Tag: Artificial Intelligence

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 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 recent years, automation and artificial intelligence (AI) have witnessed a surge in popularity, and it’s anticipated to expand as organizations become more dependent on AI solutions to address issues. Numerous tech giants, like Amazon, Apple, and Alibaba, have already started to explore the possibilities of implementing AI and robotics in their organizations.

The introduction of robots into the workplace is set to alter how C-level decision-makers will do business, as they need to share space with robots as coworkers and learn new skills as robots will gradually take over tedious and dangerous tasks on behalf of their employees. There will be a shift in job responsibilities and obligations, creating bandwidth for strategic planning for better business development in sections where robotics are not utilized. Functional leaders in customer-facing roles may identify the best methods to serve clients and use automation to deliver personalized products and services on demand.

Robotics is becoming a game changer in various industries throughout the world.

Chief Operating Officers (COOs)

COOs will play an important role in transforming the workplace by integrating AI and robotics, creating a digital strategy for automating services, and streamlining operations. Thus, COOs will drive and manage the organization’s transformation into a human-robot workforce; however, they must update their knowledge of technologies by understanding the changes and how they can affect the business. For instance, in a manufacturing company, the role of COOs will be to assess the need for automation technologies like IoT and blockchain in a department. After evaluation, they should come up with an investment strategy by analyzing how AI and robots will reshape the manufacturing industry and streamline the supply chain.

Chief Information Officers (CIOs)

CIOs will have to adjust to technology issues and work closely with other C-suits as they navigate a new landscape of risk and compliance. They will have the liberty to explore and evaluate the areas of data management, analytics, and cybersecurity. With automation technology and robot workers having a positive impact on the organization, CIOs will witness changes in function becoming more deeply integrated.

Other tech leaders, like CTOs and CDOs, may be joined by Chief Robotics Officers (CROs), who will help in navigating how robots will perform, providing robust road maps, and setting strategies for future developments.

Robotics and artificial intelligence (AI) will change the workplace as some job roles will be replaced by robots and automation, but the technology will also lead to the creation of new jobs and highly valued responsibilities. This development will also affect the C-suite, as robots will minimize their responsibilities and help in creating robust strategies in this digital era. Large-scale enterprises and SMEs must prepare their employees for collaboration with new technologies by providing adequate L&D opportunities, upskilling, reskilling, and giving them the bandwidth to accept the change.
To Know More, Read Full Article @ https://ai-techpark.com/robotics-is-changing-the-roles-of-c-suites/

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Diversity and Inclusivity in AIDigital Twins Shaping Industries

Chatbots, powered by artificial intelligence (AI), are fundamentally changing how businesses operate and enhancing productivity and efficiency. Chatbots are computer programs designed to simulate conversation with human users via text or voice. From simple FAQ bots to complex virtual agents, chatbots are automating business processes and transforming how companies interact with customers and employees. The industry is still at the nascent stage but holds great promise and potential for the future.

In this post, we’ll explore the key ways chatbots are improving business productivity and efficiency.

Driving Business Efficiency

As AI systems, chatbots continuously improve through machine learning. They utilize data from past interactions to deliver ever more accurate responses and perform tasks more efficiently over time. Natural language interfaces allow chatbots to understand context and intent, engage in complex dialogue, and complete tasks just as a human assistant would. Companies are leveraging the technology to come up with optimal marketing strategies with AI and chatbots.

From customer service agents to sales reps and administrative staff, chatbots are taking on roles humans performed in the past at lower cost and with higher consistency. They don’t need holidays, sick days, or coffee breaks.  For many routine, repetitive tasks, chatbots simply offer a more efficient alternative. Intelligent chatbots are providing tremendous ROI through increased productivity and cost savings. But there are still domains where chatbots can’t function properly. Human touch and help are required in the form of on-demand tech support for various things like cybersecurity, cloud, office printer setup, computers, and network help.

Transforming Customer Experience

Today’s customers expect ultra fast, personalized, and seamless experiences. Intelligent chatbots provide a superior level of convenience by serving customers anytime, anywhere at the pace they expect. With NLP and machine learning, chatbots analyze customer data and past interactions to make recommendations and tailor experiences to individual needs and preferences.

Chatbots are revolutionizing industries from e-commerce retail to banking and travel. They minimize wait times, reduce human errors, and allow staff to focus on higher value functions like complex problem solving and building customer relationships. By streamlining the customer journey, chatbots drive satisfaction, loyalty, and revenues.

From large enterprises to smaller businesses, chatbots are fundamentally changing how companies operate; enhancing productivity, efficiency, and the customer experience. By automating repetitive tasks and processes, chatbots enable staff to focus on more meaningful, revenue-driving work. With intelligent self-learning capabilities, chatbots will only expand their capabilities and business value over time. Its clear conversational AI is transforming engagement across industries, delivering tangible returns on investment, and driving competitive advantage.
To Know More, Read Full Article @ https://ai-techpark.com/impact-of-chatbots-on-business-productivity-and-efficiency/

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AI and RPA in Hyper-automation

Generative AI Applications and Services

In this technologically dominated era, the integration of artificial intelligence (AI) has become a trend in numerous industries across the globe. With this development of technology, AI brings potential risks like malicious attacks, data leakage, and tampering.

Thus, companies are going beyond traditional security measures and developing technology to secure AI applications and services and ensure they are ethical and secure. This revolutionary discipline and framework is known as AI Trust, Risk, and Security Management (AI TRiSM), which makes AI models reliable, trustworthy, private, and secure.

In this article, we will explore how chief information security officers (CISOs) can strategize an AI-TRiSM environment in the workplace.

Five Steps on How C-suite Can Promote Trustworthy AI in Their Organization 

The emergence of new technologies is likely to drive more potential risks; however, with the help of these five essential steps, CISOs and their teams can promote AI TRiSM solutions:

Defining AI Trust Across Different Departments

At its core, AI trust is the confidence that employees and other stakeholders have in a company that governs its digital assets. AI trust is driven by data accessibility, transparency, reliability, security, privacy, control, ethics, and responsibility. A CISO’s role is to educate employees on the concept of AI trust and how it is established inside a company, which differs depending on the industry and stakeholders. 

Develop an AI trust framework that helps achieve your organization’s strategic goals, such as improving customer connections, maximizing operational excellence, and empowering business processes that are essential to your value proposition. Once built, implement methods for measuring and improving your AI trust performance over time.

Ensure a Collaborative Leadership Mindset

As IT organizations rely on technology for back-office operations and customer-facing applications, IT leaders face the challenge of balancing business and technical risks, potentially leading to prioritizing one over the other.

CISOs and IT experts should evaluate the data risks and vulnerabilities that may exist in various business processes, such as finance, procurement, employee benefits, marketing, and other operations. For example, marketing and cybersecurity professionals might collaborate to determine what consumer data can be safely extracted, how it can be safeguarded, and how to communicate with customers accordingly.

As a CISO, you can adopt a federated model of accountability for AI trust that unites the C-suite around the common objective of seamless operation without hampering customers’ and organizations’ data. 

In conclusion, as businesses grapple with growing datasets and complicated regulatory environments, AI emerges as a powerful tool for overcoming these issues, ensuring efficiency and dependability in risk management and compliance. AI Trust, Risk, and Security Management (AI TRiSM) may assist businesses in protecting their AI applications and services from possible threats while ensuring they are utilized responsibly and compliantly.
To Know More, Read Full Article @ https://ai-techpark.com/tackling-ai-trism-in-ai-models/

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Data Analytics Trends in 2023

AI Impact on E-commerce

Artificial Intelligence will improve our lives in many ways, from safe, automated mobility to time-saving tasks. With constant innovation, AI systems will become far more capable, possibly equaling or exceeding human-level performance at most intellectual tasks.

AI is one of the most important technologies of our time and its impact is comparable to the industrial and scientific revolutions. For business leaders to utilise AI’s full capabilities it’s crucial to ensure AI is aligned with human intent and the promise of the AI product itself.

The best way to express your intent is to review examples of how the algorithm behaves and provide feedback. Human feedback can be used to steer AI products very efficiently by shaping the evolving dataset to reflect the developers’ intentions and user expectations.

What is AI alignment and why is it important?

AI alignment is a field of AI safety research that aims to ensure that AI systems achieve their desired outcomes and work properly for humans. It aims to create an end-to-end process where an AI-based system can align its output with desired human preferences.

Imagine playing darts, or any game for that matter, but not agreeing on what the board looks like or what you get points for. If the designer of an AI system cannot express consistent and clear expectations through feedback, the system won’t know what to learn.

It all comes back to iteration

Contrary to common belief, AI alignment is not actually a technology problem, it’s a people problem. Ultimately, the ability of the AI system to learn the right kind of rules comes down to the ability of the product developer or service provider to express what it is that they want the product to do.

If we don’t figure out a better way to do this, we will see a lot of disappointment in the next few years and it’s going to be very difficult to realise the potential of AI. So, it’s in our collective interest to get this right. If business and technology leaders can collaborate closely on alignment, it will help create better products and in turn benefit humans day to day-to-day lives.

We live in a fast-changing world, and expectations evolve quickly. If you assemble a large dataset, you must expect it to evolve. The challenge now is to shape your data with this evolution in mind which in turn informs your AI products. Alignment is the way forward, and the key is to approach it with an iterative mindset. The challenge now is to explore and shape your data with this evolution in mind which in turn informs your AI products.To Know More, Read Full Article @ https://ai-techpark.com/understanding-ai-alignment/
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