#data science and machine learning
Explore tagged Tumblr posts
herovired12 ¡ 9 months ago
Text
Elevate Your Career with a Data Science and Machine Learning Course Online
Are you looking to advance your career in the dynamic field of data science and machine learning? Consider enrolling in an online course! These programs offer flexible schedules and comprehensive curriculums to equip you with the latest skills and techniques. Visit our official website to learn more.
0 notes
tooldart ¡ 11 months ago
Text
0 notes
healthylifewithus ¡ 1 year ago
Text
Complete Excel, AI and Data Science mega bundle.
Unlock Your Full Potential with Our 100-Hour Masterclass: The Ultimate Guide to Excel, Python, and AI.
Why Choose This Course? In today’s competitive job market, mastering a range of technical skills is more important than ever. Our 100-hour comprehensive course is designed to equip you with in-demand capabilities in Excel, Python, and Artificial Intelligence (AI), providing you with the toolkit you need to excel in the digital age.
To read more click here &lt;<
Become an Excel Pro Delve deep into the intricacies of Excel functions, formulae, and data visualization techniques. Whether you’re dealing with basic tasks or complex financial models, this course will make you an Excel wizard capable of tackling any challenge.
Automate Your Workflow with Python Scripting in Python doesn’t just mean writing code; it means reclaiming your time. Automate everyday tasks, interact with software applications, and boost your productivity exponentially.
If you want to get full course click here &lt;<
Tumblr media
Turn Ideas into Apps Discover the potential of Amazon Honeycode to create custom apps tailored to your needs. Whether it’s for data management, content tracking, or inventory — transform your creative concepts into practical solutions.
Be Your Own Financial Analyst Unlock the financial functionalities of Excel to manage and analyze business data. Create Profit and Loss statements, balance sheets, and conduct forecasting with ease, equipping you to make data-driven decisions.
Embark on an AI Journey Step into the future with AI and machine learning. Learn to build advanced models, understand neural networks, and employ TensorFlow. Turn big data into actionable insights and predictive models.
Master Stock Prediction Gain an edge in the market by leveraging machine learning for stock prediction. Learn to spot trends, uncover hidden patterns, and make smarter investment decisions.
Who Is This Course For? Whether you’re a complete beginner or a seasoned professional looking to upskill, this course offers a broad and deep understanding of Excel, Python, and AI, preparing you for an ever-changing work environment.
Invest in Your Future This isn’t just a course; it’s a game-changer for your career. Enroll now and set yourself on a path to technological mastery and unparalleled career growth.
Don’t Wait, Transform Your Career Today! Click here to get full course &lt;<
Tumblr media
1 note ¡ View note
prashanth94 ¡ 1 year ago
Text
1 note ¡ View note
mostlysignssomeportents ¡ 1 year ago
Text
The surprising truth about data-driven dictatorships
Tumblr media
Here’s the “dictator’s dilemma”: they want to block their country’s frustrated elites from mobilizing against them, so they censor public communications; but they also want to know what their people truly believe, so they can head off simmering resentments before they boil over into regime-toppling revolutions.
These two strategies are in tension: the more you censor, the less you know about the true feelings of your citizens and the easier it will be to miss serious problems until they spill over into the streets (think: the fall of the Berlin Wall or Tunisia before the Arab Spring). Dictators try to square this circle with things like private opinion polling or petition systems, but these capture a small slice of the potentially destabiziling moods circulating in the body politic.
Enter AI: back in 2018, Yuval Harari proposed that AI would supercharge dictatorships by mining and summarizing the public mood — as captured on social media — allowing dictators to tack into serious discontent and diffuse it before it erupted into unequenchable wildfire:
https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330/
Harari wrote that “the desire to concentrate all information and power in one place may become [dictators] decisive advantage in the 21st century.” But other political scientists sharply disagreed. Last year, Henry Farrell, Jeremy Wallace and Abraham Newman published a thoroughgoing rebuttal to Harari in Foreign Affairs:
https://www.foreignaffairs.com/world/spirals-delusion-artificial-intelligence-decision-making
They argued that — like everyone who gets excited about AI, only to have their hopes dashed — dictators seeking to use AI to understand the public mood would run into serious training data bias problems. After all, people living under dictatorships know that spouting off about their discontent and desire for change is a risky business, so they will self-censor on social media. That’s true even if a person isn’t afraid of retaliation: if you know that using certain words or phrases in a post will get it autoblocked by a censorbot, what’s the point of trying to use those words?
The phrase “Garbage In, Garbage Out” dates back to 1957. That’s how long we’ve known that a computer that operates on bad data will barf up bad conclusions. But this is a very inconvenient truth for AI weirdos: having given up on manually assembling training data based on careful human judgment with multiple review steps, the AI industry “pivoted” to mass ingestion of scraped data from the whole internet.
But adding more unreliable data to an unreliable dataset doesn’t improve its reliability. GIGO is the iron law of computing, and you can’t repeal it by shoveling more garbage into the top of the training funnel:
https://memex.craphound.com/2018/05/29/garbage-in-garbage-out-machine-learning-has-not-repealed-the-iron-law-of-computer-science/
When it comes to “AI” that’s used for decision support — that is, when an algorithm tells humans what to do and they do it — then you get something worse than Garbage In, Garbage Out — you get Garbage In, Garbage Out, Garbage Back In Again. That’s when the AI spits out something wrong, and then another AI sucks up that wrong conclusion and uses it to generate more conclusions.
To see this in action, consider the deeply flawed predictive policing systems that cities around the world rely on. These systems suck up crime data from the cops, then predict where crime is going to be, and send cops to those “hotspots” to do things like throw Black kids up against a wall and make them turn out their pockets, or pull over drivers and search their cars after pretending to have smelled cannabis.
The problem here is that “crime the police detected” isn’t the same as “crime.” You only find crime where you look for it. For example, there are far more incidents of domestic abuse reported in apartment buildings than in fully detached homes. That’s not because apartment dwellers are more likely to be wife-beaters: it’s because domestic abuse is most often reported by a neighbor who hears it through the walls.
So if your cops practice racially biased policing (I know, this is hard to imagine, but stay with me /s), then the crime they detect will already be a function of bias. If you only ever throw Black kids up against a wall and turn out their pockets, then every knife and dime-bag you find in someone’s pockets will come from some Black kid the cops decided to harass.
That’s life without AI. But now let’s throw in predictive policing: feed your “knives found in pockets” data to an algorithm and ask it to predict where there are more knives in pockets, and it will send you back to that Black neighborhood and tell you do throw even more Black kids up against a wall and search their pockets. The more you do this, the more knives you’ll find, and the more you’ll go back and do it again.
This is what Patrick Ball from the Human Rights Data Analysis Group calls “empiricism washing”: take a biased procedure and feed it to an algorithm, and then you get to go and do more biased procedures, and whenever anyone accuses you of bias, you can insist that you’re just following an empirical conclusion of a neutral algorithm, because “math can’t be racist.”
HRDAG has done excellent work on this, finding a natural experiment that makes the problem of GIGOGBI crystal clear. The National Survey On Drug Use and Health produces the gold standard snapshot of drug use in America. Kristian Lum and William Isaac took Oakland’s drug arrest data from 2010 and asked Predpol, a leading predictive policing product, to predict where Oakland’s 2011 drug use would take place.
Tumblr media
[Image ID: (a) Number of drug arrests made by Oakland police department, 2010. (1) West Oakland, (2) International Boulevard. (b) Estimated number of drug users, based on 2011 National Survey on Drug Use and Health]
Then, they compared those predictions to the outcomes of the 2011 survey, which shows where actual drug use took place. The two maps couldn’t be more different:
https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00960.x
Predpol told cops to go and look for drug use in a predominantly Black, working class neighborhood. Meanwhile the NSDUH survey showed the actual drug use took place all over Oakland, with a higher concentration in the Berkeley-neighboring student neighborhood.
What’s even more vivid is what happens when you simulate running Predpol on the new arrest data that would be generated by cops following its recommendations. If the cops went to that Black neighborhood and found more drugs there and told Predpol about it, the recommendation gets stronger and more confident.
In other words, GIGOGBI is a system for concentrating bias. Even trace amounts of bias in the original training data get refined and magnified when they are output though a decision support system that directs humans to go an act on that output. Algorithms are to bias what centrifuges are to radioactive ore: a way to turn minute amounts of bias into pluripotent, indestructible toxic waste.
There’s a great name for an AI that’s trained on an AI’s output, courtesy of Jathan Sadowski: “Habsburg AI.”
And that brings me back to the Dictator’s Dilemma. If your citizens are self-censoring in order to avoid retaliation or algorithmic shadowbanning, then the AI you train on their posts in order to find out what they’re really thinking will steer you in the opposite direction, so you make bad policies that make people angrier and destabilize things more.
Or at least, that was Farrell(et al)’s theory. And for many years, that’s where the debate over AI and dictatorship has stalled: theory vs theory. But now, there’s some empirical data on this, thanks to the “The Digital Dictator’s Dilemma,” a new paper from UCSD PhD candidate Eddie Yang:
https://www.eddieyang.net/research/DDD.pdf
Yang figured out a way to test these dueling hypotheses. He got 10 million Chinese social media posts from the start of the pandemic, before companies like Weibo were required to censor certain pandemic-related posts as politically sensitive. Yang treats these posts as a robust snapshot of public opinion: because there was no censorship of pandemic-related chatter, Chinese users were free to post anything they wanted without having to self-censor for fear of retaliation or deletion.
Next, Yang acquired the censorship model used by a real Chinese social media company to decide which posts should be blocked. Using this, he was able to determine which of the posts in the original set would be censored today in China.
That means that Yang knows that the “real” sentiment in the Chinese social media snapshot is, and what Chinese authorities would believe it to be if Chinese users were self-censoring all the posts that would be flagged by censorware today.
From here, Yang was able to play with the knobs, and determine how “preference-falsification” (when users lie about their feelings) and self-censorship would give a dictatorship a misleading view of public sentiment. What he finds is that the more repressive a regime is — the more people are incentivized to falsify or censor their views — the worse the system gets at uncovering the true public mood.
What’s more, adding additional (bad) data to the system doesn’t fix this “missing data” problem. GIGO remains an iron law of computing in this context, too.
But it gets better (or worse, I guess): Yang models a “crisis” scenario in which users stop self-censoring and start articulating their true views (because they’ve run out of fucks to give). This is the most dangerous moment for a dictator, and depending on the dictatorship handles it, they either get another decade or rule, or they wake up with guillotines on their lawns.
But “crisis” is where AI performs the worst. Trained on the “status quo” data where users are continuously self-censoring and preference-falsifying, AI has no clue how to handle the unvarnished truth. Both its recommendations about what to censor and its summaries of public sentiment are the least accurate when crisis erupts.
But here’s an interesting wrinkle: Yang scraped a bunch of Chinese users’ posts from Twitter — which the Chinese government doesn’t get to censor (yet) or spy on (yet) — and fed them to the model. He hypothesized that when Chinese users post to American social media, they don’t self-censor or preference-falsify, so this data should help the model improve its accuracy.
He was right — the model got significantly better once it ingested data from Twitter than when it was working solely from Weibo posts. And Yang notes that dictatorships all over the world are widely understood to be scraping western/northern social media.
But even though Twitter data improved the model’s accuracy, it was still wildly inaccurate, compared to the same model trained on a full set of un-self-censored, un-falsified data. GIGO is not an option, it’s the law (of computing).
Writing about the study on Crooked Timber, Farrell notes that as the world fills up with “garbage and noise” (he invokes Philip K Dick’s delighted coinage “gubbish”), “approximately correct knowledge becomes the scarce and valuable resource.”
https://crookedtimber.org/2023/07/25/51610/
This “probably approximately correct knowledge” comes from humans, not LLMs or AI, and so “the social applications of machine learning in non-authoritarian societies are just as parasitic on these forms of human knowledge production as authoritarian governments.”
Tumblr media
The Clarion Science Fiction and Fantasy Writers’ Workshop summer fundraiser is almost over! I am an alum, instructor and volunteer board member for this nonprofit workshop whose alums include Octavia Butler, Kim Stanley Robinson, Bruce Sterling, Nalo Hopkinson, Kameron Hurley, Nnedi Okorafor, Lucius Shepard, and Ted Chiang! Your donations will help us subsidize tuition for students, making Clarion — and sf/f — more accessible for all kinds of writers.
Tumblr media
Libro.fm is the indie-bookstore-friendly, DRM-free audiobook alternative to Audible, the Amazon-owned monopolist that locks every book you buy to Amazon forever. When you buy a book on Libro, they share some of the purchase price with a local indie bookstore of your choosing (Libro is the best partner I have in selling my own DRM-free audiobooks!). As of today, Libro is even better, because it’s available in five new territories and currencies: Canada, the UK, the EU, Australia and New Zealand!
Tumblr media
[Image ID: An altered image of the Nuremberg rally, with ranked lines of soldiers facing a towering figure in a many-ribboned soldier's coat. He wears a high-peaked cap with a microchip in place of insignia. His head has been replaced with the menacing red eye of HAL9000 from Stanley Kubrick's '2001: A Space Odyssey.' The sky behind him is filled with a 'code waterfall' from 'The Matrix.']
Tumblr media
Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg
CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en
 — 
Raimond Spekking (modified) https://commons.wikimedia.org/wiki/File:Acer_Extensa_5220_-_Columbia_MB_06236-1N_-_Intel_Celeron_M_530_-_SLA2G_-_in_Socket_479-5029.jpg
CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en
 — 
Russian Airborne Troops (modified) https://commons.wikimedia.org/wiki/File:Vladislav_Achalov_at_the_Airborne_Troops_Day_in_Moscow_%E2%80%93_August_2,_2008.jpg
“Soldiers of Russia” Cultural Center (modified) https://commons.wikimedia.org/wiki/File:Col._Leonid_Khabarov_in_an_everyday_service_uniform.JPG
CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en
831 notes ¡ View notes
datascienceunicorn ¡ 3 months ago
Text
The Data Scientist Handbook 2024
HT @dataelixir
16 notes ¡ View notes
theinsaneapp ¡ 2 years ago
Text
Physics Informed Neural Networks
Tumblr media
Physics-informed neural networks, a deep learning method that bridges the gap between machine learning and scientific computing.
PINN has superior approximation and generalization capabilities, which made it gain popularity in solving high-dimensional partial differential equations (PDEs), and has been used in various applications such as weather modeling, healthcare, and manufacturing.
365 notes ¡ View notes
mindblowingscience ¡ 10 months ago
Text
Cornell quantum researchers have detected an elusive phase of matter, called the Bragg glass phase, using large volumes of X-ray data and a new machine learning data analysis tool. The discovery settles a long-standing question of whether this almost–but not quite–ordered state of Bragg glass can exist in real materials. The paper, "Bragg glass signatures in PdxErTe3 with X-ray diffraction Temperature Clustering (X-TEC)," is published in Nature Physics. The lead author is Krishnanand Madhukar Mallayya, a postdoctoral researcher in the Department of Physics in the College of Arts and Sciences (A&S). Eun-Ah Kim, professor of physics (A&S), is the corresponding author. The research was conducted in collaboration with scientists at Argonne National Laboratory and at Stanford University.
Continue Reading.
43 notes ¡ View notes
kaerinio ¡ 2 months ago
Text
Tumblr media
it's monday. why tf am i sitting here having to professionally pop off at someone???
11 notes ¡ View notes
gardenfractals ¡ 1 year ago
Text
Tumblr media Tumblr media
Locally Linear Embedding (LLE) approaches
46 notes ¡ View notes
chambersevidence ¡ 5 months ago
Text
Search Engines:
Search engines are independent computer systems that read or crawl webpages, documents, information sources, and links of all types accessible on the global network of computers on the planet Earth, the internet. Search engines at their most basic level read every word in every document they know of, and record which documents each word is in so that by searching for a words or set of words you can locate the addresses that relate to documents containing those words. More advanced search engines used more advanced algorithms to sort pages or documents returned as search results in order of likely applicability to the terms searched for, in order. More advanced search engines develop into large language models, or machine learning or artificial intelligence. Machine learning or artificial intelligence or large language models (LLMs) can be run in a virtual machine or shell on a computer and allowed to access all or part of accessible data, as needs dictate.
9 notes ¡ View notes
d0nutzgg ¡ 1 year ago
Text
Tumblr media
Tonight I am hunting down venomous and nonvenomous snake pictures that are under the creative commons of specific breeds in order to create one of the most advanced, in depth datasets of different venomous and nonvenomous snakes as well as a test set that will include snakes from both sides of all species. I love snakes a lot and really, all reptiles. It is definitely tedious work, as I have to make sure each picture is cleared before I can use it (ethically), but I am making a lot of progress! I have species such as the King Cobra, Inland Taipan, and Eyelash Pit Viper among just a few! Wikimedia Commons has been a huge help!
I'm super excited.
Hope your nights are going good. I am still not feeling good but jamming + virtual snake hunting is keeping me busy!
41 notes ¡ View notes
tooldart ¡ 11 months ago
Text
1 note ¡ View note
subrage ¡ 6 months ago
Text
Truth speaking on the corporate obsession with AI
Hilarious. Something tells me this person's on the hellsite(affectionate)
7 notes ¡ View notes
datascienceunicorn ¡ 3 months ago
Text
HT @dataelixir
11 notes ¡ View notes
theinsaneapp ¡ 2 years ago
Text
How ChatGPT Is Trained (Source: OpenAI)
Tumblr media
223 notes ¡ View notes