Major Trends Shaping Semantic Technologies This Year

As we have stepped into the realm of 2024, the artificial intelligence and data landscape is growing up for further transformation, which will drive technological advancements and marketing trends and understand enterprises’ needs. The introduction of ChatGPT in 2022 has produced different types of primary and secondary effects on semantic technology, which is helping IT organizations understand the language and its underlying structure.

For instance, the semantic web and natural language processing (NLP) are both forms of semantic technology, as each has different supportive rules in the data management process.

In this article, we will focus on the top four trends of 2024 that will change the IT landscape in the coming years.

Reshaping Customer Engagement With Large Language Models

The interest in large language models (LLMs) technology came to light after the release of ChatGPT in 2022. The current stage of LLMs is marked by the ability to understand and generate human-like text across different subjects and applications. The models are built by using advanced deep-learning (DL) techniques and a vast amount of trained data to provide better customer engagement, operational efficiency, and resource management.

However, it is important to acknowledge that while these LLM models have a lot of unprecedented potential, ethical considerations such as data privacy and data bias must be addressed proactively.

Importance of Knowledge Graphs for Complex Data

The introduction of knowledge graphs (KGs) has become increasingly essential for managing complex data sets as they understand the relationship between different types of information and segregate it accordingly. The merging of LLMs and KGs will improve the abilities and understanding of artificial intelligence (AI) systems. This combination will help in preparing structured presentations that can be used to build more context-aware AI systems, eventually revolutionizing the way we interact with computers and access important information.

As KGs become increasingly digital, IT professionals must address the issues of security and compliance by implementing global data protection regulations and robust security strategies to eliminate the concerns.  

Large language models (LLMs) and semantic technologies are turbocharging the world of AI. Take ChatGPT for example, it's revolutionized communication and made significant strides in language translation.

But this is just the beginning. As AI advances, LLMs will become even more powerful, and knowledge graphs will emerge as the go-to platform for data experts. Imagine search engines and research fueled by these innovations, all while Web3 ushers in a new era for the internet.

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The Evolution of AI-Powered Wearables in the Reshaping Healthcare Sector

The amalgamation of artificial intelligence (AI) and wearable technology has transformed how healthcare providers monitor and manage patients’s health through emergency responses, early-stage diagnostics, and medical research.

Therefore, AI-powered wearables are a boon to the digital era as they lower the cost of care delivery, eliminate healthcare providers’ friction, and optimize insurance segmentations. According to research by MIT and Google, these portable medical devices are equipped with large language models (LLMs), machine learning (ML), deep learning (DL), and neural networks that provide personalized digital healthcare solutions catering to each patient’s needs, based on user demographics, health knowledge, and physiological data.

In today’s article, let’s explore the influence of these powerful technologies that have reshaped personalized healthcare solutions.

Integration of AI in Wearable Health Technology

AI has been a transforming force for developing digital health solutions for patients, especially when implemented in wearables. However, 21st-century wearables are not just limited to AI but employ advanced technologies such as deep learning, machine learning, and neural networking to get precise user data and make quick decisions on behalf of medical professionals.

This section will focus on how ML and DL are essential technologies in developing next-generation wearables.

Machine Learning Algorithms to Analyze Data

Machine learning (ML) algorithms are one of the most valuable technologies that analyze the extensive data gathered from AI wearable devices and empower healthcare professionals to identify patterns, predict necessary outcomes, and make suitable decisions on patient care.

For instance, certain wearables use ML algorithms, especially for chronic diseases such as mental health issues, cardiovascular issues, and diabetes, by measuring heart rate, oxygen rate, and blood glucose meters. By detecting these data patterns, physicians can provide early intervention, take a closer look at patients’s vitals, and make decisions.

Recognizing Human Activity with Deep Learning Algorithms

Deep learning (DL) algorithms are implemented in wearables as multi-layered artificial neural networks (ANN) to identify intricate patterns and find relationships within massive datasets. To develop a high-performance computing platform for wearables, numerous DL frameworks are created to recognize human activities such as ECG data, muscle and bone movement, symptoms of epilepsy, and early signs of sleep apnea. The DL framework in the wearables learns the symptoms and signs automatically to provide quick solutions.

However, the only limitation of the DL algorithms in wearable technology is the need for constant training and standardized data collection and analysis to ensure high-quality data.

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Modernizing Data Management with Data Fabric Architecture

Data has always been at the core of a business, which explains the importance of data and analytics as core business functions that often need to be addressed due to a lack of strategic decisions. This factor gives rise to a new technology of stitching data using data fabrics and data mesh, enabling reuse and augmenting data integration services and data pipelines to deliver integration data.

Further, data fabric can be combined with data management, integration, and core services staged across multiple deployments and technologies.

This article will comprehend the value of data fabric architecture in the modern business environment and some key pillars that data and analytics leaders must know before developing modern data management practices.

The Evolution of Modern Data Fabric Architecture

Data management agility has become a vital priority for IT organizations in this increasingly complex environment. Therefore, to reduce human errors and overall expenses, data and analytics (D&A) leaders need to shift their focus from traditional data management practices and move towards modern and innovative AI-driven data integration solutions.

In the modern world, data fabric is not just a combination of traditional and contemporary technologies but an innovative design concept to ease the human workload. With new and upcoming technologies such as embedded machine learning (ML), semantic knowledge graphs, deep learning, and metadata management, D&A leaders can develop data fabric designs that will optimize data management by automating repetitive tasks.

Key Pillars of a Data Fabric Architecture

Implementing an efficient data fabric architecture needs various technological components such as data integration, data catalog, data curation, metadata analysis, and augmented data orchestration. Working on the key pillars below, D&A leaders can create an efficient data fabric design to optimize data management platforms.

Collect and Analyze All Forms of Metadata

To develop a dynamic data fabric design, D&A leaders need to ensure that the contextual information is well connected to the metadata, enabling the data fabric to identify, analyze, and connect to all kinds of business mechanisms, such as operational, business processes, social, and technical.

Convert Passive Metadata to Active Metadata

IT enterprises need to activate metadata to share data without any challenges. Therefore, the data fabric must continuously analyze available metadata for the KPIs and statistics and build a graph model. When graphically depicted, D&A leaders can easily understand their unique challenges and work on making relevant solutions.

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Artificial Intelligence is Revolutionizing Drug Discovery and Material Science

In recent years, artificial intelligence (AI) in the pharmaceutical industry has gained significant traction, especially in the drug discovery field, as this technology can identify and develop new medications, helping AI researchers and pharmaceutical scientists eliminate the traditional and labor-intensive techniques of trial-and-error experimentation and high-throughput screening.

The successful application of AI techniques and their subsets, such as machine learning (ML) and natural language processing (NLP), also offers the potential to accelerate and improve the conventional method of accurate data analysis for large data sets. AI and ML-based methods such as deep learning (DL) predict the efficacy of drug compounds to understand the accrual and target audience of drug use.

For example, today’s virtual chemical databases contain characterized and identified compounds. With the support of AI technologies along with high-performance quantum computing and hybrid cloud technologies, pharmaceutical scientists can accelerate drug discovery through existing data and the experimentation and testing of hypothesized drugs, which leads to knowledge generation and the creation of new hypotheses.

The Role of ML and DL in Envisioning Drug Effectiveness and Toxicity

In this section, we will understand the role of the two most important technologies, i.e., machine learning and deep learning, which have helped both AI researchers and pharmaceutical scientists develop and discover new drugs without any challenges:

Machine learning in drug discovery

Drug discovery is an intricate and lengthy process that requires the utmost attention to identify potential drug candidates that can effectively treat various acute and chronic drugs, which can transform the pharmaceutical industry by speeding up the prediction of toxicity and efficacy of potential drug compounds, improving precision, and decreasing costs. Based on the large set of data, ML algorithms can identify trends and patterns that may not be visible to pharma scientists, which enables the proposal of new bioactive compounds that offer minimum side effects in a faster process. This significant contribution prevents the toxicity of potential drug compounds by addressing whether the drug interacts with the drug candidates and how the novel drug pairs with other drugs.

Deep learning in drug discovery

Deep learning (DL) is a specialized form of machine learning that uses artificial neural networks to learn and examine data. The DL models in the pharmaceutical industry have different algorithms and multiple layers of neural networks that read unstructured and raw data, eliminating the laborious work of AI engineers and pharma scientists. The DL model can handle complex data through images, texts, and sequences, especially during “screen polymers for gene delivery in silico.” These data were further used to train and evaluate several state-of-the-art ML algorithms for developing structured “PBAE polymers in a machine-readable format.”

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Navigating the Future With the Integration of Deep Learning in Big Data Analytics

In the fast-growing digital world, deep learning (DL) and big data are highly used methods for data scientists. Numerous companies, such as Yahoo, Amazon, and Google, have maintained data in Exabytes, which helps generate large amounts of data with the help of big data analytics and deep learning tools and techniques.

Earlier data scientists used traditional data processing techniques, which came with numerous challenges in processing large data sets. However, with technological advancements in recent years, data scientists can utilize big data analytics, a sophisticated algorithm based on machine learning and deep learning techniques that process data in real-time and provide high accuracy and efficiency in business processes.

In recent times, it has been witnessed that DL methods are extensively used in healthcare, finance, and IT for speech recognition, learning methods in language processing, and image classification, especially when incorporated into various hybrid learning and training mechanisms for processing data with high speed.

Today’s exclusive AI Tech Park article aims to discuss integrating deep learning methods into big data analytics, analyze various applications of deep learning in big data analytics, and discuss the future of big data and deep learning.

Efficient Deep Learning Algorithms in Big Data Analytics

Deep learning is a subset of machine learning (ML), and it is considered the trendiest topic as DL is adopted in almost every field where big data is involved.

Every year, IT companies generate trillions of GBs of data, which makes extracting useful information a challenging task for them. Therefore, the answer to such a problem is deep learning, which automatically learns the hidden structure and patterns in the raw data using ML techniques.

Some deep learning models and algorithms show great potential in unleashing the complexity of patterns within big data analytics. In this section, we will take a glance at the effective ways data scientists can utilize deep learning techniques to implement big data analytics:

Preparing the Data

The initial step to implementing deep learning in big data analytics is data preparation. The quality of data used in training data learning models must be accurate to the model prepared by data scientists and IT professionals. Therefore, it is essential to ensure that the data is well structured and clean and should work as a problem solver.

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AI Ethics: A Boardroom Imperative

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

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

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

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

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

Understanding the AI Life Cycle From an Ethical Perspective

Identify the Ethical Guidelines

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

Conducting Assessments

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

Data Collection and Pre-Processing Practice

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

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Unlocking Growth in Uncertainty: 5 E-Commerce Experience Innovations

The economic downturn is dramatically impacting consumer budgets, making shoppers think twice about their spending. This puts pressure on ecommerce merchants to adapt the online shopping experiences to maximize profitable conversions.

Meeting this challenge requires a focus on five key areas:

Enhance ecommerce site search with dynamic ranking and merchandising:

With every cent counting more, merchants should closely track behavioral data around how products are performing and adjust how high they rank in onsite searches. If a product can’t stand on its own and deliver significant sales, it needs to be less visible. Those less visible products can still be shown to those who might be interested through personalization. Equally, search can rank products based on margin and inventory, so shoppers aren’t shown out of stock items. Modern site search platforms leverage similar technology to ChatGPT, such as large language models and image recognition/deep learning.

Make category pages work harder

Merchants are increasingly focused on driving more traffic directly to category pages, as they try to shorten the path to purchase. With most category pages consisting of rows of product images, the downside is that shoppers that don’t buy will click away without ever seeing the brand messaging and offers that typically exist on a store’s home page.

Merchants can rectify this by displaying more personalized editorial on category pages, highlighting USPs, brand values, discounts, offers, and wider inventory. The aim should be to encourage visitors to explore more of the site, with fewer bounces, more pages-per-visit, and ultimately more sales.

Reward VIP customers

It’s always easiest to generate sales from your most loyal customers—particularly in downturns when buyers become more risk averse. This makes it imperative that merchants segment their returning customers based on customer lifetime value, and invest in delivering custom experiences to repeat purchasers. Reward visiting VIP customers with tailored content and promotions that makes them feel special and valued, including early access to sales, exclusive offers or limited availability products that others can’t get.

Win over socially conscious shoppers

Because budgets have tightened, more shoppers are comparing the costs and benefits of different sites. Brands therefore need to be especially focused on highlighting areas that provide value, such as socially conscious initiatives. These appeal to consumers who are looking to only buy ethically sourced clothing, demand a commitment to sustainability, or only shop organic or locally produced, for example.

Merchants need to be able to recognize shoppers’ social values, and tailor their shopping experience with products or content that appeals. Socially conscious filters can allow shoppers to tailor their experience in line with their values, so they only see organic or sustainable merchandise for example. And socially conscious visual badging, which demonstrates how each purchase benefits a specific cause, such as dollars donated to charity, can really help shoppers feel engaged.

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Harnessing the Power of Quantum Computing for Enhanced Machine Learning

Quantum computing (QC) and machine learning (ML) are the two most hot technologies that are being adopted in the IT field. QC has the power of quantum physics to perform computation by providing an unprecedented level of scalability and accuracy; on the other hand, ML has deep learning capabilities and intelligent automation as leverage to scale out large data sets. Thus, the combination of these two applications, i.e., QC and ML, can create new opportunities that could solve complex problems with greater accuracy and efficiency than the traditional way of computing could.

In this article, we will dive into how to implement quantum machine learning (QML) and what the best practices are for AI technologists.

Success Story- Quantum Machine Learning in Automotive Industry

The BMW Group is among the first automotive firms to take an interest in quantum computing. In 2021, BMW Group issued the Quantum Computing Challenge in association with AWS to crowdsource innovations around specific use cases, believing that quantum computing could benefit businesses by solving complex computing problems.

The objective was to determine if the image-trained machine learning system presently in use to detect fractures in produced elements might be improved. To properly train the model, high-resolution photographs of the manufactured components were required. In addition, the organization required a lot of them because those kinds of defects are quite uncommon. There is potential for improvement because obtaining and storing these photos requires time and memory.

BMW Group gave a statement that, “In light of the required human expertise to hand-tune algorithms, machine learning (ML) techniques promise a more general and scalable approach to quality control. Quantum computing may one day break through classical computational bottlenecks, providing faster and more efficient training with higher accuracy.”

After implementing the QML solution, the BMW Group has witnessed 97% accuracy by enhancing the classical algorithm by orchestrating quantum processing unit (QPU) calculations at a crucial part of the analysis. The Quantum model was trained on 40% of the whole dataset. In contrast, the Benchmark model was trained on 70%, which implies that the classical approach is more efficient and manages to provide accurate predictions without unnecessary inputs.

Future Implementation of Quantum Machine Learning

Quantum machine learning (QML) algorithms have the potential to solve maximum problems in a much faster time than the classical algorithm. According to IBM researcher Kristan Temme, there is strong evidence that QML is emerging at a significant speed in all industries. He quotes, “At this point, I’d say it’s a bit difficult to exactly pinpoint a given application that would be of value.”

There are also proven examples where QML has been an advantageous technology over classical computing.

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Mitigating Algorithmic Bias in AIOps: Strategies for Fairness and Transparency

The business world is increasingly turning to artificial intelligence (AI) systems and machine learning (ML) algorithms to automate complex and simple decision-making processes. Thus, to break through the paradigm in the field of IT operations, IT professionals and top managers started opting for AIOps platforms, tools, and software, as they promised to streamline, optimize, and automate numerous tasks quickly and efficiently. However, there are a few shortcomings, like algorithmic bias, that have been a major concern for IT professionals and other employees in the company.

Key Technologies in Addressing Algorithmic Biases

With the use of cutting-edge AIOps technologies, IT professionals can understand and explore the algorithmic biases in the system. Thus, here are a few key technologies that will help you detect such issues:

Time Series Analysis

When having abundant data, time series analysis emerges as a crucial tool in AIOps as it records data over time by tracking users’ behavior, network activity, and system performance. Algorithms should represent temporal dependencies, trends, and seasonality to detect biases effectively. AIOps uses a time series analysis method that includes autoregressive models, moving averages, and recurrent neural networks to examine the time-stamped data for deviation and identify abnormalities quickly.

Unsupervised Learning Techniques

Unsurprised learning is an essential component of AIOps for detecting algorithm biases and unwanted labeled data, which is necessary for traditional supervised learning but with limited knowledge. To discover issues, techniques like clustering and dimensionality reduction are crucial in revealing hidden structures within data.

Machine Learning and Deep Learning

The use of ML and deep learning techniques helps in regulating the different established standards, which enables the AIOps system to learn patterns and relationships from complicated and massive data and also enables it to detect analogous biases.

While not all scenarios involving algorithmic bias are concerning, they can have major negative effects when the stakes are high. We have seen that algorithmic prejudice poses a severe threat to human privacy, with lives, livelihoods, and reputations at stake, as well as concerns about data integrity, consent, and security. Integrated AIOps ensure that IT professionals and managers avoid bias and unfairness in their AI and ML models by considering any subjective elements associated with people, locations, products, etc. in their training data and models.

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Embracing Quantum Machine Learning to Break Through Computational Barriers

In our previous articles, we have highlighted how machine learning (ML) and artificial intelligence (AI) can revolutionize IT organizations. But there is another very powerful resource that has the potential to change the traditional way of computing, which is called quantum computing (QC). In today’s article, we will highlight how to overcome computing limitations with quantum machine learning (QML) and what tools and techniques this technology can offer. But first, let’s take a quick glimpse of what quantum computing is.

Quantum computing is currently an emerging field that requires the development of computers based on the principles of quantum mechanics. Recently, scientists, technologists, and software engineers have found advancements in QC, which include increasingly stable qubits, successful demonstrations of quantum supremacy, and efficient error correction techniques. By leveraging entangled qubits, quantum computing enables dramatic advances in ML models that are faster and more accurate than before.

Usefulness of Utilizing Quantum Computing in Machine Learning

Quantum computing has the power to revolutionize ML by allowing natural language processing (NLP), predictive analytics, and deep learning tasks to be completed properly and with greater accuracy than the traditional style of computing methods. Here is how using QC will benefit technologists and software engineers when applied properly in their company:

Automating Cybersecurity Solutions

As cybersecurity is constantly evolving, companies are seeking ways to automate their security solutions. One of the most promising approaches is QML, as it is a type of AI that uses quantum computing to identify patterns and anomalies in large-scale datasets. This allows the companies to identify and respond to threats faster and reduce the cost of manual processes.

Accelerate Big Data Analysis

Quantum computing has gained traction in recent years as a potentially revolutionary technology that can be accurate in computing tasks and improve the speed of completing tasks. However, researchers are nowadays investigating the potential of QML for big data analysis. For example, a team of researchers from the University of California recently developed a QML algorithm that can analyze large-scale datasets more quickly and accurately than traditional ML algorithms.

The potential of QML algorithms is immense, and training them properly can be a major challenge for IT professionals and technologists. Researchers are finding new ways to address these problems related to the training of quantum machine learning algorithms.

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