Aditya Abeysinghe

Is software defined security changing security in modern applications? – By Aditya Abeysinghe Software defined security Software defined security (SDS) virtualizes security functions in a security network and abstracts it from hardware. It is not possible to virtualize every security aspect and hardware is necessary for some security functions in a system. Network functions including firewalls, intrusion detection, and access controls can be used in virtual networking with software defined networking and then added for SDS. Therefore, SDS has changed the use of security in networks by transitioning from traditional hardware to software-based components. Why transition to SDS? Network segmentation is used in networks to divide networks into sections so that one network section could separate from other sections. This reduces issues when one or more machines or virtual machines get attacked. When networks are segmented and when one section is attacked the other sections could operate without being attacked. ...

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Emerging marketing trends with ML – By Aditya Abeysinghe Marketing has improved its reach to target customers over the last decade with the use of newer technologies. While marketing using online websites and mobile applications are the widely used methods, new methods of online marketing are rising that use machine learning models. These emerging marketing methods have enhanced marketing to reach a large volume and variety of market types. Analyzing target market segments Machine learning (ML) is used to analyze target markets. The common method of finding target markets is to group people of similar characteristics such as by location, gender and purchasing methods. However, this is often difficult for large businesses that market services or products due to the large variety of customers. ML is used to identify people with similar behavior based on methods such as classification and clustering. ML could also be used with other systems to ...

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The changing nature of large scalable AI and ML models – By Aditya Abeysinghe Distributed AI Artificial Intelligence (AI) models are often costly to operate due to high Central Processing Unit (CPU) power required to process data. AI models also depend on additional constraints such as volatile memory, secondary memory and power. Most medium and large scale AI models are processed on cloud or remote data center servers which have high CPU power and memory compared to small scale processing devices due to these issues. However, sending data to a remote server is often not viable due to extra costs of data communication, costs of maintaining and monitoring servers in cloud and due to issues with privacy. Distributed AI is a method to distribute AI models outside a server to a location close to a data source. With distributed AI, AI models are deployed at either the source or at ...

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Rise of image generation methods in AI – By Aditya Abeysinghe Image-to-image translation Image-to-image translation is the method of transforming an image from one domain to another domain. Image-to-image translation is usually done using deep learning models such as generative adversarial networks. Models used for this method are trained with image pairs, i.e. the input and the output image. Some complex models may even use more advanced methods. Image translation is used in several applications such as for converting color model of an image such as between RGB (Red, Green, Blue) and other color schemes, for converting from one form to other forms such as an image of a summer background to an image of a winter background etc. Image creation using text Text-to-image generation is another method that is used for creating an image. In this method, a diffusion Artificial Intelligence (AI) model translates text to images. The model ...

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Changing cybersecurity use with AI models  – By Aditya Abeysinghe   Use of AI and Machine learning There are many uses of Artificial Intelligence (AI) and machine learning (ML) in cybersecurity. The advantage of using AI and ML for cybersecurity is that AI and ML models can learn and then identify attacks which are known and/or unknown in a system. Models which are based on AI and ML can identify anomalies when there is a change of usual traffic. In classification, content is classified as attacks based on the output of a model. Benefits of AI models to identify attacks AI models can be used to automate detection of attacks more effectively than by using other methods. AI models are used to enhance anomaly identification or classifications in networks with less time than manual identification based on analysis. Unlike other attack classification and anomaly detection methods, methods that use AI ...

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Emerging methods in application design – By Aditya Abeysinghe Agnostic methodology Agnostic methodology is a development method used when an app is designed to work on several platforms. Several tools and technologies exist which can be used to create an app to be used with cross platforms. Usually, the user interface and the components used to determine the logic of functions of the app can be used with multiple platforms. The backend is usually in the form of an Application Program Interface (API) which is used to bridge the functionality of the app with a database. Native methodology Native methodology is a development method used when an app is designed to work only on a single platform. Tools used to design and test could operate only in one platform and executables of these apps can only execute in that platform. However, as most users use the same app with multiple ...

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Are smart device automated systems enhancing cultures? By Aditya Abeysinghe What is a smart device? A smart device is an electronic device that can connect to other devices, share data, and often control other devices. Smart devices are different from other devices as a smart device can automate most of its services. These devices can share data over a wireless network or a wired network and often have considerable computational power compared to large-scale portable electronic devices. Storage and sensors can be plugged in as required to smart devices and user interfaces can be used to control and handle services. What is a smart device automated system? A smart device automated system is a group of smart devices that are connected over a network to automate functions of one another. Smart devices communicate using a short-range network and may connect to the Internet to share data over a remote network. ...

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Is virtualized computing changing application deploy methodologies? –  By Aditya Abeysinghe Bare metal server vs Virtual server A bare metal server is a single user computer used to host dedicated applications and services. These servers are not shared with other users and provide high performance. Most highly critical applications are hosted on bare metal servers due to their isolation from other user applications. As bare metal servers do not require a guest operating system (OS), there are more configuration options than in a virtual server. However, bare metal servers are more expensive compared to virtual servers and the user needs to provision and control servers. A virtual server is a server virtualized using a hypervisor and used to host applications and services. With a virtual server, a physical computer is split into many servers which share processing, network and other resources. Many servers can be used within the same hardware ...

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Narrow AI: Boundaries of modern AI – By Aditya Abeysinghe Narrow AI Narrow Artificial Intelligence (AI) is a type of AI which can handle only tasks that are assigned. These tasks need to be defined before they can be processed and narrow AI models cannot operate beyond the level of training. This constraint makes this type “weak” or narrow compared to strong types such as Artificial General Intelligence and Artificial Super Intelligence. Narrow AI is the mostly used AI type in commercial systems. Why it is narrow in AI? Narrow AI is designed to perform a task based on pre-defined models and inputs. Artificial General Intelligence-based models on the other hand can “think” and perform tasks on their own. However, the latter type requires AI which goes beyond how humans “think”. This type is still being researched and is not used in most commercial AI systems. Models which rely on training ...

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Use of Virtual Assistance with Business Intelligence – By Aditya Abeysinghe Behavioral intelligence Intelligence is important to identify the root cause of behaviors. After identifying the root cause of behaviors of other people, a person can explain these behaviors and predict behaviors based on the current behavior. Also, intelligence could be used to influence behavior of others and control one’s behavior. Behavioral intelligence (BI) is intelligence which combines these stages to control and analyze the behavior. What BI consists of BI can be used in nearly every use case people meet in their daily lives. Digital BI is described in this context where BI is used to control and analyze behavior of people in a digital environment. Intelligence Quotient (IQ) is the first element of BI. It is the amount of intelligence, measured using a test, a person contains measured relative to other people. IQ is important simply to find ...

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