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Shares of Vodafone Idea rallied over 5 percent on October 11, extending gains from the previous session. The 7 percent rise in two days came after the company moved the Supreme Court regarding ...

Vodafone Idea stays in green with 5% rally after SC nod to hear plea on AGR dues

AGR1

agr1

HackerNoon Reporter: Please tell us briefly about your background. I’ve had first-hand insight into how broken the global food supply chain is having left school and trained as a chef. After that, I spent close to a decade in the food and beverage industry - working in the Uk, Australia, Spain, and on cruise ships. What's your startup called? And in a sentence or two, what does it do? AGR1 (Agricultural Growing Revolutionised, farm to plate in 1 day) is a state of the art hydroponic farming company, that specialises in low land, natural light hydroponics, growing produce that’s Nakd of chemicals/pesticides then delivering to kitchens within hours of harvest, in reusable containers that are the same size as that which kitchens are already using making our brand ‘Nakd Produce’ the most sustainable and freshest there is in the market. What is the origin story? After years of being in F&B, a short time in finance in Moscow then Hong Kong, my son Henry Xander Harrison was born in 2014. That night when holding him, I realised I had to change who I was, my priorities, and what I did. A month later I was shown hydroponics, after a couple of years working for companies that claimed to be about making a better world, I realised that there aren’t any who I believe are doing it right. What do you love about your team, and why are you the ones to solve this problem? the diversity of experience, knowledge, and the commitment to making AGR1 and Nakd a success. If you weren’t building your startup, what would you be doing? Why talk about hypotheticals? At the moment, how do you measure success? What are your core metrics? Growing the best produce, having happy customers. What’s most exciting about your traction to date? We’re the first people to do hydroponics in Johor. Our produce is served daily to the royal family. Our customers love our produce and boxing system. What technologies are you currently most excited about, and most worried about? And why? Most excited about the development of the hydroponic and start ag industry. Most worried would have to be drone dogs…. Because Drone dogs!! Along with robot-controlled farms(we need jobs for people! Not robots. What drew you to get published on HackerNoon? What do you like most about our platform? We need to get attention for what we’re doing. You guys have a cool setup. What advice would you give to the 21-year-old version of yourself? I wouldn’t, I’ve lived, made mistakes, now I have a son and a mission. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Not as many people genuinely care about food security as they should, especially the people in positions of power to make an impact now before it’s too late. Vote for AGR1 for Johor Bahru. Startup of the Year

Startup Interview: "Not as many people genuinely care about food security as they should"

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! Toshima Altplus Guangzhou, China came out as the Startup of the Year in ! Leyaoyao Guangzhou Hong Kong, Hong Kong In , is declared the Startup of the Year! Hong Kong Episode Six Kunming In , is the winner! Kunming Green A Sapporo In , is declared the Startup of the Year! Sapporo Polar Star Space Shirokanedai came out as the Startup of the Year in ! Nupp1 Shirokanedai Wuhan In , is the winner! Wuhan CSICEE Baoan, China In , is declared the Startup of the Year! Baoan Jumper Baoshan, China In , is declared the Startup of the Year! Baoshan Renren Games Chengdu, China came out as the Startup of the Year in ! Ju You Xi Chengdu Fujisawa, Japan In , is the winner! Fujisawa Scohia Pharma Fukuoka, Japan In , is declared the Startup of the Year! Fukuoka Kyulux Haidian, China came out as the Startup of the Year in ! 51vv Haidian Hongkou, China In , is the winner! Hongkou Kidstone Jiading, China In , is declared the Startup of the Year! Jiading IMRobotic Kawasaki, Japan came out as the Startup of the Year in ! QD Laser, Inc. Kawasaki Kyoto, Japan In , is declared the Startup of the Year! Kyoto Hacarus Minato, Japan In , is the winner! Minato Visionary Fun Nagoya, Japan came out as the Startup of the Year in ! Prodrone Nagoya Ningbo, China The winner in is . Ningbo OlymTech Corporation Aomori In , is declared the Startup of the Year! Aomori Forte Japan Bandai-cho In , is the winner. Bandai-cho Delta-Fly Pharm Changchun In , the Startup of the Year is . Changchun Kaidi Animation Fuchu In , is declared the Startup of the Year! Fuchu TMS Fukuoka-shi The winner in is . Fukuoka-shi SkyDisc Fukushima In , the Startup of the Year is . Fukushima Matsuri Technologies Gotanda In , is the winner. Gotanda Basset Hachioji In , is declared the Startup of the Year! Hachioji Adriakaim Harbin The winner in is J . Harbin oining Technology Iwaki In , is the winner. Iwaki Tao Engineering Izumo In , the Startup of the Year is . Izumo PuREC Kagoshima In , is declared the Startup of the Year! Kagoshima Farmers Support Kamakura In , the Startup of the Year is . Kamakura KAYAC Kami-meguro The winner in is . Kami-meguro Buzzreach Kanagawa In , is the winner. Kanagawa LoiLo Kashiwa In , the Startup of the Year is . Kashiwa Intelligent Surfaces Kita In , is declared the Startup of the Year! Kita Ax Robotix Kobe The winner in is . Kobe Coconomi Koriyama In , the Startup of the Year is . Koriyama Tocoton Kumamoto In , is the winner. Kumamoto Bloom Technology Kunitachi In , is declared the Startup of the Year! Kunitachi Trys Liaocheng The winner in is . Liaocheng Jinzheng Animation Minami In , is declared the Startup of the Year! Minami ATLAB Miyazaki In , is the winner. Miyazaki Caster Nagano In , is declared the Startup of the Year! Nagano Precision Forestry Nagaoka The winner in is . Nagaoka Plant Form Nihombashi-hamacho In , the Startup of the Year is . Nihombashi-hamacho Shizen Energy Niigata In , is declared the Startup of the Year! Niigata UniBio Japan Nishi-shinagawa In , is the winner. Nishi-shinagawa Play Heart Okayama In , is the winner! Okayama Arch Farm Okinawa In , is declared the Startup of the Year! Okinawa BlogNinja Otemachi In , the Startup of the Year is . Otemachi beBit, Inc Qingdao Shi In , is the winner! Qingdao Shi Manyoubang Quanzhou In , is declared the Startup of the Year! Quanzhou KungFu Animation Group Roppongi The winner in is . Roppongi Holo-X Saitama In , the Startup of the Year is . Saitama GameTector Sendai In , is the winner! Sendai Sendai Smart Machines Setagaya In , is declared the Startup of the Year! Setagaya Skyestate Shenzhen In , the Startup of the Year is . Shenzhen Aiator Shinjuku In , is declared the Startup of the Year! Shinjuku Scalar Shizuoka In , is the winner! Shizuoka PiPhotonics Sumida The winner in is . Sumida Holotch Tachikawa In , the Startup of the Year is . Tachikawa PlusOne Takamatsu In , is declared the Startup of the Year! Takamatsu Ken Robotec Tokushima In , is the winner! Tokushima DennoKotsu Toyonaka In , the Startup of the Year is . Toyonaka FISH BIOTECH Tsukuba In , is declared the Startup of the Year! Tsukuba WARPSPACE Wakayama In , the Startup of the Year is . Wakayama 4D Sensor Xiangzhou In , is the winner! Xiangzhou ALLiN Edutech Group Xianyang The winner in is . Xianyang TONLY Xiaoshan In , is declared the Startup of the Year! Xiaoshan GONGHUI Yamaguchi In , the Startup of the Year is . Yamaguchi Archis Yangpu The winner in is . Yangpu Tmxmall Yau Tsim Mong In , is the winner! Yau Tsim Mong Airnugget Yeoksamdong In , the Startup of the Year is . Yeoksamdong WizSchool Yichang In , is declared the Startup of the Year! Yichang Hubei Kento Electronic Yokohama In , is the winner! Yokohama Next Innovation Yubei In , the Startup of the Year is . Yubei Ophiux Network Yueqing The winner in is . Yueqing Yueqing Easythink Media Zhuhai In , is the winner! Zhuhai Aijuba Hongo In , the Startup of the Year is . Hongo Smile Robotics, Inc Admiralty In , is declared the Startup of the Year! Admiralty BUNCH Southern In , the Startup of the Year is . Southern Retykle Yongdungpo In , is the winner! Yongdungpo Push News Taichung In , is declared the Startup of the Year! Taichung Wondercise Cyberport, Hong Kong In , the Startup of the Year is . Cyberport Animoca Brands Hefei, China In , is the winner! Hefei Tsinglink New Territories, Hong Kong The winner in is . New Territories, Hong Kong Zeek Dezhou In , the Startup of the Year is . Dezhou Dezhou Baji Kitakyushu In , is declared the Startup of the Year! Kitakyushu Reif Seongnam In , is the winner! Seongnam EOFlow Yanshi In , the Startup of the Year is . Yanshi CASI Vision Top Startup Cities in South Asia Hyderabad, India The Startup of the Year in is with 962 votes Hyderabad Quixy Lahore, Pakistan is the Startup of the Year! It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

Authors:
(1) Yanpeng Ye, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, and these authors contributed equally to this work;
(2) Jie Ren, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, China, and these authors contributed equally to this work;
(3) Shaozhou Wang, GreenDynamics Pty. Ltd, Kensington, NSW, Australia (shaozhou@greendynamics.com.au);
(4) Yuwei Wan, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and Department of Linguistics and Translation, City University of Hong Kong, Hong Kong, China;
(5) Imran Razzak, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia;
(6) Tong Xie, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW, Australia (tong@greendynamics.com.au);
(7) Wenjie Zhang, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia (wenjie.zhang@unsw.edu.au). Editor’s note: This article is part of a broader study. You’re reading Part 3 of 9. Read the rest below. Table of Links Abstract and Introduction
Methods
Data preparation and schema design
LLMs training, evaluation and inference
Entity resolution
Knowledge graph construction
Result
Discussion
Conclusion and References Data preparation and schema design Material experts undertook the annotation of nine distinct categories from 75 abstracts of research papers, act as LLM’s training dataset. These categories encompassed core labels - "Name," "Formula," or "Acronym" - which consistently represent specific materials across diverse papers, albeit in varied forms. Additionally, supplementary labels included "Descriptor," "Structure/Phase," "Application," "Property," "Synthesis," and "Characterization." The "Property" label served to delineate quantitative properties such as ’length,’ ’specific surface area,’ and ’mass,’ whereas the "Descriptor" label encapsulated qualitative attributes like ’stable,’ ’vertically,’ and ’safe.’ Since from the application aspects, developers always concern about its own properties, we also annotated the essential parameters as "Property" directly linked to specific "Application". For instance, attributes like ’specific capacity’ for lithium-ion batteries, ’energy conversion efficiency’ for solar cells, and ’H2 production rate’ for hydrogen evolution reaction were systematically annotated. As the Figure 2 shows, the central node, "Material," is pivotal and connects to nodes that describe its nomenclature, composition, and various attributes. Specifically, "Material" is consisted of "Formula", "Name" and "Acronym". "Structure/Phase" describing its physical form; "Application" denoting its practical uses; and "Property" outlining its inherent characteristics. The "Material" node is also associated with "Descriptor" node that provide additional qualitative information. Moreover, the "Application" node branches out to "Property", "Descriptor" and a "Domain" suggesting further specification and contextual relevance of application. To save the source of nodes and relations, each node is connected to a "Digital Object Identifier (DOI)" node. If we want to determine the source article from which the relation is derived, we can obtain the result by querying the intersection of "DOI" node neighbors at both ends of the relation. Given the inherent complexity of sentences and the variability in terminologies across abstracts, a normalization process was employed after the initial extraction. This normalization ensured uniform representation of entities with similar meanings. For example, terms such as ’Lithium-Ion Battery’ and ’Li-ion batteries’ were standardized to ’lithium-ion battery,’ while phrases like ’solution casting method,’ ’solvent post-treatment method,’ and ’solution-based deposition’ were simplified to ’solution-processed.’ All annotated entities underwent this normalization process, ensuring consistency and facilitating effective training of LLMs. The field of functional materials encompasses a wide range of areas. At this stage, our priority is to focus on energy materials. We download 150,000 abstracts of peer-reviewed research articles of energy material science includes battery, solar cell and catalyst from the Web of Science. Each abstract was stored in a JSON file format, structured as "DOI - text," facilitating seamless processing and analysis. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Yanpeng Ye, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, and these authors contributed equally to this work; (2) Jie Ren, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, China, and these authors contributed equally to this work; (3) Shaozhou Wang, GreenDynamics Pty. Ltd, Kensington, NSW, Australia (shaozhou@greendynamics.com.au); (4) Yuwei Wan, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and Department of Linguistics and Translation, City University of Hong Kong, Hong Kong, China; (5) Imran Razzak, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia; (6) Tong Xie, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW, Australia (tong@greendynamics.com.au); (7) Wenjie Zhang, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia (wenjie.zhang@unsw.edu.au). Authors: Authors: (1) Yanpeng Ye, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, and these authors contributed equally to this work; (2) Jie Ren, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, China, and these authors contributed equally to this work; (3) Shaozhou Wang, GreenDynamics Pty. Ltd, Kensington, NSW, Australia (shaozhou@greendynamics.com.au); (4) Yuwei Wan, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and Department of Linguistics and Translation, City University of Hong Kong, Hong Kong, China; (5) Imran Razzak, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia; (6) Tong Xie, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW, Australia (tong@greendynamics.com.au); (7) Wenjie Zhang, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia (wenjie.zhang@unsw.edu.au). Editor’s note: This article is part of a broader study. You’re reading Part 3 of 9. Read the rest below. Editor’s note: This article is part of a broader study. You’re reading Part 3 of 9. Read the rest below. Editor’s note: This article is part of a broader study. You’re reading Part 3 of 9. Read the rest below. Table of Links Abstract and Introduction Methods Data preparation and schema design LLMs training, evaluation and inference Entity resolution Knowledge graph construction Result Discussion Conclusion and References Abstract and Introduction Abstract and Introduction Methods Methods Data preparation and schema design Data preparation and schema design LLMs training, evaluation and inference LLMs training, evaluation and inference Entity resolution Entity resolution Knowledge graph construction Knowledge graph construction Result Result Discussion Discussion Conclusion and References Conclusion and References Data preparation and schema design Material experts undertook the annotation of nine distinct categories from 75 abstracts of research papers, act as LLM’s training dataset. These categories encompassed core labels - "Name," "Formula," or "Acronym" - which consistently represent specific materials across diverse papers, albeit in varied forms. Additionally, supplementary labels included "Descriptor," "Structure/Phase," "Application," "Property," "Synthesis," and "Characterization." The "Property" label served to delineate quantitative properties such as ’length,’ ’specific surface area,’ and ’mass,’ whereas the "Descriptor" label encapsulated qualitative attributes like ’stable,’ ’vertically,’ and ’safe.’ Since from the application aspects, developers always concern about its own properties, we also annotated the essential parameters as "Property" directly linked to specific "Application". For instance, attributes like ’specific capacity’ for lithium-ion batteries, ’energy conversion efficiency’ for solar cells, and ’H2 production rate’ for hydrogen evolution reaction were systematically annotated. As the Figure 2 shows, the central node, "Material," is pivotal and connects to nodes that describe its nomenclature, composition, and various attributes. Specifically, "Material" is consisted of "Formula", "Name" and "Acronym". "Structure/Phase" describing its physical form; "Application" denoting its practical uses; and "Property" outlining its inherent characteristics. The "Material" node is also associated with "Descriptor" node that provide additional qualitative information. Moreover, the "Application" node branches out to "Property", "Descriptor" and a "Domain" suggesting further specification and contextual relevance of application. To save the source of nodes and relations, each node is connected to a "Digital Object Identifier (DOI)" node. If we want to determine the source article from which the relation is derived, we can obtain the result by querying the intersection of "DOI" node neighbors at both ends of the relation. Given the inherent complexity of sentences and the variability in terminologies across abstracts, a normalization process was employed after the initial extraction. This normalization ensured uniform representation of entities with similar meanings. For example, terms such as ’Lithium-Ion Battery’ and ’Li-ion batteries’ were standardized to ’lithium-ion battery,’ while phrases like ’solution casting method,’ ’solvent post-treatment method,’ and ’solution-based deposition’ were simplified to ’solution-processed.’ All annotated entities underwent this normalization process, ensuring consistency and facilitating effective training of LLMs. The field of functional materials encompasses a wide range of areas. At this stage, our priority is to focus on energy materials. We download 150,000 abstracts of peer-reviewed research articles of energy material science includes battery, solar cell and catalyst from the Web of Science. Each abstract was stored in a JSON file format, structured as "DOI - text," facilitating seamless processing and analysis. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

This AI Reads Science Papers Like a Pro, Even When Humans Can’t Agree on the Words

Talk to anyone in AI, analytics, or data science, and they’ll tell you synthetic data is the future. But ask them what they mean by “synthetic data,” and you’ll get wildly different answers. That’s because synthetic data isn’t just one thing—it’s a broad category with multiple use cases and definitions. And that ambiguity makes conversations confusing. So, let’s cut through the noise. At its core, synthetic data operates along two key dimensions. The first is a spectrum ranging from filling in missing data in an existing dataset to generating entirely new datasets. The second distinguishes between interventions at the raw data level versus interventions at the insights or outcomes level. Imagine these dimensions as axes on a chart. This creates four quadrants, each representing a different type of synthetic data: data imputation, user creation, insights modeling, and manufactured outcomes. Each one serves a distinct function, and if you’re working with data in any capacity, you need to know the difference. Data Imputation: Filling in the Blanks While some might argue that data imputation isn’t truly synthetic data, modern imputation techniques have evolved beyond simple mean or median substitution. Today, advanced imputation leverages machine learning and generative AI models, making the generated values more sophisticated and contextually relevant than ever before. Data imputation sits at the intersection of missing data and raw data intervention. This means we’re working with existing datasets that have gaps, and our goal is to generate plausible values to complete them. Unlike other types of synthetic data, imputation isn’t about creating entirely new information—it’s about making incomplete data more usable. Example: A market research firm conducting media effectiveness studies might have gaps in its audience response data due to missing survey responses. Instead of discarding incomplete datasets, imputation techniques—such as statistical modeling or machine learning—can generate realistic estimates, ensuring analysts can still draw meaningful insights from the data. User Creation: Fake People, Real Insights User creation lies between new data generation and raw data intervention. Instead of modifying existing data, this approach fabricates entirely new user profiles and behaviors. It’s particularly useful when real user data isn’t available, is sensitive, or needs to be scaled artificially. User creation is a game-changer for testing products, improving security, and training AI models. Example: A streaming service might create synthetic user profiles to test its recommendation engine without exposing real customer data. Cybersecurity firms do the same to simulate attack scenarios and train fraud detection systems. Insights Modeling: Patterns Without the Privacy Risks Insights modeling operates at the intersection of existing data and intervention at the insights level. Instead of manipulating raw data points, it creates datasets that preserve the statistical properties of real-world data without exposing actual records. This makes it ideal for privacy-sensitive applications. Insights modeling also allows researchers to scale insights from pre-existing datasets, particularly when gathering large-scale data is impractical. This is common in marketing research, where data collection can be cumbersome and costly. However, this approach requires a solid foundation of real-world training data. Example: A market research firm conducting copy testing might use insights modeling to scale its normative database. Instead of relying solely on collected survey responses, the firm can generate synthetic insights models that extrapolate patterns from existing normative data. This allows brands to test creative performance against a broader, more predictive dataset without continuously gathering new survey responses. Manufactured Outcomes: When the Data Doesn’t Exist Yet Manufactured Outcomes sit at the extreme end of both new data generation and insights-level intervention. This approach involves generating entirely new datasets from scratch to simulate environments or scenarios that don’t yet exist but are essential for AI training, modeling, and simulations. Sometimes, the data you need simply doesn’t exist—or is too expensive or dangerous to collect in the real world. That’s where Manufactured Outcomes come in. This process generates entirely new datasets, often to train AI systems in environments that are difficult to replicate. Example: Self-driving car companies generate synthetic road scenarios—like a pedestrian suddenly jaywalking—to train their AI on rare but critical situations that might not appear often in real-world driving footage. Risks and Considerations of Synthetic Data While synthetic data provides powerful solutions, it isn’t without risks. Each type of synthetic data has its own challenges that can impact data quality, reliability, and ethical use. Here are some key concerns to keep in mind: Bias Propagation: If the underlying data used for imputation, insights modeling, or Manufactured Outcomes contains bias, those biases can be reinforced or even amplified.
Lack of Real-World Representativeness: User creation and data manufacture may generate data that seems realistic but fails to capture the nuances of actual user behavior or market conditions.
Overfitting and False Confidence: Insights modeling, when improperly applied, can create data that aligns too closely with the training set, leading to misleading conclusions.
Regulatory and Ethical Concerns: Privacy laws like GDPR and CCPA still apply to synthetic data if it can be reverse-engineered to identify real individuals. Key Questions to Ask When Evaluating Synthetic Data To ensure synthetic data meets quality standards, consider these questions: What is the source of the original data? Understanding the foundation of synthetic data helps assess potential biases and limitations.
How was the synthetic data generated? Different methods—machine learning, statistical models, or rule-based systems—impact the reliability of synthetic data.
Does the synthetic data maintain the statistical integrity of real-world data? Ensure the generated data behaves similarly to actual data without merely duplicating it.
Can the synthetic data be audited or validated? Reliable synthetic data should have validation mechanisms in place.
Does it comply with regulatory and ethical guidelines? Just because data is synthetic doesn’t mean it’s exempt from privacy regulations.
Is there a process to update the underlying data models? Synthetic data is only as good as the real-world data it is based on. Ensuring a process for continuously updating the foundational dataset prevents models from becoming outdated and misaligned with current trends. Wrapping It Up Synthetic data is a broad term, and if you’re working in AI, analytics, or any data-driven field, you need to be clear on what kind you’re dealing with. Are you filling in missing data (imputation), creating test users (user creation), generating anonymized patterns (insights modeling), or building brand-new datasets from scratch (manufactured outcomes)? Each of these plays a different role in how we use and protect data, and understanding them is key to making informed decisions in the rapidly evolving world of AI and data science. So next time someone throws around the term “synthetic data,” ask them: Which kind? Talk to anyone in AI, analytics, or data science, and they’ll tell you synthetic data is the future. But ask them what they mean by “synthetic data,” and you’ll get wildly different answers. That’s because synthetic data isn’t just one thing—it’s a broad category with multiple use cases and definitions. And that ambiguity makes conversations confusing. So, let’s cut through the noise. At its core, synthetic data operates along two key dimensions. The first is a spectrum ranging from filling in missing data in an existing dataset to generating entirely new datasets. The second distinguishes between interventions at the raw data level versus interventions at the insights or outcomes level. Imagine these dimensions as axes on a chart. This creates four quadrants, each representing a different type of synthetic data: data imputation, user creation, insights modeling, and manufactured outcomes . Each one serves a distinct function, and if you’re working with data in any capacity, you need to know the difference. data imputation, user creation, insights modeling, and manufactured outcomes Data Imputation: Filling in the Blanks While some might argue that data imputation isn’t truly synthetic data, modern imputation techniques have evolved beyond simple mean or median substitution. Today, advanced imputation leverages machine learning and generative AI models, making the generated values more sophisticated and contextually relevant than ever before. Data imputation sits at the intersection of missing data and raw data intervention . This means we’re working with existing datasets that have gaps, and our goal is to generate plausible values to complete them. Unlike other types of synthetic data, imputation isn’t about creating entirely new information—it’s about making incomplete data more usable. missing data raw data intervention Example: A market research firm conducting media effectiveness studies might have gaps in its audience response data due to missing survey responses. Instead of discarding incomplete datasets, imputation techniques—such as statistical modeling or machine learning—can generate realistic estimates, ensuring analysts can still draw meaningful insights from the data. Example: User Creation: Fake People, Real Insights User creation lies between new data generation and raw data intervention . Instead of modifying existing data, this approach fabricates entirely new user profiles and behaviors. It’s particularly useful when real user data isn’t available, is sensitive, or needs to be scaled artificially. new data generation raw data intervention User creation is a game-changer for testing products, improving security, and training AI models. Example: A streaming service might create synthetic user profiles to test its recommendation engine without exposing real customer data. Cybersecurity firms do the same to simulate attack scenarios and train fraud detection systems. Example: Insights Modeling: Patterns Without the Privacy Risks Insights modeling operates at the intersection of existing data and intervention at the insights level . Instead of manipulating raw data points, it creates datasets that preserve the statistical properties of real-world data without exposing actual records. This makes it ideal for privacy-sensitive applications. existing data intervention at the insights level Insights modeling also allows researchers to scale insights from pre-existing datasets, particularly when gathering large-scale data is impractical. This is common in marketing research, where data collection can be cumbersome and costly. However, this approach requires a solid foundation of real-world training data. Example: A market research firm conducting copy testing might use insights modeling to scale its normative database. Instead of relying solely on collected survey responses, the firm can generate synthetic insights models that extrapolate patterns from existing normative data. This allows brands to test creative performance against a broader, more predictive dataset without continuously gathering new survey responses. Example: Manufactured Outcomes: When the Data Doesn’t Exist Yet Manufactured Outcomes sit at the extreme end of both new data generation and insights-level intervention . This approach involves generating entirely new datasets from scratch to simulate environments or scenarios that don’t yet exist but are essential for AI training, modeling, and simulations. new data generation insights-level intervention Sometimes, the data you need simply doesn’t exist—or is too expensive or dangerous to collect in the real world. That’s where Manufactured Outcomes come in. This process generates entirely new datasets, often to train AI systems in environments that are difficult to replicate. Example: Self-driving car companies generate synthetic road scenarios—like a pedestrian suddenly jaywalking—to train their AI on rare but critical situations that might not appear often in real-world driving footage. Example: Risks and Considerations of Synthetic Data While synthetic data provides powerful solutions, it isn’t without risks. Each type of synthetic data has its own challenges that can impact data quality, reliability, and ethical use. Here are some key concerns to keep in mind: Bias Propagation: If the underlying data used for imputation, insights modeling, or Manufactured Outcomes contains bias, those biases can be reinforced or even amplified. Lack of Real-World Representativeness: User creation and data manufacture may generate data that seems realistic but fails to capture the nuances of actual user behavior or market conditions. Overfitting and False Confidence: Insights modeling, when improperly applied, can create data that aligns too closely with the training set, leading to misleading conclusions. Regulatory and Ethical Concerns: Privacy laws like GDPR and CCPA still apply to synthetic data if it can be reverse-engineered to identify real individuals. Bias Propagation: If the underlying data used for imputation, insights modeling, or Manufactured Outcomes contains bias, those biases can be reinforced or even amplified. Bias Propagation: Lack of Real-World Representativeness: User creation and data manufacture may generate data that seems realistic but fails to capture the nuances of actual user behavior or market conditions. Lack of Real-World Representativeness: Overfitting and False Confidence: Insights modeling, when improperly applied, can create data that aligns too closely with the training set, leading to misleading conclusions. Overfitting and False Confidence: Regulatory and Ethical Concerns: Privacy laws like GDPR and CCPA still apply to synthetic data if it can be reverse-engineered to identify real individuals. Regulatory and Ethical Concerns: Key Questions to Ask When Evaluating Synthetic Data To ensure synthetic data meets quality standards, consider these questions: What is the source of the original data? Understanding the foundation of synthetic data helps assess potential biases and limitations. How was the synthetic data generated? Different methods—machine learning, statistical models, or rule-based systems—impact the reliability of synthetic data. Does the synthetic data maintain the statistical integrity of real-world data? Ensure the generated data behaves similarly to actual data without merely duplicating it. Can the synthetic data be audited or validated? Reliable synthetic data should have validation mechanisms in place. Does it comply with regulatory and ethical guidelines? Just because data is synthetic doesn’t mean it’s exempt from privacy regulations. Is there a process to update the underlying data models? Synthetic data is only as good as the real-world data it is based on. Ensuring a process for continuously updating the foundational dataset prevents models from becoming outdated and misaligned with current trends. What is the source of the original data? Understanding the foundation of synthetic data helps assess potential biases and limitations. What is the source of the original data? How was the synthetic data generated? Different methods—machine learning, statistical models, or rule-based systems—impact the reliability of synthetic data. How was the synthetic data generated? Does the synthetic data maintain the statistical integrity of real-world data? Ensure the generated data behaves similarly to actual data without merely duplicating it. Does the synthetic data maintain the statistical integrity of real-world data? Can the synthetic data be audited or validated? Reliable synthetic data should have validation mechanisms in place. Can the synthetic data be audited or validated? Does it comply with regulatory and ethical guidelines? Just because data is synthetic doesn’t mean it’s exempt from privacy regulations. Does it comply with regulatory and ethical guidelines? Is there a process to update the underlying data models? Synthetic data is only as good as the real-world data it is based on. Ensuring a process for continuously updating the foundational dataset prevents models from becoming outdated and misaligned with current trends. Is there a process to update the underlying data models? Wrapping It Up Synthetic data is a broad term, and if you’re working in AI, analytics, or any data-driven field, you need to be clear on what kind you’re dealing with. Are you filling in missing data (imputation), creating test users (user creation), generating anonymized patterns (insights modeling), or building brand-new datasets from scratch (manufactured outcomes)? Each of these plays a different role in how we use and protect data, and understanding them is key to making informed decisions in the rapidly evolving world of AI and data science. So next time someone throws around the term “synthetic data,” ask them: Which kind?

Everyone in AI Loves Synthetic Data—But No One Can Agree on What It Is

Almal in KI is lief vir sintetiese data - maar niemand kan saamstem oor wat dit is nie

Alle i AI elsker syntetiske data - men ingen kan blive enige om, hvad det er

A todo el mundo en IA le encantan los datos sintéticos, pero nadie se pone de acuerdo sobre qué son

כולם ב-AI אוהבים נתונים סינתטיים - אבל אף אחד לא יכול להסכים על מה זה

AI業界の誰もが合成データを好むが、それが何なのかについては誰も同意できない

AI에 종사하는 모든 사람은 합성 데이터를 좋아하지만 그것이 무엇인지에 대해서는 아무도 동의할 수 없습니다.

Visi dirbtinio intelekto darbuotojai mėgsta sintetinius duomenis, bet niekas negali susitarti, kas tai yra

एआईमा सबैलाई सिंथेटिक डेटा मन पर्छ - तर यो के हो भन्ने कुरामा कोही पनि सहमत हुन सक्दैनन्।

AI nisqapi llapa runakunan Datos Sintéticos nisqakunata munakunku —Ichaqa manan pipas huk yuyaylla kayta atinmanchu Ima kasqanmanta

AI-யில் உள்ள அனைவரும் செயற்கைத் தரவை விரும்புகிறார்கள் - ஆனால் அது என்ன என்பதை யாரும் ஏற்றுக்கொள்ள முடியாது.

ทุกคนใน AI ชื่นชอบข้อมูลสังเคราะห์ แต่ไม่มีใครสามารถตกลงกันได้ว่ามันคืออะไร

Lahat ng nasa AI ay Gustung-gusto ang Synthetic Data—Ngunit Walang Makakasundo sa Ano Ito

Author:
(1) Andrew J. Peterson, University of Poitiers (andrew.peterson@univ-poitiers.fr). Table of Links Abstract and Introduction Related Work The media, filter bubbles and echo chambers Network effects and Information Cascades Model collapse Known biases in LLMs A Model of Knowledge Collapse Results Discussion and References Appendix Comparing width of the tails Defining knowledge collapse Abstract While artificial intelligence has the potential to process vast amounts of data, generate new insights, and unlock greater productivity, its widespread adoption may entail unforeseen consequences. We identify conditions under which AI, by reducing the cost of access to certain modes of knowledge, can paradoxically harm public understanding. While large language models are trained on vast amounts of diverse data, they naturally generate output towards the ‘center’ of the distribution. This is generally useful, but widespread reliance on recursive AI systems could lead to a process we define as “knowledge collapse”, and argue this could harm innovation and the richness of human understanding and culture. However, unlike AI models that cannot choose what data they are trained on, humans may strategically seek out diverse forms of knowledge if they perceive them to be worthwhile. To investigate this, we provide a simple model in which a community of learners or innovators choose to use traditional methods or to rely on a discounted AI-assisted process and identify conditions under which knowledge collapse occurs. In our default model, a 20% discount on AI-generated content generates public beliefs 2.3 times further from the truth than when there is no discount. Finally, based on the results, we consider further research directions to counteract such outcomes. Introduction Before the advent of generative AI, all text and artwork was produced by humans, in some cases aided by tools or computer systems. The capability of large language models (LLMs) to generate text with near-zero human effort, however, along with models to generate images, audio, and video, suggest that the data to which humans are exposed may come to be dominated by AI-generated or AI-aided processes. Researchers have noted that the recursive training of AI models on synthetic text may lead to degeneration, known as “model collapse” (Shumailov et al., 2023). Our interest is in the inverse of this concern, focusing instead on the equilibrium effects on the distribution of knowledge within human society. We ask under what conditions the rise of AI-generated content and AI-mediated access to information might harm the future of human thought, information-seeking, and knowledge. The initial effect of AI-generated information is presumably limited, and existing work on the harms of AI rightly focuses on the immediate effects of false information spread by “deepfakes” (Heidari et al., 2023), bias in AI algorithms (Nazer et al., 2023), and political misinformation (Chen and Shu, 2023). Our focus has a somewhat longer time horizon, and probes the impact of widespread, rather than marginal adoption. Researchers and engineers are currently building a variety of systems whereby AI would mediate our experience with other humans and with information sources. These range from learning from LLMs (Chen, Chen, and Lin, 2020), ranking or summarizing search results with LLMs (Sharma, Liao, and Xiao, 2024), suggesting search terms or words to write as with traditional autocomplete (Graham, 2023; Chonka, Diepeveen, and Haile, 2023), designing systems to pair collaborators (Ball and Lewis, 2018), LLM-based completion of knowledge bases sourced from Wikipedia (Chen, Razniewski, and Weikum, 2023), interpreting government data (Fisher, 2024) and aiding journalists (Opdahl et al., 2023), to cite only a few from an ever-growing list. Over time, dependence on these systems, and the existence of multifaceted interactions among them, may create a “curse of recursion” (Shumailov et al., 2023), in which our access to the original diversity of human knowledge is increasingly mediated by a partial and increasingly narrow subset of views. With increasing integration of LLM-based systems, certain popular sources or beliefs which were common in the training data may come to be reinforced in the public mindset (and within the training data), while other “long-tail” ideas are neglected and eventually forgotten. Such a process might be reinforced by an ‘echo chamber’ or information cascade effect, in which repeated exposure to this restricted set of information leads individuals to believe that the neglected, unobserved tails of knowledge are of little value. To the extent AI can radically discount the cost of access to certain kinds of information, it may further generate harm through the “streetlight effect”, in which a disproportionate amount of search is done under the lighted area not because it is more likely to contain one’s keys but because it’s easier to look there. We argue that the resulting curtailment of the tails of human knowledge would have significant effects on a range of concerns, including fairness, inclusion of diversity, lost-gains in innovation, and the preservation of the heritage of human culture. In our simulation model, however, we also consider the possibility that humans are strategic in actively curating their information sources. If, as we argue, there is significant value in the tai’ areas of knowledge that come to be neglected by AI-generated content, some individuals may put in additional effort to realize the gains, assuming they are sufficiently informed about the potential value. Summary of Main Contributions We identify a dynamic whereby AI, despite only reducing the cost of access to certain kinds of information, may lead to “knowledge collapse,” neglecting the long-tails of knowledge and creating an degenerately narrow perspective over generations. We provide a positive knowledge spillovers model with in which individuals decide whether to rely on cheaper AI technology or invest in samples from the full distribution of true knowledge. We examine through simulations the conditions under which individuals are sufficiently informed to prevent knowledge collapse within society. Finally, we conclude with an overview of possible solutions to prevent knowledge collapse in the AI-era. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Author: (1) Andrew J. Peterson, University of Poitiers (andrew.peterson@univ-poitiers.fr). Author: Author: (1) Andrew J. Peterson, University of Poitiers (andrew.peterson@univ-poitiers.fr). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work The media, filter bubbles and echo chambers The media, filter bubbles and echo chambers Network effects and Information Cascades Network effects and Information Cascades Model collapse Model collapse Known biases in LLMs Known biases in LLMs A Model of Knowledge Collapse A Model of Knowledge Collapse Results Results Discussion and References Discussion and References Appendix Appendix Appendix Comparing width of the tails Comparing width of the tails Defining knowledge collapse Defining knowledge collapse Abstract While artificial intelligence has the potential to process vast amounts of data, generate new insights, and unlock greater productivity, its widespread adoption may entail unforeseen consequences. We identify conditions under which AI, by reducing the cost of access to certain modes of knowledge, can paradoxically harm public understanding. While large language models are trained on vast amounts of diverse data, they naturally generate output towards the ‘center’ of the distribution. This is generally useful, but widespread reliance on recursive AI systems could lead to a process we define as “knowledge collapse”, and argue this could harm innovation and the richness of human understanding and culture. However, unlike AI models that cannot choose what data they are trained on, humans may strategically seek out diverse forms of knowledge if they perceive them to be worthwhile. To investigate this, we provide a simple model in which a community of learners or innovators choose to use traditional methods or to rely on a discounted AI-assisted process and identify conditions under which knowledge collapse occurs. In our default model, a 20% discount on AI-generated content generates public beliefs 2.3 times further from the truth than when there is no discount. Finally, based on the results, we consider further research directions to counteract such outcomes. Introduction Before the advent of generative AI, all text and artwork was produced by humans, in some cases aided by tools or computer systems. The capability of large language models (LLMs) to generate text with near-zero human effort, however, along with models to generate images, audio, and video, suggest that the data to which humans are exposed may come to be dominated by AI-generated or AI-aided processes. Researchers have noted that the recursive training of AI models on synthetic text may lead to degeneration, known as “model collapse” (Shumailov et al., 2023). Our interest is in the inverse of this concern, focusing instead on the equilibrium effects on the distribution of knowledge within human society. We ask under what conditions the rise of AI-generated content and AI-mediated access to information might harm the future of human thought, information-seeking, and knowledge. The initial effect of AI-generated information is presumably limited, and existing work on the harms of AI rightly focuses on the immediate effects of false information spread by “deepfakes” (Heidari et al., 2023), bias in AI algorithms (Nazer et al., 2023), and political misinformation (Chen and Shu, 2023). Our focus has a somewhat longer time horizon, and probes the impact of widespread, rather than marginal adoption. Researchers and engineers are currently building a variety of systems whereby AI would mediate our experience with other humans and with information sources. These range from learning from LLMs (Chen, Chen, and Lin, 2020), ranking or summarizing search results with LLMs (Sharma, Liao, and Xiao, 2024), suggesting search terms or words to write as with traditional autocomplete (Graham, 2023; Chonka, Diepeveen, and Haile, 2023), designing systems to pair collaborators (Ball and Lewis, 2018), LLM-based completion of knowledge bases sourced from Wikipedia (Chen, Razniewski, and Weikum, 2023), interpreting government data (Fisher, 2024) and aiding journalists (Opdahl et al., 2023), to cite only a few from an ever-growing list. Over time, dependence on these systems, and the existence of multifaceted interactions among them, may create a “curse of recursion” (Shumailov et al., 2023), in which our access to the original diversity of human knowledge is increasingly mediated by a partial and increasingly narrow subset of views. With increasing integration of LLM-based systems, certain popular sources or beliefs which were common in the training data may come to be reinforced in the public mindset (and within the training data), while other “long-tail” ideas are neglected and eventually forgotten. Such a process might be reinforced by an ‘echo chamber’ or information cascade effect, in which repeated exposure to this restricted set of information leads individuals to believe that the neglected, unobserved tails of knowledge are of little value. To the extent AI can radically discount the cost of access to certain kinds of information, it may further generate harm through the “streetlight effect”, in which a disproportionate amount of search is done under the lighted area not because it is more likely to contain one’s keys but because it’s easier to look there. We argue that the resulting curtailment of the tails of human knowledge would have significant effects on a range of concerns, including fairness, inclusion of diversity, lost-gains in innovation, and the preservation of the heritage of human culture. In our simulation model, however, we also consider the possibility that humans are strategic in actively curating their information sources. If, as we argue, there is significant value in the tai’ areas of knowledge that come to be neglected by AI-generated content, some individuals may put in additional effort to realize the gains, assuming they are sufficiently informed about the potential value. Summary of Main Contributions We identify a dynamic whereby AI, despite only reducing the cost of access to certain kinds of information, may lead to “knowledge collapse,” neglecting the long-tails of knowledge and creating an degenerately narrow perspective over generations. We provide a positive knowledge spillovers model with in which individuals decide whether to rely on cheaper AI technology or invest in samples from the full distribution of true knowledge. We examine through simulations the conditions under which individuals are sufficiently informed to prevent knowledge collapse within society. Finally, we conclude with an overview of possible solutions to prevent knowledge collapse in the AI-era. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

AI Knows Best—But Only If You Agree With It

AI ведае лепш за ўсё, але толькі калі вы з гэтым згодныя

La IA sabe más, pero solo si estás de acuerdo con ella

Tekoäly tietää parhaiten – mutta vain jos olet sen kanssa samaa mieltä

AIは最もよく知っている—ただし、あなたが同意した場合のみ

AI ຮູ້​ດີ​ທີ່​ສຸດ—ແຕ່​ຖ້າ​ເຈົ້າ​ເຫັນ​ດີ​ກັບ​ມັນ​ເທົ່າ​ນັ້ນ

AI хамгийн сайн мэддэг, гэхдээ та үүнтэй санал нийлж байгаа тохиолдолд л болно

एआईले सबैभन्दा राम्रो जान्दछ—तर यदि तपाईं सहमत हुनुहुन्छ भने मात्र

AI știe cel mai bine, dar numai dacă ești de acord cu asta

AI-க்கு நன்றாகத் தெரியும் - ஆனால் நீங்கள் அதை ஏற்றுக்கொண்டால் மட்டுமே.

AI ዝበለጸ ይፈልጥ-ግን ምስኡ ምስ እትሰማማዕ ጥራይ

Pinakamahusay na Alam ng AI—Pero Kung Sang-ayon Ka Dito

人工智能最了解——但前提是你同意它的观点

GEORGE TOWN, Cayman Islands, November 15th, 2024/Chainwire/--Prosper, a decentralized protocol bridging institutional-grade Bitcoin mining power on-chain and aiming to unlock the potential of Bitcoin through liquidity farming, today announced that it has entered into a long-term hosting service agreement with BITMAIN, the world's leading manufacturer of digital currency mining servers through its brand Antminer. Under the agreement, BITMAIN and its affiliates will provide hosting services for Prosper’s foundation-owned Bitcoin miners, bringing high-quality operational standards for $PROS token holders. The first batch of hashrate under this agreement is expected to come online throughout November via Antpool’s mining pool platform and accessible via Prosper’s v1 web app. Earlier this quarter, Prosper announced a pivot in its strategic direction to focus on Bitcoin mining and unlock Bitcoin’s potential in liquidity farming. Prosper has entered into an agreement with BITMAIN to enhance execution quality for its miners and $PROS token holders. This also highlights Prosper’s value proposition as the only Web3 Bitcoin mining project that has secured multiple top-notch industry players as key service providers. About BITMAIN Since its foundation in 2013, BITMAIN is the world's leading manufacturer of digital currency mining servers through its brand ANTMINER, which has long maintained a global market share and leading position in technology, serving customers across over 100 countries and regions. The company's R&D center is situated in Singapore, and it has multiple branches and subsidiaries across the globe, including but not limited to Hong Kong, the United States, Malaysia, and the United Arab Emirates. About Prosper Prosper is a decentralized protocol for a community that truly believes in Bitcoin, providing full exposure across Bitcoin's value layers through bridging institutional-grade Bitcoin mining power on-chain, and unlocking the potential of Bitcoin through liquidity farming. For more information, users can visit prosper-fi.com or follow us on X (formerly Twitter). Contact Prosper contact@prosper-fi.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here GEORGE TOWN, Cayman Islands, November 15th, 2024/Chainwire/--Prosper, a decentralized protocol bridging institutional-grade Bitcoin mining power on-chain and aiming to unlock the potential of Bitcoin through liquidity farming, today announced that it has entered into a long-term hosting service agreement with BITMAIN, the world's leading manufacturer of digital currency mining servers through its brand Antminer. Under the agreement, BITMAIN and its affiliates will provide hosting services for Prosper’s foundation-owned Bitcoin miners, bringing high-quality operational standards for $PROS token holders. The first batch of hashrate under this agreement is expected to come online throughout November via Antpool’s mining pool platform and accessible via Prosper’s v1 web app . v1 web app v1 web app Earlier this quarter, Prosper announced a pivot in its strategic direction to focus on Bitcoin mining and unlock Bitcoin’s potential in liquidity farming. Prosper has entered into an agreement with BITMAIN to enhance execution quality for its miners and $PROS token holders. This also highlights Prosper’s value proposition as the only Web3 Bitcoin mining project that has secured multiple top-notch industry players as key service providers. About BITMAIN Since its foundation in 2013, BITMAIN is the world's leading manufacturer of digital currency mining servers through its brand ANTMINER, which has long maintained a global market share and leading position in technology, serving customers across over 100 countries and regions. The company's R&D center is situated in Singapore, and it has multiple branches and subsidiaries across the globe, including but not limited to Hong Kong, the United States, Malaysia, and the United Arab Emirates. About Prosper Prosper is a decentralized protocol for a community that truly believes in Bitcoin, providing full exposure across Bitcoin's value layers through bridging institutional-grade Bitcoin mining power on-chain, and unlocking the potential of Bitcoin through liquidity farming. For more information, users can visit prosper-fi.com or follow us on X (formerly Twitter) . Prosper Prosper prosper-fi.com prosper-fi.com X (formerly Twitter) X (formerly Twitter) Contact Prosper contact@prosper-fi.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Prosper Enters Into Long-term Agreement With BITMAIN To Provide Bitcoin Miner Hosting Services

أبرمت شركة Prosper اتفاقية طويلة الأجل مع شركة BITMAIN لتوفير خدمات استضافة Bitcoin Miner

Prosper ukax BITMAIN ukampiw jaya pacha aruskipäwir mantawayi, Bitcoin Miner Hosting Services ukar churañataki

Prosper, Bitcoin Miner Hosting xidmətlərini təmin etmək üçün BITMAIN ilə uzunmüddətli müqavilə bağlayır

Prosper firma un acuerdo a largo plazo con BITMAIN para proporcionar servicios de alojamiento para mineros de Bitcoin

Prosper tekee pitkäaikaisen sopimuksen BITMAINin kanssa Bitcoin Miner -isännöintipalvelujen tarjoamisesta

प्रोस्पर ने बिटकॉइन माइनर होस्टिंग सेवाएं प्रदान करने के लिए बिटमैन के साथ दीर्घकालिक समझौता किया

Prosper が BITMAIN と長期契約を締結し、ビットコインマイナーのホスティングサービスを提供

Prosper, BITMAIN과 비트코인 채굴 호스팅 서비스 제공을 위한 장기 계약 체결

Prosper Ekoti Na Boyokani Ya Milayi Na BITMAIN Pona Kopesa Ba Services Ya Hébergement Ya Bitcoin Miner

Prosper noslēdz ilgtermiņa līgumu ar BITMAIN, lai nodrošinātu Bitcoin Miner mitināšanas pakalpojumus

प्रोस्परले बिटकोइन माइनर होस्टिङ सेवाहरू प्रदान गर्न BITMAIN सँग दीर्घकालीन सम्झौतामा प्रवेश गर्छ

Prosper Yinjira mumasezerano maremare hamwe na BITMAIN Gutanga Serivisi zo Kwakira Bitcoin Miner

Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. Please tell us your name, what you currently do, and where you work: My name is Maria Carolina. I'm a data scientist with 4.5 years of experience, currently working at a tech company in Brazil. How did you get into Data Science? My journey to data science is unique, I guess. I began my career as an agronomist, completing advanced degrees including a master's and doctorate, and worked as both a postdoctoral researcher and university professor (did not like teaching undergrad very much though). When I first learned about data science through international websites and blogs, it was still an emerging field in Brazil, with very few local job opportunities. So that seemed pretty impossible to me. But somehow, despite living far from the country's major financial centers where most tech jobs were concentrated (and I could not move out of state for a job), I managed to break into the field. My agricultural background was the main reason, I joined a company focused on agribusiness. What does your day-to-day role involve? My role varies depending on current projects, but research is a constant and significant component. Some tasks require several days of research before any coding begins (which I really enjoy). What technology or programming languages do you frequently use to get your work done? I primarily use Python, which is standard across our team. Given our focus on agribusiness, I also work extensively with GIS software, particularly QGIS (a big fan of this tool), and SQL. What do you like the most about your job? The research-intensive nature of my work is what I enjoy most. It feels like an extension of my academic research background but with the added satisfaction of developing practical products. I have to stay current with the latest research, state-of-the-art developments in the field, statistical analysis, machine learning techniques, and efficient programming practices. What is something you think people do not know about data science or data scientists? I understand that there is a wide variety of data roles. Even though data scientist is just one of them, there are many different profiles that a data scientist can embody, depending on the company, client, and projects. I don't know if everyone realizes the diversity of possibilities that this role provides - and of course, how much it evolves and seems to change by the year. But I believe that any data scientist should be very interested in research and learning, and I say this in addition to knowing how to run models for example (which of course also requires a lot of study). While technical skills are crucial, a fundamental curiosity and passion for research and continuous learning are essential traits for any data scientist. What are some trends in data science that you’re particularly excited about? I'm particularly excited about AI's evolving role in data science (although at the same time, I feel the need (daily) to continue learning much more about what we already call "traditional"). While it began by streamlining basic tasks, I believe we're moving toward more sophisticated and complex applications of AI. If you weren't a Data Scientist, what would you be doing? I would probably be in a research position (if I managed to, of course), as I'm deeply passionate about the scientific process - forming hypotheses, developing methodologies, testing assumptions, and documenting findings. What are 3 quick ways for someone to get into Data Science and land a job within a year? For aspiring data scientists, my advice is to: Develop a genuine interest in continuous learning and problem-solving (NEVER ends….and maybe that's very good)
Focus on understanding underlying concepts and their connections (understanding the reasons behind the things!)
Balance your studies between statistics, programming, and business domains (no one knows all…) Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. Welcome to HackerNoon ’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE . HackerNoon HERE Please tell us your name, what you currently do, and where you work: My name is Maria Carolina. I'm a data scientist with 4.5 years of experience, currently working at a tech company in Brazil. How did you get into Data Science? My journey to data science is unique, I guess. I began my career as an agronomist, completing advanced degrees including a master's and doctorate, and worked as both a postdoctoral researcher and university professor (did not like teaching undergrad very much though). When I first learned about data science through international websites and blogs, it was still an emerging field in Brazil, with very few local job opportunities. So that seemed pretty impossible to me. But somehow, despite living far from the country's major financial centers where most tech jobs were concentrated (and I could not move out of state for a job), I managed to break into the field. My agricultural background was the main reason, I joined a company focused on agribusiness. What does your day-to-day role involve? My role varies depending on current projects, but research is a constant and significant component. Some tasks require several days of research before any coding begins (which I really enjoy). What technology or programming languages do you frequently use to get your work done? I primarily use Python , which is standard across our team. Given our focus on agribusiness, I also work extensively with GIS software, particularly QGIS (a big fan of this tool), and SQL. Python What do you like the most about your job? The research-intensive nature of my work is what I enjoy most. It feels like an extension of my academic research background but with the added satisfaction of developing practical products. I have to stay current with the latest research, state-of-the-art developments in the field, statistical analysis, machine learning techniques, and efficient programming practices. What is something you think people do not know about data science or data scientists? I understand that there is a wide variety of data roles. Even though data scientist is just one of them, there are many different profiles that a data scientist can embody, depending on the company, client, and projects. I don't know if everyone realizes the diversity of possibilities that this role provides - and of course, how much it evolves and seems to change by the year. But I believe that any data scientist should be very interested in research and learning, and I say this in addition to knowing how to run models for example (which of course also requires a lot of study). data scientist While technical skills are crucial, a fundamental curiosity and passion for research and continuous learning are essential traits for any data scientist. What are some trends in data science that you’re particularly excited about? I'm particularly excited about AI's evolving role in data science (although at the same time, I feel the need (daily) to continue learning much more about what we already call "traditional"). While it began by streamlining basic tasks, I believe we're moving toward more sophisticated and complex applications of AI. If you weren't a Data Scientist, what would you be doing? I would probably be in a research position (if I managed to, of course), as I'm deeply passionate about the scientific process - forming hypotheses, developing methodologies, testing assumptions, and documenting findings. What are 3 quick ways for someone to get into Data Science and land a job within a year? For aspiring data scientists, my advice is to: Develop a genuine interest in continuous learning and problem-solving (NEVER ends….and maybe that's very good) Focus on understanding underlying concepts and their connections (understanding the reasons behind the things!) Balance your studies between statistics, programming, and business domains (no one knows all…) Develop a genuine interest in continuous learning and problem-solving (NEVER ends….and maybe that's very good) Focus on understanding underlying concepts and their connections (understanding the reasons behind the things!) Balance your studies between statistics, programming, and business domains (no one knows all…)

From Agronomist to Data Scientist: My Journey into the World of AI and Data Science

Hey Hackers! Cropler has been nominated in HackerNoon's Startups of The Year 2024  awards in Warsaw, Poland under the Emerging Tech category and others. Please vote for us here: 1️⃣ https://hackernoon.com/startups/europe/europe-warsaw-poland 2️⃣ https://hackernoon.com/startups/industry/iot 3️⃣ https://hackernoon.com/startups/industry/hardware 4️⃣ https://hackernoon.com/startups/industry/climatetech Meet Cropler Cropler is the world’s first real-time remote photo plant monitoring technology that is about to become an AI-powered digital agronomical assistant. Cropler uses smart AI-powered mobile agricameras to help farmers and agribusinesses identify the key phenophases, diseases, and inputs efficiently to perform necessary agricultural operations in a timely manner. The Winning Team 1️⃣ Nik Bedunkevich
LinkedIn
Founder & CEO
With over 10 years in software and 7+ years in hardware development, Nik's leadership in precision agriculture, backed by his experience at OneSoil, where he co-founded and led hardware development, has been instrumental. His background in Radio Engineering and his ability to integrate data exchange and soil analysis sensors have enabled Cropler to push the boundaries of AI-driven agricultural solutions.2️⃣ Marie SinkovaLinkedIn
CMO
Marie’s 6+ years in marketing, including her impactful work at MEDvidi and EduDo, have significantly boosted our brand’s visibility. Her ability to develop engaging content and manage successful teams has increased revenue and brand recognition while minimizing negative reviews. Her strategic approach in various marketing areas has laid a solid foundation for Cropler development.3️⃣ Andrii ZbrytskyiLinkedIn
BDM
With over 17 years in the agritech area, Andri Zbrytskyi has witnessed firsthand the ins and outs of clients resulting in better client/corporate relations, sales and even more business development for the company. AI agriculture solutions have found strategic partnerships thanks to his efforts and these have increased expanding the market base.4️⃣ Xenia NiaborskayaLinkedIn
Head of Design
Xenia’s experience of more than 4 years in UX/UI design has played a key role in ensuring that our user interfaces work as expected and look good as well. Her capability in business-centered design and the process of user-centered design has improved the usability of our platform tremendously.5️⃣ Eugene VoronetsLinkedIn
Tech Adviser
Eugene’s 10+ years in backend development, particularly his expertise in high-load systems, ensures the robustness and scalability of our technology. His work at OneSoil, building APIs and fostering collaborations with industry leaders like John Deere and AWS, has played a critical role in shaping Cropler’s technical infrastructure. What Cropler does differently We aim to revolutionize agriculture with Cropler's global plant monitoring technology, which uses AI-powered agricameras and remote sensing data to provide farmers, agronomists, and agribusinesses with predictive field insights and global yield analytics for optimized crop production. Cropler’s unique, AI-powered plant health data allows farmers and agribusinesses to address issues before they escalate, unlike drones or weather stations, which often provide information only after problems have already progressed and plants experience severe damage. Early disease and crop stress detection ensures that farmers can act timely to maximize yields and optimize fertilizers and pesticides application. Cropler isn’t just about monitoring—it’s about driving actionable insights that make farming more efficient, sustainable, and profitable. Milestones and achievements 🏆 357 devices deployed
🏆 Revenue of $53k in 2024
🏆 AI disease & leaf damages integration
🏆 iOS & Android app release
🏆 10 dealer centers in multiple countries
🏆 16 countries (EU + USA) have chosen our devices
🏆 USA & EU issued certificates for our devices
🏆 EIT Food Test Farm winner Finalist of 2024
🏆 China Overseas Talents Innovation and Entrepreneurship Competition
🏆 Partnerships with key market players (Varda Field ID, AGRIVI, SAS Agro, Kalo, Basf (Xarvio), Bayer, Syngenta etc.) Lessons learned 1️⃣ This was quite a challenge in building AI tools that could accurately adapt to the vast complexities of agronomic data. Initial models developed were rather far from the precision that agronomists required, struggling with symptom and stage variability across different crops. However, we further invested in our AI capabilities, enhancing our models by adding extensive data sets and collaborations with agricultural experts. This achieved two things: it not only enhanced the precision of our AI but also made our tools an inseparable companion for any agronomist by providing them with second-to-none insights and recommendations. 2️⃣ Another challenge was to convince the highly practical and quite skeptical agronomists to adopt this high-tech tool called Cropler. So, we shifted strategy and focused our efforts on education and hands-on involvement, showing agronomists not only how but why our technology worked. 3️⃣ One of the most significant challenges of launching the international expansion of our solution was overcoming the many regulatory, environmental, and operational hurdles spanning across continents, which drastically changed from region to region. We learned flexibility and local partnerships. By aligning ourselves with local agronomic experts and adapting our technology to meet regional regulations and needs, the stage was set for global impact-development of preparations for a major rollout in Europe by 2025. 4️⃣ Last, but not least, was a technical and logistical challenge to scale our technology from pilot projects to widespread usage by agronomists. So, we invested in scalable architecture and robust systems, supporting high demand for real-time data processing and global access. Our platform is not a tool; it's a 24/7 digital agronomist turning that data into actionable and timely advice. What Startups of The Year means to us Entering into HackerNoon's Startups of the Year is not an opportunity for Cropler but rather a stage to revolutionize the agribusiness panorama. We come to introduce our pioneering AI agri-camera technology that's going to change the world and make agriculture more predictable and effective. This is a moment in the spotlight to galvanize an industry for the attention of global investors, tech enthusiasts, and potential partners who are committed to transforming agriculture through innovation as much as we are. To be present there is already a loud statement of the capability and vision that we have. More than that, we are not just in attendance; we are leading from the front in this charge into the future where technology and sustainability come together to remake the standard of agriculture around the world. The insights and feedback that we get here from this eclectic tech community are very critical; it fuels our drive toward the 2025 launch of our Global Analytics. For us, this is more than a competition; this is a launching pad toward growth, visibility, and impact. Join us while we lead the way to a more efficient, sustainable, and predictable agricultural future! Vote for us today: https://hackernoon.com/startups/europe/europe-warsaw-poland Hey Hackers! Cropler has been nominated in HackerNoon's Startups of The Year 2024 awards in Warsaw, Poland under the Emerging Tech category and others. Cropler Cropler HackerNoon's Startups of The Year 2024 HackerNoon's Startups of The Year 2024 HackerNoon's Startups of The Year 2024 Emerging Tech category and others. Emerging Tech Emerging Tech Please vote for us here: Please vote for us here: 1️⃣ https://hackernoon.com/startups/europe/europe-warsaw-poland https://hackernoon.com/startups/europe/europe-warsaw-poland https://hackernoon.com/startups/europe/europe-warsaw-poland 2️⃣ https://hackernoon.com/startups/industry/iot https://hackernoon.com/startups/industry/iot https://hackernoon.com/startups/industry/iot 3️⃣ https://hackernoon.com/startups/industry/hardware https://hackernoon.com/startups/industry/hardware 4️⃣ https://hackernoon.com/startups/industry/climatetech https://hackernoon.com/startups/industry/climatetech Meet Cropler Cropler is the world’s first real-time remote photo plant monitoring technology that is about to become an AI-powered digital agronomical assistant. Cropler uses smart AI-powered mobile agricameras to help farmers and agribusinesses identify the key phenophases, diseases, and inputs efficiently to perform necessary agricultural operations in a timely manner. Cropler is the world’s first real-time remote photo plant monitoring technology that is about to become an AI-powered digital agronomical assistant. Cropler uses smart AI-powered mobile agricameras to help farmers and agribusinesses identify the key phenophases, diseases, and inputs efficiently to perform necessary agricultural operations in a timely manner. Cropler Cropler agricameras The Winning Team 1️⃣ Nik Bedunkevich LinkedIn Founder & CEO With over 10 years in software and 7+ years in hardware development, Nik's leadership in precision agriculture, backed by his experience at OneSoil, where he co-founded and led hardware development, has been instrumental. His background in Radio Engineering and his ability to integrate data exchange and soil analysis sensors have enabled Cropler to push the boundaries of AI-driven agricultural solutions. 2️⃣ Marie Sinkova LinkedIn CMO Marie’s 6+ years in marketing, including her impactful work at MEDvidi and EduDo, have significantly boosted our brand’s visibility. Her ability to develop engaging content and manage successful teams has increased revenue and brand recognition while minimizing negative reviews. Her strategic approach in various marketing areas has laid a solid foundation for Cropler development. 3️⃣ Andrii Zbrytskyi LinkedIn BDM With over 17 years in the agritech area, Andri Zbrytskyi has witnessed firsthand the ins and outs of clients resulting in better client/corporate relations, sales and even more business development for the company. AI agriculture solutions have found strategic partnerships thanks to his efforts and these have increased expanding the market base. 4️⃣ Xenia Niaborskaya LinkedIn Head of Design Xenia’s experience of more than 4 years in UX/UI design has played a key role in ensuring that our user interfaces work as expected and look good as well. Her capability in business-centered design and the process of user-centered design has improved the usability of our platform tremendously. 5️⃣ Eugene Voronets LinkedIn Tech Adviser Eugene’s 10+ years in backend development, particularly his expertise in high-load systems, ensures the robustness and scalability of our technology. His work at OneSoil, building APIs and fostering collaborations with industry leaders like John Deere and AWS, has played a critical role in shaping Cropler’s technical infrastructure. 1️⃣ Nik Bedunkevich Nik Bedunkevich LinkedIn LinkedIn LinkedIn LinkedIn Founder & CEO Founder & CEO With over 10 years in software and 7+ years in hardware development, Nik's leadership in precision agriculture, backed by his experience at OneSoil, where he co-founded and led hardware development, has been instrumental. His background in Radio Engineering and his ability to integrate data exchange and soil analysis sensors have enabled Cropler to push the boundaries of AI-driven agricultural solutions. 2️⃣ Marie Sinkova Marie Sinkova LinkedIn LinkedIn LinkedIn LinkedIn CMO CMO Marie’s 6+ years in marketing, including her impactful work at MEDvidi and EduDo, have significantly boosted our brand’s visibility. Her ability to develop engaging content and manage successful teams has increased revenue and brand recognition while minimizing negative reviews. Her strategic approach in various marketing areas has laid a solid foundation for Cropler development. 3️⃣ Andrii Zbrytskyi Andrii Zbrytskyi LinkedIn LinkedIn LinkedIn LinkedIn BDM BDM With over 17 years in the agritech area, Andri Zbrytskyi has witnessed firsthand the ins and outs of clients resulting in better client/corporate relations, sales and even more business development for the company. AI agriculture solutions have found strategic partnerships thanks to his efforts and these have increased expanding the market base. 4️⃣ Xenia Niaborskaya Xenia Niaborskaya LinkedIn LinkedIn LinkedIn LinkedIn Head of Design Head of Design Xenia’s experience of more than 4 years in UX/UI design has played a key role in ensuring that our user interfaces work as expected and look good as well. Her capability in business-centered design and the process of user-centered design has improved the usability of our platform tremendously. 5️⃣ Eugene Voronets Eugene Voronets LinkedIn LinkedIn LinkedIn LinkedIn Tech Adviser Tech Adviser Eugene’s 10+ years in backend development, particularly his expertise in high-load systems, ensures the robustness and scalability of our technology. His work at OneSoil, building APIs and fostering collaborations with industry leaders like John Deere and AWS, has played a critical role in shaping Cropler’s technical infrastructure. What Cropler does differently We aim to revolutionize agriculture with Cropler's global plant monitoring technology, which uses AI-powered agricameras and remote sensing data to provide farmers, agronomists, and agribusinesses with predictive field insights and global yield analytics for optimized crop production. global plant monitoring technology, Cropler’s unique, AI-powered plant health data allows farmers and agribusinesses to address issues before they escalate, unlike drones or weather stations, which often provide information only after problems have already progressed and plants experience severe damage. Early disease and crop stress detection ensures that farmers can act timely to maximize yields and optimize fertilizers and pesticides application. Cropler isn’t just about monitoring—it’s about driving actionable insights that make farming more efficient, sustainable, and profitable. Cropler isn’t just about monitoring—it’s about driving actionable insights that make farming more efficient, sustainable, and profitable. Milestones and achievements 🏆 357 devices deployed 🏆 Revenue of $53k in 2024 🏆 AI disease & leaf damages integration 🏆 iOS & Android app release 🏆 10 dealer centers in multiple countries 🏆 16 countries (EU + USA) have chosen our devices 🏆 USA & EU issued certificates for our devices 🏆 EIT Food Test Farm winner Finalist of 2024 🏆 China Overseas Talents Innovation and Entrepreneurship Competition 🏆 Partnerships with key market players (Varda Field ID, AGRIVI, SAS Agro, Kalo, Basf (Xarvio), Bayer, Syngenta etc.) 🏆 357 devices deployed 🏆 Revenue of $53k in 2024 🏆 AI disease & leaf damages integration 🏆 iOS & Android app release 🏆 10 dealer centers in multiple countries 🏆 16 countries (EU + USA) have chosen our devices 🏆 USA & EU issued certificates for our devices 🏆 EIT Food Test Farm winner Finalist of 2024 🏆 China Overseas Talents Innovation and Entrepreneurship Competition 🏆 Partnerships with key market players (Varda Field ID, AGRIVI, SAS Agro, Kalo, Basf (Xarvio), Bayer, Syngenta etc.) Lessons learned 1️⃣ This was quite a challenge in building AI tools that could accurately adapt to the vast complexities of agronomic data. Initial models developed were rather far from the precision that agronomists required, struggling with symptom and stage variability across different crops. However, we further invested in our AI capabilities, enhancing our models by adding extensive data sets and collaborations with agricultural experts. This achieved two things: it not only enhanced the precision of our AI but also made our tools an inseparable companion for any agronomist by providing them with second-to-none insights and recommendations. 2️⃣ Another challenge was to convince the highly practical and quite skeptical agronomists to adopt this high-tech tool called Cropler. So, we shifted strategy and focused our efforts on education and hands-on involvement, showing agronomists not only how but why our technology worked. agronomists 3️⃣ One of the most significant challenges of launching the international expansion of our solution was overcoming the many regulatory, environmental, and operational hurdles spanning across continents, which drastically changed from region to region. We learned flexibility and local partnerships. By aligning ourselves with local agronomic experts and adapting our technology to meet regional regulations and needs, the stage was set for global impact-development of preparations for a major rollout in Europe by 2025. global impact-development 4️⃣ Last, but not least, was a technical and logistical challenge to scale our technology from pilot projects to widespread usage by agronomists. So, we invested in scalable architecture and robust systems, supporting high demand for real-time data processing and global access. Our platform is not a tool; it's a 24/7 digital agronomist turning that data into actionable and timely advice. Our platform is not a tool; it's a 24/7 digital agronomist turning that data into actionable and timely advice. Our platform is not a tool; it's a 24/7 digital agronomist turning that data into actionable and timely advice. What Startups of The Year means to us Entering into HackerNoon's Startups of the Year is not an opportunity for Cropler but rather a stage to revolutionize the agribusiness panorama. We come to introduce our pioneering AI agri-camera technology that's going to change the world and make agriculture more predictable and effective. This is a moment in the spotlight to galvanize an industry for the attention of global investors, tech enthusiasts, and potential partners who are committed to transforming agriculture through innovation as much as we are. revolutionize the agribusiness To be present there is already a loud statement of the capability and vision that we have. To be present there is already a loud statement of the capability and vision that we have. More than that, we are not just in attendance; we are leading from the front in this charge into the future where technology and sustainability come together to remake the standard of agriculture around the world. The insights and feedback that we get here from this eclectic tech community are very critical; it fuels our drive toward the 2025 launch of our Global Analytics. For us, this is more than a competition; this is a launching pad toward growth, visibility, and impact. For us, this is more than a competition; this is a launching pad toward growth, visibility, and impact. Join us while we lead the way to a more efficient, sustainable, and predictable agricultural future! Vote for us today: https://hackernoon.com/startups/europe/europe-warsaw-poland Vote for us today: https://hackernoon.com/startups/europe/europe-warsaw-poland Vote for us today: https://hackernoon.com/startups/europe/europe-warsaw-poland

AI-Driven Solutions for Modern Agritech: Interview with SOTY 2024 Nominee, Cropler

How to Create a Decentralized Marketplace for Agricultural Products on the Rootstock (RSK)

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Sida loo Abuuro Goob Suuq ah oo Baahsan oo loogu talagalay Alaabooyinka Beeraha ee RSK Testnet

ከመይ ጌርና ኣብ RSK Testnet ንፍርያት ሕርሻ ዘይተማእከለ ዕዳጋ ንፈጥር

Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 26 of 29. COUNT XIV: TORTIOUS INTERFERENCE WITH CONTRACT (Against the OpenAI For-Profit Entities) 329. Plaintiff re-alleges and incorporates by reference paragraphs 1 through 328 inclusive, as though fully set forth herein. 330. The OpenAI For-Profit Entities knew Musk had a valid contract with Altman and OpenAI, Inc. and that the agreement required OpenAI, Inc.’s technology to be predominantly open source for the benefit of humankind, not for commercial gain. Aside from the name “OpenAI”—an appellation ironically shared by almost all the OpenAI For-Profit Entities—these Defendants all had knowledge of the charitable purpose for which Musk co-founded the non-profit, as, on information and belief, Altman and Brockman have at all relevant times been their officers, agents, employees, and/or owners whose knowledge and intent is imputed to them. 331. On information and belief, the OpenAI For-Profit Entities intended to disrupt Altman and OpenAI, Inc.’s performance of their contract with Musk and/or knew that such disruption of their performance was certain or substantially certain to occur as a result of their conduct. 332. The OpenAI For-Profit Entities induced OpenAI, Inc. to breach its agreement with Musk and engaged in independently wrongful conduct by siphoning the non-profit’s most valuable assets into their for-profit apparatus. On information and belief, the OpenAI For-Profit Entities currently employ much of the non-profit’s former staff, including Altman and Brockman, house its research and intellectual property, and have facilitated rampant self-dealing. 333. On information and belief, Altman, a major stakeholder in OpenAI’s for-profit apparatus, could have donated his money to the non-profit like Musk but instead, Altman put the majority of his investment in the moneymaking scheme made possible by the OpenAI For-Profit Entities. 334. Defendants intentionally concealed their wrongful conduct, which prevented Musk from discovering their scheme, notwithstanding his exercise of due diligence. 335. As a direct and proximate result of the OpenAI For-Profit Entities’ conduct, acts, and omissions alleged hereinabove, Musk is entitled to recover the damages he sustained and will sustain, including any income, gains, compensation, profits, and advantages obtained, received, or to be received by Defendants, or any of them, arising from the wrongful acquisition of Musk’s contributions to OpenAI, Inc., including prejudgment interest. Musk is entitled to an order requiring Defendants, jointly and severally, to render an accounting to ascertain the amount of such proceeds. 336. As a direct and proximate result of Defendants’ wrongful conduct, acts, and omissions alleged hereinabove, Musk has been damaged, and Defendants have been and will continue to be unjustly enriched, in an amount that shall be assessed at trial, but which vastly exceeds $75,000, and for which restitution and/or non-restitutionary disgorgement is appropriate. Such should include the imposition of a constructive trust; a declaration by this Court that Defendants are jointly and severally the constructive trustee(s) for the benefit of Musk; and an order that Defendants convey to Musk all of the profits, assets, property, and ill-gotten gains received or to be received by Defendants, which are traceable to Musk’s wrongfully acquired financial and other contributions to OpenAI, Inc. 337. Defendants’ wrongful conduct, acts, and omissions have proximately caused and will continue to cause Musk substantial injury and damage, much of which cannot be reasonably or adequately measured or compensated in money damages. The harm this wrongful conduct will cause to Musk is both imminent and irreparable, and the amount of damage sustained by Musk will be difficult to ascertain if such wrongful conduct is allowed to continue without restraint. Musk is entitled to an injunction during the pendency of this action, and permanently enjoining Defendants, their officers, agents, and employees, and all persons acting in concert with them, from engaging in such further tortious conduct. 338. Defendants’ wrongful conduct constitutes oppression, fraud, and/or malice under Cal. Civ. Code § 3294, entitling Musk to an award of punitive damages appropriate to punish or set an example of Defendants in an amount to be determined at trial. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on August 05, 2024, deadline.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 26 of 29. Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 26 of 29. HackerNoon’s Legal PDF Series here COUNT XIV: TORTIOUS INTERFERENCE WITH CONTRACT (Against the OpenAI For-Profit Entities) 329. Plaintiff re-alleges and incorporates by reference paragraphs 1 through 328 inclusive, as though fully set forth herein. 330. The OpenAI For-Profit Entities knew Musk had a valid contract with Altman and OpenAI, Inc. and that the agreement required OpenAI, Inc.’s technology to be predominantly open source for the benefit of humankind, not for commercial gain. Aside from the name “OpenAI”—an appellation ironically shared by almost all the OpenAI For-Profit Entities—these Defendants all had knowledge of the charitable purpose for which Musk co-founded the non-profit, as, on information and belief, Altman and Brockman have at all relevant times been their officers, agents, employees, and/or owners whose knowledge and intent is imputed to them. 331. On information and belief, the OpenAI For-Profit Entities intended to disrupt Altman and OpenAI, Inc.’s performance of their contract with Musk and/or knew that such disruption of their performance was certain or substantially certain to occur as a result of their conduct. 332. The OpenAI For-Profit Entities induced OpenAI, Inc. to breach its agreement with Musk and engaged in independently wrongful conduct by siphoning the non-profit’s most valuable assets into their for-profit apparatus. On information and belief, the OpenAI For-Profit Entities currently employ much of the non-profit’s former staff, including Altman and Brockman, house its research and intellectual property, and have facilitated rampant self-dealing. 333. On information and belief, Altman, a major stakeholder in OpenAI’s for-profit apparatus, could have donated his money to the non-profit like Musk but instead, Altman put the majority of his investment in the moneymaking scheme made possible by the OpenAI For-Profit Entities. 334. Defendants intentionally concealed their wrongful conduct, which prevented Musk from discovering their scheme, notwithstanding his exercise of due diligence. 335. As a direct and proximate result of the OpenAI For-Profit Entities’ conduct, acts, and omissions alleged hereinabove, Musk is entitled to recover the damages he sustained and will sustain, including any income, gains, compensation, profits, and advantages obtained, received, or to be received by Defendants, or any of them, arising from the wrongful acquisition of Musk’s contributions to OpenAI, Inc., including prejudgment interest. Musk is entitled to an order requiring Defendants, jointly and severally, to render an accounting to ascertain the amount of such proceeds. 336. As a direct and proximate result of Defendants’ wrongful conduct, acts, and omissions alleged hereinabove, Musk has been damaged, and Defendants have been and will continue to be unjustly enriched, in an amount that shall be assessed at trial, but which vastly exceeds $75,000, and for which restitution and/or non-restitutionary disgorgement is appropriate. Such should include the imposition of a constructive trust; a declaration by this Court that Defendants are jointly and severally the constructive trustee(s) for the benefit of Musk; and an order that Defendants convey to Musk all of the profits, assets, property, and ill-gotten gains received or to be received by Defendants, which are traceable to Musk’s wrongfully acquired financial and other contributions to OpenAI, Inc. 337. Defendants’ wrongful conduct, acts, and omissions have proximately caused and will continue to cause Musk substantial injury and damage, much of which cannot be reasonably or adequately measured or compensated in money damages. The harm this wrongful conduct will cause to Musk is both imminent and irreparable, and the amount of damage sustained by Musk will be difficult to ascertain if such wrongful conduct is allowed to continue without restraint. Musk is entitled to an injunction during the pendency of this action, and permanently enjoining Defendants, their officers, agents, and employees, and all persons acting in concert with them, from engaging in such further tortious conduct. 338. Defendants’ wrongful conduct constitutes oppression, fraud, and/or malice under Cal. Civ. Code § 3294, entitling Musk to an award of punitive damages appropriate to punish or set an example of Defendants in an amount to be determined at trial. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 05, 2024, deadline.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 05, 2024, deadline.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. deadline.com

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