Autore: martechcubejohn

We believe that the journey of developing software is as tough as quality assurance (QA) engineers want to release high-quality software products that meet customer expectations and run smoothly when implemented into their systems. Thus, in such cases, quality assurance (QA) and software testing are a must, as they play a crucial role in developing good software.

Manual testing has limitations and many repetitive tasks that cannot be automated because they require human intelligence, judgment, and supervision.

As a result, QA engineers have always been inclined toward using automation tools to help them with testing. These AI tools can help them understand problems such as finding bugs faster, and more consistently, improving testing quality, and saving time by automating routine tasks.

This article discusses the role of AI in the future of QA engineering. It also discusses the role of AI in creating and executing test cases, why QA engineers should trust AI, and how AI can be used as a job transformer.

The Role of AI in Creating and Executing Test Cases

Before the introduction of AI (artificial intelligence), automation testing and quality assurance were slow processes with a mix of manual and automatic processes.

Earlier software was tested using a collection of manual methodologies, and the QA team tested the software repetitively until and unless they achieved consistency, making the whole method time-consuming and expensive.

As software becomes more complex, the number of tests is naturally growing, making it more and more difficult to maintain the test suite and ensure sufficient code coverage.

AI has revolutionized QA testing by automating repetitive tasks such as test case generation, test data management, and defect detection, which increases accuracy, efficiency, and test coverage.

Apart from finding bugs quickly, the QA engineers use AI by using machine learning (ML) models to identify problems with the tested software. The ML models can analyze the data from past tests to understand and identify the patterns of the programs so that the software can be easily used in the real world.

AI as a Job Transformer for QA Professionals

Even though we are aware that AI has the potential to replace human roles, industrialists have emphasized that AI will bring revolutionary changes and transform the roles of QA testers and quality engineers.

Preliminary and heavy tasks like gathering initial ideas, research, and analysis can be handled by AI. AI assistance can be helpful in the formulation of strategies and the execution of these strategies by constructing a proper foundation.

The emergence of AI has brought speed to the process of software testing, which traditionally would take hours to complete. AI goes beyond saving mere minutes; it can also identify and manage risks based on set definitions and prior information.

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

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Can you tell us about your journey and what motivated you to co-found ForwardLane, particularly focusing on AI’s role in financial services?

My journey into fintech came to me when I worked at the multi-asset alternative asset manager, CQS. There, we could find insights and act on them far ahead of financial institutions. When I saw how difficult it was for advisors to get to insights, I came up with the vision of an AI co-pilot for every financial advisor. With EMERGE, that vision is now a reality. EMERGE analyzes all your data to uncover opportunities and deliver insights tailored to each user and client.

ForwardLane is known for its proactive and personalized advisory platform. How does AI play a pivotal role in achieving this level of personalization and what sets it apart from traditional approaches?

EMERGE combines AI, data aggregation of portfolio, market data, marketing, behavioral, demographic and psychographic and natural language generation to provide hyper-personalized guidance. Imagine having a data scientist, a personal communications coach, and a strategist dedicated to each client – that’s the power of EMERGE. It detects signals and recommends next actions unique to the individual.

You’ve been a noted commentator on AI’s application in financial services. Could you share some specific examples of how AI has benefited asset managers and insurance distribution?

EMERGE digests and learns from your data enterprise-wide to reveal new distribution opportunities. It informs your sales teams which clients to focus on and what to talk about. It can create advisor profile briefs on the fly, and then recommend an engagement plan. The ROI can be game-changing.

ForwardLane’s AI platform combines NLP with enterprise data aggregation. Could you elaborate on how this combination enhances client engagement and provides personalized content?

EMERGE’s hybrid AI extracts insights you never knew existed from both structured and unstructured data. This gives a 360-degree view of each client by connecting the dots across siloed datasets. EMERGE GPT has all of these insights to seed accurate answers and provide advice on how to engage clients effectively.

In the context of ForwardLane’s offerings, could you explain how the API framework seamlessly integrates insights into existing workflows and CRM systems?

EMERGE seamlessly integrates guidance into your existing platforms. Imagine having your CRM proactively guide your next best action for each client interaction.

You have expertise in a wide range of areas including global capital markets, derivatives, and high-performance applications. How have these areas of expertise contributed to the development of ForwardLane’s technology and solutions?

My background in hedge fund quant finance, high-performance computing and high-frequency trading technology allows EMERGE to leverage institutional-grade analytics in a turnkey platform. The inefficiencies in large organizations, led us to create new tools to make life easier for enterprise users. EMERGE democratizes insight creation, data science, effective communications and client engagement for all users

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

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Researching IoT Security

Blockchain, AI, and Quantum Computing

The Internet of Things (IoT) represents one of the most significant technological evolutions of our time. With the proliferation of connected devices, from home appliances to complex industrial machinery, IoT has seamlessly integrated into the fabric of our daily lives. This integration has not come without its challenges, particularly in terms of security.

As IoT devices become more ubiquitous, they also grow in complexity. The sensors, connected medical devices, and critical infrastructure systems we rely upon every day are now composed of countless components sourced from an increasing number of providers. This complexity is not just a matter of physical parts but extends deeply into the software that powers these devices.

Amidst this complexity lies a significant concern: data security. Many IoT devices manage data within corporate control environments, but this data is often sensitive and proprietary. The marketplace, unfortunately, is rife with misinformation and misunderstandings, leading to valid concerns about unauthorized access, data breaches, and privacy violations. These concerns are well-founded, as the potential risks include vulnerabilities in critical medical devices, connected vehicles, and key infrastructure systems, which could have significant impacts if exploited.

The Intricacies of IoT Device Software Supply Chains

Embedded devices, which form a substantial part of the IoT ecosystem, consist of intricate layers of third-party software. Unlike cloud or web software, these devices often include proprietary software from various hardware components, making the supply chain more complex and opaque. This complexity is compounded by the fact that these hardware components often come with less available public information than, for example, open-source projects on GitHub. This scenario demands a high level of software transparency, especially given the slower and less frequent update cycles in realms requiring device recertification.

The Critical Need for Software Transparency in IoT

Software transparency in IoT is not merely a best practice; it is a necessity. The complexity and opacity of embedded device supply chains make it nearly impossible to effectively assess and manage security risks without a clear understanding of the software components within these devices. This transparency becomes crucial in light of recent regulatory pushes focusing on IoT and embedded system security, such as the European Union Cyber Resilience Act (EU CRA) and the NIST Cyber Trust Mark.

The future of IoT security is a collaborative effort, one that requires manufacturers, software developers, and security experts to work together. It involves not only implementing robust security protocols but also embracing transparency at every stage of the development and deployment process. As we continue to invest in standards like SBOMs and VEX, and collaborate with industry leaders, we are paving the way for a future where IoT devices are not just functionally robust but also secure and transparent.

To Know More, Read Full Article @ https://ai-techpark.com/enhancing-iot-security-through-software-transparency/

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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

Many businesses have learned the hard way that not every AI project leads to glory and success. In fact, a 2023 CIO.com survey found that more than half of AI projects fail to produce actionable results at all. There are many reasons for this, but one of the biggest causes we frequently see is a disconnect between the data scientists who are actually building the models and the end users who would consume or use the models.

 Most data scientists would agree that deep data exploration of all the relevant data is crucial to any analytics project. Unfortunately, these same data scientists are regularly faced with tight deadlines and often have no clear way to quantify the ROI for data exploration. As a result, data scientists frequently do not spend as much time as they would like when framing and scoping new projects and exploring the corresponding data. Additionally, the onus of data exploration typically falls to the data scientist who may be fairly removed from the end users within the organization. This means that when data exploration happens, it happens apart from the business analysts closest to decision-making. As a result, organizations miss out on domain expertise that could guide bigger data-based projects such as AI.

The New AI: Analyst-powered Intelligence

There’s an enormous opportunity for companies to upskill their analysts. With AI-powered analytics, they can accomplish data exploration without getting blocked by too much data, too many correlations between the attributes, or an inability to find signal in a dataset.

Say a financial services company wants to boost its business in lines of credit for SMBs. Maximizing this opportunity requires the company to understand who their ideal customer is and how best to reach them. Using AI-powered analysis, the analyst can find groups of businesses that would be strong candidates for credit extensions and understand why they were recommended.

Armed with this insight, the analyst then collaborates with the marketing and environmental-social-governance (ESG) teams to identify the ideal customer persona to target, then prioritize the appropriate business development projects, such as chatbots that can alert the sales team when these customers interact with the website.

From start to finish, the analyst partners with their business team to get the best results out of the right AI projects. Moreover, the same AI-driven analytics platform can be used by the data science team to solve more complex problems that an analyst may not have the specific skillset for yet. It’s a win all around for the organization.

Surface Hidden Opportunities

When analysts have the power of advanced analytics in their hands they can discover business advantages buried within mountains of data. Decision-makers can have confidence that any AI project proposal that emerges as a result of deep analyses has emerged organically from data and was put together in full collaboration with those on the business side—ensuring there’s value in pursuing it.

To Know More, Read Full Article @ https://ai-techpark.com/why-ai-use-cases-fail-and-what-to-do-about-it/

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Improve Clinical Efficiency with AI

Blockchain, AI, and Quantum Computing

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

Artificial Intelligence (AI) has been developing at a rapid pace and has been integrated into a growing number of applications across every industry. AI continues to widen its capabilities to assist in a variety of daily tasks but, as can be expected with any Internet-based technology, AI also has a dark side. As cyberattacks have grown in volume and complexity over the last few years due to Covid-19, what could cybersecurity and AI look like going forward? If you want to know more about how Covid-19 affected cybersecurity, check out our blog “Cybersecurity in the post Covid-19 world.”

Preserving Privacy Around Artificial Intelligence

The cost of implementation for these types of integrated AI systems can be very high, making it an unattainable option for smaller businesses. Unfortunately, on the threat front, cybercriminals can use AI to devise and launch increasingly more complex cyber attacks. A study from 2023 by Blackberry stated that 51% of IT decision makers believe there will be a successful cyberattack credited to ChatGPT within the year.

Some malware architects have used AI to recreate malware strains and techniques described only in research publications, introducing an entirely new level of cyberattacks. For example, Chat GPT has successfully written functional malware that is capable of stealing sensitive files, encrypting hard drive content, and more. While this malware is not yet sophisticated, the speed and scale at which it can be produced is alarming. Additionally, other AI models have the capability to make attacks even more sophisticated by impersonating the voices of people and demanding money transfers. We can expect to see more attacks that are highly targeted social engineering attacks. Cybersecurity experts also state that AI-created deep fakes are finding ways to bypass biometric authentication, thus gaining access to protected systems.

We are still in the early stages of AI. These AI integrated systems need to be constantly monitored as they are far from perfect and can be prone to errors and biases. But it is clear AI products will continue to improve with time. When AI is used for corporate purposes, it is important that businesses which incorporate these AI systems ensure the technology is used for ethical purposes. These AI systems must be monitored to prevent them from being engineered to act against the corporate assets, and are not used to invade user privacy or circumvent traditional security measures – the  double-edged sword when it comes to security. While AI can provide benefits in threat detection and response capabilities, it can also pose a significant threat – be sure that your data is protected.

Simplify your data security needs. Encryptionizer is easy to deploy. It is a cost-effective way to proactively and transparently protect your sensitive data that allows you to quickly and confidently meet your security requirements. With budget considerations in mind, we have designed an affordable data security platform that protects, manages, and defends your data, while responding to the ever changing compliance requirements.
To Know More, Read Full Article @ https://ai-techpark.com/impact-of-artificial-intelligence-on-cybersecurity/

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Automated Driving Technologies Work

Researching IoT Security

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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Importance of AI Ethics

In today’s interconnected business environment, companies regularly rely on third parties for critical business functions like supply chain, IT services, and more. While these relationships can provide efficiency and expertise, they also introduce new cybersecurity risks that must be managed. More than 53% of businesses worldwide have suffered at least one cyber attack in the past 12 months and one in five firms attacked said it was enough to threaten the viability of the business. Recent high-profile breaches like the SolarWinds attack have highlighted the dangers of supply chain compromises. Implementing a comprehensive third party risk management program is essential for security. In this post, we’ll explore key strategies and best practices organizations can use to defend against cyber threats from third party relationships.

Limit Access and Segment Third Parties

Once a third party relationship is established, limit their access to only what is required for their role. Segment them into their own virtual network or cloud environment isolated from your core infrastructure. Implement the principle of least privilege access for their credentials. Disable unnecessary ports, protocols, and services. Lock down pathways between your network and the third party. The goal is to reduce their potential impact and restrict lateral movement if compromised.

Continuously Monitor for Threats

Monitor third party networks vigilantly for signs of compromise. Deploy tools like intrusion detection systems that generate alerts for anomalous behavior. Monitor for unusual data transfers, unauthorized changes, malware, and other IOCs. Conduct vulnerability scans and penetration testing against your third parties’ environments. Audit their logs and security events for issues impacting your security posture. The goal is early detection that can limit damage from a third party breach.

Practice Incident Response Plans

Even rigorous security can still experience incidents. Develop plans for quickly responding to a breach impacting a third party. Define escalation protocols and response team roles. Maintain contacts for your third parties’ security staff. Institute plans for containment, eradication, and recovery activities to limit the impact on your organization. Practice responding to mock third party breach scenarios to smooth out the process. Effective incident response can significantly reduce the damage from real world attacks.

Foster Strong Relationships with Third Parties

While security requirements and controls are critical, also focus on building strong relationships with your vendors, suppliers, and partners. Collaborate to improve security on both sides. Offer guidance and training to enhance their practices and controls. Recognize those who exceed expectations. Build rapport at the executive level so security is taken seriously. Cybersecurity does not have to be adversarial – work together to protect against shared threats.

Third party risk management is essential in modern interconnected business ecosystems. Businesses can no longer rely solely on their own security – all external connections must be assessed and managed.
To Know More, Read Full Article @ https://ai-techpark.com/third-party-risk-management-strategies-against-cyber-threats/

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