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Drive Digital Success: Radically Power Up Your Cloud Transformation with AI Magic

In today’s rapidly evolving digital landscape, the twin pillars of cloud computing and artificial intelligence (AI) are transforming the way businesses operate. The synergy between these two technologies is reshaping industries, driving innovation, and propelling organizations forward. As the cloud computing market continues to expand, projected to reach a staggering $947 billion by 2026, the AI market is poised to grow over five times to $309 billion. Rather than viewing them as separate entities, it is essential for enterprise leaders to recognize the profound impact that AI and cloud computing have on each other and the potential for greater innovation that can be achieved by harnessing their combined power. The Symbiotic Relationship: AI and Cloud Computing Automation forms the foundation of the symbiotic connection between AI and cloud computing. By integrating AI capabilities into the cloud environment, organizations gain access to advanced functionalities that enhance performance, drive efficiency, and unlock valuable insights. Cloud-based Software-as-a-Service (SaaS) companies are incorporating AI technologies into their offerings, empowering end-users with enhanced functionality and personalized experiences. From voice-activated digital assistants like Siri and Alexa to AI-powered pricing modules, the seamless integration of AI and cloud computing is revolutionizing daily tasks, simplifying processes, and optimizing operations. Streamlining Operations with AI-powered Cloud Management One of the primary areas where AI is transforming cloud computing is in the realm of cloud management. As AI technologies become increasingly sophisticated, private and public cloud platforms are leveraging these capabilities to monitor and manage their instances more effectively. With the ability to automate essential operations and self-heal in the event of a problem, AI-powered cloud management systems are revolutionizing IT infrastructure. AI-driven automation enables IT teams to offload routine tasks, liberating their time to focus on strategic initiatives that drive business value. By leveraging AI for cloud management, organizations can achieve greater operational efficiency, reduce manual interventions, and improve overall system performance. Driving Innovation with Dynamic Cloud Services Artificial Intelligence as a service is transforming how businesses utilize cloud-based tools and services. For example, imagine a cloud-based retail module equipped with AI capabilities that help brands optimize their product pricing in real-time. By analyzing factors such as demand, inventory levels, competition sales, and market trends, AI-powered pricing modules can automatically adjust product prices, ensuring they remain competitive and profitable. The integration of AI and cloud computing enables businesses to leverage dynamic cloud services that adapt and respond to changing market conditions. This level of agility and flexibility allows organizations to stay ahead of the curve, optimize operations, and deliver exceptional customer experiences. Enhancing Data Management with AI in the Cloud The growth of data in today’s digital landscape presents both opportunities and challenges for organizations. AI tools and techniques are being deployed in cloud computing environments to tackle the complexities of data management effectively. From data recognition and ingestion to classification and real-time analysis, AI-powered solutions are revolutionizing the way organizations handle massive volumes of data. In sectors such as finance, AI-driven cloud data management solutions help financial institutions analyze thousands of transactions daily, providing real-time data insights to clients and detecting fraudulent activities. By leveraging AI in data management, organizations can improve marketing strategies, enhance customer service, and optimize supply chain operations. The Benefits of Cloud Transformation with AI The amalgamation of AI and cloud computing offers a multitude of benefits, empowering organizations to thrive in the digital age. Let’s explore some of the key advantages that cloud transformation with AI brings to businesses: 1. Intelligent Automation for Enhanced Efficiency AI-powered cloud computing enables businesses to automate tedious and repetitive tasks, improving overall operational efficiency. By leveraging machine learning and advanced analytics, organizations can streamline processes, reduce manual interventions, and enhance productivity. This intelligent automation frees up valuable resources, allowing IT teams to focus on strategic initiatives that drive innovation and business growth. 2. Cost Optimization and Scalability Cloud transformation with AI presents significant cost optimization opportunities for businesses. By migrating to the cloud, organizations can reduce upfront costs associated with hardware procurement, maintenance, and infrastructure management. AI-powered cloud services offer flexible subscription models, allowing businesses to access advanced technologies without incurring substantial upfront expenses. Furthermore, AI systems can extract insights from vast amounts of data, enabling organizations to make informed decisions and optimize resource allocation. The scalability of cloud computing combined with AI capabilities allows businesses to align their resources with fluctuating demands, ensuring cost-efficiency and operational agility. 3. Seamless Data Management and Analytics The integration of AI and cloud computing revolutionizes data management and analytics. AI-powered tools enable organizations to process, analyze, and derive valuable insights from vast datasets. Implementing advanced AI algorithms and intricate machine learning methodologies enables enterprises to decipher concealed patterns, identify irregularities, and execute precision-focused, data-informed decisions with enhanced velocity and accuracy. Cloud-based AI solutions facilitate seamless data integration, ensuring that organizations can harness the full potential of their data assets. Improved data management and analytics empower businesses to gain a competitive edge, optimize processes, and drive innovation. 4. Enhanced Security and Risk Mitigation Cloud transformation with AI brings robust security capabilities to organizations. AI-powered cloud security solutions offer advanced threat detection and prevention mechanisms, protecting sensitive data and critical infrastructure from cyber threats. With the power of machine learning algorithms, these solutions have the ability to recognize patterns, identify anomalies, and take proactive measures in response to security incidents. Additionally, AI-powered risk management systems help organizations identify and mitigate potential risks across various domains. From fraud detection to compliance monitoring, AI-driven cloud security solutions provide businesses with comprehensive protection against emerging threats. Conclusion: Embracing Cloud Transformation with AI The convergence of AI and cloud computing is revolutionizing businesses across industries. By harnessing the power of AI in the cloud, organizations can achieve digital transformation, drive innovation, and gain a competitive edge. The seamless integration of AI capabilities into cloud computing environments empowers businesses to automate processes, optimize operations, and unlock valuable insights from vast amounts of data. Cloud transformation with AI offers numerous benefits, including enhanced efficiency, cost optimization, seamless data management, and robust security. With the ever evolving digital landscape, organizations must embrace the potential of AI and cloud computing to rise in the era of digital disruption. By leveraging the combined power of AI and cloud computing, businesses can unlock new opportunities, deliver exceptional customer experiences, and pave the way for a successful future.

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Low-Code and AI Integration: A Powerful Combination for the Future

It is hard to stay ahead of the competition in a digital market that is changing so quickly. Automation and innovation are two things that businesses can adopt to make themselves stay ahead in the market. Do you still not believe that putting these two fantastic elements together could make the future even better? If not, then let me give you some numbers to back up what I’m saying:The market for low-code development platforms is anticipated to grow at a CAGR of 27.8% to reach $148.5 billion by the year 2030. Low-code platforms are a game-changer for firms interested in streamlining their goods’ development processes. They will discover that these platforms fit their demands. These businesses will find that these platforms meet their needs.Similarly, artificial intelligence (AI) has substantially transformed the process by which humans derive meaning from data and apply that meaning to the solution of problems in the actual world. The whole market value for artificial intelligence is expected to rise at a CAGR of 37.3% between 2023 and 2030. It is anticipated that by 2030, it will reach $1,811.8 billion.In this blog, we’ll delve deep into the benefits of combining Low-Code with AI and why it’s a smart move for current businesses to do so.But before diving in, let’s first understand what each of these technologies brings to the table.Understanding Low-Code DevelopmentLow-code is a way of making software that lets you make tools and processes with little or no coding. In a low-code development tool, you don’t have to use complicated computer languages. Instead, you can use visual interfaces with simple reasoning and drag-and-drop features. These platforms are becoming more popular because they are a quick and easy option to traditional software creation.Low-code platforms give developers an open, visual setting where they can build simple to complicated apps using a graphical user interface. With advanced tools like drag-and-drop modelers, pre-built templates, simple user interfaces, etc., low-code platforms make it easier for developers to create and launch apps much more quickly. This helps organizations grow, make more money, and get better returns than average.Instead of writing computer code by hand, a low-code development system provides a programming setting for making software applications with graphical user interfaces and configuration.IT customers can add building blocks to processes and apps with the help of low-code creation tools. These basic parts hide the code behind actions and orders, so IT doesn’t have to write the code for interfaces and business applications by hand.Source:CompactUnderstanding The Power of Artificial IntelligenceArtificial intelligence is when a digital computer or a robot managed by a computer can do things that intelligent people usually do.A layman would probably relate AI with robots. But an expert in the field of AI would define artificial intelligence as a collection of programs that can function autonomously, generating desired outcomes without human intervention. Artificial intelligence refers to computer programs that have been developed in such a way so as to think and behave like humans. These computers can imitate human performance thanks to their capacity for learning and adaptation. The development of technology like AI has the potential to significantly improve the standard of living in the future.Source:Harvard Business ReviewThe objective of AI is to help and enhance human capacities to facilitate them in making the complex choices and decisions easier. Just like all the technologies that have been developed over so many years help humans in easing the labor and improving our decision making, similarly AI’s goal is to do the same, just in a more efficient manner.The Synergy of Low-Code and AINow, let’s explore how integrating Low-Code with AI can be a game-changer:Accelerated DevelopmentIt is well-known that low-code platforms allow for rapid application development. When coupled with AI capabilities, they can swiftly develop and implement AI-driven solutions, drastically cutting down on time-to-market.Access to Advanced CapabilitiesAI provides low-code applications with an additional layer of intelligence. Even if you are not an expert in artificial intelligence, you may easily integrate capabilities such as predictive analytics, chatbots, and image recognition into your system.Enhanced User ExperiencesArtificial intelligence can personalize user experiences by analyzing user behavior and preferences. When this functionality is integrated into low-code apps, it guarantees that consumers will receive personalized information and services.Data-Driven Decision-MakingAI’s data analytics features allow Low-Code apps to offer timely analysis and suggestions. Companies may now act quickly based on accurate data.Efficiency and AutomationAutomating complicated operations using Low-Code allows less time spent on repetitive tasks and fewer mistakes because of AI’s capacity to learn and adapt.Use Cases of Low-Code and AI Integration Customer Service ChatbotsChatbots powered by artificial intelligence are a great way to improve customer service on low-code platforms by responding to inquiries and fixing problems in real-time.Predictive Analytics ApplicationsBuild apps that use AI to predict market trends, client behavior, or the need for equipment maintenance. This will help you make more proactive decisions.Image and Voice Recognition AppsDevelop applications that recognize photos, sounds, or text, making them perfect for use in healthcare, retail, and the entertainment industry.Data Analysis DashboardsUse artificial intelligence to power data analysis dashboards and provide organizations with new insights.The Future of Low-Code and AI IntegrationThe combination of Low-Code and AI is positioned to play a crucial role as organizations seek efficient and intelligent solutions. More complex applications, which naturally integrate quick innovation with superior AI capabilities, are in the future’s sight.The rising trend of combining low-code with AI has revolutionized business application creation and deployment. The combination of low code and AI has a promising future. We anticipate increasingly sophisticated applications that combine quick development with cutting-edge AI capabilities as the technology advances. From healthcare to manufacturing to retail, several sectors stand to benefit from these applications.The capabilities and accessibility of low-code platforms will improve, allowing more people to build apps.Gartner predicted that in 2024 more than half of all new apps would be developed using low-code or no-code development platforms.Another research conducted by Forrester entails that low-code development will save the time required to bring new apps to market by as much as 80%.It’s estimated that generative AI might add trillions of dollars to global GDP through increasing productivity. Recent research shows that, across the 63 application cases studied, generative AI might add the equivalent of $2.6 to $4.4 trillion annually, much more than the United Kingdom’s GDP in 2021 (at $3,1 trillion). The total efficiency of AI would increase by 15-40 percent.More and more application development activities will be automated with the help of AI, allowing programmers to devote their time to more strategic endeavors.Applications that employ low code and AI will be smarter and better able to learn and adapt to new circumstances.Low-code platforms and artificial intelligence will be utilized to create software that is currently impossible to develop.With low-code and AI working together, enterprises will be able to:Reduce application development costs and timelines.Raise the bar for application quality.Boost the efficiency of their IT processes.Get a leg up on the competition.Businesses can make faster, more informed decisions based on data thanks to Low-Code platforms enhanced by AI in the next few years. Decisions will no longer be made manually but instead guided by AI algorithms, thanks to these apps’ ability to present insights and propose actions.Prototype and proof-of-concept creation will be sped up with the help of Low-Code and AI. As a result, businesses will have a shorter time frame from idea to execution when testing AI-driven functionality. This skill will encourage creative problem-solving in all sectors.Artificial intelligence integrated Low-Code apps will reimagine consumer experiences, from chatbots with natural language comprehension to predictive analytics that foresees customer demands. The companies of the future will be the ones that can provide customers with highly customized experiences that AI powers.ConclusionThe combination of low-code platforms and artificial intelligence has the potential to alter the application delivery process for enterprises radically. This potent mix is the key to a more productive and creative future, whether your goal is to improve user experiences, automate processes, or make choices based on data.At Aziro (formerly MSys Technologies), we specialize in both Low-Code development and AI integration. Contact us today to explore how this potent alliance can drive your business forward. Contact us at marketing@aziro.com right away to begin discussing the opportunities that await us.

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Revolutionizing Industries: The Power of Image Recognition in 2023 and beyond

IntroductionThe field of image recognition has been at the forefront of the exponential growth in technology that we’ve seen in recent years. At a compound annual growth rate (CAGR) of 17.4% from 2020 to 2025, the worldwide image recognition market is forecast to be worth $38.2 billion in 2025. This state-of-the-art AI technology has quickly spread across a wide range of industries, reshaping practices, boosting output, and providing better service to end users. In this blog post, we’ll look at how image recognition already creates waves across various sectors and transforms the business world.What is Image Recognition?Image recognition is a subfield of computer vision that includes instructing computers to read and comprehend images. It’s a method for teaching computers to “see” and understand visual content like people do. Machine learning algorithms that examine visual data for patterns, shapes, and characteristics do this.Identifying objects in images is the focus of the computer vision subfield known as image recognition. It’s a fast-expanding industry with several potential uses, including autonomous vehicles and medical diagnosis.An image is initially dissected into its component pixels by image recognition algorithms. After collecting this data, the system will examine the patterns inside the pixels to see whether they match any recognized items. The term “feature extraction” is commonly used to describe this process.The system can perform object classification after it has recognized its characteristics. This is accomplished by comparing the object’s attributes to those of other items in a database.A wide range of items can be taught to image recognition systems. Training the algorithm with more data will improve its accuracy.Source:Great LearningHow does Image Recognition work?Typically, one of two methods is used by image recognition systems:1. Traditional methods:These ways of identifying things use details that were made by hand. People make hand-crafted features to fit the task at hand.2. Machine learning:These approaches use machine learning algorithms to learn the features essential to the job naturally. Machine learning methods are becoming more and more popular because they can learn to recognize objects more correctly than traditional approaches.Now, let’s look at how image recognition is revolutionizing the various industries:1. Health care: a precise diagnosis can save livesNew diagnostic tools and therapeutics are being created with image recognition, which is predicted to grow the medical imaging market to $320.8 billion by 2025. Image recognition is starting to change the way treatment is done. It helps doctors diagnose diseases with an accuracy that can’t be beaten. AI-driven picture recognition is saving lives by giving early and accurate assessments. For example, it can find tumors in X-rays and abnormalities in pathology slides.2. Retail: Changing the way people shopIn the retail world, picture recognition makes in-store and online shopping much more personal. Smart shelves can tell when a product is running low and restock it; visual search makes it easy for customers to find what they want. With virtual try-on, you don’t have to guess when you buy clothes online, which makes customers happier.3. Self-driving cars will make the road saferImage recognition is a crucial technology being utilized in the development of self-driving automobiles, which is predicted to grow to a market size of $86.6 billion by 2025. Image recognition is the key to self-driving cars for the auto business. The eyes of these cars are cameras and sensors that use image recognition to see where they are going, find barriers, and make sure the trip is safe. Picture recognition is the way to go as we move towards self-driving cars.4. Agriculture: Transforming the way crops are managedImage recognition powered by AI is making crop control better in agriculture. Drones with cameras take pictures of farms, which can be used to find diseases and pests in real-time. This makes it possible to make exact changes, cutting down on harmful chemicals and increasing food yields.5. Security: Making safety betterImage recognition technology helps security systems all over the world. The worldwide security market was worth USD 119.75 billion in 2022, and it is anticipated to expand at a CAGR of 8.0% from 2023 to 2030. The proliferation of security systems may be attributed to the growth in criminal activity, terrorism, fraudulent schemes worldwide, and stricter regulatory regulations. Face recognition, finding objects, and finding unusual things make places safer. This technology keeps us safe from airports to houses by letting us know who is around and who might be a threat.6. E-commerce: A Revolution in the Way We ShopVisual shopping is changing the way people shop online. Consumers can find goods by taking pictures of them thanks to image recognition, which drives visual search. Product tagging makes online shopping more accessible, and virtual try-ons for clothes and items improve the user’s experience.7. Content Moderation: Making the Internet a Safe PlaceImage recognition is increasingly used to moderate material on social media apps and websites. This technology instantly finds and eliminates dangerous or inappropriate material, making the Internet safer for people of all ages.8. Protecting the environment: helping with conservation effortsImage recognition helps keep the world healthy. It helps keep track of the number of animals, find criminal trapping, and measure deforestation. AI-powered systems that can discover reusable materials also make it easier to get responsibly rid of trash.9. Accessibility: Making everyone feel welcomeImage recognition is one of the most essential parts of making the digital world easier to use. It turns the words in pictures into speech, so people who can’t see can still get information. Object recognition apps help with everyday jobs by figuring out what things are in real-time.10. Problems and ethical things to think aboutAs picture recognition is increasingly used, problems with bias, privacy, and data protection must be solved. For AI to reach its full potential, it is crucial to ensure its methods are fair and safe.ConclusionImage recognition is more than just a technology tool in 2023 and beyond. It’s a driving force of progress, transforming whole sectors while raising productivity and bettering people’s lives. We must prioritize ethical issues and data protection as we embrace the ever-expanding capabilities of image recognition to guarantee that these developments will be used for society’s greater good. We may look forward to a future where image recognition continues to give us agency, ushering in more intelligent, secure, and individually tailored interactions in various fields.Aziro (formerly MSys Technologies): Facilitating Your Organization’s Digital Evolution Here at Aziro (formerly MSys Technologies), we firmly believe in the game-changing potential of tools like image recognition. Our digital services are made to help companies of all sizes and in all industries take advantage of cutting-edge technologies like image recognition. Our team of professionals is here to assist you with all aspects of digital transformation, from designing user-friendly interfaces to expanding your data resources.We’re here to help your company become more responsive to market changes, data-driven, and capable of producing intelligent, scalable solutions. Our extensive digital offerings include everything you need, including mobility, analytics, the Internet of Things, artificial intelligence/machine learning, and big data.Are you prepared to speed up your transition into the technological future? Contact us at marketing@aziro.com right away so we can begin discussing the opportunities that await us.

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Ethics and Responsibility in Artificial Intelligence: A Guide for Business Leaders

This guide outlines key principles for transparent, fair, accountable, and socially responsible AI implementation. Learn how to ensure ethical use of AI and improve your business operations while acting in an ethical and responsible manner.A few facts to know before we move forward. Read onArtificial Intelligence (AI) has become an essential part of many businesses today. As per MarketsnadMarkets, the artificial intelligence market is projected to be valued at $86.9 billion by 2022 in terms of revenue, with a forecasted compound annual growth rate (CAGR) of 36.2% from 2022 to 2027.As AI continues to advance and become more widespread, it is important for business leaders to understand the ethical considerations and responsibilities that come with its use.In just two months following its launch, ChatGPT achieved a remarkable milestone of reaching an estimated 100 million monthly active users in January 2023, making it the fastest-growing consumer application ever.In this article, we will explore the ethics and responsibilities of AI and provide a guide for business leaders to ensure their AI systems are used ethically and responsibly.TransparencyOne of the primary ethical considerations of AI is transparency. AI systems can be complex, and it can be challenging to understand how they make decisions. However, it is essential that businesses ensure that their AI systems are transparent in their decision-making processes.Transparency means that the rationale behind the system’s decisions should be clear and understandable to humans. This is particularly important if the decisions made by the AI system could impact individuals or communities. For example, if an AI system is used to determine creditworthiness, the criteria used to make that determination must be clear and explainable.FairnessAnother critical ethical consideration of AI is fairness. AI systems should not discriminate against any particular group of people. Business leaders must ensure that their AI systems are trained on unbiased data and that they are regularly audited to detect and address any bias in their algorithms.Bias can be introduced into AI systems in many ways. For example, if an AI system is trained on historical data that reflects societal biases, the system may perpetuate those biases. It is crucial that businesses take steps to mitigate bias in their AI systems to ensure that they are fair to all individuals.PrivacyAI systems often require large amounts of data to function effectively. However, this data must be collected and used responsibly. Business leaders must protect the privacy of their users’ data. They should only collect data that is necessary for the functioning of their AI systems, and they must have strict security measures in place to prevent unauthorized access to this data.The collection and use of personal data is governed by laws and regulations in many jurisdictions. Business leaders must ensure that they are complying with all applicable laws and regulations, including the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States.AccountabilityAnother critical ethical consideration of AI is accountability. Business leaders must take responsibility for the actions of their AI systems. This means that they should have mechanisms in place to detect and correct any errors or harm caused by the AI systems. They should also be prepared to compensate individuals or communities that have been adversely affected by their AI systems.AI systems can make mistakes, just like humans. However, when an AI system makes a mistake, the consequences can be far-reaching and potentially devastating. It is crucial that businesses have processes in place to ensure that they are accountable for the actions of their AI systems.Human OversightAI systems should not be used to replace human decision-making entirely. Business leaders must ensure that there is always human oversight of AI systems and that humans have the ability to intervene if necessary.Humans can provide critical oversight to AI systems. They can identify errors or biases that may be present in the system and intervene to correct them. Additionally, humans can bring a level of empathy and understanding to decision-making that AI systems may not be capable of.Social ResponsibilityAI systems can have far-reaching social implications. Business leaders must consider the impact that their systems may have on society and take steps to mitigate any negative effects.For example, an AI system may be used to automate a particular task, which could result in job losses for individuals who previously performed that task. It is crucial that businesses consider the broader social implications of their AI systems and take steps to mitigate any negative effects.Continual ImprovementFinally, AI systems should be continually monitored and improved upon. Business leaders must have processes in place to regularly evaluate their AI systems and ensure that they are performing as intended. This includes monitoring the data used to train the system and ensuring that it remains relevant and unbiased.As technology continues to evolve, AI systems will also need to be updated and improved. Business leaders must have a plan in place to ensure that their AI systems are continually updated and improved to meet the changing needs of their business and society as a whole.Final ThoughtsThe ethical considerations and responsibilities of AI are crucial for business leaders to understand. AI systems have the potential to impact individuals and communities in significant ways, and it is essential that they are used ethically and responsibly.Business leaders must ensure that their AI systems are transparent, fair, protect privacy, accountable, have human oversight, consider social responsibility, and are continually improving. By following these ethical principles, businesses can use AI systems to improve their operations while also ensuring that they are acting in a socially responsible and ethical manner.Let Your Business Take a Leap Forward with Aziro (formerly MSys Technologies)Looking to take your business to the next level? Look no further than Aziro (formerly MSys Technologies). Our digital services can help you deliver new and exciting experiences to your clients, thanks to our bespoke touchpoints and modernized end-user experiences.Our expert architects can help you create innovative, user-friendly software that will keep your customers coming back for more. We make your business agile using microservices and machine learning-powered processes, creating multichannel experiences that work across platforms.We’ll also help you up your data skills, with robust data governance capabilities, information silo unification, and flexible data architecture. Our digital services cover everything from mobility to analytics to IoT, AI, and big data, creating scalable, intelligent products and custom solutions.Ready to accelerate your business? Contact Aziro (formerly MSys Technologies) today at marketing@aziro.com and let’s get started.

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5 Top AI Challenges in Cybersecurity You shouldn’t Overlook

Advancement in technologies has created umpteen opportunities for cybercriminals to steal data. The rise in the use of cloud technology has accelerated the process of sharing of data online – information is now available irrespective of place and time. The odds are far more favorable than before for cybercriminals to get into your system. Organizations are firefighting cyber threats at two fronts – from amateur script artists who consider hacking more as awarding than rewarding, and attacks backed by organized crime syndicate with intentions to de-stabilize operations and damage the economy. Per a report by Security Intelligence, the average cost of a data breach is $3.92 million as of 2019. Cybersecurity Ventures predicts that the damage to the world due to cybercrime will reach $6 trillion annually by 2021. This represents the greatest transfer of economic wealth in history, risks the incentives for innovation and investment, and will be more profitable than the global trade of all major illegal drugs combined. This amount will only climb up until we do away with the firefighting approach and think more proactively, It takes a thief to catch a thief To beat some in their own game, you must think like them. If they are fast, you must be fast; if they are cutting-edge, you must be cutting edge. To counter the threats posed by cybercriminals, organizations ought to be faster. It requires to do away with traditional security measures and embrace new age, automation-driven practices that could put us ahead of any hacker. The regular practice includes securing only mission-critical parts within an infrastructure. This leaves room open for hackers to target non-critical components. Therefore, organizations must implement comprehensive and robust cybersecurity procedures that cover every component within an infrastructure. Further, organizations should align themselves with the practice of leveraging automated scripts to facilitate continuous monitoring and reporting in real-time. Ushering an era of proactive cybersecurity via Machine Learning Artificial intelligence (AI) and machine learning (ML) gives an edge to modern software that are primarily created to protect from unethical cyber practices. With AI and ML, the cybersecurity software products get an extra sense to underline concurrent behavioral patterns of the workflows, assess its threat level and, based on it, alert the concerned team. The key reason why AI/Ml can perform such activity is its ability to gauge data, compare it with past actions, and derive an inference. This inference provides the security team an insight into future events that could lead to a possible cyber-attack. However, AI application is still in the nascent stages. Per IDC, one in four AI project usually ends up failing. This means there are challenges we must counter in order to make AI a success. These challenges become significant when the matter is about the organization’s data security. Let us now analyze 5 top challenges that prevent the successful implementation of AI/Ml for cybersecurity. 1. Non-aligned internal processes Most companies have optimized their infrastructure, especially its security components, by investing in tools and platforms. Yet, we see that they face security hurdles and fail to safeguard themselves against an external attack. This is a result of a lack of internal process improvements and cultural change that prevents capitalizing the investments in security operation centers. Further, the lack of automation and fragmented processes creates a less robust playground to defense against cybercriminals. 2. Decoupling of storage systems Most organizations do not leverage data broker tools like RabbitQ and Kafka to initiate analytics of the data outside the system. They do not decouple storage systems and compute layers, which doesn’t allow AI scripts to execute effectively. Further, a lack of decoupling of storage systems increases the possibilities of vendor lock-ins in case of a change in the product or platform. 3. The issue of malware signature Signatures are like fingerprints of malicious code that assist security teams in finding the malware and raising an alert. The signatures do not match the growing number of malware every year. The concern is that any change in the script of the virus makes the signature invalid. In short, signatures will only help debug malware if the code is pre-established by security teams. 4. The increasing complexity of data encryption The rise in the use of sophisticated and advanced data encryption strategies are making it difficult to isolate an underlying threat. The most common way to monitor external traffic is via deep packet inspection (DPI) that helps filter external packets. However, these packets consist of a predefined code characteristic that can be weaponized to infiltrate in the system by the hackers. Further, the complex nature of DPI puts pressure on the firewall, slowing down the infrastructure speed. 5. Choosing the right AI use cases More than 50 percent of the AI implementation project fails in the first go. This is because organizations try to adopt AI on a company-wide level. They often neglect the importance of baby steps – narrowing down on AI-based use cases. Thus, they miss out on initial learning curves and fail to absorb critical hiccups that often jeopardize the AI projects. AI/ML isn’t a magic bullet rather AI/Ml isn’t a cure-all to the activities of cybercriminals. Rather, a fierce defense that is rooted in intelligence and intuition. AI/ML will help create intelligent systems that work as a potent defensive force against activities. They could detect and alter, but they can’t reason why and how these activities were triggered. It is the security teams that need to carry out root-cause analysis of the incident/s and then remediate it. Take Away Mature processes, cultural alignment, and skillful teams and choosing the right AI use cases in cybersecurity are the key to the success. For this, security teams must carry out an internal audit and tick mark areas in infrastructure that are the most vulnerable. Ideally, they can start with data filtering to segregate unauthenticated sources. This isn’t the thumb rules, though. The bottom line is taking mindful steps towards adopting AI for cybersecurity.

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4 AI and Analytics trends to watch for in 2020-2021

Never did we imagine the fictional robotic characters in novellas to become a reality. However, we wished, didn’t we? The theory of ‘Bots equal to Brains’ is now becoming a possibility. The mesmerizing and reverence Artificial Intelligence (AI) that we as children saw in the famous TV show- The Richie Rich has now become a plausible reality. Maybe, we are not fully prepared to leverage AI/Robotics as part of our daily lives; however, it has already created a buzz, profoundly among the technology companies. AI has found a strong foothold in the realms of data analytics and data insights. Companies have started to leverage advanced algorithms garnering actionable insights form a vast set of data for smart customer interactions, better engagement rates, and newer revenue streams. Today, Intelligence-driven Machine Learning intrigues most companies in different industries globally; however, not all exploit its true potentials. Combining AI with Analytics can help us drive intelligent automation delivering enriched customer experiences. Defining AI in Data Analytics This can be broad. However, to summarize, it means using AI in gathering, sorting, analyzing a large chunk of unstructured data, and generating valuable and actionable insights driving quality leads. Big players triggering the storm around AI AI may sound scary or fascinating in the popular imagination; however, some of the global companies have understood its path-breaking impact and invested in it to deliver smart outputs. Many big guns like IBM, Google, and Facebook are at the forefront, driving the AI bandwagon for better human and machine co-ordination. Facebook, for instance, implements advanced algorithms triggering automatic photo tagging options and relevant story suggestions (based on user search, likes, comments, etc.). However, with big players triggering the storm around AI, marketers are slowly realizing the importance of humongous data available online for brand building and acquiring new customers. Hence, we can expect a profound shift towards AI application in Data Analytics in the future. What’s in store for Independent Software Vendors (ISVs) and Enterprise teams With the use of machine learning algorithms, Independent Software Vendors and Enterprise teams can personalize the product offerings using sentimental analysis, voice recognition, or engagement patterns. The application of AI can automate the tasks while giving a fair idea of their expectations and needs. This could help product teams in bringing out innovative ideas. Product specialists can also differentiate between bots and people, prioritize responses based on customers, and identify competitor strategies concerning customer engagements. One of the key elements that AI will gain weight among product marketers will be its advantage in real-time response. The changing business dynamics and customer preferences make it crucial to draft responses in real-time and consolidate customer trust. Leveraging AI will ensure that you, as a brand, are ready to meet customer needs without wasting any time. Let us understand a classic example of how real-time intelligent social media analytics can create new opportunities. Lets read about 4 AI and Analytics trends to watch for in 2020-2021 1. Conversational UI Conversational UI is a step ahead from pre-fed and templated chatbots. Here, you actually make a UI that talks to users with human language. It allows users to tell a computer what it needs. Within conversational UI, there is written communication where you would type in a chatbox and voice assistant that facilitate oral communication. We could see more focus on voice assistants in the future. For example, we are already experiencing a significant improvement in the “social” skills of Crotona, Siri, and OK Google. 2. 3D Intelligent Graphs With the help of data visualization, insights are presented interactively to the users. It helps create logical graphs consisting of key data points. It provides an easy to use dashboard where data can be viewed to reach to the conclusion. It helps quickly grasp the overall pattern, understand the trend, and strike out elements that require attention. Such interactive, 3D graphs are increasingly used by online learning institutes to make learning interactive and fun. You will also see 3D graphs used by data scientists to formulate advanced algorithms. 3. Text Mining It is a form of Natural Language Processing that used AI to study phrases or text and detect underlying value. It helps organizations to segregate information from emails, social media posts, product feedbacks, and others. Businesses can leverage text mining to extract keywords, important topic names, or highlight the sentiment – positive, neutral, or negative. 4. Video and Audio Analytics This will become a new normal in the coming few years. Video Analytics is computer-supported facial recognition, gesture recognition used to get relevant and sensitive information from video and audios to reduce human efforts and enhance security. You can use it in parking assistance, traffic management, access authorization, among others. Can AI get CREEPY? There is a growing concern over breach of privacy by the unethical use of AI. Are the concerns far-fetched? Guess not! It is a known fact that some companies use advanced algorithms to track your details such as phone numbers, anniversaries, addresses, etc. However, some do not limit to the aforementioned data, foraying into our web-history, traveling details, shopping patterns, etc. Imagine your recent picture on Twitter or Facebook, which has a privacy setting activated used by a company to create your bio. This is undoubtedly creepy! Data teams should chalk down key parameters to acquire data and share information with the customers. Even if you have access to individual customer information like their current whereabouts, a favorite restaurant, or favorite team, one should refrain from using it while interacting with customers. It is your wisdom to diligently using customer data without intruding on their privacy. Inference Clearly, the importance of analytics and the use of AI for adding value to the process of data analysis is going up through 2020. With data operating in silos, most organizations are finding it difficult to manage, govern, and extract value out of their unstructured data. This will make them lose on a competitive edge. Therefore, we would experience a rise of data as a service that will instigate the onboarding of specialized data-oriented skills, finely grained business processes, and data-critical functions.

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AI artificial intelligence Big Data Analytics

How to build an AI app using Tensorflow and Android

Abstract:This article describes a case study on building a mobile app that recognizes objects using machine learning. We have used Tensorflow Lite. Tensorflow Lite machine learning (ML) is an open source library provided by Google. This article mentions a brief on Tensorflow Lite.Object Identification in Mobile app (Creative visualization)Tensorflow Lite:Using Tensorflow, Implement the Machine Learning (ML) or Artificial Intelligence(AI)-powered applications running on mobile phones. ML adds great power to our mobile application. TensorFlow Lite is a lightweight ML library for mobile and embedded devices. TensorFlow works well on large devices and TensorFlow Lite works really well on small devices, as that it’s easier, faster and smaller to work on mobile devices.Machine Learning:Artificial Intelligence is the science for making smart things like building an autonomous driving car or having a computer drawing conclusions based on historical data. It is important to understand that the vision of AI is in ML. ML is a technology where computer can train itself.Neural Network:Neural network is one of the algorithms in Machine learning. One of the use cases of neural networks is, if we have a bunch of images, we can train the neural network to classify which one is the image of a cat or the image of a dog. There are many possible use cases for the combination between ML and mobile applications, starting from image recognition.Machine Learning Model Inside our Mobile Applications:Instead of sending all raw images to the server, we can extract the meaning from the raw data, then send it to the server, so we can get much faster responses from cloud services.This ML model runs inside our mobile application so that mobile application can recognize what kind of object is in each image. So that we can just send the label, such as a cat, dog or human face, to the server. That can reduce the traffic to server. We are going to use Tensorflow Lite in mobile app.TensorFlow Lite Architecture:DEMO: Build an application that is powered by machine learning — Tensorflow Lite in Androidhttps://www.youtube.com/watch?v=olQNKvMbpRgGithub link:Pull below source code, import into Android Studio.https://github.com/Chokkar-G/machinelearningapp.git(Screencast)Tensorflow Lite object detectionThis post contains an example application using TensorFlow Lite for Android App. The app is a simple camera app that classifies images continuously using a pretrained quantized MobileNets model.Workflow :Step 1: Add TensorFlow Lite Android AAR:Android apps need to be written in Java, and core TensorFlow is in C++, a JNI library is provided to interface between the twoThe following lines in the app’s build.gradle file, includes the newest version of the AARbuild.gradle:repositories { maven {url ‘https://google.bintray.com/tensorflow'} } dependencies { // …compile ‘org.tensorflow:tensorflow-lite:+’ }Android Asset Packaging Tool should not compress .lite or .tflite in asset folder, so add following block.android { aaptOptions {noCompress “tflite”noCompress “lite”} }Step 2: Add pretrained model files to the projecta. Download the pretrained quantized Mobilenet TensorFlow Lite model from herehttps://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zipb. unzip and copy mobilenet_quant_v1_224.tflite and label.txt to the assets directory: src/main/assets(Screencast) Placing model file in assets folderStep 3: Load TensorFlow Lite Model:The Interpreter.java class drives model inference with TensorFlow Lite.tflite = new Interpreter(loadModelFile(activity));Step 4: Run the app in device.Conclusion:Detection of objects like a human eye has not been achieved with high accuracy using cameras, i.e., cameras cannot replace a human eye. Detection refers to identification of an object or a person by training a model by itself. However, we do have great potential in MI. This was just a simple demonstration for MI. We could create a robot that changes its behavior and its way of talking according to who’s in front of it (a child/ an adult). We can use deep learning algorithm to identify skin cancer, or detect defective pieces and automatically stop a production line as soon as possible.References:https://www.tensorflow.org/mobile/tflite/https://www.tensorflow.org/mobile/

Aziro Marketing

artificial intelligence blog

Artificial Intelligence Taking over Wall Street trading

One of the biggest reason trading decisions are affected is because of human emotions. Machines and algorithms can make complicated decisions and execute trades at a rate which no human can match and is not influenced by emotions. The parameters these algorithms take into consideration are price variations, macroeconomic data volume changes similar to accounting information of different corporate companies and news articles from various times topredict the nature of a particular stock.Stock prediction can be done using the company's historical data. This historical data can be used to perform either Linear regression or Support Vector Regression depending on the complexity of the system, to discover trends in the stock market. The algorithm can access various real time news papers and journals to retrieve the latest news and information regarding a specific company. This data is then processed and analysed along with the historical data and data derived from the quarterly results and press releases of that company. This helps in predicting a stock price of a specific company.If we need to analyze the whole market, consisting of more than 6000 companies listed in the New York Stock Exchange, we can do that too in the similar manner by navigating through the regulatory filings, social media posts, real time news feed and other finance related metrics also involving elements such as correlations and valuations in order to predict investments which are considered undervalued.AI is already in use by institutional traders and are incorporated in tools used for stock trading. Some of which are completely automated and are used by Hedge Funds. Most of these systems can detect minute changes caused by a number of factors and historical data. As a result thousands of trades are performed on single day.An interesting example:It was noticed that, everytime Anne Hathaway was mentioned in the news, the share price of Berkshire Hathaway increased. This was probably because, there was some algorithm from a trading firm running automatic trades whenever it came across “Hathaway” in the news.This particular example is a false positive and the fact that this system can run automatic trades based on real time news feed is pretty interesting. This technique requires data ingestion, sentiment analysis and entity detection.If the system or algorithm can detect and react to positive news feed faster than anybody else in the market, then one can make the profit that is the leap(or decrease) in price.Citation: http://www.eurekahedge.com/Research/News/1614/Artificial-Intelligence- AI-Hedge-Fund-Index- Strategy-Profile

Aziro Marketing

artificial intelligence blog

Using Predictive Artificial Intelligence for the future of Healthcare

Artificial Intelligence (A.I) is used in all the sectors for improvement and better outcome. For example NASA used Google AI to find new planets in the galaxy. This has become a news for the world. Would like to give you a brief introduction on AI on healthcare. Using AI in healthcare will reduce the complications which come into existence when a person goes through an surgery and also can give him best possible information depending or diet to not have a surgery. We could balance the ethics and efficiency of the healthcare industry. By using AI, points which are overlooked or missed in urgency can be taken care at the primary level than at a critical level. Since I came across an health emergency in my family, so thought how I can use AI to contribute in healthcare. I determined the best possible match of Kidney Transplantation, started reading the information present around me to find out the information related to these organs. In my findings I found out that each have many complications can also lead to death of an individual. Transplant option would be thought of when the kidneys stop functioning entirely. This condition is called end-stage renal disease (ESRD) or end-stage kidney disease (ESKD). If you reach this point, your doctor is likely to recommend dialysis. In addition to the dialysis, your doctor will inform you whether you are a good candidate for kidney transplant. You will need to be healthy enough to have major surgery and go through a strict, lifelong medication after surgery to be a good candidate for a transplant. You must also be willing and able to follow all instructions from your doctor and take your medications regularly. If your doctor thinks you’re good candidate for a transplant, and you’re interested in the procedure, you’ll need to be evaluated at a transplant center. This examination usually involves several visits to the hospital to assess your physical, psychological and familial condition. The doctors will run tests on your blood and urine and give you a complete information to ensure you’re healthy enough for surgery. The Matching Process At the time of evaluation for transplant, blood tests would be conducted to determine your blood type (A, B, AB, or O) and your human leukocyte antigen (HLA). HLA is a group of antigens which is located on the surface of your white blood cells, they are responsible for your body’s immune response. If donor and your HLA type matches, then your body will not reject the kidney. Each person has six antigens, three from each biological parent. The more antigens matches you have with the donor, the greater the chance of a successful transplant. Once a best possible donor has been identified, another test has to be take up to ensure sure that your antibodies won’t attack the donor’s organ. This is done by mixing a small amount of your blood with the donor’s blood. The transplant can’t be taken up if your blood forms any antibodies in response to the donor’s blood. If the blood shows no antibody reaction which is called “negative crossmatch”, i.e. transplant can proceed. Algorithm Process During this process, we take the old data related to this and train our engine. So how does the engine work? Engine works but connecting all the hospital present around that town/city/country, it will recognise the best possible chances of the success. We get to know this from the data present with us from the old cases. Our engine reads the information and gets best possible procedure to go ahead. For a simple example, a person in a certain city will be in need to transplantation and he does get suitable match. There is one more person in another city with the same problem. The donor who is ready for that person matches with this person1 and donor of person1 matches with person2. We can plan the best possible match and provide the information to concerned person such that a best possible help will be provided to patients who are in the hospital. The algo helps doctors to get to know the complication which many come to a person while undergoing the procedure such that a solution can be ready while the process is going on. This will reduced the risk of the patients who undergo major operations. So we are looking forward to work on this and make it a best scenario for showing the capabilities of the AI engine which will increase the life expectancy of a human being.

Aziro Marketing

artificial intelligence blog

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