Suramya's Blog : Welcome to my crazy life…

September 3, 2024

Indian String Theorists find new formula for calculating Pi

Filed under: Science Related — Suramya @ 5:11 PM

I always thought the formula for Pi was simple, 22/7 but apparently that is not the case. There are multiple mathematicians who have spent a significant time coming up with a formula for calculating Pi precisely. For example, Madhava an Indian scholar, who lived from 1350 to 1425, found that pi equals 4 multiplied by a series that begins with 1 and then alternately subtracts or adds fractions in which 1 is placed over successively higher odd numbers (so 1/3, 1/5, and so on). One way to express this would be:


A formula presents how pi can be calculated using a series developed by the Indian scholar Madhava.

While the formula is quite simple to implement and calculate it takes a long time to get accurate results. There are other formulas as well to calculate Pi. The latest one was found when physicists Arnab Priya Saha and Aninda Sinha of the Indian Institute of Science were exploring the String Theory and instead found a completely new formula for calculating Pi. They published their findings in their Paper (Field Theory Expansions of String Theory Amplitudes)

Saha and Sinha discovered the following formula which shows that Madhava’s formula is only a special case of a much more general equation for calculating pi.


A formula presents a way of calculating pi that was identified by physicists Arnab Priya Saha and Aninda Sinha.

I tried understanding the math behind the formula but it didn’t really make much sense to me so I am just going to quote the explanation given by Scientific American here instead of trying to explain it myself. 🙂

This formula produces an infinitely long sum. What is striking is that it depends on the factor λ , a freely selectable parameter. No matter what value λ has, the formula will always result in pi. And because there are infinitely many numbers that can correspond to λ, Saha and Sinha have found an infinite number of pi formulas.

If λ is infinitely large, the equation corresponds to Madhava’s formula. That is, because λ only ever appears in the denominator of fractions, the corresponding fractions for λ = ∞ become zero (because fractions with large denominators are very small). For λ = ∞, the equation of Saha and Sinha therefore takes the following form:


Saha and Sinha’s formula can be adapted based on the assumption of an infinitely large parameter.

The first part of the equation is already similar to Madhava’s formula: you sum fractions with odd denominators. The last part of the sum (–n)n – 1, however, is less familiar. The subscript n – 1 is the so-called Pochhammer symbol. In general, the expression (a)n corresponds to the product a x(a + 1) x (a + 2) x … x (a + n – 1). For example, (5)3 = 5 x 6 x 7 = 210. And the Pochhammer symbol in the above formula therefore results in: (–n)n – 1 = (–n) x (–n + 1) x (–n + 2) x … x (–n + n – 3) x (–n + n – 2).

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As the two string theorists report, however, pi can be calculated much faster for smaller values of λ. While Madhava’s result requires 100 terms to get within 0.01 of pi, Saha and Sinha’s formula for λ = 3 only requires the first four summands. “While [Madhava’s] series takes 5 billion terms to converge to 10 decimal places, the new representation with λ between 10 [and] 100 takes 30 terms,” the authors write in their paper.

Source: Hacker News: String Theorists Accidentally Find a New Formula for Pi

– Suramya

August 31, 2024

NASA has a site that uses LandSat images to spell a given name

Filed under: Astronomy / Space,Interesting Sites,My Thoughts — Suramya @ 8:30 PM

NASA satellites take a lot of photos of earth and they are available online to view but that doesn’t make it fun to look at them. So they have a site that spells out your name using landsat imagery. Which is a pretty cool way to showcase the images. You can try it out at the You Name in Landsat site.

Here’s how my name looks:

Suramya: Spelled using landsat images
Suramya: Spelled using landsat images

Hovering the cursor on each image gives you the name and location of the geological/geographical image used.

Source: Mastodon.world: @davidho

– Suramya

August 30, 2024

Admiral Grace Hopper’s NSA Lecture from 1982 on Future Possibilities: Data, Hardware, Software, and People

Filed under: Computer Software,Tech Related — Suramya @ 6:05 PM

Grace Hopper is one of the founders of Programming languages and was the first person to devise the theory of machine-independent programming languages which she then used to develop the FLOW-MATIC programming language and COBOL. She had a phenomenal impact on the field of Computer Science/Engineering and her lectures are extremely interesting to watch as even after 40 years the concepts she talks about are still relevant. The NSA has finally released the video recording of a 1982 lecture by Adm. Grace Hopper titled “Future Possibilities: Data, Hardware, Software, and People.”

Initially they refused to do so because “With digital obsolescence threatening many early technological formats, the dilemma surrounding Admiral Hopper’s lecture underscores the critical need for and challenge of digital preservation. This challenge transcends the confines of NSA’s operational scope. It is our shared obligation to safeguard such pivotal elements of our nation’s history, ensuring they remain within reach of future generations. While the stewardship of these recordings may extend beyond the NSA’s typical purview, they are undeniably a part of America’s national heritage.”.

Thankfully after a massive push from the all over the world to get NSA to release the video saner minds prevailed and the entirety of the lecture has been released in two parts. You can watch them below:


Capt. Grace Hopper on Future Possibilities: Data, Hardware, Software, and People (Part One, 1982)


Capt. Grace Hopper on Future Possibilities: Data, Hardware, Software, and People (Part Two, 1982)

Since I don’t trust online systems to keep information available indefinitely, I have also archived the lectures on my system so if they disappear in the future I will have copies I can publish.

– Suramya

August 27, 2024

MIT Researchers publish AI risk database exposing 700+ ways AI can be risky

Filed under: Artificial Intelligence,Computer Software,My Thoughts — Suramya @ 10:44 AM

AI (or rather what is call AI right now), is not really intelligent but it does have a lot of risks associated with using it. We all know about the Deep Fakes and the hallucinations etc but those are not the only risks of using generative AI. The researchers at MIT have cataloged the over 700 risks of using generative AI.

The risks posed by Artificial Intelligence (AI) are of considerable concern to academics, auditors, policymakers, AI companies, and the public. However, a lack of shared understanding of AI risks can impede our ability to comprehensively discuss, research, and react to them. This paper addresses this gap by creating an AI Risk Repository to serve as a common frame of reference.

This comprises a living database of 777 risks extracted from 43 taxonomies, which can be filtered based on two overarching taxonomies and easily accessed, modified, and updated via our website and online spreadsheets. We construct our Repository with a systematic review of taxonomies and other structured classifications of AI risk followed by an expert consultation. We develop our taxonomies of AI risk using a best-fit framework synthesis. Our high-level Causal Taxonomy of AI Risks classifies each risk by its causal factors (1) Entity: Human, AI; (2) Intentionality: Intentional, Unintentional; and (3) Timing: Pre-deployment; Post-deployment. Our mid-level Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental, and (7) AI system safety, failures, & limitations. These are further divided into 23 subdomains. The AI Risk Repository is, to our knowledge, the first attempt to rigorously curate, analyze, and extract AI risk frameworks into a publicly accessible, comprehensive, extensible, and categorized risk database. This creates a foundation for a more coordinated, coherent, and complete approach to defining, auditing, and managing the risks posed by AI systems.

They have published a paper on it: The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks From Artificial Intelligence that you should check out. They have also made their entire database available to copy for free as well.

Check it out if you have some free time.

Source: Boingboing.net: MIT’s AI risk database exposes 700+ ways AI could ruin your life.

– Suramya

August 26, 2024

Anime character breaks free from Blender by hijacking its controls

Filed under: Humor,Tech Related — Suramya @ 10:58 AM

Kensyouen_Y has created a video using Blender depicting an Anime character model who becomes sentient and starts playing around with Blender’s UI, messing around with different tools and functionalities, changing her own hair color via Shader Nodes, and eventually crashing the software with her boisterous high jinks.

This is a phenomenally creative video, something that I couldn’t create in a 100 years. 🙂 Check it out below.


Anime character breaks free and hijack’s the 3D software

Source: Boingboing.net: Anime character breaks free: Watch her hijack 3D software in video

– Suramya

August 25, 2024

Browse Open source clones of classic video games

Filed under: Computer Software,Tech Related — Suramya @ 2:19 AM

There are a lot of games that can no longer be played because the systems to run the games are no longer in production and it is illegal to modify their code to work on the new systems or operating systems or emulators. That is where open source comes into play, developers have dedicated a lot of time creating open source clones of their favorite games.

You can access the list and instructions on how to install/play them at: https://osgameclones.com/, which gathers open-source or source-available remakes of great old games in one place.

A Remake is a game where the executable and sometimes the assets as well are remade open source. Some of these games aren’t exact remakes but evolution of original ones, which were eventually open sourced.
A Clone is a game which is very similar to or heavily inspired by a game or series.
An Official project is the official source code release for a game that was formerly closed-source, maintained by the original creators and has minimal changes.
A Similar game is one which has similar gameplay but is not a clone.
A Tool is not a game, but something that assists in playing or modding the game, such as a high resolution patch, or resource extractor.

I see Open source versions of Classics like Decent II, Doom II/III and many more on the site. Check it out if you have some free time.

Source: Boingboing.net: Open source clones of classic video games

August 22, 2024

Correct Pronunciation of SAP

Filed under: Humor,My Thoughts — Suramya @ 8:05 PM

Saw this sign while stuck in Bangalore traffic and it made me laugh. I mean, how annoyed do you have to be about the miss pronunciation of the name to take actual billboards out to correct people.


We are SAP (es-ay-pea)

– Suramya

August 21, 2024

First three Post-Quantum Encryption Algorithms released by NIST

Filed under: Computer Security,My Thoughts,Quantum Computing — Suramya @ 8:30 PM

NIST has been reviewing algorithms as part the the PQC (Post Quantum Cryptography) Standardization process for over 8 years now and they have released the first three standards for post-quantum cryptography. These standards will allow systems to protect their data and communications with encryption that are not vulnerable to Quantum Computers. Current standards and tools rely on complex math problems that are difficult or impossible to solve using conventional computers but are vulnerable to a sufficiently capable quantum computer which would be able to process potential solutions very quickly.

The new standards are designed for two essential tasks for which encryption is typically used: general encryption, used to protect information exchanged across a public network; and digital signatures, used for identity authentication. NIST announced its selection of four algorithms — CRYSTALS-Kyber, CRYSTALS-Dilithium, Sphincs+ and FALCON — slated for standardization in 2022 and released draft versions of three of these standards in 2023. The fourth draft standard based on FALCON is planned for late 2024.

While there have been no substantive changes made to the standards since the draft versions, NIST has changed the algorithms’ names to specify the versions that appear in the three finalized standards, which are:

  • Federal Information Processing Standard (FIPS) 203, intended as the primary standard for general encryption. Among its advantages are comparatively small encryption keys that two parties can exchange easily, as well as its speed of operation. The standard is based on the CRYSTALS-Kyber algorithm, which has been renamed ML-KEM, short for Module-Lattice-Based Key-Encapsulation Mechanism.
  • FIPS 204, intended as the primary standard for protecting digital signatures. The standard uses the CRYSTALS-Dilithium algorithm, which has been renamed ML-DSA, short for Module-Lattice-Based Digital Signature Algorithm.
  • FIPS 205, also designed for digital signatures. The standard employs the Sphincs+ algorithm, which has been renamed SLH-DSA, short for Stateless Hash-Based Digital Signature Algorithm. The standard is based on a different math approach than ML-DSA, and it is intended as a backup method in case ML-DSA proves vulnerable.

Similarly, when the draft FIPS 206 standard built around FALCON is released, the algorithm will be dubbed FN-DSA, short for FFT (fast-Fourier transform) over NTRU-Lattice-Based Digital Signature Algorithm.

This is a significant step in ensuring our data and systems are protected against threats that are on the horizon. The Register has a good article on this topic (NIST finalizes trio of post-quantum encryption standards) that I highly recommend you check out.

Sources:
* Mastodon.social
* Schneier.com: NIST Releases First Post-Quantum Encryption Algorithms

August 19, 2024

Raksha Bandhan over the years

Filed under: My Thoughts — Suramya @ 11:32 PM

Yesterday was Rakhi and as folks know I have a lot of sisters so end up with a ton of Rakhi’s being tied on my hand. For those who don’t know, Rakhi or Raksha Bandhan as it is formally called is a Hindu festival where sisters tie a talisman or amulet called the rakhi around the wrists of their brothers who symbolically protect them and receive a gift in return. The sisters do a Tilak, tie the Rakhi and then give a piece of sweet. My sisters don’t know how to give just a piece of sweet so I usually have a full laddu/other sweets (usually quite large) stuffed in my mouth for every Rakhi being tied.

It is an annual event and so I felt like sharing some Rakhi pics from over the years. I don’t have the pics from 1986-1994 and a few other years as they are not yet digitized:



1985, Surabhi tying the Rakhi on her own for the first time


1994


1998


2003


2004


2005


2006


2007


2008


2009


2012


2013


2014


2015


2016


2017


2019


2022


2023


2024


2024

I am blessed to have so many sisters and loving family to support me in life.

– Suramya

July 29, 2024

Detecting AI-Generated Videos using MISLnet

Filed under: Artificial Intelligence,My Thoughts — Suramya @ 11:43 PM

With new technology and ‘AI’ it is becoming easier and easier to create fake images that look realistic enough to fool the casual eye. The problem is that this can be used to promote lies or scams etc. So we need to be able to identify if a given image is AI generated or real. Unfortunately, this is something that is easier said than done because as soon as the detector comes up with a way to identify fake images, the generators make changes to fix the issue resulting in a on-going game of whack-a-mole. That being said, it is important that we can identify and there is a lot of fascinating work that is happening in this space.

In an actually useful implementation of AI, researchers have trained a system called MISLnet that searches for statistical traces left in synthetic images by their source generator. It looks for relationships between pixel color values that are present in images taken by a digital camera which are not there in the AI generated image. This allows the system to identify AI generated images with over 98% accuracy.

I read the paper Beyond Deepfake Images: Detecting AI-Generated Videos(PDF) and honestly a lot of it went over my head. But based on tests it seems that MISLnet does perform well in identifying AI generated images.

The new tool the research project is unleashing on deepfakes, called “MISLnet”, evolved from years of data derived from detecting fake images and video with tools that spot changes made to digital video or images. These may include the addition or movement of pixels between frames, manipulation of the speed of the clip, or the removal of frames.

Such tools work because a digital camera’s algorithmic processing creates relationships between pixel color values. Those relationships between values are very different in user-generated or images edited with apps like Photoshop.

But because AI-generated videos aren’t produced by a camera capturing a real scene or image, they don’t contain those telltale disparities between pixel values.

The Drexel team’s tools, including MISLnet, learn using a method called a constrained neural network, which can differentiate between normal and unusual values at the sub-pixel level of images or video clips, rather than searching for the common indicators of image manipulation like those mentioned above.

The tool specifically targets images taken with a digital camera. It does not take into consideration that the image might have been taken by an Analog camera or is a scan of a printed images. In both those scenarios the relationships between pixel color values that the tool uses to identify real images will not exist, potentially leading the tool to falsely classify the image as fake or AI generated.

That being said, this is pretty interesting research and I am looking forward to testing the tool once it is released for general use.

Source: Schneier on Security: New Research in Detecting AI-Generated Videos

– Suramya

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