#Most Important Concepts of Data Science

[cromacampusinstitute]

The most important concept in Data Science is data understanding and preparation, as it forms the foundation for all subsequent analyses. This involves collecting, cleaning, and organizing raw data to ensure it is accurate, complete, and ready for analysis.

[catboybiologist]

Many billionaires in tech bros warn about the dangerous of AI. It's pretty obviously not because of any legitimate concern that AI will take over. But why do they keep saying stuff like this then? Why do we keep on having this still fear of some kind of singularity style event that leads to machine takeover?

The possibility of a self-sufficient AI taking over in our lifetimes is... Basically nothing, if I'm being honest. I'm not an expert by any means, I've used ai powered tools in my biology research, and I'm somewhat familiar with both the limits and possibility of what current models have to offer.

I'm starting to think that the reason why billionaires in particular try to prop this fear up is because it distracts from the actual danger of ai: the fact that billionaires and tech mega corporations have access to data, processing power, and proprietary algorithms to manipulate information on mass and control the flow of human behavior. To an extent, AI models are a black box. But the companies making them still have control over what inputs they receive for training and analysis, what kind of outputs they generate, and what they have access to. They're still code. Just some of the logic is built on statistics from large datasets instead of being manually coded.

The more billionaires make AI fear seem like a science fiction concept related to conciousness, the more they can absolve themselves in the eyes of public from this. The sheer scale of the large model statistics they're using, as well as the scope of surveillance that led to this point, are plain to see, and I think that the companies responsible are trying to play a big distraction game.

Hell, we can see this in the very use of the term artificial intelligence. Obviously, what we call artificial intelligence is nothing like science fiction style AI. Terms like large statistics, large models, and hell, even just machine learning are far less hyperbolic about what these models are actually doing.

I don't know if your average Middle class tech bro is actively perpetuating this same thing consciously, but I think the reason why it's such an attractive idea for them is because it subtly inflates their ego. By treating AI as a mystical act of the creation, as trending towards sapience or consciousness, if modern AI is just the infant form of something grand, they get to feel more important about their role in the course of society. Admitting the actual use and the actual power of current artificial intelligence means admitting to themselves that they have been a tool of mega corporations and billionaires, and that they are not actually a major player in human evolution. None of us are, but it's tech bro arrogance that insists they must be.

Do most tech bros think this way? Not really. Most are just complict neolibs that don't think too hard about the consequences of their actions. But for the subset that do actually think this way, this arrogance is pretty core to their thinking.

Obviously this isn't really something I can prove, this is just my suspicion from interacting with a fair number of techbros and people outside of CS alike.

[thewinastudyblog]

some advice i have for future computer science students

as soon as you learn data structures & complexity, run, don’t just walk, RUN to leetcode while the knowledge is still fresh in your mind. your entire career and whether you’ll get a well-paying job vs an average paying job depends on how good you are at leetcode.

build as many projects as you can, and i’m not talking tutorial projects that take a few hours, i’m talking big projects. working on a project for a month or two will get you really far.

if you don’t have an internship, do not waste your summers, learn new technologies, languages, concepts and build projects you can put in your cv.

try to participate in hackathons and coding competitions. it’s okay if you fail, but you’ll learn a lot.

learn how to read documentation. most tutorials don’t even cover a quarter of what a language, framework or software has to offer. the sooner you make reading documentation a habit, the better it is. and yes i know, documentation is long and hard to read. my advice is only read the sections that are relevant to you in the moment. something i also personally do is look at the code examples at the same time as i am reading the paragraphs, it really helps easily absorb the information.

try not to use chatgpt. and if you do, then at least use it for stuff you know you can do yourself and will be able to correct if the bot gets it wrong. using chatgpt is a very slippery slope and the more you use it the less you learn.

the math is important. math teaches you how to reason and how to develop better logical thinking. just because you don’t see yourself using the xyz theorem you’ve learnt anytime in the future doesn’t mean the math is useless.

be prepared to get comfortable with erros, issues, bugs and just problems in general. you’ll be coding 30% of the time and debugging 70% of the time (i’m exaggerating but sometimes it feels like this is the case lol), and that’s okay, it’s how we learn and the sooner you embrace it the better. if you’re someone who easily gets frustrated, then this is a heads up.

learn as you go. there is no such thing as waiting until you know everything before you start on a project. the only way and the best way to learn in this field is practice, so build, build, and build.

these are all the ones i could think of for now. feel free to comment your thoughts and questions <3

[ayeforscotland]

What is Dataflow? Part 2: Diagrams

This is the second part of a couple of posts about Dataflow, particularly why it's important for the world going forward and relating to the Crowd Strike IT disaster.

Read the first part here.

Before I get into this one today, I wanted to address a couple of things.

Firstly, Dataflow is something that nearly every single person can understand. You do NOT:

Need to have a degree in Computing Science

Need to work in IT

Need to be a data analyst / Spreadsheet master

If any of you see the word 'Data' and feel your eyes glazing over, try and snap out of it because, if you're anything like me, Dataflow is much more approachable as a concept.

Secondly, what do I mean by IT?

Traditionally in most of our media the all-encompassing 'IT department' handles everything to do with technology. But every business works differently and there are many job titles with lots of crossover.

For example, you can be an infrastructure engineer where your focus is on building and maintaining the IT infrastructure that connects your organisation internally and externally. This is a completely different role from an Application Portfolio Manager who is tasked with looking after the Applications used in business processes.

Both are technical people and come under the banner of 'IT' - but their roles are focused in different areas. So just bear that in mind!

Now that's out of the way, let's begin! This one will be a little bit deeper, and questions welcome!

An Intro to Diagrams

You probably do not need a history of why pictures are important to the human race but to cover our bases, ever since we put traced our hands on a cave wall we have been using pictures to communicate.

Jump forward in time and you have engineers like Leonardo Da Vinci drafting engineering schematics.

You get the idea, humans have been creating diagrams (Pictures) for thousands of years. Centuries of refinement and we have much more modern variations.

And there's one main reason why diagrams are important: They are a Common Language.

In this context, a Common Language helps bridge a language gap between disciplines as well as a linguistic gap. A Spanish electrician and a German electrician should be able to refer to the same diagram and understand each other, even if they don't know each other's language.

The reason they can do this is because they're are international standards which govern how electrical diagrams are created.

A Common Language for Digital?

Here's an image I've shown to clients from governments and institutions to global organisations.

Everything around us, from the products we use to the bridges we drive over and the buildings we live, work, enjoy and shop in had diagrams backing them.

You would not build a skyscraper without a structural engineering diagram, you would not build an extension on your house if an architect couldn't produce a blueprint.

Why is there not an equivalent for the Digital World and for Dataflow?

Where is the Digital Common Language?

This is the bit where the lightbulb goes on in a lot of people's heads. Because, as I mentioned in Part 1, the flow of data is the flow of information and knowledge. And the common mistake is that people think of dataflow, and only ever think about the technology.

Dataflow is the flow of information between People, Business Processes *and* Technology Assets.

It is not reserved to Technology specialists. When you look at the flow of data, you need to understand the People (Stakeholders) at the top, the processes that they perform (and the processes which use the data) and the technology assets that support that data.

The reason why this is important is because it puts the entire organisation in context.

It is something that modern businesses fail to do. They might have flow charts and network diagrams, and these are 'alright' in specific contexts, but they fall to pieces when they lack the context of the full organisation.

For example, here is a Network Diagram. It is probably of *some* value to technical personnel who work in infrastructure. Worth bearing in mind, some organisations don't even have something like this.

To be absolutely clear, this diagram will hold some value for some people within the organisation. I'm not saying it's completely useless. But for almost everyone else, it is entirely out of context, especially for any non-technical people.

So it doesn't help non-technical people understand why all of these assets are important, and it doesn't help infrastructure teams articulate the importance of any of these assets.

What happens if one of those switches or routers fails? What's the impact on the organisation? Who is affected? The diagram above does not answer those questions.

On the other side of the business we have process diagrams (aka workflow diagrams) which look like this.

Again we run into the same problem - This is maybe useful for some people working up at the process layer, but even then it doesn't provide context for the stakeholders involved (Are there multiple people/departments involved throughout) and it doesn't provide any context for technical personnel who are responsible for maintaining the technology that supports this process.

In short, nobody has the big picture because there is not a common language between Business & IT.

Conclusion

So what do we do? Well we need to have a Common Language between Business & IT. While we need people with cross-functional knowledge, we also need a common language (or common framework) for both sides of the organisation to actually understand each other.

Otherwise you get massively siloed departments completely winging their disaster recovery strategies when things like Crowd Strike goes down.

Senior Management will be asked questions about what needs to be prioritised and they won't have answers because they aren't thinking in terms of Dataflow.

It's not just 'We need to turn on everything again' - It's a question of priorities.

Thing is, there's a relatively simple way to do it, in a way that looking at any engineering diagram feels simple but actually has had decades/centuries of thought behind it. It almost feels like complete common sense.

I'll save it for Part 3 if you're interested in me continuing and I'll make a diagram of my blog.

The important thing is mapping out all the connections and dependencies, and there's not some magic button you press that does it all.

But rigorous engineering work is exactly that, you can't fudge it with a half-arsed attempt. You need to be proactive, instead of reacting whenever disaster strikes.

[local-hellhound-steals-spaghetti]

Damn like… I realised today how much my training methodology is changing after listening to Chirag Patel on Micheal Shikashio’s podcast.

Because I love Chirag Patel’s work and he was the one who recommended by dolphin internship when he visited Wolf Park. But I realised like… even with dolphin training how much behaviourism and functional behavioural analysis sticks around as this Logical and Scientific means of modifying behaviour.

But the sterile language now kind of makes me feel a bit icky? Like you’re talking about living individual beings as if they’re formulas and things to input stimulus into…?

I remember how much we were told as interns with the dolphins to not use “emotional” language. And I agree objectivity is important and data is important.

But to me it feels like the discussion becomes about consequences and antecedents rather than welfare and emotions and that animal’s autonomy and needs being met. As well as their overall health and wellness. And boiling that down to “antecedents” kind of feels super reductive?

This might be a weird reach but it also feels very … patriarchal? I mean science has been dominated by male bias and is only just starting to see the benefits of diversity in research… but this whole idea of emotions being unscientific or putting emotions into little boxes to define them… Behaviourism has done a lot of damage in a lot of ways - especially when you look at how it’s been used on autistic people to “modify undesirable behaviours.”

Anyone in behaviour consulting fields needs to be questioning whether it’s ethical to be modifying certain behaviours. Especially from an ethological standpoint.

Idk I have tried to simplify behaviour modification concepts for my clients so I don’t overload them with jargon, but it made me realise how much the jargon can distract you from the fact that you are working with living, emotional beings and how they’re feeling is important.

Maybe I’m overthinking this but I’d be curious to hear what others takes are on this - how behaviourism is still one of the most prevalent sciences in animal behaviour fields but human psychology has evolved beyond that. Should we give animals more credit for the emotions that drive behavioural responses and acknowledge the importance of their behavioural wellness, rather than looking at behaviour in a sterile manner?

[academicfever]

This is a good starting point but its not exhaustive by any means...

#Research 101: Part 1

##    How to find a good research topic?

It’s best to familiarize yourself with a discipline or topic as broadly as possible by looking beyond academia

Tips:

Be enthusiastic, but not unrealistic. For example, you might be tempted to throw yourself into finding out to what extent an entire economy has become circular, but it may already be challenging and tricky enough to find out which building materials are being recycled in the construction sector, and in what ways.

Be open-minded but beware of cul-de-sacs. You should always find out first whether enough is known about a topic already, or you might find yourself wasting a lot of time on it.

Be creative but stay close to the assignment. This starts with the topic itself; if one learning objective of the assignment is to carry out a survey, it isn’t helpful to choose a topic for which you need to find respondents on the other side of the world. One place where you can look for inspiration is current events. 

Although professors and lecturers tend to be extremely busy, they are often enthusiastic about motivated and smart students who are interested in their research field. You do need to approach them with focused questions, though, and not just general talk such as: ‘Do you know of a good topic for me?’ In many cases, a good starting point is the scholar themselves. Do a search on them in a search engine, take a look at their university web page, read recent publications,

In most university towns, you’ll come across organizations that hold regular lectures, debates, and thematic evenings, often in partnership with or organized by university lecturers and professors. If you’re interested in transdisciplinary research where academic knowledge and practical knowledge come together, this is certainly a useful place to start your search.

If you want to do interdisciplinary research, it is essential to understand and work with concepts and theories from different research fields, so that you are able to draw links between them (see Menken and Keestra (2016) on why theory is important for this). With an eye to your ‘interdisciplinary’ academic training, it is therefore a good idea to start your first steps in research with concepts and theories.

##How to do Lit Review:

Although texts in different academic disciplines can differ significantly in terms of structure, form, and length, almost all academic articles (research articles and literature reports) share a number of characteristics:

They are published in scholarly journals with expert editorial boards

These journals are peer-reviewed

These articles are written by authors who have no direct commercial or political interest in the topic on which they are writing

There are also non-academic research reports such as UN reports, data from statistics institutes, and government reports. Although these are not, strictly speaking, peer-reviewed, the reliability of these sources means that their contents can be assumed to be valid

You can usually include grey literature in your research bibliography, but if you’re not sure, you can ask your lecturer or supervisor whether the source you’ve found meets the requirements.

Google and Wikipedia are unreliable: the former due to its commercial interests, the latter because anyone, in principle, can adjust the information and few checks are made on the content.

disciplinary and interdisciplinary search machines with extensive search functions for specialized databases, such as the Web of Science, Pubmed, Science Direct, and Scopus

Search methods All of these search engines allow you to search for scholarly sources in different ways. You can search by topic, author, year of publication, and journal name. Some tips for searching for literature: 1. Use a combination of search terms that accurately describes your topic. 2. You should use mainly English search terms, given that English is the main language of communication in academia. 3. Try multiple search terms to unearth the sources you need. a. Ensure that you know a number of synonyms for your main topic b. Use the search engine’s thesaurus function (if available) to map out related concepts.

During your search, it is advisable to keep track of the keywords and search combinations you use. This will allow you to check for blind spots in your search strategy, and you can get feedback on improving the search combinations. Some search engines automatically keep a record of this.

Exploratory reading How do you make a selection from the enormous number of articles that are often available on a topic? Keep the following four questions in mind, and use them to guide your literature review: ■■ What is already known about my topic and in which discipline is the topic discussed? ■■ Which theories and concepts are used and discussed within the scope of my topic, and how are they defined? ■■ How is my topic researched and what different research methods are there? ■■ Which questions remain unanswered and what has yet to be researched?

$$ Speed reading:

Run through the titles, abstracts, and keywords of the articles at the top of your list and work out which ideas (concepts) keep coming back.

Next, use the abstract to figure out what these concepts mean, and also try to see whether they are connected and whether this differs for each study.

If you are unable to work out what the concepts mean, based on the context, don’t hesitate to use dictionaries or search engines.

Make a list of the concepts that occur most frequently in these texts and try to draw links between them.

A good way to do this is to use a concept map, which sets out the links between the concepts in a visual way.

All being well, by now you will have found a list of articles and used them to identify several concepts and theories. From these, try to select the theories and concepts that you want to explore further. Selecting at this stage will help you to frame and focus your research. The next step is to discover to what extent these articles deal with these concepts and theories in similar or different ways, and how combining these concepts and theories leads to different outcomes. In order to do this, you will need to read more thoroughly and make a detailed record of what you’ve learned.

next: part 2

part 3

part 4

last part

[sueeeeesworld]

Hello,

My name is Xue Yan. I am a Ph.D. student in Applied Health Sciences at the University of Illinois at Urbana-Champaign. My academic advisor, Dr. Liza Berdychevsky, and I conduct a study titled “Sex Views and Sexual Self-concept”.

If you are currently 18 years old or older and willing to share your opinions about sexual-related topics, please allow me to invite you to participate in this study.

If you agree to participate, you will take part in a survey, taking approximately 20 minutes. All information collected from this survey is anonymous and will be treated as strictly confidential. Your name will not appear on this survey and the information you provided will be grouped with other participants’ information to protect your identity. Please click on the link below or scan the QR code to participate:

Your participation would be much appreciated and extremely important, as it would provide valuable insights to contribute to people’s sexual justice and effective sexual health education.

Thank you very much for your time and consideration!

Xue Yan

Department of Recreation, Sport and Tourism Management

College of Applied Health Sciences University of Illinois Urbana-Champaign

xueyan3@illinois.edu

[probablyasocialecologist]

We’re often told that it would be unfeasible for everyone on the planet to live good lives—that if there wasn’t some degree of poverty—or at least lower living standards—in the rest of the world, then we’d blow right through the ecological limits of the planet. Even if it’s not said explicitly, the argument is that some people need to be poor in order for us in the Global North to live good lives. There’s a lot wrong with this assumption on a lot of different levels, but most importantly—it’s empirically inaccurate. It is possible, in fact, for everybody on the planet to have their needs met and to live a good life and make it happen, in fact, with only 30 percent of current global resource and energy use. That might sound unbelievable, right? Well, that’s capitalist realism for you. Because not only is it believable—it’s based on solid research and empirical data. It would, however, require ending capitalism and moving towards eco-socialism. So yes, it’s possible. But it won’t be easy. To discuss the research behind these exciting findings we’ve brought on economic anthropologist Jason Hickel. Jason is a professor at the The Institute for Environmental Science and Technology at the Autonomous University of Barcelona, and the author of the books The Divide: A Brief Guide to Global Inequality and its Solutions and Less is More: How Degrowth will Save the World. He’s the lead author of the paper “How much growth is required to achieve good lives for all? Insights from needs-based analysis” published in the journal World Development Perspectives, and which we’ll be discussing today.  As you may know, Jason is a regular guest on the show and was on most recently to discuss two other fascinating and important papers he recently co-authored, “Imperialist appropriation in the world economy: Drain from the global South through unequal exchange, 1990–2015” published in journal Global Environmental Change and "Unequal exchange of labour in the world economy" published in the journal Nature Communications. What assumptions go into traditional economic thinking and how have they limited the way we conceptualize poverty and how we address it? How do we conceive of good lives—and how does our current economic system limit these conceptions and perpetuate environmental destruction and social immiseration? What would an economic system that is designed around meeting actual human and planetary needs look like? And, perhaps most importantly, how do we get there? These are just some of the questions we discuss in this fascinating conversation with economic anthropologist Jason Hickel.

[stone2stars]

Not science questions but I thought it'd be an interesting ask anyway. Consider this as.. data collection.

1) What do you think is the most important skill/ability to have, to grasp scientific concepts easily?

2) Where's the line that separates being confident from being egotistical? Is there such a line in the first place?

[People do say that being overly self assured can lead one to overlook their own faults but (speaking from an academic setting's pov), as long as one is aware that they aren't immune from making mistakes & that there's always room to learn and improve; can that extra confidence really pose a hurdle in their learning journey?]

Most important skill?

Pattern recognition.

Not memory, not intelligence, not even math.

Scientific understanding comes from noticing the invisible threads between ideas. Whether you're looking at chemical reactivity, data points, or a star system's behavior, it’s all pattern logic. People who train their brains to ask "What’s repeating?" and "What changes when I tweak this variable?" pick things up faster because they don’t memorize. They connect.

Confidence vs ego.

The line’s real. It's subtle. It moves.

Confidence says “I’ve studied enough to try this.” Ego says “I don’t need to study to be right.”

The difference isn't loudness, it’s feedback tolerance. A confident person can still hear criticism without combusting. An egotistical one treats it like radiation.

You’re right that self-assurance helps—hell, it’s vital in a field where half your work is going to fail. But once your belief in your own ideas outweighs your willingness to stress-test them? You’ve already started mislearning.

Bottom line: It’s not about humility vs arrogance. It’s about keeping your mind porous enough to let better answers in.

Otherwise, you’re not doing science. You’re doing cosplay.

[mariacallous]

Arvind Narayanan, a computer science professor at Princeton University, is best known for calling out the hype surrounding artificial intelligence in his Substack, AI Snake Oil, written with PhD candidate Sayash Kapoor. The two authors recently released a book based on their popular newsletter about AI’s shortcomings.

But don’t get it twisted—they aren’t against using new technology. “It's easy to misconstrue our message as saying that all of AI is harmful or dubious,” Narayanan says. He makes clear, during a conversation with WIRED, that his rebuke is not aimed at the software per say, but rather the culprits who continue to spread misleading claims about artificial intelligence.

In AI Snake Oil, those guilty of perpetuating the current hype cycle are divided into three core groups: the companies selling AI, researchers studying AI, and journalists covering AI.

Hype Super-Spreaders

Companies claiming to predict the future using algorithms are positioned as potentially the most fraudulent. “When predictive AI systems are deployed, the first people they harm are often minorities and those already in poverty,” Narayanan and Kapoor write in the book. For example, an algorithm previously used in the Netherlands by a local government to predict who may commit welfare fraud wrongly targeted women and immigrants who didn’t speak Dutch.

The authors turn a skeptical eye as well toward companies mainly focused on existential risks, like artificial general intelligence, the concept of a super-powerful algorithm better than humans at performing labor. Though, they don’t scoff at the idea of AGI. “When I decided to become a computer scientist, the ability to contribute to AGI was a big part of my own identity and motivation,” says Narayanan. The misalignment comes from companies prioritizing long-term risk factors above the impact AI tools have on people right now, a common refrain I’ve heard from researchers.

Much of the hype and misunderstandings can also be blamed on shoddy, non-reproducible research, the authors claim. “We found that in a large number of fields, the issue of data leakage leads to overoptimistic claims about how well AI works,” says Kapoor. Data leakage is essentially when AI is tested using part of the model’s training data—similar to handing out the answers to students before conducting an exam.

While academics are portrayed in AI Snake Oil as making “textbook errors,” journalists are more maliciously motivated and knowingly in the wrong, according to the Princeton researchers: “Many articles are just reworded press releases laundered as news.” Reporters who sidestep honest reporting in favor of maintaining their relationships with big tech companies and protecting their access to the companies’ executives are noted as especially toxic.

I think the criticisms about access journalism are fair. In retrospect, I could have asked tougher or more savvy questions during some interviews with the stakeholders at the most important companies in AI. But the authors might be oversimplifying the matter here. The fact that big AI companies let me in the door doesn’t prevent me from writing skeptical articles about their technology, or working on investigative pieces I know will piss them off. (Yes, even if they make business deals, like OpenAI did, with the parent company of WIRED.)

And sensational news stories can be misleading about AI’s true capabilities. Narayanan and Kapoor highlight New York Times columnist Kevin Roose’s 2023 chatbot transcript interacting with Microsoft's tool headlined “Bing’s A.I. Chat: ‘I Want to Be Alive. 😈’” as an example of journalists sowing public confusion about sentient algorithms. “Roose was one of the people who wrote these articles,” says Kapoor. “But I think when you see headline after headline that's talking about chatbots wanting to come to life, it can be pretty impactful on the public psyche.” Kapoor mentions the ELIZA chatbot from the 1960s, whose users quickly anthropomorphized a crude AI tool, as a prime example of the lasting urge to project human qualities onto mere algorithms.

Roose declined to comment when reached via email and instead pointed me to a passage from his related column, published separately from the extensive chatbot transcript, where he explicitly states that he knows the AI is not sentient. The introduction to his chatbot transcript focuses on “its secret desire to be human” as well as “thoughts about its creators,” and the comment section is strewn with readers anxious about the chatbot’s power.

Images accompanying news articles are also called into question in AI Snake Oil. Publications often use clichéd visual metaphors, like photos of robots, at the top of a story to represent artificial intelligence features. Another common trope, an illustration of an altered human brain brimming with computer circuitry used to represent the AI’s neural network, irritates the authors. “We're not huge fans of circuit brain,” says Narayanan. “I think that metaphor is so problematic. It just comes out of this idea that intelligence is all about computation.” He suggests images of AI chips or graphics processing units should be used to visually represent reported pieces about artificial intelligence.

Education Is All You Need

The adamant admonishment of the AI hype cycle comes from the authors’ belief that large language models will actually continue to have a significant influence on society and should be discussed with more accuracy. “It's hard to overstate the impact LLMs might have in the next few decades,” says Kapoor. Even if an AI bubble does eventually pop, I agree that aspects of generative tools will be sticky enough to stay around in some form. And the proliferation of generative AI tools, which developers are currently pushing out to the public through smartphone apps and even formatting devices around it, just heightens the necessity for better education on what AI even is and its limitations.

The first step to understanding AI better is coming to terms with the vagueness of the term, which flattens an array of tools and areas of research, like natural language processing, into a tidy, marketable package. AI Snake Oil divides artificial intelligence into two subcategories: predictive AI, which uses data to assess future outcomes; and generative AI, which crafts probable answers to prompts based on past data.

It’s worth it for anyone who encounters AI tools, willingly or not, to spend at least a little time trying to better grasp key concepts, like machine learning and neural networks, to further demystify the technology and inoculate themselves from the bombardment of AI hype.

During my time covering AI for the past two years, I’ve learned that even if readers grasp a few of the limitations of generative tools, like inaccurate outputs or biased answers, many people are still hazy about all of its weaknesses. For example, in the upcoming season of AI Unlocked, my newsletter designed to help readers experiment with AI and understand it better, we included a whole lesson dedicated to examining whether ChatGPT can be trusted to dispense medical advice based on questions submitted by readers. (And whether it will keep your prompts about that weird toenail fungus private.)

A user may approach the AI’s outputs with more skepticism when they have a better understanding of where the model’s training data came from—often the depths of the internet or Reddit threads—and it may hamper their misplaced trust in the software.

Narayanan believes so strongly in the importance of quality education that he began teaching his children about the benefits and downsides of AI at a very young age. “I think it should start from elementary school,” he says. “As a parent, but also based on my understanding of the research, my approach to this is very tech-forward.”

Generative AI may now be able to write half-decent emails and help you communicate sometimes, but only well-informed humans have the power to correct breakdowns in understanding around this technology and craft a more accurate narrative moving forward.

[cognitivejustice]

The state of citizen science in Ukraine

The GROMADA project is a partnership between CEOBS, the universities of Copenhagen, Hamburg and Lund, Italian NGO Systasis and Greek education company Web2Learn. The project has brought together academics, lawyers, civil society activists and students to explore the potential of citizen science to detect war-related harms in Ukraine. Another crucial component of the GROMADA project is its focus on civic evidence collection for legal accountability.

We had initially assumed that the concept of “citizen science” — or indeed “civilian science” — was not widely known in Ukraine. However, the issue proved to be purely terminological. International citizen science platforms, such as iNaturalist or GBIF, are popular among amateur scientists and environmental activists in Ukraine. In 2024, the Science at Risk initiative published its White Book for Citizen Science in Ukraine, which summarised the pre-war citizen science projects and their new wartime outlook.

Numerous local projects have developed in Ukraine under the umbrella of “civic environmental monitoring”. Save Dnipro, an environmental monitoring and advocacy organisation, is an example of a civic initiative that has made it to the national scale and now closely cooperates with the government in environmental policymaking. Another group, Stop Poisoning Kryvyi Rih, has developed civic water monitoring protocols and tested local sources in Dnipropetrovsk Oblast for their suitability as an emergency water source during the war. Elsewhere, the Dovkola Network of citizen science developed its Green Book of Environmental Monitoring in Ukraine. Importantly, it is felt that citizen science has a considerable, and still quite untapped potential, for countering misinformation and establishing environmental truths.

Citizen science’s potential role in investigating environmental crimes is also relevant and increasingly under the spotlight. However, it is still limited by procedural constraints, the admissibility of evidence and lack of case law in most jurisdictions. The Formosa case in Texas, where the judge found a petrochemical company liable for violating the US Clean Water Act on the basis of evidence collected by the civic group, is an important example of citizen-driven data supporting litigation.

Russia’s war against Ukraine has provided an impetus for new citizen science initiatives. The most notable ones are led by professional environmentalists or involve cooperation with academia, and try to support investigative authorities in collecting evidence of environmental crimes. The Ukrainian Scientific Centre for the Ecology of the Sea and the Let’s Do It Ukraine youth organisation joined water and sediment sampling efforts on the Black Sea coast after the Kakhovka Dam breach, with the data made available to the investigative authorities.

To date, the GROMADA project has engaged a wide range of Ukrainian citizen science practitioners and activists, as well as lawyers working on accountability issues, as it has explored their needs and their perceptions of citizen science in the context of the war. The war itself has created important challenges for civic monitoring. Access to many government data portals is closed, routine environmental inspections are not taking place and baseline data is often unavailable. Importantly, evidence collected by citizen science initiatives within a criminal investigation must remain confidential during the pre-trial phase, limiting the possibility of informing communities about the extent of harm and any risks it may have generated. Ensuring communities’ right to access information may require alternative ways of organising this work, together with dedicated funding.

[datasciencewithmohsin]

Simple Linear Regression in Data Science and machine learning

Simple linear regression is one of the most important techniques in data science and machine learning. It is the foundation of many statistical and machine learning models. Even though it is simple, its concepts are widely applicable in predicting outcomes and understanding relationships between variables.

This article will help you learn about:

1. What is simple linear regression and why it matters.

2. The step-by-step intuition behind it.

3. The math of finding slope() and intercept().

4. Simple linear regression coding using Python.

5. A practical real-world implementation.

If you are new to data science or machine learning, don’t worry! We will keep things simple so that you can follow along without any problems.

What is simple linear regression?

Simple linear regression is a method to model the relationship between two variables:

1. Independent variable (X): The input, also called the predictor or feature.

2. Dependent Variable (Y): The output or target value we want to predict.

The main purpose of simple linear regression is to find a straight line (called the regression line) that best fits the data. This line minimizes the error between the actual and predicted values.

The mathematical equation for the line is:

Y = mX + b

: The predicted values.

: The slope of the line (how steep it is).

: The intercept (the value of when).

Why use simple linear regression?

click here to read more https://datacienceatoz.blogspot.com/2025/01/simple-linear-regression-in-data.html

[replicantapologist]

How do phylogeny and taxonomy relate to each other?

I'm writing this because phylogeny is a very dear concept to me, and I hate seeing it being misunderstood. And because @glitter-stained asked me to elaborate on a vague post lol.

(and then I turned it into sort of an exercise too).

Now, I know that this is super super niche, and that for 99.99% percent of the people, it doesn't matter. Anyway, I saw a post which implied that phylogeny and taxonomy are concepts which are opposite to each other, or that using one excludes using the other, and that's not at all how things are.

So, what are taxonomy and phylogeny?

Taxonomy

We love putting things in neat little boxes and classifying them, saying this is a fish and this is a dog and so on. If I asked you to think of a fish and of a canine, you'd think of two very beings. Right?

Taxonomy is the science of creating those boxes. Then we put the things that are similar in the same box (which would be classification, but the two parts of this task are intertwined, of course). I guess 😬.

You may be familiar with Linnaeus' classification, with its hierarchical categories of Kingdom, Phylum, Class, Order, Family, Genus, Species.

Linnaeus' system had three kingdoms - plants, animals and minerals.

The taxonomy system we use nowadays is based on Linnaeus', but with some changes. For example, we don't use kingdom anymore (the broader hierarchical category being the three domains: Bacteria, Archaea and Eukarya (everything that isn't bacteria or Archaea, basically)). Of course, we only classify living beings.

There is a method to that creation of boxes and deciding in which box to put something.

Linnaeus, for example, grouped organisms by their physical traits. That led to boxes that don't make sense anymore nowadays. For example, Linnaeus put Fungi in the plant kingdom. I guess that, looking at the visual of plants and fungi, that would make sense...

But wait, why does it not make sense to put Fungi and plants together anymore?

Well, that's because nowadays we believe that the most correct way of grouping organisms is by reflecting their inferred evolutionary relationships.

And how do we do that? By constructing phylogenetic trees!

Phylogeny

A phylogeny is a hypothesis of evolutionary relationships between organisms of different species.

These guys here:

Getting into the details of how to make phylogenies is extremely outside the scope of this post (and is a hot topic in Biology. People have gone to war because of this. Rivalries were born because of this. Academic insults were exchanged because of this). Basically, we collect data about organisms (used to be morphology, now we are in the era of molecular phylogenies, but this also is a hot topic) and use models to determine how the changes of certain features reflects their evolutionary relationships.

An important concept, however, is that of monophyly.

A monophyletic group is a group of lineages which comprise their ancestor and *all its descendants*.

Using that image above, for example:

B and C form a monophyletic group, which well call BC. Because it includes the ancestor (the node) and all of its descendants (in this case, species B and C).

Now, A and B don't form a monophyletic group, because this group doesn't include all the ancestor and all of its ancestors (C is left out). The group AB is what we call a paraphiletic group.

And I'm saying this because sometimes, we used in the past a phylogeny that we have now determined to not best reflect the evolutionary relationships anymore. For example, in school I learned about the group "Bryophytes" which is useful when were studying similar plants, but that does not include a group comprising an ancestor and all its descendants. "Bryophytes" is a paraphiletic group.

Why am I talking about that?

Well, because inferring monophylies is of one of the goals in the construction of phylogenetic trees. Sometimes we can't determine that, sometimes we have different hypotheses for how to resolve the tree (yeah, war, rivalries and academic insults too), but we want to solve that. In the ideal world, we'll have a tree only with monophyletic groups, and that is what will allow us to... CLASSIFY THINGS.

And that's how we reconcile taxonomy and phylogeny.

Taxonomy and phylogeny are friends! Or, they should be. Then again, this is perhaps a good moment to say that I am veeeeery Dobzhansky biased, and I take his "Nothing in Biology makes sense except in the light of evolution" words to heart. But so does Biology in general nowadays. Modern taxonomy is phylogeny based.

In the platonic world, we have all monophyletic groups resolved (always keeping in mind that these trees are *hypotheses*, and that we can always find ones that better describe the evolutionary relationships).

Look at this beautiful phylogeny of flowering plants (angiosperms) by level of order:

Now, I not only mentioned fishs and dogs above because they were random examples hehe.

You see, this arbitrary thing of creating boxes leads us to funny situations where the technical term for a thing doesn't correspond to the popular one. If you are pedantic enough. And funny 👍.

Case in point: Sarcopterygii.

If you look at Wikipedia, this group will be defined as follows:

Sounds very much like a fish, doesn't it?

But let's take a look at the phylogeny of Sarcopteriigy:

Look who's there in the right corner...

Tetrapoda! (Which comprises amphibians, birds and mammals). Which includes dogs.

So...

This entire post, just to say that I, typing this post, and you, a human reading this post, and a dog, are fish! This is what happens when we put thing into boxes 🤷‍♀️.

This is the (more) niche version of the "Dinosaurs are still alive because the avian dinosaurs are birds, only non-avian dinosaurs are extinct" joke. (The technical basis in the same: monophyly).

This one:

Anyway. Thanks for coming to my TED talk!

Anyone who made it this far, feel free to ask any questions and/or complement and/or correct anything 🥰

[cyberstudious]

what's it like studying CS?? im pretty confused if i should choose CS as my major xx

hi there!

first, two "misconceptions" or maybe somewhat surprising things that I think are worth mentioning:

there really isn't that much "math" in the calculus/arithmetic sense*. I mostly remember doing lots of proofs. don't let not being a math wiz stop you from majoring in CS if you like CS

you can get by with surprisingly little programming - yeah you'll have programming assignments, but a degree program will teach you the theory and concepts for the most part (this is where universities will differ on the scale of theory vs. practice, but you'll always get a mix of both and it's important to learn both!)

*: there are some sub-fields where you actually do a Lot of math - machine learning and graphics programming will have you doing a lot of linear algebra, and I'm sure that there are plenty more that I don't remember at the moment. the point is that 1) if you're a bit afraid of math that's fine, you can still thrive in a CS degree but 2) if you love math or are willing to be brave there are a lot of cool things you can do!

I think the best way to get a good sense of what a major is like is to check out a sample degree plan from a university you're considering! here are some of the basic kinds of classes you'd be taking:

basic programming courses: you'll knock these out in your first year - once you know how to code and you have an in-depth understanding of the concepts, you now have a mental framework for the rest of your degree. and also once you learn one programming language, it's pretty easy to pick up another one, and you'll probably work in a handful of different languages throughout your degree.

discrete math/math for computer science courses: more courses that you'll take early on - this is mostly logic and learning to write proofs, and towards the end it just kind of becomes a bunch of semi-related math concepts that are useful in computing & problem solving. oh also I had to take a stats for CS course & a linear algebra course. oh and also calculus but that was mostly a university core requirement thing, I literally never really used it in my CS classes lol

data structures & algorithms: these are the big boys. stacks, queues, linked lists, trees, graphs, sorting algorithms, more complicated algorithms… if you're interviewing for a programming job, they will ask you data structures & algorithms questions. also this is where you learn to write smart, efficient code and solve problems. also this is where you learn which problems are proven to be unsolvable (or at least unsolvable in a reasonable amount of time) so you don't waste your time lol

courses on specific topics: operating systems, Linux/UNIX, circuits, databases, compilers, software engineering/design patterns, automata theory… some of these will be required, and then you'll get to pick some depending on what your interests are! I took cybersecurity-related courses but there really are so many different options!

In general I think CS is a really cool major that you can do a lot with. I realize this was pretty vague, so if you have any more questions feel free to send them my way! also I'm happy to talk more about specific classes/topics or if you just want an answer to "wtf is automata theory" lol

[nochi-quinn]

I've been reading Time to Orbit: Unknown at a friend's suggestion and just got caught up. I started off just making notes of stuff I wanted to post once I was done so I wouldn't get accidentally spoiled in possible notes or that just wouldn't be relevant later, but quickly became a liveblog just because of the kind of person I am. it makes very little sense in places and sometimes there are multiple chapters in between any commentary, but here it is:

it is remarkably easy to procure an axe on colony ship Courageous

kudzu is inevitable

“In your medical opinion, doctor,” I ask, “what in the everloving fuck?”

no dogs on luna. they just run right off the damn thing.

tinera. you agree.

“You would be truly amazed just how often ‘serving humanity’ and ‘obeying the law’ are, in fact, diametrically opposed concepts.”

“Sure, we’ll make sure to be better liars next time,” Tinera says. “That’s all I ask.”

Okay I can't quote this entire conversation about the kill switch but let it be known I really want to

“If you go to jail, should be for something cool.”

Crew bonding by making fun of the captain for their cringe childhood interests

“Do you guys remember that factory in Sengki?” Adin asks. “Where the AI to the apartment building that housed most of its workforce noticed that all its residents were sleep deprived and added two hours to the clocks, throwing production into chaos for a month?” comrade Sengki AI

they're Vaults. they're Vaults in Space.

“Everything is wrong with everything" if that's not a fucking mood

countdown to throwing Sands out an airlock

tal. you agree.

I deeply love every time they talk about "pre-Neocambrian" anything, it feels so accurate to how we talk about ancient socities now, just reading so much into everything based on far too little data to be so sure of ourselves

"Captain Sands rubs his temples" welcome to the Courageous

you can't kill MOVIE NIGHT, you absolute monster

can we PLEASE throw Sands out an airlock

"That’s how science works, right? (I’m not a scientist.)"

found the cannibalism

before going to the next chapter, I don't believe Sands figured out shit about shit, he just wants to Solve A Problem so he looks like A Good Captain

If Sands Has No Haters I Am Dead

“Are you asking how many this Friend killed, or how many it was convicted for?” “Which is the bigger number?” “Guess,” it says, with a little smile.

I'm going to shove Sands in a locker and then throw the locker out an airlock

fuck yeah adin, get his ass

FUCK YEAH ASPEN GET HIS ASS

aspen no

(I don't know what I expected honestly)

“I still want to try violence, actually,”

FUCK YEAH ADIN GET HIS JOB

“You’re both utterly terrible examples of humanity that the universe would be better off without! It’s not a competition.”

just saw No Mercy Percy in the Patreon box

(it's weirdly heartwarming to see the patreon box grow over time)

ohh, I like the Texan paper flower custom, that's really sweet

Truly fascinating how the instant Sands is in the ground I'm back to smiling at every other line

like this will obviously change but I feel like it's important to note

you KICK meringue

you CANNOT, 118 chapters in, casually drop that aspen is colorblind. like.

I knew shit was going too well

would it be weird to have one eye be colorblind and the other not? what would that even look like?

"minor injuries" YOU LOST AN EYE

aspen is going to burn every copy of every single one of their books

("they're probably digital" they will burn the computers)

BEE MOVIE SURVIVED

“It says ‘This product was manufactured in a facility that processes peanuts.’” oh adin bb

"It’s not eavesdropping. It’s sociological research."

oh, they's cannibals

that's cannibal behavior

Aspen talking like Dinesh now that they're trying to speak Texan is my favorite

"when you change your mind" is just the most casually gross thing to say about tinera not wanting to get her hand fixed

if the eyeball starts talking to them uchikoshi is gonna sue

“Aspen,” Tinera says, “has anyone ever told you that you’re a nerd?” “Not so much, these days. You’re usually distracted by Tal being a much bigger nerd. Like light pollution.”

the Hylaran politics in general and regarding the colonists specifically is reminding me of old stargate episodes and I'm here for it

aspen is obviously daniel

okay teleportation was not on my bingo card

oh, they've got meningitis. that's not great.

"The problem with talking to an AI is that I can’t punch it."

okay so they've DON'T got meningitis. not sure this is better.

Bobby Tables mentioned

Bobby Tables plot device!

hey. hey aspen. whatcha doin buddy

HEY ASPEN???

and a little child will lead them

oh god kim's gonna try to fix tinera's hand isn't she

oh god kim tried to fix tinera's hand AND the friend's brain didn't she

I knew my immortal was gonna show up in here at some point

fascinated by how hylaran society is pretty much literally a big kindergarten class bc it simply makes so much fucking sense

tal doing complex flower crown math

"As your captain, I order you to enjoy pancake dinner with me.”

DANDELION I'm gonna cry

pretty sure their "repair indefinitely" plan is how you get the space station from outer worlds

or, how dandelion's explaining it, the quarian fleet

my reaction to the "they'll see how right we are eventually" thing is so strong and I do not know why

noooo greyed-out "next" button my behated

[vdrawsrobots]

Fighting Communication and Gundam Science in G-Fighter

I originally posted this on twitter, but I figured it would be easier to read here--- and I'd love to know anybody's thoughts about my rambles/speculation :) Edited/expanded a little because I don't have to worry about word count hehe

A possible interpretation of how "fighting communication" could be interpreted as a more literal phenomenon instead of Martial Arts Magic when considering the way Fighter-Gundam connections are described in the G-Fighter entertainment bible. Warning for spoilers under the read more!

Gundams in G-Fighter are (to an extent) powered by the emotions of the pilot. That emotional state has an influence on how well they can pilot/communicate their intentions to their Gundam, and in some cases, on the behavior of the Gundam itself.

This is alluded to a few times in the show--- the most direct acknowledgment I can find would be Dr. Kasshu referring to Shining’s “Emotional Energy System” being primarily powered by anger. It's also explored in the training that Schwarz Bruder gives to Domon in order to control Shining's super mode--- in order to properly communicate the pilot's intentions, their emotions must be calm and regulated, easier to translate into data that the Gundam can understand. Therefore, his techniques focus on getting Domon to see past just striking out in blind anger. It's about intention and clarity of communication.

This is more explicitly stated in the Entertainment Bible (which does a better job explaining it than I ever could):

This emotional data is then translated into something that the Gundam’s system can understand— therefore, the clearer/more controlled the emotional input, the more accurate the translation, and the better the pilot’s intentions can be “understood” by their machine. The Gundam understands the pilot on a level that no other human can, a pure knowing of the pilot's consciousness (or "soul").

As stated, mental training is also just as important as physical. After all, when this communication is unclear, it can have disastrous consequences. Master Asia encourages Domon to just get mindlessly mad in their fight in order to weaken him. Kyoji’s extreme emotional state when crashing to Earth, combined with the physical damage to Ultimate’s computer system, accidentally teaches the Devil Gundam to hate humanity.

No wonder the technique Schwarz teaches Domon is about clarity, control and inward understanding of emotion and intent.

Given this translation of emotional feedback into transferrable data, the concept of “Fighting Communication” and Gundam Fighters understanding each other through combat could be interpreted literally.

Battles in G-Fighter involve a lot of physical contact--- obviously, as they are battles, but there are a lot of scenes of the Gundams joining hands when struggling against each other, or otherwise maintaining prolonged physical connection at high-intensity moments of combat.

Often it is during these scenes when characters refer to fighting communication, or what the fists of their opponent are trying to tell them (all the references to the soul of a fighter being expressed through their fists, etc). Another important thing to notice here is that both pilots are in a heightened emotional state. They're also in as close to perfect connection with their Gundams as they can--- they're putting everything on the line, going all-out to win, and so is their machine. Gundam and pilot are one--- implying a very high flow of emotional data and neural input from Gundam to pilot in these moments.

Could it be then that this physical contact accidentally allows for some accidental crossover of emotional feedback? Is there an unintentional spillover caused by the Gundam reaching its limits to handle so much raw emotion, or possible crossover in the same way that electricity jumps between conductors? It's not impossible to imagine that the “understanding of the soul” the Fighter experiences is being caused by the accidental crossover of emotional feedback between the Gundams in these moments of prolonged physical contact— therefore literally allowing fighters to see into and understand each other’s true feelings in the heat of high-intensity combat.

And since the way emotion/thought is translated into something understandable to the machine in a way that goes beyond human comprehension, it could also be speculated that this could occur for the pilots, as well. That the high-intensity causes the literal blurring of the lines between the minds of two pilots (think of something similar to the Drift in Pacific Rim). Maybe for a Gundam Fighter, a battle with your opponent really does allow you to understand them on a deeper level than just words can allow. Maybe a fighter really can communicate with their fists in a way they don't know how to with words.

Or maybe I'm reaching and reading too much into it :) either way, let me know your thoughts if you have any!! I love writing about g-fighter, both fic and analysis (??? i guess???) stuff like this, and tumblr lets me be as long-winded as I want >:) I have a lot of Thoughts about each pilot's specific relationship with their Gundam, and I'd like to write more about them sometime soon...