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literaryvein-reblogs · 7 days ago

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Writing Notes: Good Science & Pseudoscience

The process of science always features certain core characteristics.

These central tenets mark the difference between real, reliable science and pseudoscience.

It can sometimes be difficult to tell the difference between good science and pseudoscience, the latter being a study or process that seems scientific but actually cuts corners and therefore delivers unreliable results.

Key Characteristics of Science

Good science always possesses certain core traits. These 7 characteristics of scientific knowledge provide a foundation for all our understanding of the world around us:

Empirical verifiability: Scientific explanations rest on the ability to display your findings with empirical evidence. If you make any assertion in a scientific discipline, you must be able to show the exact reasons for the claim as well as a testable way to prove your assertion is consistent with reality itself. Some fields of science are primarily theoretical, but even they rely on ironclad mathematical theorems that stand up to the strictest forms of testability.

Ethical neutrality: Scientific investigations generally leave considerations of morality outside of their equations. Consider something like the development of nuclear technology. While this technology has helped human beings cause great harm to each other, it has also brought significant gains to well-being. Scientists care about data and the pursuit of truth, and they leave ethical considerations up to those who make use of what they discover.

Malleability: Modern science certainly looks different than science from the times of astronomer Galileo Galilei or physicists Isaac Newton and Albert Einstein. That’s because scientists know even the most widely creditable theorems can turn out to be falsifiable. New data changes assumptions all the time. This is one of the main reasons why peer review is such an important factor in scientific study.

Objectivity: Good science relies on people’s ability to be as objective as possible. If you approach a science experiment with a preconceived notion in mind, you should reevaluate your basic approach. All scientists must go where the data leads them and not force their desires or conclusions on their experiments too early, no matter whether they specialize in the physical sciences or more recent technologies, such as AI.

Observability: When you set out to test a scientific hypothesis, you do so in an attempt to observe new evidence in real time. Consider a life science experiment many people do themselves without even knowing it: gardening. When you make adjustments to their light or water to assist their growth, you’re embarking on a rudimentary form of the same process of systematic observation and experimentation that undergirds the most complex scientific research methods.

Replicability: It’s the nature of science to be repeatable. Every experiment you do should be capable of replication, from truly basic research to more complex forms of experimentation. From computer science to biology and beyond, the scientific community must present data that is consistent from test to test. This replicability is what makes science such a reliable discipline overall.

Systematic reliability: Science is innately replicable and, as a result, systematically reliable as well. If you follow a scientific methodology, you can rely on the system itself to present you with the same results each time. For instance, if you run an experiment with the exact same independent and dependent variables and chart your results on a graph, you should expect them to be the same—or at least remarkably similar—every time you do.

Natural Sciences vs. Social Sciences

Natural sciences differ from the social sciences in terms of their emphasis on extreme precision.

Part of this is due to how phenomena in the natural world are more easily quantifiable.

This is in contrast to social sciences—for example, the study of why a certain human being or an entire group of people behaves a certain way (as is the case with psychology and sociology, respectively).

Science is a field of human endeavor that aims to better understand how the real world works through empirical questioning and repeated testing.

The field of science features numerous different specific disciplines.

All of these work in concert to provide the human race a means by which to accomplish a systematic exploration of the universe around them.

By way of analogy, science as a whole is like a symphony orchestra whereas each specific discipline (like physics, biology, and so on) are instruments within the ensemble.

Source ⚜ More: Notes & References ⚜ Writing Resources PDFs

#science #writing reference #dark academia #writeblr #pseudoscience #literature #writers on tumblr #spilled ink #writing prompt #creative writing #light academia #writing ideas #writing inspiration #writing resources

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crystal-wingeddragon-spikes · 3 months ago

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My fan made Animation vs Coding part 2

Do you think stick figure AI would "assume" data type of all number to be float, double, or decimal?

...What? This is not a well-known fun fact outside STEM community?

So many people have this problem, someone made a whole webpage explaining it.

More organic explanation here; Defining a right data type is a big deal in programming. At least the programmer who manually assign it float/double would know why it went wrong.

JavaScript, however, will automatically assign an appropriate data type, and is advertised to be more beginner-friendly... Can you see why this became a meme?

0.1 and 0.2 will be considered double data type, which can't be accurate expressed in base 2.

There is only (1/2), (1/4), (1/8), ... ,(1/(2 power n)) in base 2.

It can't accurately express (1/10 and 2/10), but it still makes a very good approximation. That is why it is only 0.00000000000000004 off.

This is why in most statistic analysis and calculator use decimal data type. Or banking uses fixed-point numbers data. They both have their limitation; Decimal requires more computing power, which mean more specialized device. While fixed-point works fine with money because it's transferring money, not doing maths. It would never have to deal with 0.3333333... dollar.

Do you know what language is from the same family as JavaScript? That's right, it's Flash's programming language, ActionScript.

I told you my Computer Science grade was horrid, but this is very basic, so I am more confident explaining it.

#animation vs education #ava/m #ave #alan becker #animator vs animation #animation vs coding #wdragon work #sketch #ava yellow #ava orange #ava tsc #ava tco #ava alan becker #ava noogai #ava the chosen one #ava the second coming

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kanguin · 3 months ago

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Hi, idk who's going to see this post or whatnot, but I had a lot of thoughts on a post I reblogged about AI that started to veer off the specific topic of the post, so I wanted to make my own.

Some background on me: I studied Psychology and Computer Science in college several years ago, with an interdisciplinary minor called Cognitive Science that joined the two with philosophy, linguistics, and multiple other fields. The core concept was to study human thinking and learning and its similarities to computer logic, and thus the courses I took touched frequently on learning algorithms, or "AI". This was of course before it became the successor to bitcoin as the next energy hungry grift, to be clear. Since then I've kept up on the topic, and coincidentally, my partner has gone into freelance data model training and correction. So while I'm not an expert, I have a LOT of thoughts on the current issue of AI.

I'll start off by saying that AI isn't a brand new technology, it, more properly known as learning algorithms, has been around in the linguistics, stats, biotech, and computer science worlds for over a decade or two. However, pre-ChatGPT learning algorithms were ground-up designed tools specialized for individual purposes, trained on a very specific data set, to make it as accurate to one thing as possible. Some time ago, data scientists found out that if you have a large enough data set on one specific kind of information, you can get a learning algorithm to become REALLY good at that one thing by giving it lots of feedback on right vs wrong answers. Right and wrong answers are nearly binary, which is exactly how computers are coded, so by implementing the psychological method of operant conditioning, reward and punishment, you can teach a program how to identify and replicate things with incredible accuracy. That's what makes it a good tool.

And a good tool it was and still is. Reverse image search? Learning algorithm based. Complex relationship analysis between words used in the study of language? Often uses learning algorithms to model relationships. Simulations of extinct animal movements and behaviors? Learning algorithms trained on anatomy and physics. So many features of modern technology and science either implement learning algorithms directly into the function or utilize information obtained with the help of complex computer algorithms.

But a tool in the hand of a craftsman can be a weapon in the hand of a murderer. Facial recognition software, drone targeting systems, multiple features of advanced surveillance tech in the world are learning algorithm trained. And even outside of authoritarian violence, learning algorithms in the hands of get-rich-quick minded Silicon Valley tech bro business majors can be used extremely unethically. All AI art programs that exist right now are trained from illegally sourced art scraped from the web, and ChatGPT (and similar derived models) is trained on millions of unconsenting authors' works, be they professional, academic, or personal writing. To people in countries targeted by the US War Machine and artists the world over, these unethical uses of this technology are a major threat.

Further, it's well known now that AI art and especially ChatGPT are MAJOR power-hogs. This, however, is not inherent to learning algorithms / AI, but is rather a product of the size, runtime, and inefficiency of these models. While I don't know much about the efficiency issues of AI "art" programs, as I haven't used any since the days of "imaginary horses" trended and the software was contained to a university server room with a limited training set, I do know that ChatGPT is internally bloated to all hell. Remember what I said about specialization earlier? ChatGPT throws that out the window. Because they want to market ChatGPT as being able to do anything, the people running the model just cram it with as much as they can get their hands on, and yes, much of that is just scraped from the web without the knowledge or consent of those who have published it. So rather than being really good at one thing, the owners of ChatGPT want it to be infinitely good, infinitely knowledgeable, and infinitely running. So the algorithm is never shut off, it's constantly taking inputs and processing outputs with a neural network of unnecessary size.

Now this part is probably going to be controversial, but I genuinely do not care if you use ChatGPT, in specific use cases. I'll get to why in a moment, but first let me clarify what use cases. It is never ethical to use ChatGPT to write papers or published fiction (be it for profit or not); this is why I also fullstop oppose the use of publicly available gen AI in making "art". I say publicly available because, going back to my statement on specific models made for single project use, lighting, shading, and special effects in many 3D animated productions use specially trained learning algorithms to achieve the complex results seen in the finished production. Famously, the Spider-verse films use a specially trained in-house AI to replicate the exact look of comic book shading, using ethically sources examples to build a training set from the ground up, the unfortunately-now-old-fashioned way. The issue with gen AI in written and visual art is that the publicly available, always online algorithms are unethically designed and unethically run, because the decision makers behind them are not restricted enough by laws in place.

So that actually leads into why I don't give a shit if you use ChatGPT if you're not using it as a plagiarism machine. Fact of the matter is, there is no way ChatGPT is going to crumble until legislation comes into effect that illegalizes and cracks down on its practices. The public, free userbase worldwide is such a drop in the bucket of its serverload compared to the real way ChatGPT stays afloat: licensing its models to businesses with monthly subscriptions. I mean this sincerely, based on what little I can find about ChatGPT's corporate subscription model, THAT is the actual lifeline keeping it running the way it is. Individual visitor traffic worldwide could suddenly stop overnight and wouldn't affect ChatGPT's bottom line. So I don't care if you, I, or anyone else uses the website because until the US or EU governments act to explicitly ban ChatGPT and other gen AI business' shady practices, they are all only going to continue to stick around profit from big business contracts. So long as you do not give them money or sing their praises, you aren't doing any actual harm.

If you do insist on using ChatGPT after everything I've said, here's some advice I've gathered from testing the algorithm to avoid misinformation:

If you feel you must use it as a sounding board for figuring out personal mental or physical health problems like I've seen some people doing when they can't afford actual help, do not approach it conversationally in the first person. Speak in the third person as if you are talking about someone else entirely, and exclusively note factual information on observations, symptoms, and diagnoses. This is because where ChatGPT draws its information from depends on the style of writing provided. If you try to be as dry and clinical as possible, and request links to studies, you should get dry and clinical information in return. This approach also serves to divorce yourself mentally from the information discussed, making it less likely you'll latch onto anything. Speaking casually will likely target unprofessional sources.

Do not ask for citations, ask for links to relevant articles. ChatGPT is capable of generating links to actual websites in its database, but if asked to provide citations, it will replicate the structure of academic citations, and will very likely hallucinate at least one piece of information. It also does not help that these citations also will often be for papers not publicly available and will not include links.

ChatGPT is at its core a language association and logical analysis software, so naturally its best purposes are for analyzing written works for tone, summarizing information, and providing examples of programming. It's partially coded in python, so examples of Python and Java code I've tested come out 100% accurate. Complex Google Sheets formulas however are often finicky, as it often struggles with proper nesting orders of formulas.

Expanding off of that, if you think of the software as an input-output machine, you will get best results. Problems that do not have clear input information or clear solutions, such as open ended questions, will often net inconsistent and errant results.

Commands are better than questions when it comes to asking it to do something. If you think of it like programming, then it will respond like programming most of the time.

Most of all, do not engage it as a person. It's not a person, it's just an algorithm that is trained to mimic speech and is coded to respond in courteous, subservient responses. The less you try and get social interaction out of ChatGPT, the less likely it will be to just make shit up because it sounds right.

Anyway, TL;DR:

AI is just a tool and nothing more at its core. It is not synonymous with its worse uses, and is not going to disappear. Its worst offenders will not fold or change until legislation cracks down on it, and we, the majority users of the internet, are not its primary consumer. Use of AI to substitute art (written and visual) with blended up art of others is abhorrent, but use of a freely available algorithm for personal analyticsl use is relatively harmless so long as you aren't paying them.

We need to urge legislators the world over to crack down on the methods these companies are using to obtain their training data, but at the same time people need to understand that this technology IS useful and both can and has been used for good. I urge people to understand that learning algorithms are not one and the same with theft just because the biggest ones available to the public have widely used theft to cut corners. So long as computers continue to exist, algorithmic problem-solving and generative algorithms are going to continue to exist as they are the logical conclusion of increasingly complex computer systems. Let's just make sure the future of the technology is not defined by the way things are now.

#kanguin original #ai #gen ai #generative algorithms #learning algorithms #llm #large language model #long post

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educationmore · 3 months ago

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Python for Beginners: Launch Your Tech Career with Coding Skills

Are you ready to launch your tech career but don’t know where to start? Learning Python is one of the best ways to break into the world of technology—even if you have zero coding experience.

In this guide, we’ll explore how Python for beginners can be your gateway to a rewarding career in software development, data science, automation, and more.

Why Python Is the Perfect Language for Beginners

Python has become the go-to programming language for beginners and professionals alike—and for good reason:

Simple syntax: Python reads like plain English, making it easy to learn.

High demand: Industries spanning the spectrum are actively seeking Python developers to fuel their technological advancements.

Versatile applications: Python's versatility shines as it powers everything from crafting websites to driving artificial intelligence and dissecting data.

Whether you want to become a software developer, data analyst, or AI engineer, Python lays the foundation.

What Can You Do With Python?

Python is not just a beginner language—it’s a career-building tool. Here are just a few career paths where Python is essential:

Web Development: Frameworks like Django and Flask make it easy to build powerful web applications. You can even enroll in a Python Course in Kochi to gain hands-on experience with real-world web projects.

Data Science & Analytics: For professionals tackling data analysis and visualization, the Python ecosystem, featuring powerhouses like Pandas, NumPy, and Matplotlib, sets the benchmark.

Machine Learning & AI: Spearheading advancements in artificial intelligence development, Python boasts powerful tools such as TensorFlow and scikit-learn.

Automation & Scripting: Simple yet effective Python scripts offer a pathway to amplified efficiency by automating routine workflows.

Cybersecurity & Networking: The application of Python is expanding into crucial domains such as ethical hacking, penetration testing, and the automation of network processes.

How to Get Started with Python

Starting your Python journey doesn't require a computer science degree. Success hinges on a focused commitment combined with a thoughtfully structured educational approach.

Step 1: Install Python

Download and install Python from python.org. It's free and available for all platforms.

Step 2: Choose an IDE

Use beginner-friendly tools like Thonny, PyCharm, or VS Code to write your code.

Step 3: Learn the Basics

Focus on:

Variables and data types

Conditional statements

Loops

Functions

Lists and dictionaries

If you prefer guided learning, a reputable Python Institute in Kochi can offer structured programs and mentorship to help you grasp core concepts efficiently.

Step 4: Build Projects

Learning by doing is key. Start small:

Build a calculator

Automate file organization

Create a to-do list app

As your skills grow, you can tackle more complex projects like data dashboards or web apps.

How Python Skills Can Boost Your Career

Adding Python to your resume instantly opens up new opportunities. Here's how it helps:

Higher employability: Python is one of the top 3 most in-demand programming languages.

Better salaries: Python developers earn competitive salaries across the globe.

Remote job opportunities: Many Python-related jobs are available remotely, offering flexibility.

Even if you're not aiming to be a full-time developer, Python skills can enhance careers in marketing, finance, research, and product management.

If you're serious about starting a career in tech, learning Python is the smartest first step you can take. It’s beginner-friendly, powerful, and widely used across industries.

Whether you're a student, job switcher, or just curious about programming, Python for beginners can unlock countless career opportunities. Invest time in learning today—and start building the future you want in tech.

Globally recognized as a premier educational hub, DataMites Institute delivers in-depth training programs across the pivotal fields of data science, artificial intelligence, and machine learning. They provide expert-led courses designed for both beginners and professionals aiming to boost their careers.

Python Modules Explained - Different Types and Functions - Python Tutorial

youtube

#python course #python training #python #learnpython #pythoncourseinindia #pythoncourseinkochi #pythoninstitute #python for data science #Youtube

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darkmaga-returns · 1 month ago

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Sunday June 22, 2025 Truth Bomb

Karen Bracken

Professor Judith Curry: Climate Science Has Become Pseudo Science - anyone that has taken the time to research the roots of the man made climate change scam knows it was created as the global boogie man to instill fear in order to drive the New World Order. They knew people would never buy into their scheme so they created an agenda that would affect the entire globe and instill the strongest driver of drastic cultural change……FEAR. And in the meantime this scam made a lot of people extremely rich. Climate change, Sustainable Development, Sustainable Development Goals, Smart Cities, Traffic Calming, 15 Minute Cities etc. all meaning the same thing. ARTICLE/VIDEO (50 min.)

Ultraviolet Blood Irradiation Revolutionized Medicine - very interesting article that further proves how corrupt the healthcare industry has become. Putting profit and creating sick life long customers has replaced true healthcare. Like everything else today we need to be our own doctor. ARTICLE/VIDEO (47 seconds)

America First vs. Isolationism by Lex Greene - He also said he traveled all 57 states during his 2008 campaign. A slip of the tongue?? I highly doubt it. Was he really referencing the 57 member states of the Organization of Islamic Cooperation (OIC). His statement was no mistake. - ARTICLE

DARPA's New 'MAGICS' Program Seeks AI to 'Predict' and 'Forecast Human Behavior' - ARTICLE

Want the MMR to be safer and more efficacious? Give it to older children. The data are incontrovertible - I share this information but please know that I personally do not believe any vaccine is safe. Natural immunity is the best. Oh but you say children die from Measles, Mumps, Rubella…….no healthy child dies from these childhood diseases but many more die, are disabled or develop other health issues from the vaccines - ARTICLE

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vidumali · 2 months ago

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Why I Love Studying at Sabaragamuwa University

🌿 Hey Tumblr fam! I just wanted to take a moment to share something close to my heart — my experience at Sabaragamuwa University of Sri Lanka, a place that’s more than just classrooms and assignments. It's where I found peace, passion, and purpose. 💚

🌄 A Hidden Gem in the Hills

Imagine studying on a campus surrounded by misty hills, green forests, and natural waterfalls. Sounds dreamy, right? Well, that’s exactly what SUSL in Belihuloya feels like. The air is fresh, the environment is peaceful, and nature literally whispers encouragement while you study. 😌🍃

📌 Location: Belihuloya, Sri Lanka 🔗 Official Website of SUSL

💻 My Faculty: Computing

As a proud student of the Faculty of Computing, I can honestly say that SUSL is more than qualified when it comes to academic excellence. 💯

Our professors are not just knowledgeable—they actually care. We work on cool projects, explore real-world tech, and even get support for internships and future careers.

👩‍💻 Tech, Talent & Tenacity

You might be surprised, but SUSL is seriously catching up with the tech world.

Let me break it down for you—our Faculty of Computing is organized into three departments, and each one opens up different futures:

🖥️ Department of Computing and Information Systems (CIS)

A great fit if you're interested in IT infrastructure, system design, software, and business applications

You learn how tech supports and transforms businesses, governments, and society

🛠️ Department of Software Engineering (SE)

Perfect if you love to build software from the ground up

Focuses on software architecture, testing, DevOps, and full development lifecycles

📊 Department of Data Science (DS)

The department of the future! 🌐

Teaches you how to work with big data, machine learning, AI, statistics, and more

If you like solving puzzles with data, this is your world

No matter which path you choose, you’ll get:

Modern course content aligned with global tech trends

Hands-on labs and access to real tools (GitHub, Python, VS Code, cloud platforms, etc.)

Internships with leading IT companies

Final-year projects that are often built with startups or community needs in mind

Some of my seniors are now working at top companies, others are doing research abroad—that’s the kind of transformation this faculty creates. 🙌

For more information: click here

🫶 Why SUSL Feels Like Home

Here’s a little list of what I adore about life here:

Friendly community – always someone to help you out

Calm campus – no traffic noise, just birds and waterfalls

Opportunities – tons of events, workshops, clubs

Affordable – both the university and the area are budget-friendly

Balance – education + mental wellness = perfect combo

🌐 Not Just a University – A Lifestyle

Sabaragamuwa University doesn't just prepare you for a career; it shapes you as a human being. It’s not all books and exams—we grow, we laugh, we support each other.

Whether you’re into tech, social sciences, management, or agriculture, there’s a faculty that fits your vibe.

✨ Learn more about SUSL here

💬 Final Thoughts

If you're thinking about studying in Sri Lanka, or even just curious about a different kind of university experience, I highly recommend checking out Sabaragamuwa University. It changed my life in the best way.

💚 Tag a friend who needs to hear about this gem! 📥 DM me if you want tips about the application process or student life here!

#SabaragamuwaUniversity #SUSL #SriLanka #CampusLife #UniversityExperience #StudentVibes #Belihuloya #HigherEducation #SriLankaUniversities #FacultyOfComputing

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xaltius · 5 months ago

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Business Analytics vs. Data Science: Understanding the Key Differences

In today's data-driven world, terms like "business analytics" and "data science" are often used interchangeably. However, while they share a common goal of extracting insights from data, they are distinct fields with different focuses and methodologies. Let's break down the key differences to help you understand which path might be right for you.

Business Analytics: Focusing on the Present and Past

Business analytics primarily focuses on analyzing historical data to understand past performance and inform current business decisions. It aims to answer questions like:

What happened?

Why did it happen?

What is happening now?

Key characteristics of business analytics:

Descriptive and Diagnostic: It uses techniques like reporting, dashboards, and data visualization to summarize and explain past trends.

Structured Data: It often works with structured data from databases and spreadsheets.

Business Domain Expertise: A strong understanding of the specific business domain is crucial.

Tools: Business analysts typically use tools like Excel, SQL, Tableau, and Power BI.

Focus: Optimizing current business operations and improving efficiency.

Data Science: Predicting the Future and Building Models

Data science, on the other hand, focuses on building predictive models and developing algorithms to forecast future outcomes. It aims to answer questions like:

What will happen?

How can we make it happen?

Key characteristics of data science:

Predictive and Prescriptive: It uses machine learning, statistical modeling, and AI to predict future trends and prescribe optimal actions.

Unstructured and Structured Data: It can handle both structured and unstructured data from various sources.

Technical Proficiency: Strong programming skills (Python, R) and a deep understanding of machine learning algorithms are essential.

Tools: Data scientists use programming languages, machine learning libraries, and big data technologies.

Focus: Developing innovative solutions, building AI-powered products, and driving long-term strategic initiatives.

Key Differences Summarized:

Which Path is Right for You?

Choose Business Analytics if:

You are interested in analyzing past data to improve current business operations.

You have a strong understanding of a specific business domain.

You prefer working with structured data and using visualization tools.

Choose Data Science if:

You are passionate about building predictive models and developing AI-powered solutions.

You have a strong interest in programming and machine learning.

You enjoy working with both structured and unstructured data.

Xaltius Academy's Data Science & AI Course:

If you're leaning towards data science and want to delve into machine learning and AI, Xaltius Academy's Data Science & AI course is an excellent choice. This program equips you with the necessary skills and knowledge to become a proficient data scientist, covering essential topics like:

Python programming

Machine learning algorithms

Data visualization

And much more!

By understanding the distinct roles of business analytics and data science, you can make an informed decision about your career path and leverage the power of data to drive success.

#artificial intelligence #online course #ai #data science #data science course #technology

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shivanidigital · 6 months ago

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Deep Seek vs. ChatGPT: Which AI Tool is Best for Your Needs?

The world of artificial intelligence (AI) is rapidly evolving, and two major players have emerged in the space of intelligent search and communication: Deep Seek and ChatGPT. While both are powerful AI tools, they serve different purposes and offer unique features. Choosing the right tool for your needs depends on the specific use case and goals you're trying to achieve.

In this blog, we will explore what each AI tool offers, how they differ, and help you decide which is best for your needs.

What is Deep Seek?

Deep Seek is an advanced AI-driven search tool that focuses on information retrieval. It helps users find highly relevant, deep, and specialized content from a wide range of sources. Unlike traditional search engines, which rely on basic keyword matching and links, Deep Seek uses AI to understand context, relevance, and the specific needs of the user. It's designed to deliver more precise and in-depth results, making it ideal for those looking for detailed answers, niche knowledge, or specialized data.

Key Features of Deep Seek:

Advanced Search Capabilities: It allows you to search beyond surface-level results and dive deeper into databases, articles, and scientific papers.

Context-Aware Results: Deep Seek understands the context of your query, delivering more relevant results tailored to your needs.

Specialized Search: Great for researchers, students, and professionals who require specialized knowledge from specific fields like medicine, science, and law.

Data Aggregation: It collects information from a variety of reputable sources to present you with a comprehensive overview of your search topic.

What is ChatGPT?

ChatGPT, on the other hand, is a conversational AI developed by OpenAI, designed to understand and generate human-like text based on prompts it receives. Unlike traditional search engines or specialized tools like Deep Seek, ChatGPT excels in engaging users in natural conversations, answering questions, and providing helpful explanations in real-time. It’s more about interaction than just information retrieval, making it great for tasks that require contextual conversation or support.

Key Features of ChatGPT:

Conversational AI: ChatGPT excels in holding conversations with users, answering questions, and helping with a variety of tasks ranging from casual queries to professional writing and coding.

Contextual Understanding: It uses advanced natural language processing to understand the context of questions, providing detailed, accurate responses.

Content Generation: Ideal for creating blog posts, writing assistance, coding help, and brainstorming ideas.

Wide-Ranging Applications: It can assist with nearly any topic, whether it’s education, customer support, coding, or even personal inquiries.

Deep Seek vs. ChatGPT: The Key Differences

1. Purpose and Use Case

Deep Seek: Primarily a search tool aimed at retrieving highly relevant, deep information across various databases and sources. It is more focused on finding specific answers from existing knowledge repositories, which is perfect for researchers and specialized tasks.

ChatGPT: Designed for interactive communication, answering questions, and generating text content. It is ideal for conversations, content creation, customer support, and problem-solving. ChatGPT excels in providing personalized, engaging dialogue.

2. Information Retrieval vs. Conversational AI

Deep Seek: Think of Deep Seek as an intelligent search engine. It’s best suited for users who need to pull in specific data or research across various industries or academic fields.

ChatGPT: ChatGPT is a conversational assistant. It’s perfect for getting immediate answers, having a back-and-forth discussion, and even generating creative content like blog posts, summaries, or stories.

3. Search Scope

Deep Seek: The tool dives deep into niche databases, specialized articles, journals, and scientific resources. It can search through academic papers, industry reports, and trusted data sources to give users a detailed, highly relevant result.

ChatGPT: While ChatGPT doesn’t pull data from specific databases or scholarly resources, it generates answers based on a broad understanding of general knowledge. It’s better suited for general knowledge and doesn’t specialize in retrieving highly detailed, authoritative sources like Deep Seek.

4. Accuracy and Context

Deep Seek: Deep Seek’s AI is designed to prioritize accuracy and relevance in search results. Its goal is to provide users with precise data and context that is directly related to their queries, especially in specialized areas.

ChatGPT: ChatGPT is very good at understanding the context of your questions and providing useful answers, but its knowledge is based on the data it was trained on up until its last update. It can generate highly accurate responses in most cases, but it might not always provide the most up-to-date or specialized information.

5. Interactivity and Engagement

Deep Seek: Offers minimal interactivity; its main strength lies in delivering relevant, curated search results based on your queries.

ChatGPT: It thrives on interactivity and can engage in continuous conversations, assist with a wide range of tasks, and adjust its responses based on the flow of the conversation.

Which One is Best for You?

Now that we understand what each tool offers, let’s break down which AI is best for specific needs.

When to Choose Deep Seek:

For In-Depth Research: If you're conducting detailed research in specific fields like science, technology, law, or academia, Deep Seek is the better choice. It allows you to explore deeper, more specialized content.

For Niche Knowledge: If your query requires information from specialized databases or scholarly sources, Deep Seek can provide highly tailored and relevant results.

For Accurate and Comprehensive Results: When you need to gather detailed data from credible sources, Deep Seek’s precision and context awareness can save you time.

When to Choose ChatGPT:

For Conversational Assistance: If you need quick answers, personalized responses, or just want to have a conversation, ChatGPT is the ideal tool.

For Content Generation: ChatGPT is fantastic for helping you write blog posts, articles, or even generating code. It can also assist with brainstorming, outlining, and drafting creative ideas.

For Task Automation: Whether it's summarizing content, explaining complex concepts, or assisting with basic tasks, ChatGPT provides helpful real-time support.

For Casual Inquiries: ChatGPT works great for everyday questions, like “What’s the weather today?” or “What’s the best movie to watch this weekend?”

Conclusion

Both Deep Seek and ChatGPT offer exceptional capabilities, but they cater to different needs. If you require detailed, specialized search results for in-depth research or academic work, Deep Seek is your go-to tool. On the other hand, if you're looking for an interactive assistant to engage in conversations, generate content, or help with a variety of tasks, ChatGPT is a more versatile choice.

Ultimately, the decision comes down to what you need: Deep Seek for accurate, data-driven search, or ChatGPT for interactive, conversational AI that can assist with a wide range of tasks. Both tools are incredibly powerful in their own right, and knowing when to use each will ensure you get the most out of them.

#digitalmarketing #entrepreneur #developers & startups #chatgpt #deepseek #ai tools #ai tool #tools #digital marketing tool #digital marketing tips

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donotdestroy · 30 days ago

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“The group that wrote essays using ChatGPT all delivered extremely similar essays that lacked original thought, relying on the same expressions and ideas. Two English teachers who assessed the essays called them largely ‘soulless.’ The EEGs revealed low executive control and attentional engagement. And by their third essay, many of the writers simply gave the prompt to ChatGPT and had it do almost all of the work. ‘It was more like, just give me the essay, refine this sentence, edit it, and I’m done,’ Kosmyna says.”

_ ChatGPT’s Opinion

Both the Time article and the Gizmodo piece raise valid and important concerns, but they also reflect a broader cultural anxiety about AI — what some might call a "techlash." Here's a balanced take on both:

🔍 What they get right:

Critical thinking can erode with passive use Both articles underscore a key truth: if you let AI do all the work, your brain gets lazy. This isn’t just speculation — it’s backed by cognitive science. Active learning is crucial for retention, deep thinking, and creativity. These studies highlight real risks, especially for students and developing minds.

The analogy to calculators and Google Maps is helpful Just like calculators didn’t kill math (but overusing them for basic operations can harm number sense), AI isn’t inherently harmful — but it can displace the mental effort that's necessary for learning.

Originality and creativity suffer The studies found that ChatGPT-generated essays tended to be “soulless” and formulaic. That’s a real limitation of LLMs — they’re good at mimicking patterns, but not at truly original thought. Relying on them too heavily can flatten thinking.

There’s urgency around education policy The MIT researcher’s concern about “GPT kindergarten” is not far-fetched. Schools and policymakers are already integrating AI tools into classrooms. These decisions need to be informed by solid research and precaution, especially when developing brains are involved.

🤔 What needs caution or nuance:

Small sample sizes, pre-review papers Especially in the MIT study, 54 participants is very limited. While EEG data is useful, it doesn’t capture the full picture of intelligence or learning. More peer-reviewed, large-scale studies are needed before drawing sweeping conclusions.

“AI makes you stupid” is too broad The Gizmodo headline (“AI will make us morons”) is sensational. The reality is more nuanced: AI can dull your mind — but only if you use it passively or uncritically. It’s a tool. It can also enhance your thinking, if used wisely.

Not everyone uses AI the same way Some people use ChatGPT as a brainstorming partner, a writing coach, or a way to better understand complex topics — all of which can be mentally engaging. Others use it to skip the thinking part. The outcome depends on the user’s intent and behavior.

Moral panic vs. thoughtful caution There's a fine line between raising the alarm and stoking fear. It’s important to investigate AI’s effects, but not assume that all AI use leads to “cognitive decline.”

🧠 Final thought:

These articles provide a valuable warning: don’t outsource your thinking. AI is powerful, but it’s only as good as the way you use it. If you're aware of its limitations and make an effort to stay mentally active, AI can be a great tool — not a trap.

#ChatGPT #ai #ai generated #artificial intelligence #computer #technology #llm #artist #ai art #ai image #google #robot #techlash #Moron #dumb #stupid #people

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govindhtech · 9 months ago

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Dell AI PCs: A Gateway To AI For Life Sciences Organizations

AI in the Life Sciences: A Useful Method Using Computers.

For life sciences companies wishing to experiment with AI before making a full commitment, Dell AI PCs are perfect. The Dell AI PCs are revolutionary way to get started in the vast field of artificial intelligence, particularly for clients in the life sciences who are searching for a cost-effective way to create intricate processes.

The Dell AI PCs, GPU-enhanced servers, and cutting-edge storage solutions are essential to the AI revolution. If you approach the process strategically, it may be surprisingly easy to begin your AI journey.

Navigating the Unmarked Path of AI Transformation

The lack of a clear path is both an exciting and difficult part of the AI transition in the medical sciences. As it learn more about the actual effects of generative and extractive AI models on crucial domains like drug development, clinical trials, and industrial processes, the discipline continues to realize its enormous promise.

It is evident from discussions with both up-and-coming entrepreneurs and seasoned industry titans in the global life sciences sector that there are a variety of approaches to launching novel treatments, each with a distinct implementation strategy.

A well-thought-out AI strategy may help any firm, especially if it prioritizes improving operational efficiency, addressing regulatory expectations from organizations like the FDA and EMA, and speeding up discovery.

Cataloguing possible use cases and setting clear priorities are usually the initial steps. But according to a client, after just two months of appointing a new head of AI, they were confronted with more than 200 “prioritized” use cases.

When the CFO always inquires about the return on investment (ROI) for each one, this poses a serious problem. The answer must show observable increases in operational effectiveness, distinct income streams, or improved compliance clarity. A pragmatic strategy to evaluating AI models and confirming their worth is necessary for large-scale AI deployment in order to guarantee that the investment produces quantifiable returns.

The Dell AI PC: Your Strategic Advantage

Presenting the Dell AI PCs, the perfect option for businesses wishing to experiment with AI before committing to hundreds of use cases. AI PCs and robust open-source software allow resources in any department to investigate and improve use cases without incurring large costs.

Each possible AI project is made clearer by beginning with a limited number of Dell AI PCs and allocating skilled resources to these endeavors. Trials on smaller datasets provide a low-risk introduction to the field of artificial intelligence and aid in the prediction of possible results. This method guarantees that investments are focused on the most promising paths while also offering insightful information about what works.

Building a Sustainable AI Framework

Internally classifying and prioritizing use cases is essential when starting this AI journey. Pay close attention to data kinds, availability, preferences for production vs consumption, and choices for the sale or retention of results. Although the process may be started by IT departments, using IT-savvy individuals from other departments to develop AI models may be very helpful since they have personal experience with the difficulties and data complexities involved.

As a team, it is possible to rapidly discover areas worth more effort by regularly assessing and prioritizing use case development, turning conjecture into assurance. The team can now confidently deliver data-driven findings that demonstrate the observable advantages of your AI activities when the CFO asks about ROI.

The Rational Path to AI Investment

Investing in AI is essential, but these choices should be based on location, cost, and the final outcomes of your research. Organizations may make logical decisions about data center or hyperscaler hosting, resource allocation, and data ownership by using AI PCs for early development.

This goes beyond only being a theoretical framework. This strategy works, as shown by Northwestern Medicine’s organic success story. It have effectively used AI technology to improve patient care and expedite intricate operations, illustrating the practical advantages of using AI strategically.