Tag: Artificial Intelligence

Marketing organizations are increasingly adopting artificial intelligence to help analyze data, uncover insights, and deliver efficiency gains, all in the pursuit of optimizing their campaigns. The era of AI augmentation to assist marketing professionals will continue to gain momentum for at least the next decade. As AI becomes more pervasive, this shift will inevitably reshape the makeup and focus for marketing teams everywhere.

Humans will retain control of the marketing strategy and vision, but the operational role of machines will increase each year. By 2025, it is projected that 70% of lower-level administrative duties will largely disappear as artificial intelligence tools become more deeply entwined in the operations of marketing departments. Similarly, many analytical positions will become redundant, with smart chatbots expected to assume up to 60% of daily responsibilities.

However, the jobs forecast is not all doom and gloom because the demand for data scientists will explode. The ability to aggregate and analyze massive amounts of data will become one of the most sought-after skillsets for the rest of this decade. By 2028, the number of data science positions is expected to grow by 30%, remaining immune to economic pressures. These roles will be less susceptible to budget cuts, highlighting the critical importance of data analysis in the evolving marketing landscape.

Effects of the AI Rollout on Marketing Functions

As generative AI design tools are increasingly adopted, one thorny issue involves copyright protection. Many new AI solutions scrape visual content without being subjected to any legal or financial consequences. In the year ahead, a lot of energy and effort will be focused on finding a solution to the copyright problem by clarifying ownership and setting out boundaries for AI image creation. This development will drive precious cost and time savings by allowing marketing teams to embrace AI design tools more confidently, without the fear of falling into legal traps.

In addition, AI will become more pivotal as marketing teams struggle to scale efforts for customer personalization. The gathered intelligence from improved segmentation will enable marketing executives to generate more customized experiences. In addition, the technology will optimize targeted advertising and marketing strategies to achieve higher engagement and conversion levels.

By the end of 2024, most customer emails will be AI-generated. Brands will increasingly use generative AI engines to produce first drafts of copy for humans to review and approve. However, marketing teams will have to train large language models (LLMs) to fully automate customer content as a way of differentiating their brands. By 2026, this practice will be commonplace, enabling teams to shift their focus to campaign management and optimization.

To Know More, Read Full Article @ https://ai-techpark.com/future-of-marketing-with-ai-augmentation/ 

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The intersection of artificial intelligence (AI) and digital advertising to create truly engaging experiences across global audiences and cultures is reaching an inflection point. Companies everywhere are leveraging powerful trends in AI, machine learning and apps for performance marketing.

Today’s AI and machine learning technologies are allowing apps to understand speech, images, and user behavior more naturally. As a result, apps with AI capabilities are smarter and more helpful, and companies are using these technologies to create tailored experiences for customers, regardless of language or background. AI is leveling the playing field by making advanced data tools accessible to anyone, not just data scientists.

Kochava has incorporated AI and machine learning across our diverse solutions portfolio for years, such as within our advanced attribution and fraud prevention products. We have also adopted advanced technologies, like large language models (LLMs) to develop new tools.

Many organizations are instituting internal restructuring with a focus on enhancing the developer experience. The aim is to leverage the full potential of AI for smart applications, providing universal access to advanced tech tools, while adapting to changes in app store policies. Engineering teams are spearheading the development of self-service platforms managed by product teams. The primary objective is to optimize developers’ workflows, speeding up the delivery of business value, and reducing stress. These changes improve the developer experience which can help companies retain top talent.

From an overall organizational structure perspective, in pursuit of a more efficient and effective approach, Kochava is focused on enhancing developer experiences, leveraging AI for intelligent applications, democratizing access to advanced technologies, and adapting to regulatory changes in app marketplaces.

Reimagining the Future

The software and applications industry is one that evolves particularly quickly. The app market now represents a multibillion-dollar sector exhibiting no signs of slowing. This rapid growth and constant change presents abundant opportunities for developers to build innovative new applications while pursuing their passions. For app developers, monitoring trends provides inspiration for maintaining engaging, innovative user experiences.

As AI integration increases, standards will develop to ensure AI can automatically interface between applications. It will utilize transactional and external data to provide insights. Applications will shift from set features to AI-driven predictions and recommendations tailored for each user. This advances data-driven decision making and transforms the experience for customers, users, teams, and developers.

To Know More, Read Full Article @ https://ai-techpark.com/generative-ai-marketing-trends/ 

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You may have seen the acronym ‘RAG’ floating around in relation to artificial intelligence. What the heck is RAG and why is everyone talking about it?

RAG stands for Retrieval-Augmented Generation and combines a generative model with a retrieval system to enhance or augment (as the name suggests) AI responses with more accurate and current data. So this means there are two portions to it: the generative model, which generates human-like text, and the retrieval system, which supplements the generative model’s output.

As with any emerging technology, before implementing it within your organization, it’s wise to understand it, as well as its potential benefits, and truly consider why you should – or should not – use it. Let’s explore what RAG is and the impact it can have on your business.

The RAG Process

There are four components to the process flow of a RAG process: query processing, retrieval, integration, and generation. These components are what allow you to truly specialize a Large Language Model (LLM) with a knowledge base of your choosing..

Retrieval: The retriever is a component that searches and selects relevant information from a large database or knowledge base based on the input query.

Knowledge Base: This is the collection of data or information sources that the retriever accesses to find content relevant to the query.

Re-ranker/Selector: The re-ranker or selector evaluates and chooses the best output from the generated responses, ensuring relevance and quality.

Generation: This component integrates the retrieved information into the language generation process, synthesizing it with the input to produce a coherent response.

Now that we’ve outlined the process RAG uses to produce more effective AI responses, let’s examine why RAG is more effective than other models.

Benefits of RAG

There are numerous benefits of implementing RAG, but the major benefits include preventing hallucination, control over the knowledge base used and flexibility in updating information like price changes or product stock.

Preventing Hallucination: RAG reduces the occurrence of generating false or nonsensical information by grounding responses in verified data, enhancing the accuracy and reliability of AI-generated content, crucial for areas where precision is vital.

Control Over the Models Knowledge: RAG allows precise control over the information sources, enabling organizations to tailor content generation to their specific standards and requirements, thus ensuring consistency and alignment with organizational values.

Flexibility on Updating Information: With RAG’s ability to process real-time data, it excels in applications requiring current information, such as AI sales agents and market analysis, ensuring that businesses can offer accurate, timely data to their clients.

To Know More, Read Full Article @ https://ai-techpark.com/why-everyone-is-raving-about-rag/ 

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In the last few years, we have witnessed that the digital landscape’s boundary between reality and fiction has become increasingly blurred thanks to the advent of deepfake technology. While the intention of developing deep fake technology was purely for entertainment and other legitimate applications, in recent times it has become infamous for spreading misinformation. This technology can also manipulate the cybersecurity domain by confusing or influencing users, exploiting their trust, and bypassing traditional security measures.

Numerous cybersecurity experts have raised questions about deep fake technology playing a multifaceted role and risking national security and prohibited information sources.

Today’s exclusive AITech Park article will explore the nature, risks, real-life impacts, and measures needed to counter these advanced threats.

Decoding DeepFakes

At its core, deep fakes are a part of artificial intelligence (AI) and machine learning (ML) that leverages sophisticated AI algorithms to superimpose or replace elements within audio, video, or images and develop hyper-realistic simulations of individuals saying or doing things they never did.

As the availability of personal information rises online, cybercriminals are investing in technology to exploit deep fake technology, especially with the introduction of social engineering techniques for phishing attacks, as it can mimic the voices and mannerisms of trusted individuals. Cyber attackers orchestrate complicated schemes to mislead unsuspecting targets into revealing sensitive information or transferring funds.

The Progression of Deep Fakes

Deepfakes have opened a new portal for cyber attackers, ranging from suave spear-phishing to the manipulation of biometric security systems. Spear phishing is a common form of deep fake phishing that develops near-perfect impersonation of trusted figures, making a gigantic leap by replicating writing style, tonality, or mincing exact email design. This realistic initiation of visuals and voice can tend to pose an alarming threat to organizations and stakeholders, raising serious concerns about privacy, security, and the integrity of digital content.

For instance, there are cases registered where cyber attackers impersonate business associates, vendors, suppliers, business partners, or C-level executives and make payment requests, demand bank information, or ask for invoices and billing addresses to be updated to steal sensitive data or money. Another example is business email compromise (BEC), which is a costlier form of cybercrime, as these scams are possibly conducted for financially damaging organizations or individuals.

In this era of digitization, we can say that we are navigating the uncharted territory of generative AI (GenAI), where we need to understand the importance of collaboration, stay vigilant, and take measures to combat the threat of deepfakes. The question here shouldn’t be whether we can completely eradicate the threat but how we acclimate our strategies, systems, and policies to mitigate deepfake threats effectively.

To Know More, Read Full Article @ https://ai-techpark.com/the-rise-of-deep-fake-technology/ 

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Despite remarkable progress in pharmaceuticals, more than 7,000 diseases persist without efficacious treatments. Many medical conditions remain underfunded and overlooked, leading to low success rates in new drug discovery endeavors.

The journey from identifying a potential molecule to developing a market-ready medicine is an extensive, laborious, and expensive process. However, quantum computing (QC) offers the potential to revolutionize this journey by addressing complex challenges within the healthcare supply chain and even creating new medications from scratch. Nevertheless, the integration of QC into drug research remains a gradual process.

Today, we delve into the transformative impact of QC on drug research and its promising prospects in the realm of healthcare.

Enhancing Drug Research Efficiency with Quantum Computing

Drug discovery entails intricate processes that blend computational simulations with laboratory experimentation. QC introduces novel discovery approaches, enabling the selection of candidate molecules with desired properties without the need for exhaustive screening procedures. Leveraging artificial intelligence (AI) and machine learning (ML) alongside QC's unique computational principles accelerates drug development, particularly for diseases such as cancer and Alzheimer's, where traditional methods have fallen short.

Democratizing Drug Development with Quantum Computing

QC not only promises to streamline drug development processes but also democratize access to them. Cloud-based QC services provide researchers, ranging from startups to established pharmaceutical firms, with access to quantum computing resources. This accessibility reduces barriers to entry in the pharmaceutical industry, empowering a wider range of stakeholders to participate in drug development endeavors.

Future Trends of Quantum Computing in Drug Discovery

The future of QC in the pharmaceutical industry is rapidly evolving, especially with the emergence of hybrid quantum-classical systems. These systems combine quantum and classical computing techniques to address complex challenges more efficiently. Collaborative ecosystems between pharmaceutical companies, technology firms, and academic institutions are also on the rise, particularly in the realm of QC-enabled drug discovery. Such collaborations aim to leverage quantum algorithms to enhance ML capabilities in drug design and discovery processes, promising groundbreaking advancements in the field.

In conclusion, QC stands poised to revolutionize drug discovery, offering improvements in accuracy and accelerating the overall process. By harnessing the power of quantum bits and algorithms, researchers can address current challenges in drug development and expedite the delivery of novel treatments. As research and innovation in QC continue to advance, its role in transforming the pharmaceutical industry and improving patient outcomes will undoubtedly become increasingly significant.

To Know More, Read Full Article @ https://ai-techpark.com/the-intersection-of-quantum-computing-and-drug-discovery/ 

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The rise of Generative AI (GenAI) has enormous potential for the banking and finance industries. By utilizing GenAI, banks and credit unions speed applications from submission to approval, save time and effort, and deliver a desirable customer experience.

A recent report from the Society for Human Resource Management (SHRM) and The Burning Glass Institute details how GenAI will have an outsized role on the banking and finance industries. The report lists Morgan Stanley, Bank of America and Northwest Mutual as some of the organizations that are most likely to capitalize on the implementation of GenAI. Their study also measures GenAI exposure among several different professional industries; “investment banking and securities dealing and brokerage” measured third highest while “mortgage and nonmortgage loan brokers” ranked highest overall. If SHRM and The Burning Glass Institute are so convinced that GenAI will profoundly alter how financial institutions operate, what will that change look like and why does it matter?

GenAI is distinct from other forms of automation by its ability to automate what is typically considered knowledge work. This represents a sea change in how professional industries, including financial services, will implement automation technology in their workplaces. In fact, financial services are especially dependent on repetitive manual processes requiring specialized knowledge. Processes like loan underwriting and credit card applications require knowledge workers to manually input data and individually connect with customers or members, which takes up the majority of workers’ time and tasks.  GenAI excels in automating repetitive, manual tasks—such as data processing and pattern identification—streamlining operations and freeing up valuable time for knowledge workers.

The applications of GenAI within financial services manifest in both evident and nuanced ways, each offering distinct advantages to forward-thinking institutions. Many industries have begun employing GenAI solutions as chatbots for customer service, and financial services are no exception. GenAI-powered chatbots, operational around the clock, offer an immediate response to customer inquiries, significantly reducing the need for direct intervention by skilled professionals and enhancing service efficiency.  However, these solutions become even more compelling for financial institutions when embedded in the bank or credit union’s broader systems. For example, a loan applicant can interact with a GenAI-enabled chatbot and get a real-time status update on their loan status by providing a few identifying details. In this way, GenAI increases efficiency while also directly improving the customer or member experience.

GenAI technology is novel, and its implementations are sure to evolve further in the coming months and years. However, its potential for financial services is undeniable. In order for banks and credit unions to take full advantage of this nascent technology, financial institutions need to create AI policies, complete digital transitions and start exploring and investing in GenAI use cases now.

To Know More, Read Full Article @ https://ai-techpark.com/how-generative-ai-enhances-credit-unions-and-community-banks/ 

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In the past few years, we have witnessed rapid advancements in the field of quantum computing (QC), which triggers the potential revolutionization in various industries, such as healthcare, supply chain, and manufacturing. This technology can perform complex computations at an unimaginable speed when compared to classical computers, even against quantum threats.

According to the National Institute of Standards and Technology (NIST), the post-quantum cryptography (PQC) standards are expected to be completed by 2024, allowing quantum vendors and experts to keep up with the six QC trends that intersect machine learning (ML) and artificial intelligence (AI).

In today’s exclusive AI Tech Park article, we will delve into the top six quantum computing trends for 2024, providing detailed insight for quantum vendors and experts to harness the transformative power of this cutting-edge technology.

Quantum-Sensing Technologies

The implementation of quantum sensing technologies will enable IT organizations, quantum vendors, and experts to achieve unprecedented levels of sensitivity and precision in measuring and detecting applications. In 2024, businesses will leverage quantum sensor tools and applications for environmental monitoring, medical diagnostics, and mineral exploration to gather actionable insights and make informed decisions based on highly accurate data.

Quantum-Safe Cryptography

With the arrival of quantum computers, traditional cryptographic algorithms will become absolute and vulnerable to quantum attacks. Therefore, organizations will adopt quantum-safe cryptography solutions and technology to protect their sensitive data and communications from quantum threats. The implementation of quantum-safe algorithms, such as quantum key distribution or lattice-based cryptography, will become essential tools for securing digital assets and guaranteeing data privacy in a post-quantum world.

Quantum Machine Learning

Quantum computing, when intersected with ML, enables businesses to leverage quantum algorithms for pattern recognition, optimization, and predictive analytics. The quantum machine learning (QML) algorithms will unlock new insights from large data sets, accelerate model training processes, and enable more accurate predictions in numerous domains. The quantum vendors and experts can further explore the possibilities of integrating QML into the data and analysis section to make data-driven decisions to streamline innovation and develop a competitive advantage in this digital world.

To Know More, Read Full Article @ https://ai-techpark.com/the-top-six-quantum-computing-trends-for-2024/ 

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Digital twin (DT) is a rapidly growing concept that has gained traction as it can improve product designs, optimize performance at an industrial level, and create proactive maintenance services. This upgrading technology has started taking shape on an entirely new and different scale as it has become the pillar for futuristic smart cities.

In the scenario of smart cities, digital twins work as virtual replicas of the city’s assets, such as buildings, road lighting systems, energy and grid capabilities, and mobility solutions. However, it is not enough to develop a third-dimensional (3D) model of these sources. Therefore, the digital twin of smart cities pairs the 3D information with spatial modeling (for building the environment), simulations and mathematical models (for workable electric and mechanical systems), and other components that use real-time data feeds from the Internet of Things (IoT) platforms.

In this exclusive AITech Park, we will explore how digital twins will help smart cities evolve in 2024.

Twinning With the New Age Smart Cities

With the introduction of digital twins in the construction field, this technology has the potential to unlock data that was traditionally trapped in silos.

When constructing a new building, the digital twin is developed from the initial phases of the project by the architects, engineers, and construction (AEC) teams to work together to define each other’s performance goals and get the desired outcomes. Now, as the project progresses, the data is continuously collected and fed into the model using any digital twin solution. When the infrastructure is handed over to the owner, the virtual twin collects operations data that will fine-tune performance and manage maintenance in the long term.

As the digital twin mostly revolves around data supplies, it’s the physical twin that helps in performing predictions and simulations in response to real-world conditions. For instance, in the construction industry, the physical twin can be used to align a building’s solar facade that follows the path of the sun and modifies airflow to minimize the spread of germs.

Therefore, it is evident that DT allows the AEC teams to connect better throughout the entire assignment lifecycle, from design to decommissioning. Further, integrating static data aids in specifying the segment and creating maintenance schedules based on the dynamic data of occupancy rates and environmental conditions.

When DT is combined with building information modeling (BIM), the AEC team is well connected to data, which processes dynamic, real-time, bidirectional information management, bringing out the full potential of integrated workflows and information sharing with clients.

As DT is integrated with artificial intelligence (AI) and machine learning (ML), this technology will evolve from being a conceptual tool to becoming more competent and autonomous as software capabilities expand. The application areas for digital twins will continue to reach new heights in the coming years and will change the way AEC teams create, use, and optimize physical spaces and multiple processes.

To Know More, Read Full Article @ https://ai-techpark.com/urbanizing-smart-cities-with-digital-twins/ 

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Today, in the field of technology, product management is rapidly changing because of artificial intelligence (AI) and machine learning (ML). With these quick advancements in technology and the ever-growing reliance on data-driven decision-making, product managers find themselves at odds; they must forget old ways to learn new ones that fit into this digital age.

Rather than simply managing cutting-edge products or services developed by others, a product manager in today’s IT organization should be viewed as someone who can transform everything about them using any new technique or technology available while also engaging stakeholders like never before.

This article gives an overview of what the digital world means for you as a product manager and some popular certifications in this area.

The Role of Product Managers in the Digital World

Product managers should know the different technologies that are currently being used to process data, understand what each one does best, and how they can be applied.They need not only technical skills but also business acumen to identify many areas where innovation is possible within an organization through the use of data-driven strategies. These strategies will then guide them towards coming up with insights that will push for invention around those areas, leading to the successful launch of new products or services under their control.

Data Analysis and Interpretation

Product managers need to analyze large and complex datasets and identify trends, patterns, and insights to make informed decisions on product development optimization. They also need to collaborate with data scientists to develop product models, perform necessary statistical analysis, and conduct A/B testing.

Product Vision and Strategy

The PM needs to work closely with different teams, which include business stakeholders, data scientists, and software engineers, to identify the product vision and roadmap. Along with that, PM needs to develop business cases to create a data-driven presentation and communicate the product vision and strategy to their stakeholders.

User Experience and Design

Collaboration with UI and UX designers to create user-friendly and intuitive interfaces that enable customers to interact with data-driven services and products. The product managers need to conduct user research and usability testing to comprehend the customer’s needs and preferences and develop user personas and journey maps to inform product development and optimize UX. Let’s use an understanding of the top four trending product management certification courses that product managers can consider to build a strong portfolio in the competitive market.

To Know More, Read Full Article @ https://ai-techpark.com/the-power-of-ai-with-product-management-certifications/ 

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In the 21st century, people are searching for an abode that will provide better public infrastructure and easily accessible resources that will make their lives easier.

Traditional cities often grapple with major issues of inadequate infrastructure, huge population growth, inefficient resource and waste management, and traffic congestion, aiming at a lack of urban development.

However, the introduction of smart cities represents a pivotal shift towards embracing new-age technologies to solve some of the most pressing challenges of urban living and make cities have better infrastructure, public services, and sustainable growth.

The concept of smart cities emerged as a transformative trend in the fields of technology and architecture that will reshape the urban landscape and revolutionize the way people interact with our environment. By integrating technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data analytics, architects and IT professionals can set new standards for service delivery, sustainability, and livability.

In 2024, IT professionals and architects will be at the forefront of this environmental sustainability movement, leveraging technology and innovative design principles to develop cities that are technologically advanced, sustainable, and efficient to cater to the different needs of each resident.

In today’s exclusive AITech Park article, we will explore the emerging trend of smart cities and how IT professionals and architects can play a pivotal role in the development of these cities.

Towards Zero Waste

In 2024, architects will be more focused on eliminating the challenges of waste management to create resilient and sustainable cities by implementing smart waste management systems that have sensor-driven bins and smart waste collection vehicles that will optimize waste collection routes and reduce fuel consumption. Advanced waste-to-energy technologies are used to convert organic waste into renewable energy sources, minimizing landfill usage and mitigating environmental impacts.

Innovative Solutions for Water Sustainability

According to a report by the U.N. World Water Development Report 2023, water scarcity is one of the biggest crises that the world is facing, as it was revealed that 2 billion people (26% of the population) lack safe drinking water, while 3.6 billion (46%) lack access to safely managed sanitation.

Therefore, to curb these issues and strategize for water conservation and management, architects and IT professionals can implement IoT-enabled water meters to monitor water usage in real-time, enabling residents to optimize water consumption and identify leakages.

Bottom Line

As the world’s population continues to grow at an unprecedented rate, the essentiality of smart cities becomes more pronounced, as they provide a blueprint to address the challenges of urbanization and strive to reach the different goals related to improving urban lifestyle, achieving economic growth, and environmental sustainability.

To Know More, Read Full Article @ https://ai-techpark.com/the-emergence-of-smart-cities-in-2024/ 

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