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TEKNOLOGI

How Blockchain and Big Data Can Work Together

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How Blockchain and Big Data Can Work Together

Big data and blockchain work well together by providing more security and integrity. 

One is transforming data management while the other is changing the nature of transactions altogether. Could they create an even more significant impact on the industries by binding together – big data for blockchain or blockchain for big data?.

Big data technologies first came into the picture at the dawn of this millennium to meet the computational needs of large datasets in the Internet-era. Proprietary applications like BigTable by Google and ZooKeeper at Yahoo showcased the potential of big data. However, the potential could only be tapped into after open-source projects such as the Hadoop File System (HDFS) and Hadoop MapReduce hit the market. Since then, big data has snowballed to transform how companies manage their data in the 21st century. Satoshi Nakamoto, an anonymous mystic individual, introduced the world to blockchain in 2008. It was developed in an attempt to solve the problem of double spending in transactions by eliminating the need for a third party in financial transactions. Blockchain also gave the world its first digital cryptocurrency – the bitcoin. Since then, the concept of blockchain has rapidly evolved to provide robust solutions to problems persisting in a wide array of industries. Now that both big data and blockchain are established as effective tools to tackle issues in different domains, we look forward to – possible methods of integrating both big data and blockchain to deliver even better solutions to specific problems, or as we’ve called it in this article, blockchain for big data and big data for blockchain.

How Big Data Works With Blockchain

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A lot of governments have had trouble with the anonymity clause of blockchain. Despite being favored for its security and infallibility, blockchains are turned down for not being able to track stakeholders in transactions, thus being a preferred choice for illegal trade. Big data applications can help make blockchains trackable by managing structured datasets of wallet addresses and their owner details. This kind of infrastructure can convince governments to adopt blockchain as a platform for transactions that demand speed, safety, reliability, and traceability – thanks to big data for blockchain.

The Close Ties Between Blockchain and Big Data

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Big data is comfortably dealing with huge sets of data, but some issues in its infrastructure have posed a problem in the widespread adoption of the technology. The big data infrastructure is centralized to a server location that offers complete unconditional control of data to the ones who have access to the server. This ‘ownership’ creates a problem when big data infrastructure is to be shared between different companies or even different regional offices of the same company. Besides, having multiple copies at different locations is not a solution because it puts a burden on resources and also creates confusion while determining the most updated data resource. Furthermore, now that big data resources are being traded among different entities, the legitimacy of a data resource poses a concern. With a blockchain for big data, we can create a decentralized data resource to which every one has full access. We can also track updates to the data resource on the blockchain, eliminating the need for and confusion due to multiple copies. Moreover, data transactions can be verified for legitimacy using blockchain concepts like proof-of-work or proof-of-stake and at the same time blockchain can provide a robust financial platform for data transactions between entities.

It is incredible how both of these technologies – big data and blockchain – can together significantly improve the usability of each other. The techniques can help create a hybrid infrastructure on pillars of big data and blockchain. The infrastructure will be flexible for different application types, like its parents – big data and blockchain.


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TEKNOLOGI

How Artificial Intelligence Can Bring People Together

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How Artificial Intelligence Can Bring People Together

Artificial intelligence (AI) enables people to spend more time with those that matter the most.

AI is bringing people together, whether it’s assisting in the planning of a family vacation, getting to a place securely, allowing everyone to see each other on a video call, or making gift shopping a little easier.

The continuous evolution of AI has the potential to revolutionize the way we live, work, and interact with each other.

While AI is often portrayed as a divisive force that could lead to job losses and social inequality, it also has the power to bring people together and create new opportunities for collaboration and cooperation. In this article, we will explore some of the ways in which AI can be used to bridge social, cultural, and linguistic barriers and foster greater understanding and empathy among diverse communities.

1. Breaking Down Language Barriers

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Source: Nature Magazine

One of the most significant benefits of AI is its ability to facilitate communication across different languages. Machine learning algorithms can now accurately translate spoken or written language in real-time, allowing people who speak different languages to communicate with each other more easily. This technology has been particularly valuable for businesses operating in global markets, enabling them to connect with customers and partners from different regions and cultures.

The benefits of AI-powered translation extend beyond the business world. For example, the United Nations has been using AI-powered translation to help bring together representatives from different countries and facilitate discussions on important global issues. By breaking down language barriers, AI is helping to promote greater collaboration and understanding among people from diverse backgrounds.

2. Enhancing Cross-Cultural Empathy

Another way in which AI can bring people together is by enhancing cross-cultural empathy. Artificial intelligence can be combined with virtual reality (VR) and augmented reality (AR) technologies to enable people to experience different cultures and perspectives in a more immersive and engaging way. By simulating real-world scenarios and environments, AI can help people to better understand and appreciate different cultures, religions, and ways of life.

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Source: Science Direct

AI-powered chatbots are also being used to promote cross-cultural empathy. These chatbots are programmed to engage in conversations with users from different cultures and backgrounds, using natural language processing and machine learning algorithms to understand and respond to different communication styles and cultural norms. By promoting greater empathy and understanding, AI-powered chatbots can help to break down social and cultural barriers and foster greater social cohesion.

3. Improving Access to Healthcare

AI is also being used to improve access to healthcare, particularly in underserved and marginalized communities. For example, AI-powered diagnostic tools can help to identify diseases and conditions more accurately and quickly than traditional diagnostic methods. This technology is particularly valuable in developing countries, where access to healthcare is often limited, and where the burden of disease is highest.

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Source: Deloitte

In addition, AI-powered chatbots and virtual assistants are being used to provide healthcare information and support to patients in remote or underserved areas. These chatbots can answer common medical questions, provide basic health advice, and even monitor patients’ symptoms and suggest appropriate treatments. By improving access to healthcare, AI is helping to promote better health outcomes and reduce healthcare disparities, bringing people together by improving the quality of life for everyone.

4. Promoting Social Inclusion

Artificial intelligence can help to promote social inclusion by creating new opportunities for people with disabilities and other marginalized groups. For example, AI-powered assistive technologies can help people with visual or hearing impairments to navigate their environment more easily, communicate with others, and access information more effectively. These technologies can also help to break down barriers to education and employment, providing new opportunities for people who might otherwise be excluded from these areas.

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Source: McKinsey

AI-powered social networks and online communities can also help to bring people with similar interests and experiences together, regardless of their physical location or social background. These communities can provide a sense of belonging and support, and can help to break down social barriers by connecting people who might not otherwise have the opportunity to interact with each other.

Slutsats

Artificial intelligence has the potential to bring people together in a variety of ways, from breaking down language barriers to promoting social inclusion and empathy. By harnessing the power of AI, we can create a more connected and inclusive society, where people from diverse backgrounds can come together to collaborate, learn from each other, and share their experiences.

It is important to state that AI is not a panacea for social problems. Like any technology, AI can be used for both good and bad purposes, and its impact depends largely on how it is developed and deployed. It is therefore essential to ensure that AI is developed in a responsible and ethical manner, taking into account the potential risks and unintended consequences of its use.

Moreover, it is important to recognize that AI is not a substitute for human interaction and connection. While AI can help to facilitate communication and understanding across different cultures and languages, it cannot replace the depth and richness of face-to-face interaction and human connection.

Artificial intelligence has the potential to bring people together and create a more connected and inclusive society. By breaking down barriers to communication, promoting cross-cultural empathy, improving access to healthcare, and promoting social inclusion, AI can help to foster greater understanding, collaboration, and social cohesion. However, it is important to ensure that AI is developed and deployed in a responsible and ethical manner, and to recognize that it is not a substitute for human interaction and connection.

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TEKNOLOGI

How to Reverse Video with 5 Free Video Editing Software

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How to Reverse Video with 5 Free Video Editing Software

With so many professional video makers available on the market, it becomes a daunting task to select suitable software for creating a reversed video.

We have rounded up the most popular video editors suitable for professionals with different levels of experience. You can leverage them to produce top-level content without wasting much time.

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#1 Movavi

Regardless of the operating system you prefer, this free video reverser is exactly what you need if you prioritize quality and a streamlined interface. After downloading and installing it on your device, you just need to add a video clip that you want to edit by dragging and dropping it onto the timeline. Then, click on the Properties icon and tick the Reverse box. Once you have previewed the result, save it in any supported format that you need to use. The program supports MP4, WMV, MKV, and 3GP formats, among many more.

#2 iMovie

This widely-used video editor stands out for its high-fidelity filters. Using it, you can produce professional-looking reverse clips for improving the online presence of your brand and developing your business. It supports quick file sharing to social media networks and comes with handy file management tools. To reverse your clip, you just need to go to the Project browser, select a video, and double-click on it to open the Clip inspector. Then, you need to select the Reverse checkbox and confirm your choice.

#3 Kinovea

You can use this reverse video editor without paying a dime to perform basic video enhancement tasks. As 97.8% of internet users in the U.S. aged between 18 and 24 years enjoy watching videos online, it becomes crucial to use video editing tools to reach out to a younger audience. Kinovea has a special feature in the Motion menu for creating a reverse effect. You can preview the changes on the main screen. Once you are satisfied with the result, go to the File menu and click Save.

#4 Adobe Premiere Pro

Being one of the most advanced programs for editing videos, Adobe Premiere Pro is included in the Creative Cloud package, which makes it quite affordable. It integrates with other Adobe products, which makes it perfect for professionals and beginners alike. Besides enhancing your videos by adding to them transitions, you can use this software to achieve a reverse video effect.

#5 Blackmagic DaVinci Resolve Studio 18

While you can access this powerful video editor for free, it also has a paid version. It allows you to perform color-grading like a pro, reverse your clips, and edit 8K footage. You just need to right-click on your video, select the option Change Clip Speed, and tick the Reverse Speed checkbox to achieve the desired effect.

Summing Up

Using these free tools, you can quickly reverse your videos to create attention-grabbing content for your followers and subscribers. This effect is easy to emulate even if you don’t have extensive experience in video editing. Using this technique, you can bring your point across more efficiently and create viral videos for your target audience.

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HOW BUSINESSES CAN USE PRESCRIPTIVE ANALYTICS FOR LOGISTICS MANAGEMENT

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HOW BUSINESSES CAN USE PRESCRIPTIVE ANALYTICS FOR LOGISTICS MANAGEMENT

When multiple variants and uncertainties are included while making informed decisions, it becomes increasingly difficult for businesses to make efficient operations.

However, the use of analytics for favorable outcomes has been steadily growing, as industry giants like Google and Netflix and many such companies utilize analytics to improve their processes and serve their customers better. Amidst this, the logistics sector can utilize ‘prescriptive analytics’ that makes data-driven decisions and determines the best course of action. Prescriptive analysis is a type of data analysis that uses mathematical models, algorithms and other techniques to generate specific recommendations or solutions to a problem or decision-making situation. It is used to identify the best course of action to achieve a desired outcome, taking into account constraints and uncertainties. Let’s understand the role of prescriptive analytics in logistics in detail.

WHY BUSINESSES MUST USE PRESCRIPTIVE ANALYTICS FOR LOGISTICS

Businesses use prescriptive analytics in logistics to improve efficiency and optimize supply chain operations. By analyzing data from various sources, such as transportation costs, inventory levels and customer demand, prescriptive analytics can provide insight into what actions should be taken to improve performance. For example, a business may use prescriptive analytics to optimize delivery routes, reducing transportation costs and improving delivery times. It can also be used to identify bottlenecks in the supply chain, such as inventory shortages or delays and to develop strategies to address these issues. Additionally, prescriptive analytics can be used to predict future demand for products and to optimize inventory levels, reducing the risk of stockouts and improving customer satisfaction.

6 STEPS BUSINESSES NEED TO FOLLOW TO USE PRESCRIPTIVE ANALYTICS FOR LOGISTICS MANAGEMENT

Predictive analytics provides a streamlined, comprehensive process for efficient logistics management.

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Stage 1: Defining the Problem

Before starting the process, professionals need to identify the logistics problem or decision-making situation that needs to be addressed. They should clearly define the objectives, constraints and desired outcomes.

Stage 2: Collecting and Preparing Data

Next, they should gather relevant data from various sources, such as inventory levels, transportation costs and customer demand and prepare the data for analysis by cleaning, transforming and normalizing it.

Stage 3: Modeling the Problem

Later, businesses can use mathematical models and algorithms to represent the logistics problem and the relationships between the different variables. They must ensure that the model is able to generate specific recommendations or solutions based on the input data.

Stage 4: Analyzing the Data

Next, businesses can use the model to analyze the data and generate specific recommendations or solutions. This step may involve running simulations, sensitivity analyses and other techniques to evaluate different scenarios and identify the best course of action.

Stage 5: Communicating and Implementing the Solution

Post the analysis of the data, businesses must communicate the results and recommendations to all decision-makers and stakeholders. Thus, they pave the way to implement the solution and monitor its performance to ensure it is achieving the desired outcome.

Stage 6: Continuously Monitoring and Optimizing

Lastly, businesses should continuously monitor the performance of the implemented solution and make adjustments as needed. They can use the feedback and updated data to refine the model and improve future decision-making.

Slutsats

Through prescriptive analytics, the logistics domain can now reduce complexities and uncertainties along with enhancing performance and mitigating risks. This will enable companies to get a competitive edge in the market.

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