Tag: Deep Learning

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.

To Know More, Read Full Article @ https://ai-techpark.com/adapting-to-economic-uncertainty/

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AI in Medical Imaging: Transforming Healthcare

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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.

To Know More, Read Full Article @ https://ai-techpark.com/best-practices-for-quantum-machine-learning/ 

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AI and Blockchain Revolution

AI and RPA in Hyper-automation

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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.

To Know More, Read Full Article @ https://ai-techpark.com/algorithmic-biases-solutions/ 

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Ethics in the Era of Generative AI

Generative AI for SMBs and SMEs

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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.

To Know More, Read Full Article @ https://ai-techpark.com/overcoming-limitations-with-quantum-ml/ 

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Safeguarding Business Assets

Cloud Computing Frameworks

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In the fast-moving business world, IT professionals and enthusiasts cannot ignore the use of machine learning (ML) in their companies. Machine learning tends to give a better insight into improving business performance, like understanding trends and patterns that human eyes generally miss out on. Thus, Machine learning (ML) and artificial intelligence (AI) aren’t just words; rather, they have the potential to change the industry positively. Through this article, we will focus on the importance of implementing machine learning and its use cases in different industries that will benefit you in the present and future.

The Usefulness of ML in Different Industries

Machine learning is a game-changer, and let’s see here how different industries have made the best use of it:

Predictive Analytics for Recommendations

Predictive analytics are generally used to identify opportunities before an event occurs. For example, identifying the customers that have spent the most time on your e-commerce website will result in profit for your company in the long run. These insights are only possible through predictive analytics, which allows your company to optimize market spending and focus on acquiring customers that will generate profit.

 Automate Decision-making

Automated and intelligent decision-making solutions and tools can be used by you to make quick decisions for efficient teamwork. For instance, some industries require strict adherence to compliance, which can only be applied by decision-management tools that help in maintaining records of legal protocols. These tools can make quick decisions if the business fails to obey any compliance rules.

 Creating a Data-Driven Culture

Creating a data-driven culture helps in getting numbers and insights that are generated through data. A data-driven organization not only empowers your teams but also improves your decision-making efficiency and effectiveness. One such example of a data-driven culture is DBS Bank, which has embraced AI and data analytics to provide customers with personalized recommendations. This is helping the customers and the bank authorities make better financial decisions and also improving customer loyalty. By embracing a data-driven culture, DBS Bank has also invested in training employees in data analytics and big data.

Machine learning is an important tool for making automated decisions in various business processes. These models help you identify errors and make unbiased and informed decisions. By analyzing data through customer interaction, preference, and behavior, ML algorithms can help identify the correct patterns and trends, which will help your company in the long run.

To Know More, Read Full Article @ https://ai-techpark.com/ml-helps-make-decisions/ 

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Digital Patient Engagement Platforms

The rise and understandability of AI systems have become serious topics in the AI tech sector as a result of AI’s rise. The demand for Explainable AI (XAI) has increased as these systems become more complicated and capable of making crucial judgments. This poses a critical question: Does XAI have the capacity to completely replace human positions, or does it primarily empower human experts?

Explainability in AI is an essential component that plays a significant and growing role in a variety of industry areas, including healthcare, finance, manufacturing, autonomous vehicles, and more, where their decisions have a direct impact on people’s lives. Uncertainty and mistrust are generated when an AI system makes decisions without explicitly stating how it arrived at them.

A gray area might result from a black box algorithm that is created to make judgments without revealing the reasons behind them, which can engender mistrust and reluctance. The “why” behind the AI’s decisions has left human specialists baffled by these models. For instance, a human healthcare provider may not understand the reasoning behind a diagnosis made by an AI model that saves a patient’s life. This lack of transparency can make specialists hesitant to accept the AI’s recommendation, which could cause delays in crucial decisions.

Importance of Explainable AI

The demand for AI solutions continues to grow across diverse industries, from healthcare and finance to transportation and customer service. However, as AI systems become more integrated into critical decision-making processes, the need for transparency and accountability increases. In high-stakes scenarios like healthcare diagnosis or loan approval, having the ability to explain AI decisions becomes crucial to gain user trust, regulatory compliance, and ethical considerations.

Empowering Human Experts with Explainable AI

Enhanced Decision Making: By providing interpretable explanations for AI outputs, experts can better understand the underlying reasoning behind the model's decisions. This information can be leveraged to validate and refine predictions, leading to more informed and accurate decisions.

Collaboration between Humans and AI: Explainable AI fosters a more collaborative relationship between human experts and AI systems. The insights provided by AI models can complement human expertise, leading to more robust solutions and new discoveries that would have been challenging for humans or AI to achieve independently.

Reduced Bias and Discrimination: XAI techniques can help identify biases in AI models and uncover instances of discrimination. By understanding the factors influencing predictions, experts can take corrective measures and ensure fairness in the AI system's behavior.

Trust and Acceptance: Transparency in AI models builds trust among users and stakeholders. When experts can validate the reasoning behind AI decisions, they are more likely to accept and embrace AI technologies, leading to smoother integration into existing workflows.

To Know More, Visit @ https://ai-techpark.com/xai-dilemma-empowerment/ 

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