Sankey solutions: IT Consulting & Services | Digital Transformation

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In recent years, problem-solving techniques have undergone a fundamental transformation due to the increasing adoption of large language models (LLMs). Earlier, to solve any tasks with computers, programs had to be created. These programs were composed of carefully constructed commands written in different programming languages. But since LLMs were developed, completing these kinds of tasks has just required textual prompts.

Because of their text-to-text structure, LLMs are remarkably capable of handling a wide range of tasks with a single model. Initially, models such as GPT-2 and GPT-3 were used in zero and few-shot learning demos to highlight this capability. But LLMs become even more interesting when customized to match human preferences and commands, opening the door to the creation of well-known generative applications like chat-based search experiences, information-seeking dialogue agents, and coding assistants. 

LLMs’ adaptability and usefulness have driven them to recognition in both scientific and popular cultures.  

Prompt engineering is a related field that has emerged at the same time as this rise in popularity. A more sophisticated form of engineering known as prompt engineering is the generation of input requests for large language models (LLMs) that direct them to produce desired outputs. 

It comprises improving prompts’ responsiveness to user inputs through iterative tuning. When simple prompts don’t produce the desired results, this iterative procedure is especially helpful. 

Let’s look at some common prompting tactics in depth.

Source – https://realpython.com/practical-prompt-engineering/

Types of Prompting Techniques

1. ZERO-SHOT PROMPTING:

Zero-shot prompting involves presenting tasks to a model without providing any task-specific examples. The model is expected to generate a response based solely on the given prompt and its pre-existing knowledge.

In this example, the model generates a recipe for a classic Italian pasta dish based solely on the given prompt, without any specific instructions or ingredients provided.

When zero-shot doesn’t work, it’s recommended to provide demonstrations or examples in the prompt which leads us to one-shot and few-shot prompting.

 

2. ONE-SHOT PROMPTING:

One-shot prompting involves showing the model one task-specific example before presenting the actual prompt. This example serves as a reference for the model, guiding its understanding of the task and expected response. For instance, consider the below example:

In this example, the model is shown as one example of a response to a query about a country’s capital before being asked to respond to a new query. It uses the example to understand the task and generate a response for the new query accordingly.

 

3. FEW-SHOT PROMPTING:

Few-shot prompting involves providing the model with a few task-specific examples to enhance its performance on a particular task. The model learns from these examples to generalize its understanding and improve its accuracy on similar tasks. For instance, consider a scenario where the model is trained to classify customer reviews as positive or negative based on a few labelled examples:

Here, the model accurately identifies the mixed sentiment of the review by leveraging the few-shot examples provided during training.

 

4. MULTI-SHOT PROMPTING:

Multi-shot prompting expands on the concept of one-shot prompting by providing the model with multiple task-specific examples to guide its output. These examples offer a broader understanding of the task and allow the model to produce more accurate and comprehensive responses. For example, consider a translation task where the model is provided with multiple examples of English-to-French translations:

Examples: 

  1. English to French Translation: “Hello, how are you?” Output: “Bonjour, comment ça va?”
  2. English to French Translation: “Thank you, I am well.” Output: “Merci, je vais bien.”

By analyzing multiple examples, the model can generate translations that capture the nuances of the language more effectively. 

 

5. CHAIN-OF-THOUGHT PROMPTING:

Chain-of-thought prompting involves breaking down the input-output process into a series of coherent reasoning steps. This technique aids complex tasks like common sense reasoning and arithmetic by providing structured responses. For instance, consider a math word problem where the model follows a series of intermediate reasoning steps to arrive at the final answer: 

Here, the model generates a chain of thought to solve the problem step-by-step, leading to the final answer.

The integration of prompt engineering revolutionizes various aspects of customer interaction and service across industries:

How can organizations leverage prompt techniques?

1. CONVERSATIONAL AI:

The rise of chatbots and virtual assistants emphasizes the need for seamless, contextual conversations. Prompt engineering empowers brands to create AI companions that adapt to user tone, understand intent, and provide personalized responses. From recommending recipes to scheduling appointments, AI companions offer tailored assistance with a touch of wit.

2. RECOMMENDER SYSTEMS:

Prompt-engineered LLMs analyze individual user data and preferences to deliver personalized product recommendations. These systems can offer intuitive and engaging shopping experiences, helping customers discover unexpected favourites based on their evolving tastes.

3. CUSTOMER SERVICE APPLICATIONS:

Customer service transforms with prompt-engineered AI capable of analyzing complaints, suggesting solutions, and seamlessly escalating issues. AI-driven customer service centres streamline routine tasks, allowing human agents to focus on complex cases and foster deeper customer relationships.

4. MARKETING:

Companies are expanding generative AI efforts in marketing, leveraging prompt engineering for content generation and SEO optimization. Fine-tuning prompts to fit specific niches enables the creation of tailored marketing materials, driving engagement and enhancing brand visibility.

How prompt techniques can be used across industries?

  • HEALTHCARE:
  • Prompting techniques support medical chatbots in providing preliminary diagnoses, analyzing medical imaging data for improved diagnostics, and offering personalized mental health support.

  • FINANCE:
  • In finance, prompting is used for automating financial data analysis, enhancing fraud detection and prevention, and streamlining customer service through intelligent chatbots.

  • MANUFACTURING:
  • Prompting aids in optimizing production processes, enhancing supply chain management with predictive analytics, and improving product quality control through automated systems.

  • EDUCATION:
  • In the education sector, prompting facilitates personalized learning experiences, collaborative learning, and the development of smart tutoring systems for improved academic outcomes.

    Sankey Solutions leverages prompt engineering to enhance efficiency and effectiveness in various aspects of its operations.

    Sankey employs prompt engineering to enrich its internal knowledge base. Developers use prompt-engineered AI to curate and refine a comprehensive repository of best practices and troubleshooting guides. By providing developers with prompt-based access to this knowledge base, Sankey ensures quick and accurate resolution of technical queries and challenges.

    Within its internal project management processes, Sankey implements prompting techniques to facilitate communication and collaboration among development teams. Prompts are utilized to streamline task assignments, progress tracking, and issue resolution. By incorporating prompt engineering into project management tools and workflows, Sankey enhances team productivity and project efficiency.

    Sankey enhances its capacity to provide forefront tech solutions that satisfy the diverse requirements of its clients and support business growth by adopting rapid development.

    The automotive industry is currently in the midst of a profound transformation, with electric vehicles (EVs) leading the charge toward a more sustainable and eco-friendly future. As automakers across the globe gear up to meet the rising demand for EVs, advanced machine learning (ML) is emerging as a key enabler, propelling the sector into a new era of innovation and performance. In this comprehensive blog post, we will take an in-depth and technical journey into the transformative role of advanced machine learning in the automotive EV sector, examining cutting-edge technological innovations, and providing a broader perspective on the implications of this evolving landscape.

    Machine Learning’s Crucial Role in EV Advancement

    Before we dive into the technical intricacies, let’s understand the fundamental role of machine learning in the electric vehicle industry.

    Machine learning, a subset of artificial intelligence, empowers machines to learn and make decisions by processing and analyzing data. In the context of electric vehicles, ML is a multifaceted tool that goes far beyond traditional automotive engineering. It plays a pivotal role in multiple aspects, enhancing performance, safety, energy efficiency, and the overall driving experience.

    Battery Management and Optimization

    • Data-Driven Insights: Machine learning algorithms continuously monitor and optimize the performance of EV batteries. These algorithms process data related to temperature, charge cycles, and usage patterns to predict battery health and provide real-time recommendations for charging, discharging, and thermal management.
    • Extending Battery Life: The result is not only optimized energy utilization but also the extension of battery lifespan, addressing a key concern for EV owners.

    Range Prediction and Optimization

    • Eliminating Range Anxiety: Machine learning algorithms predict range with remarkable accuracy. By factoring in variables like driving habits, weather conditions, and traffic patterns, these algorithms offer real-time range estimates. This helps drivers plan their journeys more effectively and alleviates range anxiety, a common concern among EV users.

    Safety and Autonomous Driving

    • Advanced Driver-Assistance Systems (ADAS): ML is instrumental in the development of advanced driver-assistance systems, enhancing safety in electric vehicles. These systems rely on ML algorithms for object detection, lane keeping, adaptive cruise control, and other critical safety features.
    • Towards Full Autonomy: The foundation laid by ML-driven ADAS systems paves the way for full autonomy in the future. EVs are increasingly becoming intelligent, self-driving machines, thanks to machine learning’s continuous learning capabilities.

    Energy Recovery and Regeneration

    • Optimized Regenerative Braking: Machine learning models analyze data related to driving behavior and traffic conditions to determine the ideal moments to engage regenerative braking. This energy recovery process captures and stores energy during deceleration, contributing to increased efficiency and longer battery life.

    Charging Infrastructure and Grid Management

    • Grid Optimization: Machine learning is not limited to the vehicle itself. It also plays a crucial role in the development of charging infrastructure and grid management. Predictive algorithms anticipate charging demand and adapt to grid conditions, optimizing the charging process for EV owners. This reduces charging times and minimizes the strain on the electrical grid.

    Predictive Maintenance

    • Proactive Maintenance: Predictive maintenance is a game-changer for electric vehicle manufacturers. Machine learning algorithms analyze sensor data from various vehicle components to identify potential issues before they lead to breakdowns. This proactive approach reduces maintenance costs, improves vehicle reliability, and enhances driver satisfaction.

    Innovative Applications of Machine Learning in EVs

    As we venture deeper into the technical landscape, it’s crucial to highlight some of the innovative applications of machine learning in electric vehicles that are shaping the future of mobility.

    1. Natural Language Processing (NLP)

    Machine learning has powered voice-activated systems in EVs. These systems are becoming increasingly sophisticated, enabling drivers to interact with their vehicles more naturally. They can perform tasks such as adjusting climate control, navigation, and entertainment systems with voice commands.

    2. Energy-Efficient Route Planning

    Machine learning-driven route planning is taking electric vehicles to the next level. These algorithms consider factors such as elevation changes and traffic conditions to determine the most energy-efficient path for EVs. This feature not only maximizes range but also minimizes energy consumption during trips.

    3. Interior Comfort Optimization

    Machine learning processes sensor data from within the vehicle to automatically adjust settings for interior comfort. This includes personalized climate control, seat adjustments, and even cabin fragrances based on driver and passenger preferences.

    Challenges and Considerations

    Amid this wave of technological advancements, it’s crucial to address the challenges and considerations that come with integrating machine learning in electric vehicles.

    1. Data Privacy and Security

    The collection and analysis of vast amounts of data from electric vehicles raise concerns about data privacy and security. Manufacturers must implement robust safeguards to protect sensitive information and ensure compliance with data protection regulations.

    2. Hardware and Computing Power

    Implementing advanced machine learning algorithms requires increasingly powerful onboard computing hardware. This introduces considerations related to energy efficiency and heat management to avoid draining the vehicle’s battery.

    3. Scalability and Compatibility

    Manufacturers need to ensure that ML algorithms are scalable and compatible with various EV models. This requires standardized data formats and interfaces to enable seamless integration of new features and updates across different vehicle lines.

    4. Regulatory Compliance

    The deployment of machine learning in EVs must adhere to regulatory standards. Manufacturers need to work closely with regulatory bodies to ensure that their systems meet safety and performance requirements while also remaining compliant with evolving regulations in the automotive industry.

    The Road Ahead: Collaborations and Innovations

    Overcoming these challenges and fully harnessing the potential of machine learning in the EV sector requires close collaboration between automakers, tech companies, and research institutions. Innovations in hardware, software, and data management will be instrumental in driving the industry forward.

    Optimizing battery performance and management is crucial in addressing challenges associated with IoT device batteries. Our trained model effectively predicts outcomes on new data, and the Battery Insights module leverages its output to generate valuable insights into IoT device battery life. These insights not only offer a comprehensive understanding of battery performance but also inform strategic enhancements in device design. By tackling battery management challenges proactively, we aim to enhance the overall efficiency and longevity of IoT devices in the below architecture diagram.

    The architectural illustration depicts a cloud computing system leveraging Azure App Service for web application hosting and Azure IoT Core for IoT device connectivity and management.

    The web application employs Raw Data from IoT devices for feature extraction and transformation. The Feature Extraction process discerns vital features like temperature, humidity, and vibration. Feature Transform converts these features into a format compatible with the machine learning model, storing it in an SQL database for data analysis and model training.

    Azure Service Bus facilitates communication between the web application and IoT devices, ensuring reliable and secure interactions. Azure IoT Core enables secure communication between IoT devices and the cloud, while the Notification module sends user notifications based on machine learning model outputs.

    After completing the training and tuning process, the resulting model is deployed to the Trained Model module. This trained model predicts on new data and the Battery Insights module utilizes its output to generate insights into IoT device battery life, informing device design enhancements.

    In its entirety, the architectural diagram illustrates a thoughtfully crafted cloud computing system. It utilizes Azure App Service, Azure IoT Core, and Azure Service Bus, ensuring the secure and reliable hosting of a web application, effective management of IoT devices, and the execution of data analysis and machine learning processes.

    The successful implementation of a robust Battery Management system by Sankey Solutions speaks to the company’s demonstrated expertise in delivering effective solutions. This achievement not only underscores Sankey Solutions’ commitment to providing high-quality services in the specialized field of Battery Management but also serves as a testament to the company’s capabilities and dedication in meeting the unique and demanding needs of its prestigious clients. The accomplishment reflects Sankey Solutions’ position as a trusted and reliable partner in delivering innovative solutions for optimal battery performance and management.

    Conclusion

    In this technical and comprehensive exploration, we have unveiled the transformative role of advanced machine learning in advancing the automotive EV sector. From battery management and range prediction to autonomous driving, energy recovery, and innovative applications like NLP and energy-efficient route planning, machine learning is redefining the electric vehicle landscape.

    As electric vehicles continue to gain momentum in the automotive market, the role of machine learning will become increasingly pronounced. The road ahead promises further innovations and advancements that will not only benefit EV owners but also contribute to a cleaner and more sustainable future of transportation. This remarkable journey holds immense promise and potential, and it is through the fusion of machine learning and electric vehicles that we are steering toward a more innovative, efficient, and eco-friendly driving experience.

    – Kalpana Sha Iyer

    Business Head – UK | Europe

    kalpana.s@sankeysolutions.com

    Software-driven vehicles, also known as autonomous vehicles, are revolutionizing the automotive industry with their cutting-edge technology and potential to transform transportation as we know it. While the concept of self-driving cars might have initially seemed like science fiction, significant advancements in artificial intelligence and sensing technologies have made it a reality. As these vehicles become increasingly viable and accessible, businesses have a unique opportunity to leverage software-driven vehicles to drive growth, enhance efficiency, and explore new possibilities. In this article, we will explore how businesses can harness the potential of software-driven vehicles to unlock growth and stay ahead in the competitive landscape.

    Streamlining Logistics and Supply Chain

    For businesses heavily reliant on logistics and supply chain operations, software-driven vehicles offer a transformative advantage. Autonomous delivery vehicles can optimize route planning, minimize delivery times, and reduce operational costs. By leveraging AI-powered autonomous trucks or drones, businesses can achieve faster and more efficient last-mile deliveries, leading to increased customer satisfaction and loyalty.

    Enhanced Mobility Services

    Companies in the transportation and ride-hailing sectors can significantly benefit from the adoption of software-driven vehicles. By incorporating autonomous ride-hailing services, businesses can reduce the need for human drivers, leading to lower operational costs and increased availability of services. Additionally, the potential for reduced traffic congestion and more efficient rides can improve customer experiences, attracting more users to the platform and fostering business growth.

    Innovating Service and Convenience

    Businesses in various industries can innovate their service offerings with the integration of autonomous vehicles. For example, restaurants and food delivery services can deploy autonomous delivery vehicles to offer contactless and speedy deliveries. Retail businesses can also explore the possibility of autonomous stores on wheels, providing on-demand products and services in various locations, catering to customer preferences and demands.

    Data-Driven Business Insights

    Software-driven vehicles generate vast amounts of data through their sensors and onboard systems. Businesses can leverage this data to gain valuable insights into customer behavior, traffic patterns, and market trends. These data-driven insights can inform business strategies, optimize operations, and drive product or service innovation, ultimately leading to improved business growth and profitability.

    Fostering Innovation and Partnerships

    As software-driven vehicles continue to evolve, businesses can explore innovative collaborations and partnerships. Companies involved in developing AI algorithms, sensor technologies, and infrastructure for autonomous vehicles offer exciting possibilities for cross-industry collaboration. By collaborating with technology providers, businesses can accelerate their own growth and stay at the forefront of industry trends.

    Creating New Revenue Streams

    As software-driven vehicles continue to evolve, businesses can explore innovative collaborations and partnerships. Companies involved in developing AI algorithms, sensor technologies, and infrastructure for autonomous vehicles offer exciting possibilities for cross-industry collaboration. By collaborating with technology providers, businesses can accelerate their own growth and stay at the forefront of industry trends.

    Conclusion

    Software-driven vehicles represent a paradigm shift in the automotive industry, opening up numerous possibilities for businesses to drive growth and embrace innovation. From streamlining logistics and supply chain operations to providing enhanced mobility services and creating new revenue streams, the integration of autonomous vehicles can lead to a more efficient, customer-centric, and profitable business ecosystem.

    However, adopting software-driven vehicles requires careful consideration of the technological landscape, regulatory frameworks, and safety concerns. Businesses must invest in robust cybersecurity measures, comprehensive testing protocols, and employee training to ensure the successful integration of autonomous technology.

    As the technology continues to mature and become more widespread, businesses that embrace software-driven vehicles early on will gain a competitive advantage, setting themselves up for sustained growth and success in the dynamic world of autonomous transportation.

    – Kalpana Sha Iyer

    Business Head – UK | Europe

    kalpana.s@sankeysolutions.com

    The biggest reason for the tech giants to switch to EV technology is because of the growing concerns about the environment and depleting reserves of fossil fuels. Electric vehicles are going to solve major problems as they are low on maintenance, you can save money spent on gas, and they provide better performance compared to gasoline-powered vehicles.

    As technology improves electric vehicles will be the driving force for the automobile sector which will boost the development of the data and vehicle analytics. Industry leaders are accelerating the speed of automotive technology innovation as they develop new concepts of connected, autonomous, and shared mobility. According to industry experts and analysts, by 2030, there will be 6.4 million electric cars on UK roads, improving air quality as well as the transport sector’s contribution to the country’s carbon emissions. It is expected that there will be between roughly 8 million and 11 million hybrid or electric cars in the UK.

    So, What are the challenges in the EV world today?

    Like every evolving industry, there are several challenges today that are plaguing the EV industry.

    Below are the top 5 challenges:

    1. Insufficient charging stations
    2. Low Mileage
    3. Lack of Service centers
    4. Lack of skilled labor (Breakdown & maintenance)
    5. Problems with battery management

    Other challenges:

    1. No clarity in government policies
    2. Supply chain problems
    3. Trends-chatbots
    4. Higher vehicle cost

    So, How technology could address the above top 5 challenges?

    In-sufficient charging stations:

    While it is consuming time to install more charging stations at workplaces and public destinations, Technology could optimize availability by locating nearby public and private charging stations ensuring the availability, Pre-booking charging stations, and Effortless payments.

    Mileage optimization:

    Technology Tools could keep track of Service schedules and the Status of Servicing, help send notifications on Tyre pressure monitoring, and optimized interval for servicing required due to Harsh braking.

    Lack of service centers:

    Technological advancements include Pre-booking of servicing, Location tracking of centers, and Preventive maintenance to reduce going to centers.

    Lack of Skilled Labor:

    This challenge could be addressed through Learning Management solutions which include videos, Blogs, case studies, and articles to upgrade their skills.

    Battery management:

    The battery system is the heart of an electric vehicle, and managing its health is critical for ensuring a smooth and uninterrupted operation. A critical need of EV owners and manufacturers is the real-time monitoring and management of the battery system, which can help them manage important parameters such as battery health and charging status.

    The BMS provides detailed information on the battery’s health, state of charge, and charging status, which can help EV owners and manufacturers optimize battery performance and extend battery life. Real-time monitoring can help owners and manufacturers identify the best time to charge their EVs, reducing the risk of overcharging or undercharging, which can damage the battery and reduce its lifespan.

    Business Impact

    Below are a few important benefits of above to any EV manufacturing company   

    • Increased efficiency, profits, and performance
    • Boosted productivity and output levels
    • The improved overall growth of the company

    Conclusion

    Numerous technological advancements and innovations could be used to overcome all the traditional challenges and problems, which are a showstopper. Technology-driven trends such as shared mobility, electrification, connectivity, and autonomous driving are leading to increased efficiency, profits, and performance leading to the overall growth of the company. Sankey Solutions has associated with several brands that are there in the EV space and now understands the EV ecosystem thoroughly. Our solutions have been thoughtfully designed with a focus on key factors such as quality, performance, cost, and time to market. These are broadly addressing most of the challenges mentioned above for global automotive players.

    – Kalpana Sha Iyer

    Head of Business, Growth and Strategy – UK & EUROPE

    Sankey Business Solutions

    For further queries contact: kalpana.s@sankeysolutions.com

    The automotive industry is currently in the midst of a profound transformation, with electric vehicles (EVs) leading the charge toward a more sustainable and eco-friendly future. As auto

    In Chinese astrology, each year is related to a Chinese zodiac animal according to a 12-year cycle. Those who were born in the years 2020, 2008, 1996, and backwards are known as Rats. People born in these years are said to be optimistic and likeable.

    However, in cybersecurity, RAT (Remote Access Trojan) stands for the polar opposite of likeable: a nasty tool used by bad actors. For many years, RATs have been used to remotely and covertly control victims’ computers. The cunning RAT can gain access to computer users’ files and hardware resources such as webcams and microphones, as well as act as a keylogger, data stealer, and launchpad for other malware attacks. Worse, the use of RATs in enterprise-level attacks is on the rise.

    In this blog, one of our Sankians – Shubham Dhumal, a Solution Analyst will discuss the impact of this malware…

    “As a tech enthusiast, I often receive requests for assistance with device-related issues. One such instance was when a relative approached me with a smartphone behaving erratically. The phone was automatically sending SMS messages, installing apps without permission, and opening undesirable websites on the browser, among other issues.

    To troubleshoot the issue, I began investigating the device’s settings, paying close attention to any third-party apps with permissions that may be causing the problem. After some time, I concluded that the most effective solution would be to format the device. However, deep down, I knew that this was not merely an OS issue or a bug in an application; there was something else at play.

    To address any lingering doubts and conduct a final check before formatting the device, I decided to install antivirus software from the Play Store. After installing the software, I initiated a scan of the device, and within a few minutes, the screen turned red and displayed a list of compromised applications. The antivirus software efficiently cleared and uninstalled all the compromised applications.

    After completing this process, I asked my relative to use the device for a few days and report any similar issues. It is essential to ensure that the problem is resolved completely and that there are no traces of any malware or other malicious elements left on the device. Taking such measures can prevent the recurrence of similar problems and ensure the device’s optimal performance.

    Out of curiosity, I searched for some of the listed malware during the scanning process and noticed that some of the listed malwares were Remote Access Trojans (RATs). Intrigued by this discovery, I delved deeper into the subject and learned that these malicious programs can enable hackers to gain full access to one’s phone. RATs can read and send text messages, access contact lists and call logs, record audio, and video, and even track the device’s location.

    It was clear to me that having such malware on a device is a significant threat to personal, financial, and professional security. RATs cannot be installed or accessed directly, and usually require a host to execute the attack. Given the increasing sophistication of cybersecurity threats, it is crucial to take proactive measures to safeguard one’s devices and data from malicious attacks.

    I asked my relative further to understand the reason behind the installation of such applications. He downloaded APK files from unauthorized websites and stores on the internet. His rationale for resorting to these sources was that they provided access to more advanced features and removed the limitations imposed by the original applications. It was then that I began to realize how these tactics work.

    Essentially, attackers gain access to the original application files and modify them using specialized tools to unlock new features and offers, which are typically available for a fee. These modified applications are then distributed to unknown sources, tempting users with the alluring prospect of accessing these advanced features for free. It may sound like an attractive proposition, but it comes at a significant cost: the security and integrity of one’s device and data.

    But there’s always another side of the coin worth talking about. There is another aspect to consider as to why someone would modify an application in this way. Modifying an application is an easy task, and it’s not much time-consuming at present. In some cases, attackers modify the application code and add a malicious piece of code, which acts as a host for executing malicious activities. This code can exploit the permissions granted by the user to the host application and gain access to sensitive data or other functionalities of the device.

    Therefore, it is essential to exercise caution while downloading and installing applications, especially from unknown or unverified sources. Such practices can pose a significant risk to the security and privacy of one’s device and data. It is recommended to stick to trusted sources and official app stores for downloading applications, and regularly update the device’s operating system and security software to stay protected from potential threats.

    The damage inflicted by a RAT can be severe, with significant impacts on personal, financial, and professional fronts. The attacker can steal sensitive information, compromise financial transactions, and breach confidential data, among other things. It is crucial to take preventative measures to avoid RAT attacks, such as avoiding suspicious websites and sources, maintaining updated security software, and regularly scanning devices for malware.

    In addition to conventional data security practices, it is crucial to avoid utilizing cracked software and instead purchase authorized versions exclusively. Opting for authorized versions instead of free, cracked versions is a wise investment as the latter may result in several multifaceted payments. It is strongly advised to refrain from downloading applications from unverified resources and to exercise caution when utilizing unauthorized or modified versions of applications. It is vital to prioritize data security by taking such precautionary measures.”

    In conclusion, the safety of our data is of utmost importance in today’s digital world. As individuals, we must prioritize cybersecurity practices to ensure that our sensitive information is protected from potential threats. Along with standard security measures, such as strong passwords and two-factor authentication, avoiding the use of cracked software and only purchasing authorized versions can significantly reduce the risk of a data breach. It is vital to stay vigilant and informed about potential threats to our data and take appropriate steps to mitigate them. By adopting a proactive approach to cybersecurity, we can ensure the safety and privacy of our data, and consequently, safeguard our digital lives.

    makers across the globe gear up to meet the rising demand for EVs, advanced machine learning (ML) is emerging as a key enabler, propelling the sector into a new era of innovation and performance. In this comprehensive blog post, we will take an in-depth and technical journey into the transformative role of advanced machine learning in the automotive EV sector, examining cutting-edge technological innovations, and providing a broader perspective on the implications of this evolving landscape.

    Personal computing’s definition is and has been changing to one that is much busier and more collaborative. This transformation, especially sped up by a pandemic, has been the perfect breeding ground for web apps to act as a center of activity. There is a dispute over whether having a single dominating browser would be advantageous for all browsers. 

    It’s worth noting that the engines we use now allow for a variety of consumer-friendly experiences. 

    The changing preferences of today’s digital natives’ alias millennials make it unarguably essential for organizations to ensure their consumers stick to their solutions. Especially tech startups where the majority are digital natives, fleeting in an information-overloaded environment, looking for swift solutions to chronic problems. This is proved by the demand for leading food delivery and local service brands. Today’s productivity nerds hustle and live their lives to the fullest simultaneously. To cater to their needs at an opportune moment is a must for the service industry in the tech space. Now is the time to also follow the legacy of super-apps for web browsers.  

    Is Chrome becoming obsolete?

    The technology landscape is constantly evolving, and it is always possible for a new product or service to emerge that surpasses Chrome in terms of popularity or functionality. However, it is difficult to predict exactly what the future may hold, and it is important to remember that Chrome has a strong track record of adapting to new developments in the tech industry. 

    There is no proof at this time that Chrome will soon become obsolete. While it is true that there are some capabilities in other web browsers that Chrome may not have, this does not necessarily mean that Chrome is fading into obscurity. 

    In truth, Chrome has a solid history of consistently updating and enhancing its capabilities, and it continues to be a top choice for many users because of its speed, security, and extensive library of add-ons and integrations. 

    Additionally, selecting a web browser is frequently a matter of personal preference, and various users may favour other features or functionalities. Assuming that there will always be a market for a range of different browsers, it is unlikely that any particular web browser would become obsolete. 

    Google’s Dominance

    Google Chrome is currently the most widely used web browser in the world, with a market share of around 68%. This has been the case for several years, and it is due in large part to Chrome’s reputation for being fast, user-friendly, and secure. 

    There are other web browsers available, such as Mozilla Firefox, Microsoft Edge, and Safari, but Chrome has consistently been the most popular choice among users. 

    Google’s dominance in the browser industry is also due in part to its integration with other Google products and services, such as Search, Gmail, and Google Drive. This makes it convenient for users who are already invested in the Google ecosystem. 

    It is worth noting that the market share of web browsers can vary by region and can change over time, as users may switch to different browsers based on their needs and preferences. However, Chrome has remained a dominant player in the browser industry for many years. 

    Consumer preferences

    Factors that can influence consumer preferences in this industry include the availability of new features, changes in technology and user experience, and shifts in the overall market landscape. For example, if a new web browser is released that offers significantly better performance or more advanced features than existing browsers, it may attract a large number of users and cause a shift in consumer preferences. 

    Additionally, if an existing browser fails to keep up with new technological developments or fails to address user needs and concerns, it may lose market share to other browsers. Overall, the preferences of consumers in the browser industry can be influenced by a variety of factors, and it is important for browser developers to stay attuned to the needs and expectations of their users in order to remain competitive. 

    New Emerging Web Browsers

    SigmaOS and Arc are examples of new and emerging browsers that are starting to disrupt the browser marketplace. These browsers are designed to offer a more user-friendly and customizable experience than traditional browsers, such as Chrome and Firefox. For example, SigmaOS is built on the open-source Chromium browser and focuses on providing a more streamlined and efficient browsing experience. It also includes built-in privacy and security features, such as anti-tracking and anti-phishing tools, to help keep users safe online. Similarly, Arc is a browser that aims to put the user in control of their browsing experience by providing a wide range of customization options and tools. 

    One of the keyways in which browsers like SigmaOS and Arc are disrupting the market is by providing a more personalized and tailored browsing experience. This is in contrast to traditional browsers, which often have a one-size-fits-all approach and can be difficult for users to customize to their preferences. By making it easy for users to customize and control their browsing experience, these new browsers are able to appeal to a wider range of users and are able to gain market share from more established browsers. 

    With the increasing amount of personal data being shared online, many users are becoming more concerned about protecting their privacy and keeping their information safe. Browsers like SigmaOS and Arc are addressing this need by incorporating advanced security and privacy features, such as anti-tracking and anti-phishing tools, to help keep users safe and secure while they browse. 

    In summary, browsers like SigmaOS and arc are disrupting the browser market by providing personalized and efficient browsing experiences and emphasizing privacy and security. However, they are relatively new in the market, it is yet to be seen how they will fare in the long run. 

    How tech plays a role

    Technology plays a crucial role in the development and evolution of new browsers. Here are a few ways in which technology is driving the emergence of new browsers: 

    1. Advancements in web technologies: As new web technologies such as HTML5, CSS3, and JavaScript continue to be developed and become more widely adopted, new browsers are able to take advantage of these technologies to provide a more rich and more interactive browsing experience. This includes features such as better support for multimedia, improved animation and graphics, and more responsive and dynamic web pages.
    2. Improved performance: With technological advancements in processors and memory, new browsers are able to run faster and more efficiently than ever before. This allows them to handle more demanding web pages and applications and provide a smoother and more responsive browsing experience. 
    3. Increased focus on security and privacy: As the internet becomes an increasingly important part of our lives, the need for secure and private browsing has become more important than ever. New browsers are using technology such as encryption, anti-tracking, and anti-phishing tools to keep users’ data and privacy safe. 
    4. Personalization and customization: With the help of machine learning and data analysis, browsers can provide personalized browsing experiences for different users by analyzing users’ browsing habits and preferences. This allows browsers to suggest new pages, features, and content to match users’ needs. 
    5. Mobile optimization: With the increasing use of mobile devices to access the internet, new browsers are optimized for small screens and touch-based interfaces, as well as to use fewer data and batteries. 

    Conclusion

    In short, technology plays a major role in the emergence of new browsers by enabling them to provide a faster, more secure, and more personalized browsing experience, as well as to adapt to the growing use of mobile devices. This allows new browsers to stand out in the market and to meet the ever-evolving needs of internet users. 

    Beyond that, the future is still somewhat uncertain. For the tech industry, the possibilities seem to be worth the risks. Web browsing is more popular than Facebook or Google, and windows and tabs are where most of modern life takes place. More than 25 years have passed since the definition of a browser was radically reevaluated. The open web is due for a comeback, and so is the web browser. And if any challenger can dethrone Chrome, it might contribute to defining how the next 20 years of existence will be. 

    When customers are accustomed to receiving food deliveries in 15 minutes and experiencing ultra-rapid 5G internet speeds, waiting a whole day for a transaction to complete becomes bothersome.

    Traditional banking, the industry standard for hundreds of years, has drifted behind by today’s tech-savvy users. The obstacles that conventional financial institutions are currently facing include prolonged response times, security risks, and fixed business hours. And so, the fintech world came up with a solution: neobanks.

    According to Statista.com, the market size of neobanks was estimated to be $35 billion in 2020, and it is anticipated that the market will expand at a CAGR of 21.26% from now until 2027.

    Neobanks, in other words, will severely impact every aspect of the financial services industry.

    Let us first understand what are neobanks.

    Neobanks is a specific kind of challenger bank that only conducts business online. These are recently founded banks that are independent of major conventional banks. They can offer their services via mobile and desktop devices despite their absence of any physical branches. They mandate that all of their customers go through a digital onboarding procedure, typically on a smartphone. Neobanks can fall into one of two categories: those with a banking license and those without one.

    Neobanks Vs Traditional Banks

    Let us understand the basic difference between neobanks vs traditional banks. The traditional retail banking approach has failed to retain consumers since digitalization and the pandemic. Neobanks provide highly tailored services at cheaper prices by leveraging technologies like artificial intelligence (AI), automation, and cloud computing -something that traditional banks lack. Additionally, Neobanks’ unique digital presence is possibly their biggest edge over traditional banks. Neobanks benefit from lower operating expenses as they do not need to invest in infrastructure or physical branches. Neobanks can offer minimal or no fees and high-interest rates on deposits to their customers as a result, passing along these advantages to them. They can provide inexpensive international payments and money transfers since they exclusively use the internet to conduct business.

    Technology’s Role in Neobanking

    Technology has enabled Neobanks to become more successful, efficient, and innovative. It goes without saying that technology has aided Neobanks in cutting costs and enhancing customer experience. As a result that they are entirely digital, Neobanks can automatically collect transactional data, liberating resources for new services and goods.

    Neobanks have evolved to improve the ability to realize their full potential by integrating the newest technologies, such as analytics, artificial intelligence, voice interfaces, etc. In addition to offering services around-the-clock, technology will also assist in locating solutions in much quicker and more effective methods. Technology would also make it easier to find relevant financial services, eliminating the delay.

    Automation of processes:
    The ability to do banking transactions online has been the most underrated change for customers. Neobanks have made it redundant to wait in line for hours at a bank branch to send money abroad. The transaction can be started in just five minutes, and you can monitor its progress in real-time from the comfort of your own home. Additionally, by digitizing the entire process, technology has allowed Neobanks to complete KYC, AML checks, etc. in a far more rigorous manner.

    Technology-enabled design as brand edge:
    Organizations in both the B2C and B2B sectors now recognize that design can be a powerful tool for building brand recognition. Artificial intelligence and Machine learning are connecting data and user experiences, while sleek user interfaces attract people and make the digital journey easier. Neobanks utilize these AI / ML technologies to forecast a user’s needs and offer specialized services or products by using personal data to create a personalized experience.

    Future Trends

    Neobanks would benefit as they move into the future by getting informed of trends and adhering to them. Neobanks’ ability to succeed more quickly might be accelerated by following the most current developments. A few trends that may help determine the future of Neobanks in the banking business are listed below.

    • Banking Super Apps:
      Super-apps are one of these customized tools which perform the task differently but effectively. Banking super apps demonstrate that consumers today are interested in more than simply banking. Choices made by consumers are now heavily interconnected. Neobanks have begun combining several services like banking, insurance, real estate, mobile payments, etc. under one platform and branding it a “Super-App” in order to cater to this need. These super-apps have special abilities. They place a high value on the demands of the customer, focus on those needs, and have excellent user experience design.

     

    • Banking On Hyper-Personalization:
      One of the determining factors for Neobanks in the future would be the ability to customize financial solutions to meet customer needs. Transactional data is in abundance at Neobanks. Neobanks might use their data analytics expertise to integrate with app analytics and transactional data to tailor their offerings to their customers. Neobanks can develop hyper-personalized data and improve their customer experience by using AI and ML.

     

    • Advanced Analytics/ Predictive Analytics:
      Implementing such digital capabilities can further improve the client experience. Advanced analytics evaluates the available data and delivers practical insights. Advanced analytics can analyze transactions, expenditures, and investment data from neobanks customers and offer a solution that could help the Neobank strengthen its points of strength or weakness. These data need to be utilized to predict the requirement of the future. Customers would benefit from the ability to forecast and gain from a new beginning. Additionally, this is not a one-time strategy. The Neobanks are gathering these data points to develop a useful understanding, specifically for the needs of future customers. This would include not only predicting but also personalizing the experience to the needs of the customer. And that would be the true future for neobanks and pure CX.

    Conclusion

    In conclusion, although banking has changed throughout the years—from cheques to demand draft to mobile banking to invisible banking to connected banking to digital banking—but the fundamental idea—that customers should be valued—remains the same. And Neobanking is the ideal solution for its tech-savvy customers, which attracts time and money savings. The future business model, market share, and commercial success of Neobanks are challenging to predict. Still, it can be anticipated that the banking industry will nevertheless face disruptive changes stemming from the widespread adoption of internet technology, and the rise of cryptocurrencies.

    It is no longer surprising that the industrial sector is looking for a way to reimagine themselves in this competitive world. Every industry has embraced technological advances and is working to develop products that meet the demands of high-tech consumers. The banking and finance sector is one of them. Banks have been trying to incorporate AI systems for years in an effort to enhance customer service and beat off growing competition from upstarts outside the traditional financial services sector.

     

    Artificial Intelligence has grown the evolution of the banking sector. According to a survey, AI will boost the banking and finance sector by at least USD 1.2 trillion by 2035. Presently, AI is helping to save costs for banks, and there is a prediction that by 2023, it will be worth $447 billion.

     

    AI is more than just a driver of revenue generation and cost reduction. It is a result of how important it is in determining the banking landscape. Due to the rise of intelligent tools and numerous internet-connected devices, consumer demand is rising. People believe that the development of AI will provide them more control over their lives.

     

    Banks and financial service providers are currently re-evaluating their offerings to better meet customer expectations. Predictive analysis plays a pivotal

    Why Do Banks Require Use of AI With Predictive Analytics?

     

    The AI bend has been triggered; thus, the emphasis is not only on the volume of data collection but also on its norm to derive useful insights. External variables include the many functions played by services, theft, security, corporate intelligence, uncertainty, consumer services, and more. It should now be seen as a network of interconnected functions where data is received in a hub-and-spoke configuration. Instead of using the present record-keeping systems, AI makes it easier to create these data centres. Banks must spend on creating consolidated data sets, which should contain meaningful, accessible, and contextualized data rather than just bytes of information.

     

    Therefore, in the coming years, the potential of AI and predictive analytics will grow and continue to assist banks in making wiser decisions. By utilizing AI and maximizing the potential of their combined data sets. The following are some examples of applications of predictive analytics technologies in the banking sector:

     

    Credit Scoring:

    With the technological advancements, financial lenders can now lower their risk by utilizing a variety of client data. Relevant data is analyzed and distilled into a single value known as a credit score that represents the lending risk using statistical and machine learning. A lender might be more confident in a customer’s creditworthiness the higher their credit score. Credit scoring, a type of AI technology based on predictive modelling, estimates the probability that a customer will miss a transaction, become overdue, or be insolvent. The time it takes to assess a company’s financial situation is reduced by automated credit decisioning systems made possible by data-driven AI technologies. By examining a larger number of data points for a shorter period of time and producing quicker credit scores, it enables closer monitoring of its actions and creditworthiness.

    Fraud detection:

    The majority of laborious, time-consuming processes have been replaced by quick, convenient real-time payments as cashless transactions have evolved. But with many conveniences comes a surge in phishing, application fraud, identity fraud, and card skimming, among other online criminal activities. Using enhanced pattern detection, combining several analytics techniques can serve as efficient anti-fraud solutions and stop criminal activities. IDENCHECK and SHERLOCK by CRIF are two such products created by the Indian credit information business CRIF (Center for Research in International Finance). The former is intended to improve your current KYC verification procedures by giving you the ability to digitally check against public databases maintained by the government and other organizations, whereas the latter introduces a potent anti-fraud solution that simplifies it than ever to identify and look into application and identity frauds.

    Collections:

    Given the number of customers who frequently miss payments, collections have become a crucial operation for banks. What is required, however, is the proper harnessing of energies i.e., by streamlining the collections process, predictive analytics enables banks to properly differentiate between the various portfolio risks. It supports defaulting

    Cross-selling:

    Where there are several products available, effective cross-selling of products can be achieved by examining the existing customer behaviour patterns. With the assistance of this study, banks will be able to target their sales and marketing efforts and determine which specific products should be sold to which customers. And all of this leads to cross-selling that is more effective, boosting revenue and improving customer relations. Cross-selling another product to an existing customer is very beneficial because it might be difficult for banks today to retain one profitable customer.

     

    The possibilities listed above represent just a small portion of what banks can accomplish with predictive analytics. Banks should recognize the significance of data science, implement it into their decision-making, and create strategies based on valuable insights from their customer data in order to acquire a competitive edge.

     

    Conclusion

    A new realm of the FinTech sector called predictive analytics in finance has the potential to significantly alter how data analytics is currently done. Real-time predictive analytics technology is already being successfully used by many businesses to improve their understanding of client demands, internal processes, markets, and other factors. 

     

    We all are aware that every bank’s journey toward digitization will be different because every institution has different problems that necessitate different solutions. Customers are becoming less tolerant of opaque procedures that take weeks to complete in the age of immediate approvals and one-click internet ordering. Using cutting-edge technology to understand your customers and anticipate their preferences, the system anticipates certain outcomes in order to better maximize conversions, engagements, and retention. The development of predictive analytics solutions using effective Big Data processing, Artificial Intelligence, and Machine Learning technologies is one of the many fintech development services provided by Sankey Solutions.

    When new tactics overpower old traditions, the explosion of influence occurs automatically. Likewise, addressing the Fintech industry, a medley of financial industry and technology has thrived so far; bridging the hill, bringing in profits, and achieving huge heights. Former traditions like financial services and banks have backed themselves up by taking off to a technology-savvy Fintech industry based on a business model, providing financial services which comprise payment investments and lending services. This approach is followed by the whole nation making it efficient, safe, and better. Talking about the trading and investment companies who have augmented their service portfolio by inculcating several technologies launching day by day as the Fintech market blushes. People in banking tracts, trading & investments have acknowledged that technologies like artificial intelligence, big data, cryptocurrency, and blockchain have altered the topography in investment management, risk mitigation, and optimizing portfolios.

     

    AMCs-Fintech white labeling
    For financial institutions, it looks hard to play in the saturated market of Fintech where technology is already enough to conquer the market profit and fulfill the customer’s needs. In this case, when organizations are looking to cultivate their product or service, they white label it with a desirable tech company to lessen the cost and intensify the brand awareness to the targeted audience. A diversified approach to innovation and the ability to test value can fill the service gaps of these AMC’s by including Fintech services in their business model.

    Fintech Partnering with AMC’s
    This kind of partnership alleviates the resource building of one company that cannot make a profit in this market. To pull this company up, the investment unions have concentrated on partnering or collaborating with Fintech innovation to solve the challenges of procurement like integration, culture misalignment, risk management, synergy, time, and fund management. This trend is anticipated to accelerate in the future. This partnership can bring abrupt speed for people to adopt alternative lending platforms with the benefit of evaluating Opportunities.

    Many Fintech companies around the world have shaped the nation with their powerful technological resources. These industries bring new-fangled solutions to the market which have acted as a barrier entry to other financial service providers. However, lack of technology can be a great reason for institutions to get acquired by technology-rich unicorns.

     

    Asset management companies to include Fintech solutions
    Still, there are innovators and leaders belonging to the AMC world inculcating innovative services with ingenious technology like text analytics, natural language processing, algorithm trading, cryptocurrency IoT, and other solutions.

    Let’s understand some of the technologies in short!

     

    Data analytics
    This form of technology includes the use of types of computer programs for analyzing and deriving the meaning to help implicate the indicators of future performance of the company by assessing consumer sentiments and other analytics.

     

    Natural Language Processing
    It is a space for research that happens at the intersection of computer science and artificial intelligence which helps to interpret the human language and results in providing useful insights about the trends, interest rates, policies aggregate, output, and inflammation expectations of the business.

     

    Algorithmic trading
    This type of innovative trading is a computerized buying and selling of financial methods in pre-specified rules and guidelines set for execution. Also, high-frequency trading is a form of algorithmic trading that helps to design vast quantities of granulated financial data to place trades automatically when the condition arrives.

     

    Cryptocurrency
    This technology ensures that safe and verified payments transpire during transactions off summing dollars to cut off the labor-intensive tax for accounting. Cryptocurrency has fostered a Fintech offshoot in AMC’s hence this technology can benefit Fintech.

    With these technologies in hand, the investment market has sophisticatedly enhanced financial services such as investment, financial advisory, and product comparison. View to which there are high chances of customers getting habituated with services that are digital, mobile-friendly, and interactive. The traditional wall is not yet over. There are founders in the financial sector who are lingering behind and have to rethink the depth and the scope technology brings with them in order to stay competitive and survive in the market. Thus, exchange demands the nexus of trends and developments to resolve the financial issues in terms of their several services and features. Foreseeing the growth, asset management companies can result in lowering the transactional costs of financial services, as well as relying entirely on digital access. Henceforth, businesses and consumers both incur gigantic benefits from offering these individuals effective financial services by turning or adopting Fintech practices.

     

    If this topic interests your time and you are an AMC looking forward to enhancing your service portfolio, contact us for more information.

    Evolution in Technology

    Evolution in Technology is fascinating! In simplified terms, “Evolution is Change”. It brings a change in social dynamics and human life as quickly as the swing of a magic wand from Harry Potter.

    We as homo sapiens have come a long way from evolving as a biological life form, but the evolution that is overlooked in many senses is the growth in technology. We have achieved throughout the decades with many noteworthy and remarkable people contributing to this change.

    I personally love this representation of evolution with the perspective of technology immersed within. How we came from the plantation and hunting for food to building civilizations, industrialization, automation, to socializing etc. We all can notice the human energy spent, getting reduced in proportional to evolution and time, all thanks to technology.

    I envision a world where people won’t have to do most of the repetitive & mechanical work and all that’d be left for people to do will be live and explore life while being creative in work. What a beautiful imagination!

    Quantum Computers

    After throwing my opinions on evolution, now let’s untangle the concept called as Quantum Computers. I’ll gracefully tell you why Quantum Computers could be the next step to evolution. To gain your attention, here’s a quote from a pretty popular person in the technology world

    What is Quantum Computing and How is it different from normal computers?

    To help answer this question, let’s understand a well-known paradox widely known as “Schrodinger’s Cat

    Schrodinger’s Cat is a thought experiment in which a cat is placed in a sealed box with a radioactive source and a poison that will be triggered if an atom of the radioactive substance decays. Quantum physics suggests that the cat is both alive and dead (a superposition of states), until someone opens the box and, in doing so, changes the quantum state. Quantum Superposition is a system that has two different states that can define it and it’s possible for it to exist in both.

    Quantum entanglement is the state where two systems are so strongly correlated that gaining information about one system will give immediate information about the other no matter how far apart these systems are. The outcome of the measurements on the individual qubits could be 0 or 1(Cat is dead or alive). However, the outcome of the measurement on one qubit will always be correlated to the measurement on the other qubit.

    Computers used today can only encode information in bits that take the value of 1 or 0—restricting their ability. So, basically every operation we make on a computer and the internet is processed in the language of 1’s and 0’s.

    Quantum Computing, on the other hand, uses quantum bits or qubits. It has something (a particle or an electron), for example – that adopts two possible states, and while it is in superposition, the quantum computer and specially built algorithms harness the power of both these states I.e., the unique ability of subatomic particles that allows them to exist in more than one state (a 1 and a 0 at the same time).

    Quantum Computers – Evolution, not a Myth!

    Superposition and Entanglement in quantum physics are the fundamentals that empower quantum computers to handle operations at speeds exponentially higher than conventional computers with much lesser energy consumption.

    Quantum Computing can solve specific problems with lesser energy which will open gates for new revolutions helping reduce the need for human efforts. Trending applications include running simulations and data analyses, such as chemical or drug trials.

    Google has been spending billions of dollars on its plan to build its quantum computer by 2029. Hence, in my opinion, Quantum Computers are here to stay!

    To Conclude:

    Imagine going back a century in time and telling a person that in the next 5-6 decades, he/she will be able to communicate with a person in a different part of the world. You’d be ignored and laughed at. Now similarly, imagine a person coming to you from the future and laughing at your job while bragging that it is done by computers in the future. How would you react? ?

    BLOG BY: ROHIT HEGDE