technology – DevopsCurry

Challenges with Big Data & the Need for Big Data Analytics

Mon, 02 Sep 2024 16:57:05 +0000

In this article, we are discussing the challenges businesses face in managing big data and how big data analytics and its associated tools solve this issue.

Introduction to Big Data & Big Data Analytics

Image source: Big Data Analytics: Its Technologies and Tools

Businesses, especially large-scale ones, need to store huge amounts of data. For example, Instagram has tons of data to store – including the billions of past and upcoming posts, the likes and comments on each post, and user information like profile data. It also tracks how people use the platform (known as consumer behavior) to recommend reels to them.

An e-commerce site like Amazon also stores data about their millions of customers – like their searches, purchases, wishlist, etc. – to recommend products to them. Plus, it also keeps track of its own inventory and stocks.

In this way, several other platforms like Facebook, Netflix, and YouTube, also operate on massive amounts of data sets and still work efficiently. How?

So the secret to managing and analyzing such massive data sets is big data analytics. But first, what is big data?

Big data and its challenges

Big data, in simple words, are large complex data sets that grow continuously. They are often described using the 5 V’s:

Volume: Volume refers to the massive size of data sets that modern businesses use.
Velocity: Velocity refers to the speed at which big data grows. For example, Instagram has 1000+ new posts every second.
Variety: Variety refers to the various types of data that businesses acquire from different sources and formats like emails, social media posts, audio, images, etc.
Veracity: Veracity refers to the accuracy and credibility of data as it can often be inaccurate, false, or incomplete.
Value: Value refers to the usefulness of data and how much it benefits the business.

Big data, due to its huge size, poses a variety of challenges which are as follows:

Firstly, storage is a major concern with big data as it keeps on growing from terabytes to even petabytes and exabytes.
Big data can be either structured, semi-structured, or unstructured. This diversity makes it difficult to analyze using traditional technologies.
Maintaining security and privacy across huge data sets is also difficult.
With such massive amounts of data, inaccuracies like false data, duplicate data, or conflicting data are inevitable.

With all these challenges, it is clear that analyzing big data manually or using traditional techniques is quite inefficient and impractical.

Hence, specialized tools and technologies are implemented for analyzing big data, giving rise to the term big data analytics.

What is big data analytics

IBM defines big data analytics as “…the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights.” It involves the following steps:

Image source: Big Data Analytics: How It Works and Its Benefits

Data collection: The first step of big data analytics is to collect data, which can be found in structured, semi-structured, or unstructured form.
Data processing and cleaning: This step involves organizing the acquired data, removing any duplicate data, and formatting the data properly.
Data analysis: Now that the data has been organized, it becomes much easier to analyze it in various ways. For example, it can be used to create graphs or predict trends.

Next, let’s discuss the types of big data analytics…

Types of big data analytics

Based on its function, big data analytics can be divided into 4 types:

Descriptive analytics: Descriptive analytics describes past data in ways. It is used to summarize historical data, generate reports, or help interpret unstructured data through visualization (as in graphs). It answers the question ‘What happened?’ It is used by businesses to analyze their traffic and engagement on their website or social media handles.
Diagnostic analytics: Diagnostic analytics helps diagnose problems and the cause of certain events. It answers the question ‘Why did it happen?’ For example, if a company is witnessing lower sales, diagnostic analytics can help find its cause by analyzing factors like ratings, customer reviews, competition, etc.
Predictive analytics: Predictive analytics, as the name suggests, uses AI, machine learning, and data mining techniques to analyze past data to predict future trends. It answers the question ‘What might happen?’ By predicting consumer and market trends, it helps businesses make informed decisions and stay a step ahead of their competitors.
Prescriptive analytics: Based on the data provided by the previous three types, prescriptive analytics helps to find the solution to a business problem or help make a decision as per upcoming trends. It answers the question ‘What to do about it?’ It is used by the airline industry to adjust ticket prices as per customer demand, weather, destination, and other factors.

Big data analytics tools

The following are 3 of the popular big analytics tools:

APACHE Hadoop: Hadoop was developed in 2005 and is one of the most popular and widely used tools for big data analytics. It is a free Java-based open-source platform that is used by large companies like Amazon, Microsoft, Uber, etc.
Tableau: Tableau, launched in 2003, is an analytics tool that helps businesses create interactive data visualizations like graphs and charts. In addition to descriptive analytics, it also helps recognize patterns and predict trends. It has various products like Tableau Prep, Tableau Server, and Tableau Desktop to suit diverse business needs.
Power BI: Power BI is a cloud-based analytics tool developed in 2014 by Microsoft for the Microsoft ecosystem. Like Tableau, Power BI also offers data visualization features. It has a Q & A feature powered by Natural Language Processing (NLP) which allows its users to question their data.

Advantages of big data analytics

Saves time: Big data analytics tools help businesses collect and analyze vast amounts of real-time data from various sources. Thus, it saves time and helps businesses make quicker decisions.
Improved decisions: Along with speed, it also helps businesses make calculated decisions. Predictive analytics, in particular, help with risk assessment and forecasting trends to help businesses devise marketing strategies.
Customer satisfaction: Big data analytics can also be used to better understand customer needs and pain points. These insights can then be used by businesses to improve their existing products and even create new ones, thus improving customer experience.

Conclusion

Handling and analyzing huge amounts of ever-growing data sets can be time-consuming through traditional methods. Big data comes with several challenges like storage problems, security concerns, and possible inaccuracy. Thus, modern technologies like Hadoop and Power BI are the only way through which large companies can analyze vast amounts of past and real-time data efficiently and stay ahead of their competition.

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Overall Information On 5G Technology

Tue, 27 Aug 2024 04:50:51 +0000

5G Technology Explained: Key Features and Impact

In this article, we will be focusing on how 5G technology evolved from 1G, its key features and concepts, and its impact on various industries.

Introduction to 5G Technology

A few years ago, 4G was the fastest that anyone could use. However, with its launching in 2018, 5G completely revolutionized every sector and corner of the modern world. With its ultra-low latency and 100 times faster speed than 4G, it has significantly improved connectivity across the globe.

But how? How is 5G so fast and powerful?

So before discussing the technologies that make 5G what it is, let’s first get a brief overview of how it all started from the beginning…

From 1G to 5G: The Evolution of Mobile Networks

Image credits: https://www.drishtiias.com/daily-news-analysis/5g-in-india/print_manually

The first generation of cellular networks i.e. 1G technology was introduced in the 1980s and used analog signals. Being the first of its kind, it was the slowest and had a speed limit of around 2.4 kbps with which it could only manage voice calls within a limited coverage.

1G was succeeded by the second generation or 2G launched in the early 1990s. It replaced analogue signals with digital signals instead. It had a data speed of 64kbps, thus enabling better-quality voice calls, text (SMS) messaging, and multimedia (MMS) messaging.

The third generation or 3G appeared in the early 2000s with a data transfer speed of 2mbps and a bandwidth of 2100MHz. It improved network coverage and allowed faster data transfer along with video streaming/calling.

Next in the line came the fourth generation or 4G which was first commercially used in Norway near the end of 2009. 4G brought in features like LTE or Long-term Evolution and VoLTE or Voice over LTE. This enabled high-quality video streaming/chatting, online gaming, social media, instant messaging, and faster download speed. 4G is the most widely used cellular network presently, although it may not be so in the coming years.

5G or the fifth generation is the latest and the most powerful in today’s world. It was launched in 2018 in the United Nations. It has surpassed previous generations with its 10-20 Gbps speed, negligible latency, and increased capacity. In short, it’s best in almost every way.

Now let’s see how 5G does so…

5G Network Architecture: Key Concepts & Features

Massive MIMO

In massive multiple-input multiple-output or massive MIMO technology, a large number of antenna elements are present on a single base station (hence, massive). This large number of antennas enables the station to receive multiple signals (multiple input) and send multiple signals (multiple output) simultaneously.

In the evolution of the massive MIMO, the first in the line was SISO or single-input single-output, which used a single antenna for receiving and transmitting signals. Then came single-user MIMO in which multiple antennas served a single device, followed by multi-user MIMO in which multiple users could connect to the same network. Massive MIMO is simply an expanded version of multi-user MIMO where several users can use the same network with much more connectivity.Massive MIMO is what gives 5G its huge network capacity and data transfer speed.

Network slicing

Each user has different connectivity needs at different times – for example, a person streaming HD videos has very different needs from a person simply surfing on the web. The former requires a network with higherbandwidth to reduce buffering while the latter requires a low-latency network for faster site loading speed. Normally in this case, both the users would receive the same connectivity even if one of them is underusing them – which is a waste of resources. Hence, to reduce wastage and provide an optimized network to its users, network slicing is used in 5G.

Network slicing creates several independent virtual networks (or slices) over a common physical network. These virtual networks or slices are different from each other in terms of performance and can be used to serve the specific needs of the user.

Beamforming

Image credits: 5G NR Wireless technology for enhanced user experience

Beamforming technology allows 5G networks to direct signals in specific directions towards a particular user or area instead of sending them in all directions. It is made possible by MIMO technology where an array of antennas concentrate several signals along the same path to improve coverage in a specific area. Beamforming enhances signal quality and reduces interference/noise from neighbouring cells or signals.

5G Service Categories

The enhanced capabilities of 5G technology can be compiled into 3 service categories…

eMBB:Enhanced Mobile Broadband or eMBB is a concept in 5G technology that revolves around faster internet speed, higher data capacity and wider coverage.
uRLLC:Latency refers to the time it takes for a server to respond to a user request. For example, the time it takes for a website to load when a user clicks on its link. In 5G technology, Ultra-reliable low latency communication or uRLLC refers to latency as low as 1 millisecond which results in higher reliability. uRLLC is of key importance in critical cases like remote surgery or self-driving cars.
mMTC: 5G technology allows a large number of devices to connect to the same network and work simultaneously thanks to Massive MIMO. This capability of a 5G network is referred to as Massive Machine-Type Communications or mMTC.

Impact of 5G

Economic output: According to a 2019 report by IHS Markit, it is estimated that 5G will produce a global economic output of $13.2 trillion by 2035. Out of this, the highest contribution of $1.5 trillion is expected from the information and communications sector and a minimum contribution of $121 billion from the hospitality sector.
Virtual Reality (VR) and Augmented Reality (AR): 5G’s ultra-low latency and high data transfer speed offer smoother real-time interactions in AR/VR. 5G-supported AR/VR technology has found various applications like virtual company meetings, remote surgery for training purposes, VR/AR video games, real-time collaboration on 3D models, etc.
Internet of Things (IoT):The Internet of Things or IoT refers to an internet network that connects several physical devices and allows data exchange between them. Smart home devices like lighting systems and thermostats are a common application of IoT. 5G technology, with its mMTC capability and higher network efficiency, has allowed IoT to expand its reach to rural and remote areas. It has also opened up the possibility for the development of smart cities and smart factories.

Conclusion

The launching of 5G network has transformed every industry and sector – from enhancing personal mobile experiences to empowering space technologies. It has achieved this capability through a combination of supporting technologies and hardware – likemassive MIMO, network slicing and beam forming.However, although 5G is so powerful, making it available in all places will demand significant time and cost. Hence, 5G is expected to work alongside and complement the well-established and widely used 4G network, instead of replacing it completely.

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Come About To Know DevOps CAMS Model

Shiwani Sharma — Tue, 14 May 2024 07:03:21 +0000

DevOps CAMS Model/Principle

Firstly, without going further about the CAMS model and principle of DevOps, it’s important to know detailed information about what DevOps is all about. Though we have many blogs about DevOps, you can go through our previous blog to see more information about DevOps. Here while starting this blog, we are going to mention a few about about DevOps.

What is DevOps ?

A Process that integrates IT operations, practice, tools, software development And contributes the outstanding characteristics of software with the endless delivery. It characterizes the take on the renewal of programmable infrastructure and expenditure, software development, industrialization. In a company, it stimulates alliance and transmission.

DevOps have some procedures such as the CI/CD tool (Continuous Integration/ Continuous Delivery) with an intensity of task automation. Microservice, Container, and Executing together with the DevOps methodologies. Though it is clear that it has some methodologies, it is not a technology.

Wikipedia Definition Of DevOps: DevOps is a methodology in the software development and IT industry. Used as a set of practices and tools, DevOps integrates and automates the work of software development (Dev) and IT operations (Ops) as a means for improving and shortening the systems development life cycle.

The two words define DevOps (software development and Operations) and in other words, you can say the assortment of software development and operation is known as DevOps. It enhances the speed and quality of the application that has been delivering to an enormous extent and that’s why it’s becoming more prominent for the organization. It provides you with the faster speed, security for your code, delivered quickly, these are some of the important features of using DevOps.

What is the CAMS principle in DevOps?

It is very important to understand the purpose of DevOps for your organization, either you are in any role such as a software developer or a system administration.

The CAMS principle of DevOps helps to solve problems and provides solutions. CAMS stands for Culture, Automation, Measurement, and Sharing.

C – Culture

A – Automation

M – Measurement

S – Sharing

Let’s discuss all these separately as below:

Culture

Culture in DevOps refers to the way both operations and development teams work together, with shared routines and goals. Embracing failure as an opportunity to learn and promoting continuous learning and improvement is an essential aspect of DevOps culture. It bound the behaviors, attitude, norms and values shared by any members of the organization. In short, culture in CAMS model refers to stimulate a communicative and collective environment where all the team member works together with having a common goals. DevOps recommend a culture where a single person take the responsibility for the security, reliability and quality of their work all over the lifecycle of software delivery. In the journey of DevOps organization that categorize culture is were often see development in innovation, collaboration and overall performance.

Automation

Automation is a crucial element in the DevOps principle, as it reduces manual tasks in the software delivery process. By automating various stages of software development, such as testing, building, handling, and deployment, teams can achieve quicker and more reliable releases while minimizing human errors. Automation is the best choice in terms of consuming time, money and efforts. You can easily says that automation is the important pillars of the DevOps CAMS model that also helps in maintaining the stability and repeatability, and make certain that deployments are to be expected and reproducible all over the separate environments.

Measurement

Measurement is a key component to track and improve the company’s performance. Key Performance Indicators (KPIs) are often used to measure improvements. Some relevant questions related to measurement include: How satisfied are our clients? How can our company improve collaboration to enhance performance? If you are utilizing the measurements it will helps in find out the progress is being made in the calculative direction. Measurement consists of analyzing and evaluate different aspects of the software delivery pipeline and the system as a whole. The very basis aim of measurement is to collect the data that gives insights into the quality, efficiency and performance of the operation and development processes.

In the measurement some key metrics are as : Customer satisfaction, Deployment frequency, lead time, MTTR(Mean Time To Recover) and change the failure rate.

Sharing

Sharing is the final principle of the CAMS model. It emphasizes the practice of sharing knowledge and information across the entire team within the organization. By fostering knowledge sharing, continuous improvement can be achieved. Sharing plays an important role in transparency, fostering collaboration and continuous improvement within an organization. It is also very important to share the knowledge for braking down silos between several teams within the organization. When overall we are talking about the perception then we can say that sharing is the primary to the success of DevOps initiatives, as it encourages transparency, collaboration and continuous improvement all over the software delivery and development.

Conclusion:

The CAMS model provides an effective delivery process, promotes collaboration, and ensures customer satisfaction in DevOps practices. The two words define DevOps (software development and Operations) and in other words, you can say the assortment of software development and operation is known as DevOps. The CAMS principle of DevOps helps to solve problems and provides solutions. CAMS stands for Culture, Automation, Measurement, and Sharing.

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Blockchain Technology

Shiwani Sharma — Mon, 06 Nov 2023 08:39:36 +0000

What Is Blockchain Technology ?

Blockchain is a secure database that can be shared simultaneously, where all the information is found through a network of participants. It is also known as a ledger or distributed database and DLT (Distributed Ledger Technology). In other terms, it is a process that records information that is difficult for the system to be transformed or exploited. It is allocated between the nodes of a computer network, both in the public and private sectors. One of the most famous public blockchain networks is known as the Bitcoin blockchain. If anyone wants to open a Bitcoin wallet, they can easily do so.

Blockchain can also record transactions through different computers or nodes. In blockchain, digital assets are circulated, and they cannot be transmitted or copied. It provides management solutions and transparency to all types of global industries, such as the food supply chain, gaming industry, healthcare data, etc.

Decentralization is the most important concept in Blockchain technology. Only the distributed ledger via nodes can be connected to the chain, while no computer or organization has control over the chain. The blockchain nodes also help save the networking working and sustain several copies of the chain.

Definition Of BlockChain As Per Wikipedia:

A blockchain is a decentralized, distributed, and often public, digital ledger consisting of records called blocks that are used to record transactions across many computers so that any involved block cannot be altered retroactively, without the alteration of all subsequent blocks

Use Case Of BlockChain

Some other important use cases of blockchain are in healthcare, cryptocurrency, accounting, cybersecurity, etc. One of the important use cases of blockchain is in cryptocurrency.

Cryptocurrency: One of the most important uses of blockchain is in cryptocurrency. Cryptocurrency refers to digital currency used to buy goods and services, such as Bitcoin, Litecoin, etc. Crypto uses bitcoin to work with both the public ledger and to improve the security system of crypto. The first cryptocurrency (Bitcoin) blockchain application occurred in 2009. Blockchain helps in cryptocurrency by providing security and enabling mobility, meaning crypto can be sent to any place in the world.

Healthcare: Blockchain also plays an important role in the healthcare industry. Here, blockchain is used to handle electronic medical records and clinical trials data. Blockchain helps to handle and capture all the relevant health records of patients that provide a consolidate system available to approve the healthcare providers. In short, Blockchain helps in Electronic Health Records (EHRs). It is also utilized to track the supply chain of pharmaceuticals, and it assure with the safety and authenticity by capturing their journey from individual manufacture to consumer.

Real Estate: Blockchain helps in property title and to capture the ownership records, this will decrease the possibility over the ownership of properties as well it hold the latest investment opportunities for particular institutions and individuals. It will also help in the verification and listing of property that means buyers and sellers can easily identified property data or the information. When you execute the blockchain technology in the real estate industry, it can be beneficial to decrease of fraud happened and some mistakes in real estate management and transaction. Blockchain is useful for the landlords as well they can utilized it to streamline the application and acceptance the process for securing resident privacy.

Insurance: It boost up the transparency, efficiency and security. Blockchain plays an important role in Insurance sector, the main reason of it is it helps to detect fraud detection and prevention.

Benefits Of Blockchain

One of the important benefits of blockchain is recording transactions as a database. The three most important benefits of blockchain in any organization are:

Time efficiency
Cost efficiency
High-level security

Some of others benefits are as follow:

Data Integrity: This is very important benefit because due to this features it is difficult to corrupt the data and this features is also beneficial for industry as like supply chain management.

Enhance User Trust: Block chain has the transparent and tamper – resistance nature and due to this it enhance the trust level of user. In some methods, it’s important to build trust as like in voting system, financial transactions etc.

Decentralization: One of the important benefit of blockchain is decentralization that provide trust among the participants. It also helps to enhance the security because it decrease the risk of a single point of failure.

Conclusion: Blockchain technology is a process that records information that is difficult for the system to be transformed or exploited. It is allocated between the nodes of a computer network, both in the public and private sectors. We have taken the name of decentralization as a benefit of blockchain and it is the most important concept in Blockchain technology. Only the distributed ledger via nodes can be connected to the chain, while no computer or organization has control over the chain. The blockchain nodes also help save the networking working and sustain several copies of the chain.

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Exploring the Good and Bad Of Artificial Intelligence (AI)

Shiwani Sharma — Thu, 19 Oct 2023 14:31:04 +0000

Currently, the term Artificial Intelligence (AI) has become a buzzword in society. You hear this word daily from someone’s mouth, indicating its rising popularity and the curiosity of every individual to know what will emerge next from this AI technology. It has also become an integral part of our daily lives. AI was first invented in 1950 and the idea of AI introduced or came in the world by a computer scientists John McCarthy.

Artificial Intelligence (AI) is a process that combines both science and engineering to create intelligent computer programs and machines.

As the name suggests, AI refers to digital computers executing tasks with human input. It is akin to using computers to comprehend human intelligence. One of the most renowned applications of AI is OpenAI’s ChatGPT. However, ChatGPT represents just a small part of AI, demonstrating how AI technology is beneficial and utilized in today’s era. AI finds applications in various industries such as Healthcare, Finance, Edtech, and more. Python, Java, Julia, and R are some of the programming languages commonly associated with AI development, with no other programming languages being interchangeable in AI.

We have discussed in detail what AI is, its Types, and how you can utilize AI in different industries in our previous blog: https://devopscurry.com/artificial-intelligence-an-overview/.

Now, in this blog, you will learn more about its advantages and disadvantages, so let’s discuss it in detail below:

Advantages of Artificial Intelligence

Decreasing Human Error: One of the most significant advantages of AI is its ability to reduce human errors. AI is designed to perform tasks while maintaining consistency, which helps decrease errors caused by human factors. AI aids in data analysis, particularly when processing large amounts of data, as it can recognize patterns that humans might struggle to identify. This potential is crucial in industries such as cybersecurity, fraud detection, and finance.
Automation: Artificial Intelligence can automate repetitive tasks, freeing human workers to focus on more strategic and creative aspects of their jobs. Most human tasks can now be completed by AI, improving companies’ efficiency and productivity. AI’s automation also leads to cost savings by reducing labor costs, thereby enhancing an organization’s profitability. Another benefit of automation is the reduction of the workload for human employees, maintaining consistency in performing tasks, which is challenging in industries with strict daily requirements.
Always Available: AI systems can be utilized continuously without the need for breaks, holidays, or rest. You can use AI platforms 24/7, making them available every day and at any time. This is crucial in applications such as data processing and customer support, where companies require these services to be available for their customers or clients, including chatbots that interact with customers to resolve their queries at any time, day or night. This feature is essential for both consumers and businesses.
Faster Decision Making: In various domains, faster decision-making is a significant advantage of AI, as it can process vast amounts of data quickly, identify complex scenarios, and assist in the decision-making process. It also helps retrieve real-time data from different sources, such as social media and sensors, enabling organizations to make decisions based on current information, which is vital in emergency responses and financial trading.
Continuous Learning: AI has two models: machine learning and deep learning, both of which benefit from continuous learning. This allows the Artificial Intelligence system to improve, adapt, and stay relevant over time. As a user, you have the opportunity to learn many things from AI, as its models are updated regularly.
Speed & Accuracy: All the tasks that AI perform, done with good accuracy and high speed. The tasks is completed with high speed but less amount of error occurs. This advantage is much effective and valuable in autonomous vehicles and medical diagnosis. AI also helps to find out the huge datasets quickly, trends & search out the patterns in a friction of the time it would take a human analyze.

Disadvantages of Artificial Intelligence

Lack Of Creativity: Although AI reduces human labor, it cannot match the creative capabilities of humans. In situations requiring a deep understanding of creativity, human emotions, and complex social interactions, AI may fall short.
Lack Of Job Availability: AI has the power to replace many human jobs, especially those involving data analysis and repetitive tasks. While AI benefits organizations in many ways, it can lead to unemployment, which is a concern in countries like India where people are already struggling to find jobs.
High Costs: AI technologies can be very expensive to develop and implement, making them unaffordable for small-scale industries. The high costs associated with AI become a disadvantage, as not every industry can utilize it.
Security Risks: One of the major disadvantages of AI is its security risks. Nowadays, with the help of AI, people are misusing it for hacking and spreading disinformation, posing significant security threats. AI algorithms are used to invade privacy and launch cyberattacks, making the digital landscape less secure.
Privacy Issues: Another disadvantage of AI is privacy concerns. AI systems often collect and analyze large amounts of data, raising worries about privacy violations and the potential for surveillance.

Image Credit: https://medium.com/marketing-in-the-age-of-digital/when-it-comes-to-customer-service-whats-the-benefit-and-drawbacks-of-ai-over-human-interaction-13e61672562d

From the image above, you will gain a better understanding of the pros and cons of AI. Some of the advantages and disadvantages have been mentioned in the previous sections, while others can be observed in the images.

Conclusion: Artificial Intelligence offers numerous advantages, including the reduction of human errors, automation, 24/7 availability, and the facilitation of faster decision-making. Another advantage is continuous learning, which allows AI systems to remain important and dynamic tools in our world.

However, it is essential to acknowledge the disadvantages of AI, such as its lack of creativity, limited job opportunities, high costs, security risks, and privacy issues. While there are many advantages and disadvantages, we have highlighted only a few to provide you with a deeper understanding of artificial intelligence.

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