Tag: Deep Learning

Data has always been the backbone of business operations, highlighting the significance of data and analytics as essential business functions. However, a lack of strategic decision-making often hampers these functions. This challenge has paved the way for new technologies like data fabric and data mesh, which enhance data reuse, streamline integration services, and optimize data pipelines. These innovations allow businesses to deliver integrated data more efficiently.

Data fabric can further combine with data management, integration, and core services across multiple technologies and deployments.

This article explores the importance of data fabric architecture in today’s business landscape and outlines key principles that data and analytics (D&A) leaders need to consider when building modern data management practices.

The Evolution of Modern Data Fabric Architecture

With increasing complexities in data ecosystems, agile data management has become a top priority for IT organizations. D&A leaders must shift from traditional data management methods toward AI-powered data integration solutions to minimize human errors and reduce costs.

Data fabric is not merely a blend of old and new technologies; it is a forward-thinking design framework aimed at alleviating human workloads. Emerging technologies such as machine learning (ML), semantic knowledge graphs, deep learning, and metadata management empower D&A leaders to automate repetitive tasks and develop optimized data management systems.

Data fabric offers an agile, unified solution with a metadata-driven architecture that enhances access, integration, and transformation across diverse data sources. It empowers D&A leaders to respond rapidly to business demands while fostering collaboration, data governance, and privacy.

By providing a consistent view of data, a well-designed data fabric improves workflows, centralizes data ecosystems, and promotes data-driven decision-making. This streamlined approach ensures that data engineers and IT professionals can work more efficiently, making the organization’s systems more cohesive and effective.

Know More, Read Full Article @ https://ai-techpark.com/data-management-with-data-fabric-architecture/

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In recent years, computer vision (CV) has appeared as a transformative technology that reshapes the landscape of numerous industries by allowing machines to analyze and understand visual information around them.

According to tech leaders, computer vision is often referred to as the eyes of artificial intelligence (AI), which makes it a transformative technology that not only revolutionizes the industries that adapted it but also becomes a cornerstone for the advancement of AI. With more technological advancements, the convergence of CV with IoT, big data analytics (BDA), and automation has given rise to smart work that remains competitive and improves productivity and efficiency.

In this blog, we will learn about the critical role that computer vision plays in pushing the boundaries and creating new avenues for different industries in this digital world.

The Core of Computer Vision

Computer vision is a field of study that enables computers to replicate human visual systems and is often considered a subset of artificial intelligence that collects information from digital images and videos and further processes it to define different attributes. CV relies on way recognition approaches to self-train and comprehend visual data. Earlier ML algorithms were used for computer vision applications; now deep learning (DL) methods have developed as a better solution for this domain. Therefore, with more training with data and algorithms, CV now works much the same as human vision.

These capabilities make computer vision more useful in different industries that range from healthcare and logistics to manufacturing and financial services.

Computer Vision Use Cases

Computer vision technology has tremendous potential to revolutionize numerous industries by providing an automated technique to identify minute defects in products. With the help of ML algorithms, computer vision systems can detect slight variations in outcome quality that may not be observable by the human eye.

The healthcare industry has already advanced with new-age robotic surgeries, but computer vision has quite a multifold effect that can help in performing even delicate and complex procedures. According to a recent report by Statista, more than 20.21% of healthcare institutions and hospitals are implementing CV in their daily processes. This technology can be improved by real-time, high-resolution photographs of the surgical site, allowing the surgeon to have a better idea and acquaintance with the procedure.

Computer vision is an area that tech researchers are still researching and looking for further development in. As we navigate into the future of intelligent technologies, computer vision can redefine boundaries that machines can archive and further open new doors to new possibilities that will reshape the way we interact with the world around us.

To Know More, Read Full Article @ https://ai-techpark.com/computer-vision-in-different-industries/

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Large Language Models (LLMs) are considered to be an AI revolution, altering how users interact with technology and the world around us. Especially with deep learning algorithms in the picture data, professionals can now train huge datasets that will be able to recognize, summarize, translate, predict, and generate text and other types of content.

As LLMs become an increasingly important part of our digital lives, advancements in natural language processing (NLP) applications such as translation, chatbots, and AI assistants are revolutionizing the healthcare, software development, and financial industries.

However, despite LLMs’ impressive capabilities, the technology has a few limitations that often lead to generating misinformation and ethical concerns.

Therefore, to get a closer view of the challenges, we will discuss the four limitations of LLMs devise a decision to eliminate those limitations, and focus on the benefits of LLMs.

Limitations of LLMs in the Digital World

We know that LLMs are impressive technology, but they are not without flaws. Users often face issues such as contextual understanding, generating misinformation, ethical concerns, and bias. These limitations not only challenge the fundamentals of natural language processing and machine learning but also recall the broader concerns in the field of AI. Therefore, addressing these constraints is critical for the secure and efficient use of LLMs.

Let’s look at some of the limitations:

Contextual Understanding

LLMs are conditioned on vast amounts of data and can generate human-like text, but they sometimes struggle to understand the context. While humans can link with previous sentences or read between the lines, these models battle to differentiate between any two similar word meanings to truly understand a context like that. For instance, the word “bark” has two different meanings; one “bark” refers to the sound a dog makes, whereas the other “bark” refers to the outer covering of a tree. If the model isn’t trained properly, it will provide incorrect or absurd responses, creating misinformation.

Misinformation

Even though LLM’s primary objective is to create phrases that feel genuine to humans; however, at times these phrases are not necessarily to be truthful. LLMs generate responses based on their training data, which can sometimes create incorrect or misleading information. It was discovered that LLMs such as ChatGPT or Gemini often “hallucinate” and provide convincing text that contains false information, and the problematic part is that these models point their responses with full confidence, making it hard for users to distinguish between fact and fiction.

To Know More, Read Full Article @ https://ai-techpark.com/limitations-of-large-language-models/

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Explore how AI innovation and sustainable data center management intersect, focusing on energy-efficient strategies to balance performance and environmental impact.

With all that’s being said about the growth in demand for AI, it’s no surprise that the topics of powering all that AI infrastructure and eking out every ounce of efficiency from these multi-million-dollar deployments are hot on the minds of those running the systems.  Each data center, be it a complete facility or a floor or room in a multi-use facility, has a power budget.  The question is how to get the most out of that power budget?

Balancing AI Innovation with Sustainability

Optimizing Data Management: Rapidly growing datasets that are surpassing the Petabyte scale equal rapidly growing opportunities to find efficiencies in handling the data.  Tried and true data reduction techniques such as deduplication and compression can significantly decrease computational load, storage footprint and energy usage – if they are performed efficiently. Technologies like SSDs with computational storage capabilities enhance data compression and accelerate processing, reducing overall energy consumption. Data preparation, through curation and pruning help in several ways – (1) reducing the data transferred across the networks, (2) reducing total data set sizes, (3) distributing part of the processing tasks and the heat that goes with them, and (4) reducing GPU cycles spent on data organization​.

Leveraging Energy-Efficient Hardware: Utilizing domain-specific compute resources instead of relying on the traditional general-purpose CPUs.  Domain-specific processors are optimized for a specific set of functions (such as storage, memory, or networking functions) and may utilize a combination of right-sized processor cores (as enabled by Arm with their portfolio of processor cores, known for their reduced power consumption and higher efficiency, which can be integrated into system-on-chip components), hardware state machines (such as compression/decompression engines), and specialty IP blocks. Even within GPUs, there are various classes of GPUs, each optimized for specific functions. Those optimized for AI tasks, such as NVIDIA’s A100 Tensor Core GPUs, enhance performance for AI/ML while maintaining energy efficiency.

Adopting Green Data Center Practices: Investing in energy-efficient data center infrastructure, such as advanced cooling systems and renewable energy sources, can mitigate the environmental impact. Data centers consume up to 50 times more energy per floor space than conventional office buildings, making efficiency improvements critical.  Leveraging cloud-based solutions can enhance resource utilization and scalability, reducing the physical footprint and associated energy consumption of data centers.

To Know More, Read Full Article @ https://ai-techpark.com/balancing-brains-and-brawn/

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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.

To Know More, Read Full Article @ https://ai-techpark.com/top-four-semantic-technology-trends-of-2024/ 

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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.

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

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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.

To Know More, Read Full Article @ https://ai-techpark.com/data-management-with-data-fabric-architecture/ 

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Explainable AI Is Important for IT

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.”

To Know More, Read Full Article @ https://ai-techpark.com/ai-in-drug-discovery-and-material-science/ 

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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.

To Know More, Read Full Article @ https://ai-techpark.com/deep-learning-in-big-data-analytics/

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Information Security and the C-suite

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

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

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

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

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

Understanding the AI Life Cycle From an Ethical Perspective

Identify the Ethical Guidelines

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

Conducting Assessments

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

Data Collection and Pre-Processing Practice

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

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

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