Types of Learning in AI, Explained

AI is touted as a groundbreaking development in technology, and with good reason. The ability to automate time-consuming, expensive, or monotonous tasks is amazing. Just take a look at the buzz created over applications like ChatGPT.

Personalization is another big benefit of AI, both in terms of marketing and entertainment. Predicting future outcomes is another area where AI gets to shine. However, AI is not as all-powerful as some people may think. To get an AI model to work effectively and efficiently, it must first be trained on one or more sets of data.

As well as this, the model must be given some guidelines and algorithms to make use of. This field is known as AI learning, and there are many categories of learning within this. Read on to discover the main types of AI learning and how they’re used, as well as their advantages and drawbacks.

When talking about AI learning, we mean the way that AI models are trained to enhance their performance in certain tasks. In most cases, the model is provided with some input data. This can be labeled with the correct output, or unlabelled. The main idea is for the AI model to learn from this data, usually calculating an appropriate function.

This can then be used to predict future outputs based on new input data. The model is often also provided with certain algorithms to do its job. The significance of AI learning is that it’s essential to improve the accuracy and consistency of AI models.

This is crucial in many applications, but especially those where high accuracy is necessary — think medical diagnosis, self-driving vehicles, or financial planning. Training AI models well can also help them be versatile, and be applied to new situations.

At a high level, there are 4 main categories of learning in AI: supervised, semi-supervised, unsupervised, and reinforcement learning. We’ll explain what these mean next.

This is a form of learning where the dataset that the machine is being trained on has inputs that are already labeled. Because the inputs are mapped to outputs, the machine has a helping hand here.

The idea is that, once the machine has been trained and has recognized the relationships between input and output, it will be able to calculate outputs without supervision. For example, we could train a machine on input data related to stock prices.

This dataset would be vast, with hundreds or thousands of examples of inputs. If successful, the machine would calculate a function that could map inputs to outputs, and use this to predict future stock prices.

Generally, there are two kinds of algorithms used within supervised learning: classification algorithms and regression algorithms. Classification algorithms are concerned with finite, discrete data, i.e. colors or shapes, regression algorithms work with continuous data, i.e. numbers.

Meanwhile, supervised learning can potentially give us an exact answer, but it does require a lot of computational power. Supervised learning is often used in image and speech recognition, medical diagnosis, and fraud detection.

This is sort of like supervised learning, but with not as many inputs labeled, and even these may not be completely accurate. Semi-supervised learning is a type of AI learning mostly used where completely labeling the inputs would be expensive computationally, or labeling is particularly difficult.

In addition, if the labeled data is in short supply or contains a lot of noise, the unlabelled data can help to mitigate this. However, semi-supervised learning can’t have as high accuracy as fully supervised learning.

Overall, semi-supervised learning aims to strike a balance between accuracy and computational costs. A real-life analogy would be if a student received support from a teacher, but was then left to solve problems based on their gained knowledge. This type of learning is used in image and text classification, as well as speech recognition.

You may imagine that unsupervised learning refers to learning where the input data isn’t labeled. In this case, you’d be correct. No figurative teacher is guiding the machine, which must figure out the appropriate functions by itself.

Unsurprisingly, this is a much more complex process to execute than supervised learning, but the potential results here are much greater. In theory, mastering unsupervised learning will lead to exponential leaps in AI capabilities, as machines will be completely able to teach themselves. Naturally, this is extremely hard to achieve, since the machine must rely on its own logic completely.

As an example, take a variety of shapes. These will have differences, such as the number of corners and sides, as well as internal and external angles. The machine will attempt to figure out these differences and the patterns of the shapes to predict future outputs.

Unsupervised learning is mainly used where we want to group similar data points (known as clustering), detect anomalies in data, or study generative models so that new data can be created.

All in all, unsupervised learning can be used on more complicated tasks than supervised learning, but does tend to be more inaccurate and difficult. It’s used in applications such as anomaly detection, information extraction, and network analysis.

When it comes to this type of learning in AI, no training data is used, but the machine does receive some help. The guidance is in the form of an “environment,” meaning it’s provided with goals, prescribed actions, and feedback on its performance.

In some sense, this is similar to supervised learning, although there are no data labels and the feedback received can be appreciably noisy. The machine learns only through trial and error and is incentivized to improve its performance through rewards and penalties for its actions.

As such, the machine will learn to maximize the “points” it receives, improving its efficiency. Reinforcement learning is used in scenarios where certain actions are desirable, such as training robots to perform tasks or play games, financial trading, and autonomous vehicles.

This learning type has many advantages because it’s suitable for elaborate problems and closely simulates the way that humans learn to act. However, substantial amounts of data are required, as well as a lot of computations.

The number of algorithms in AI learning is very extensive, but some of the most common ones are listed here. For a closer look at some of the algorithms, check out our article on supervised learning.

Now that we’ve covered the learning types at the top of the hierarchy, it’s time to delve a little deeper. There are many types of AI learning, but some are more common than others. These are listed in the table below, along with the category of learning that they fall into.

There are quite a lot of types of learning here, so we’ll examine each one briefly.

This makes use of ensemble algorithms, where multiple models are combined to improve accuracy. Generally, two types of ensemble learning are used. The first is where results from multiple models trained on different algorithms are combined, and the second is where models are trained one after the other on the same data, correcting the previous model’s errors as they go.

Ensemble learning tends to be used in both regression and classification tasks. It helps to make models more robust so that they’re less easily affected by anomalous data. However, ensemble algorithms can be complicated and laborious, and the results may be difficult to comprehend due to many models being used.

The term “transfer” comes from the principle of this learning where the machine uses its knowledge from one task to improve its performance on another. In essence, it “transfers” what it has learned to this new situation.

This type is used a lot in natural language processing and image classification and helps to reduce the computational power needed. As the model is trained on a specific task with test data, this can then be applied to related tasks with a lot more data to process.

This results in lower costs and better performance than using a model that hasn’t been trained already. Like with any model, there are some drawbacks, however. Transfer learning can only be used when tasks are related in some way, and isn’t always the cheaper option. If a lot of adjusting is needed to find the best model for the task, costs can easily add up.

Online learning can also be called incremental learning. This is because the model is updated gradually as it receives new data. The machine is trained on these inputs as it receives them, updating its parameters as it goes. Online learning can be very useful when memory is restricted, and all the related data cannot be stored in one instance.

This type is also helpful when data is changing significantly over time because the model updates itself systematically. Online learning can be used for text classification and fraud detection, cybersecurity, and most situations where data is changing in real time.

This type of AI learning can require more fine-tuning, however, and can be prone to fitting itself to noisy or incorrect data as it receives inputs one after the other.

As a subtype of supervised learning, active learning permits the model to ask human operator questions as it’s being trained. It’s often used where it would be expensive to label and collect data and can be considered a strategy for approaching problems that would usually be semi-supervised.

The amount of data needed can be minimized by asking for support from a human. Active learning applies to bioinformatics (the field of using technology to interpret biological data), natural language processing, and image recognition.

Although it’s extremely useful, active learning isn’t appropriate for all learning problems and is very dependent on the strategy used to select the best data examples. As such, a high degree of discernment is required from the operator.

Transductive learning can be considered a form of supervised learning, as it uses labeled data. However, it doesn’t use this to create a general function but to make predictions based on specific inputs. In this way, transductive learning doesn’t assume consistency between the training dataset and the test dataset.

Naturally, transductive learning is limited in that it’s not suitable for generalizing to new inputs, but is handy when the distribution of input data is prone to change. This type has similar applications to active learning, such as in the fields of natural language processing, bioinformatics, and image recognition.

This type of model is used in a situation where we have groups of labeled data, but where individual inputs are unlabelled. This is often done because inputs may be duplicated, i.e. several data values may be identical.

You can think of this data as being labeled in “bags,” rather than individually. As such, multi-instance learning aims to categorize these “bags,” and is advantageous where labeling each input would be expensive or time-consuming.

This can reduce the accuracy, but the learning type is still useful for image and speech recognition, as well as medical diagnosis. The model could, for example, aim to predict if a patient has a particular disease based on their medical history as a whole.

In contrast to multi-instance learning, multi-task learning works with one dataset but aims to solve multiple problems at once. The objective here is to generalize more accurately across the tasks, by informing the model from the results of each task.

Multi-task learning can be used in natural language processing, where tasks often have underlying similarities. When tasks are unrelated, however, this learning type won’t be appropriate. The model must also balance between completing each task, which can lead to subpar performance. Generally, though, this makes for a more efficient model if tasks are interrelated.

When we try to determine a specific outcome from a general rule, we’re said to be using deductive reasoning. Therefore, deductive learning is concerned with achieving a specific result given a set of general premises, using logic.

This can be used in natural language processing, where we want to decipher specific meanings from unstructured text. Deductive learning is also used in knowledge-based systems, where knowledge is applied to new situations to give specialized advice.

Examples would be financial planning and medical diagnosis, where an expert may not always be available. However, deductive learning is limited to the knowledge and rules it must follow and is therefore highly dependent on this data as well as relatively incapable of handling uncertainty.

Inductive reasoning refers to when we use specific cases to predict more generalized outcomes. This can be thought of as the opposite process of deductive learning. In this way, inductive learning attempts to generalize new data from pre-existing data.

To be clear, inductive learning is used in many other types of AI learning that we’ve covered. The exception would be deductive learning.

As such, inductive learning is used in virtually all of the applications previously discussed. It’s rarely used for unsupervised learning, but it’s possible that the algorithm can cluster data using patterns it has identified in the input data.

Finally, let’s discuss self-supervised learning. This is almost a hybrid of unsupervised and supervised learning. A problem that’s typically considered an unsupervised learning problem is represented as a supervised learning problem.

The approach is similar since the model is using data to make predictions. But the data comes in the form of modified input data. The initial task is then to recreate the original input data. As such, labeling data isn’t required to form predictions from the input data.

This can result in more data and resources being needed. But this is useful in situations where labeled data is scarce and unlabelled data is abundant. Most AI learning applications can make use of self-supervised learning, except for those that require a lot of labeled data.

We’ve talked about the main types of AI learning that are being employed. So, at this stage, it would be useful to distinguish between “narrow” and “general” AI. All of the learning types mentioned here fall under narrow AI. This is because the models are designed to perform specific tasks, but can’t apply this knowledge to completely new areas.

On the other hand, general AI aims to possess what’s known as general intelligence. This is often seen as close to human intelligence. In this sense, general intelligence refers to the ability to learn and then apply the skills and knowledge gained to entirely new scenarios, being able to successfully perform any task that a human would be capable of.

One of the most recent approaches is deep learning. This is where a neural network is constructed that’s based on the structure of the human brain. In a neural network, interconnected nodes mimic the brain’s neurons.

Overall, general AI is seen as one of the ultimate goals of AI development. There has been promising progress in this regard. However, many more advancements will be needed before we’re close to accomplishing general AI.

To conclude, there’s a huge range of learning types within the field of AI learning, which can be considered supervised, unsupervised, semi-supervised, or reinforcement learning. All types of narrow AI have their own pros and cons, so which one is suitable for you depends on the task at hand.

While all of these are impressive advancements and have their place, the overarching objective of many people in the AI field is to develop general AI, which could possess human-like intelligence. We’ll have to wait and see if we get there but, so far, the progress made in narrow AI is certainly impressive and has already shaped our technological world.

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NEAR Price Prediction 2023-2032: Is the NEAR Protocol a Good Investment?

Near Protocol (NEAR) is a blockchain platform with significant attention and partnerships within the cryptocurrency industry. Initially established by a small team in San Francisco, NEAR Protocol has rapidly expanded its influence and formed collaborations with industry giants. As a result, it has witnessed a consistent upward trajectory since its inception.

Cryptocurrencies continue to gain traction as a viable asset class, and investors are becoming more aware of the potential for long-term returns. In this NEAR Price Prediction, we will look at the current price state of the NEAR Protocol and its price predictions between 2023 – 2032 to determine if it is a good investment opportunity.

The latest NEAR Protocol price analysis shows that the currency has recovered from its previous low of $1.19 and is now trading at the $1.21 mark after a bullish movement today. NEAR price has been on a consistent uptrend since the early hours of trading when it experienced an initial surge of over 1 percent, which further decreased to 0.56 percent later in the day.

The currency is currently trading between resistance and support levels at $1.24 and $1.18 respectively, however, if it can break through either of these marks then further bullish growth is expected. NEAR Protocol has seen an overall decrease of 19.24 percent in the last 7 days but is still up by 0.56 percent in the last 24 hours and looks set for further gains if the current trend continues.

On the daily chart, NEAR Protocol has seen a consolidation process in its price over the past few days as it has been trading in a tight range of $1.15 to $1.25 which is indicative of a strong buying pressure in the market. The momentum has shifted to the bullish side and it appears that traders are beginning to take advantage of this situation as the currency is currently trading above its resistance level at $1.20.

The market capitalization of NEAR Protocol is currently $1.10 billion with a circulating supply of 916 million tokens. It’s worth noting that the coin has seen an increase in trading volume over the past 24 hours, which could be a sign of further bullish growth if the current market sentiment remains positive. Currently, the 24-hour trading volume for the coin is $58.3 million displaying an increase of 6.08 percent.

Technical indicators on the 1-day timeframe are showing a bearish trend with the MACD line and signal line below the zero line. The histogram is still in the negative territory but has started to move towards the positive region, indicating a possible reversal of the trend. The RSI line is also below the 50-mark portraying bearish pressure in the market. The moving average of the coin is also displaying a bearish sentiment as it slopes downwards.

The hourly chart for NEAR protocol shows that the coin has been forming an uptrend pattern on the chart, which suggests that the bulls are gaining control of the market. The price is still settled above the $1.20 level, which is acting as immediate support for the NEAR price. If the buying pressure loses momentum and the price drops below this mark, then it could result in a further dip toward the next support level at $1.18.

Technical indicators on the chart also confirm the bullish sentiment in the market with both the MACD line and signal line trending upwards. The histogram has moved into a positive region as well, indicating a sustainable uptrend pattern forming in the market. 

The Relative Strength Index (RSI) line is at the 36 indexes moving away from the oversold territory, which suggests buying pressure in the market. The moving average of the NEAR protocol has a value of 1.20, indicating that the current price is trading higher than the average.

Overall, the NEAR Protocol price analysis shows that the currency is in a good position to make further gains if it can break through its resistance levels. However, investors should be cautious of the bearish momentum in the market and look out for any sudden dips that could result in a further decrease. With that said, it appears that NEAR Protocol has plenty of room to grow as long as buyers maintain their control over the market.

The NEAR Protocol price prediction for 2023 estimates NEAR to attain a minimum price of $1.74 and an average value of $1.81.Near is predicted to attain a maximum price of $1.99.

Our NEAR protocol price forecast for 2024 is expected to trade at a minimum price of $2.53, with an average of $2.60 and a maximum price of $3.04 by the end of 2024.

The NEAR forecast for 2025 suggests a continuation of price rise with a minimum value of $3.60, an average price of $3.71, and a maximum value of nearly $4.38.

The NEAR Protocol technical analysis and projections for 2026 anticipate the minimum price to be around $5.45, with an average trading price of $ 5.60 and a maximum value of nearly $6.19 by the end of 2026.

In 2027, our NEAR price prediction for 2027 estimates Near protocol’s price to be trading at a minimum price today of $7.70, with an average price of $7.98 and a maximum price value nearly reaching $9.47 by the end of 2027.

The NEAR Protocol price prediction for 2028 suggests the bullish sentiment will continue with a minimum price of $11.42, an average trading volume of the price of nearly $11.82, and a maximum value of $13.46 by the end of 2028.

In 2029, our NEAR protocol price prediction forecasts NEAR to be trading at a minimum of $16.54, with an average price of nearly $17.01 and a maximum value of$19.92 by the end of 2029.

The NEAR Protocol forecast for 2030 suggests a sustained bullish sentiment with a minimum value of $24.52, an average trading price of nearly $25.20, and a maximum value of $28.42 by the end of 2030.

The Near Protocol price forecast for 2031 suggests NEAR Protocol price is forecast to reach the lowest possible level of $35.61 in 2031. As per our findings, the NEAR price could reach the maximum level of $42.80 with an average forecast price of $36.62.

In 2032, our NEAR Protocol price prediction estimates that the NEAR Protocol price could reach a minimum of $52.26, with an average value of $53.72 and a maximum value of nearly $61.32 by the end of 2032.

According to the business analysts latest data gathered, the Near price forecast is bullish for the next year as the business analyst predicts NEAR price to rise by 6.69% and reach $ 1.274097 by June 16, 2023. According to the current Near Protocol price trend, the general NEAR Protocol price prediction suggests the market sentiment is bearish, with 5 technical analysis indicators signaling bullish and 26 signaling bearish signals.

The long-term Near price predictions by Coincodex are bullish as the websites suggest the crypto-market cap might rebound as they suggest. In the best-case scenario, the NEAR price prediction for 2026 is $ 107.32 if it follows Facebook’s growth. If NEAR Protocol follows Internet growth, the prediction for 2026 would be $ 12.31.

DigitalCoinPrice has a bullish outlook on Near future prices and projects, and the market expert suggests all technical indicators, the 200-day SMA will drop soon, and the price will hit $1.90 by the end of December. By December 2023, 2024, NEAR Protocol’s short-term 50-Day SMA shows a $1.68.

The long-term price forecast for Near is for Near Protocol to be worth trading at a minimum price of $4.63, an average price of $5.37, and a maximum price forecast of $5.49.DigitalCoinPrice estimates the price of Near to attain a maximum price of $9.00, while by 2032, they expect Near to attain a maximum price of $23.26.

Technewsleader has a relatively bullish Near protocol price forecast, estimating a one-year price change of $2.13, with a trading range of $4.04 to $5.09 in 2026. The long-term price forecast by Technewsleader estimates Near to trade at a range of $12.31 to $14.77 in 2029, with a maximum possible value of $6.10  in five years. Technewsleader estimates Near Protocol to unlock its full potential in 2032, reaching a maximum price of $25.40.

According to Altcoin Doctor, a popular cryptocurrency market analyst based on Youtube, Near has the highest price and potential to spike rapidly, and they believe it can reach $10.0 by mid-2024.

As per Altcoin Doctor, Protocol Near price prediction suggests Near could hit a low of $0.97 in 2021 but will gain momentum towards 2022 and beyond. He further suggests that the long-term price forecast for Near is bullish, with an expected trade value of $31.97 by the end of 2026. The Youtuber has given a technical analysis of future price points for the cryptocurrency and believes that Near has a lot of potential to move up in price.

The Near Protocol (NEAR) began its journey in August with a vision of creating a scalable and permissionless blockchain. In October 2020, the first known trade value for NEAR was recorded at $1.072, and the price briefly spiked to $1.884 on the same day. However, the asset experienced a subsequent downturn, finding support at $0.538 by November 5th. NEAR then saw a recovery, closing the year with an annual trade price of $1.459.

The year 2021 Near Protocol price movements show an uptrend, as NEAR began trading at $1.305. By March 13th, it reached a new all-time high (ATH) of $7.572. However, a prolonged downward movement in the market pushed the price down to $1.537 by July 19th. An upward trend followed, driving the price to $11.776 on September 9th and further to $13.168 on October 26th.

On November 15th, NEAR Protocol launched its “Nightshade” sharding solution, coinciding with a price of $12.046. The year concluded with an annual trade price of $15.793.In early 2022, NEAR Protocol (NEAR) experienced significant growth. It reached its all-time high of $20.42 on January 16th. However, it underwent a correction in February and dipped below the $10 level. The price began to surge again in April, reaching a peak of $19.64 on April 8th.

However, like many other cryptocurrencies, the current value of NEAR was impacted by the broader market downturns during this year’s crypto crashes. On June 18th, its value plummeted below $5, dropping further to $2.90.

The downward trend continued throughout the end of October and into November, with NEAR falling even lower. Eventually, it dipped below its launch price, reaching a 52-week low of $1.44 on November 21st.

NEAR closed 2022 at $1.2747 and has been trading between $1.16 to $1.16 since then. The price of Near is down -15.76% in the last 30 days, while in the past week, NEAR has declined by -13.83%.

NEP-141 $NEAR token Integration with Binance. NEAR Protocol, a leading blockchain network, has been making headlines with its recent collaborations and partnerships. One notable partnership is with Binance, a prominent cryptocurrency exchange. Through this collaboration, Binance’s custody solution has added support for NEAR Protocol’s native NEP-141 $NEAR token. This integration enables NEAR token holders to securely store their assets while taking advantage of the liquidity opportunities offered by Binance. The partnership aims to attract financial institutions seeking trusted digital asset custody and settlement services to join the NEAR ecosystem. The collaboration reflects Near Protocol’s commitment to driving growth and innovation in the decentralized web space.

Onboarding a billion users to Web3. In addition to the Binance partnership, NEAR Protocol has announced a new funding strategy to fuel growth within its ecosystem. The protocol plans to evaluate its capital allocation over the past few years and make strategic adjustments to promote further expansion. The NEAR Foundation, responsible for developing and promoting NEAR, aims to deploy capital effectively to support growth initiatives while raising awareness about NEAR and onboarding a billion users to Web3. The funding strategy showcases NEAR Protocol’s dedication to driving expansion and innovation in the decentralized web.

Partnership with Mirae Asset Securities. Furthermore, NEAR Protocol has entered into a collaborative research agreement with Mirae Asset Securities, a leading financial services company in South Korea. This memorandum of understanding aims to strengthen the partnership between blockchain and Web 3.0 and expand the scope of technical collaboration. With its global blockchain network, Near Protocol provides developer-friendly tools and libraries to facilitate the development and deployment of decentralized applications (DApps).

Mirae Asset Securities aims to leverage Near Protocol’s technological capabilities to enhance its expertise and leadership in the blockchain-related financial industry. The collaboration aims to apply successful overseas cases to the domestic financial infrastructure and play a leading role in the globalization market capitalization of South Korea’s financial sector.

Additionally, Near Protocol has collaborated with MARBLEX, a company focused on multichain expansion, to enhance its Web3 networks. MARBLEX will provide MBX services to the NEAR Protocol ecosystem through its MARBLEX WARP Bridge, including MBX games, wallets, DEX, and NFT. This collaboration aims to improve the accessibility of MBX Narratives to NEAR Protocol users and gradually integrate MARBLEX’s proprietary blockchain into future projects. The partnership will also involve cooperative marketing initiatives to establish a strong global presence.

These recent developments highlight Near Protocol’s efforts to forge strategic partnerships, foster growth within its ecosystem, and expand the use cases of its blockchain technology. With collaborations with industry leaders like Binance, Mirae Asset Securities, and MARBLEX, Near Protocol aims to accelerate the adoption of Web3 and drive innovation in the decentralized web space.

Near Protocol is a decentralized platform built on the proof-of-stake consensus model. It was created to enable developers to quickly and easily build, deploy, and scale applications with minimal overhead costs. Unlike other blockchain platforms, NEAR provides an intuitive user experience via its wallets and software development kits (SDKs).

NEAR Protocol, also known as NEAR, is a layer-one blockchain platform designed to address the limitations of existing blockchains and provide a community-run cloud computing infrastructure. It aims to offer improved transaction speeds, higher throughput, and enhanced interoperability, making it an ideal environment for decentralized applications (DApps). 

One notable feature of NEAR Protocol is its use of human-readable account names, simplifying the user experience compared to the complex cryptographic wallet addresses commonly used on other blockchains like Ethereum. This user-friendly approach helps lower the entry barrier for developers and users alike.

To tackle scaling challenges, NEAR Protocol introduces innovative solutions. It has developed its consensus mechanism called “Doomslug,” which optimizes block production and reduces the time required for block confirmations. This enhances the overall performance and efficiency of the network.

NEAR Protocol is being developed by the NEAR Collective, a community-driven initiative responsible for updating the platform’s codebase and releasing regular updates to the ecosystem. The collective’s overarching goal is to build a platform that offers high security for managing valuable assets such as money, identity, and high performance to make blockchain applications practical and accessible for everyday users.

Numerous projects are being built on NEAR Protocol, showcasing its versatility and potential. For example, Flux is a protocol on NEAR that enables developers to create markets based on various assets, commodities, and real-world events. Mintbase, another project on NEAR, is an NFT (Non-Fungible Token) minting platform allowing users to create and trade unique digital assets.

With its focus on scalability, usability, and developer-friendly features, NEAR Protocol aims to contribute to the advancement of decentralized applications and foster the growth of Web 3.0, a vision of a more decentralized and user-centric internet.

NEAR Protocol tackles scalability challenges by implementing sharding, which involves dividing the network into smaller fragments called shards. Each shard is responsible for processing a specific portion of the network’s code, enabling parallel computation and enhancing the network’s capacity as the number of nodes increases. This approach reduces the computational load on individual nodes and improves transaction speeds and efficiency.

NEAR Protocol utilizes a Proof-of-Stake (PoS) consensus mechanism to achieve consensus among network nodes. Validators, who want to participate in transaction validation, must stake their NEAR tokens. Token holders who prefer not to operate a node can delegate their stake to validators of their choice. Validators are chosen through an auction system at regular intervals, with those holding larger stakes having more influence in the consensus process.

Validators in NEAR Protocol have distinct roles. Some validate chunks, which are aggregations of transactions from a specific shard, while others produce blocks containing chunks from all shards. Additionally, nodes called “fishermen” monitor the network, detect any malicious behavior, and report it. If a validator engages in improper behavior, their stake can be penalized. This system incentivizes validators to act honestly and ensures the security and integrity of the network.

Erik Trautman, Illia Polosukhin, and Alexander Skidanov co-founded NEAR Protocol (NEAR). Erik Trautman is an entrepreneur with a background in finance and Wall Street. Before NEAR Protocol, he founded Viking Education, an organization focused on training software developers. Illia Polosukhin brings over a decade of industry experience to NEAR Protocol, including three years at Google, where he worked on machine learning projects. Alexander Skidanov is a computer scientist who previously worked at Microsoft and later joined memSQL as the director of engineering.

In addition to the co-founders, NEAR Protocol boasts a talented team of experienced developers. The team includes several individuals who have achieved recognition, such as International Collegiate Programming Contest (ICPC) gold medalists and winners. The NEAR Protocol team also emphasizes their expertise in building scalable, sharded systems, which aligns with the protocol’s focus on improving blockchain scalability.

The NEAR token serves multiple purposes within the NEAR Protocol ecosystem. First and foremost, it is used to pay for transaction fees incurred when executing operations on the blockchain. Users need to hold and spend NEAR tokens to interact with decentralized applications (DApps) and perform transactions on the network.

In addition to transaction fees, NEAR tokens play a role in storing data on the blockchain. Users must provide collateral in the form of NEAR tokens to store their data securely and reliably on the network.

NEAR Protocol employs a token reward system to incentivize and reward various stakeholders within the ecosystem. Transaction validators, who play a crucial role in securing and validating the network, receive NEAR token rewards. These rewards are distributed every epoch and amount to approximately 4.5% of the total NEAR supply annually.

Furthermore, developers who create smart contracts on the NEAR platform receive a portion of the transaction fees generated by their contracts. This incentivizes developers to build and deploy innovative applications on the NEAR blockchain. The remaining portion of each transaction fee is burned, which reduces the supply of NEAR tokens and potentially increases their value over time.

NEAR Protocol is designed to support a variety of tokens, including those “wrapped” from other chains, as well as non-fungible tokens (NFTs). This versatility allows for interoperability and the seamless integration of different token standards within the NEAR ecosystem.

NEAR has established a bridge with Ethereum, enabling users to transfer ERC-20 tokens from the Ethereum network to NEAR. This bridge facilitates the movement of assets between the two blockchains, expanding the possibilities for users and developers within the NEAR ecosystem.

Resources allocated to the protocol treasury are distributed by the NEAR Foundation, a Switzerland-based non-profit dedicated to protocol maintenance, the entire crypto ecosystem with funding, and guiding the protocol’s governance. Technical upgrades to the NEAR crypto network are carried out by the Reference Maintainer, selected by the NEAR Foundation board. However, all nodes in the network must consent to updates by upgrading their software. Eventually, oversight of the Reference Maintainer will be conducted by community-elected representatives.

NEAR Protocol aims to pull ahead in the crowded race to provide the infrastructure for Web 3.0 and has sought to distinguish itself through its unique developer and user-friendly features.

The governance of the NEAR Protocol involves multiple entities and mechanisms to ensure effective management and decision-making within the ecosystem. The NEAR Foundation, a non-profit organization based in Switzerland, is crucial in protocol maintenance, ecosystem funding, and guiding governance processes. The foundation is responsible for allocating resources from the protocol treasury, which supports the development and growth of the NEAR ecosystem.

The Reference Maintainer handles technical upgrades and maintenance of the NEAR crypto network. The NEAR Foundation board initially selects the Reference Maintainer, but consensus from all network nodes is required to implement updates. This ensures that the majority of the network participants agree upon changes to the protocol. In the future, the oversight of the Reference Maintainer will transition to community-elected representatives, allowing for a more decentralized and community-driven governance approach.

NEAR Protocol is positioning itself as a leading infrastructure provider for Web 3.0 and distinguishes itself through its developer and user-friendly features. By focusing on ease of use and providing robust tools and resources for developers, NEAR aims to attract and empower a vibrant developer community. The protocol strives to create an environment that fosters innovation and encourages the creation of user-friendly decentralized applications.

NEAR Protocol (NEAR) stands out from other blockchain platforms due to its unique features and technologies. One notable innovation is the implementation of Nightshade, a variation of sharding. Nightshade enables parallel processing of transactions across multiple sharded chains, significantly improving the blockchain’s transaction throughput. With up to 100,000 transactions per second and near-instant transaction finality, NEAR Protocol achieves high performance while keeping transaction fees low.

NEAR Protocol also focuses on improving user experience and developer friendliness. It utilizes human-readable addresses, making the onboarding process simpler and more intuitive for users. Additionally, NEAR provides modular components that allow developers to quickly start projects like token contracts or non-fungible tokens (NFTs). By lowering entry barriers and offering developer-friendly tools, NEAR Protocol aims to attract a wide range of developers and facilitate the creation of innovative decentralized applications.

NEAR Protocol’s commitment to ecosystem growth is evident in its substantial ecosystem funding initiatives. With an $800 million fund, NEAR aims to support projects accelerating growth within the protocol ecosystem. The funding includes partnerships with Proximity Labs, and it encompasses scaling support for existing projects and startup grants for 20 selected startups. NEAR Protocol focuses on funding teams involved in decentralized finance (DeFi), NFTs, DAOs, and gaming, actively seeking to revolutionize how people interact with money and drive the adoption of Web3 technologies.

Moreover, NEAR Protocol aims to make blockchain technology accessible to a broader audience. The release of a JavaScript software development kit (JS SDK) allows over 20 million JavaScript programmers from the Web2 world to enter the blockchain and Web3 space easily. By leveraging JavaScript’s familiarity and widespread usage, NEAR Protocol empowers developers to build applications in a language they are proficient in, fostering broader adoption and expanding the developer community within the NEAR ecosystem.

In summary, NEAR Protocol’s unique features, including Nightshade for scalability, emphasis on user experience and developer friendliness, substantial ecosystem funding initiatives, and the introduction of a JavaScript SDK, set it apart from other blockchain platforms, positioning it as a promising contender in the blockchain industry.

NEAR Protocol has been experiencing significant expansion in its ecosystem, driven by its focus on user experience and ease of project deployment. The network recently launched the public beta version of Sender, a non-custodial mobile wallet app. The app, already connected to over 20 leading projects within the NEAR ecosystem, has garnered over 300,000 downloads of its web extension. Sender Labs, the wallet developer, received seed funding from Binance Labs and Metaweb Ventures, followed by a private funding round.

Since the mainnet launch of NEAR Protocol in 2020, the ecosystem has witnessed the introduction of nine decentralized applications (dApps) with a total value locked (TVL) of approximately $285 million as of the time of writing. This demonstrates the growing activity and adoption within the NEAR ecosystem.

NEAR Protocol has also strongly emphasized security. The Rainbow NEAR-Ethereum bridge successfully defended against two attacks, with the second attempt resulting in the hacker losing 2.5 ETH. Additionally, NEAR announced successful mitigation of two vulnerabilities on Aurora, its Ethereum sidechain, through its bug bounty program. These security measures underline the protocol’s commitment to maintaining a robust and secure environment for users and developers.

The NEAR Protocol team remains confident in their dynamically sharded Proof-of-Stake blockchain’s scalability, security, and sustainability. By combining the power of Proof-of-Stake and sharding, NEAR aims to become one of the most scalable, secure, and environmentally friendly blockchain networks in the cryptocurrency space.

(NEAR) can be purchased from several reputable cryptocurrency exchanges. Here are some popular platforms where you can buy NEAR:

Binance: Binance is one of the largest and most well-known cryptocurrency exchanges globally. You can buy NEAR directly with popular cryptocurrencies like Bitcoin (BTC) or Ethereum (ETH) on the Binance platform.

Huobi Global: Huobi is another prominent cryptocurrency exchange that offers NEAR trading pairs. You can trade cryptocurrencies like Bitcoin, Ethereum, and Tether (USDT) for NEAR on Huobi Global.

Mandala Exchange: Mandala Exchange is a user-friendly cryptocurrency exchange that provides NEAR trading options. You can buy NEAR using various cryptocurrencies supported on the platform.

OKEx: OKEx is a reputable cryptocurrency exchange where you can buy and trade NEAR. It offers NEAR trading pairs with popular cryptocurrencies such as Bitcoin and Ethereum.

NEAR Protocol (NEAR) stands out among platforms like Microsoft Azure or Amazon Web Services due to its unique selling points and fundamental features. One key aspect is the distributed network of validators, which ensures the security of the NEAR network without a single point of failure. This decentralized approach prevents hacking, tampering, and data loss, setting it apart from centrally-controlled cloud platforms. NEAR Protocol’s primary goal is to empower users by giving them control over their assets, data, and governance.

Scalability is a standout feature of NEAR Protocol. The network performs impressively through its Doomslug block production technique and Nightshade sharding system. NEAR can process up to 100,000 transactions per second, surpassing competitors like Solana. Additionally, transactions on the NEAR network finalize in less than a second, accompanied by extremely low transaction fees. The combination of Nightshade and Doomslug enhances throughput, efficiency, and network capacity, enabling lightning-fast transactions.

NEAR Protocol also prioritizes simplicity and convenience. Its use of human-readable account names makes cryptocurrency more accessible to users worldwide, reducing the complexity often associated with wallet addresses.

Interoperability is another strength of NEAR. The “Rainbow Bridge” allows users to bring tokens from Ethereum and easily build Ethereum projects using the Aurora Ethereum Virtual Machine. This seamless interoperability expands the possibilities for users and developers within the NEAR ecosystem.

NEAR Protocol’s commitment to sustainability is noteworthy. It became the world’s first climate and carbon-neutral blockchain in 2021, demonstrating its dedication to environmental responsibility.

Regarding cost-effectiveness, NEAR Protocol offers significantly lower transaction fees than other blockchain platforms. Developers are also empowered to earn 30% of transaction fees, giving them an additional incentive to contribute to the ecosystem.

Near Protocol (NEAR) is a blockchain platform with significant attention and partnerships within the cryptocurrency industry. It offers a developer-friendly environment for building decentralized applications (DApps) and addresses scalability challenges through its innovative sharding technology.

The protocol has made notable strides in expanding its ecosystem through partnerships with industry giants such as Binance and Mirae Asset Securities. These collaborations aim to enhance liquidity, foster growth, total market capitalization and drive innovation within the NEAR ecosystem.

From a technical perspective, NEAR Protocol’s use of Nightshade sharding and a Proof-of-Stake consensus mechanism enables high throughput, fast transaction finality, and low fees. The protocol’s commitment to user experience and developer-friendliness, exemplified by its human-readable account names and JavaScript SDK, further sets it apart in the blockchain space.

When considering NEAR Protocol as an investment opportunity, it’s essential to weigh multiple factors, including its partnerships, technological innovations promising growth, and market trends. Price predictions from various sources suggest a potential upward trajectory for NEAR, with projections ranging from $1.74 to $61.32 by 2032.

However, it’s important to note that cryptocurrency investments are inherently risky and subject to market volatility. Investors must conduct their research, assess risk tolerance, and seek professional advice before making investment decisions.

NEAR Protocol’s progress, ecosystem expansion, and partnerships indicate a promising future for the platform. With its focus on scalability, usability, and developer-friendly features, NEAR aims to contribute to the advancement of decentralized applications and the growth of Web 3.0.