Autore: martechcubejohn

For shoppers, this is the perfect time to celebrate the holiday season, and for the e-commerce sector, it is an ideal time to get many reviews. However, during this time of the year, E-commerce stores face problems with understaffed teams, supply chain woes, and changing customer behaviors, which, when combined, create the storm of the century.

As consumer behavior and preferences change every season, e-commerce merchants continuously seek innovative strategies to engage and satisfy their customers. With the help of artificial intelligence (AI), with its automation capabilities and data-driven insights, it can upscale this landscape and generate a higher conversion rate.

In this article, we will find out more about how e-commerce merchants can use AI to benefit themselves during this festive season.

Benefits of AI in E-commerce  

The benefits of artificial intelligence are immense, as it helps e-commerce companies provide personalized experiences to their target audience and enhance day-to-day operations effectively. Let’s explore each advantage carefully:

Personalized Shopping Experiences

AI can analyze your user’s behavior to provide more personalized recommendations to help customers discover products that align with their festive needs and preferences. It can also optimize target advertising campaigns, enabling retailers to customize their marketing efforts, resulting in a seamless and individual shopping experience for customers.

Reduced Operational Costs

Implementing AI in e-commerce allows your business to reduce costs through automation of tasks, which diminishes the need for manual labor. AI enables you to manage your inventory and automate customer interaction, leading to savings in operational costs, empowering businesses, and getting a clear picture of the marketing challenges.

Increased Sales Opportunities

AI in e-commerce aids in forecasting emerging market demand during the festive season, enabling online retailers to strategically introduce and position their products and estimate opportunities for cross-selling and upselling. This means your business can unlock new sales channels for efficient sales processes and broaden its market presence.

The Black Friday and Cyber Monday retail scene is a huge change, as online retailers have to make sure to adjust their operations as consumer behaviors and expectations evolve rapidly. So, to stay in the game of profitable e-commerce shops, they have to rely on artificial intelligence (AI) to improve efficiency, cut extra costs, and balance operations through automation and less human interaction.

To Know More, Read Full Article @ https://ai-techpark.com/ai-in-the-e-commerce-sector/ 

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Information Security and the C-suite

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The acceleration in the adoption of cloud technology has revolutionised the business landscape, and in doing so, significantly altered the cybersecurity ecosystem. The vast potential of cloud technology, such as its scalability, adaptability, and cost-effectiveness, has not gone unnoticed by nefarious entities seeking opportunities for exploitation. As businesses across ASEAN continue their transition to the cloud, they are increasingly confronted with escalating incidents of data breaches, ransomware attacks, and insider threats.

Therefore, it’s vital for organisations to devise and implement a robust cloud-specific incident response plan. Such a plan can help minimise the impact of security incidents, accelerate recovery time, and ensure optimal data protection in this rapidly evolving digital space.

Cloud Incident Response (IR) today needs to grapple with a radically different set of challenges, including data volume, accessibility, and the speed at which threats can multiply within cloud architectures. The interplay of various components, such as virtualization, storage, workloads, and cloud management software, intensifies the complexity of securing cloud environments.

That being said, Cloud IR cannot be done in isolation of the company’s overall incident response activities and business continuity plans. When possible, cloud security tools should use the same SOC, SOAR, and communication tools currently being used to secure other company elements. Using the same infrastructure ensures that suspicious and threatening cloud activities receive an immediate and appropriate response.

Creating an effective response plan involves understanding and managing the unique cloud platforms, being fully aware of data storage and access, and adeptly handling the dynamic nature of the cloud. Specifically:

Managing the Cloud Platform: The administrative console, the control centre of each cloud platform, facilitates the creation of new identities, service deployment, updates, and configurations impacting all cloud-hosted assets. This becomes an attractive target for threat actors, considering it offers direct access to the cloud infrastructure and user identities.

Understanding Data in the Cloud: The cloud hosts data, apps, and components on external servers, making it crucial to maintain correct configurations and timely updates. This is vital not just to prevent external threats, but also to manage internal vulnerabilities, such as misconfigurations, given the inherent complexity and size of cloud networks.

In conclusion, as businesses in the ASEAN region increasingly embrace cloud technologies, the need for a well-defined cloud IR plan has never been more crucial. By efficiently identifying signs of cloud-based threats, mitigating breaches, and limiting or eliminating damage, organisations can secure their cloud infrastructures, enhance their response processes, and reduce time to resolution.

To Know More, Read Full Article @ https://ai-techpark.com/strengthening-your-cybersecurity/ 

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Black Friday and Cyber Monday are excellent opportunities for customers to get hold of some great deals, while for retailers, it is an outstanding time to clear up their stocks. But, unfortunately, even cybercriminals utilize this time to execute scams and crimes that affect businesses.

According to global collective research in 2022, there is $41 billion in fraud damages reported from the e-commerce industry. However, it is anticipated that by the end of 2023, the loss will surpass $48 billion.

As cyber criminals initiate new routine scams around the time of these events, it is the right time for CISOs and other IT teams to step up their vigilance plan of action to counter such malicious attacks and protect their business as well as the interest of their valued customers.

With the help of this article, we will delve deep into some useful tips to create a secure online shopping experience.

Key Actions for Black Friday Cybersecurity

During such frenzied festive seasons, the CISOs and IT managers should be cautious as threat actors are on their toes to ruin businesses with their scams and deceiving tactics.

So, to prevent such incidents here are four essential key actions you can conduct:

Create a Robust Cybersecurity Plan

Planning a robust cybersecurity plan during events and festivals, like Black Friday or Cyber Monday can involve multiple approaches. It has been witnessed that cyber actors are ahead in the game by using tactics like:

Custom site designs for the event or early bird deals to fool customers into clicking on them and impersonating them as your customers.

It has been seen that customers are attracted to clickbait that forces your company to get their sensitive information, like credit or debit card details, addresses, mobile numbers, and many more.

Thus, chalking out the areas where monitoring is needed or what steps and protocols are needed to eliminate these incidents will save you valuable money and resources in turn giving your customers the retail therapy they deserve.

Implement Automated Data Security and Compliance

You need to ensure that your website or application follows all the rules and regulations in terms of data security and compliance.

Implement automated data security and compliance services that scan your network and notify you in real-time of any suspicious activity, which allows you to promptly act before any damage occurs.

Automated solution tools like Scrut, Vanta, Drata, and Tugboat Logic help employees monitor and report threats promptly.

The only standard approach to implement these tools would require adequate routine team training with appropriate knowledge transfer for personnel to operate these tools and defeat cyber criminals ahead of time!

To Know More, Read Full Article @ https://ai-techpark.com/ai-on-black-friday/ 

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Effective Machine Identity Management

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In response to rapidly changing workplace needs, many companies launched into scaling up their tech stacks and providing employees with new tools that promised greater efficiency, improved productivity, and a better digital experience. Research shows, however, that 40% of employees and 44% of executives believe that employees are either somewhat or significantly over-provisioned by tech at work. As a result, workplaces now are grappling with an abundance of tools that were poorly matched both to employees’ needs and to specific workplace challenges.

The underlying disconnect is this: companies focused too much on providing new equipment to teams in an attempt to make broad, sweeping improvements in productivity or to accelerate business transformations. In turn, they fell short of providing the right equipment to the right employee at the right time.

Determine benchmarks to prepare for scaling.

Organizations looking to scale and grow need clear markers of success long before they level up their investment in IT systems to prevent too large of an investment or too little preparation. Setting benchmarks is essential. Companies can achieve this step by comparing historical trends based on data instead of guesswork. This must happen alongside real-time data for a full picture of the digital employee experience (that is, each employee’s experience with the tech stack allocated to them—whether good or bad).

Context around certain IT moves and decisions, as well as the impact of those moves on workplace productivity and performance, is crucial for enabling strategic planning. For instance, it’s possible to parcel data into meaningful, informative sets based on the workplace environment (hybrid or remote), the employee experience with the digital tools they need for their roles, and the systems used.

Benchmarks will be critical for growth planning, including any M&A plans on the docket, enterprise-wide system integrations (such as EMR rollouts for healthcare organizations), or widespread software updates. Armed with easily-digestible benchmark data, IT teams can sort out any issues ahead of an influx of talent, system mergers, and digital transformation projects.

In these scenarios, IT leaders can emerge as true business heroes, instead of the old days when the “IT hero” was associated with reactively saving a company from extended downtime. The IT executives’ ability to tie downtime, latency, and systems issues (and more) to the business’s bottom line—based on data-informed calculations—will elevate strategic planning. The monetary value of an organization’s IT health is rapidly increasing as companies look to eliminate redundancies, streamline workflows, and create better digital employee experiences. Up until now, measuring the baseline of IT health—and tying that baseline to a financial tally—has been cumbersome and inefficient. Now, IT leaders can determine the issues affecting productivity through a single dashboard of a digital experience platform, enabling companies to quickly measure the impact of software, hardware, and network issues on workplace productivity, in turn immediately remedying any issues.

To Know More, Read Full Article @ https://ai-techpark.com/cost-if-it-issues/ 

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Effective Machine Identity Management

Neural Network Technology

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With its enormous calculation speed, quantum computing (QC) has the potential to tackle a wide range of issues that traditional computers struggle to handle. The study of the nexus between quantum computing and machine learning is known as quantum machine learning (QML). Many small and medium-sized businesses (SMEs) and small-to-medium-sized businesses (SMBs) are searching for ways to use QML in all areas of science and technology. However, building quantum computers is a significant technical challenge; thus, with the help of QML algorithms and applications, numerous IT companies like IBM, Google, Xanadu, and many others have created several libraries and platforms that work as a subset of QC.

Top Three Scalable Quantum ML Platforms

Researchers and IT engineers have formulated a few QML cloud services that allow CEOs and IT professionals of SMEs and SMBs to have early access to quantum processing. Thus, to conduct a task properly, you need the right QML tools and applications. Here is a list of the top three QML platforms and tools:

IBM: Quantum Experience

IBM Q Experience, a product of IBM launched in May of 2016, is a cloud-based platform for programming and running quantum circuits on IBM’s quantum computers. It comes with several tools for creating and evaluating quantum machine-learning algorithms. Currently, this platform is offered on many transmon qubit processor-equipped quantum devices and simulators. Those with five and sixteen qubits can be accessed by everyone. However, the IBM Q Cloud Network offers devices up to 65 qubits.

Rigetti Computing: Forest

Forest, the first quantum-first cloud computing platform, was developed by a California-based startup IT company, Rigetti Computing. Its quantum processors (QPUs) are made available to users via the cloud and are seamlessly linked with classical computing infrastructure thanks to Quantum Cloud Services (QCS). With the help of the Forest SDK, users may create quantum programs in Quil, compile them, and execute them using a simulator.

Xanadu: Xanadu Quantum Cloud

Xanadu, an IT business started in Canada, offers Xanadu Quantum Cloud, which has three completely programmable photonic quantum computers, through cloud-based access. The company has a full-stack Python library with the codename Strawberry Fields, which is for designing, optimizing, and utilizing photonic quantum computers. This platform makes access to a unique set of near-term applications possible within quantum chemistry, finance, logistics, and cloud-based quantum machine learning.

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

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AI in Medical Imaging: Transforming Healthcare

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The rapid advancement of artificial intelligence (AI) is revolutionising our lives and work, making processes more efficient. Technologies like large-scale machine learning and natural language processing models, such as ChatGPT, are pushing the boundaries of what was once confined to the realm of science fiction. However, a significant challenge remains in bridging the gap between technical brilliance and real-world application.

While AI has made significant progress in virtual environments, the introduction of AI-powered general-purpose robots in the physical world still faces substantial obstacles. Why is this the case, and how can we address these barriers? We explore the topic in more detail below.

Energy efficiency stands out as a primary obstacle. At its core, a robot is essentially a self-propelled computer. Anyone who has used a laptop knows that even the best devices struggle to operate for more than a few hours without recharging. With robots, energy demands are even higher due to internal processes and physical movement. Safety considerations prevent them from relying on tethered connections, necessitating extended battery life.

Unfortunately, current robot mechanics and autonomous systems lack the energy efficiency required for sustained operation. They require frequent and extended charging periods to perform optimally. While the first generation of robots is utilised in industrial settings for manufacturing, they remain constantly tethered to a power source. Although there are general-purpose robots available, like Sanctuary’s Phoenix humanoid, they are still cumbersome and expensive. It will likely take five to ten more iterations before we achieve a model that is truly independent, freely moving, and capable of performing various tasks.

To bridge this gap, we must start with smaller and simpler applications that gradually lead to full AI integration in the physical world. Cobots, which are robots designed for simple tasks, can play a crucial role in this process. Examples include self-driving wheelchairs, robots cleaning building facades, or autonomous technology performing complex, focused tasks like a smoke-diving robot searching for people or a drone fixing power lines. The key is focusing on single-duty performance, not only to enhance energy efficiency but also to achieve the highest standard of work.

Mechanical efficiency is another critical aspect. By improving the way robots move, potentially by utilising artificial muscles and joints to mimic human motion, we can reduce their energy requirements. However, achieving fully functional humanoid technology is still a considerable distance away.

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

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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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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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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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Cloud Computing Frameworks

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Data science is a rapidly evolving field. How does Tredence stay ahead of the curve and ensure its solutions incorporate the latest advancements and best practices in the industry?

At Tredence, we constantly innovate to stay ahead in the rapidly evolving data science field. We have established an AI Center of Excellence, fueling our innovation flywheel with cutting-edge advancements.

We’ve built a Knowledge Management System that processes varied enterprise documents and includes a domain-specific Q&A system, akin to ChatGPT. We’ve developed a co-pilot integrated data science workbench, powered by GenAI algorithms and Composite AI, significantly improving our analysts’ productivity.

We’re also democratizing data insights for business users through our GenAI solution that converts Natural Language Queries into SQL queries, providing easy-to-understand insights. These are being implemented across our client environments, significantly adding value to their businesses.

How does Tredence leverage data science to address specific challenges faced by businesses and industries?

Tredence, as a specialized AI and technology firm, delivers bespoke solutions tailored to businesses’ unique needs, leveraging cutting-edge data science concepts and methodologies. Our accelerator-led approach significantly enhances time to value, surpassing traditional consulting and technology companies by more than 50%. Tredence offers a comprehensive suite of services that cover the entire AI/ML value chain, supporting businesses at every stage of their data science journey.

Our Data Science services empower clients to seamlessly progress from ideation to actionable insights, enabling ML-driven data analytics and automation at scale and velocity. Tredence’s solutioning services span critical domains such as Pricing & Promotion, Supply Chain Management, Marketing Science, People Science, Product Innovation, Digital Analytics, Fraud Mitigation, Loyalty Science, and Customer Lifecycle Management.

Focusing on advanced data science frameworks, Tredence excels in developing sophisticated Forecasting, NLP models, Optimization Engines, Recommender systems, Image and video processing algorithms, Generative AI Systems, Data drift detection, and Model explainability techniques. This comprehensive approach enables businesses to harness the full potential of data science, facilitating well-informed decision-making and driving operational efficiency and growth across various business functions. By incorporating these data science concepts into their solutions, Tredence empowers businesses to gain a competitive advantage and capitalize on data-driven insights.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-arnab-sen/ 

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