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Bitget: Top 4 in global daily trading volume!
Please also display BTC in AR62.10%
New listings on Bitget : Pi Network
BTC/USDT$86631.13 (+1.72%)Fear at Greed Index44(Fear)
Altcoin season index:0(Bitcoin season)
Coins listed in Pre-MarketPAWS,WCTTotal spot Bitcoin ETF netflow -$157.8M (1D); -$22M (7D).Welcome gift package para sa mga bagong user na nagkakahalaga ng 6200 USDT.Claim now
Trade anumang oras, kahit saan gamit ang Bitget app. I-download ngayon
Bitget: Top 4 in global daily trading volume!
Please also display BTC in AR62.10%
New listings on Bitget : Pi Network
BTC/USDT$86631.13 (+1.72%)Fear at Greed Index44(Fear)
Altcoin season index:0(Bitcoin season)
Coins listed in Pre-MarketPAWS,WCTTotal spot Bitcoin ETF netflow -$157.8M (1D); -$22M (7D).Welcome gift package para sa mga bagong user na nagkakahalaga ng 6200 USDT.Claim now
Trade anumang oras, kahit saan gamit ang Bitget app. I-download ngayon
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Price calculator
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kategorya ng Crypto
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XAI presyoXAI
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Quote pera:
PHP
Kinukuha ang data mula sa mga third-party na provider. Ang pahinang ito at ang impormasyong ibinigay ay hindi nag-eendorso ng anumang partikular na cryptocurrency. Gustong i-trade ang mga nakalistang barya? Click here
₱0.{7}4975+0.10%1D
Price chart
Last updated as of 2025-04-02 17:57:44(UTC+0)
Market cap:--
Ganap na diluted market cap:--
Volume (24h):--
24h volume / market cap:0.00%
24h high:₱0.{7}4975
24h low:₱0.{7}4928
All-time high:₱0.{4}1111
All-time low:₱0.{9}5053
Umiikot na Supply:-- XAI
Total supply:
100,000,000,000,000XAI
Rate ng sirkulasyon:0.00%
Max supply:
--XAI
Price in BTC:0.{13}1008 BTC
Price in ETH:0.{12}4616 ETH
Price at BTC market cap:
--
Price at ETH market cap:
--
Mga kontrata:
0xD16E...3fbB08B(BNB Smart Chain (BEP20))
Ano ang nararamdaman mo tungkol sa XAI ngayon?
Tandaan: Ang impormasyong ito ay para sa sanggunian lamang.
Presyo ng XAI ngayon
Ang live na presyo ng XAI ay ₱0.{7}4975 bawat (XAI / PHP) ngayon na may kasalukuyang market cap na ₱0.00 PHP. Ang 24 na oras na dami ng trading ay ₱0.00 PHP. Ang presyong XAI hanggang PHP ay ina-update sa real time. Ang XAI ay 0.10% sa nakalipas na 24 na oras. Mayroon itong umiikot na supply ng 0 .
Ano ang pinakamataas na presyo ng XAI?
Ang XAI ay may all-time high (ATH) na ₱0.{4}1111, na naitala noong 2024-05-03.
Ano ang pinakamababang presyo ng XAI?
Ang XAI ay may all-time low (ATL) na ₱0.{9}5053, na naitala noong 2024-05-03.
Bitcoin price prediction
Kailan magandang oras para bumili ng XAI? Dapat ba akong bumili o magbenta ng XAI ngayon?
Kapag nagpapasya kung buy o mag sell ng XAI, kailangan mo munang isaalang-alang ang iyong sariling diskarte sa pag-trading. Magiiba din ang aktibidad ng pangangalakal ng mga long-term traders at short-term traders. Ang Bitget XAI teknikal na pagsusuri ay maaaring magbigay sa iyo ng sanggunian para sa trading.
Ayon sa XAI 4 na teknikal na pagsusuri, ang signal ng kalakalan ay Malakas na nagbebenta.
Ayon sa XAI 1d teknikal na pagsusuri, ang signal ng kalakalan ay Malakas na nagbebenta.
Ayon sa XAI 1w teknikal na pagsusuri, ang signal ng kalakalan ay Malakas na nagbebenta.
Ano ang magiging presyo ng XAI sa 2026?
Batay sa makasaysayang modelo ng hula sa pagganap ng presyo ni XAI, ang presyo ng XAI ay inaasahang aabot sa ₱0.{7}4922 sa 2026.
Ano ang magiging presyo ng XAI sa 2031?
Sa 2031, ang presyo ng XAI ay inaasahang tataas ng -2.00%. Sa pagtatapos ng 2031, ang presyo ng XAI ay inaasahang aabot sa ₱0.{7}9140, na may pinagsama-samang ROI na +83.49%.
XAI price history (PHP)
The price of XAI is -68.78% over the last year. The highest price of in PHP in the last year was ₱0.{4}1111 and the lowest price of in PHP in the last year was ₱0.{9}5053.
TimePrice change (%)
Lowest price
Highest price 
24h+0.10%₱0.{7}4928₱0.{7}4975
7d-28.01%₱0.{7}4806₱0.{7}7097
30d+63.03%₱0.{7}3052₱0.{6}2601
90d+16.45%₱0.{8}9306₱0.{6}2958
1y-68.78%₱0.{9}5053₱0.{4}1111
All-time-78.43%₱0.{9}5053(2024-05-03, 335 araw ang nakalipas )₱0.{4}1111(2024-05-03, 335 araw ang nakalipas )
XAI impormasyon sa merkado
XAI's market cap history
XAI holdings by concentration
Whales
Investors
Retail
XAI addresses by time held
Holders
Cruisers
Traders
Live coinInfo.name (12) price chart
XAI na mga rating
Mga average na rating mula sa komunidad
4.4
Ang nilalamang ito ay para sa mga layuning pang-impormasyon lamang.
XAI sa lokal na pera
1 XAI To MXN$01 XAI To GTQQ01 XAI To CLP$01 XAI To HNLL01 XAI To UGXSh01 XAI To ZARR01 XAI To TNDد.ت01 XAI To IQDع.د01 XAI To TWDNT$01 XAI To RSDдин.01 XAI To DOP$01 XAI To MYRRM01 XAI To GEL₾01 XAI To UYU$01 XAI To MADد.م.01 XAI To OMRر.ع.01 XAI To AZN₼01 XAI To SEKkr01 XAI To KESSh01 XAI To UAH₴0
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Last updated as of 2025-04-02 17:57:44(UTC+0)
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Ang mga tao ay nagtatanong din tungkol sa presyo ng XAI.
Ano ang kasalukuyang presyo ng XAI?
The live price of XAI is ₱0 per (XAI/PHP) with a current market cap of ₱0 PHP. XAI's value undergoes frequent fluctuations due to the continuous 24/7 activity in the crypto market. XAI's current price in real-time and its historical data is available on Bitget.
Ano ang 24 na oras na dami ng trading ng XAI?
Sa nakalipas na 24 na oras, ang dami ng trading ng XAI ay ₱0.00.
Ano ang all-time high ng XAI?
Ang all-time high ng XAI ay ₱0.{4}1111. Ang pinakamataas na presyong ito sa lahat ng oras ay ang pinakamataas na presyo para sa XAI mula noong inilunsad ito.
Maaari ba akong bumili ng XAI sa Bitget?
Oo, ang XAI ay kasalukuyang magagamit sa sentralisadong palitan ng Bitget. Para sa mas detalyadong mga tagubilin, tingnan ang aming kapaki-pakinabang na gabay na Paano bumili ng .
Maaari ba akong makakuha ng matatag na kita mula sa investing sa XAI?
Siyempre, nagbibigay ang Bitget ng estratehikong platform ng trading, na may mga matatalinong bot sa pangangalakal upang i-automate ang iyong mga pangangalakal at kumita ng kita.
Saan ako makakabili ng XAI na may pinakamababang bayad?
Ikinalulugod naming ipahayag na ang estratehikong platform ng trading ay magagamit na ngayon sa Bitget exchange. Nag-ooffer ang Bitget ng nangunguna sa industriya ng mga trading fee at depth upang matiyak ang kumikitang pamumuhunan para sa mga trader.
Saan ako makakabili ng crypto?
Video section — quick verification, quick trading

How to complete identity verification on Bitget and protect yourself from fraud
1. Log in to your Bitget account.
2. If you're new to Bitget, watch our tutorial on how to create an account.
3. Hover over your profile icon, click on “Unverified”, and hit “Verify”.
4. Choose your issuing country or region and ID type, and follow the instructions.
5. Select “Mobile Verification” or “PC” based on your preference.
6. Enter your details, submit a copy of your ID, and take a selfie.
7. Submit your application, and voila, you've completed identity verification!
Ang mga investment sa Cryptocurrency, kabilang ang pagbili ng XAI online sa pamamagitan ng Bitget, ay napapailalim sa market risk. Nagbibigay ang Bitget ng madali at convenient paraan para makabili ka ng XAI, at sinusubukan namin ang aming makakaya upang ganap na ipaalam sa aming mga user ang tungkol sa bawat cryptocurrency na i-eooffer namin sa exchange. Gayunpaman, hindi kami mananagot para sa mga resulta na maaaring lumabas mula sa iyong pagbili ng XAI. Ang page na ito at anumang impormasyong kasama ay hindi isang pag-endorso ng anumang partikular na cryptocurrency.
Bitget Insights
Mahnoor-Baloch007
1d
AI agents and AI are related but distinct concepts in the field of artificial intelligence.
AI (Artificial Intelligence)
1. Definition: AI refers to the broad field of study focused on creating intelligent machines that can perform tasks that typically require human intelligence.
2. Characteristics: AI systems can process and analyze large amounts of data, learn from experiences, and make decisions based on that data.
3. Examples: AI-powered chatbots, image recognition systems, and natural language processing tools.
AI Agents
1. Definition: AI agents are a specific type of AI system that can autonomously perform tasks on behalf of a user or another system.
2. Characteristics: AI agents have the ability to design their own workflow, utilize available tools, and interact with external environments to achieve complex goals.
3. Examples: AI-powered trading bots, autonomous vehicles, and smart home systems.
Key Differences
1. Autonomy: AI agents have a higher level of autonomy compared to traditional AI systems, allowing them to make decisions and take actions independently.
2. Interactivity: AI agents can interact with their environment and other systems, whereas traditional AI systems may only process data internally.
3. Proactivity: AI agents can anticipate and prevent problems, whereas traditional AI systems may only react to problems after they occur.
4. Complexity: AI agents often require more complex decision-making and problem-solving capabilities compared to traditional AI systems.
In summary, while AI refers to the broader field of artificial intelligence, AI agents are a specific type of AI system that can autonomously perform tasks, interact with their environment, and make decisions independently.
Thank you...🙂
$BTC $ETH $SOL $PI $XRP $DOGE $SHIB $SUNDOG $MEME $AI $XAI $PEPECOIN $PIPPIN $ORAI $ETC $WHY $U2U
SUNDOG+6.45%
BTC+1.73%

Crypto_inside
1d
Machine learning ❌ Traditional learning. 🧐😵💫
Machine learning and traditional learning are two distinct approaches to learning and problem-solving.
Traditional Learning:
1. Rule-based: Traditional learning involves explicit programming and rule-based systems.
2. Human expertise: Traditional learning relies on human expertise and manual feature engineering.
3. Fixed models: Traditional learning uses fixed models that are not updated automatically.
Machine Learning:
1. Data-driven: Machine learning involves learning from data and improving over time.
2. Algorithmic: Machine learning relies on algorithms that can learn from data and make predictions.
3. Adaptive models: Machine learning uses adaptive models that can update automatically based on new data.
Key Differences:
1. Learning style: Traditional learning is rule-based, while machine learning is data-driven.
2. Scalability: Machine learning can handle large datasets and complex problems, while traditional learning is limited by human expertise.
3. Accuracy: Machine learning can achieve higher accuracy than traditional learning, especially in complex domains.
Advantages of Machine Learning:
1. Improved accuracy: Machine learning can achieve higher accuracy than traditional learning.
2. Increased efficiency: Machine learning can automate many tasks, freeing up human experts for more complex tasks.
3. Scalability: Machine learning can handle large datasets and complex problems.
Disadvantages of Machine Learning:
1. Data quality: Machine learning requires high-quality data to learn effectively.
2. Interpretability: Machine learning models can be difficult to interpret and understand.
3. Bias: Machine learning models can perpetuate biases present in the training data.
When to Use Machine Learning:
1. Complex problems: Machine learning is well-suited for complex problems that require pattern recognition and prediction.
2. Large datasets: Machine learning can handle large datasets and identify trends and patterns.
3. Automating tasks: Machine learning can automate many tasks, freeing up human experts for more complex tasks.
When to Use Traditional Learning:
1. Simple problems: Traditional learning is well-suited for simple problems that require explicit programming and rule-based systems.
2. Small datasets: Traditional learning is suitable for small datasets where machine learning may not be effective.
3. Human expertise: Traditional learning relies on human expertise and manual feature engineering, making it suitable for domains where human expertise is essential.
Thank you...🙂
$BTC $ETH $SOL $PI $AI $XAI $BGB $BNB $DOGE $DOGS $SHIB $BONK $MEME $XRP $ADA $U2U $WUF $PARTI $WHY
BTC+1.73%
BGB-1.56%

Crypto_inside
1d
What is Q-learning...🤔🤔??
Q-learning is a type of reinforcement learning algorithm used in machine learning and artificial intelligence. It's a model-free, off-policy learning algorithm that helps agents learn to make decisions in complex, uncertain environments.
Key Components:
1. Agent: The decision-maker that interacts with the environment.
2. Environment: The external system with which the agent interacts.
3. Actions: The decisions made by the agent.
4. Rewards: The feedback received by the agent for its actions.
5. Q-function: A mapping from states and actions to expected rewards.
How Q-learning Works:
1. Initialization: The agent starts with an arbitrary Q-function.
2. Exploration: The agent selects an action and observes the resulting state and reward.
3. Update: The agent updates its Q-function based on the observed reward and the expected reward for the next state.
4. Exploitation: The agent chooses the action with the highest Q-value for the current state.
Advantages:
1. Simple to implement: Q-learning is a straightforward algorithm to understand and code.
2. Effective in complex environments: Q-learning can handle complex, dynamic environments with many states and actions.
Disadvantages:
1. Slow convergence: Q-learning can require many iterations to converge to an optimal policy.
2. Sensitive to hyperparameters: The performance of Q-learning is highly dependent on the choice of hyperparameters.
Q-learning is a powerful algorithm for reinforcement learning, but it can be challenging to tune and may not always converge to an optimal solution.
Thank you...🙂
$BTC $ETH $SOL $PI $AI $XAI $XRP $BGB $BNB $DOGE $DOGS $SHIB $BONK $FLOKI $U2U $WUF $WHY $SUNDOG $COQ $PEPE
SUNDOG+6.45%
BTC+1.73%

Crypto_inside
1d
What is Machine learning..🤔🤔??
Machine learning is a subset of artificial intelligence (AI) that involves training algorithms to learn from data and make predictions, decisions, or recommendations without being explicitly programmed.
Key Characteristics:
1. Learning from data: Machine learning algorithms learn patterns and relationships in data.
2. Improving over time: Machine learning models improve their performance as they receive more data.
3. Making predictions or decisions: Machine learning models make predictions, decisions, or recommendations based on the learned patterns.
Types of Machine Learning:
1. Supervised Learning: The algorithm learns from labeled data to make predictions.
2. Unsupervised Learning: The algorithm learns from unlabeled data to identify patterns.
3. Reinforcement Learning: The algorithm learns through trial and error to achieve a goal.
4. Semi-supervised Learning: The algorithm learns from a combination of labeled and unlabeled data.
5. Deep Learning: A subset of machine learning that uses neural networks with multiple layers.
Machine Learning Applications:
1. Image Recognition: Image classification, object detection, and facial recognition.
2. Natural Language Processing (NLP): Text classification, sentiment analysis, and language translation.
3. Speech Recognition: Speech-to-text and voice recognition.
4. Predictive Analytics: Forecasting, regression, and decision-making.
5. Recommendation Systems: Personalized product recommendations.
Machine Learning Algorithms:
1. Linear Regression: Linear models for regression tasks.
2. Decision Trees: Tree-based models for classification and regression.
3. Random Forest: Ensemble learning for classification and regression.
4. Support Vector Machines (SVMs): Linear and non-linear models for classification and regression.
5. Neural Networks: Deep learning models for complex tasks.
Machine Learning Tools and Frameworks:
1. TensorFlow: Open-source deep learning framework.
2. PyTorch: Open-source deep learning framework.
3. Scikit-learn: Open-source machine learning library.
4. Keras: High-level neural networks API.
Machine learning has numerous applications across industries, including healthcare, finance, marketing, and more. Its ability to learn from data and improve over time makes it a powerful tool for solving complex problems.
Thank you...🙂
$BTC $ETH $SOL $PI $AI $XAI $BGB $BNB $DOGE $SHIB $FLOKI $BONK $U2U $WUF $WHY $SUNDOG $PARTI $XRP
SUNDOG+6.45%
BTC+1.73%

Kanyalal
2d
AI agents and AI are related but distinct concepts in the field of artificial intelligence.
AI (Artificial Intelligence)
1. Definition: AI refers to the broad field of study focused on creating intelligent machines that can perform tasks that typically require human intelligence.
2. Characteristics: AI systems can process and analyze large amounts of data, learn from experiences, and make decisions based on that data.
3. Examples: AI-powered chatbots, image recognition systems, and natural language processing tools.
AI Agents
1. Definition: AI agents are a specific type of AI system that can autonomously perform tasks on behalf of a user or another system.
2. Characteristics: AI agents have the ability to design their own workflow, utilize available tools, and interact with external environments to achieve complex goals.
3. Examples: AI-powered trading bots, autonomous vehicles, and smart home systems.
Key Differences
1. Autonomy: AI agents have a higher level of autonomy compared to traditional AI systems, allowing them to make decisions and take actions independently.
2. Interactivity: AI agents can interact with their environment and other systems, whereas traditional AI systems may only process data internally.
3. Proactivity: AI agents can anticipate and prevent problems, whereas traditional AI systems may only react to problems after they occur.
4. Complexity: AI agents often require more complex decision-making and problem-solving capabilities compared to traditional AI systems.
In summary, while AI refers to the broader field of artificial intelligence, AI agents are a specific type of AI system that can autonomously perform tasks, interact with their environment, and make decisions independently.
Thank you...🙂
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SUNDOG+6.45%
BTC+1.73%
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