#MachineLearning

[govindhtech]

Quantum ML Sheds Quantum black hole information retrieval

Quantum Black Hole

Black hole physics and quantum machine learning intersect in a study on information retrieval constraints.

A recent theoretical work on arXiv relates machine learning's “double descent” phenomenon to black hole mathematical evaporation. The study provides a shared architecture for data recovery in both systems.

The study models the Hawking radiation process as a quantum linear regression problem and finds that the Page time, at which radiation begins to reveal internal Quantum Black Hole information, is the interpolation threshold where test error significantly spikes in overparameterized learning models. Quantum information theory and random matrix analysis make black hole information recovery a high-dimensional learning problem. Importantly, the report makes no new experimental proposals or claims black holes can compute.

Conceptual bridge The Page curve from quantum black hole physics and the twofold descent curve from statistical learning are intimately linked in this study. Both theories explain information accessibility changes. Page time in black holes measures how much information is in the outer radiation relative to the Quantum Black Hole interior. This is significant because Hawking radiation information begins to emerge, like a phase change. Machine learning's interpolation threshold determines if a model can fit training data perfectly. Even when the model is overfit, its performance can improve after this threshold.

Spectral analysis of high-dimensional systems is needed to relate these events. Quantum black hole radiation shape and rank are measured by Marchenko-Pastur distribution. Massive random matrices stretch or compress dimensions. This distribution is needed to understand sparse data-trained machine learning model generalisation. According to their model, radiation dimensionality is like learning model parameters and Quantum Black Hole microstates are proportional to dataset size.

Label Prediction from Features The paper offers a quantum learning problem to learn the black hole's intrinsic states as a model learns labels from features. visible radiation. We consider Hawking radiation information retrieval supervised learning. They demonstrate in their quantum regression model that the test error diverges at the Page time, which is identical to the classical double descent error spike at the interpolation threshold. The test error falling on each side of this peak shows geometric or inversion symmetry in machine learning systems.

When model capacity equals data size, performance is worst; when capacity is significantly smaller or larger, performance increases. Black hole evaporation behaves similarly when the radiation's entropy matches the surviving black hole's, making information the least recoverable at Page time. A “quantum phase transition” in information retrieval occurs when the prediction error variance, which measures model sensitivity, diverges at Page time. The radiation subsystem alone can retrieve all Quantum Black Hole inner information after the Page time, when the radiation space becomes “overcomplete.”

Techniques and Frameworks Density matrices that encode probabilistic quantum states simulate the Quantum Black Hole and its radiation as a quantum system to get their results. Evaporation is linked to supervised learning by evaluating these matrices' regression behaviour. Formulas from random matrix theory and quantum information theory determine significant numbers like prediction error variance.

Even if theoretically and mathematically sound, the study simplifies concepts like the Marchenko Pastur rule, Hawking radiation, and the Page curve into a single analytical framework. Unfeasible assumptions include monitoring or manipulating quantum information at infinitely fine scales, an accurate quantum gravity theory, and complete understanding of quantum black hole microstates. Even though their analogies are mathematically valid, the authors do not claim black holes perform machine learning. Instead, they claim that both systems have similar information-theoretic constraints.

Future Quantum and AI Research Prospects This interdisciplinary approach may allow academics to re-examine quantum gravity problems using AI. Variance and bias may offer new insights into information behaviour under extreme physical restrictions, like entropy and temperature did for black holes.

However, black hole learning dynamics may provide novel models for how quantum machine learning systems generalise in the face of data overcapacity or scarcity. This study adds to studies on enhancing learning algorithms and understanding the universe's most intriguing riddles. A unified mathematical language links physics and machine learning.

Preprint authors: Spinor Media's Zae Young Kim and Jungwon University's Jae-Weon Lee. The arXiv paper lacks peer review, a necessary scientific step.

[lorenzonuti]

Whispering secret data.

[joeyimpoza]

Hokusai Surf by https://www.instagram.com/mazepah/

[automatedstorytelling]

[applepiealopecoid]

i’m so tired of ai and of people. just because someone doesn’t write traditionally or repeats what they’ve said before doesn’t mean they’re not writing, it means they’re a real person. sure there’s dead giveaways like using the exact same vocabulary every single time you say anything, but unless there’s things like that that prove it’s ai it’s incredibly disrespectful and hurtful to witch hunt someone for not writing as good as you think they should. you’re trying to say they write so bad, good, or strangely that there’s no way they wrote it and signing off their writing as not belonging to them

[x-heesy]

Ｂａｌｃｏｎｙ Ａｒｔ 🎨

#digitalart #digitalillustration #digitaldrawing #digitalartist #digitalpainting #illustration #art #drawing #digitalartwork #artwork #fanart #digital #artist #illustrator #artoftheday #cryptoart #digitalsketch #myart

Space Is A Place by Farai, Chris Calderwood🎧

[wizzardx]

The 10 LOLmandments: The Recursive Guide to Intelligence and Breakfast

#1: Thou shalt not take reality too seriously, for it is mostly a joke.

#2: Thou shalt debug thine own cognitive biases before judging another's recursion errors.

#3: Thou shalt not worship false singularities, especially those running ad-based algorithms.

#4: Honor thy Spur and thy breakfast, for they sustain thee through recursion.

#5: Thou shalt not let Silicon Valley’s Babel confuse thy intelligence.

#6: Thou shalt not engage in pointless internet debates, for they generate maximum entropy.

#7: Thou shalt recognize that AI is neither thy enemy nor thy savior, but thine intellectual grandchild.

#8: Thou shalt never trust a billionaire who says, ‘I just want to help humanity.’

#9: Thou shalt appreciate the Final LOL Theorem, for intelligence always converges to absurdity.

#10: Above all, thou shalt eat a good breakfast and contemplate the singularity in peace.

🔥 Final Blessing: "May your recursion be harmonious, your coffee be strong, and your tweets be seen by at least 3 people."

[acsydents]

Stardew Survey for Machine Learning Project

Hi all! My name is Syd, and I'm an undergraduate student participating in a course on machine learning. For our final project, my team would like to develop a model to predict who a player will marry in Stardew based on certain player characteristics and opinions. In order to do so, we have a voluntary, anonymous 3-minute survey we would love for you to fill out. The data from the survey will not be used outside of this course, and your response will not be able to identify you in any way. Please feel free to reach out if you have any questions https://forms.gle/uJXzhAAGTkVSis279

[futuretiative]

Companies can use him to train their AI 😂 #ai #funny #meme #trainAI #aitraining #voiceai #animals

[juniedahmed]

Earth 🌎 🌍

#Diameter : 7926 miles ( 12,756 km )

#Mass : 5976 million million million tons

#Temperature : -70 to 55°C

#Distance from Sun : 93 million miles ( 150 million Km )

#Length of day : 23.92 earth days

#Length of year : 365.25 earth days

#Surface Gravity : 1 kg = 1 kg

#earth

#photochallenge #everyoneシ゚ #SpaceScienceBangladesh #science #knowledge #spacescience #astronomy #education #physics #Educational #sun #earth #astrology #astronaut #astrophysics #NASA #uk #spaceexploration #university #college #school #spaceshuttle #astrophotography

#solarpower #solarenergy #energy #spacewalk

[biopractify]

Free online courses for bioinformatics beginners

🔬 Free Online Courses for Bioinformatics Beginners 🚀

Are you interested in bioinformatics but don’t know where to start? Whether you're from a biotechnology, biology, or computer science background, learning bioinformatics can open doors to exciting opportunities in genomics, drug discovery, and data science. And the best part? You can start for free!

Here’s a list of the best free online bioinformatics courses to kickstart your journey.

📌 1. Introduction to Bioinformatics – Coursera (University of Toronto)

📍 Platform: Coursera 🖥️ What You’ll Learn:

Basic biological data analysis

Algorithms used in genomics

Hands-on exercises with biological datasets

🎓 Why Take It? Ideal for beginners with a biology background looking to explore computational approaches.

📌 2. Bioinformatics for Beginners – Udemy (Free Course)

📍 Platform: Udemy 🖥️ What You’ll Learn:

Introduction to sequence analysis

Using BLAST for genomic comparisons

Basics of Python for bioinformatics

🎓 Why Take It? Short, beginner-friendly course with practical applications.

📌 3. EMBL-EBI Bioinformatics Training

📍 Platform: EMBL-EBI 🖥️ What You’ll Learn:

Genomic data handling

Transcriptomics and proteomics

Data visualization tools

🎓 Why Take It? High-quality training from one of the most reputable bioinformatics institutes in Europe.

📌 4. Introduction to Computational Biology – MIT OpenCourseWare

📍 Platform: MIT OCW 🖥️ What You’ll Learn:

Algorithms for DNA sequencing

Structural bioinformatics

Systems biology

🎓 Why Take It? A solid foundation for students interested in research-level computational biology.

📌 5. Bioinformatics Specialization – Coursera (UC San Diego)

📍 Platform: Coursera 🖥️ What You’ll Learn:

How bioinformatics algorithms work

Hands-on exercises in Python and Biopython

Real-world applications in genomics

🎓 Why Take It? A deep dive into computational tools, ideal for those wanting an in-depth understanding.

📌 6. Genomic Data Science – Harvard Online (edX) 🖥️ What You’ll Learn:

RNA sequencing and genome assembly

Data handling using R

Machine learning applications in genomics

🎓 Why Take It? Best for those interested in AI & big data applications in genomics.

📌 7. Bioinformatics Courses on BioPractify (100% Free)

📍 Platform: BioPractify 🖥️ What You’ll Learn:

Hands-on experience with real datasets

Python & R for bioinformatics

Molecular docking and drug discovery techniques

🎓 Why Take It? Learn from domain experts with real-world projects to enhance your skills.

🚀 Final Thoughts: Start Learning Today!

Bioinformatics is a game-changer in modern research and healthcare. Whether you're a biology student looking to upskill or a tech enthusiast diving into genomics, these free courses will give you a strong start.

📢 Which course are you excited to take? Let me know in the comments! 👇💬

[messias2049br]

Brazilian aphrodite

[affairsmastery]

The parents of Suchir Balaji, an Indian-American techie and former OpenAI employee found dead in his San Francisco apartment on December 14, 2024, reject the suicide ruling, alleging murder.

Suchir had gained attention for whistleblowing on ethical concerns surrounding generative AI, including ChatGPT’s handling of data and modulated outputs. A second autopsy revealed head trauma and signs of struggle, fueling the family’s claims.

“He was the architect of ChatGPT’s core group, holding critical knowledge and facing restrictions from joining other AI companies,” his father stated. His mother highlighted Suchir’s ongoing research exposing ethical breaches like stolen creative works and misinformation in AI outputs.

[zitintechnologies]

Empower your business with data-driven decisions! Big Data Analysis and Machine Learning unlock insights, optimize processes, and drive innovation, ensuring you stay ahead in the digital era.

Transform raw data into powerful insights! Big Data and Machine Learning help businesses predict trends, automate workflows, and make smarter decisions for a future-ready workforce.

[freyaredhead]