#AI in research projects

[jamespotter7860]

AI and Document Insights: Simplifying Complex Research Projects with Photon Insights

As research is an inexact science, keeping track of vast amounts of data can be daunting. Complicated projects often include reviewing multiple documents, extracting relevant insights from them, synthesizing findings from various sources and synthesizing these into one cohesive research report. Unfortunately, this process can be time consuming and subject to human error, making accuracy and efficiency an ongoing struggle for researchers. Thanks to Artificial Intelligence (AI), platforms like Photon Insights are revolutionizing how researchers handle document insights; streamlining complex projects more efficiently while increasing productivity — this article explores how AI improves document insights while Photon Insights helps researchers navigate projects more successfully than ever before!

Researching Document Insights to Gain New Insights

Documenting insights is vital for researchers across disciplines for multiple reasons, including:

1. Information Overload: Researchers often face an overwhelming amount of information from academic articles, reports, and studies that needs to be processed efficiently to obtain valuable insights for meaningful analysis. Extracting key insights efficiently is paramount.

2. Improved Understanding: Accurate insights help researchers grasp complex topics, identify trends and understand the repercussions of their findings.

3. Evidence-Based Decision Making: Documented insights enable researchers to support their conclusions with solid evidence, which is key for maintaining credibility within academic and corporate environments.

4. Streamlined Collaboration: When conducting multidisciplinary research projects, sharing insights among team members is paramount for cohesive progress and informed decision-making.

Challenges Involve Traditional Document Analysis

Traditional methods for document analysis present several hurdles.

1. Time-Consuming Processes: Reviewing and extracting information from numerous documents manually can take considerable time, limiting research progress.

2. Risk of Human Error: Manual analysis can lead to inaccuracies due to human interpretation, leading to discrepancies and discrepancies within data.

3. Difficulties with Handling Unstructured Data: Research data often contains unstructured content that makes analysis and derivation of insights difficult without using specialist software tools.

4. Limited Collaboration: Sharing insights between team members can be cumbersome when using static documents and manual processes as means for sharing insight.

How AI Is Transforming Document Insights

Document analysis with artificial intelligence (AI) offers several significant advantages for researchers looking to simplify complex projects:

Automated Data Extraction Processes (ADEPs)

AI algorithms can automatically extract relevant data from documents, significantly shortening manual analysis time and freeing researchers up to focus on interpreting their findings rather than collecting information.

Keyword Focus: Automated Data Extraction and Time Efficiency

Photon Insights employs advanced data extraction techniques that enable researchers to quickly gather insights from various documents, streamlining their workflow.

2. Natural Language Processing (NLP)

Natural Language Processing (NLP) allows AI to understand human language, providing insights from unstructured sources like articles and reports. NLP identifies key themes, concepts, and sentiments that make complex texts easier for researchers to grasp the main points.

Keyword Focus: Natural Language Processing and Text Analysis

Researchers can leverage Photon Insights’ NLP capabilities to extract meaningful insights from large volumes of documents, deepening their understanding of complex subjects.

Enhance Search Capabilities

AI-powered search functions allow researchers to query documents using natural language, and return results that are contextual rather than simply keyword matching. This feature improves accuracy and efficiency of research processes.

Keyword Focus: Improve Search, Contextual Queries

Photon Insights provides advanced search functionalities that enable users to quickly locate the information they require, creating smoother research workflows.

Intelligent Summarization (ISS)

AI can produce concise summaries of lengthy documents, outlining only the key information. This allows researchers to quickly assess which documents warrant further study and make informed decisions.

Keyword Focus: Intelligent Summarization, Rapid Insights

Photon Insights provides intelligent summarization tools to enable researchers to gain quick and immediate insights from large amounts of text, saving both time and effort in the process.

5. Collaborative Features

AI-driven platforms can enhance collaboration by allowing team members to easily share insights, comments, and annotations in real time — an indispensable feature that ensures all team members stay informed throughout the research process.

Keyword Focus: Collaborative Features, Real-Time Sharing

Photon Insights encourages collaboration among researchers by enabling them to engage with each other’s findings and insights seamlessly — thus creating a more productive research environment.

Photon Insights Advantage

Photon Insights stands out as an invaluable tool for researchers seeking to leverage AI for document insights. Here’s how it enhances research experiences:

1. Comprehensive Document Management system.

Photon Insights allows users to efficiently organize and manage their documents, providing easy access to relevant materials — an essential step in maintaining an efficient research workflow.

2. User-Friendly Interface

The platform’s intuitive user interface makes navigating documents and extracting insights much simpler, making it ideal for researchers of all skill levels.

3. Customizable Dashboards

Researchers can create customized dashboards that represent their specific research interests and priorities, providing for more focused data analysis and insights.

Integration of Other Tools

Photon Insights provides users with seamless integration between various research tools and databases, enabling them to streamline their workflows and maximize research capabilities.

5. Continuous Development and Learning

Photon Insights’ AI algorithms learn from user interactions, continually honing in on relevance for each researcher to ensure they get the most relevant and up-to-date results possible. This ensures they receive relevant and valuable data.

Case Studies of Success With Photon Insights

Consider these case studies as examples of AI’s effectiveness in document insights:

Case Study 1: Academic Research

Academic researchers investigating climate change made use of Photon Insights to rapidly review hundreds of scientific articles. With its automated data extraction and intelligent summarization features, this team was able to synthesize critical findings more quickly for publication as an extensive review paper with wide appeal.

Case Study 2: Corporate Analysis

Photon Insights helped a corporate research department streamline their market analysis process. Utilizing its NLP capabilities, the team were able to extract sentiment data from industry reports and news articles, providing real-time market intelligence insights for informed strategic decisions.

Case Study 3 — Healthcare Research

Photon Insights was used by a healthcare research group to analyze patient data and clinical studies. With its automated extraction of relevant insights, the team were able to quickly identify trends in treatment outcomes which ultimately resulted in improved care strategies and protocols.

Future of AI and Document Insights

As AI technology develops further, its role in document insights may grow increasingly significant. A number of trends may determine its development:

1. Greater Automation: Automating document analysis will further increase efficiency, enabling researchers to focus on interpretation and application instead.

2. Advances in AI Capabilities: Advancements in artificial intelligence algorithms will increase both accuracy and depth of insights drawn from complex documents.

3. Integrating Emerging Technologies: When combined, AI and emerging technologies such as blockchain and augmented reality could create new avenues for document insights and analysis.

4. Emphasis on Ethical AI: As AI becomes more integrated into research, attention to ethical considerations will become ever more essential to ensure fairness, transparency, and accountability.

AI is revolutionizing how researchers manage document insights, streamlining complex projects and improving overall efficiency. From automating data extraction and natural language processing to intelligent summarization capabilities, AI enables researchers to navigate large volumes of information with ease.

Photon Insights stands at the forefront of this transformation, offering an AI-powered suite of tools designed to optimize document analysis and foster collaboration. As research requirements increase, adopting solutions like Photon Insights will become essential in meeting those demands while increasing productivity and gaining insights. With so much data out there already available online, AI solutions such as Photon Insights offer key differentiators that will lead to success both academically and corporately alike.

[reality-detective]

— Military Insider —

They panicked when they saw the future — Project Lööking Glass 🤔

[quaranmine]

On Wednesday before I gave my presentation I confessed to a new employee that I was worried it would be too long and she brightly told me her life hack was to just let AI rewrite things for her. She said I should put in all my talking points and ask ChatGPT to give me a five minute exactly presentation. I was like....how is the most polite possible way (since this is a new colleague I shouldn't get off on the wrong foot with) that I can express that I will Not be taking this advice. Ever. I told her that I didn't think we were allowed to use ChatGPT at this job (we most certainly are not, it is a nightmare for any type of protected information) and also that I prefer to write all of my own work. Despite my best efforts the last part of that was still passive aggressive, lol.

Something about being a writer makes it so that it's almost offensive to me for someone to suggest I use AI to do my work instead? Like, the day I reach the point where I let AI write something for me is the day y'all need to be checking me for brain damage because clearly I'm losing it

[redfish-blu]

thinking about the human brain organoids in the petri dishes that grew eye receptacles, process light, taught themselves how to play pong, and the researchers testing on them refuse to give an opinion on whether they are conscious or not (but believe they are sentient).

[ivo3d]

Another scene candidate for digital theater background projection.

[froyogotlowbro]

I have opinions on AI art that i think might get me castrated but like. I truly don't think artists (such as myself as well) should be too afraid of AI only because

When ever the majority of consumers find out something was made with AI fully, that product is seen as not worthy to put money into because literally anyone could make it. (Example Nicki Minaj's AI Album covers)

AI art can (and has been though they won't admit to it) used as really good inspiration and a way to teach artist how to do painting methods or style techniques.

and most importantly: AI art can't be copyrighted so even if it does get "good enough to replace artist", it wont be able to be protected or make a decent profit due to lacking humanity. Shout out to that monkey for making that law a thing.

Obviously people are going to use AI art to pretend they actually drew something but I would consider that as something akin to "artists" who trace or steal other's art where it's going to happen no matter what and that sucks but like. literally what can someone do but block, call out and move on.

[jcmarchi]

Toward a code-breaking quantum computer

New Post has been published on https://thedigitalinsider.com/toward-a-code-breaking-quantum-computer/

Toward a code-breaking quantum computer

The most recent email you sent was likely encrypted using a tried-and-true method that relies on the idea that even the fastest computer would be unable to efficiently break a gigantic number into factors.

Quantum computers, on the other hand, promise to rapidly crack complex cryptographic systems that a classical computer might never be able to unravel. This promise is based on a quantum factoring algorithm proposed in 1994 by Peter Shor, who is now a professor at MIT.

But while researchers have taken great strides in the last 30 years, scientists have yet to build a quantum computer powerful enough to run Shor’s algorithm.

As some researchers work to build larger quantum computers, others have been trying to improve Shor’s algorithm so it could run on a smaller quantum circuit. About a year ago, New York University computer scientist Oded Regev proposed a major theoretical improvement. His algorithm could run faster, but the circuit would require more memory.

Building off those results, MIT researchers have proposed a best-of-both-worlds approach that combines the speed of Regev’s algorithm with the memory-efficiency of Shor’s. This new algorithm is as fast as Regev’s, requires fewer quantum building blocks known as qubits, and has a higher tolerance to quantum noise, which could make it more feasible to implement in practice.

In the long run, this new algorithm could inform the development of novel encryption methods that can withstand the code-breaking power of quantum computers.

“If large-scale quantum computers ever get built, then factoring is toast and we have to find something else to use for cryptography. But how real is this threat? Can we make quantum factoring practical? Our work could potentially bring us one step closer to a practical implementation,” says Vinod Vaikuntanathan, the Ford Foundation Professor of Engineering, a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and senior author of a paper describing the algorithm.

The paper’s lead author is Seyoon Ragavan, a graduate student in the MIT Department of Electrical Engineering and Computer Science. The research will be presented at the 2024 International Cryptology Conference.

Cracking cryptography

To securely transmit messages over the internet, service providers like email clients and messaging apps typically rely on RSA, an encryption scheme invented by MIT researchers Ron Rivest, Adi Shamir, and Leonard Adleman in the 1970s (hence the name “RSA”). The system is based on the idea that factoring a 2,048-bit integer (a number with 617 digits) is too hard for a computer to do in a reasonable amount of time.

That idea was flipped on its head in 1994 when Shor, then working at Bell Labs, introduced an algorithm which proved that a quantum computer could factor quickly enough to break RSA cryptography.

“That was a turning point. But in 1994, nobody knew how to build a large enough quantum computer. And we’re still pretty far from there. Some people wonder if they will ever be built,” says Vaikuntanathan.

It is estimated that a quantum computer would need about 20 million qubits to run Shor’s algorithm. Right now, the largest quantum computers have around 1,100 qubits.

A quantum computer performs computations using quantum circuits, just like a classical computer uses classical circuits. Each quantum circuit is composed of a series of operations known as quantum gates. These quantum gates utilize qubits, which are the smallest building blocks of a quantum computer, to perform calculations.

But quantum gates introduce noise, so having fewer gates would improve a machine’s performance. Researchers have been striving to enhance Shor’s algorithm so it could be run on a smaller circuit with fewer quantum gates.

That is precisely what Regev did with the circuit he proposed a year ago.

“That was big news because it was the first real improvement to Shor’s circuit from 1994,” Vaikuntanathan says.

The quantum circuit Shor proposed has a size proportional to the square of the number being factored. That means if one were to factor a 2,048-bit integer, the circuit would need millions of gates.

Regev’s circuit requires significantly fewer quantum gates, but it needs many more qubits to provide enough memory. This presents a new problem.

“In a sense, some types of qubits are like apples or oranges. If you keep them around, they decay over time. You want to minimize the number of qubits you need to keep around,” explains Vaikuntanathan.

He heard Regev speak about his results at a workshop last August. At the end of his talk, Regev posed a question: Could someone improve his circuit so it needs fewer qubits? Vaikuntanathan and Ragavan took up that question.

Quantum ping-pong

To factor a very large number, a quantum circuit would need to run many times, performing operations that involve computing powers, like 2 to the power of 100.

But computing such large powers is costly and difficult to perform on a quantum computer, since quantum computers can only perform reversible operations. Squaring a number is not a reversible operation, so each time a number is squared, more quantum memory must be added to compute the next square.

The MIT researchers found a clever way to compute exponents using a series of Fibonacci numbers that requires simple multiplication, which is reversible, rather than squaring. Their method needs just two quantum memory units to compute any exponent.

“It is kind of like a ping-pong game, where we start with a number and then bounce back and forth, multiplying between two quantum memory registers,” Vaikuntanathan adds.

They also tackled the challenge of error correction. The circuits proposed by Shor and Regev require every quantum operation to be correct for their algorithm to work, Vaikuntanathan says. But error-free quantum gates would be infeasible on a real machine.

They overcame this problem using a technique to filter out corrupt results and only process the right ones.

The end-result is a circuit that is significantly more memory-efficient. Plus, their error correction technique would make the algorithm more practical to deploy.

“The authors resolve the two most important bottlenecks in the earlier quantum factoring algorithm. Although still not immediately practical, their work brings quantum factoring algorithms closer to reality,” adds Regev.

In the future, the researchers hope to make their algorithm even more efficient and, someday, use it to test factoring on a real quantum circuit.

“The elephant-in-the-room question after this work is: Does it actually bring us closer to breaking RSA cryptography? That is not clear just yet; these improvements currently only kick in when the integers are much larger than 2,048 bits. Can we push this algorithm and make it more feasible than Shor’s even for 2,048-bit integers?” says Ragavan.

This work is funded by an Akamai Presidential Fellowship, the U.S. Defense Advanced Research Projects Agency, the National Science Foundation, the MIT-IBM Watson AI Lab, a Thornton Family Faculty Research Innovation Fellowship, and a Simons Investigator Award.

[essektheylyss]

I have finished the last annoying part of my quarter and thank FUCK.

[aokozaki]

Remember gang, the part of AI generation that sucks is the "content scaping basically the entire internet without permission" part. That's the part we dislike, and the technology is otherwise useful.

Animators for Into The Spiderverse creating their own tools to automate one of the most fiddly steps? Cool.

People putting the internet in a blender and using the outputted slurry to replace hiring artists? Kinda shit.

Aiding artists in their work? Yay! Using algorythmic content instead of artists? Boo!

[psychotaxonomy]

The Average Face of Each Zodiac Sign

The average faces for each zodiac sign calculated using Midjourney's AI to aggregate images of public figures of diverse ages with Sun/Moon/Rising and/or 3+ inner planet stelliums in each zodiac sign.

I attempted to assemble as much ethnic diversity as I could, though quality data is often lacking on people traditionally marginalized by Western history, so more work is needed on that front. Some images represent a "racial average" of 1 of each White, Black, Latinx, East Asian and West Asian appearing-averages, in efforts to make a universal standard.

Others are simply an average of the entire database.

Others are averaged by visual "types". For instance, Cancer stellium women with Gemini-Scorpio as a secondary influence seem more prone to appear in historic databases than any other sort of Cancer stellium, and are visually very identifiable. So much so that I created a category for this combination of signs amid "standard Cancer".

These averages are continually being updated as new data comes in. Each image references ~15-20 individuals whose natal chart uniquely reflects a stellium (3+ significant chart aspects) in the given sign. In most instances, gender is based on assigned sex at birth.

Referencing data gathered by friends at the Astrofaces Project and the user-submitted database at Astroseek.com, I've been able to check my averages of renowned public figures sourced in the Astrodatabank & Astrotheme databases to determine the accuracy/legitimacy of each visual average.

Notes:

You may not resemble your sun sign, because another sign may predominate your chart. Visit a site like Astro-charts.com to make a free chart & see which sign is most dominant in your chart. For maximum accuracy, you will need correct birth time & place in addition to birth date. Appearance seems to be most astrologically influenced by Sun sign, Rising sign (1st house / Ascendant), and Moon, but also by concentrations of the same sign in inner planets such as Mercury, Mars, Venus, Jupiter and Saturn. Outer celestial bodies like Uranus, Neptune & Pluto, which are said to have generational significance rather than individual significance in most cases, due to their very slow (years long) rotations around the sun.

[just-gay-thoughts]

I occasionally make posts about history stuff and whatever grabs hold of my little bastard brain (as soon as I get one week without a major assignment due I'll make a new one, we're just at that fun part of the semester), so if anyone has ideas for fun stuff I'd gladly take them👉🏻👈🏻

And if you're new here and want to read about the history of sheet ghosts or queer saints it's under the #gay history time tag

[d0nutzgg]

Life Updates: buildspace, an Idea, and some Concepts

Hey everyone, thanks for all the support on my prior posts having to do with machine learning! I know I didn't mention this really but recently I was accepted into S4 of buildspace which is like a "school" for creatives. I am doing Nights & Weekends but it has been a lot of fun since I started! We just did our first project which was creating the idea we will be working on throughout this "season" of buildspace. This was mine:

I know you all can tell I am really passionate about helping others with the research I do with machine learning / AI. I am wanting to make it into a nonprofit type business while I am in buildspace for s4.

I have some Proof of Concepts already which you can check out on my Kaggle here:

However, I am working on another project today that uses Logistic Regression and XGBoost models stacked together to predict heart failure mortality. I plan on doing a full walk through of the project to help show investors and buildspace what my goal is for my business.

What are your ideas on this? Do you think I should go for it? What are your dreams if you are a software engineer yourself? I want to hear from you all!

[autumnalwalker]

One WIP One Song

Self-indulgently jumping on the open tag from @blind-the-winds's tag game post.

Rules: For each of your WIPs, share a song you associate with it. Then tag as many people as you'd like. If you get tagged again, you can do it with a whole new round of songs

The Archivist's Journal: "Once Upon a Me" by Rachie

Empty Names: "Heroes" by emmy curie

And as a bonus, a song for the untitled solarpunk witch story that I wrote a couple chapters for about a year ago, back before I started posting things online, and would like to revisit someday:

"She is a Mirror of Me" by S.J. Tucker

Passing the tag on to @words-after-midnight, @ceph-the-ghost-writer, @cljordan-imperium, @writingpotato07, and an open tag for anyone who wants to do what I did and join in without an explicit invitation.

[safety-pin-punk]

Was trying to make a cd tonight and discovered that the iTunes account is lacking some crucial music. But also. I dont wanna set up the whole system just to rip a few songs off spotify

Uhg

[queen-mabs-revenge]

This is the untold story of the Santiago Boys. A story that will get you to rethink everything you know about technology and politics. A story that will leave you asking: What if? Decades before Big Tech stole our future, these rebellious engineers dreamed of a different digital universe. Imagine a world where technology serves the people, not corporations. Where big data helps democracy, not ruins it. A place where the impossible always becomes possible. Their ideas were bold, their goals noble. But as their dream is about to become reality, powerful forces crush it. Why is their story not better known, and what really happened to the Santiago Boys? Have they really resurfaced in Silicon Valley? Spies, terrorist attacks, startups, and much human drama -- it's all here. The Santiago Boys have lessons to teach us, and on the 50th anniversary of the Chilean Coup, their story is more relevant than ever.

Upcoming 9 part podcast series about Project Cybersyn - there's a sign up on the linked page to get an update when it drops!