#“notion graph integration”

[futuretiative]

Graphy.app Data Visualization Made Easy (and Fast!)

Stop struggling with data! Graphy.app makes creating beautiful, impactful graphs a breeze. See how easy it is!" #datavisualization #graphs #dataanalysis #graphyapp #productivity #businesstools #analytics

Don't forget to like, comment, and subscribe for more AI content!

[dissertationwritinghelpforstds]

How to Conduct a Literature Review Using Digital Tools (with Notion Template)

Embarking on a literature review is a fundamental component of academic research that can often appear overwhelming due to the sheer volume of relevant articles and sources. However, leveraging digital tools like Notion can substantially streamline and enhance this process. By providing a structured approach, Notion enables researchers to manage their literature reviews with greater efficiency and organization. This comprehensive guide will walk you through a methodical literature review workflow using Notion, explore various digital tools, and offer a Notion template to facilitate your research.

The Benefits of Using Notion

Notion is an advanced organizational tool that integrates the functionalities of note-taking, project management, and database creation into a single platform. Its versatility is particularly advantageous for managing a literature review. Here are several key benefits of using Notion:

Integration of Pages and Databases: Notion allows for seamless linking of pages and embedding of databases within other pages. This interconnected structure facilitates comprehensive data management and easy navigation between related information.

Customizable Filters and Sorting: Users can create custom properties and apply filters to databases, which enables sophisticated sorting and retrieval of data tailored to specific research needs.

Efficient Data Management: Notion supports the transfer and management of data from Excel sheets, enhancing the organization and accessibility of research materials.

In my workflow, Notion plays a central role through two primary databases: the ‘literature tracker’ and the ‘literature notes’ matrix. These databases are instrumental in tracking papers and synthesizing information to construct a coherent argument.

Stages to Literature Review Workflow

1. The Literature Search

The initial phase of a literature review involves a systematic search for relevant sources. This step is critical for building a comprehensive and well-rounded review.

Identify Keywords: Begin by developing a list of keywords that are pertinent to your research questions. Engage with your supervisor or colleagues to refine this list, ensuring it encompasses all relevant terms. As you progress, be prepared to adjust your keywords based on emerging research trends and findings.

Utilize Database Search Tools: Employ established databases such as Web of Science, Scopus, Google Scholar to locate pertinent literature. These platforms offer extensive search functionalities and access to a broad range of academic papers. Additionally, set up email alerts for new publications related to your keywords. This proactive approach ensures that you remain informed about the latest developments in your field.

Library Building and Recommendations: Manage your literature library using tools like Mendeley, which facilitates the organization of references and offers recommendations for related papers. Mendeley’s sharing capabilities also enable collaboration with colleagues, enhancing the collective management of research resources.

2. Literature Mapping Tools

Literature mapping tools are invaluable for visualizing the relationships between papers and identifying key research themes.

Citation Gecko: This tool constructs a citation tree from ‘seed papers,’ illustrating the connections between various studies through their citation relationships. It is particularly useful for uncovering seminal works and understanding the progression of research topics.

Connected Papers: Connected Papers uses a similarity algorithm to generate a graph of related papers based on a given key paper. This tool provides insights into related research that may not be immediately evident through direct citation links, helping to broaden your understanding of the field.

3. The Literature Tracker

An organized literature tracker is essential for managing and reviewing research papers effectively.

Organize with Notion: Utilize Notion’s customizable properties to document essential details of each paper. This includes metadata such as title, author, publication date, keywords, and summary. The ability to filter and sort this data simplifies the process of managing large volumes of literature.

Database Views: Notion offers various database views, such as the kanban board, which can be used to track your reading workflow. This visual representation aids in monitoring your progress and managing tasks associated with your literature review.

4. The Literature Synthesis Matrix

The synthesis matrix is a crucial component for organizing and synthesizing information from the literature.

Second Pass of Papers: After an initial screening, populate the ‘literature notes’ database with detailed information from the papers you deem relevant. This should include comprehensive notes on the paper’s summary, key results, methodology, critiques, and any future work suggested.

Relational Databases: Leverage Notion’s relational database capabilities to link related papers and create a synthesis matrix. This matrix helps in identifying connections between different studies and assists in constructing a coherent narrative for your literature review.

5. Writing Your Literature Review

Writing a literature review involves synthesizing the collected information into a structured and insightful analysis.

Identify Research Themes: Use your literature matrix to pinpoint key research themes and questions. These themes will form the basis of your literature review sections and guide the development of your thesis statement(s).

Summarize and Evaluate Sources: Focus on the most significant sources for each theme, summarizing their key points and critically evaluating their contributions. This involves assessing the strengths and weaknesses of each study and linking related research to provide a comprehensive overview.

Situate Your Research: Clearly articulate the research gap your study addresses, justifying your research approach based on the identified gaps and the synthesis of the reviewed literature.

6. Iterating Your Literature Review

A literature review is a dynamic process that requires regular updates and revisions.

Regular Updates: Continuously update your literature review as new research emerges. Balance the time spent on reading with the progress of your own research to ensure that your review remains current and relevant.

 Notion Template

To facilitate your literature review process, I have developed a Notion template that includes:

A Literature Tracker Database: For recording and managing details of relevant papers.

A Literature Notes Database: For detailed notes and synthesis of the literature.

Predefined Properties: For filtering and sorting entries according to specific research needs.

You can duplicate and customize this template to fit your research requirements.

Useful Resources

Here are some additional resources that can aid in the literature review process:

The Literature Review: Step-by-Step Guide for Students

3 Steps to Save You From Drowning in Your Literature Review

How to Write a Literature Review

How to Become a Literature Searching Ninja

Mind the Gap

7 Secrets to Write a PhD Literature Review The Right Way

By following this structured approach and utilizing digital tools like Notion, you can streamline your literature review process, enhance organization, and ensure that your research is thorough and well-founded. This methodology not only simplifies the review process but also provides a robust framework for developing a strong thesis or dissertation.

Investing in your academic future with Dissertation Writing Help For Students means choosing a dedicated professional who understands the complexities of dissertation writing and is committed to your success. With a comprehensive range of services, personalized attention, and a proven track record of helping students achieve their academic goals, I am here to support you at every stage of your dissertation journey.

Feel free to reach out to me at helpmyassignment51@gmail.com to commence a collaborative endeavor towards scholarly excellence. Whether you seek guidance in crafting a compelling research proposal, require comprehensive editing to refine your dissertation, or need support in conducting a thorough literature review, I am here to facilitate your journey towards academic success. and discuss how I can assist you in realizing your academic aspirations.

[coming-up-with-usernames-is-hard]

PhD Blog Week 8

Courses

CFT: More messing around with correlators, radial quantisation, lots of things that are almost vertex operator algebras without ever saying that

Diff Top: Main complaint this week is that he keeps mentioning bundles without defining them, but I finished the assignment so that's good

Lie Algebras: Root systems, mostly just linear algebra, should finish the classification this week

Talks

Integrable Systems Seminar: Spherical double affine Hecke algebra, still don't really know what it is but despite that the talk was actually one of the most understandable seminars I've been to

Algebra Seminar: More Yangians, still don't know what a Yangian is, followed for about ten minutes before getting lost. The preseminar was good, but didn't help follow the main seminar

Example Showcases: Mostly pretty good this week. The first was on geometric applications to number theory, simple start showing that some equation has no solutions over the integers, then the schemes appeared and I was lost. This seems to be a common occurrence. I should learn what a scheme is. Second talk was on Feynman diagrams and how they arise in QFT, it was a good explanation of a lot of physics in just half an hour, and it was nice to be back on familiar ground after the étale fundamental group. Third one was on the Witt and Weyl algebras, a good explanation but I felt that the talk lacked a conclusion. The final talk was on topological data analysis, very interesting application of things that I don't know that well, and a little bit of rep theory.

Reading Groups

Complex Geometry: The Weyl group part 2, I missed part 1 but it still mostly made sense

Infinity Categories: Really interesting this week. We looked at how to define monoidal categories, the approach taken was to move away from the notion of a biniary product and phrase symmetric monoidal categories as co-cartesian fibrations, then the monoidal structure is naturally induced from maps in FinSet_*, and replacing finite sets with ordered sets gives a non-symmetric structure. Then this (much more complicated) definition generalises to infinity categories.

Categories: The 2-category of categories, after infinity-categories 2 feels like a very small number! I've also agreed to give a talk in a couple of weeks so I need to brush up on universal properties

Supervisor Meeting

Finally heard from my second supervisor as we discussed Wronskian solutions to differential equations, not my favourite area of maths but hopefully once it's related to the algebra I won't have to deal with the differential equations much

Teaching

Two tutorials this week, the work sheet was harder this week as they've just started graph theory, the final question was to prove that the utilities problem has no solution which was a pretty big ask

[melonisopod]

The thing about Oberon is that it's hard to talk about the two sides of his personality - Oberon the Fairy King and Vortigern the Abyssal Insect - without sounding like they're two separate halves of a split personality. When that's the thing, they're both him. And what those two halves of him want can at times be actively contradictory.

Vortigern in the general sense is just a terminal for the Abyssal Worm, a manifestation of a doomsday device that's meant to destroy Fairy Britain. He's reincarnated at least three times, and frankly just wants to do his job and be done. "Vortigern" isn't even a proper name, but really a title.

'Oberon' (or the Saint Graph of him) was in a sense, a role given to him by the Welsh Fairies, at a time when his very being was basically on life support. He's still ultimately aware of his role as Vortigern, so he isn't 'brainwashed,' but the role of 'Oberon' was so thoroughly integrated into his being that it is essentially now part of who he is.

Taking the mantle of 'Oberon' was a mixed blessing, and really more of a curse than anything. 'Oberon' is the reason why everything he says is twisted into lies, but one specific feature of 'Oberon' (namely, A Midsummer Night's Dream) gave way to the notion that there is someone who will love him, no matter how twisted or amoral he is. Of course, 'Titania' is a fabrication, yet she represents something that he (both the Fairy King mantle and the doomsday device) lack.

Before the Welsh Fairies influenced him, Vortigern wanted nothing. At least, that we know of. He was content simply to destroy everything because that's what he's created to do. But now, he's the Fairy King. He has to be the Fairy King. And isn't there someone out there who would love him unconditionally?

What results is someone who is at times fully at odds with himself - he hated the feeling of being crawled on by insects, and yet they're what saved his life and welcomed him to their home. They were weak and powerless, but how can he fault them for this when he's also terribly weak on his own? He is the Fairy King, even if he isn't, because that's what the Welsh Fairies wished for. He wishes someone, anyone, could ever love him despite his cruel and twisted nature.

He hates Fujimaru because they come from Proper Human History, a world that draws a line between 'reality' and 'fantasy' and separates him from his beloved Titania, and hates them further because they attempt to sustain a Lostbelt that should have long been dead. And yet, the grow to love Ritsuka, come to wonder if they are their dear Titania they were searching for...

...obviously none of this sits with him well because this person is his enemy, whom he hates. What is he going to do now? Well, Oberon accomplished his goal, there's nothing left, and yet, why did he answer that summon? Is he here because he's finally found his Titania in Ritsuka? Or is he simply biding his time to fill out 'Oberon's' plan to destroy Proper Human History?

He's not sure. He's going to sit in his room and mope about it.

[kohakhearts]

when people first meet me and inquire about my studies im generally hit with two different responses, being 1) “wow, that’s an unusual combination”/“you don’t see that often”/etc. and 2) “you must be SO smart!” (or its evil twin, “you must hate yourself ha-ha”), and while the first is obviously a better response than the second, both are kinda…awkward to react to.

like? IS it an unusual combination of interests, or is it actually that most institutions make it exceptionally difficult for people to pursue stem and arts concurrently? and that we don’t often talk about the heavy crossover between stem and the arts because we’re so culturally obsessed with this notion that the world is split into Art People and Science People (also known as English People and Math People)?

and how would my interest in a science make me any smarter than someone in my program who chose to pursue a minor in history instead of physics? also, NO, i don’t hate myself. obviously taking stem classes after spending years believing im “not a math person” has lowered my gpa, but that’s not really something i care about, because at the end of the day i find the subject endlessly fascinating and i enjoy my classes very much, and i get better at math every semester because i have no choice. because it’s just…a method of communication. it’s a language. you practice, you improve - but you have to be consistent and intentional about it. the same way you have to be consistent and intentional about analyzing fictional texts and historical documents.

which is to say that like. you are using the same skills. i tutored a high school student last year who looked at me like i was crazy for saying that close reading a short story is functionally the same as solving an algebra problem. you collect like terms. then you compare and contrast them to make a statement about them - it’s human nature to seek refuge in what is familiar even if it is simultaneously traumatic, or x = 2 and y = -2. you can chart it, you can graph it, you can draw it. listen, isn’t there something so inherently beautiful about the word integral? it’s something intrinsic, baked into a person or a thing - the fundamental values formed within you by tiny, infinitesimal pieces: moments, experiences - they coalesce into something completely different, but still. you can go back. you can find the pieces. define them, pick them apart, put them together again in new ways. expand them, contract them, equate them to something else just to understand them.

half the study of mathematics is called analysis, for god’s sake. what is the study of art if not analysis? is it not the goal of the artist, the writer, to make sense of our place in the world? and is this not what we do in physics, too? look at the world and try to find reason in it? as the poet spends their life trying to make the intangible tangible, the particle physicist attempts to study dark matter. when we form a sentence, we utilize a complex system of equations that are so second-nature to us we don’t even register that’s what we’re doing - but there’s a reason this branch of linguistics is called syntactic calculus.

like…believe me. if you told my teenage self i’d be taking calculus-based courses in university, i wouldn’t have believed it. i teach high school students now who tell me they know they aren’t good at english, but it doesn’t matter to them because they do so well in math. and i get it. i do. but it’s disappointing, too, because i think my knowledge of math has made me a better reader and writer. and it feels like most people are missing out on that connection, because they feel like it’s impossible to make. but any experimentalist can tell you there’s an art to the scientific process. any musician or poet can tell you that great art is dictated by numbers - rhythm, rhyme and metre, all of it. the only group of people as interested in conceptual symmetry as physicists are artists.

anyway, all i’m saying is like - one is not more essential than the other, these things are inextricably linked, these things are as fundamental to human existence as breathing. there’s a reason why astronomers defer to shakespeare to name newly discovered bodies in space, you know? we've all gotta learn to love the math in our art and the artistry behind math.

[animehouse-moe]

How I'm Tracking My Manga Reading Backlog

I'm bad at keeping up with reading sometimes. I'll read newer releases while still forgetting about some, want to re-read something even though I haven't started on another series, and leave droves of titles sitting on my shelves staring at me.

I got tired of that, and also tired of all these different tracking websites and apps that don't do what I want. So, with Notion and a few other tools, I've set out to make my own, and I like it! So I thought, hey, why not share how I'm doing it and see how other people keep track of their lists, so that's why I'm here. Enough rambling though, let me lead you through why I decided to make my own.

So, the number 1 challenge: Automation. In truth, it's far from perfect and is the price I pay for being lazy. But, I can automate a significant chunk of the adding process. I've yet to find a proper way to go from barcode scanning on my phone to my reading list, but I can go pretty easily from an amazon listing to the reading list. With it I grab: title, author, publisher, page count, and cover image.

So what do I use?

Well, it's a funky and interesting thing called 'Bardeen' that allows you to scrape webpages (among other things), collect and properly structure the desired information, and then feed it right into your Notion database. It's a little odd to try and figure out at first, but it's surprisingly intuitive in how it works! Once you have your template setup, you just head to the webpage (I've found Amazon the best option) and hit the button for the scraper you've built, and it puts it into Notion.

It saves an inordinate amount of time in populating fields by hand, and with the help of templates from Notion, means that the only fields left "empty" are the dated fields for tracking reading.

Thanks to Bardeen, the hardest (and really only) challenge is basically solved. Not "as" simple as a barcode, but still impressively close. Now, since the challenge is out of the way, how about some fun stuff?

Data visualization is incredibly fun for all sorts of people. Getting to see a breakdown of all the little pieces that make up your reading habits is very interesting. Sadly, Notion doesn't have the ability to build charts from your own databases, so you need a tool.

The one I ended up settling on was 'Grid.is', as it has a "direct" integration/embed with Notion.

Sure, it has its own "limitations", but they pose absolutely zero concern as to how I want to set up my own data visualization. You can have (as far as I know) an unlimited number of graphs/charts on a single page, and you can choose to embed that page as a single entity, or go along and embed them as independent links. Either way, the graphs are really great and there's a lot of customization and options in regards to them. Also, incredibly thankful for the fact that there's an AI assistant to create the charts for you. The way that Notion data's read in is horrendous, so the AI makes it infinitely easier than what it appears as at first.

And yes, all those little popups and hover behaviors are preserved in the embeds.

Well, I suppose rather than talking about the tertiary tools, I should talk about what I'm doing with Notion itself, no?

Alright, so, like all Notion pages it starts with a database. It's the central core to keeping track of data and you can't do without it. Of course, data is no good if you can't have it properly organized, so how do I organize it?

With tags, of course! I don't have a massive amount of tags in place for the database, but I am considering adding more in terms of genre and whatnot. Regardless, what I have for the entries currently is: Title, Reading Status (TBR, Reading, Read, etc.), Author, Format (manga or LN), Date Started, Date Completed, Pages, and Publisher.

In addition to those "displayed" tags, I have two tertiary fields. The first is an image link so that entries can display an image in the appropriate view. The second, and a bit more of a pain, is a formula field used to create a proper "title" field so that Notion can sort effectively (they use lexicographic, so numbers end up sorted as letters instead). This is the poorly optimized Notion formula I used, as I don't have much experience with how they approach stuff like this. It just adds a leading zero to numbers less than 10 so that it can be properly sorted.

Of course this list view isn't my default view though, the calendar from the top of this post is. Most of the time though, I don't have it set to the monthly view, but rather weekly. Following up that view though, I've got my "up next" tab. This tab's meant to track all the titles/entries that I'm about to read. Things I'm planning to read today, tomorrow, or the day after. Sorta a three day sliding window to help me keep on top of the larger backlog and avoid being paralyzed by choice. It's also the only view that uses images currently.

Following that, I've got my "To Be Read" gallery. I wanted to use a kanban board but notion will only display each category as a single column, so I chose this view instead, which makes it much easier to get a better grasp of what's in the list. I've been considering adding images to this view, but I need to toy around with it some more. Either way, the point is to be able to take a wider look at what I've got left in my TBR and where I might go next.

So overall, I've ordered these views (though the list view I touch on "first" is actually the last of the views) in order from "most recent" to "least recent", if that makes any sense. Starting with where I've finished, moving to where I go next, what I have left, and then a grouping of everything for just in case.

It's certainly far from a perfect execution on a reading list/catalogue, but I think personally speaking that it checks off basically all of the boxes I required it to, and it gives me all the freedom that I could ever want - even if it means I have to put in a bit of elbow grease to make things work.

[tagbintech]

Top Remote Work Tools Empowering Virtual Employees in 2025

The rise of virtual employees has reshaped the future of work, especially in 2025. From startups to global enterprises, businesses are increasingly depending on remote work tools to streamline operations, maintain collaboration, and enhance productivity. These tools are not just facilitating remote work—they’re empowering a new breed of agile, AI-assisted, borderless employees.

In this article, we’ll explore the top remote work tools empowering virtual employees in 2025, the trends driving their growth, and how these platforms are shaping the future of work.

Why Remote Work Tools Matter More Than Ever in 2025

The hybrid and remote work culture has shifted from temporary to transformational. Companies today need reliable, intelligent, and scalable tools to:

• Enable seamless communication across time zones

• Track performance and outcomes

• Support mental well-being and employee engagement

• Automate routine tasks

• Ensure data security and compliance

With the advent of AI integration, immersive workspaces, and asynchronous collaboration, 2025’s remote tools are smarter, faster, and deeply personalized.

Top Remote Work Tools Empowering Virtual Employees in 2025

1. Slack AI 2025 Edition

Category: Communication & Collaboration

Why It’s Top: Slack has evolved into an AI-powered messaging hub with built-in bots that summarize threads, auto-prioritize tasks, and suggest responses in real-time.

Features:

• AI-thread summarization

• Language translation for global teams

• Calendar & task syncing with voice commands

• Custom workflow builders

Best For: Remote teams needing intelligent communication across time zones.

2. Zoom 2.0 + Immersive Workspaces

Category: Video Conferencing & Virtual Presence

Why It’s Top: Zoom’s 2025 version includes spatial audio, AR-enabled rooms, and AI-assisted meeting summaries.

Features:

• Immersive VR meeting rooms

• Real-time AI translation

• Post-meeting action item summaries

• Smart background noise cancellation

Best For: Virtual employees seeking deeper, more interactive remote engagement.

3. ClickUp AI

Category: Project Management

Why It’s Top: ClickUp’s AI engine helps remote workers plan, manage, and automate tasks across complex workflows. In 2025, it integrates voice-to-task automation and team behavior analytics.

Features:

• Smart task assignment via AI

• Goal alignment tracking

• Visual dashboards and reports

• Workflow automation

Best For: Remote teams managing large-scale projects.

4. Notion AI WorkOS

Category: Knowledge Management & Documentation

Why It’s Top: Notion’s AI-powered system allows virtual employees to create, summarize, translate, and cross-link internal documentation effortlessly.

Features:

• Auto-generated documentation

• Knowledge graph building

• AI writing assistant for internal comms

• Integrated database + chat

Best For: Remote-first teams needing centralized and collaborative knowledge hubs.

5. Time Doctor 2025 Pro

Category: Time Tracking & Productivity Analytics

Why It’s Top: Time Doctor now offers deep behavioral analytics and focus-mode alerts to help remote workers stay on track.

Features:

• Distraction alerts

• Productivity scorecards

• Screenshots & app usage reports

• Biometric login for secure access

Best For: Employers managing large virtual teams or digital agencies.

6. Loom + AI Voice Sync

Category: Async Communication

Why It’s Top: Loom now offers AI-synced voiceover translations and automated video summaries, making it easier for global teams to stay informed asynchronously.

Features:

• Voice-to-text + translations

• Auto-created meeting highlights

• Chrome + mobile integration

• AI coach for video messaging

Best For: Startups and creative teams needing flexible communication.

7. Trello with Butler AI

Category: Task Management

Why It’s Top: Trello’s Butler automation system has been enhanced with AI-based task prediction, enabling virtual workers to get proactive reminders and workflow suggestions.

Features:

• Custom rule-based automation

• Predictive task due dates

• Power-ups for every department

• Kanban-style UI

Best For: Solopreneurs or agile teams managing simple sprints.

8. Microsoft Teams with Copilot AI

Category: Enterprise Collaboration

Why It’s Top: Microsoft Teams now uses Copilot to summarize meetings, schedule next steps, and even respond to emails using context-aware AI.

Features:

• AI-generated insights

• Outlook + SharePoint integration

• Data compliance tools

• Copilot document editing

Best For: Large enterprises with remote departments across continents.

How These Tools Shape the Future of Work

In 2025, these platforms aren't just tools—they're digital workspaces powered by:

• Artificial Intelligence for predictive workflows

• Augmented Reality for immersive collaboration

• Behavioral Analytics for smarter team management

• Voice & Language AI for global communication

• Automation to reduce repetitive tasks

This transformation means virtual employees can be just as (or more) productive, engaged, and innovative as on-site teams—no matter where they are in the world.

Future Trends in Remote Work Tools

1. Integration Ecosystems

Apps will become more modular, integrating smoothly with CRMs, HR software, payroll, and even health platforms.

2. Multilingual Collaboration

Built-in AI translations and cultural sensitivity tools will empower truly global teams.

3. Well-being Dashboards

Future platforms will include mental health tracking, focus analysis, and burnout alerts to support remote worker wellness.

4. Outcome-Driven Metrics

Tools will move from time-tracking to value-tracking—rewarding results, not hours.

[longlistshort]

For Camille Henrot’s imaginative installation for A Number of Things at Hauser & Wirth, a variety of sculptures, including several from her Abacus series, are surrounded by paintings from her Dos and Don’ts series. There’s a playfulness to both, but something a bit darker too. Walking on the soft floor among the sculptures, there is a feeling of childlike wonder, while at the same time, in combination with the paintings, you are reminded of the rules and restrictions that are imposed on us, starting when we are very young, and how they become more oppressive with age.

From the gallery’s press release-

Evoking children’s developmental tools, shoes, distorted graphs and counting devices, new large-scale bronze sculptures from the artist’s ‘Abacus’ series (2024)—presented alongside recent smaller-scaled works—address the friction between a nascent sense of imagination and society’s systems of signs. The exhibition will also feature vibrant new paintings from Henrot’s ongoing ‘Dos and Don’ts’ series. Initiated in 2021, the ‘Dos and Don’ts’ series combines printing, painting and collage techniques where etiquette books become the palimpsest for play with color, gesture, texture and trompe l’oeil. The artworks will emerge from a flooring intervention—conceived and designed with Charlap Hyman & Herrero—that transforms the gallery into a site of sensory experimentation. Henrot’s exhibition vivaciously sets the stage for the arbitrary nature of human behavior to circulate freely between rule and exception.

As viewers enter the gallery, they will be greeted by a pack of dog sculptures tied to a pole, as if left unattended by their walker. Shaped from steel wool, aluminum sheets, carved wood, wax, chain and other unexpected materials, Henrot’s creatures speak to the ever-unfolding effects of human design and domestication. As an extension of Henrot’s ongoing interest in relationships of dependency, the dogs stand in as the ultimate image of attachment. Henrot’s latest ‘Abacus’ sculptures unite the utilitarianism of the ancient calculating tool with the arches and spirals of a children’s bead maze—a toy popularized in the 1980s as a heuristic diversion in pediatric waiting rooms and nursery schools. Through these formal associations, an instinctive sense of play collides with the learned impulse to search out patterns and impose order. With their biomorphic contours, opaline patinas and quadruped or biped anatomies, these works seem charged with a lifeforce of their own. Hovering between pure abstraction and their multivalent referents, Henrot’s bronzes invite our unfettered, sensuous engagement, even as they allude to the symbolic systems that tyrannize our imaginations.

Behavioral conditioning is a central concern of Henrot’s ‘Dos and Don’ts’ series. These richly layered paintings consider the idea of ‘etiquette’ as it relates to society at large: its codes of conduct, laws and notions of authority, civility and conformity. The works feature collaged fragments of invoices from an embryology lab; a note conjugating the German verb ‘to be;’ dental X-rays; digital error messages; children’s school homework; and to-do lists, among other things. Together, they build on Henrot’s interest in making sense of the urge to organize and categorize information—a theme that has been prevalent in her practice since her groundbreaking film ‘Grosse Fatigue’ (2013). The ‘Dos and Don’ts’ series distorts its source material to reveal the constructed, performative nature of any social identity, while acknowledging the emotional security that behavioral mimicry and groupthink can provide. As the exhibition’s almost childlike title suggests, ‘A Number of Things’ brings together a disparate but related group of works that collectively address the enormously difficult task that is living, learning and growing in society. With tenderness for the most banal traces of our existences, Henrot offers a meditation on the competing impulses to both integrate and resist the unquestioned structures of society in our everyday lives.

‘There’s a reason why, in English, the word ‘politics,’ ‘polite’ and ‘police’ all sound the same—they are all derived from the Greek word polis or city, the Latin equivalent is civitas, which also gives us ‘civility,’ ‘civic’ and a certain modern understanding of ‘civilization.’ —David Graeber, ‘The Dawn of Everything’ (2021)

In the video walkthrough with Henrot (below) she discusses many of the inspirations for the work.

This exhibition closes 4/12/25.

[fronzennews]

Neuro-Symbolic AI Advancing Artificial General Intelligence Solutions

Neuro-symbolic AI is emerging as a key player in the race towards achieving Artificial General Intelligence (AGI). By fusing the capabilities of neural networks with symbolic reasoning, this innovative approach addresses existing limitations in traditional AI methodologies.

2. Understanding the Integration of Neural Networks and Symbolic AI

Neuro-symbolic AI crafts a synergistic connection between two dominant paradigms in AI: neural networks, which focus on deep learning and pattern recognition, and symbolic AI, which emphasizes structured reasoning through logic and rules. 2.1 The Role of Neural Networks Neural networks excel in identifying patterns across extensive datasets, learning to mimic complex behaviors, and enhancing predictive accuracy, which allows them to tackle a variety of applications from image recognition to natural language processing. 2.2 The Importance of Symbolic AI On the other hand, symbolic AI employs structured rules, logical constraints, and relational graphs to represent knowledge. This facet is crucial for tasks that require coherent reasoning and explanations, bridging the gap between diverse notions within AI applications.

3. Overcoming Current AI Weaknesses with Neuro-Symbolic AI

The integration of neuro-symbolic AI seeks to tackle significant challenges faced by contemporary AI systems, notably the lack of generalizability, transparency, and robustness. 3.1 Enhancing Generalizability One of the major pitfalls of current AI is the inability to generalize well in unfamiliar contexts. By incorporating symbolic knowledge, neuro-symbolic AI enhances adaptability, allowing models to better navigate new scenarios and reason effectively even with scarce data. 3.2 Promoting Transparency and Interpretability The lack of transparency is a rampant concern with traditional neural networks, often referred to as "black boxes." Neuro-symbolic AI, with its reliance on symbols and rules, renders the decision-making process more explicit, fostering trust and understanding among users. 3.3 Boosting Robustness Neuro-symbolic AI also fortifies the resilience of AI systems. By leveraging symbolic structures, it can reduce the incidence of hallucinations—erroneous outputs that sound plausible but lack factual accuracy—thereby enhancing the reliability of AI models.

4. Essential Capacities Required for AGI

For the realization of AGI, robust AI systems must demonstrate key attributes, including grounding in knowledge, the ability to learn and adapt, and alignment with human values. 4.1 Grounding in Real-World Knowledge Grounding involves understanding entities and concepts in a real-world context. neuro-symbolic AI achieves this by embedding symbolic knowledge within neural frameworks, ensuring that AI operates based on an informed comprehension of its surroundings. 4.2 Instructibility and Learning AGI systems ought to evolve in response to user feedback. Neuro-symbolic AI supports this by integrating user inputs into both its neural and symbolic components, allowing for continual learning and adaptation based on real-world interactions. 4.3 Alignment with Human Values Ensuring that AI systems align with societal expectations and principles is paramount. Neuro-symbolic AI utilizes symbolic reasoning to navigate ethical considerations, thereby training systems to make decisions that are congruent with human intentions and values.

5. Applications of Neuro-Symbolic AI Across Industries

Neuro-symbolic AI holds substantial promise across a variety of sectors, demonstrating its versatile capabilities and potential transformative impact. 5.1 Healthcare and Medicine The integration of neuro-symbolic AI can revolutionize medical diagnostics by bolstering accuracy and minimizing biases in decision-making processes. It promises a more nuanced understanding of patient data and medical histories. 5.2 Finance In the finance sector, neuro-symbolic AI facilitates better decision-making through transparency and reliability. It helps in complex financial modeling and risk assessments, making the outcomes more interpretable for analysts. 5.3 Criminal Justice Neuro-symbolic AI contributes to fairer and more robust judicial processes by enhancing the reliability of AI tools in legal settings, thereby mitigating biases and inaccuracies when analyzing criminal data. 5.4 Autonomous Systems In autonomous vehicles and cyber-physical systems, neuro-symbolic AI improves safety by enabling better object detection and decision-making capabilities, leading to more reliable navigation in complex environments.

6. Addressing Hallucinations and Data Requirements

Neuro-symbolic AI presents a solution to the common issue of hallucinations found in traditional neural networks, while also discussing the essential data requirements for the success of its symbolic components. 6.1 Mitigating Hallucinations By using knowledge graphs, neuro-symbolic AI can validate the outputs from neural networks. This capability reduces the risk of generating misleading information and ensures that responses are both contextually appropriate and factually accurate. 6.2 Understanding Data Requirements Although neuro-symbolic AI aims to reduce the amount of data needed for training, it still relies on high-quality, well-structured data for the symbolic components. The use of graph databases is pivotal in supporting this structured knowledge representation.

7. Recent Research and Development Trends in Neuro-Symbolic AI

The field of neuro-symbolic AI is experiencing rapid advancements, focusing on various critical aspects that enhance its capacity and applicability. 7.1 Knowledge Representation Innovations Researchers are actively exploring innovative methods for integrating symbolic and neural representations to construct effective knowledge graphs, which are vital for commonsense reasoning within AI. 7.2 Advances in Learning and Inference Emerging techniques in this domain are improving AI’s capabilities in planning, zero-shot recognition, and overall generalization, making AI systems more adept in handling unforeseen queries. 7.3 Improved Logic and Reasoning Models Efforts are underway to bolster logical consistency in AI outputs through the development of more sophisticated models, such as logic tensor networks, that prioritize both reasoning fidelity and operational safety.

8. Future Directions and Challenges in Neuro-Symbolic AI

While neuro-symbolic AI signifies a considerable leap forward, various challenges remain to be addressed to secure its potential for AGI. 8.1 Incremental Learning Challenges A major challenge lies in designing symbolic systems that can grow and adapt based on new insights and experiences, particularly how to efficiently evolve with user interactions. 8.2 Context-Aware Inference Mechanisms Developing systems that adjust their reasoning processes based on situational context is essential for the practical deployment of neuro-symbolic AI, which could lead to more nuanced interactions. 8.3 Ensuring Explainability As AI becomes more integrated into everyday life, achieving detailed explainability for complex reasoning chains remains crucial for fostering trust and user acceptance.

9. Conclusion

In summary, neuro-symbolic AI stands as a transformative approach in the pursuit of Artificial General Intelligence, merging the robustness of neural networks with the precision of symbolic reasoning. Its impact extends across various fields, holding the promise to reshape the AI landscape for the better. For more insightful articles on similar topics, feel free to explore my blog at FROZENLEAVES NEWS. This enhanced article improves readability and helps highlight the crucial aspects of neuro-symbolic AI without cluttering the text. Read the full article

[unsdggoal4qualityeducation]

OPEN BOOK EXAMINATION (OBE)

DR SEKAR SRINIVASAN - UN Educationist

OBE-1 INTRODUCTION

▪️Critical thinking promotion is the main outcome of OBE

▪️Problem solving skills for applying conceptual knowledge with deeper understanding of the subject matter.

▪️One should think critically in real world situations.

▪️Reduced memorization pressure. Decreases the examination stress and phobia.

▪️If the students are clear with concepts then accessing resources becomes easier.

▪️It promotes resource management skills.

▪️Text books , notes and online materials should be effectively managed and skillful navigation for solutions is much desired in today’s digital age.

▪️Learning engagement is promoted which directly increases the motivation to explore more real life solutions and situations and application during learning.

▪️This increases the interest in the subject matter.

▪️Application of knowledge assessment is primarily done in OBE.

▪️Ability to apply theoretical knowledge to practical situations.

▪️OBE presents open ended questions that require analysis and synthesis of information.

▪️Student’s comprehension ability is needed more towards measure of learning outcomes.

▪️Over dependence on resources during exams reduces to certain extent individual performance skills with freedom of imagination and it provides a travel in the set matrix.

▪️Limitation to access the complete resources plays a crucial role for all stakeholders with a common point.

▪️Time management for potentially impacting their performance is a major parameter of consideration in OBE.

▪️OBE may conceal deficiencies in understanding so it becomes challenge for educators in assessing mastery.

▪️Only with vigilant monitoring and implementation, OBE can ensure ethical standards and academic integrity.

OBE-2 BASIC REQUIREMENTS FOR TEACHERS AND THEIR ROLE

▪️Facilitating the transfer of information from text books to student’s minds.

▪️Retrieval methods of learning concepts during examination.

▪️Shortcut methods for accessing the facts from resource materials.

▪️Provide ample mind mapping and concept maps for every individual topics as well as links or chain for each unit.

▪️Improved and effective utilization of color codes during preparation for ready and quick access from resources to manage optimum time utility during exams.

▪️To do away with the wrong notion towards regular studies. Stress “MEANS ALWAYS JUSTIFY THE ENDS”

▪️Notes taking during class room activities (lectures) is to be strengthened with advanced research methodologies available under this topic. Certain foreign universities provides this as a bridge course introductory topic for specialization.

▪️The OBE envisages more on how the course concepts work together rather than simply compare or evaluate information.

▪️Here the algorithm of formation of flowcharts, the steps involved in solving the problem in arriving at the solution is more stressed.

▪️Teacher should look for a structural breakups with logical arguments which leads to solutions.

▪️Self tests by teachers for familiarizing OBE is essential and practical outcome experiences should be communicated with curiosity and innovative creativity.

OBE – 3 ROLE OF STUDENTS IN OBE

Students should be familiar with all nuances of OBE.

They should not hesitate to get:

▪️Clarification from teachers for even silly doubts in the following.

▪️Exactly what is allowed and not allowed in exam halls.

▪️Wether citation of sources to be side tracked in answers.

▪️Learning methodologies and recapitalization techniques.

▪️Time management vistas.

▪️Organization of resources with the flow of question paper patterns.

▪️Minimum or optimum usage of resources for maximum time saving and complete presentation of answers to the point.

▪️Mastering shortcuts, color coding, emphasis underlining with markers, key points access methods, page reference index, boxing formulae and graph points.

▪️Crystal clear summary of each topic and core concept dealt under each unit, their interlink as chain for other chapters or furtherance in applications.

▪️Preparation should focus on Application, Analysis, Synthesis, Comparisson, Evaluation and problem solving

▪️Maximum number of repeated rehearsals as model tests with both content capture as well as time management.

▪️Answers should be direct, simple, self explanatory and to the point.

▪️Surely you can answer by yourself, some questions without reference.

▪️Paraphrasing a given answer with a structured presentation should be mastered instead of long paragraphs.

▪️Concise and well supported answers will attract more credit points.

▪️Final revision and review of answers for clarity and accuracy OBE will be more useful.

▪️Controversial or materials with distorted facts of ambiguity may yield undesirable or poor credits and lead to conflicts in answering. ▪️So be familiar with the right selection of resource materials well in advance.

▪️One has to put in maximum efforts with perseverance and accommodative mentality when venturing into untreated paths of this journey.

▪️Life is a journey of challenges and risks. OBE will definitely prepare you to build self confidence in making you more self reliant and to face any platform in finding rational yet objective solutions on win-win borders.

With best wishes, Regards Dr. Sekar Srinivasan UN EDUCATIONIST.

[futuretiative]

Imagine Explainers: AI-Powered Video Explanation Tool | @futuretiative

Imagine Explainers is an AI tool designed to quickly generate explainer videos on a wide range of topics. #ImagineExplainers #AIVideo #VideoGeneration #Explainers #AIAnimation #EdTechAI #ArtificialIntelligence #ContentCreation #VideoMarketing #EducationTechnology #MachineLearning #AITools #TechInnovation #FutureOfVideo #EasyVideoCreation

It's part of a growing trend of AI-powered video creation platforms that aim to simplify the video production process.

Imagine Explainers, and similar AI video tools, offer a promising solution for creating explainer videos. They are particularly useful for educational purposes, content creation at scale, and situations where speed and cost-effectiveness are paramount. However, it's important to be aware of the limitations in terms of creative control, potential quality issues, and the need for accuracy. As AI technology continues to evolve, these tools are likely to improve and become even more versatile.

Don't forget to like, comment, and subscribe for more AI content!

"data visualization" "graph maker" "create graphs" "data analysis tools" "chart maker" "easy data visualization" "online graph maker" "ai graph generator" "data visualization software" "business graph maker" "data reporting tools" "interactive graph maker" "data dashboard tools" "graph creation online" "data presentation tools" "google sheets graph integration" "notion graph integration" "business intelligence tools" "analytics software" "reporting software" "data storytelling" "infographic maker"

[excelrthane]

Hadoop and Spark: Pioneers of Big Data in the Data Science Realm

Introduction:

In the realm of data science, the sheer volume, velocity, and variety of data have given rise to the phenomenon known as big data. Managing and analysing vast datasets necessitates specialised tools and technologies. This article explores big data and delves into two prominent technologies, Hadoop and Spark, integral parts of any comprehensive data scientist course, that play pivotal roles in handling the complexities of big data analytics.

Understanding Big Data:

Big data refers to datasets that are too large and complex to be processed by traditional data management and analysis tools. The three Vs—Volume, velocity, and variety—characterise big data. Volume refers to the massive amount of data generated, Velocity denotes the speed at which data is generated and processed, and Variety encompasses the diverse sources and formats of data. 

Specialised technologies are required to harness the potential insights from big data, and two of the most prominent ones are Hadoop and Spark, as extensively covered in a Data Science Course in Mumbai.

Hadoop: The Distributed Processing Powerhouse

Hadoop, an open-source framework, is synonymous with big data processing.

Created by the Apache Software Foundation, Hadoop facilitates the distribution of large data sets' storage and processing across clusters of standard hardware. It comprises two primary elements: the HDFS for data storage and the MapReduce programming model for efficient data processing.

1. Hadoop Distributed File System (HDFS):

At the core of Hadoop is its distributed file system, HDFS. HDFS breaks down vast datasets into smaller segments, usually 128 MB or 256 MB, and disperses these segments throughout various nodes in a cluster. This enables parallel processing, making it possible to analyse massive datasets concurrently, a key topic in any data science course.

2. MapReduce Programming Model:

Hadoop employs the MapReduce programming model for distributed data processing. MapReduce breaks down a computation into two phases—Map and Reduce. The Map phase processes and filters the input data, while the Reduce phase aggregates and summarises the results. This parallelised approach allows Hadoop to process vast amounts of data efficiently.

While Hadoop revolutionised big data processing, the evolution of technology led to the emergence of Apache Spark.

Spark: The High-Performance Data Processing Engine

Apache Spark, an open-source, fast, and general-purpose cluster-computing framework, addresses some of the limitations of Hadoop, providing quicker and more versatile extensive data processing capabilities.

1. In-Memory Processing:

One of Spark's key differentiators is its ability to perform in-memory processing, reducing the need to read and write to disk. Consequently, Spark outpaces Hadoop's MapReduce in terms of speed, particularly for repetitive algorithms and dynamic data analysis, due to its more efficient processing capabilities.

2. Versatility with Resilient Distributed Datasets (RDDs):

Spark brings forth the notion of Resilient Distributed Datasets (RDDs), a robust and fault-tolerant array of elements designed for parallel processing. RDDs can be cached in memory, enabling iterative computations and enhancing the overall speed and efficiency of data processing.

3. Advanced Analytics and Machine Learning Libraries:

Spark offers high-level APIs in Scala, Java, Python, and R, making it accessible to a broader audience. Additionally, Spark includes libraries for machine learning (MLlib) and graph processing (GraphX), expanding its utility beyond traditional batch processing.

Comparing Hadoop and Spark:

While both Hadoop and Spark are integral components of the extensive data ecosystem, they cater to different use cases and have distinct advantages.

Hadoop Advantages:

  Well-suited for batch processing of large datasets.

  Proven reliability in handling massive-scale distributed storage and processing.

Spark Advantages:

Significantly faster than Hadoop, especially for iterative algorithms and interactive data analysis.

Versatile with support for batch processing, interactive queries, streaming, and machine learning workloads.

Conclusion:

In the ever-expanding landscape of data science, big data technologies like Hadoop and Spark, critical components in a Data Science Course in Mumbai, are crucial in unlocking insights from vast and complex datasets. With its distributed file system and MapReduce paradigm, Hadoop laid the foundation for scalable data processing. Spark brought about advancements that address the evolving needs of the data-driven era. As part of a data science course, understanding these technologies equips data scientists and analysts with the capability to derive significant insights, thus fueling innovation and guiding decision-making across various sectors.

[whatisdatabase]

Airtable Competitors

Notion: The All-in-One Workspace Notion stands out as a versatile tool that goes beyond traditional databases and project management. It offers a collaborative workspace where teams can create notes, documents, and databases seamlessly. Notion’s block-based structure allows for easy customization, making it an attractive choice for those seeking a comprehensive solution for collaboration and information.

Trello: Agile Project Management

Trello is known for its simplicity and effectiveness in managing projects using boards, lists, and cards. While it may not offer the depth of database capabilities like Airtable, its intuitive interface and flexibility in task management make it a strong competitor, especially for teams focused on agile project management.

Click here To read more about Top 10 Airtable Competitors

Asana: Streamlined Task and Project Management

Asana excels in providing a streamlined platform for task and project management. Its user-friendly interface and robust collaboration features make it a top choice for teams seeking a balance between simplicity and functionality. Asana’s focus on task dependencies and timelines positions it as a worthy alternative to Airtable competitors for project-centric teams.

Monday.com: Work Operating System

Monday.com takes a unique approach by positioning itself as a “Work Operating System.” It provides a highly visual and customizable platform for managing projects, workflows, and team collaboration. With features like Kanban boards and Gantt charts, Monday.com offers a comprehensive solution for those who value visual representations of their work processes.

Coda: The Doc for Teams

Coda combines the power of documents and databases, allowing teams to create interactive documents that integrate seamlessly with their workflows. With a flexible structure that includes tables, graphs, and text, Coda is suitable for teams looking for a tool that can evolve alongside their diverse collaboration needs.

Follow us on

Pinterest

Tumblr

Flipboard

Scoop IT

FaceBook

[sciencestyled]

When Paintbrush Meets Petri Dish: A Snobbishly Witty Romp Through the Art-Science Gala

Ah, the age-old debate: science versus art. One adorned in white lab coats, the other swathed in splattered smocks. But let's not get too carried away with stereotyping – after all, not all scientists are absent-minded geniuses (only about 97.3%, statistically speaking) and not all artists are beret-wearing, espresso-sipping dreamers (the remaining 2.7%, presumably). This essay, my dear reader, is a whimsical yet sagacious exploration of the delightful tango between science, art, and education.

Now, let's set the scene. In one corner, we have Science, the methodical, evidence-seeking powerhouse of human progress. In the other, Art, the creative, emotionally-charged soul of human expression. And caught in the middle? Education, the beleaguered referee trying to make sense of it all. The question on everyone's lips (or at least on those lips belonging to people who ponder such things) is: can art and science coexist? Better yet, can they collaborate to enhance education? Spoiler alert: Yes, they can, and it's fabulously fascinating.

Firstly, let's tackle the notion of art advancing science education. Gone are the days when science was taught with the dryness of a cracker left out in the Sahara. Enter stage right: Art. Imagine, if you will, a biology class where students learn about cell structure through creating detailed, colorful models. Or a physics lesson where the principles of motion are explored through kinetic sculptures. It's not just about making science 'pretty'; it's about using art to provide a more engaging, memorable, and comprehendible learning experience. After all, who wouldn't prefer to learn about the solar system by crafting a vibrant, to-scale mural over memorizing dusty textbook diagrams?

But wait, there's more! Art doesn't just doll up science education; it also introduces a much-needed dose of creative thinking. Creativity isn't just for the artsy types, you know. It's a crucial skill in scientific inquiry. Hypothesis formation, after all, is essentially coming up with an educated guess – and what is that if not a creative process? By integrating artistic methods into science education, we're training the next generation of scientists to think outside the proverbial Petri dish.

Now, flip the script. How does science aid in creating art? Ah, dear reader, this is where it gets juicy. For starters, let's talk materials. Where do you think those vibrant pigments, durable resins, and nifty 3D printing materials come from? A little thing called chemistry, that's where. And let's not forget about the digital art world, which owes its entire existence to computer science and technology.

But it's not just about the tools. Science can also inspire art. Take fractals, for example. These infinitely complex patterns, found in everything from snowflakes to seashells, have become a muse for countless artists, leading to mesmerizing works that blend the boundaries of art and mathematics. Or consider the field of bioart, where artists use living tissues and bacteria as their medium. It's not just avant-garde; it's a commentary on the very nature of life itself, courtesy of biology.

Let's dive deeper into this rabbit hole. The intersection of art, science, and education can also play a pivotal role in societal issues. Think climate change, for instance. Scientific reports and graphs might make eyes glaze over, but an impactful piece of art that viscerally depicts the consequences of global warming? That can stir hearts and spur action. By marrying the factual rigor of science with the emotional power of art, we can educate and motivate the public in ways that neither discipline could achieve alone.

And let's not forget the cultural implications. Art and science are both reflections of our society, mirroring our values, fears, and aspirations. By blending them in education, we're not just teaching facts or techniques; we're fostering a more holistic understanding of the world. We're teaching students to appreciate the beauty in a well-crafted equation and the logic in a masterful painting. It's about creating well-rounded individuals who can appreciate the wonders of the cosmos and the splendors of a canvas with equal fervor.

In conclusion, my eloquent and undoubtedly enlightened reader, the convergence of art, science, and education is not just possible; it's essential. It's a veritable feast for the mind, a carnival of creativity and intellect. It's about breaking down the false dichotomy between 'logical' science and 'emotional' art and recognizing that, at their core, both are driven by a deep, insatiable curiosity about the world. So, let's raise our test tubes and paintbrushes in a toast to this magnificent mélange, and let the learning (and laughing) begin!

[rauhauser]

R.O.E. Reconsidered

Back on June 3rd in Shall We Play A Game? I noted that I had started doing something completely new on June 10th of 2020, and that I’m finally putting it out in the open. I’ve not said anything, at least directly, about what IT actually is, but I have slyly posted at least one progress update. Somewhere. Out there.

June 10th of this year was the beginning of a sort of groundhog day, an instant replay of a the summer of 2022. Thanks to some blood work results that showed up that day I started making changes, peeling an onion so to speak, and eleven weeks later I’m amazed, but it seems the life upgrade has stuck. I wake up, I’m mentally alert, and I remain that way throughout the day. This is another improvement on par with what happened when I started a course of Noopept sixteen months ago.

Here’s the best measure of that – they were taken about two weeks apart. A gain of ten seconds on one level and fifteen on another? Those are jumps as extraordinary as what Noopept provided last year.

This new thing that I do has been in my head, backed up by copious capturing of information in Evernote. As that collection approaches 900 items I recognize a situation where my autistic curation skills are interfering with taking the next step. So I “rotated the cube” to look at another face of the problem, and made a Maltego graph of … things. Nine people. Ten documents. And 104 phrases, just the keywords I noted while collecting those nearly 900 things.

The collection started long before I had any sense of what I was going to DO with the findings. Upon reaching the 77th day of making it available, I got a curious notion, another facet of the cube to examine. Maybe I should have a look around and see if anyone else is doing anything similar. So I slipped eleven search terms into a Talkwalker Alerts setup and piped the results to an Inoreader folder. I have discovered that I am not alone. The majority of the search results are in French. Did you recall that very first item I added to Evernote?

There are others out there, doing things that are similar enough to what I am doing that they are tripping some of those alerts. These other people are playing with portions of what I am doing, but the ones I have reviewed are each interested in a single area. Nobody else is "turning the cube", they're not integrating the same disparate pieces that I am. I am going to keep looking, to see what they say, and where it is said, but I will be utterly amazed if I encounter anyone else who noticed the confluence of forces in the summer of 2022.

What does this mean in terms of the Rules Of Engagement?

I assumed I was singular at the very start and that I could be as demanding as I wanted in terms of participants. Now that I know there is an existing substrate on which I can plant things, that makes it a bit easier, because there are already interested audiences, but it makes it harder, because I am not alone in these pursuits.

Perhaps ... just maybe ... anyone who could successfully follow the trail I'm blazing ... is the sort who is already out there, pursuing their own esoteric, syncretic interests?

The next milestone is 82 days ahead, and in a nod to its ancient origins I even set a countdown timer. This will be a time of watching for me, and a time to connect more dots on that Maltego graph.

[craigbrownphd]

If you did not already know

Message-Dropout In this paper, we propose a new learning technique named message-dropout to improve the performance for multi-agent deep reinforcement learning under two application scenarios: 1) classical multi-agent reinforcement learning with direct message communication among agents and 2) centralized training with decentralized execution. In the first application scenario of multi-agent systems in which direct message communication among agents is allowed, the message-dropout technique drops out the received messages from other agents in a block-wise manner with a certain probability in the training phase and compensates for this effect by multiplying the weights of the dropped-out block units with a correction probability. The applied message-dropout technique effectively handles the increased input dimension in multi-agent reinforcement learning with communication and makes learning robust against communication errors in the execution phase. In the second application scenario of centralized training with decentralized execution, we particularly consider the application of the proposed message-dropout to Multi-Agent Deep Deterministic Policy Gradient (MADDPG), which uses a centralized critic to train a decentralized actor for each agent. We evaluate the proposed message-dropout technique for several games, and numerical results show that the proposed message-dropout technique with proper dropout rate improves the reinforcement learning performance significantly in terms of the training speed and the steady-state performance in the execution phase. … Complex-Valued Network for Matching (CNM) This paper seeks to model human language by the mathematical framework of quantum physics. With the well-designed mathematical formulations in quantum physics, this framework unifies different linguistic units in a single complex-valued vector space, e.g. words as particles in quantum states and sentences as mixed systems. A complex-valued network is built to implement this framework for semantic matching. With well-constrained complex-valued components, the network admits interpretations to explicit physical meanings. The proposed complex-valued network for matching (CNM) achieves comparable performances to strong CNN and RNN baselines on two benchmarking question answering (QA) datasets. … Variational Inverse Control With Events (VICE) The design of a reward function often poses a major practical challenge to real-world applications of reinforcement learning. Approaches such as inverse reinforcement learning attempt to overcome this challenge, but require expert demonstrations, which can be difficult or expensive to obtain in practice. We propose variational inverse control with events (VICE), which generalizes inverse reinforcement learning methods to cases where full demonstrations are not needed, such as when only samples of desired goal states are available. Our method is grounded in an alternative perspective on control and reinforcement learning, where an agent’s goal is to maximize the probability that one or more events will happen at some point in the future, rather than maximizing cumulative rewards. We demonstrate the effectiveness of our methods on continuous control tasks, with a focus on high-dimensional observations like images where rewards are hard or even impossible to specify. … EdgePool Graph Neural Network (GNN) research has concentrated on improving convolutional layers, with little attention paid to developing graph pooling layers. Yet pooling layers can enable GNNs to reason over abstracted groups of nodes instead of single nodes. To close this gap, we propose a graph pooling layer relying on the notion of edge contraction: EdgePool learns a localized and sparse hard pooling transform. We show that EdgePool outperforms alternative pooling methods, can be easily integrated into most GNN models, and improves performance on both node and graph classification. … https://analytixon.com/2023/07/19/if-you-did-not-already-know-2100/?utm_source=dlvr.it&utm_medium=tumblr