingly providing access to malicious entities.

In tandem with employee education, bolstering the organization’s defenses against ransomware requires the implementation of robust technological measures. Advanced malware detection and filtering systems play a crucial role in fortifying both email and endpoint protection. By deploying these cutting-edge solutions, companies can significantly reduce the chances of malware infiltration. Additionally, the importance of fortified password protocols cannot be overstated in the battle against ransomware. Two-factor authentication and single sign-on systems provide formidable barriers, strengthening password security and rendering unauthorized access substantially more challenging for cybercriminals.

To Know More, Read Full Article @ https://ai-techpark.com/top-trends-in-cybersecurity-ransomware-and-ai-in-2024/

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Automated Driving Technologies Work

Ethics in the Era of Generative AI

Currently, the two most dominant technologies in the world are machine learning (ML) and artificial intelligence (AI), as these aid numerous industries in resolving their business decisions. Therefore, to accelerate business-related decisions, IT professionals work on various business situations and develop data for AI and ML platforms.

The ML and AI platforms pick appropriate algorithms, provide answers based on predictions, and recommend solutions for your business; however, for the longest time, stakeholders have been worried about whether to trust AI and ML-based decisions, which has been a valid concern. Therefore, ML models are universally accepted as “black boxes,” as AI professionals could not once explain what happened to the data between the input and output.

However, the revolutionary concept of explainable AI (XAI) has transformed the way ML and AI engineering operate, making the process more convincing for stakeholders and AI professionals to implement these technologies into the business.

Why Is XAI Vital for AI Professionals?

Based on a report by Fair Isaac Corporation (FICO), more than 64% of IT professionals cannot explain how AI and ML models determine predictions and decision-making.

However, the Defense Advanced Research Project Agency (DARPA) resolved the queries of millions of AI professionals by developing “explainable AI” (XAI); the XAI explains the steps, from input to output, of the AI models, making the solutions more transparent and solving the problem of the black box.

Let’s consider an example. It has been noted that conventional ML algorithms can sometimes produce different results, which can make it challenging for IT professionals to understand how the AI system works and arrive at a particular conclusion.

After understanding the XAI framework, IT professionals got a clear and concise explanation of the factors that contribute to a specific output, enabling them to make better decisions by providing more transparency and accuracy into the underlying data and processes driving the organization.

With XAI, AI professionals can deal with numerous techniques that help them choose the correct algorithms and functions in an AI and ML lifecycle and explain the model’s outcome properly.

To Know More, Read Full Article @ https://ai-techpark.com/why-explainable-ai-is-important-for-it-professionals/

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What is ACI

Democratized Generative AI

Artificial intelligence (AI) has been a game changer in the business landscape, as this technology can analyze massive amounts of data, make accurate predictions, and automate the business process.

However, AI and ethics problems have been in the picture for the past few years and are gradually increasing as AI becomes more pervasive. Therefore, the need of the hour is for chief information officers (CIOs) to be more vigilant and cognizant of ethical issues and find ways to eliminate or reduce bias.

Before proceeding further, let us understand the source challenge of AI. It has been witnessed that the data sets that AI algorithms consume to make informed decisions are considered to be biased around race and gender when applied to the healthcare industry, or the BFSI industry. Therefore, the CIOs and their teams need to focus on the data inputs, ensuring that the data sets are accurate, free from bias, and fair for all.

Thus, to make sure that the data IT professionals use and implement in the software meet all the requirements to build trustworthy systems and adopt a process-driven approach to ensure non-bais AI systems

This article aims to provide an overview of AI ethics, the impact of AI on CIOs, and their role in the business landscape.

Understanding the AI Life Cycle From an Ethical Perspective

Identify the Ethical Guidelines

The foundation of ethical AI responsibility is to develop a robust AI lifecycle. CIOs can establish ethical guidelines that merge with the internal standards applicable to developing AI systems and further ensure legal compliance from the outset. AI professionals and companies misidentify the applicable laws, regulations, and on-duty standards that guide the development process.

Conducting Assessments

Before commencing any AI development, companies should conduct a thorough assessment to identify biases, potential risks, and ethical implications associated with developing AI systems. IT professionals should actively participate in evaluating how AI systems can impact individuals’ autonomy, fairness, privacy, and transparency, while also keeping in mind human rights laws. The assessments result in a combined guide to strategically develop an AI lifecycle and a guide to mitigate AI challenges.

Data Collection and Pre-Processing Practice

To develop responsible and ethical AI, AI developers and CIOs must carefully check the data collection practices and ensure that the data is representative, unbiased, and diverse with minimal risk and no discriminatory outcomes. The preprocessing steps should focus on identifying and eliminating the biases that can be found while feeding the data into the system to ensure fairness when AI is making decisions.

To Know More, Read Full Article @ https://ai-techpark.com/the-impact-of-artificial-intelligence-ethics-on-c-suites/

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Generative AI for SMBs and SMEs

Mental Health Apps for 2023

Explainability is crucial in AI applications. How does Kyndi ensure that the answers provided by its platform are explainable and transparent to users?

Explainability is a key Kyndi differentiator and enterprise users generally view this capability as critical to their brand as well as necessary to meet regulatory requirements in certain industries like the pharmaceutical and financial services sectors.

Kyndi uniquely allows users to see the specific sentences that feed the resulting generated summary produced by GenAI. Additionally, we further enable them to click on each source link to get to the specific passage rather than just linking to the entire document, so they can read additional context directly. Since users can see the sources of every generated summary, they can gain trust in both the answers and the organization to provide relevant information. This capability directly contrasts with ChatGPT and other GenAI solutions, which do not provide any sources or have the ability to utilize only relevant information to generate summaries. While some vendors may technically provide visibility into the sources, there will be so many to consider that it would render the information impractical to use.

Generative AI and next-generation search are evolving rapidly. What trends do you foresee in this space over the next few years?

The key trend in the short term is that many organizations were initially swept up in the hype of GenAI and then witnessed issues such as inaccuracy via hallucinations, the difficulty in interpreting and incorporating domain-specific information, explainability, and security challenges with proprietary information.

The emerging trend that organizations are starting to understand is that the only way to enable trustworthy GenAI is to implement an elegant solution that combines LLMs, vector databases, semantic data models, and GenAI technologies seamlessly to deliver direct and accurate answers users can trust and use right away. As organizations realize that it is possible to leverage their trusted enterprise content today, they will deploy GenAI solutions sooner and with more confidence rather than continuing their wait-and-see stance.

How do you think Kyndi is positioned to adapt and thrive in the ever-changing landscape of AI and search technology?

Kyndi seems to be in the right place at the right time. ChatGPT has shown the world what is possible and opened a lot of eyes to new ways of doing business. But that doesn’t mean that all solutions are enterprise ready as OpenAI openly admits that it is inaccurate too often to be usable by organizations. Kyndi has been working on this problem for 8 years and has a production-ready solution that addresses the problems of hallucinations, adding domain-specific information, explainability, and security today.

In fact, Kyndi is one of a few vendors offering an end-to-end complete solution that integrates language embeddings, LLM, vector databases, semantic data models, and GenAI on the same platform, allowing enterprises to get to production 9x faster than other alternative approaches. As organizations compare Kyndi to other options, they are seeing that the possibilities suggested by the release of ChatGPT are actually achievable right now.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-ryan-welsh-ceo-of-kyndi/

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Diversity and Inclusivity in AI

Guide to the Digital Twin Technology

We are living in an era of change, where industries are changing their traditional way of managing and streamlining organizational goals. SMEs and SMBs are gradually gaining market share and developing well-known brands, eliminating the term monopoly, as any business with an appropriate data strategy can create its own space in this competitive landscape.

To stay competitive, businesses are attracted to two potential technologies: artificial intelligence (AI) and business intelligence (BI). Combined, they offer a powerful tool that transforms raw data into implementable insight by making data accessible to BI managers. This collaboration between AI and BI enables companies to steer large-scale data efficiently and make quick business decisions.

This article provides an overview of the current landscape of AI and BI, highlighting the evolution of BI systems after integrating artificial intelligence. 

The Synergy Between BI and AI

The partnership between artificial intelligence and business intelligence has become the backbone of the modern business world.

In this competitive market, businesses across all industries strive to drive innovation and automation as an integrated strategy that reshapes organizations from a mindset of data and data-driven decision-making.

When BI managers integrate AI into BI systems in businesses, it harnesses big data’s power, providing previously inaccessible insights.

Traditionally, BI systems were focused on historical data analysis, which was collected and analyzed manually with the help of a data team, which tends to be a tedious job, and businesses often face data bias.

However, AI-powered BI systems have become a dynamic tool that uses predictive analysis and real-time decision-making skills to identify market patterns and predict future trends, providing a more holistic view of business operations and allowing your organization to make informed decisions.

The current landscape of AI-driven BI is a combination of big data analytics, machine learning (ML) algorithms, and AI in traditional BI systems, leading to a more sophisticated tool that provides spontaneous and automated analytical results.

As the AI field diversifies, the BI system will mature continuously, posing an integral role in shaping the future of business strategies across various industries.

Artificial intelligence is transforming business intelligence in numerous ways by making it a powerful tool for BI managers and their teams to work efficiently and effectively and have access to a wider range of customers. Even small businesses and enterprises are trying their hands at AI-powered BI software, intending to automate the maximum work of data analytics to make quick decisions.

In the coming years, we can expect more potential use cases of AI-powered business intelligence software and tools, helping businesses solve the greatest challenges and reach new heights.

To Know More, Read Full Article @ https://ai-techpark.com/transforming-business-intelligence-through-ai/

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Edge Computing TrendsImportance of AI Ethics

The emergence of artificial intelligence (AI) has continually reshaped a range of sectors across the business world.

However, the convenience of AI needs to be balanced against the environmental consequences and the unplanned actions that often arise from the unnecessary usage of hardware, energy, and model training. With the knowledge of digital technologies and a robust foundation to support sustainable development, chief information officers (CIOs) should consider implementing AI initiatives.

According to a survey by Gartner, it is evident that environmental issues are a top priority, and tech companies need to focus on eliminating these issues. Consequently, the CIOs are under pressure from executives, stakeholders, and regulators to initiate and reinforce sustainability programs for IT.

Thus, the combination of adopting AI and environmental sustainability requires proactive strategies that will transform your business. This article describes a framework for the adoption of green algorithms that CIOs can implement in IT organizations to support sustainable development.

AI Supporting Environmental Sustainability

For tracking a sustainable environment within an IT organization, the CIOs have to deliver mandates and requirements to track and trace their businesses’ sustainability KPIs, such as energy consumption or the percentage of carbon footprint. However, the importance of these KPIs and the effectiveness of CIOs rest in how well the research matter is integrated into their digital foundation or digital dividend into the digitized metrics of the organization.

Let’s consider an example of modern networks that are implemented in data centers that allow you and your team to monitor, manage, and minimize energy consumption. It is always advisable to use optical networks because they are more energy efficient and resilient than copper cables, as copper cables are rare earth metals and are mined and refined to transform them into strong cables. Thus, the production of fiber networks uses few raw materials and fewer plants when compared to copper cables.

There are findings that IT companies that have implemented modern networking strategies have witnessed a reduction in their environmental footprint by four times compared to those that have not.

A Five-Step Framework for Adopting Green Algorithms

The green algorithms come into play when there is a lot of complexity, cost, and carbon involved in implementing AI in IT organizations. The green algorithms can be seamlessly integrated with a range of methodologies, from natural language processing (NPL) for analyzing stakeholders’ sentiments to machine learning (ML) to enable predictive maintenance.

However, to implement green algorithms effectively, a collaborative initiative with CIOs and IT project managers is required to develop a structured approach to encourage the development of energy efficiency and environmentally responsible AI solutions that will be the backbone of modern project management.

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

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Ethics in the Era of Generative AI

Democratized Generative AI

We live in an ecosystem where we desire a personalized experience, from music to web series, and the products and services we purchase are often recommended to us based on the data that is collected by these websites or applications.

This ability lets us understand our needs and wants for a better living experience.

Similarly, in the healthcare industry, we can monitor our health and get personalized treatment with the help of artificial intelligence (AI), Natural language processing (NLP), and machine learning (ML) models and algorithms, which tech and healthcare visionaries refer to as AI in healthcare.

AI in healthcare is a promising collaboration, as it challenges the traditional way patients are treated by doctors and healthcare specialists to bring a futuristic clinical and administrative solution. Using modern-age technology, doctors, researchers, and other healthcare providers improve healthcare delivery in areas like preventive care, disease diagnosis and prediction, treatment plans, as well as care delivery and administrative work.

AI in healthcare is further helping recruiting companies contribute to consumer health swiftly. Nowadays, the increasing use of AI in consumer wearables and other medical devices is providing value in monitoring and identifying early-stage heart diseases. This AI-powered integration of sensors and devices helps healthcare service providers observe and detect life-threatening diseases at an early stage.

Nevertheless, healthcare areas are plentiful. However, this article will focus on how AI has been implemented and what the future of healthcare policies looks like for the industry.

The concept of patient-centricity focuses on AI-based prescription medicine, which offers enhanced personal treatment by empowering patients and providing visual care.

Focus Areas of AI in Healthcare

The introduction of AI in healthcare implements modern healthcare systems that are equipped to cure diseases at a rapid pace with greater accuracy, improving the quality of care through technological advancements.

The integral focus areas for artificial intelligence help in making the modern healthcare process and system more patient-centric, further fostering care delivery, strengthening disease surveillance mechanisms, and enhancing the drug discovery process.

The future of AI in healthcare holds immense potential for helping shape public and private health policies. While prioritizing education and training initiatives and embracing this technology responsibly, custodians in the health tech industry can unlock the full potential for creating innovative and lasting solutions that address the relentless healthcare challenges.

To Know More, Read Full Article @ https://ai-techpark.com/ai-in-healthcare/

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Digital Patient Engagement Platforms

Importance of AI Ethics

In today’s digital era, the merge of artificial intelligence (AI) and the Internet of Things (IoT) has kicked off a tech revolution that tends to reshape numerous industries across the globe with a vision that this transformation helps businesses enhance efficiency and drives innovations at an unprecedented pace.

For a better understanding, IoT is all about an extensive network of connected devices with an embedded sensor that collects and transfers large-scale data that is processed according to the behavior and patterns of the user to make the correct decision at the right time.

On the other hand, artificial intelligence (AI) is a technology that imitates human intelligence and behavior in computer systems, further enhanced by learning and experimenting, to develop new behaviors and skills.

These technologies, together, can solve real-world problems and create new products for businesses to enhance the customer-digital experience. This article explores the opportunities and approaches of AI and IoT that will revolutionize market paradigms.

Five Skills to Stay Relevant and Competitive in the Age of IoT and AI

The scope of implementation and importance of IoT and AI determine the need for qualified IT professionals; however, the demand for experts in these technologies requires certain technical and soft skills.

Below, we have reviewed some wanted skills that are required in an IT professional to succeed in IoT and how they can boost their career profile:

Artificial Intelligence and Machine Learning

AI and machine learning (ML) have become key technologies that have reshaped the IT field. To excel, IT engineers and IoT developers need to have a good understanding of ML and AI technologies, as these technologies are the base of developing tools and frameworks that are essential to developing IoT devices and AI applications that are further used in various application areas, like automotive, manufacturing, finance, and healthcare. Learning and understanding where and how to implement ML algorithms with the help of data sensors are used to develop smarter appliances.

The skill of big data management will be useful for predictive analytics, which is based on identifying data patterns. The IT engineers and IoT developers should also have knowledge of popular ML libraries such as Kera and Tensorflow and the ability to program in languages such as R, Python, and C++.

IoT Systems and Networking

The Internet of Things (IoT) is a blooming field in the IT industry that involves connecting physical devices (motion sensors, smart glasses, VR headsets, smart devices, trackers, drones, etc.) to the Internet. To be a master, IT engineers, and IoT developers need to have a better understanding of the IoT concept and technologies to develop robust and seamless connected devices that require a unique user interface (UI). The knowledge about IoT includes networking protocols such as Bluetooth, Zigbee, and Wi-Fi; engineers and developers should also be familiar with IoT platforms such as AWS IoT and Microsoft Azure. IT professionals who have good UI skills in visual design, analytics, wireframing, and prototyping excel at developing satisfactory devices.

To Know More, Read Full Article @ https://ai-techpark.com/exploring-the-intersection-of-ai-and-iot/

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Generative AI in Virtual Classrooms

Hadoop for Beginners

In today’s competitive world, the various sectors are at a crossroads. Companies following traditional approaches are struggling to keep pace with the fast pace of digital transformation.

So, the pace of the ongoing digital transformation demands a shift in paradigm with the help of intelligent applications (I-apps), which imbue the power of artificial intelligence (AI) and machine learning (ML) to automate tasks, get insights, and drive better decision-making that has the potential to reshape the future of various sectors.

Why Are Intelligent Apps Important for Business?

Intelligent apps are capable of continually learning the environmental, behavioral, and emotional patterns of users while completing the tasks assigned to them.  The applications are meant to forecast your requirements and present them as relevant information, ideas, or recommendations using predictive analysis, prescriptive analysis, operational vision, and product insights. Chatbots, virtual assistants, and recommendation engines on e-commerce sites are just a few examples of intelligent applications.

Let’s consider an example of Slack, a popular collaboration application that provides channels for team communication, file sharing, and connections with other productivity apps.

How Do Intelligent Apps Work?

Intelligent applications are not just clever software; they are built on a foundation of powerful technologies that enable their unique capabilities by empowering software developers to create intelligent apps that automate tasks, analyze data, and predict outcomes. Let’s take a quick glimpse into the mechanics behind how this application works:

AI and ML Technologies

Intelligent apps lie in AI and ML, as these technologies enable apps to process vast amounts of data, learn patterns, make predictions, and adapt their behavior intelligently.

Low-code and No-code Platforms

Democratizing app development, these platforms allow software developers with varying skill levels to create intelligent apps without extensive coding knowledge. These platforms provide visual interfaces, pre-built components, and drag-and-drop functionalities, making intelligent AI-powered app development more accessible.

Data Integration

Intelligent applications need seamless integration with various data sources, including databases, cloud platforms, and IoT devices, to make intelligent decisions and provide personalized experiences.

To Know More, Read Full Article @ https://ai-techpark.com/why-intelligent-applications-are-no-longer-an-option-for-business/

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Safeguarding Business AssetsCloud Computing Frameworks

The inherent inertia and inefficiency of regulators in responding to rapidly evolving sectors like AI can be attributed to several factors rooted in their nature, design, and skill sets. First, regulatory bodies are typically structured to be cautious and deliberative, prioritizing stability and risk aversion over rapid adaptation. This approach, while beneficial for maintaining systemic integrity in traditional markets, often results in a lag when faced with fast-paced technological innovations. Additionally, the design of these institutions, often bureaucratic and bound by complex legislative processes, hampers their ability to swiftly enact new policies or adapt existing ones to novel contexts. Lastly, there is often a skill and knowledge gap; regulators may lack the specialized expertise required to understand and effectively govern cutting-edge technologies, leading to a reliance on outdated frameworks or overly cautious approaches that fail to address the unique challenges and opportunities presented by sectors like cryptocurrency.

This pattern of slow and inadequate responses was most recently highlighted by the rise and fall of FTX, a major cryptocurrency exchange. In 2021, FTX quickly grew into one of the world’s largest cryptocurrency exchanges. In 2022 it collapsed in one of the most prolific financial fraud cases in US history. This failure served as a wake-up call. It demonstrated the risks inherent in the crypto market and the consequences of the US government’s slow response in establishing a comprehensive regulatory framework.

Consumers are getting screwed

All these regulations will create negative consequences for consumers if not carefully crafted or if they “inadvertently” favor large companies at the expense of smaller ones or innovation in general. Primarily, a reduction in innovation and diversity, slower access to advanced technologies, and decreased competition are a few of many concerns. The best example of this happening is in Canada. The telecom industry consists of only three players: Rogers Communications Inc., Telus Corporation, and Bell Canada. This became possible as they lobbied and bullied their way into the top to introduce regulations to stifle any competition in mobile phone and internet services.

As a result, Canadians have significantly worse coverage plans, both locally and globally, than Americans do. Mobile phone bills have skyrocketed to eye watering prices, and Canadians are often the last to get many interesting and innovative services. This extreme competition stifling has even resulted in fatal consequences. On July 8th, 2022, Rogers Communications experienced a service outage that knocked 25% of Canada offline. This resulted in many crucial services being knocked offline, including 911 services.

What do we do then?

AI must be decentralized. Period. Full stop. Allowing something as game changing and powerful to be centralized and to follow the bottom line of business corporations is akin to allowing the internet to be controlled by corporations. If this had happened, this would have resulted in a much less free and open internet than the one we have today. Consumers of the internet today have freedom of choice in where they shop from, how often they do it, and what they wish to pay, due to the many services allowed on the internet. If it had been regulated like Microsoft had attempted to do in 1995, it would be akin to shopping in a random strip mall in midwestern America.

To Know More, Read Full Article @ https://ai-techpark.com/rapid-advancement-of-ai/

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Trends in Big Data for 2023

Guide to Endpoint Security