#NLP Models

[gnosticgnoob]

I would have painted this myself but I didn't have to

[riazhatvi]

People Think It’s Fake" | DeepSeek vs ChatGPT: The Ultimate 2024 Comparison (SEO-Optimized Guide)

The AI wars are heating up, and two giants—DeepSeek and ChatGPT—are battling for dominance. But why do so many users call DeepSeek "fake" while praising ChatGPT? Is it a myth, or is there truth to the claims? In this deep dive, we’ll uncover the facts, debunk myths, and reveal which AI truly reigns supreme. Plus, learn pro SEO tips to help this article outrank competitors on Google!

Chapters

00:00 Introduction - DeepSeek: China’s New AI Innovation

00:15 What is DeepSeek?

00:30 DeepSeek’s Impressive Statistics

00:50 Comparison: DeepSeek vs GPT-4

01:10 Technology Behind DeepSeek

01:30 Impact on AI, Finance, and Trading

01:50 DeepSeek’s Effect on Bitcoin & Trading

02:10 Future of AI with DeepSeek

02:25 Conclusion - The Future is Here!

Why Do People Call DeepSeek "Fake"? (The Truth Revealed)

The Language Barrier Myth

DeepSeek is trained primarily on Chinese-language data, leading to awkward English responses.

Example: A user asked, "Write a poem about New York," and DeepSeek referenced skyscrapers as "giant bamboo shoots."

SEO Keyword: "DeepSeek English accuracy."

Cultural Misunderstandings

DeepSeek’s humor, idioms, and examples cater to Chinese audiences. Global users find this confusing.

ChatGPT, trained on Western data, feels more "relatable" to English speakers.

Lack of Transparency

Unlike OpenAI’s detailed GPT-4 technical report, DeepSeek’s training data and ethics are shrouded in secrecy.

LSI Keyword: "DeepSeek data sources."

Viral "Fail" Videos

TikTok clips show DeepSeek claiming "The Earth is flat" or "Elon Musk invented Bitcoin." Most are outdated or edited—ChatGPT made similar errors in 2022!

DeepSeek vs ChatGPT: The Ultimate 2024 Comparison

1. Language & Creativity

ChatGPT: Wins for English content (blogs, scripts, code).

Strengths: Natural flow, humor, and cultural nuance.

Weakness: Overly cautious (e.g., refuses to write "controversial" topics).

DeepSeek: Best for Chinese markets (e.g., Baidu SEO, WeChat posts).

Strengths: Slang, idioms, and local trends.

Weakness: Struggles with Western metaphors.

SEO Tip: Use keywords like "Best AI for Chinese content" or "DeepSeek Baidu SEO."

2. Technical Abilities

Coding:

ChatGPT: Solves Python/JavaScript errors, writes clean code.

DeepSeek: Better at Alibaba Cloud APIs and Chinese frameworks.

Data Analysis:

Both handle spreadsheets, but DeepSeek integrates with Tencent Docs.

3. Pricing & Accessibility

FeatureDeepSeekChatGPTFree TierUnlimited basic queriesGPT-3.5 onlyPro Plan$10/month (advanced Chinese tools)$20/month (GPT-4 + plugins)APIsCheaper for bulk Chinese tasksGlobal enterprise support

SEO Keyword: "DeepSeek pricing 2024."

Debunking the "Fake AI" Myth: 3 Case Studies

Case Study 1: A Shanghai e-commerce firm used DeepSeek to automate customer service on Taobao, cutting response time by 50%.

Case Study 2: A U.S. blogger called DeepSeek "fake" after it wrote a Chinese-style poem about pizza—but it went viral in Asia!

Case Study 3: ChatGPT falsely claimed "Google acquired OpenAI in 2023," proving all AI makes mistakes.

How to Choose: DeepSeek or ChatGPT?

Pick ChatGPT if:

You need English content, coding help, or global trends.

You value brand recognition and transparency.

Pick DeepSeek if:

You target Chinese audiences or need cost-effective APIs.

You work with platforms like WeChat, Douyin, or Alibaba.

LSI Keyword: "DeepSeek for Chinese marketing."

SEO-Optimized FAQs (Voice Search Ready!)

"Is DeepSeek a scam?" No! It’s a legitimate AI optimized for Chinese-language tasks.

"Can DeepSeek replace ChatGPT?" For Chinese users, yes. For global content, stick with ChatGPT.

"Why does DeepSeek give weird answers?" Cultural gaps and training focus. Use it for specific niches, not general queries.

"Is DeepSeek safe to use?" Yes, but avoid sensitive topics—it follows China’s internet regulations.

Pro Tips to Boost Your Google Ranking

Sprinkle Keywords Naturally: Use "DeepSeek vs ChatGPT" 4–6 times.

Internal Linking: Link to related posts (e.g., "How to Use ChatGPT for SEO").

External Links: Cite authoritative sources (OpenAI’s blog, DeepSeek’s whitepapers).

Mobile Optimization: 60% of users read via phone—use short paragraphs.

Engagement Hooks: Ask readers to comment (e.g., "Which AI do you trust?").

Final Verdict: Why DeepSeek Isn’t Fake (But ChatGPT Isn’t Perfect)

The "fake" label stems from cultural bias and misinformation. DeepSeek is a powerhouse in its niche, while ChatGPT rules Western markets. For SEO success:

Target long-tail keywords like "Is DeepSeek good for Chinese SEO?"

Use schema markup for FAQs and comparisons.

Update content quarterly to stay ahead of AI updates.

🚀 Ready to Dominate Google? Share this article, leave a comment, and watch it climb to #1!

Follow for more AI vs AI battles—because in 2024, knowledge is power! 🔍

[pencilrecords]

Growing in a tall man's shadow

There is a debate happening in the halls of linguistics and the implications are not insignificant.

At question is the idea of recursion: since the 1950s, linguists have held that recursion is a defining characteristics of human language.

What happens then, when a human language is found to be non-recursive?

Here, Noam Chomsky, who first placed the idea of recursion on the table, is the tall man.

And, Daniel Everett, a former missionary to the Piraha tribe in the Amazon forest, is the upstart.

At stake is one of the most important ideas in modern linguistics: recursion.

Does a human language have to be recursive? That's the question Everett poses; and advances the argument that recursion is not inherent to being human.

From the Youtube description of the documentary:

Deep in the Amazon rainforest, the Pirahã people speak a language that defies everything we thought we knew about human communication. No words for colors. No numbers. No past. No future. Their unique way of speaking has ignited one of the most heated debates in linguistic history. For 30 years, one man tried to decode their near-indecipherable language—described by The New Yorker as “a profusion of songbirds” and “barely discernible as speech”. In the process, he shook the very foundations of modern linguistics and challenged one of the most dominant theories of the last 50 years: Noam Chomsky’s Universal Grammar. According to this theory, all human languages share a deep, innate structure—something we are born with rather than learn. But if the Pirahã language truly exists outside these rules, does it mean that everything we believed about language was wrong? If so, one of the most powerful ideas in linguistics could crumble.

Documentary: The Amazon Code

Directed by: Randal Wood, Michael O’Neill

Production : Essential Media, Entertainment Production, ABC Australia, Smithsonian Networks & Arte France

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-==-=-=-=-=-=-=-

I think that there is more to come with this story, so here is a running list of info by and from people who interact with the idea on a regular basis and actually know what they're talking about:

The Battle of the Linguists - Piraha Part 2 by K. Klein

[aibyrdidini]

SEMANTIC TREE AND AI TECHNOLOGIES

Semantic Tree learning and AI technologies can be combined to solve problems by leveraging the power of natural language processing and machine learning.

Semantic trees are a knowledge representation technique that organizes information in a hierarchical, tree-like structure.

Each node in the tree represents a concept or entity, and the connections between nodes represent the relationships between those concepts.

This structure allows for the representation of complex, interconnected knowledge in a way that can be easily navigated and reasoned about.

CONCEPTS

Semantic Tree: A structured representation where nodes correspond to concepts and edges denote relationships (e.g., hyponyms, hyponyms, synonyms).

Meaning: Understanding the context, nuances, and associations related to words or concepts.

Natural Language Understanding (NLU): AI techniques for comprehending and interpreting human language.

First Principles: Fundamental building blocks or core concepts in a domain.

AI (Artificial Intelligence): AI refers to the development of computer systems that can perform tasks that typically require human intelligence. AI technologies include machine learning, natural language processing, computer vision, and more. These technologies enable computers to understand reason, learn, and make decisions.

Natural Language Processing (NLP): NLP is a branch of AI that focuses on the interaction between computers and human language. It involves the analysis and understanding of natural language text or speech by computers. NLP techniques are used to process, interpret, and generate human languages.

Machine Learning (ML): Machine Learning is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed. ML algorithms can analyze data, identify patterns, and make predictions or decisions based on the learned patterns.

Deep Learning: A subset of machine learning that uses neural networks with multiple layers to learn complex patterns.

EXAMPLES OF APPLYING SEMANTIC TREE LEARNING WITH AI.

1. Text Classification: Semantic Tree learning can be combined with AI to solve text classification problems. By training a machine learning model on labeled data, the model can learn to classify text into different categories or labels. For example, a customer support system can use semantic tree learning to automatically categorize customer queries into different topics, such as billing, technical issues, or product inquiries.

2. Sentiment Analysis: Semantic Tree learning can be used with AI to perform sentiment analysis on text data. Sentiment analysis aims to determine the sentiment or emotion expressed in a piece of text, such as positive, negative, or neutral. By analyzing the semantic structure of the text using Semantic Tree learning techniques, machine learning models can classify the sentiment of customer reviews, social media posts, or feedback.

3. Question Answering: Semantic Tree learning combined with AI can be used for question answering systems. By understanding the semantic structure of questions and the context of the information being asked, machine learning models can provide accurate and relevant answers. For example, a Chabot can use Semantic Tree learning to understand user queries and provide appropriate responses based on the analyzed semantic structure.

4. Information Extraction: Semantic Tree learning can be applied with AI to extract structured information from unstructured text data. By analyzing the semantic relationships between entities and concepts in the text, machine learning models can identify and extract specific information. For example, an AI system can extract key information like names, dates, locations, or events from news articles or research papers.

Python Snippet Codes for Semantic Tree Learning with AI

Here are four small Python code snippets that demonstrate how to apply Semantic Tree learning with AI using popular libraries:

1. Text Classification with scikit-learn:

```python

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.linear_model import LogisticRegression

# Training data

texts = ['This is a positive review', 'This is a negative review', 'This is a neutral review']

labels = ['positive', 'negative', 'neutral']

# Vectorize the text data

vectorizer = TfidfVectorizer()

X = vectorizer.fit_transform(texts)

# Train a logistic regression classifier

classifier = LogisticRegression()

classifier.fit(X, labels)

# Predict the label for a new text

new_text = 'This is a positive sentiment'

new_text_vectorized = vectorizer.transform([new_text])

predicted_label = classifier.predict(new_text_vectorized)

print(predicted_label)

```

2. Sentiment Analysis with TextBlob:

```python

from textblob import TextBlob

# Analyze sentiment of a text

text = 'This is a positive sentence'

blob = TextBlob(text)

sentiment = blob.sentiment.polarity

# Classify sentiment based on polarity

if sentiment > 0:

sentiment_label = 'positive'

elif sentiment < 0:

sentiment_label = 'negative'

else:

sentiment_label = 'neutral'

print(sentiment_label)

```

3. Question Answering with Transformers:

```python

from transformers import pipeline

# Load the question answering model

qa_model = pipeline('question-answering')

# Provide context and ask a question

context = 'The Semantic Web is an extension of the World Wide Web.'

question = 'What is the Semantic Web?'

# Get the answer

answer = qa_model(question=question, context=context)

print(answer['answer'])

```

4. Information Extraction with spaCy:

```python

import spacy

# Load the English language model

nlp = spacy.load('en_core_web_sm')

# Process text and extract named entities

text = 'Apple Inc. is planning to open a new store in New York City.'

doc = nlp(text)

# Extract named entities

entities = [(ent.text, ent.label_) for ent in doc.ents]

print(entities)

```

APPLICATIONS OF SEMANTIC TREE LEARNING WITH AI

Semantic Tree learning combined with AI can be used in various domains and industries to solve problems. Here are some examples of where it can be applied:

1. Customer Support: Semantic Tree learning can be used to automatically categorize and route customer queries to the appropriate support teams, improving response times and customer satisfaction.

2. Social Media Analysis: Semantic Tree learning with AI can be applied to analyze social media posts, comments, and reviews to understand public sentiment, identify trends, and monitor brand reputation.

3. Information Retrieval: Semantic Tree learning can enhance search engines by understanding the meaning and context of user queries, providing more accurate and relevant search results.

4. Content Recommendation: By analyzing the semantic structure of user preferences and content metadata, Semantic Tree learning with AI can be used to personalize content recommendations in platforms like streaming services, news aggregators, or e-commerce websites.

Semantic Tree learning combined with AI technologies enables the understanding and analysis of text data, leading to improved problem-solving capabilities in various domains.

COMBINING SEMANTIC TREE AND AI FOR PROBLEM SOLVING

1. Semantic Reasoning: By integrating semantic trees with AI, systems can engage in more sophisticated reasoning and decision-making. The semantic tree provides a structured representation of knowledge, while AI techniques like natural language processing and knowledge representation can be used to navigate and reason about the information in the tree.

2. Explainable AI: Semantic trees can make AI systems more interpretable and explainable. The hierarchical structure of the tree can be used to trace the reasoning process and understand how the system arrived at a particular conclusion, which is important for building trust in AI-powered applications.

3. Knowledge Extraction and Representation: AI techniques like machine learning can be used to automatically construct semantic trees from unstructured data, such as text or images. This allows for the efficient extraction and representation of knowledge, which can then be used to power various problem-solving applications.

4. Hybrid Approaches: Combining semantic trees and AI can lead to hybrid approaches that leverage the strengths of both. For example, a system could use a semantic tree to represent domain knowledge and then apply AI techniques like reinforcement learning to optimize decision-making within that knowledge structure.

EXAMPLES OF APPLYING SEMANTIC TREE AND AI FOR PROBLEM SOLVING

1. Medical Diagnosis: A semantic tree could represent the relationships between symptoms, diseases, and treatments. AI techniques like natural language processing and machine learning could be used to analyze patient data, navigate the semantic tree, and provide personalized diagnosis and treatment recommendations.

2. Robotics and Autonomous Systems: Semantic trees could be used to represent the knowledge and decision-making processes of autonomous systems, such as self-driving cars or drones. AI techniques like computer vision and reinforcement learning could be used to navigate the semantic tree and make real-time decisions in dynamic environments.

3. Financial Analysis: Semantic trees could be used to model complex financial relationships and market dynamics. AI techniques like predictive analytics and natural language processing could be applied to the semantic tree to identify patterns, make forecasts, and support investment decisions.

4. Personalized Recommendation Systems: Semantic trees could be used to represent user preferences, interests, and behaviors. AI techniques like collaborative filtering and content-based recommendation could be used to navigate the semantic tree and provide personalized recommendations for products, content, or services.

PYTHON CODE SNIPPETS

1. Semantic Tree Construction using NetworkX:

```python

import networkx as nx

import matplotlib.pyplot as plt

# Create a semantic tree

G = nx.DiGraph()

G.add_node("root", label="Root")

G.add_node("concept1", label="Concept 1")

G.add_node("concept2", label="Concept 2")

G.add_node("concept3", label="Concept 3")

G.add_edge("root", "concept1")

G.add_edge("root", "concept2")

G.add_edge("concept2", "concept3")

# Visualize the semantic tree

pos = nx.spring_layout(G)

nx.draw(G, pos, with_labels=True)

plt.show()

```

2. Semantic Reasoning using PyKEEN:

```python

from pykeen.models import TransE

from pykeen.triples import TriplesFactory

# Load a knowledge graph dataset

tf = TriplesFactory.from_path("./dataset/")

# Train a TransE model on the knowledge graph

model = TransE(triples_factory=tf)

model.fit(num_epochs=100)

# Perform semantic reasoning

head = "concept1"

relation = "isRelatedTo"

tail = "concept3"

score = model.score_hrt(head, relation, tail)

print(f"The score for the triple ({head}, {relation}, {tail}) is: {score}")

```

3. Knowledge Extraction using spaCy:

```python

import spacy

# Load the spaCy model

nlp = spacy.load("en_core_web_sm")

# Extract entities and relations from text

text = "The quick brown fox jumps over the lazy dog."

doc = nlp(text)

# Visualize the extracted knowledge

from spacy import displacy

displacy.render(doc, style="ent")

```

4. Hybrid Approach using Ray:

```python

import ray

from ray.rllib.agents.ppo import PPOTrainer

from ray.rllib.env.multi_agent_env import MultiAgentEnv

from ray.rllib.models.tf.tf_modelv2 import TFModelV2

# Define a custom model that integrates a semantic tree

class SemanticTreeModel(TFModelV2):

def __init__(self, obs_space, action_space, num_outputs, model_config, name):

super().__init__(obs_space, action_space, num_outputs, model_config, name)

# Implement the integration of the semantic tree with the neural network

# Define a multi-agent environment that uses the semantic tree model

class SemanticTreeEnv(MultiAgentEnv):

def __init__(self):

self.semantic_tree = # Initialize the semantic tree

self.agents = # Define the agents

def step(self, actions):

# Implement the environment dynamics using the semantic tree

# Train the hybrid model using Ray

ray.init()

config = {

"env": SemanticTreeEnv,

"model": {

"custom_model": SemanticTreeModel,

},

}

trainer = PPOTrainer(config=config)

trainer.train()

```

APPLICATIONS

The combination of semantic trees and AI can be applied to a wide range of problem domains, including:

- Healthcare: Improving medical diagnosis, treatment planning, and drug discovery.

- Finance: Enhancing investment strategies, risk management, and fraud detection.

- Robotics and Autonomous Systems: Enabling more intelligent and adaptable decision-making in complex environments.

- Education: Personalizing learning experiences and providing intelligent tutoring systems.

- Smart Cities: Optimizing urban planning, transportation, and resource management.

- Environmental Conservation: Modeling and predicting environmental changes, and supporting sustainable decision-making.

- Chatbots and Virtual Assistants:

Use semantic trees to understand user queries and provide context-aware responses.

Apply NLU models to extract meaning from user input.

- Information Retrieval:

Build semantic search engines that understand user intent beyond keyword matching.

Combine semantic trees with vector embeddings (e.g., BERT) for better search results.

- Medical Diagnosis:

Create semantic trees for medical conditions, symptoms, and treatments.

Use AI to match patient symptoms to relevant diagnoses.

- Automated Content Generation:

Construct semantic trees for topics (e.g., climate change, finance).

Generate articles, summaries, or reports based on semantic understanding.

RDIDINI PROMPT ENGINEER

[pinolitas]

I'm pretty sure all those tools people recognize as ai are actually machine learning because isn't ai supposed to be predictive rather than generative

[bunchacrunchcake]

AI is retarted part 1 million

Find me 20 of X company in my location:

[Lists 20 companies.]

How many of these are real, how many did you make up?

"You're absolutely right to question this. Looking back at my response, I need to be more honest"

[lists 10 of the companies as fake]

I do a google search and find out one of the fake companies is real.

"You're absolutely correct, and I apologize for the error. This highlights an important issue with my previous response: I made assumptions about which companies were "hallucinated" without carefully re-checking the search results." So, um, real question, what are you guys using this for? THERAPY???!!! ROMANCE ?!!!! RUNNING A BUSINESS?!! CUSTOMER SERVICE?!!!

Y'all are dumber than the AI.

[jenniferphilop0420]

AI Software Development in USA for Smart Business Growth

Artificial Intelligence (AI) is revolutionizing industries across the globe—and nowhere is this transformation more evident than in the United States. As businesses race to stay competitive, AI software development in USA has emerged as a cornerstone for driving innovation, boosting efficiency, and enabling smart business growth. From personalized customer experiences to automation of complex operations, AI solutions are becoming vital for modern enterprises.

Why the USA Leads in AI Software Development

The United States remains a global leader in AI innovation due to its advanced technological infrastructure, top-tier talent, and thriving startup ecosystem. Leading universities and research labs fuel continual innovation, while tech giants like Google, Microsoft, and IBM heavily invest in AI R&D.

What makes AI software development in USA particularly compelling is the convergence of cutting-edge technologies, agile development processes, and a business-friendly environment. Whether it’s Silicon Valley, New York, or Austin, AI development firms in the U.S. are setting the pace for digital transformation.

Benefits of AI Software Development for Businesses

1. Enhanced Customer Experience

AI-powered chatbots, virtual assistants, and recommendation engines offer personalized interactions that increase customer satisfaction and loyalty. U.S.-based AI developers build intelligent systems that learn from user behavior and deliver tailored solutions in real time.

2. Automation of Repetitive Tasks

AI automates mundane and repetitive tasks, freeing up employees to focus on strategic initiatives. From automating emails to processing invoices, American AI development companies design custom automation tools that improve productivity and reduce operational costs.

3. Predictive Analytics for Smarter Decisions

AI enables businesses to make data-driven decisions by analyzing large datasets and forecasting future trends. Companies in the USA are building AI tools that help executives anticipate market changes, customer needs, and inventory demands with high accuracy.

4. Scalability and Flexibility

AI solutions developed in the USA are highly scalable, allowing businesses to grow without limitations. Cloud-based AI software ensures seamless integration with existing systems and easy expansion as your business evolves.

Key Industries Leveraging AI in the USA

● Healthcare

AI is transforming diagnostics, drug discovery, and patient care. U.S. firms are leading the way in developing AI applications that analyze medical images, predict disease progression, and enhance treatment plans.

● Finance

Financial institutions are using AI for fraud detection, risk assessment, and algorithmic trading. AI software development in USA enables banks and fintech firms to make faster and more accurate financial decisions.

● Retail & E-Commerce

AI enhances inventory management, personalized marketing, and customer support. U.S.-based retailers use AI to analyze consumer behavior and optimize the buyer journey.

● Manufacturing

Predictive maintenance, robotic process automation, and smart logistics are made possible with AI. American manufacturers are embracing AI to streamline production and reduce downtime.

● Real Estate

AI-powered valuation tools, virtual property tours, and smart contract systems are gaining popularity. Real estate companies in the USA are leveraging AI to improve property management and customer engagement.

Choosing the Right AI Software Development Company in the USA

When selecting a partner for AI software development in USA, businesses should consider several critical factors:

✔ Expertise & Experience

Look for firms with a proven track record in developing AI applications across various industries.

✔ Customization

Choose a company that offers tailor-made AI solutions suited to your business goals, rather than one-size-fits-all platforms.

✔ Integration Capabilities

The AI software should seamlessly integrate with your existing systems and technologies.

✔ Ongoing Support

AI models need regular updates and optimization. A reliable U.S. AI development partner offers continuous support and upgrades.

✔ Compliance & Security

Ensure the company follows U.S. data privacy regulations like HIPAA and GDPR, especially if your application involves sensitive data.

Popular AI Technologies Used in U.S. Software Development

Machine Learning (ML) – Enables systems to learn and improve over time.

Natural Language Processing (NLP) – Powers chatbots, sentiment analysis, and voice assistants.

Computer Vision – Used for image recognition and video analytics.

Robotic Process Automation (RPA) – Automates business workflows.

Generative AI – Builds new content, such as text, images, and even code.

Future Trends in AI Software Development in USA

🔹 Generative AI in Business Applications

Generative AI tools like GPT and DALL·E are being customized for business use—from content generation to product design.

🔹 AI and Edge Computing

U.S. companies are exploring AI at the edge to reduce latency and process data locally for faster insights.

🔹 AI Ethics and Responsible AI

As AI adoption grows, U.S. developers are increasingly focused on building ethical and transparent AI systems.

🔹 AI for SMBs

AI is no longer just for large enterprises. American AI developers are creating cost-effective solutions tailored for small and mid-sized businesses.

Conclusion: Fuel Your Business with AI Innovation

Investing in AI software development in USA is not just a trend—it's a strategic move for future-proofing your business. With world-class talent, cutting-edge tools, and a history of tech leadership, the USA is the ideal destination for businesses seeking innovative and scalable AI solutions. Whether you’re in healthcare, finance, retail, or manufacturing, leveraging AI can unlock unprecedented growth, efficiency, and customer satisfaction.

Need help getting started? Partner with a top-tier AI software development company in the USA to craft intelligent solutions tailored to your business needs.

[aibyrdidini]

MACHINE LEARNING AND SUBFIELDS

Learning machine learning (ML) involves a foundational understanding of various subfields, including deep learning (DL), natural language processing (NLP), large language models (LLMs), neural networks, and algorithms.

Here's a breakdown of these concepts with an example using Python.

Machine Learning (ML):

ML algorithms learn from data to make predictions or classifications.

Eg: Spam filtering in emails.

Deep Learning (DL):

A subfield of ML using artificial neural networks with multiple hidden layers to process complex data.

Eg: Image recognition software.

Natural Language Processing.

(NLP):

Deals with the interaction between computers and human language.

Eg: Chatbots and machine translation.

Large Language Models (LLMs):

Advanced NLP models trained on massive amounts of text data to perform complex tasks.

Eg: Generating different creative text formats, like poems or code.

Neural Networks:

Inspired by the human brain, they consist of interconnected nodes (neurons) that process information.

Eg: Used in image recognition and recommendation systems.

Algorithms:

Step-by-step instructions to solve a problem or perform a task.

Eg: Machine learning algorithms use various mathematical formulas to make predictions.

Python for Machine Learning:

Python is a popular language for ML due to its readability and extensive libraries like Scikit-learn and TensorFlow.

Example: Predicting Restaurant Customer Churn.

Here's a basic Python snippet using Scikit-learn to build an ML model that predicts customer churn (likelihood of a customer not returning) for a restaurant:

# Import libraries

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

# Load data (replace with your data)

data = ...

target = ...

# Split data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2)

# Train the model

model = LogisticRegression()

model.fit(X_train, y_train)

# Make predictions on test set

predictions = model.predict(X_test)

# Evaluate the model's performance

# ...

This is a simplified example, but it demonstrates how ML can be applied to real-world problems using Python. As you progress in your learning journey, you'll delve deeper into these areas to create more sophisticated models.

RDIDINI PROMPT ENGINEER

[innovaticsblog]

Empower AI solutions with the transformative capabilities of natural language processing (NLP). Discover how NLP fuels intelligent communication and decision-making.

[aichatbot08]

Leading AI Software Development Solutions in Malaysia

Empowering businesses in Malaysia with cutting-edge AI Software Development in Malaysia, tailored software solutions, and innovative automation tools to drive growth and efficiency.

[sinoeurovoices]

中國研究技藝的挑戰與創新

中國研究環境的變化，以及新型數據研究技術的進展，幾乎同時成為研究社群難以忽視的議題。然而，新型數據研究技術對於研究者，究竟是突破限制的利器，抑或使研究技藝更形黯淡，遠遠未有定論。可以想見的是：兩者之間的張力，已帶來源源不絕的壓力和動力，促使偏好不同方法的研究者精煉技藝以因應變化。《上報》與國立清華大學《當代中國研究通訊》合作策劃「新冷戰下的中國研究」專題，第二篇由陳至潔教授執筆，他回顧了自身十多年來在中國研究領域的求索、困境與轉折。儘管挑戰撲面而來，但活用豐富多樣的技術方法，切入長期關注的現象脈絡，進而反省視野局限，也能夠形成因應變化的策略及議程。這些經驗扎實而具體，實用且具建設性。中國畢竟沒有靜止，現象依然錯綜紛呈，因應策略之下，挑戰背後充滿機遇。 中國政治自2010年代開始進入加強社會管控的時期，21世紀頭十年相對開放的社會政策受到抑制。習近平成為中共領導人之後，極為重視國家安全與…

[fuerst-von-plan1]

Fortschritte der KI in der Echtzeitkommunikation: Anwendungen und Innovationen

Die rasante Entwicklung der Künstlichen Intelligenz (KI) hat weitreichende Auswirkungen auf zahlreiche Bereiche der Gesellschaft, insbesondere in der Echtzeitkommunikation. Die Fähigkeit von KI-Systemen, große Datenmengen in Echtzeit zu analysieren, Muster zu erkennen und kontextbezogene Informationen bereitzustellen, revolutioniert die Art und Weise, wie Menschen miteinander kommunizieren und…

[aidevelop]

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[philomathresearch]

AI in Market Research: How It Is Disrupting Our Industry

Explore how AI is transforming market research by enhancing data analysis, insights, and decision-making, industry innovation and efficiency. For more detail visit here : https://www.philomathresearch.com/blog/2024/10/23/ai-in-market-research-how-it-is-disrupting-our-industry/

[noeyecontactpleasse]

I do want to acknowledge the fact that since GROK doesn't act like a neo nazi, that means that the dev team responsible for its training have done active work to teach GROK what racism is and how to properly respond in opposition to it. That's an active intentional process that the dev team deserves credit for. Demographic-aware models are still cutting-edge so any new innovations in that space are welcomed.

elon musk "creating" an ai chatbot named grok and charging people money to use it is already funny, but the funnier part of how while pretty much other every chatbot in existence almost instantly get's corrupted and becomes racist, musk managed to almost completely on accident make one that's. not racist or transphobic or antisemitic. like wow