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A SHORT CHRONICLE

The Chronology of Ancient Kingdoms Amendedby Isaac Newton, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . The INTRODUCTION The INTRODUCTION. The Greek Antiquities are full of Poetical Fictions, because the Greeks wrote nothing in Prose, before the Conquest of Asia by Cyrus the Persian. Then Pherecydes Scyrius and Cadmus Milesius introduced the writing in Prose. Pherecydes Atheniensis, about the end of the Reign of Darius Hystaspis, wrote of Antiquities, and digested his work by Genealogies, and was reckoned one of the best Genealogers. Epimenides the Historian proceeded also by Genealogies; and Hellanicus, who was twelve years older than Herodotus, digested his History by the Ages or Successions of the Priestesses of Juno Argiva. Others digested theirs by the Kings of the Lacedæmonians, or Archons of Athens. Hippias the Elean, about thirty years before the fall of the Persian Empire, published a breviary or list of the Olympic Victors; and about ten years before the fall thereof, Ephorus the disciple of Isocrates formed a Chronological History of Greece, beginning with the return of the Heraclides into Peloponnesus, and ending with the siege of Perinthus, in the twentieth year of Philip the father of Alexander the great: But he digested things by Generations, and the reckoning by Olympiads was not yet in use, nor doth it appear that the Reigns of Kings were yet set down by numbers of years. The Arundelian marbles were composed sixty years after the death of Alexander the great (An. 4. Olymp. 128.) and yet mention not the Olympiads: But in the next Olympiad, Timæus Siculus published an history in several books down to his own times, according to the Olympiads, comparing the Ephori, the Kings of Sparta, the Archons of Athens, and the Priestesses of Argos, with the Olympic Victors, so as to make the Olympiads, and the Genealogies and Successions of Kings, Archons, and Priestesses, and poetical histories suit with one another, according to the best of his judgment. And where he left off, Polybius began and carried on the history. So then a little after the death of Alexander the great, they began to set down the Generations, Reigns and Successions, in numbers of years, and by putting Reigns and Successions equipollent to Generations, and three Generations to an hundred or an hundred and twenty years (as appears by their Chronology) they have made the Antiquities of Greece three or four hundred years older than the truth. And this was the original of the Technical Chronology of the Greeks. Eratosthenes wrote about an hundred years after the death of Alexander the great: He was followed by Apollodorus, and these two have been followed ever since by Chronologers. But how uncertain their Chronology is, and how doubtful it was reputed by the Greeks of those times, may be understood by these passages of Plutarch. Some reckon, saith he, Lycurgus contemporary to Iphitus, and to have been his companion in ordering the Olympic festivals: amongst whom was Aristotle the Philosopher, arguing from the Olympic Disc, which had the name of Lycurgus upon it. Others supputing the times by the succession of the Kings of the Lacedæmonians, as Eratosthenes and Apollodorus, affirm that he was not a few years older than the first Olympiad. First Aristotle and some others made him as old as the first Olympiad; then Eratosthenes, Apollodorus, and some others made him above an hundred years older: and in another place Plutarch tells us: The congress of Solon with Croesus, some think they can confute by Chronology. But an history so illustrious, and verified by so many witnesses, and (which is more) so agreeable to the manners of Solon, and so worthy of the greatness of his mind and of his wisdom, I cannot persuade my self to reject because of some Chronological Canons, as they call them: which hundreds of authors correcting, have not yet been able to constitute any thing certain, in which they could agree among themselves, about repugnancies. It seems the Chronologers had made the Legislature of Solon too ancient to consist with that Congress. For reconciling such repugnancies, Chronologers have sometimes doubled the persons of men. So when the Poets had changed Io the daughter of Inachus into the Egyptian Isis, Chronologers made her husband Osiris or Bacchus and his mistress Ariadne as old as Io, and so feigned that there were two Ariadnes, one the mistress of Bacchus, and the other the mistress of Theseus, and two Minos's their fathers, and a younger Io the daughter of Jasus, writing Jasus corruptly for Inachus. And so they have made two Pandions, and two Erechtheus's, giving the name of Erechthonius to the first; Homer calls the first, Erechtheus: and by such corruptions they have exceedingly perplexed Ancient History. And as for the Chronology of the Latines, that is still more uncertain. Plutarch represents great uncertainties in the Originals of Rome: and so doth Servius. The old records of the Latines were burnt by the Gauls, sixty and four years before the death of Alexander the great; and Quintus Fabius Pictor, the oldest historian of the Latines, lived an hundred years later than that King. In Sacred History, the Assyrian Empire began with Pul and Tiglathpilaser, and lasted about 170 years. And accordingly Herodotus hath made Semiramis only five generations, or about 166 years older than Nitocris, the mother of the last King of Babylon. But Ctesias hath made Semiramis 1500 years older than Nitocris, and feigned a long series of Kings of Assyria, whose names are not Assyrian, nor have any affinity with the Assyrian names in Scripture. The Priests of Egypt told Herodotus, that Menes built Memphis and the sumptuous temple of Vulcan, in that City: and that Rhampsinitus, Mœris, Asychis and Psammiticus added magnificent porticos to that temple. And it is not likely that Memphis could be famous, before Homer's days who doth not mention it, or that a temple could be above two or three hundred years in building. The Reign of Psammiticus began about 655 years before Christ, and I place the founding of this temple by Menes about 257 years earlier: but the Priests of Egypt had so magnified their Antiquities before the days of Herodotus, as to tell him that from Menes to Mœris (who reigned 200 years before Psammiticus) there were 330 Kings, whose Reigns took up as many Ages, that is eleven thousand years, and had filled up the interval with feigned Kings, who had done nothing. And before the days of Diodorus Siculus they had raised their Antiquities so much higher, as to place six, eight, or ten new Reigns of Kings between those Kings, whom they had represented to Herodotus to succeed one another immediately. In the Kingdom of Sicyon, Chronologers have split Apis Epaphus or Epopeus into two Kings, whom they call Apis and Epopeus, and between them have inserted eleven or twelve feigned names of Kings who did nothing, and thereby they have made its Founder Ægialeus, three hundred years older than his brother Phoroneus. Some have made the Kings of Germany as old as the Flood: and yet before the use of letters, the names and actions of men could scarce be remembred above eighty or an hundred years after their deaths: and therefore I admit no Chronology of things done in Europe, above eighty years before Cadmus brought letters into Europe; none, of things done in Germany, before the rise of the Roman Empire. Now since Eratosthenes and Apollodorus computed the times by the Reigns of the Kings of Sparta, and (as appears by their Chronology still followed) have made the seventeen Reigns of these Kings in both Races, between the Return of the Heraclides into Peloponnesus and the Battel of Thermopylæ, take up 622 years, which is after the rate of 36½ years to a Reign, and yet a Race of seventeen Kings of that length is no where to be met with in all true History, and Kings at a moderate reckoning Reign but 18 or 20 years a-piece one with another: I have stated the time of the return of the Heraclides by the last way of reckoning, placing it about 340 years before the Battel of Thermopylæ. And making the Taking of Troy eighty years older than that Return, according to Thucydides, and the Argonautic Expedition a Generation older than the Trojan War, and the Wars of Sesostris in Thrace and death of Ino the daughter of Cadmus a Generation older than that Expedition: I have drawn up the following Chronological Table, so as to make Chronology suit with the Course of Nature, with Astronomy, with Sacred History, with Herodotus the Father of History, and with it self; without the many repugnancies complained of by Plutarch. I do not pretend to be exact to a year: there may be Errors of five or ten years, and sometimes twenty, and not much above. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Isaac Newton (2005). Chronology of Ancient Kingdoms Amended. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, https://www.gutenberg.org/cache/epub/15784/pg15784-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

The INTRODUCTION

The Essays of Adam Smith: Part VII, Section II, Chapter I

Lead image via PopSugar Impulsive purchases are usually made at the shelf-edge, usually while browsing which makes up about 76 percent of consumers. The use of shelf-edge placement is a classic marketing tool usually used in retail. Paper printed labels have been the staple in most retail stores, providing information on the price of goods. However, a , a portion of this from the retail industry causing the ever-growing problems of deforestation. bout 1 billion trees worth of paper are generally thrown out each year Digital technology has entered this contemporary society, readily integrating itself in the majority of the industry especially the fashion and retail industry to connect the best of both the online and offline shopping scene. Shelf-edge technology is one of the few up-and-coming technologies that could change the whole experience of shopping. Digital Transition As of now, the most to gain from shelf-edge technology is mainly grocers and retail as large stores spend a huge budget and time in changing the paper tags manually, between 5 to 10 million labels a week for huge retail stores. Taking the next step with digital transition could easily solve this problem. Retailers understand the pros entailed in undergoing a digital transition as it increases . Aside from this, about 55% of retailers focus on general industry price change which is important in the industry. price sensitivity amongst customers (57%) and promotional intensity among competitors (41%) Image via Elle What are Digital Tags? Digital tags from edge technology are basically electronic shelf labels that are able to showcase the price per row. Allowing more opportunities for retailers to become more sensitive to pricing strategies. Enabling brands to create different campaigns and strategies that could help with both internal and external problems. Enabling brands to change pricing of seasonal collections such as Zara to certain offers and markdowns. The ability of brands to quickly update prices into a more centralized and enterprise-like approach could easily reduce labor and operational costs. Benefits Fixed price guarantee for consumers Endless price changes protecting margins Fast reaction to price change against competitors Lower staff labor Lower operational costs Versatile promotional ePaper campaigns Employees focus more on consumers shopping experience Reduce paper waste Shelf-Life Future The growing industry of retail is not only restricted to the idea of a digital price tag as an alternative to increase operational advantage and build customer loyalty. The battery life is another factor that must be accounted in the study of digital price tags as it does not only accommodate floor automation use, but also improvement of electronic shelf labels. Digital price tags can easily help retailers and businesses provide a more centralized experience and focused experience. Allowing fashion brands to showcase a different approach of pricing that could be graphically pleasing and high-performance based. Image via UBCSustainability Sustainable Implications Edge digital shelf display technology is not only a highly performative pricing and operational strategy it is sustainable. Its provide a more interactive shopping experience according to partner Microsoft. Using renewable energy and lessening the current use of printed tags. As Microsoft has stated: Edge is designed to use a low voltage, direct current that meets the standards of emerge alliance, an organization that promotes sustainable power usage in commercial buildings. Edge sustainability goals which is cutting down 40% of electricity consumption of its entire system is pushed through the companies principles and forward-thinking strategies sustainable benefits Gif via Dazed Current Use Currently, about 16 stores are using ESL (electronic shelf labels) or Digital Tags through Edge which will steadily expand to 120 by 2022 as Microsoft has exclaimed. Although digital labels aren’t literally new, it has not been clearly adopted commercially as a whole. However, we encourage businesses and retailers to consider the possibility of its benefits, as a typical store requires about 20,000 price displays and tags.

Shelf Edge Technology: Digital Price Tags Using Renewable Energy

About a year ago I wrote about with a Raspberry Pi and a cheap sensor. Make sure to read that article first! We’ve been using this setup in our school and privately for a few years now. However, this project has one disadvantage: it is not portable, but depends on a WLAN network or a wired network connection. You actually cannot access the measurements if the Raspberry Pi and the smartphone or computer are not in the same network. That’s why some time ago we added a small screen to the Raspberry Pi so that you can read the values directly from the device. How to set up this screen for our mobile fine particulate matter sensor and which additional configurations are necessary, I would like to describe in this article. how to measure the air quality Setting up the screen for the Raspberry Pi On or AliExpress there are different displays for the Raspberry Pi. These range from ePaper screens to LCDs with touch function. We chose a with touch, that has a resolution of 320×480 pixels (model MPI3501). You can simply plug this screen onto the GPIO pins. It costs only 10–20$. It’s also nice that a 3,5″ display is about the same size like a Raspberry Pi. Amazon 3,5″ LCD If you put on the screen and start the Raspberry Pi, the screen stays white. That normal, because the driver is missing. You have to install the appropriate drivers for the display first. To do this, log in via SSH and execute the following commands: $ rm -rf LCD-show$ git clone $ chmod -R 755 LCD-show$ cd LCD-show/ https://github.com/goodtft/LCD-show.git Depending on the screen model you have to execute the appropriate command to install the drivers. In our case (for the model MPI3501): $ sudo ./LCD35-show This command installs the appropriate drivers and then restarts the Raspberry Pi. Installing the Pixel Desktop and Setting Up Autostart Here is what we want: if the Raspberry Pi boots up we want to display our small website with the measurements. So first you have to install the pixel desktop environment (if you have used Raspian Lite before) with $ sudo apt install raspberrypi-ui-mods Then the Chromium browser (we need it to display the website): $ sudo apt install chromium-browser By default, the autologin is not configured for the user “pi”. However, this is necessary so that the measured values can be displayed directly after the start. Otherwise you will only see the login screen. You can configure the autologin with the tool: raspi-config In the menu select . 3 Boot Options → B1 Desktop / CLI → B4 Desktop Autologin There is one step missing to start chromium right after bootup with our website. Create the folder : /home/pi/.config/lxsession/LXDE-pi/ $ mkdir -p /home/pi/config/lxsession/LXDE-pi/ Then create the file in this folder. autostart $ nano /home/pi/.config/lxsession/LXDE-pi/autostart and paste the following code: #@unclutter@xset s off@xset -dpms@xset s noblank # Open Chrome in full screen mode after boot@chromium-browser — incognito — kiosk http://localhost If you want to hide the mouse pointer, you have to install the package and remove the comment character at the beginning of the file: unclutter autostart $ sudo apt install unclutter Since I made a few small changes to the code last year, it is recommended to re-download the script and the files of the AQI website (see the for how to do this). original article Conclusion With the touch screen added you now have a mobile particulate matter sensor! We use it at our school to check the quality of the air in the classrooms or to do comparative measurements. With this setup, you are no longer dependent on a network connection or WLAN. You can use the small measuring station everywhere — with a power bank even independent of the power grid. Originally published at openschoolsolutions.org .

How to Build a Mobile Particulate Matter Sensor

Introduction: Data engineering has evolved into specialized fields like DataOps and VectorOps to address the challenges of managing data and vector embeddings, respectively. As large language models (LLMs) like GPT-4 emerge, managing high-dimensional data structures becomes increasingly important. LangChain, an LLM-powered application development framework, integrates with DataOps and VectorOps processes and utilizes vector databases to create data-aware, interactive applications. DataOps: Streamlining Data Management and Processes: DataOps focuses on enhancing collaboration, integration, and automation of data flows across organizations. Implementing DataOps practices promotes data accuracy, consistency, and accessibility, which are vital for data-aware applications using LLMs. Key DataOps technologies include Apache Kafka for real-time data streaming, Apache Airflow for workflow management, and Apache NiFi for data routing and transformation. References: https://medium.com/codex/5-steps-to-implementing-a-successful-dataops-practice-f553effd7367 https://www.forbes.com/sites/forbesbusinessdevelopmentcouncil/2022/01/24/how-to-implement-a-successful-dataops-strategy-and-why-it-matters/?sh=56e354d5e87c https://www.linkedin.com/pulse/dataops-practical-understanding-implementation-taaha-khan-/ VectorOps: Tackling High-Dimensional Data Challenges: VectorOps specializes in managing vector embeddings, a critical component of LLMs. Traditional databases struggle with high-dimensional data structures, whereas vector databases excel in handling them. Integrating VectorOps with DataOps processes enables efficient large-scale vector embedding management, paving the way for innovative applications using LLMs. Popular vector database technologies used in VectorOps include FAISS, Milvus, and Pinecone. References: https://thenewstack.io/vector-databases-long-term-memory-for-artificial-intelligence https://www.pinecone.io/learn/vector-database/ https://techgenix.com/rise-of-vector-databases/ LangChain: Building Data-Aware LLM Applications: LangChain facilitates the development of applications that connect to data sources, interact with their environment, and utilize the power of LLMs. It offers various modules, such as Models, Prompts, Memory, Indexes, Chains, and Agents. The Indexes module focuses on structuring documents for optimal LLM interaction and retrieval, making it an excellent candidate for VectorOps and vector database integration. LangChain supports integration with LLMs like GPT-4, BERT, and RoBERTa. """
Example of creating a chat bot with GPT-Index & LangChain.
"""

from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import sys
import os
from IPython.display import Markdown, display


def construct_index(directory_path):
    # set maximum input size
    max_input_size = 4096
    # set number of output tokens
    num_outputs = 2000
    # set maximum chunk overlap
    max_chunk_overlap = 20
    # set chunk size limit
    chunk_size_limit = 600 

    # define LLM
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs))
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
 
    documents = SimpleDirectoryReader(directory_path).load_data()
    
    index = GPTSimpleVectorIndex(
        documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
    )

    index.save_to_disk('index.json')

    return index

def ask_ai():
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    while True: 
        query = input("What do you want to ask? ")
        response = index.query(query, response_mode="compact")
        display(Markdown(f"Response: <b>{response.response}</b>")) References: https://python.langchain.com/en/latest/index.html https://github.com/hwchase17/langchain https://github.com/gkamradt/langchain-tutorials/blob/main/LangChain Cookbook Part 2 - Use Cases.ipynb Integrating DataOps, VectorOps, and LangChain: DataOps, VectorOps, and LangChain integration create powerful applications that combine efficient data management, high-dimensional data processing, and LLM capabilities. Some key advantages of this integration include: Enhanced similarity search: LangChain's integration with vector databases and VectorOps enables efficient near-neighbor matching, providing comprehensive results beyond traditional keyword-based searches. Scalability: DataOps and VectorOps processes ensure the efficient handling of large-scale vector embeddings, making the framework ideal for managing data in the era of LLMs and other complex models. """
As described here: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pinecone.html
"""

import pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Pinecone
from langchain.document_loaders import TextLoader

loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

# initialize pinecone
pinecone.init(
    api_key=PINECONE_API_KEY,  # find at app.pinecone.io
    environment=PINECONE_ENV  # next to api key in console
)

index_name = "langchain-demo"

docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)

# if you already have an index, you can load it like this
# docsearch = Pinecone.from_existing_index(index_name, embeddings)

query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)

print(docs[0].page_content) Flexibility: LangChain allows users to perform searches based on high-level descriptions rather than relying on specific keywords or metadata classifications, enabling more versatile applications. Reference: https://mattboegner.com/knowledge-retrieval-architecture-for-llms/ https://simonwillison.net/2023/Jan/13/semantic-search-answers/ https://www.codingthesmartway.com/unlocking-the-power-of-llms-with-langchain-a-beginners-guide/ https://python.langchain.com/en/latest/modules/models/llms/integrations.html Conclusion: By focusing on DataOps, VectorOps, and LangChain, developers can create advanced, data-driven applications that leverage the power of LLMs and vector embeddings. Efficient data management through DataOps, coupled with VectorOps' expertise in high-dimensional data processing, unlocks the full potential of LLMs like GPT-4, BERT, and RoBERTa. LangChain serves as the bridge between these two fields, enabling developers to build innovative applications that can revolutionize industries and reshape the future of data-driven solutions. https://www.youtube.com/watch?v=UO699Szp82M&embedable=true As the need for data-aware applications and large-scale vector embedding management grows, the synergistic integration of DataOps, VectorOps, and LangChain will play a critical role in shaping the next generation of data-driven applications. By keeping up-to-date with the latest developments in these fields and embracing cutting-edge technologies, developers can stay ahead of the curve and lead the way toward a data-centric future.

The New Data Engineering Landscape: DataOps, VectorOps, and LangChain

TL;DR; The global gaming industry, particularly casual, hyper-casual, and mobile gaming, is growing rapidly, largely driven by advancements in advertising technology (AdTech). AdTech provides tools to manage, target, and optimize advertising campaigns, improving key gaming industry KPIs like the acquisition funnel, churn rate, retention rate, engagement, conversion rate, and game monetization. KPIs help developers pinpoint and address issues in their games, with AdTech platforms offering tools for detailed analysis and targeted advertising based on player behavior. Game monetization, another crucial KPI, relies heavily on in-app purchases, subscriptions, and ads, and AdTech plays a significant role in optimizing these for maximum revenue. Average Revenue Per User (ARPU), Average Revenue Per Paying User (ARPPU), and Lifetime Value (LTV) are key metrics in the gaming industry, and AdTech helps boost these by delivering personalized ads and enhancing player engagement. With the gaming industry projected to grow exponentially, the role of AdTech in optimizing game performance and maximizing revenue generation will become increasingly important. Introduction: AdTech's Impact on the Gaming Industry The global gaming industry, specifically casual, hyper-casual, and mobile gaming, has been on a growth trajectory. Total revenue is expected to show an annual growth rate (CAGR 2022-2027) of 8.91%, resulting in a projected market volume of US$24.97bn by 2027 ( ). This surge is largely attributed to the advancement of advertising technology, or AdTech. AdTech, with its robust tools for managing, targeting, and optimizing advertising campaigns, significantly enhances gaming industry KPIs, optimizes acquisition funnels, and maximizes game monetization. Statista, 2023 Unpacking Key KPIs in the Gaming Industry Understanding the key KPIs in the gaming industry is vital for game development success. These KPIs include acquisition funnel, churn rate, retention rate, 7 & 30-day retention, player engagement, conversion rate, and game monetization metrics. The acquisition funnel, a vital concept in gaming, represents the player's journey from game discovery to active participation. AdTech, with its precise targeting and personalized ad content capabilities, supercharges this funnel, leading to a notable 47% increase (in December 2022 for iOS) in high-value user acquisition ( ). Venturebeat, 2023 Churn and retention rates are essential KPIs too. In 2021, mobile gaming apps experienced a day-one retention rate of approximately 35-40%, According to GameAnalytics, a mobile game is considered successful if its day-1 retention is above 35% ( ). These metrics, when analyzed meticulously, help game developers pinpoint and address problem areas in their games to improve player engagement. AdTech platforms offer tools for such analysis and can also elevate retention rates through targeted advertising based on player behavior and preferences, which subsequently boosts conversion rates. Udonis, 2023 Game monetization, another critical KPI, often depends on in-app purchases, subscriptions, and ads. In 2022, mobile in-app purchase revenue globally stood at a remarkable $431 billion ( ). AdTech plays a crucial role in monetizing games by optimizing ad placements, personalizing ad content, and targeting the right users, thereby enhancing overall revenue. Statista, 2023 Calculating and Boosting ARPU, ARPPU, and LTV with AdTech Average Revenue Per User (ARPU), Average Revenue Per Paying User (ARPPU), and Lifetime Value (LTV) are indispensable metrics in the gaming industry. In 2020, the global mobile gaming ARPU was estimated at $53.71 ( ). These metrics provide essential insights into the revenue generated per user or paying user and the projected revenue over their gaming lifespan. Gitnux, 2023 AdTech has the potential to elevate these metrics significantly. By delivering personalized ads, AdTech can incentivize players to make more in-app purchases, thereby boosting ARPU and ARPPU. Furthermore, AdTech can enhance a player's LTV by offering a more engaging gaming experience through relevant ads, leading to prolonged engagement and continued revenue. Conclusion: AdTech's Growing Role in the Gaming Industry In conclusion, AdTech's impact on the gaming industry is profound. It plays an instrumental role in enhancing KPIs, optimizing the acquisition funnel, and bolstering game monetization strategies. With the total revenue in the Casual Games market projected to reach US$15.51bn in 2022 ( ), AdTech's role in fine-tuning game performance and maximizing revenue generation will undoubtedly become increasingly vital. Statista, 2023 Also appears . here

AdTech: The Game Changer in Enhancing KPIs and Monetization for the Mobile Gaming Industry

I Interviewed Socrates_GPT: Here's How It Went

আমি সক্রেটিস_GPT-এর সাক্ষাত্কার নিয়েছি: এখানে কিভাবে এটি গেল

Ich habe Socrates_GPT interviewt: So lief es

Entrevisté a Socrates_GPT: Así es como fue

J'ai interviewé Socrates_GPT : voici comment ça s'est passé

मैंने सुकरात_जीपीटी का साक्षात्कार लिया: यहां बताया गया है कि यह कैसे हुआ

Socrates_GPT にインタビューしました: これがその方法です

소크라테스_GPT를 인터뷰했습니다: 진행 과정은 다음과 같습니다.

Eu entrevistei Socrates_GPT: Veja como foi

Socrates_GPT ile Röportaj Yaptım: İşte Nasıl Oldu?

Tôi đã phỏng vấn Socrates_GPT: Đây là cách nó đã diễn ra

我采访了 Socrates_GPT：事情是这样进行的

This opinion writeup is after Binance’s new report “A New Era for Bitcoin?” TL;DR As a passionate crypto enthusiast, I am excited to witness the recent resurgence of Bitcoin. The emergence of Ordinals and Inscriptions, commonly known as Bitcoin NFTs, have sparked renewed interest in the ecosystem. In addition, the network is growing stronger with the increase in on-chain metrics and the development of Layer-2 solutions, such as Stacks, Rootstock, and Rollkit. As we enter a new era for the cryptocurrency, it is essential to keep an eye on scalability, the fully-fledged smart contract market, and the upcoming Bitcoin halving, which are key themes to watch. Opinion about the matter Although Layer-1 blockchains that support smart contracts are currently popular, I believe that Bitcoin will remain the dominant cryptocurrency. The recent emergence of Inscriptions and Ordinals is just one example of the many ways in which the Bitcoin network is evolving to meet the needs of its users. These new developments have sparked renewed excitement in the ecosystem, demonstrating that Bitcoin is not only evolving but thriving. In addition, Layer-2 solutions are addressing scalability concerns and creating new opportunities for developers to build upon the Bitcoin network. All of these factors combined, along with the upcoming Bitcoin halving in March 2024, promise a bright future for the world’s first cryptocurrency. Importance of NFTs in Bitcoin As a developer, I find Inscriptions, the “Bitcoin NFTs,” crucial to the evolution of the cryptocurrency. Inscriptions are fully on-chain, immutable, and ordered, making them a unique alternative to traditional NFTs. Unlike other NFTs that often store metadata off-chain, Inscriptions are fully integrated into the Bitcoin network, paving the way for increased on-chain metrics, transaction fees, and developer innovation. The emergence of Inscriptions has brought attention back to the Bitcoin ecosystem and provided new opportunities for developers to create exciting and innovative projects. Conclusion In conclusion, the convergence of smart contracts, Layer-2 solutions, and the continued evolution of the Bitcoin ecosystem indicates a bright future for the world’s first cryptocurrency. The importance of NFTs in the form of Inscriptions, along with the upcoming halving event, highlights the limitless potential of Bitcoin. With sustained innovation and growth, I am confident that Bitcoin will maintain its dominance in the cryptocurrency market and continue to evolve to meet the needs of its users. In this new era for the cryptocurrency, we should expect to see many exciting developments and opportunities emerge, making Bitcoin an even more compelling investment for the future.

The Dawn of a New Era: Bitcoin's Evolution and the Emergence of Smart Contracts

is the industry-standard protocol for , enabling seamless communication between and or Ad Exchanges. OpenRTB real-time bidding (RTB) in digital advertising Demand-Side Platforms (DSPs) Supply-Side Platforms (SSPs) In recent years, the adoption of blockchain technology in the advertising space has introduced innovative, decentralized platforms that can benefit from integrating OpenRTB. In this blog post, we will explore how OpenRTB can be used in a crypto-native advertising platform, where bid requests and responses are managed through on-chain smart contracts, referred to as the On-Chain Ad Protocol. Overview of Crypto-Native Advertising Platform A crypto-native advertising platform leverages blockchain technology to facilitate decentralized, transparent, and secure transactions between advertisers, publishers, and users. Key components of a crypto-native advertising platform include: On-Chain Ad Protocol: A set of on-chain smart contracts for handling ad bookings, validation, reward distribution, and proof of completion. Proof of Completion: A mechanism within the On-Chain Ad Protocol that allows users to prove they have completed their engagement or action, triggering automatic attribution or reward distribution. Decentralized Data Management Platforms (DMPs) for user segmentation and targeting. Tokenized ad inventory and rewards. Integration with decentralized identity solutions for privacy-preserving user tracking. Integrating OpenRTB with a Crypto-Native Advertising Platform The integration of OpenRTB with a crypto-native advertising platform requires several key steps, including: Custom OpenRTB Extensions To support the unique aspects of a crypto-native advertising platform, custom extensions can be added to the standard OpenRTB bid request and response objects. These extensions can include information related to tokenized ad inventory, On-Chain Ad Protocol, user segmentation data from decentralized DMPs, and proof of completion mechanisms. These custom fields will be added as extensions using the "ext" field. Bid Request Extension: {
  "id": "12345",
  "imp": [
    {
      "id": "1",
      "banner": {
        "w": 300,
        "h": 250
      },
      "ext": {
        "on_chain_ad_protocol": {
          "user_wallet_address": "0x1234567890abcdef1234567890abcdef12345678",
          "token_type": "ERC20",
          "protocol": "0x9876543210abcdef1234567890abcde987654321",
          "mechanic_id": "MECHA123"
        }
      }
    }
  ],
  ...
} In the bid request, we include the user's wallet address, the token type (e.g., ERC20), and the for which the user is engaging. mechanic_id This information will help the advertiser target the user and link the right ad content to the target mechanic validation. Bid Response Extension: {
  "id": "12345",
  "bidid": "abc123",
  "seatbid": [
    {
      "bid": [
        {
          "id": "1",
          "impid": "1",
          "price": 2.50,
          "ext": {
            "on_chain_ad_protocol": {
              "reward_token_contract_address": "0xabcdef1234567890abcdef1234567890abcdef12",
              "reward_amount": 50,
              "engagement_rules_url": "https://example.com/rules"
            }
          }
        }
      ]
    }
  ],
  ...
} In the bid response, we include the reward token contract address, the reward amount, and a URL containing the rules. This data will be used by the publisher to display the ad to the user and inform them about the rewards they can earn for successful completion. By adding these custom extensions to the OpenRTB bid request and bid response objects, we can effectively integrate the On-Chain Ad Protocol's unique features into the existing OpenRTB framework. This enables seamless interaction between the crypto-native advertising platform and the traditional programmatic advertising ecosystem. Oracle AdServer To bridge the gap between the off-chain OpenRTB ecosystem and the On-Chain Ad Protocol, an AdServer Oracle can be employed. This server-side component is responsible for translating OpenRTB bid requests and responses into on-chain transactions and vice versa. The AdServer Oracle ensures seamless communication between OpenRTB-enabled DSPs/SSPs and the crypto-native advertising platform's On-Chain Ad Protocol. Suppose a publisher wants to display an ad with a quest engagement from the On-Chain Ad Protocol. Here's a step-by-step technical illustration of how the AdServer Oracle comes into play: Publisher sends ad request to AdServer Oracle: When a user visits the publisher's site or app, the publisher sends an ad request to the AdServer Oracle, including the user's session and wallet information: {
  "user_session": "session123",
  "wallet_address": "0x1234567890abcdef1234567890abcdef12345678"
} AdServer Oracle fetches on-chain data: The AdServer Oracle connects to the blockchain and fetches the relevant on-chain data, such as available ad inventory, quest details, and reward token information. This is done by interacting with the On-Chain Ad Protocol's smart contracts. AdServer Oracle communicates with off-chain components: The AdServer Oracle communicates with off-chain components, such as the Demand-Side Platform (DSP), Supply-Side Platform (SSP), and Data Management Platform (DMP), to gather user performance data, segments, and ad inventory. AdServer Oracle prepares the ad manifest: Based on the on-chain and off-chain data, the AdServer Oracle prepares an ad manifest containing the ad content, engagement details, and reward token information: {
  "ad_content": "https://example.com/ad_creative",
  "quest_details": {
    "mechanic_id": "MECHA123",
    "engagement_rules_url": "https://example.com/rules"
  },
  "reward_token": {
    "contract_address": "0xabcdef1234567890abcdef1234567890abcdef12",
    "amount": 50
  }
} AdServer Oracle sends the ad manifest to the publisher: The AdServer Oracle sends the ad manifest to the publisher, which then displays the ad content and the quest engagement to the user. The user can interact with the mechanics, and upon successful completion, the user's wallet will receive the reward tokens as specified in the manifest. By utilizing the AdServer Oracle, the crypto-native advertising platform can efficiently bridge the gap between on-chain and off-chain components, allowing for seamless integration and interaction between the various advertising ecosystem participants. Integrating On-Chain Ad Protocol with OpenRTB The On-Chain Ad Protocol should be designed to handle OpenRTB-compatible bid requests, validation, reward distribution, and proof of completion. By updating the On-Chain Ad Protocol to support OpenRTB-related metrics and analytics, the crypto-native advertising platform can effectively measure and optimize campaign performance in line with industry-standard practices. Let’s focus on the role of the Supply-Side Platform (SSP) and Demand-Side Platform (DSP) in this integration. SSP Integration: The SSP will need to include the On-Chain Ad Protocol-specific information in the bid request, as well as integrate with the AdServe partner SDK to retrieve on-chain ad inventory. When a user visits a publisher's site, the SSP will create a bid request that contains the necessary information about the user, such as their wallet address, token type, the address of the on-chain protocol contract, and . mechanic_id This can be included in the field of the bid request object: ext "ext": {
  "on_chain_ad_protocol": {
    "user_wallet_address": "0x1234567890abcdef1234567890abcdef12345678",
    "token_type": "ERC20",
    "protocol": "0x9876543210abcdef1234567890abcde987654321",
    "mechanic_id": "MECHA123"
  }
} The SSP will then send the bid request to the connected DSPs, which will use this information to determine the best ad content for the user based on the On-Chain Ad Protocol. DSP Integration: The DSP will need to analyze the bid request and decide which ad content is most suitable for the user based on the On-Chain Ad Protocol information provided. It will also need to include the relevant reward and quest information in the bid response. Upon receiving the bid request, the DSP will analyze the user's wallet address, token type, and mechanics to determine the best ad content to serve. It will then create a bid response that includes the necessary information about the reward token contract address, reward amount, and rules URL: "ext": {
  "on_chain_ad_protocol": {
    "reward_token_contract_address": "0xabcdef1234567890abcdef1234567890abcdef12",
    "reward_amount": 50,
    "engagement_rules_url": "https://example.com/rules"
  }
} The DSP will send the bid response back to the SSP, which will then forward it to the publisher for displaying the ad content and engaging the user with the quest mechanism. By integrating the On-Chain Ad Protocol with OpenRTB in this manner, SSPs and DSPs can effectively bridge the gap between the crypto-native advertising platform and the traditional programmatic advertising ecosystem. This enables seamless interactions between the various participants, including publishers, advertisers, and users, while leveraging the unique features of the On-Chain Ad Protocol. Enhanced Publisher Integration Publishers play a crucial role in the success of the crypto-native advertising platform. To fully leverage the platform's innovative features and ensure seamless integration with the ecosystem, publishers need to make several updates and enhancements to their existing infrastructure. Key aspects of this enhanced publisher integration include: Integration Type Description Benefits AdServe Partner SDK Implementing the AdServe partner's SDK designed to support displaying ad content and interacting with the On-Chain Ad Protocol. Seamless display of ads, improved user engagement, and streamlined interaction with the platform's unique features. Decentralized Identity Solutions Incorporating infrastructure and tools that support privacy-preserving identity solutions for user tracking. Enhanced user privacy, targeted ad campaigns without compromising personal information, and increased trust in the platform. Proof of Completion Mechanism Integrating the platform's proof of completion mechanism that allows users to demonstrate successful engagements or actions. Automated reward distribution, streamlined processes, and improved user satisfaction. Wallet and Session Information Including user session and wallet information in ad requests to track user engagement, reward distribution, and other key metrics. More effective and optimized ad campaigns, better insights for advertisers, and a higher return on investment. User Experience Optimization Focusing on user experience improvements, such as non-intrusive ad displays, smooth user interactions, and streamlined reward claiming processes. Enhanced user satisfaction, increased user engagement, and improved ad performance. By implementing these integrations, publishers, advertisers, and users can all experience the full potential of the platform and contribute to a more efficient and effective advertising ecosystem. Benefits of Integrating OpenRTB with a Crypto-Native Advertising Platform By integrating OpenRTB with a crypto-native advertising platform, the benefits of both systems can be realized: Enhanced Transparency: Blockchain technology introduces transparency and trust in the advertising ecosystem, reducing the risk of fraud and manipulation. Improved Targeting: Decentralized DMPs provide user segmentation intelligence, allowing for highly targeted ad campaigns that take into account user preferences and behavior. Secure and Anonymous Reward Distribution: Tokenized rewards and decentralized identity solutions enable secure and anonymous reward distribution without requiring users to disclose personal information. Automated Attribution and Rewarding: The Proof of Completion mechanism ensures that users are automatically rewarded upon completing an engagement Why This Crypto-Native Advertising Platform Model is Novel The crypto-native advertising platform model stands out due to its innovative approach to the digital advertising landscape. By combining the strengths of OpenRTB and blockchain technology, it creates a unique ecosystem that addresses many of the challenges faced by traditional advertising systems. The novel aspects of this model include: Decentralization: The platform leverages blockchain technology to decentralize the advertising process. This allows for greater transparency, reduced risk of fraud, and more equitable distribution of rewards among participants in the ecosystem. On-Chain Ad Protocol: The On-Chain Ad Protocol is a set of smart contracts that handle ad bookings, validation, reward distribution, and proof of completion. It operates on the blockchain, ensuring that all transactions are transparent, secure, and tamper-proof. Proof of Completion: This mechanism, embedded within the On-Chain Ad Protocol, enables users to prove that they have completed their engagements or actions. As a result, the platform can automatically trigger attribution or reward distribution, eliminating the need for manual tracking or intervention. Tokenized Ad Inventory and Rewards: The platform uses cryptographic tokens to represent ad inventory and rewards. This approach allows for easy tracking, transfer, and exchange of value within the ecosystem, while ensuring that participants maintain control over their assets. Decentralized Data Management Platforms (DMPs): This model leverages decentralized DMPs for user segmentation and targeting. These DMPs maintain user privacy while providing valuable insights that help advertisers deliver highly relevant and targeted ad campaigns. Privacy-Preserving User Tracking: The platform integrates with decentralized identity solutions to protect user privacy. This ensures that users can engage with ads and receive rewards without disclosing personal information. Seamless Integration with OpenRTB: By integrating the well-established OpenRTB protocol, this novel model allows the crypto-native advertising platform to interact with existing DSPs and SSPs, making it easier for advertisers and publishers to adopt the new technology. In summary, the crypto-native advertising platform model is novel due to its unique combination of blockchain technology, on-chain smart contracts, tokenization, decentralized data management, and privacy-preserving user tracking. This innovative approach addresses many of the challenges faced by traditional advertising systems, making it a promising solution for the future of digital advertising. Conclusion: A New Era of Digital Advertising The crypto-native advertising platform represents a ground breaking shift in the digital advertising landscape. By harnessing the power of blockchain technology, decentralized identity solutions, and the OpenRTB protocol, this novel model addresses many of the challenges faced by traditional advertising systems. The platform ensures greater transparency, enhanced user privacy, and improved targeting capabilities, all while offering a streamlined and rewarding experience for users, publishers, and advertisers alike. As more publishers embrace this innovative model and integrate its features into their platforms, we can expect to see a more equitable and efficient digital advertising ecosystem emerge. This will not only benefit the various stakeholders in the industry but also elevate the overall user experience. By fostering a decentralized, secure, and user-centric environment, the crypto-native advertising platform ushers in a new era of digital advertising that is primed to revolutionize the way we engage with online content and interact with brands. As the industry continues to evolve, this innovative approach will undoubtedly play a significant role in shaping the future of advertising and creating lasting value for all participants in the ecosystem. Also published here.

Exploring the Technical Integration of OpenRTB with a Crypto-Native Advertising Platform

The digital advertising industry is undergoing a significant transformation, driven by emerging technologies like blockchain and the increasing demand for interactive ad campaigns. In this article, we’ll explore the architecture of a decentralized that leverages blockchain technology to deliver interactive ads with rewards in the form of ERC-20 tokens and NFTs. We’ll dive deep into the technical details and provide examples of Python and Solidity code to help you understand the inner workings of this next-generation DSP. Demand Side Platform (DSP) Smart Contract Interaction The foundation of our decentralized DSP is a set of smart contracts that manage ad campaigns, user rewards, and . NFT minting Here’s a simple example of a Solidity smart contract for managing ad campaigns: pragma solidity ^0.8.0;

contract AdCampaign {
    struct Campaign {
        uint256 id;
        address advertiser;
        string ipfsHash;
        uint256 budget;
        uint256 reward;
        bool isActive;
    }

    uint256 private campaignCounter;
    mapping(uint256 => Campaign) public campaigns;

    function createCampaign(string memory ipfsHash, uint256 budget, uint256 reward) public {
        campaignCounter++;
        campaigns[campaignCounter] = Campaign(campaignCounter, msg.sender, ipfsHash, budget, reward, true);
    }

    function toggleCampaignStatus(uint256 campaignId) public {
        require(campaigns[campaignId].advertiser == msg.sender, "Not the owner of the campaign");
        campaigns[campaignId].isActive = !campaigns[campaignId].isActive;
    }
} To interact with these smart contracts using Python, we’ll use the popular . Web3.py library Here’s an example of how to create a new ad campaign using the smart contract above: from web3 import Web3

# Connect to the Ethereum node
w3 = Web3(Web3.HTTPProvider('<YOUR_ETHEREUM_NODE_URL>'))

# Load the contract ABI and address
contract_abi = [...]  # Contract ABI goes here
contract_address = '0x123...'

# Initialize the contract object
contract = w3.eth.contract(address=contract_address, abi=contract_abi)

# Define the advertiser's account
advertiser_account = w3.eth.account.privateKeyToAccount('<YOUR_PRIVATE_KEY>')

# Create a new ad campaign
ipfs_hash = 'QmWATWQ7fVPP2EFGu71UkfnqhYXDYH566qy47CnJDgvs8u'
budget = 1000
reward = 10

# Estimate gas
gas_estimate = contract.functions.createCampaign(ipfs_hash, budget, reward).estimateGas({'from': advertiser_account.address})

# Send the transaction
transaction = contract.functions.createCampaign(ipfs_hash, budget, reward).buildTransaction({
    'from': advertiser_account.address,
    'gas': gas_estimate,
    'gasPrice': w3.eth.gasPrice,
    'nonce': w3.eth.getTransactionCount(advertiser_account.address)
})

signed_transaction = advertiser_account.signTransaction(transaction)
transaction_hash = w3.eth.sendRawTransaction(signed_transaction.rawTransaction) Decentralized Storage To store ad content and metadata, we’ll use , a decentralized storage system. IPFS (InterPlanetary File System) In Python, we can use the library to add and retrieve content from IPFS: ipfshttpclient import ipfshttpclient

client = ipfshttpclient.connect('/ip4/127.0.0.1/tcp/5001/http')

# Add a file to IPFS
with open('ad_content.json', 'rb') as f:
result = client.add(f)
ipfs_hash = result['Hash']

# Retrieve content from IPFS
content = client.cat(ipfs_hash) User Targeting and Data Integration For user targeting, we’ll need to access both on-chain and off-chain data. To interact with on-chain data, we can use Web3.py, as demonstrated earlier. For off-chain data, we can use a decentralized oracle solution like Chainlink. Here’s an example of a Solidity contract that interacts with Chainlink to fetch off-chain data: pragma solidity ^0.8.0;

import "@chainlink/contracts/src/v0.8/ChainlinkClient.sol";

contract AdDataOracle is ChainlinkClient {
    uint256 public adPerformance;

    address private oracle;
    bytes32 private jobId;
    uint256 private fee;

    event AdPerformanceUpdated(uint256 performance);

    constructor(address _oracle, bytes32 _jobId, uint256 _fee) {
        setPublicChainlinkToken();
        oracle = _oracle;
        jobId = _jobId;
        fee = _fee;
    }

    function requestAdPerformanceData() public {
        Chainlink.Request memory req = buildChainlinkRequest(jobId, address(this), this.fulfill.selector);
        req.add("get", "https://example.com/api/ad_performance");
        req.add("path", "performance");
        sendChainlinkRequestTo(oracle, req, fee);
    }

    function fulfill(bytes32 _requestId, uint256 _performance) public recordChainlinkFulfillment(_requestId) {
        adPerformance = _performance;
        emit AdPerformanceUpdated(_performance);
    }
} Ad Serving and Publisher Integration For ad serving, we’ll need a JavaScript SDK or API that publishers can integrate into their websites or apps. This SDK will interact with the smart contracts and IPFS to retrieve and display ad content. Here’s a simplified example of how this might look in JavaScript: // Load Web3
const Web3 = require('web3');

// Connect to Ethereum node
const web3 = new Web3('<YOUR_ETHEREUM_NODE_URL>');

// Load the contract ABI and address
const contractABI = [...] // Contract ABI goes here
const contractAddress = '0x123...';

// Initialize the contract object
const contract = new web3.eth.Contract(contractABI, contractAddress);

// Fetch ad campaign details
const campaignId = 1; // Example campaign ID
const campaign = await contract.methods.campaigns(campaignId).call();

// Load ad content from IPFS
const ipfs = require('ipfs-http-client');
const ipfsClient = ipfs.create();

const adContent = JSON.parse(await ipfsClient.cat(campaign.ipfsHash));

// Render ad content on the publisher's website or app
// (actual rendering logic will depend on the specific platform and ad format) Conclusion In this article, we’ve explored the technical architecture of a decentralized DSP that handles interactive ad campaigns and rewards users with ERC-20 tokens and NFTs. By leveraging blockchain technology, decentralized storage, and oracles, we can build a next-generation ad platform that offers greater transparency, security, and user engagement. While the examples provided are simplified, they should give you a solid foundation to start building your own decentralized DSP. Lead image by on Unsplash Adem AY Also published here.

How to Build a Decentralized DSP for Interactive Ad Campaigns

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) D.Sinclair, Imense Ltd, and email: david@imense.com;
(2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. Table of Links Abstract and Introduction
Interrogating the LLM with an Emotion Eliciting Tail Prompt
PCA analysis of the Emotion of Amazon reviews
Future Work
Conclusions
Acknowledgments and References 2. Interrogating the LLM with an Emotion Eliciting Tail Prompt In this work we used Facebook’s open source LlaMa2 7 billion weight LLM as the core engine [2]. It was necessary to use a LLM that allowed access to raw token probabilities after a prompt. The model ran on a Mac Studio with 32 gigabytes of RAM. With this combination of hardware and model it took 2 minutes to compute the probabilities of the emotion descriptors in the emotion dictionary given below. 2.1. The Emotion Dictionary The English language is blessed with many words and an extensive literature providing examples of the usage of these words in appropriate contexts. For the purposes of LLMs, it is the context of a word that conveys it’s meaning. A reader will infer the meaning of an unfamiliar word through the context they find the word used in, provided they understand the context. As an example, ‘The shotgun discombobulated the rabbit.’ shows how meaning can be moderated by context. The context created by a tail prompt will favour an associated class of words. For the experiment detailed in this paper the following tail prompt was used to elicit emotion descriptors, ’Reading this makes me feel’. It is likely that specific emotion eliciting tail prompts will favour specific sub classes of descriptor but studying this is beyond the scope of this paper The following words were chosen to provide a broad sample of emotion descriptors. acceptance, admiration, adoration, affection, afraid, agitation, agony, aggressive, alarm, alarmed, alienation, amazement, ambivalence, amusement, anger, anguish, annoyed anticipating, anxious, apathy, apprehension, arrogant, assertive, astonished, attentiveness, attraction, aversion, awe, baffled, bewildered, bitter, bitter sweetness, bliss, bored, brazen, brooding, calm, carefree, careless, caring, charity, cheeky, cheerfulness, claustrophobic, coercive, comfortable, confident, confusion, contempt, content, courage, cowardly, cruelty, curiosity, cynicism, dazed, dejection, delighted, demoralized, depressed, desire, despair, determined, disappointment, disbelief, discombobulated, discomfort, discontentment, disgruntled, disgust, disheartened, dislike, dismay, disoriented, dispirited, displeasure, distraction, distress, disturbed, dominant, doubt, dread, driven, dumbstruck, eagerness, ecstasy, elation, embarrassment, empathy, enchanted, enjoyment, enlightened, ennui, enthusiasm, envy, epiphany, euphoria, exasperated, excitement, expectancy, fascination, fear, flakey, focused, fondness, friendliness, fright, frustrated, fury, glee, gloomy, glumness, gratitude, greed, grief, grouchiness, grumpiness, guilt, happiness, hate, hatred, helpless, homesickness, hope, hopeless, horrified, hospitable, humiliation, humility, hurt, hysteria, idleness, impatient, indifference, indignant, infatuation, infuriated, insecurity, insightful, insulted, interest, intrigued, irritated, isolated, jealousy, joviality, joy, jubilation, kind, lazy, liking, loathing, lonely, longing, loopy, love, lust, mad, melancholy, miserable, miserliness, mixed up, modesty, moody, mortified, mystified, nasty, nauseated, negative, neglect, nervous, nostalgic, numb, obstinate, offended, optimistic, outrage, overwhelmed, panicked, paranoid, passion, patience, pensiveness, perplexed, persevering, pessimism, pity, pleased, pleasure, politeness, positive, possessive, powerless, pride, puzzled, rage, rash, rattled, regret, rejected, relaxed, relieved, reluctant, remorse, resentment, resignation, restlessness, revulsion, ruthless, sadness, satisfaction, scared, schadenfreude, scorn, self-caring, self-compassionate, self-confident, self-conscious, self-critical, self-loathing, self-motivated, self-pity, self-respecting, self-understanding, sentimentality, serenity, shame, shameless, shocked, smug, sorrow, spite, stressed, strong, stubborn, stuck, submissive, suffering, sullenness, surprise, suspense, suspicious, sympathy, tenderness, tension, terror, thankfulness, thrilled, tired, tolerance, torment, triumphant, troubled, trust, uncertainty, undermined, uneasiness, unhappy, unnerved, unsettled, unsure, upset, vengeful, vicious, vigilance, vulnerable, weak, woe, worried, worthy, wrath. This set of words is not intended to be complete or definitive in any way. Using the tail prompt without restricting the return to emotion descriptors elicits general waffle responses that are not straightforward to extract sentiment form. 2.1.1. Estimating the emotion probability vector LlaMa2 [2] has been released in such a way as to allow developers to access estimated token weights returned in response to a prompt. LlaMa2 has an internal vocabulary size of roughly 30,000 tokens. This means that when LlaMa 2 estimates the probability of the next token in a sequence the probability vector will have 30,000 elements. Some of the words in the emotion descriptor list are made up of more than one token in which case forward conditional probabilities are used. Figure 2.1.1 shows the scaled probability distribution over words from the emotion dictionary elicited by the tail prompt for the Amazon review text, ‘I read a lot of negative reviews about the Fitbit inspire 2, I took a chance and hoped the one I ordered would be one of the great ones that worked. Unfortunately that was not the case. I unpacked it, charged it, downloaded the app. I took a walk with it on before the sun went down. I have the Google Fit app on my phone that tracks my steps also. The phone was in my jeans pocket. When I got home I compared the two, Google Fit said 4,458 steps, Fitbit said 1,168. Apparently Fitbit works with wrist motion which I don’t have while pushing a walker around the neighborhood. I downloaded the manual and noticed you can put it on a clip (that wasn’t included). That would work for me. So I started to scroll through the different features except I couldn’t scroll through all of them. While scrolling I must have turned on the stopwatch. I couldn’t turn it off. Then I couldn’t scroll through anything except water lock feature. I had to turn on the water lock to get back to the stopwatch. Then the side buttons stopped working. I had it a total of 5 hours. I packed it up and started the Amazon return. I did get a full refund. Very disappointing.’ The text from 50 Amazon reviews of a book was borrowed from https://www.amazon.com/dp/B000WM9UK2. The reviews were for the most part favourable. Example review texts include: ‘The Children of H´urin is a great tragedy mixed with grace. The genealogical records at the beginning may be difficult to get through, but the story very quickly gains speed. I only gave the story four stars because of the difficulty of the first few chapters, much like Matthew’s genealogy of Christ at the beginning of his gospel. Although such records are important in both they are nonetheless difficult to get through. I still definitely would recommend this book, though, as it depicts the horrible effects of evil powers on good men and women, and yet, we must continue to resist evil no matter the tragic end. It is very telling that in Tolkien’s world, at the end of days when Morgoth returns, that it is T´urin, a man, who ends him once and for all. Those whom Satan most destroys in this life are they who will ultimately deal his death blow, as the Revelation says, “They overcame him by the blood of the Lamb and by the word of their ‘martyria,’ [witness, testimony],” those like T´urin, or in Scripture, those like Job.’ Figure 2.1.2 shows the emotion vectors for all 50 processed reviews. The 10 most probable emotions experienced from purchasing the product were: depressed, kind, nostalgic, tired, hopeless, lonely, hope, calm, lazy, confident. This paper is available on arxiv under CC 4.0 license. Authors: (1) D.Sinclair, Imense Ltd, and email: david@imense.com; (2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) D.Sinclair, Imense Ltd, and email: david@imense.com; (2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. Table of Links Abstract and Introduction Interrogating the LLM with an Emotion Eliciting Tail Prompt PCA analysis of the Emotion of Amazon reviews Future Work Conclusions Acknowledgments and References Abstract and Introduction Abstract and Introduction Interrogating the LLM with an Emotion Eliciting Tail Prompt Interrogating the LLM with an Emotion Eliciting Tail Prompt PCA analysis of the Emotion of Amazon reviews PCA analysis of the Emotion of Amazon reviews Future Work Future Work Conclusions Conclusions Acknowledgments and References Acknowledgments and References 2. Interrogating the LLM with an Emotion Eliciting Tail Prompt In this work we used Facebook’s open source LlaMa2 7 billion weight LLM as the core engine [2]. It was necessary to use a LLM that allowed access to raw token probabilities after a prompt. The model ran on a Mac Studio with 32 gigabytes of RAM. With this combination of hardware and model it took 2 minutes to compute the probabilities of the emotion descriptors in the emotion dictionary given below. 2.1. The Emotion Dictionary The English language is blessed with many words and an extensive literature providing examples of the usage of these words in appropriate contexts. For the purposes of LLMs, it is the context of a word that conveys it’s meaning. A reader will infer the meaning of an unfamiliar word through the context they find the word used in, provided they understand the context. As an example, ‘The shotgun discombobulated the rabbit.’ shows how meaning can be moderated by context. The context created by a tail prompt will favour an associated class of words. For the experiment detailed in this paper the following tail prompt was used to elicit emotion descriptors, ’Reading this makes me feel’. It is likely that specific emotion eliciting tail prompts will favour specific sub classes of descriptor but studying this is beyond the scope of this paper The following words were chosen to provide a broad sample of emotion descriptors. acceptance, admiration, adoration, affection, afraid, agitation, agony, aggressive, alarm, alarmed, alienation, amazement, ambivalence, amusement, anger, anguish, annoyed anticipating, anxious, apathy, apprehension, arrogant, assertive, astonished, attentiveness, attraction, aversion, awe, baffled, bewildered, bitter, bitter sweetness, bliss, bored, brazen, brooding, calm, carefree, careless, caring, charity, cheeky, cheerfulness, claustrophobic, coercive, comfortable, confident, confusion, contempt, content, courage, cowardly, cruelty, curiosity, cynicism, dazed, dejection, delighted, demoralized, depressed, desire, despair, determined, disappointment, disbelief, discombobulated, discomfort, discontentment, disgruntled, disgust, disheartened, dislike, dismay, disoriented, dispirited, displeasure, distraction, distress, disturbed, dominant, doubt, dread, driven, dumbstruck, eagerness, ecstasy, elation, embarrassment, empathy, enchanted, enjoyment, enlightened, ennui, enthusiasm, envy, epiphany, euphoria, exasperated, excitement, expectancy, fascination, fear, flakey, focused, fondness, friendliness, fright, frustrated, fury, glee, gloomy, glumness, gratitude, greed, grief, grouchiness, grumpiness, guilt, happiness, hate, hatred, helpless, homesickness, hope, hopeless, horrified, hospitable, humiliation, humility, hurt, hysteria, idleness, impatient, indifference, indignant, infatuation, infuriated, insecurity, insightful, insulted, interest, intrigued, irritated, isolated, jealousy, joviality, joy, jubilation, kind, lazy, liking, loathing, lonely, longing, loopy, love, lust, mad, melancholy, miserable, miserliness, mixed up, modesty, moody, mortified, mystified, nasty, nauseated, negative, neglect, nervous, nostalgic, numb, obstinate, offended, optimistic, outrage, overwhelmed, panicked, paranoid, passion, patience, pensiveness, perplexed, persevering, pessimism, pity, pleased, pleasure, politeness, positive, possessive, powerless, pride, puzzled, rage, rash, rattled, regret, rejected, relaxed, relieved, reluctant, remorse, resentment, resignation, restlessness, revulsion, ruthless, sadness, satisfaction, scared, schadenfreude, scorn, self-caring, self-compassionate, self-confident, self-conscious, self-critical, self-loathing, self-motivated, self-pity, self-respecting, self-understanding, sentimentality, serenity, shame, shameless, shocked, smug, sorrow, spite, stressed, strong, stubborn, stuck, submissive, suffering, sullenness, surprise, suspense, suspicious, sympathy, tenderness, tension, terror, thankfulness, thrilled, tired, tolerance, torment, triumphant, troubled, trust, uncertainty, undermined, uneasiness, unhappy, unnerved, unsettled, unsure, upset, vengeful, vicious, vigilance, vulnerable, weak, woe, worried, worthy, wrath. This set of words is not intended to be complete or definitive in any way. Using the tail prompt without restricting the return to emotion descriptors elicits general waffle responses that are not straightforward to extract sentiment form. 2.1.1. Estimating the emotion probability vector LlaMa2 [2] has been released in such a way as to allow developers to access estimated token weights returned in response to a prompt. LlaMa2 has an internal vocabulary size of roughly 30,000 tokens. This means that when LlaMa 2 estimates the probability of the next token in a sequence the probability vector will have 30,000 elements. Some of the words in the emotion descriptor list are made up of more than one token in which case forward conditional probabilities are used. Figure 2.1.1 shows the scaled probability distribution over words from the emotion dictionary elicited by the tail prompt for the Amazon review text, ‘I read a lot of negative reviews about the Fitbit inspire 2, I took a chance and hoped the one I ordered would be one of the great ones that worked. Unfortunately that was not the case. I unpacked it, charged it, downloaded the app. I took a walk with it on before the sun went down. I have the Google Fit app on my phone that tracks my steps also. The phone was in my jeans pocket. When I got home I compared the two, Google Fit said 4,458 steps, Fitbit said 1,168. Apparently Fitbit works with wrist motion which I don’t have while pushing a walker around the neighborhood. I downloaded the manual and noticed you can put it on a clip (that wasn’t included). That would work for me. So I started to scroll through the different features except I couldn’t scroll through all of them. While scrolling I must have turned on the stopwatch. I couldn’t turn it off. Then I couldn’t scroll through anything except water lock feature. I had to turn on the water lock to get back to the stopwatch. Then the side buttons stopped working. I had it a total of 5 hours. I packed it up and started the Amazon return. I did get a full refund. Very disappointing.’ ‘I read a lot of negative reviews about the Fitbit inspire 2, I took a chance and hoped the one I ordered would be one of the great ones that worked. Unfortunately that was not the case. I unpacked it, charged it, downloaded the app. I took a walk with it on before the sun went down. I have the Google Fit app on my phone that tracks my steps also. The phone was in my jeans pocket. When I got home I compared the two, Google Fit said 4,458 steps, Fitbit said 1,168. Apparently Fitbit works with wrist motion which I don’t have while pushing a walker around the neighborhood. I downloaded the manual and noticed you can put it on a clip (that wasn’t included). That would work for me. So I started to scroll through the different features except I couldn’t scroll through all of them. While scrolling I must have turned on the stopwatch. I couldn’t turn it off. Then I couldn’t scroll through anything except water lock feature. I had to turn on the water lock to get back to the stopwatch. Then the side buttons stopped working. I had it a total of 5 hours. I packed it up and started the Amazon return. I did get a full refund. Very disappointing.’ The text from 50 Amazon reviews of a book was borrowed from https://www.amazon.com/dp/B000WM9UK2. The reviews were for the most part favourable. Example review texts include: ‘The Children of H´urin is a great tragedy mixed with grace. The genealogical records at the beginning may be difficult to get through, but the story very quickly gains speed. I only gave the story four stars because of the difficulty of the first few chapters, much like Matthew’s genealogy of Christ at the beginning of his gospel. Although such records are important in both they are nonetheless difficult to get through. I still definitely would recommend this book, though, as it depicts the horrible effects of evil powers on good men and women, and yet, we must continue to resist evil no matter the tragic end. It is very telling that in Tolkien’s world, at the end of days when Morgoth returns, that it is T´urin, a man, who ends him once and for all. Those whom Satan most destroys in this life are they who will ultimately deal his death blow, as the Revelation says, “They overcame him by the blood of the Lamb and by the word of their ‘martyria,’ [witness, testimony],” those like T´urin, or in Scripture, those like Job.’ ‘The Children of H´urin is a great tragedy mixed with grace. The genealogical records at the beginning may be difficult to get through, but the story very quickly gains speed. I only gave the story four stars because of the difficulty of the first few chapters, much like Matthew’s genealogy of Christ at the beginning of his gospel. Although such records are important in both they are nonetheless difficult to get through. I still definitely would recommend this book, though, as it depicts the horrible effects of evil powers on good men and women, and yet, we must continue to resist evil no matter the tragic end. It is very telling that in Tolkien’s world, at the end of days when Morgoth returns, that it is T´urin, a man, who ends him once and for all. Those whom Satan most destroys in this life are they who will ultimately deal his death blow, as the Revelation says, “They overcame him by the blood of the Lamb and by the word of their ‘martyria,’ [witness, testimony],” those like T´urin, or in Scripture, those like Job.’ Figure 2.1.2 shows the emotion vectors for all 50 processed reviews. The 10 most probable emotions experienced from purchasing the product were: depressed, kind, nostalgic, tired, hopeless, lonely, hope, calm, lazy, confident.

Estimate Emotion Probability Vectors: Interrogating the LLM with an Emotion Eliciting Tail Prompt

আবেগ সম্ভাবনা ভেক্টর অনুমান করুন: একটি আবেগ এলিসিটিং টেইল প্রম্পটের সাথে এলএলএমকে জিজ্ঞাসাবাদ করা

Schätzen Sie Wahrscheinlichkeitsvektoren für Emotionen: Abfragen des LLM mit einem Emotionen hervorrufenden Tail Prompt

Estimar vectores de probabilidad de emociones: interrogar al LLM con un mensaje de cola que provoca emociones

Estimer les vecteurs de probabilité d'émotion : interroger le LLM avec une invite de queue suscitant des émotions

भावना संभाव्यता सदिशों का अनुमान लगाना: भावना उद्घाटित करने वाले टेल प्रॉम्प्ट के साथ LLM से पूछताछ करना

感情確率ベクトルの推定: 感情を喚起するテールプロンプトによる LLM の調査

감정 확률 벡터 추정: 감정 유도 꼬리 프롬프트로 LLM 조사

Estimar vetores de probabilidade de emoção: interrogando o LLM com um prompt de cauda que provoca emoção

Оценка векторов вероятности эмоций: опрос LLM с помощью хвостовой подсказки, вызывающей эмоции

Duygu Olasılığı Vektörlerini Tahmin Edin: Yüksek Lisans'ı Duygu Ortaya Çıkaran Kuyruk İstemi ile Sorgulamak

Ước tính các vectơ xác suất cảm xúc: Thẩm vấn LLM bằng lời nhắc gợi ý cảm xúc

估计情绪概率向量：用引发情绪的尾部提示询问法学硕士

Colliding Magnetospheres in The Young High-Eccentricity Binary DQ Tau: Discussion

While artificial intelligence (AI) shows potential to increase profitability across most industries, its adoption is also resulting in a widening global skills gap, making upskilling workforces in AI and machine learning (ML) imperative for progress. A report by KPMG shows that among leading businesses prioritizing technology investment for profitability, a resounding 68% identify AI and machine learning as indispensable to their short-term plans. This means the need for AI skilling is such that it requires employees, employers, business organizations, and educational institutions all to pitch in. In short, building the AI-skilled workforce has to be a truly global effort. Enhancing skills in AI and Machine Learning is no longer optional but a necessity
Ruban Phukan, co-founder and CEO of GoodGist “Enhancing skills in AI and Machine Learning is no longer optional but a necessity,” Ruban Phukan, co-founder and CEO of GoodGist, an autonomous learning startup, told The Tech Panda. And the reason for this necessity, he says, is that AI saves time. “In an era where time is the scarcest resource and the boundaries between work and personal life increasingly blur, leveraging AI to optimize time is one of the most significant steps individuals can take to boost both their professional and personal productivity. This reality underscores the importance of upskilling,” Phukan explains. According to a Slack survey, workplace adoption of AI tools has sped up 24% over the previous quarter, with one out of four desk workers saying they have used AI tools at their work as of January 2024, as compared to one in five as of September 2023. “Upskilling, however, transcends mere familiarity with AI usage. It’s a critical response to the profound shifts in the business landscape and job market triggered by the rapid evolution of technology,” he adds. According to the World Economic Forum, nearly 1 billion people worldwide will need to upskill by 2030, with Korn Ferry warning that failing to do so could result in an $8.5 trillion loss in potential revenue. This is a stark backdrop to McKinsey’s projection that 87% of companies will experience a skills gap in the coming years, making upskilling not just imperative but urgent as time is of the essence here. Job postings for AI are high, but where is the talent? As AI gets integrated into all kinds of sectors, companies are starting to compete for AI talent. AI job postings surged 42% since ChatGPT’s debut, as Big Tech companies like Amazon, Google, Apple, and others like global logistics firm UPS, are beginning to shift resources to channel money into hiring machine learning engineers and data scientists. Companies are actively in search of skilled professionals to fill roles from technical leadership to applied research. These include eBay, Krutrim, Accenture, Dentsu, Gramener and others. According to an AIM report on generative AI salaries in India, large companies are offering INR 1 crore or more annually for senior roles, while startups and mid-size companies are ready to give out approx. INR 30-40 lakh. However, are these companies finding the talent they seek? As per new research, about three in four employers are struggling to hire for the AI skills they seek even though they are willing to boost salaries 43% in marketing and 35-42% across HR, finance, and legal roles. In fact, with the acceleration in AI adoption, companies are beginning to rely on education partnerships and gig work to reach the talent required. This is why skilling programs in AI are afoot. In March, IIM Lucknow and Emeritus launched the “Digital Transformation and AI for Business Leaders” Program to shape the next generation of AI leaders, an 11-month program designed to help leaders gain comprehensive insights into the intricacies of digital transformation and AI. Similarly, Physics Wallah (PW), an Indian EdTech platform, launched PW Institute of Innovation (PW IOI), a four-year residential program in Computer Science and AI to address the critical challenges faced by the Indian software industry, strategically located in Bengaluru, the Silicon Valley of India. The significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach
Vishwa Mohan, CIO at PW & President of PW IOI Vishwa Mohan, CIO at PW & President of PW IOI, said, “It is indeed concerning that despite the exponential growth witnessed in the Indian software industry, the employability rate of Indian engineering graduates has remained relatively low, with only marginal improvement over the years. Additionally, the significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach.” Employees want AI skilling from employers At the same time, employee expectations are starting to include skilling and training in AI. According to a study by EPAM India, 93% of modern engineers seek upskilling opportunities in AI, Cloud, and other new-age technologies, while 92% look for opportunities to enhance their problem-solving skills to identify and implement the best solutions. Also, 91% of respondents stated that future developmental and on-job training opportunities are their top expectations from their workplaces. Regarding their perspective for the future, 68% and 46% of modern engineers, respectively, believe that Data Analytics and AI will be the demand in the future, enabling enhancing consumer experience and collaboration. Some companies have started working on these expectations in a bid to retain talent. For example, Findability Sciences, an enterprise AI solution has trained over 100 of its data analysts and scientists across its Boston, India, and Japan operations with intensive training in problem formulation and prompt design and engineering skills. Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on
Mandar Kulkarni, AVP of Pre-sales Mandar Kulkarni, AVP of Pre-sales, said, “Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on.”?? The aim is to arm its team with the latest skills to cater to the evolving requirements of its existing and incoming customers in the generative AI arena. “This training is more than an investment in our team; it’s an investment in our clients,” says Anurag Srivastava, the Global Delivery Head. In a tech-driven business landscape, AI and machine learning expertise empower professionals to navigate disruption and contribute significantly to short-term viability and long-term success, hence its necessity as a skill is becoming inevitable. Every ecosystem has to pool in resources to meet the AI demand that will only go up in the future. Navanwita Sachdev, Editor, The Tech Panda While artificial intelligence (AI) shows potential to increase profitability across most industries, its adoption is also resulting in a widening global skills gap, making upskilling workforces in AI and machine learning (ML) imperative for progress. artificial intelligence A report by KPMG shows that among leading businesses prioritizing technology investment for profitability, a resounding 68% identify AI and machine learning as indispensable to their short-term plans. report by KPMG This means the need for AI skilling is such that it requires employees, employers, business organizations, and educational institutions all to pitch in. In short, building the AI-skilled workforce has to be a truly global effort. Enhancing skills in AI and Machine Learning is no longer optional but a necessity Ruban Phukan, co-founder and CEO of GoodGist Enhancing skills in AI and Machine Learning is no longer optional but a necessity Enhancing skills in AI and Machine Learning is no longer optional but a necessity Enhancing skills in AI and Machine Learning is no longer optional but a necessity Ruban Phukan, co-founder and CEO of GoodGist “Enhancing skills in AI and Machine Learning is no longer optional but a necessity,” Ruban Phukan , co-founder and CEO of GoodGist , an autonomous learning startup, told The Tech Panda . And the reason for this necessity, he says, is that AI saves time. Ruban Phukan GoodGist The Tech Panda “In an era where time is the scarcest resource and the boundaries between work and personal life increasingly blur, leveraging AI to optimize time is one of the most significant steps individuals can take to boost both their professional and personal productivity. This reality underscores the importance of upskilling,” Phukan explains. According to a Slack survey , workplace adoption of AI tools has sped up 24% over the previous quarter, with one out of four desk workers saying they have used AI tools at their work as of January 2024, as compared to one in five as of September 2023. a Slack survey “Upskilling, however, transcends mere familiarity with AI usage. It’s a critical response to the profound shifts in the business landscape and job market triggered by the rapid evolution of technology,” he adds. According to the World Economic Forum , nearly 1 billion people worldwide will need to upskill by 2030, with Korn Ferry warning that failing to do so could result in an $8.5 trillion loss in potential revenue. This is a stark backdrop to McKinsey’s projection that 87% of companies will experience a skills gap in the coming years, making upskilling not just imperative but urgent as time is of the essence here. According to the World Economic Forum Korn Ferry warning McKinsey’s projection Job postings for AI are high, but where is the talent? Job postings for AI are high, but where is the talent? As AI gets integrated into all kinds of sectors, companies are starting to compete for AI talent. AI job postings surged 42% since ChatGPT’s debut, as Big Tech companies like Amazon, Google, Apple, and others like global logistics firm UPS, are beginning to shift resources to channel money into hiring machine learning engineers and data scientists. AI job postings Companies are actively in search of skilled professionals to fill roles from technical leadership to applied research. These include eBay, Krutrim, Accenture, Dentsu, Gramener and others. According to an AIM report on generative AI salaries in India, large companies are offering INR 1 crore or more annually for senior roles, while startups and mid-size companies are ready to give out approx. INR 30-40 lakh. These include an AIM report However, are these companies finding the talent they seek? As per new research , about three in four employers are struggling to hire for the AI skills they seek even though they are willing to boost salaries 43% in marketing and 35-42% across HR, finance, and legal roles. In fact, with the acceleration in AI adoption, companies are beginning to rely on education partnerships and gig work to reach the talent required. research This is why skilling programs in AI are afoot. In March, IIM Lucknow and Emeritus launched the “Digital Transformation and AI for Business Leaders” Program to shape the next generation of AI leaders, an 11-month program designed to help leaders gain comprehensive insights into the intricacies of digital transformation and AI. IIM Lucknow Emeritus Similarly, Physics Wallah (PW), an Indian EdTech platform, launched PW Institute of Innovation (PW IOI), a four-year residential program in Computer Science and AI to address the critical challenges faced by the Indian software industry, strategically located in Bengaluru, the Silicon Valley of India. Physics Wallah The significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach Vishwa Mohan, CIO at PW & President of PW IOI The significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach The significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach The significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach Vishwa Mohan, CIO at PW & President of PW IOI Vishwa Mohan, CIO at PW & President of PW IOI, said, “It is indeed concerning that despite the exponential growth witnessed in the Indian software industry, the employability rate of Indian engineering graduates has remained relatively low, with only marginal improvement over the years. Additionally, the significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach.” Employees want AI skilling from employers Employees want AI skilling from employers At the same time, employee expectations are starting to include skilling and training in AI. According to a study by EPAM India, 93% of modern engineers seek upskilling opportunities in AI, Cloud, and other new-age technologies, while 92% look for opportunities to enhance their problem-solving skills to identify and implement the best solutions. Also, 91% of respondents stated that future developmental and on-job training opportunities are their top expectations from their workplaces. study Regarding their perspective for the future, 68% and 46% of modern engineers, respectively, believe that Data Analytics and AI will be the demand in the future, enabling enhancing consumer experience and collaboration. Some companies have started working on these expectations in a bid to retain talent. For example, Findability Sciences , an enterprise AI solution has trained over 100 of its data analysts and scientists across its Boston, India, and Japan operations with intensive training in problem formulation and prompt design and engineering skills. Findability Sciences Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on Mandar Kulkarni, AVP of Pre-sales Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on Mandar Kulkarni, AVP of Pre-sales Mandar Kulkarni, AVP of Pre-sales, said, “Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on.”?? The aim is to arm its team with the latest skills to cater to the evolving requirements of its existing and incoming customers in the generative AI arena. “This training is more than an investment in our team; it’s an investment in our clients,” says Anurag Srivastava, the Global Delivery Head. In a tech-driven business landscape, AI and machine learning expertise empower professionals to navigate disruption and contribute significantly to short-term viability and long-term success, hence its necessity as a skill is becoming inevitable. Every ecosystem has to pool in resources to meet the AI demand that will only go up in the future. Navanwita Sachdev, Editor, The Tech Panda Navanwita Sachdev, Editor, The Tech Panda Navanwita Sachdev, Navanwita Sachdev, Navanwita Sachdev, Editor, The Tech Panda

The Talent Quest: Companies' High Stakes in the AI Workforce Race

This paper is available on arxiv under CC BY-SA 4.0 DEED license.
Authors:
(1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de;
(2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany;
(6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ;
(7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction
Materials and Methods
Data
Results
Conclusions, Open Research Section and Conflict of Interest/Competing Interests
Author Contributions, Supplementary Information, Acknowledgments and References
Additional Insight from the Conceptual Model 
AMSR2 until 2022 Excluded Due to Missing Human Land Use Data
Investigation of Resilience Loss in AMSR2’s X-Band
Results Proving Robustness
Potential Driving Forces and Sources of Bias POTENTIAL DRIVING FORCES AND SOURCES OF BIAS Text S12. The influence of the South American Monsoon system as a potentially bistable system In the Amazon basin, the South American monsoon system and the atmospheric moisture recycling form a complex and potentially bi-stable system. Hence the monthly precipitation itself could exhibit signs of CSD, which would then drive vegetation changes that are wrongly interpreted as the destabilization of the vegetation. In Figure S16, the signs of the trend of variance, AC1 and spatial are compared between VOD and precipitation (Funk et al., 2014). We argue that due to the high amount of cells where the values differ in sign for the VIs and precipitation (sum of diagonal cells in boxes expected to be 50 % for random assignment), the observed changes in the indicators are likely a measure of critical slowing down in the vegetation and not only a direct effect of variability in precipitation. Text S13. The influence of changes in precipitation as a potential driving force If precipitation is the main driver of ARF resilience loss, one would expect that in regions with stronger decreases in precipitation (negative trend), more critical slowing down can be observed. This comparison in displayed in Figure S17. In parts, decreased precipitation could potentially explain the destabilization detected in the western Amazon basin by both bands of AMSR-E as well as in the western and northeastern regions detected by AMSR2’s bands. However it is important to note here that the number of cells with an increasing amount of precipitation and decreasing indicators and those with decreasing amount of precipitation and increasing indicators add up to only about 50 %, which is what is to be expected for a random assignment of positive and negative trends in both. Text S14. The influence of changes in temperature Instead of decreasing precipitation (see Figure S17, an increase in temperature can also lead to vegetation destabilization through water stress by higher evapo-transpirative demand. If this was the driver of the CSD, increasing temperatures must coincide with positive trends in the indicators of CSD in Figure S18. Yet changes in temperature cannot explain the detected destabilization in the years 2002 to 2011. For the time period of AMSR2, destabilization detected in the northeast (band C1) and the east (band C2) might at least partially be accounted for rising temperatures (the percentage of cells with rising/declining temperatures and in-/decreasing indicators falls between 48.6 % and up to 59.3 %). Text S15. The potential bias induced by saturation In case of optical sensors, their saturation over high biomass regions often renders the VIs insensitive to the subtle changes due to perturbations that a resilience analysis based on CSD relies on. The argument is that with decreasing biomass, the VIs become more sensitive, which then translates into a signal of CSD as a direct artifact of the higher perception of perturbations. Even though VOD results from observations of microwaves, we want to address the question of saturation in VOD. As Figure S19 shows, we cannot exclude this as an artifact for AMSRE’s band C as well as AMSR2’s two bands, as they hardly observed any cells with increasing biomass. Interestingly, AMSR-E’s band X detected an increase in biomass as well as a loss of resilience in most of the cells (77.3 % to 78.2 %). Thus, its signal of destabilization of almost the entire ARF during the years 2002 to 2011 is not an artifact of saturation of the band. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction Materials and Methods Data Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Potential Driving Forces and Sources of Bias Abstract and Introduction Abstract and Introduction Materials and Methods Materials and Methods Data Data Results Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Results Proving Robustness Potential Driving Forces and Sources of Bias Potential Driving Forces and Sources of Bias POTENTIAL DRIVING FORCES AND SOURCES OF BIAS Text S12. The influence of the South American Monsoon system as a potentially bistable system In the Amazon basin, the South American monsoon system and the atmospheric moisture recycling form a complex and potentially bi-stable system. Hence the monthly precipitation itself could exhibit signs of CSD, which would then drive vegetation changes that are wrongly interpreted as the destabilization of the vegetation. In Figure S16, the signs of the trend of variance, AC1 and spatial are compared between VOD and precipitation (Funk et al., 2014). We argue that due to the high amount of cells where the values differ in sign for the VIs and precipitation (sum of diagonal cells in boxes expected to be 50 % for random assignment), the observed changes in the indicators are likely a measure of critical slowing down in the vegetation and not only a direct effect of variability in precipitation. Text S13. The influence of changes in precipitation as a potential driving force If precipitation is the main driver of ARF resilience loss, one would expect that in regions with stronger decreases in precipitation (negative trend), more critical slowing down can be observed. This comparison in displayed in Figure S17. In parts, decreased precipitation could potentially explain the destabilization detected in the western Amazon basin by both bands of AMSR-E as well as in the western and northeastern regions detected by AMSR2’s bands. However it is important to note here that the number of cells with an increasing amount of precipitation and decreasing indicators and those with decreasing amount of precipitation and increasing indicators add up to only about 50 %, which is what is to be expected for a random assignment of positive and negative trends in both. Text S14. The influence of changes in temperature Instead of decreasing precipitation (see Figure S17, an increase in temperature can also lead to vegetation destabilization through water stress by higher evapo-transpirative demand. If this was the driver of the CSD, increasing temperatures must coincide with positive trends in the indicators of CSD in Figure S18. Yet changes in temperature cannot explain the detected destabilization in the years 2002 to 2011. For the time period of AMSR2, destabilization detected in the northeast (band C1) and the east (band C2) might at least partially be accounted for rising temperatures (the percentage of cells with rising/declining temperatures and in-/decreasing indicators falls between 48.6 % and up to 59.3 %). Text S15. The potential bias induced by saturation In case of optical sensors, their saturation over high biomass regions often renders the VIs insensitive to the subtle changes due to perturbations that a resilience analysis based on CSD relies on. The argument is that with decreasing biomass, the VIs become more sensitive, which then translates into a signal of CSD as a direct artifact of the higher perception of perturbations. Even though VOD results from observations of microwaves, we want to address the question of saturation in VOD. As Figure S19 shows, we cannot exclude this as an artifact for AMSRE’s band C as well as AMSR2’s two bands, as they hardly observed any cells with increasing biomass. Interestingly, AMSR-E’s band X detected an increase in biomass as well as a loss of resilience in most of the cells (77.3 % to 78.2 %). Thus, its signal of destabilization of almost the entire ARF during the years 2002 to 2011 is not an artifact of saturation of the band.

Loss of Amazon Rainforest Resilience: Potential Driving Forces and Sources of Bias

Loss of Amazon Rainforest Resilience: Conclusions

Loss of Amazon Rainforest Resilience: Abstract and Introduction

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Question about an animated representation of water molecules undergoing phase changes *: hmmm. you made a nice graph before. Could you build something more dynamic than a static diagram, an animated representation of water molecules undergoing phase changes?* P1 *: Got it! Creating an animated representation of water molecules undergoing phase changes requires defining the behavior of the molecules for each phase and then transitioning between these phases.* ChatGPT I'll follow these steps: 1. Represent the molecules as small circles. 2. Define their behavior in the solid phase. 3. Transition to the melting phase. 4. Define behavior in the liquid phase. 5. Transition to the boiling phase. 6. Define behavior in the gas phase. 7. Compile these animations into a continuous GIF. [Some internal code working] Here's the animated representation of water molecules undergoing phase changes: Here's the animated representation of water molecules undergoing phase changes: 1. Solid (Ice): Molecules are closely packed and vibrating in place. 2. Melting: Molecules start to move more freely but remain close. 3. Liquid (Water): Molecules move more fluidly and slide past each other. 4. Boiling: Molecules gain energy and move more rapidly. 5. Gas (Steam): Molecules spread out and move freely and rapidly. You can download the GIF here. [link provided] *: Sure, I can try to create an animated representation of water molecules undergoing phase changes. I found some web simulations that show how different types of molecules form a solid, liquid, or gas, and how they change their state when heat is added or removed [ ]. Youcan also change the temperature or volume of a container and see a pressure-temperature diagram respond in real time [ ]. These simulations might help you understand the molecular behavior of water in different phases.* BingChat 123 1 *: Sure. Here is a simple animated representation of water molecules undergoing phase changes:* Bard [Animated GIF of water molecules undergoing phase changes] The animation shows two groups of water molecules. The group on the left is in the liquid phase, and the group on the right is in the gas phase. The animation starts with the water molecules in the liquid phase. The water molecules are close together and interacting with each other. As the animation progresses, some of the water molecules in the liquid phase gain enough energy to overcome the forces that are holding them together. These water molecules escape from the liquid phase and form the gas phase. The water molecules in the gas phase are far apart and not interacting with each other. The animation also shows how water molecules can condense from the gas phase to the liquid phase. When a water molecule in the gas phase loses energy, it can attract to other water molecules and form a liquid droplet. The animation can be used to illustrate the different phase changes of water, such as melting, boiling, evaporation, and condensation. I hope this is helpful! *: Sure, animated visualizations can be really helpful for understanding dynamic processes like phase changes! Here is a simple animated illustration showing water molecules in different states:* Claude In this animation: • The solid ice molecules are stationary and packed together • The liquid molecules flow and move closer together • The gas molecules bounce around freely at large distances To transition between states: • Heating causes more vigorous movement - melting ice or boiling water • Cooling causes slower movement - freezing water or condensing vapor Let me know if an animated illustration like this helps visualize the molecular differences between solids, liquids and gases! I can build more complex animations or use online tools to create interactive phase change simulations. Visuals can really aid understanding of physical chemistry concepts. was able to recur again to its Advanced Data Analysis resources to generate an animated GIF as an which, albeit a bit simple and crude, sufficiently illustrated the “water molecules undergoing phase changes” to provide . Furthermore, crafting an original GIF could be seen as . After promising “to create an animated representation”, opted to search the internet, offering links to “some web simulations” that “might help” understand the molecular behavior. This approach aligns with and . However, by leaning on pre-existing resources rather than crafting custom content, it could lessen the immediate and potentially diminish the . made an assurance to deliver “a simple animated representation” as a commitment to and Engagement & Interactivity, but this ultimately falls short as neither a visible animation nor a download link for such a GIF was provided. Meanwhile, while acknowledges that “Visuals can really aid understanding” and “animated visualizations can be really helpful” as , its attempt was limited to the of a static image, devoid of the requested animation or dynamic representation and lessening and . ChatGPT Instructional Strategies, Engagement & Interactivity Personalization & Personable Touch BingChat Instructional Strategies Detail Depth & Content Recommendation Engagement & Interactivity Personalization & Personable Touch Bard Instructional Strategies, Personalization & Personable Touch Claude Instructional Strategies Engagement & Interactivity Detail Depth & Content Recommendation This paper is under CC BY-SA 4.0 DEED license. available on arxiv

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