#2012 algorithmic revolution

[resonancewitness]

Wang Yibo's endorsements and interesting patterns

So I have been looking into clusters of values represented by the stars’ portfolios of brand endorsements, and with Wang Yibo’s brands the picture is even more interesting. I am deliberately not going to be  looking at the history with the brands and at the revenue the endorsement brings, even if this sort of data would be totally available. I am looking at the values. 

And I can see three main groupings: 

Luxury/ stage performance/ making a statement via one’s looks group of brands. Beauty/ originality / fashion as a language to express one’s uniqueness. Chanel, Jimmy Choo, Loewe, Shu Uemura are in this group. 

Then there is a whole group related to sports/ outdoor activities/ spirit of adventure/ active youth lifestyle / embodied experience of life + healthy lifestyle (Lacoste, Jeanswest, Evisu (through the car racing connection), Lifespace probiotics, now Helly Hansen); loosely connected to this is TaiLing the electric vehicle (indirectly via the climate change positioning and Wang Yibo nature conservation activist/ China youth representative on the international “arena” roles). These seem to be most aligned with the direction his public persona has been evolving over the last year and a half. 

Another loosely aligned with this group is Edifier with the high-quality sound and music tech, because it is a very youth thing to do. 

And then there are the “minor neutrals” (Baseus, C-kafe skincare, Adolph haircare etc.) and “definite outliers” from active/ healthy lifestyle (SuperX alcohol, Master Kong Ice Tea, Franzzi cookies, Milkground processed cheese). 

I have been following these brands for a while now, looking not just at the celebrity photos, but at all the ways the brands are engaging with their customers. Because celebrity endorsement is a very particular marketing strategy, if it is tied closely to the fandom and fan engagement. It sure cuts through the noise of saturated markets, but, taken to the extreme, it can (re)create the economic side of the aftermath of incident 227. When fans’ emotions are “farmed” and converted into brands’ profit, it makes fandom rather different to what it used to be before the 2012 “algorithmic revolution” in social media. 

So I am particularly interested in what other marketing strategies brands are using to keep their own followership growing and engaged (and to make sure these are real people, and not bots). And I have been looking at different Wang Yibo’s endorsements and noticing very interesting things.

At the moment I am in a space of “is anybody seeing what I am seeing?”, so I’d ask you, dear reader. If you by chance also have been using your “detective streak” in trying to understand what is happening with the strategies that Wang Yibo’s brands are employing to interact with fans, are there any specific patterns that you have noticed?

[theenchantedecho]

"The Enchanted Echo" - Golden Trio Era (1990-2010s)

Muggle Studies Section

"The Intricacies of Muggle Digital Life: An Anthropological Exploration Beyond the Veil"

By Janice Codequill

In the sprawling metropolis of London, where the thrum of Muggle machinery harmonises with the whispered incantations of hidden wizards, there emerges a beguiling paradox: the Muggle Internet. Picture the Floo Network without soot-cloaked mantels or bursts of emerald flames—a realm where information and interaction commingle in an elaborate dance of pixels and algorithms.

A Realm of Enchantment, Sans Wand

At first glance, the Muggle Internet could be mistaken for a magical innovation—akin to Owl Post, the Floo Network, or dare I say, Apparition. However, the truth resounds louder than a Sonorus charm: this world is governed by '1s and 0s' and not an ounce of Floo Powder or phoenix feather core can be detected. It's as if Muggles have conjured their own Diagon Alley, except the entrance isn't a magical brick wall, but strings of code.

"Muggles have synthesised connectivity without recourse to any magical elements," says revered Magical Anthropologist Professor Albus Lysander, "and in doing so, they've shifted the very axis of their cultural experience."

Revolutionary Reverberations

The Internet's impact on Muggle society is not just a footnote, but a tome in its own right. It resonates with the vibrations of change, much like the enchanted pamphlets of "The Quibbler" or the whispers of Potterwatch during our darker days. A study by the Wizarding Journal of Cultural Studies found that Muggle revolutions, such as the Arab Spring, were greatly enabled by digital communication.

Dr. Lisa Montgomery, a Muggle academic, elaborates, "The internet has democratised access to information and granted platforms to those previously voiceless." This notion echoes the sentiments of many in our community who've relied on alternative channels of information during times of media censorship and societal upheaval.

The Dark Corners of the Web

Nonetheless, the Internet is not an unblemished paradise. Its darker realms bear more than a passing resemblance to Knockturn Alley. Cyberbullying, 'fake news', and online scamming lurk ominously, presenting a potential dark side to this tool. "Any form of power, magical or digital, comes with responsibility. Ethical conduct remains paramount," warns Professor Lysander.

The Ministry of Magic, in a recent report titled "Intersecting Ethical Conundrums," commented on the haunting similarities between cyber-crimes and offenses involving the Dark Arts.

Ethical Mirrorings Across Worlds

Embarking upon this anthropological study has proffered deeper perspectives on how Muggles grapple with ethical entanglements, akin to our own moral riddles surrounding love potions, Unforgivable Curses, or the surveillance capabilities of magical portraits. The precarious balance between liberty and security is a dance we are all reluctantly familiar with, regardless of our mortal or magical status.

Fusing Threads in a Shared Tapestry

In this post-war era, as we stitch our wounds and solidify our unity, the Muggle digital realm offers a mirror—imperfect yet instructive. It may not shimmer with the enchantment of a potion or offer the tactile satisfaction of a wand's swish and flick, but its resonance—a pulse almost tangible—promises potential for both great benevolence and alarming malevolence.

Illustration: A bewitching tableau of Muggle 'laptops' and 'smartphones' contrasted with wizarding essentials such as quills, crystal balls, and time-turners.

Citations:

Lysander, Professor Albus. "Magic and Muggle: Parallel Cultures," Journal of Wizarding Anthropology, 2012.

Montgomery, Dr. Lisa. "The Digital Revolution: A Muggle Perspective," Muggle Anthropology Quarterly, 2006.

"Ethical Dilemmas in the Muggle Digital Realm," Journal of Muggle Studies, 2009.

"Freedom vs. Security: A Muggle and Magical Dilemma," Magical Law Review, 2015.

"Intersecting Ethical Conundrums," Ministry of Magic Reports, 2017.

"Digital Revolution and Muggle Revolutions," Wizarding Journal of Cultural Studies, 2013.

[xaltius]

Data Science Evolution over the Decades and Future Advances

From humble beginnings rooted in statistics and early computing, data science has undergone a phenomenal transformation, evolving into one of the most impactful and sought-after fields of the 21st century. It's a journey marked by an exponential surge in data, relentless technological innovation, and a growing understanding of data's power to drive decisions and reshape industries.

Let's take a stroll through the decades and peek into the exciting future of this dynamic discipline.

The Early Days: The 1960s to 1990s - The Genesis

While the term "data science" itself gained prominence later, its foundational concepts were laid in the mid-20th century.

1960s-1970s: The Dawn of Data Analysis: Statisticians like John Tukey began advocating for "data analysis" as a distinct field, emphasizing exploratory techniques and the visual representation of data. This era saw the initial intersection of statistics and computer science, laying the groundwork for what was to come.

1980s-1990s: Data Mining Emerges: As businesses started accumulating larger datasets, the need for automated pattern recognition grew. This period witnessed the rise of "data mining," leveraging statistical methods and early machine learning algorithms to uncover hidden insights in structured data. Relational databases became the norm, and early tools for reporting and querying data emerged.

The Big Data Boom: The 2000s - The Unstructured Challenge

The turn of the millennium brought a seismic shift: the explosion of digital data.

The "Big Data" Era: The proliferation of the internet, social media, and digital transactions led to unprecedented volumes, velocities, and varieties of data (the 3 V's). Traditional data processing methods struggled to cope.

Hadoop and MapReduce: This decade saw the advent of groundbreaking technologies like Hadoop (for distributed storage) and MapReduce (for processing large datasets). These open-source frameworks became critical for handling the sheer scale of "big data," allowing for the analysis of both structured and unstructured information.

Coining the "Data Scientist": Recognizing the unique blend of skills required to navigate this new data landscape – statistics, computer science, and domain expertise – the term "data scientist" began to gain traction, eventually popularized by the Harvard Business Review in 2012 as "the sexiest job of the 21st century."

The Machine Learning Revolution: The 2010s - Prediction Takes Center Stage

This decade truly ignited the data science phenomenon, largely driven by advancements in machine learning.

Machine Learning Mainstream: Algorithms for prediction and classification, once confined to academic research, became widely accessible. Supervised and unsupervised learning techniques, like decision trees, support vector machines, and clustering, found widespread application across industries.

Deep Learning Breakthroughs: Towards the latter half of the decade, deep learning, a subset of machine learning powered by neural networks, achieved remarkable success in areas like image recognition, natural language processing, and speech synthesis, pushing the boundaries of what AI could achieve.

Cloud Computing & Democratization: The rise of cloud platforms (AWS, Azure, Google Cloud) provided scalable and affordable infrastructure for data storage and processing, democratizing data science and making advanced analytics accessible to a broader range of organizations.

Open-Source Dominance: Python and R emerged as the dominant programming languages for data science, fueled by rich ecosystems of open-source libraries (e.g., scikit-learn, TensorFlow, PyTorch, Pandas).

The Present & Near Future: Late 2010s to Mid-2020s - Specialization and Responsibility

Today, data science is characterized by increasing specialization and a stronger focus on ethical considerations.

AI Data Scientist: The emergence of specialists focusing on the entire AI model lifecycle, from advanced model architecture design to ethical deployment.

MLOps Maturation: The industrialization of machine learning model deployment and management (MLOps) is becoming crucial for ensuring models are reliable, scalable, and perform well in production.

Explainable AI (XAI): As AI models become more complex, the need to understand their decisions and ensure transparency is paramount. XAI techniques are gaining importance.

Responsible AI and Ethics: Growing awareness of algorithmic bias, fairness, and data privacy has led to a stronger emphasis on ethical AI development and data governance frameworks (like GDPR).

Real-time Analytics: The demand for instant insights from streaming data, driven by IoT and real-time business needs, is pushing the boundaries of data processing.

Augmented Analytics & AutoML: Tools that leverage AI to automate parts of the data analysis process, making data insights more accessible to "citizen data scientists" and allowing experts to focus on higher-value tasks.

Future Advances: 2025 and Beyond - The Next Frontier

The trajectory of data science promises even more revolutionary advancements:

Generative AI Proliferation: Beyond current large language models, generative AI will revolutionize content creation, drug discovery, material science, and personalized experiences, moving from experimentation to widespread production deployment.

Edge AI: Processing data closer to its source (on devices, at the "edge" of the network) will become increasingly common, enabling real-time decision-making in autonomous vehicles, smart cities, and industrial IoT.

Quantum Computing's Impact: While still in its nascent stages, quantum computing holds the potential to solve currently intractable data-intensive problems, accelerating complex simulations and optimization tasks.

Data Mesh and Data Products: Organizations will move towards more decentralized data architectures, treating data as a product with clear ownership and consumption patterns, enhancing data accessibility and quality.

Synthetic Data Generation: As privacy concerns grow and real-world data collection faces limitations, the generation of high-quality synthetic data for training AI models will become a vital capability.

Human-AI Collaboration: The future isn't about AI replacing humans, but about intelligent systems augmenting human capabilities, freeing up data scientists for more strategic, creative, and ethical considerations.

Hyper-Personalization at Scale: With more sophisticated data and AI, truly individualized experiences across healthcare, education, retail, and entertainment will become the norm.

Data science has come a long way, transforming from a niche academic pursuit into a pivotal force driving innovation across every sector. The journey has been thrilling, and as we look ahead, the potential for data to continue reshaping our world is boundless. The future of data science is not just about crunching numbers; it's about building a more intelligent, efficient, and ultimately, a better future.

[impaaktmagazine]

AI Evolution Beyond Humans :The Age of Machine Superiority

As machines eclipse human capabilities, we find ourselves searching for meaning in a world no longer centered on us.

While the wave of machines taking over human tasks is growing exponentially, it is being predicted by some of the global experts that 82% of global companies are either using or exploring the use of Artificial Intelligence (AI) Evolution in their organization.

Over the years, AI has become a driving force in reducing both operational and physical load on multiple industries. AI is certainly not just being restricted to computer science but spreading its wings across other industries; For instance, the use of neural networks in healthcare to chatbots or algorithmic trading in finance and more!

The question is “What do we really understand about AI?” If not, then it's high time that we uncover the complex forces at the crossroads of humans and machines.

Let’s begin from the very beginning!

How did this all begin?

Artificial Intelligence has evolved remarkably since its early beginnings. The journey traces back to the 1950s when British mathematician and visionary Alan Turing posed a groundbreaking question: “Can machines think?” Turing's work marked a pivotal shift—from abstract theories to practical experimentation—laying the foundation for the intelligent systems we continue to develop today.

The well-renowned Turing Test has machines on one side and humans on the other both hidden from view. The interrogator will have a natural language conversation with both and if the interrogator is not able to distinguish between machine and human - The machine is considered to be human intelligent enough to pass the test. This was a milestone for Artificial Intelligence development using modern computational techniques by igniting the spark of  “Machine having human intelligence”. This was an exceptional pivotal moment in the history of AI.

Coming into existence - In 1956 in the Dartmouth Conference “Artificial Intelligence” was coined for the first time and this was the beginning of AI as a formal field of study. Scientists taking inspiration from human intelligence and aiming to impart it to machines has always been a thing in the field of AI. 

The AI winters - The 1960s to 1970s are termed AI winters. Due to technology and data availability limitations there was almost no funding & research in the field of AI. AI was a vanished topic from newspapers, magazines, and books until in 1997 IBM’s Deep Blue defeated world chess champion Garry Kasparov. This opened the jammed research gates for AI. 

Data Explosion That Fueled AI Evolution

The shift toward an internet- and data-centric world in the 1990s and 2000s marked a pivotal turning point in the evolution of artificial intelligence. As vast amounts of digital data became available and computing power advanced rapidly, momentum in AI research surged. In 2006, Geoffrey Hinton co-authored a seminal paper that reignited interest in neural networks through the use of deep learning techniques.

By 2012, researchers from Stanford and Google—including Jeff Dean and Andrew Ng—demonstrated the potential of multi-layer neural networks. Their system gained attention for its ability to recognize images, famously identifying cats without prior labeling, showcasing the emerging power of unsupervised learning.

In 2017, the Google Brain team introduced a major breakthrough in natural language processing (NLP) with the development of the Transformer architecture. This innovation leveraged a self-attention mechanism, enabling AI systems to more effectively process sequences of data such as text.

Building on this momentum, OpenAI released the first version of GPT in 2018. This generative AI model utilized the Transformer framework to create powerful large language models (LLMs), setting the stage for the AI revolution we are witnessing today.

With these landmark achievements, artificial intelligence is rapidly transitioning from cutting-edge research to an essential part of everyday life.

The way we don’t remember life before smartphones, trust me we won’t remember life before AI too!

Inside the Mind of AI: Its Branches and How They Work

AI consists of different branches and each branch uses a different method to inculcate human intelligence into machines. These branches focus on a specific problem where machines are intelligent enough to get a breakthrough. Let’s understand some of the AI branches offered.

Machine Learning: Machine Learning is the core branch of AI which enables machines to learn from data autonomously without explicitly programming. A machine learning model is a program that can find patterns or make decisions from a previously unseen dataset. The primary machine learning models fall into broad categories - Unsupervised learning, Semi-supervised learning, and Reinforcement learning. ML powers recommendation systems that personalize user experiences in e-commerce and entertainment platforms like Netflix and Amazon.

Natural Language Processing: AI’s ability to understand, interpret and generate human language. Natural language processing (NLP) combines computational linguistics, machine learning, and deep learning models to process human language. Computational linguistics - Computational linguistics is the science of understanding and constructing human language models with computers and software tools. Deep learning is a specific field of machine learning that teaches computers to learn and think like humans. Applications like chatbots provide seamless customer interactions leveraging the power of NLP.

Neural Networks/Deep Learning: Neural networks are inspired by human brain structure and function. Artificial neural networks are computational models that mimic the structure and function of the biological brain to understand complex patterns. Neural Networks find applications across diverse fields, including image recognition, natural language processing, recommendation systems, and fraud detection.

Computer Vision: Making machines capable of interpreting visual data. Computer Vision (CV) focuses on enabling machines to analyze and interpret images and videos, mimicking human visual perception. This branch of AI is widely applied in tasks such as facial recognition, which enhances security systems, and medical imaging, where it aids in diagnosing diseases through X-rays and MRIs.

Robotics: Robotics is about designing machines to perform tasks autonomously or semi-autonomously. Machine learning, artificial intelligence (AI), and robotics are three interconnected fields transforming the world around us. These systems combine sensors, actuators, and intelligent algorithms to execute operations in dynamic environments. Industrial robots enhance manufacturing efficiency by automating assembly lines, while autonomous vehicles revolutionize transportation with real-time navigation and decision-making. Robotics also extends to healthcare, where robotic surgical assistants improve precision and patient outcomes.

Expert Systems: AI programs that simulate human expertise in specific fields. Expert systems are AI-based programs designed to emulate human decision-making in specialized domains. They rely on rule-based frameworks and knowledge bases to provide recommendations or solutions. In healthcare, diagnostic systems analyze patient data to identify diseases and suggest treatments.

AI has more branches such as fuzzy logic, Evolutionary Computation, Cognitive computing, Swarm Intelligence, and more worth giving a read about. You can check them out here.

Beyond the Buzz: Tangible AI Use Cases Transforming Everyday Operations

Okay!!!! So you are not gonna take me seriously until AI-powered robots take over - screamed the AI virtual BOT. 

Everyone, everywhere is really talking about AI. But have you ever wondered if AI is really that helpful in business to increase operational efficiency? OR is it just following the flock's opinion?

Let’s discuss some AI business implementations and I will let you pick a side. 

Bryte is the leading restorative sleep technology platform powered by AI.

The Chief Producer officer of Bryte, Rex Harris, shared a use case where he spoke about getting swamped with survey results and chat messages from users. They applied Correlation to build a Correlation machine called Claude. (In machine learning, correlation is a statistical analysis that measures how related two variables are) .Claude connects the dots in every survey result and provides you with a detailed summary report. It also looks through chat sessions to categorize and prioritize bugs and feature requests.“Interesting, 20 hours of survey work? Gone, thanks to the power of AI!". 

Let’s transition to the next use case. 

Taskade Automates tasks and supercharges workflows by crafting, training, and deploying your virtual AI Agents.

Dionis Loire (Co-Founder at Taskade) shared that before AI implementation, on average they could create 2 to 3 templates a week for customized template requests.  But now with AI in place, they jumped it to 100 templates a week. 

OKAY! That's a 35x improvement using AI! They also noticed better consumer experience and organic traffic.

One more implementation by Taskad was in the support industry. We know different sets of consumers often end up asking the same doubts and questions, and we keep answering them again and again! Taskade developed AI software for drafting and sending replies based on studying previous email responses.

Interprets New Support Emails -> Does Remote Search -> Drafts Response-> TADA Customer Query Resolved!

That was a 6/60x Improvement using AI. Moving on to our next.

Pulley is all about everything you need to manage Equity.

We all tend to forget small meeting pointers or tasks we are about to complete. Mark Erdmann Co-founder of Pulley shared a fascinating use case where they build an AI tool that instantly searches instances of you mentioning your work on Slack and recaps it for you. This saved at least one hour of work per quarter per person. The second use case they shared was creating a knowledge bot for the Slack channel. Any question asked will be answered from analysis of chat history, pre-processing raw chat before going to machine learning language models. The team of 30 users uses this 30-40 times a day. AI Saving 40 hrs of Leadership time! Let’s turn our attention to the next use case

Zapier is a workflow automation software for everyone.

Have you ever used Google Gemini to take meeting notes? Or you are still noting down the Minutes of Meeting the old way. Google’s useful feature takes meeting notes for you. If you join late, you can tell it “Catch me up during the meeting with Summary so far”. As an organizer, you also receive an email with a link recap shortly after the meeting ends. Pretty cool Check this out.

Zapier co-founder Mike Knoop took it one step ahead and built an OpenAI gong that extracts transcripts, gives out detailed summaries, recommends next steps, adds back data to CRM, and prompts sales representatives on how they should continue the deal. 

Result ??? $100 ARR per month

The Road Ahead: AI as a Tool, Partner, and a Catalyst for Change

In conclusion, AI has evolved from its early theoretical roots to become a transformative force across various industries today. With its branches ranging from machine learning and natural language processing to computer vision and robotics, AI has opened up new frontiers in problem-solving and automation. Businesses worldwide are leveraging AI for a myriad of use cases. As AI continues to advance, its potential for reshaping industries and improving efficiency remains boundless, promising a future where intelligent systems work alongside humans to tackle complex challenges.

One of the CEO’s in AI Public Consciousness 2022 said - “CEO’s have bought Ferraris in the shape of state of art AI systems. They just haven’t given any driving lessons to the staff!” …….Thoughts?

[skeeyee333]

Blog Post #3 Week 4 due 2/13

How does Everett challenge the myth that Black people are late or uninterested adopters of technology?

Everett challenges this myth by documenting the early and enthusiastic use of the internet by African diasporic communities, especially around 1995 when Black web presence began visibly growing. She mentions examples like Afrocentric content on Yahoo and the Million Woman March being organized online, showing how Black communities were not only participating in digital spaces, but they were also innovating within them.

How does mainstream media coverage of Black online activity reflect racial bias, according to Everett

Everett critiques mainstream media for portraying Black digital users as “surprising,” marginal, or only recently joining the digital world, even though they have been present just like everyone else. She highlights how coverage often emphasizes that Black digital activity is new, inadequate, or rare, making it seem out of place. This reflects a deeper bias that frames whiteness as the default digital identity, erasing the complexity and history of Black tech use.

What is one of the downsides of government monitoring for public safety?

I believe the government monitors our actions closely and often targets specific groups, particularly African Americans and Latinos, to make surveillance appear more justified. According to the article "Track and Trapped," around 40,000 African Americans and 130,000 Latinos are listed in the CalGang database. While this data may be presented as a tool for public safety, it results in tracking of innocent civilians who may look alike, leading to unfair outcomes for both targeted and untargeted groups. Although monitoring criminals can be valuable for society, the widespread surveillance of innocent people results in damage in areas that reduce justice and trust in the system, is a downside.

Should we blame AI for its algorithms? Or humans?

Artificial intelligence is essentially created by humans, meaning that its algorithms are based on what humans input into it. The ones we should blame for the lack of information that led to racist remarks are AI. The article “Algorithms of Oppression” mentions an example of how Google returned offensive results of African Americans, showing them with terms like “gorilla,” which is concerning. I believe we shouldn’t hold AI solely responsible for these outcomes. After all, it reflects the biases present in the data we feed it, which come from us. These errors reveal more about human prejudice than the technology itself.

Everett, A. (2002). The Revolution Will Be Digitized. Duke University Press. https://read.dukeupress.edu/social-text/article-abstract/20/2%20(71)/125/32619/The-Revolution-Will-Be-DigitizedAFROCENTRICITY-AND?redirectedFrom=fulltext 

Youth Justice Coalition (2012). Track and Trapped. Youth of Color, Data Gang Databases, and Gang Injunctions.

[hey-its-haliee]

[W5: I called dibs! Breaking news: Hashtag Activism - A tale of two tales]

Guys, I think we might be club snubbed...

Ah the internet... it's not just where we scroll—it’s where we exist. From calling out injustice to fueling trends, our digital lives don’t just reflect reality—they shape it. But being online isn’t enough. Digital citizenship means knowing the game: our rights, responsibilities, and the fact that platforms don’t just connect us—they control the conversation. They shape political engagement, amplify movements, and turn hashtags into rallying cries.

The question is: We’re louder than ever—but are we really being heard? 👻

Or are we just playing by the algorithm’s rules?😱

1. CTRL + Hustle: Clicktivism in Action?

_Hashtags: Raising Voices or Just Raising Eyebrows? 

Hashtag activism is the internet’s megaphone, rallying millions with a single click. Movements like #BlackLivesMatter and #MeToo have driven protests, influenced policies, and reshaped cultural conversations.

But but but but! Hear me out.

_Trending Today, Forgotten Tomorrow: Who’s Playing Who?

A viral moment isn’t always a revolution—it can just be digital theatre wrapped in pixels. Sure, social media can raise awareness at lightning speed, but keeping that momentum? That’s where things get messy.

As Cooper (2023) cites Muslic (2017):  "Social media is highly efficient at raising awareness as well as funds. However, the Internet is also incredibly fast-paced, so support for campaigns is usually short-lived.”

Sound familiar? One minute, your feed is flooded with activism, and the next, it’s back to cat memes and influencer drama. The algorithm moves on, and—let’s be real—so do most people.

_Click, Clout and Collapsed: The Internet’s 30-Minute Revolution

As you might have guessed, this does not go according to plan…

Before TikTok made activism trendy, there was Kony 2012—the campaign that turned a Ugandan warlord into the internet’s ultimate villain. It was the viral moment of 2012, with celebrities, influencers, and even your high school history teacher jumping on the bandwagon.

(Harris 2012)

(Hodgson 2012)

The result?

🚀 100 million views in six days.

The outcome?

🚫Not Kony’s arrest

…just a collective case of slacktivism burnout and the campaign’s leader having a very public meltdown. It was the ultimate reality check: the internet can make someone famous overnight, but justice? That’s a different story.

...

And nope, in case you were wondering—Joseph Kony was never arrested (#That Never Happened) Despite becoming the world’s most wanted man (at least online), he slipped through the cracks. The Ugandan military gave up the search in 2017, the U.S. packed its bags, and Kony? Well, he’s still out there, somewhere…He might have faded from our feeds, but reality? Not so much.

_Algorithm decides!

But not all movements get the same red carpet. Kony 2012 was pushed; #FreePalestine was punished. Activists face shadowbans, deletions, and content blocks. Turns out, platforms don’t just boost activism—they filter it. If the algorithm isn’t on your side… does your movement even stand a chance? But this wasn’t just a #FreePalestine problem—it was a system problem.

And someone saw this coming.

_The Internet’s Unbothered Prophet 

Meet Zeynep Tufekci - the internet’s only sane person. She didn’t just call it—she practically had a crystal ball. 

Tufekci tried to warn us, but did we listen? Nope. (We should have) She wasn’t just skeptical—she saw the design flaw in digital activism. Platforms don’t just amplify voices; they engineer movements for engagement. The system thrives on outrage, not outcomes. And guess what? The algorithm always wins. 

Virality isn’t victory. A video trends, we tear up, smash the share button, and think we’re dismantling war crimes from our dorm rooms.

…Can we do better?

2. #PaveTheWay: From Tweets to Streets

_Viral with a Vengeance: Finally, We Grew a Backbone

Not all hashtag activism is performative noise. Some movements bridge the gap between digital and real-world action. Enter #NiUnaMenos.

Born in Argentina in 2015, #NiUnaMenos (“Not One Less”) started as a viral feminist movement against gender-based violence. It evolved into real-world protests, pushed for legal reforms, and led to stronger anti-gender violence laws.

Similarly, the #GeorgeFloyd protests also kept the fire going - it turned a viral video into a global uprising, forcing a reckoning on police brutality and racial justice—proving that hashtag activism CAN work, and in fact, it’s a powerful tool…

...Just make sure the internet doesn’t move on before the “real-life” work is done.

Final Thoughts: Beyond the Scrolling, Screaming and Sharing

A tweet can trend. A movement can shake things up. But real change? That takes more than 280 characters. The fight doesn’t stop when the algorithm moves on—The question is, will you?

References:

Basu, M 2012, ‘As criticism surfaces, “KONY 2012” gains momentum faster than Susan Boyle’, CNN, viewed 15 February 2025, .

Cooper, K 2023, The Effectiveness of Online Activism: Who it is Effective For, What Issues it is Effective For, and What Time Period it is Effective For, Thesis, University at Albany, State University of New York, p. 17, viewed .

Harris, P 2012, ‘Kony 2012 organisers plan massive day of action across US cities’, The Guardian, viewed .

Hodgson, S 2012, ‘“Kony 2012” Video Illustrates the Power of Simplicity’, The New York Times, viewed .

[sanjayforsales]

How Sales Evolved the World: A Transformative Journey

Sales, a cornerstone of human interaction and economic growth, has been the silent engine driving the evolution of societies across centuries.

From barter systems to digital transactions, sales has continually adapted, reshaped, and empowered civilizations. It’s not just about transactions—it’s about creating value, building relationships, and fostering innovation.

Let’s delve into how sales has evolved over time, and how the world’s greatest sales minds have influenced this transformation.

The Early Days: Barter Systems to Currency

Sales began as simple barter systems around 6000 BC when people exchanged goods and services directly. For instance, a farmer might trade wheat for a carpenter’s crafted tools. However, as societies expanded, barter faced limitations due to its inefficiency.

The invention of currency around 600 BC in the ancient kingdom of Lydia (modern-day Turkey) revolutionized sales. Currency simplified trade, standardizing value and enabling economies to grow beyond regional boundaries.

The Industrial Revolution: Scaling Sales

The 18th-century Industrial Revolution marked a seismic shift in sales. Mass production created an abundance of goods, necessitating organized sales efforts. Businesses adopted door-to-door sales and established markets to reach customers. For instance, Singer Sewing Machines, founded in 1851, became one of the first companies to leverage installment plans and door-to-door sales to make its products accessible to the masses.

The Industrial Revolution also birthed iconic sales figures like John H. Patterson, the founder of the National Cash Register (NCR) Company. Known as the father of modern salesmanship, Patterson introduced structured sales training programs in the late 1800s. His approach emphasized understanding customer needs and crafting personalized pitches—a principle still relevant in today’s sales world.

The Digital Revolution: Sales in the Age of the Internet

The advent of the internet in the late 20th century marked another monumental evolution in sales. Businesses moved online, creating e-commerce platforms that revolutionized accessibility. Amazon, founded by Jeff Bezos in 1994, is a prime example. Bezos’s vision of "Earth’s most customer-centric company" transformed how products are sold and delivered globally.

Search engines like Google, launched in 1998, introduced a new sales frontier: digital marketing. Techniques like Search Engine Optimization (SEO) and Pay-Per-Click (PPC) advertising became indispensable. Today, websites like www.consult4sales.com help businesses navigate this complex digital landscape, offering expert insights and strategies to optimize their sales processes.

Sales Masters Who Changed the Game

Some individuals have redefined the art and science of sales. Here are a few trailblazers:

Zig Ziglar (1926-2012): Known as the "Sales Guru," Ziglar’s motivational speeches and books like Secrets of Closing the Sale continue to inspire sales professionals worldwide.

Mary Kay Ash (1918-2001): The founder of Mary Kay Cosmetics empowered women through her direct sales model. Her legacy proves that empathy and personal relationships are key to successful sales.

Dale Carnegie (1888-1955): Author of How to Win Friends and Influence People, Carnegie’s teachings on interpersonal skills have influenced countless salespeople.

Their principles—from building trust to understanding psychology—remain cornerstones in sales today.

Modern Trends Shaping Sales

Data-Driven Sales

In today’s world, data reigns supreme. Customer Relationship Management (CRM) systems like Salesforce and HubSpot have revolutionized sales. They help businesses analyze customer behavior, predict trends, and personalize interactions. For example, Netflix’s recommendation algorithm—a data-driven approach—has significantly boosted its sales by catering to user preferences.

Social Selling

The rise of social media has added a new dimension to sales. Platforms like LinkedIn, Instagram, and Twitter allow businesses to engage with customers directly. Social selling—the art of using social media to find and nurture leads—is a game-changer. Brands like Nike leverage these platforms to build authentic relationships and drive sales.

The Role of AI

Artificial Intelligence (AI) is shaping the future of sales. Chatbots, predictive analytics, and personalized recommendations streamline the sales process. Companies like Tesla use AI-powered platforms to engage potential buyers, enhancing the overall customer experience. For tailored strategies in this evolving landscape, businesses can rely on www.consult4sales.com for expert guidance.

Real-World Impact of Sales Evolution

Economic Growth

Sales drives economies. For instance, the automobile industry’s growth in the 20th century wasn’t just due to innovation but also because of stellar sales strategies. Henry Ford’s vision of affordable cars for the masses was realized through effective sales channels and financing options.

Innovation and Competition

Sales fosters competition, which in turn drives innovation. Apple’s iPhone, launched in 2007, disrupted the mobile phone market not just through its design but also through its genius sales and marketing campaigns. Steve Jobs’s presentations became legendary, blending storytelling with product features to create desire.

Empowering Individuals

Sales isn’t limited to businesses. It empowers individuals to achieve financial independence. Consider the rise of small businesses on platforms like Etsy. These entrepreneurs use digital tools to sell globally, breaking traditional barriers.

The Future of Sales

The next frontier in sales will be driven by immersive technologies like virtual reality (VR) and augmented reality (AR).

Imagine trying out a car or furniture virtually before making a purchase. Additionally, sustainability will play a crucial role. Companies aligning their sales strategies with eco-conscious values are likely to resonate with future generations.

To stay ahead in this dynamic field, businesses must adapt, innovate, and embrace digital transformation. For actionable insights, visit www.consult4sales.com—your partner in mastering the art of sales.

Sales is the heartbeat of human progress. It has evolved from simple barter systems to complex digital ecosystems, continuously shaping economies, industries, and lives. As we stand on the brink of new innovations, the essence of sales—building trust, delivering value, and fostering relationships—remains unchanged. By learning from the masters and leveraging modern tools, anyone can harness the power of sales to make a lasting impact.

For businesses seeking to navigate this ever-evolving landscape, www.consult4sales.com offers the expertise and guidance needed to thrive in the world of sales.

[completeconnection]

Advancements in AI Technology: Revolutionizing the World

Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century. From its inception as a theoretical concept in the mid-20th century to its current applications across industries, AI has grown to influence nearly every aspect of human life. This article explores the remarkable advancements in AI technology, highlighting key milestones, innovations, and real-world applications that showcase its immense potential. Alongside, we’ll also discuss how to use data analytics for better digital marketing strategies, 10 key strategies for optimizing your cloud expenses, and explore instant approved article submission sites manual verified for 2025.

The Early Days of AI

AI’s journey began in the 1950s when pioneers like Alan Turing and John McCarthy laid the theoretical groundwork. Turing’s seminal paper, Computing Machinery and Intelligence (1950), introduced the concept of machines performing tasks that require human-like intelligence. McCarthy, often referred to as the “Father of AI,” coined the term "Artificial Intelligence" in 1956 during the Dartmouth Conference, which is widely considered the birthplace of AI as a field of study.

In these early years, AI research focused on symbolic reasoning and rule-based systems. Programs like the Logic Theorist (1955) and General Problem Solver (1957) demonstrated AI’s potential in solving mathematical and logical problems. However, limitations in computing power and data hindered significant progress.

The Rise of Machine Learning

The 1980s and 1990s marked a shift towards machine learning (ML), an AI subfield focused on enabling machines to learn from data rather than relying solely on pre-programmed rules. This era saw the development of algorithms like decision trees, support vector machines, and neural networks. Neural networks, inspired by the structure of the human brain, became the foundation of modern AI advancements.

One of the defining moments was the introduction of backpropagation, an algorithm that allowed neural networks to learn more efficiently. This innovation laid the groundwork for deep learning, a subset of machine learning that would dominate AI research in the 21st century.

The Big Data Revolution

AI’s rapid progress in the 2000s and 2010s was fueled by the explosion of big data. With the proliferation of the internet, social media, and IoT devices, vast amounts of data became available for analysis. AI systems leveraged this data to improve accuracy and performance. This development also brought data analytics to the forefront, transforming digital marketing strategies by providing actionable insights into customer behavior.

For instance, search engines like Google harnessed AI to refine their algorithms, delivering more relevant search results. Similarly, recommendation systems, as seen in platforms like Netflix and Amazon, became increasingly sophisticated, tailoring content and products to individual preferences. Marketers now use data analytics to identify customer trends, optimize ad spend, and boost engagement, ultimately enhancing ROI.

Breakthroughs in Deep Learning

Deep learning emerged as a game-changer in AI, enabling machines to process unstructured data such as images, videos, and speech. In 2012, a deep neural network developed by Geoffrey Hinton and his team at the University of Toronto achieved groundbreaking results in image recognition, winning the ImageNet competition. This success demonstrated the power of deep learning in solving complex tasks.

Real-world applications soon followed:

Computer Vision: Deep learning powered advancements in facial recognition, object detection, and medical imaging. Companies like Tesla use computer vision for autonomous vehicles, enabling cars to "see" and interpret their surroundings.

Natural Language Processing (NLP): AI systems like OpenAI’s GPT series and Google’s BERT revolutionized NLP. These models can understand and generate human-like text, powering chatbots, virtual assistants, and translation services.

Speech Recognition: Virtual assistants such as Siri, Alexa, and Google Assistant rely on deep learning to convert spoken language into actionable commands.

AI in Healthcare

One of the most impactful applications of AI has been in healthcare. AI systems have demonstrated remarkable accuracy in diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. For example:

Medical Imaging: AI algorithms can analyze X-rays, MRIs, and CT scans to detect anomalies such as tumors or fractures. In some cases, these systems outperform human radiologists.

Drug Discovery: AI accelerates the drug development process by identifying potential compounds and predicting their effectiveness. During the COVID-19 pandemic, AI played a critical role in vaccine development.

Predictive Analytics: Hospitals use AI to predict patient admissions, optimize resource allocation, and prevent readmissions by analyzing patient data.

AI in Business and Industry

Businesses across industries have adopted AI to enhance efficiency, reduce costs, and improve customer experiences. Notable examples include:

Finance: AI-driven algorithms are used for fraud detection, credit scoring, and algorithmic trading. For instance, JPMorgan Chase’s AI system reviews thousands of legal documents in seconds, a task that would take human employees hundreds of hours.

Retail: AI-powered chatbots handle customer inquiries, while inventory management systems predict demand and reduce waste. Amazon’s use of AI in logistics and supply chain optimization is a prime example.

Manufacturing: AI enables predictive maintenance by monitoring equipment and identifying potential failures before they occur. Companies like Siemens and GE utilize AI to optimize production processes.

To manage costs effectively in cloud-based AI and big data implementations, businesses are adopting strategies to optimize their cloud expenses. These include monitoring usage, automating scaling, and leveraging cost-efficient storage solutions, ensuring maximum ROI from cloud investments.

Autonomous Systems

Self-driving cars are one of the most ambitious applications of AI. Companies like Waymo, Tesla, and Uber have made significant strides in developing autonomous vehicles capable of navigating complex urban environments. These systems rely on a combination of sensors, computer vision, and reinforcement learning to make real-time decisions.

Drones and robots are another area where AI has had a transformative impact. Autonomous drones are used for delivery services, disaster response, and agricultural monitoring, while robots assist in warehouses, hospitals, and even homes.

Ethical Considerations and Challenges

Despite its remarkable achievements, AI technology is not without challenges. Ethical concerns surrounding privacy, bias, and accountability have come to the forefront. For instance:

Bias in AI Systems: AI models trained on biased data can perpetuate and even amplify societal inequalities. For example, facial recognition systems have faced criticism for higher error rates in identifying individuals from certain demographic groups.

Job Displacement: The automation of tasks previously performed by humans has raised concerns about job loss in sectors like manufacturing, transportation, and customer service.

Privacy Concerns: AI systems that collect and analyze personal data, such as surveillance technologies, have sparked debates about the balance between security and privacy.

To address these issues, organizations and governments are working to establish ethical AI guidelines and regulatory frameworks. Initiatives like "Explainable AI" aim to make AI systems more transparent and accountable.

AI in Entertainment and Creativity

AI is not just about efficiency and problem-solving; it is also fueling creativity. In the entertainment industry, AI is used to create personalized content recommendations, generate music, and even write scripts. Tools like OpenAI’s DALL-E and Adobe’s Sensei enable artists to create stunning visuals with minimal effort.

AI-generated content is becoming increasingly prevalent, from realistic video game characters to deepfake videos. While these technologies offer exciting possibilities, they also raise questions about authenticity and misuse.

AI and Digital Marketing

The integration of AI and data analytics has transformed digital marketing strategies. AI enables marketers to analyze large datasets to understand customer behavior, predict trends, and personalize campaigns. Combining this with the use of instant approved article submission sites manual verified for 2025, marketers can amplify their reach and authority effectively.

For example:

AI-driven chatbots provide real-time customer support.

Predictive analytics helps optimize ad targeting.

AI tools like HubSpot and SEMrush automate SEO, enhancing visibility.

Future Prospects of AI

The future of AI holds limitless potential. Emerging areas such as quantum computing, neuromorphic computing, and AI-human collaboration are poised to redefine what machines can achieve. Key trends to watch include:

General AI: While current AI systems are specialized for specific tasks, the development of General AI—machines capable of performing any intellectual task that a human can do—remains a long-term goal.

AI in Education: Personalized learning platforms powered by AI could revolutionize education, tailoring lessons to individual student needs.

Sustainability: AI is being used to tackle global challenges like climate change by optimizing energy use, monitoring deforestation, and predicting natural disasters.

Conclusion

AI technology has come a long way from its theoretical origins, evolving into a powerful tool that is reshaping industries and improving lives. From healthcare and business to entertainment and sustainability, AI

[cheepuppp]

Activism & Protest: How Social Media Sparks (and Complicates) Revolutions

Can a hashtag change the world? Social media sure makes it seem like it. From #BlackLivesMatter to #StopAsianHate, hashtags have become rallying cries for global movements. They mobilize protests, expose injustices, and amplify voices that traditional media often ignores. But here’s the thing: while social media is a powerful tool for activism, it’s also a minefield of challenges. Let’s dive into how platforms like Twitter and Facebook are reshaping activism, for better, for worse, and for everything in between.

The Power of Going Viral: How Social Media Drives Movements

One of the best things about social media? It turns everyday people into activists overnight. Take the Hong Kong protests, for example. When the government started cracking down, protesters used Twitter and encrypted apps like FireChat to organize rallies without getting caught (Mahtani, 2019). Even when the internet was blocked, they found workarounds with mesh networks, basically offline communication systems that couldn’t be traced (Koetsier, 2019). Talk about innovation.

In Myanmar, social media played a similar role. Protesters used Facebook to document human rights violations during the military coup, rallying international attention and solidarity (Banki, 2021). The posts were raw, heartbreaking, and impossible to ignore. Social media wasn’t just a tool for spreading the wor, it became a lifeline for people fighting for democracy.

And let’s not forget the #StopAsianHate movement in the U.S. When anti-Asian violence spiked during the pandemic, hashtags like #StopAsianHate and #COVIDRacism brought communities together. Abidin and Zeng (2020) explain how these hashtags did more than raise awareness, they gave people a safe space to share their stories and demand change.

The Risks: When Social Media Bites Back

But here’s the catch: the same platforms that empower activists can also be used against them. In Hong Kong, protesters had to wear masks and ditch their phones to avoid being tracked by surveillance tech (Walters & Smith, 2019). In Myanmar, the military turned Facebook into a propaganda machine, spreading fake news to discredit the protestors (Shao, 2019). And let’s not forget how algorithms work, they prioritize engagement, which often means pushing the most divisive content to the top of our feeds. That’s great for outrage clicks, but terrible for nuanced discussions.

Sigal and Biddle (2015) caution against putting social media on a pedestal. Yes, it’s a powerful tool, but it’s not a silver bullet. Censorship, surveillance, and misinformation are real threats, and they can undermine even the most passionate movements.

The Wildcard: K-Pop Fans and the New Wave of Activism

Now, here’s where things get really interesting. Activism isn’t just happening in traditional spaces, it’s spilling into fandoms. Yes, fandoms. K-pop stans are some of the most organized communities on the internet, and they’ve started using their skills for social justice. During the Black Lives Matter protests, BTS fans raised over $1 million for the cause, rallying behind the hashtag #MatchAMillion (Cho, 2018).

This kind of “fan activism” (Jenkins & Shrestova, 2012) is a game-changer. It shows that digital communities whether they’re built around music, memes, or activism have the power to mobilize for real-world impact. Who knew streaming BTS’s latest album and fighting for justice could go hand-in-hand?

Personal Reflection

I’ll admit it, I used to think sharing hashtags and liking posts didn’t really count as activism. But during the #StopAsianHate movement, I found myself retweeting stories, signing petitions, and even joining online discussions about how to help. It felt small at first, but it also made me realize the ripple effect of social media. One share can lead to ten, then a hundred, and suddenly you’ve got a movement.

That said, I’ve also seen the limits. Clicking “share” is easy; showing up to protests or making donations takes more effort. Social media is a great starting point, but the real challenge is turning those clicks into action.

Conclusion

Social media has completely transformed activism. It gives marginalized voices a platform, connects global communities, and turns hashtags into rallying cries. But it’s not perfect. The risks of surveillance, censorship, and slacktivism are very real, and not every hashtag leads to lasting change.

Still, the potential is undeniable. Movements like #StopAsianHate, the Hong Kong protests, and K-pop fan activism show us that when digital communities come together, they can achieve incredible things. The challenge now is to take that online energy and make it count offline because that’s where revolutions really happen.

References

Abidin, C., & Zeng, J. (2020). Feeling Asian Together: Coping With #COVIDRacism on Subtle Asian Traits. Social Media + Society. https://doi.org/10.1177/2056305120948223

Banki, S. (2021). Thanks to the Internet, we know what's happening in Myanmar. But a communication blackout may be near. The Conversation.

Cho, M. (2018). 3 Ways that BTS and its Fans are Redefining Liveness. Flow: A Critical Forum on Media and Culture, 24. http://www.flowjournal.org/2018/05/bts-and-its-fans/

Jenkins, H., & Shrestova, S. (2012). Up, up and away! The potential of fan activism. Transformative Works and Cultures, 10. https://doi.org/10.3983/twc.2012.0435

Mahtani, S. (2019). Masks, cash and apps: How Hong Kong's protesters find ways to outwit the surveillance state. The Washington Post. https://www.washingtonpost.com/world/asia_pacific/masks-cash-and-apps-how-hong-kongs-protesters-find-ways-to-outwit-the-surveillance-state/2019/06/15/8229169c-8ea0-11e9-b6f4-033356502dce_story.html

Shao, G. (2019). Social media has become a battleground in Hong Kong's protests. CNBC. https://www.cnbc.com/2019/08/16/social-media-has-become-a-battleground-in-hong-kongs-protests.html

Sigal, I., & Biddle, E. (2015). Our Enduring Confusion About the Power of Digital Tools in Protest. Fibreculture Journal, 26. https://twentysix.fibreculturejournal.org/fcjmesh-007-our-enduring-confusion-about-the-power-of-digital-tools-in-protest/

[afzaal1234]

Facebook vs. Instagram: A Comparative Analysis of Social Media Giants

Social media has reshaped our world, influencing how we communicate, share, and build relationships. Among the platforms leading this digital revolution are Facebook and Instagram. Though both platforms are owned by Meta Platforms, Inc., they each offer unique features and experiences that appeal to different audiences. This blog will explore the similarities and differences between Facebook and Instagram, shedding light on their strengths and which one might be better for your social media goals.

1. A Brief Overview

Facebook, launched in 2004, was the pioneer that brought social networking into the mainstream. With its comprehensive suite of features, from status updates to event planning, Facebook became the go-to platform for billions of users. As of 2024, Facebook still boasts one of the largest active user bases worldwide, encompassing a diverse demographic from teenagers to senior citizens.

Instagram, on the other hand, emerged in 2010 and quickly carved out a niche as the visual-centric cousin of Facebook. With a focus on photo and video sharing, Instagram attracted younger audiences looking for a more curated and visually appealing social experience. By the time Facebook acquired Instagram in 2012, it was already cementing its place as a powerful contender in the social media ecosystem.

2. User Demographics

Understanding the audience each platform attracts is crucial for users and businesses deciding where to focus their attention.

Facebook caters to a broader demographic. Its extensive user base ranges from teenagers to older adults, making it ideal for reaching various age groups. According to recent data, while younger users are slightly less engaged than before, the platform still maintains significant activity among adults aged 30 and older.

Instagram skews younger, with the majority of users aged between 18 and 34. This makes it a favorite for Millennials and Generation Z. Instagram's visually driven format and features like Stories and Reels resonate with users looking for quick, engaging content.

3. Content and Features

Both platforms have evolved over time, adding new features and functionalities to maintain user engagement.

Facebook's Features:

Text and Link Sharing: Facebook's core function is sharing updates, articles, and links, making it a hub for information.

Groups and Events: These tools are key for community building and event promotion.

Marketplace: A space for users to buy and sell goods locally.

Live Streaming: Facebook Live allows users to stream events and interact in real-time.

News Feed Algorithm: Prioritizes content based on user interactions, including comments, likes, and shares.

Instagram's Features:

Visual Storytelling: Posts are primarily image- or video-based, with captions to complement them.

Stories and Highlights: Temporary 24-hour content is great for behind-the-scenes looks, announcements, and interactive posts.

Reels: Short, entertaining videos that often feature trends, music, and creativity.

Explore Page: Helps users discover new content based on their interests.

Shopping Integration: The ability to tag products directly in posts and stories enhances the shopping experience.

Comparison of Content Style:

Facebook leans toward more comprehensive, discussion-oriented content. A post on Facebook might include a longer narrative or a detailed link preview leading to an external article.

Instagram thrives on a concise, aesthetic-first approach. High-quality photos, brief captions, and videos that engage viewers within a few seconds are vital for success.

4. Engagement and Community Building

Both platforms are powerful tools for fostering interaction but in distinct ways.

Facebook is ideal for in-depth interactions. Its structure encourages dialogue through comments on posts, replies within community groups, and participation in discussions that can span a wide array of topics. The platform’s algorithm often favors longer posts and discussions, making it better suited for fostering community and detailed conversations.

Instagram, while still fostering engagement, thrives on immediacy and simplicity. Likes, shares, and quick comments dominate the interaction landscape. Features like polls, questions, and interactive stickers in Stories allow for direct user engagement that is more spontaneous and visually driven. The platform’s focus on aesthetics and trends means that maintaining a visually appealing and consistent theme can greatly enhance engagement.

5. Advertising and Business Use

Both platforms offer robust advertising solutions, yet they have different strengths.

Facebook Ads: The platform’s advanced targeting capabilities are one of its biggest strengths. Businesses can leverage extensive demographic, interest-based, and behavioral data to reach their ideal audience. The variety of ad formats, from carousel ads to video ads, provides flexibility in messaging.

Instagram Ads: While it also uses Meta's targeting tools, Instagram's ads are more integrated into the user experience. Sponsored posts and Stories often blend seamlessly into the feed, which can lead to higher engagement rates, particularly with visually appealing and dynamic content. The platform’s visual focus makes it a natural choice for fashion, beauty, and lifestyle brands.

Business Tools Comparison: Facebook’s Business Suite provides a comprehensive platform for managing marketing campaigns, analyzing data, and automating posts across both platforms. Instagram, however, has leaned into integrations like Instagram Shopping, where users can shop directly through posts, making it an essential platform for e-commerce.

6. Algorithm Differences

Facebook’s algorithm prioritizes posts based on factors such as personal interactions, relevance, and popularity, which means users often see posts from friends, groups, and pages they interact with most. This can make it harder for new content to reach users without paid promotions or consistent interaction.

Instagram’s algorithm, while also engagement-based, has a more visual focus and considers factors such as user interactions, posting times, and content type (Reels, Stories, or standard posts). The introduction of Reels, modeled after TikTok’s success, shows Instagram’s push toward quick, engaging video content.

7. Pros and Cons of Each Platform

Facebook Pros:

Versatile for a variety of content types (text, video, event pages, etc.).

Strong community-building tools through groups and events.

Robust advertising and targeting options.

Facebook Cons:

Overcrowded with content, making organic reach more difficult.

Younger demographics show signs of decreasing engagement.

Instagram Pros:

Perfect for visually driven content and brand aesthetics.

High engagement rates for visually appealing posts and interactive features.

Effective for influencer partnerships and e-commerce.

Instagram Cons:

Limited text capacity can restrict storytelling.

Highly competitive, making it challenging to stand out without strong visual content.

[aileaders]

A Spotlight on the Key AI Innovators in Modern AI Tech Revolution

The modern artificial intelligence (AI) revolution has been driven by a series of groundbreaking innovations that are shaping the future of industries and society as a whole. Behind these advancements are visionary AI innovators—engineers, researchers, and entrepreneurs—whose work is pushing the boundaries of what AI can achieve. From creating sophisticated machine learning algorithms to developing transformative AI applications, these key figures are at the forefront of the AI revolution. This article spotlights some of the most influential innovators who are shaping the future of AI technology.

Geoffrey Hinton: The Godfather of Deep Learning

Called as the “Godfather of Deep Learning,” Geoffrey Hinton, is one of the most pivotal figures in the AI revolution. A British-Canadian cognitive psychologist and computer scientist, Hinton’s work in neural networks laid the foundation for many of today’s AI systems. In the 1980s, he co-developed the backpropagation algorithm, which allows neural networks to learn from errors and improve their performance. This breakthrough was instrumental in the development of deep learning, a subset of machine learning that mimics the way the human brain processes information.

Hinton’s work gained prominence in 2012 when his research team at the University of Toronto won the ImageNet competition, a major computer vision challenge, using deep learning. Their algorithm vastly outperformed competitors, demonstrating the power of neural networks for image recognition. Since then, deep learning has become the backbone of AI applications in areas like speech recognition, natural language processing, and computer vision. Hinton continues to be an influential figure in AI research, currently working as a vice president at Google and a professor at the University of Toronto.

Yann LeCun: Pioneer of Convolutional Neural Networks

Another leading figure in the field of AI, particularly in deep learning and computer vision is Yann LeCun.  A professor at New York University and the Chief AI Scientist at Meta (formerly Facebook), LeCun is known for developing convolutional neural networks (CNNs), which have become crucial in image and video recognition. CNNs are designed to automatically and adaptively learn spatial hierarchies of features from data, making them especially effective for tasks like object detection and facial recognition.

LeCun’s contributions to AI have had a significant impact on the development of autonomous vehicles, medical imaging, and even augmented reality. His CNN architecture is widely used in AI applications that require image classification, such as in self-driving cars, where AI needs to identify road signs, pedestrians, and other vehicles. LeCun, along with Hinton and Yoshua Bengio, received the prestigious Turing Award in 2018 for their work in deep learning, marking them as central architects of the modern AI revolution.

[ethandottang]

Week 12 | Crowdsourcing in Times of Crisis

Hello Humans! We'll be talking all about crowdsoursing today! Could we do something right here in this blog? Could we turn this blog into an example of crowdsourcing? Drop anything you can think of about crowdsourcing, to let others come to this blog to get information about crowdsourcing!

Now crowdsourcing, what is it? it is a practice where tasks or inputs are solicited from a large group of people, often via the internet, has proven to be a valuable tool in times of crisis. This approach leverages the collective intelligence and resources of a community to address challenges that may be beyond the capabilities of individual entities or traditional methods. During crises, such as natural disasters, pandemics, or humanitarian emergencies, crowdsourcing can facilitate rapid and effective responses, providing crucial support to affected populations.

One of the primary benefits of crowdsourcing in crisis situations is the speed at which information can be gathered and disseminated. For instance, during the 2010 Haiti earthquake, the Ushahidi platform enabled real-time mapping of crisis information by aggregating data from various sources, including social media, text messages, and emails (Meier, 2012). This crowdsourced information helped rescue teams prioritize their efforts and allocate resources more efficiently. Similarly, during the COVID-19 pandemic, crowdsourced data platforms like Nextstrain provided real-time tracking of the virus's spread, aiding public health responses globally (Hadfield et al., 2018).

Crowdsourcing also promotes community engagement and empowerment. By involving local communities in the crisis response process, it ensures that the solutions are tailored to the specific needs and contexts of those affected. This participatory approach not only enhances the effectiveness of the response but also fosters resilience and self-reliance among community members. For example, during the Australian bushfires of 2019-2020, platforms like Fires Near Me allowed citizens to report and track fire incidents, contributing to a more coordinated and community-driven response (McLennan, 2020).

However, crowdsourcing in times of crisis is not without challenges. The accuracy and reliability of crowdsourced data can be problematic, as the information provided by untrained individuals may be inconsistent or erroneous. Furthermore, the sheer volume of data can be overwhelming, necessitating robust systems for filtering and verifying information. Addressing these challenges requires a combination of technological solutions, such as machine learning algorithms for data validation, and human oversight to ensure the credibility of the information (Goodchild & Glennon, 2010).

In conclusion, crowdsourcing is a powerful tool for crisis management, offering rapid information gathering, enhanced community involvement, and resource optimization. While challenges remain, the continued development of technological and organizational frameworks can harness the full potential of crowdsourcing to improve crisis response efforts.

References

Goodchild, M. F., & Glennon, J. A. (2010). Crowdsourcing geographic information for disaster response: a research frontier. International Journal of Digital Earth, 3(3), 231-241.

Hadfield, J., Megill, C., Bell, S. M., Huddleston, J., Potter, B., Callender, C., Sagulenko, P., Bedford, T., & Neher, R. A. (2018). Nextstrain: real-time tracking of pathogen evolution. Bioinformatics, 34(23), 4121-4123.

McLennan, B. (2020). Fires Near Me: The citizen-led bushfire information revolution. Australian Journal of Emergency Management, 35(2), 20-25.

Meier, P. (2012). Crisis mapping in action: How open source software and global volunteer networks are changing the world, one map at a time. Journal of Map & Geography Libraries, 8(2), 89-100.

[szczutek]

Activism

Activism

Judith Butler: Bodies in Alliance and Politics of the Street in Sensible Politics

Butler explores the relation of the body its representation in relation to order – otherwise called „theatre of legitimacy”. The way actions, status and context of the two in relation to place can have revolutionary effects or the opposite, and how in more modern context the media is placed into this equation. Butler also disputes the idea that the eye of the media is not a viable form of „body”/that media presence does not demonstrate in the same way physical occupation demonstrates (establishes itself in a public space). This is, however, disputable since 2012 when Butler made this argument as more commonly brief media show of solidarity has largely replaced physical occupation of space. In this way despite its larger numbers it more easily governable than a breathing crowd, through censhorship, algorithms (as Butler has made evident the fact that „the dominant media are corporately owned” and thus benefit from their role in the theatre of legitimacy.)

•Theatre of Legitimacy: theatre/appearance of a regime „is no longer unproblematically housed in public space, since public space now occurs in the midst of another action, one that displaces the power that claims legitimacy precisely by taking over the field of its effects.”

•„just as they sometimes fill or take over a public space, the material history of those structures also works on them, becoming part of their very action” à actions placed in context, targeting sites of political wounding or of social significance in order to wound.

•„Such a struggle intervenes the spatial organisation of power, which includes the allocation and restriction of spatial locations in which and by which any population may appear, which implies a spatial regulation of when and how the ‚popular will’ may appear.”

•„Tahrir Square” à 25 January Revolution in Egypt in response to police brutality, state-of-emergency laws, lack of political freedom, corruption, unemployment, inflation etc of the Hosni Mubarak regime. Butler specifically references the occupations of plazas.

•Mubarak regime’s „entrenched hierarchies [...] differentials of wealth between the military and corporate sponsors of the regime and the working people.” à In the resistance „how people cared for their various quaters within the square, the beds on the pavement” etc. The theatre of legitimacy is not only in the politics of the regime but in the values it enforces. Occupying the public space, there is no division like in the home or the workplace, women and men, abled and disabled, people of all backgrounds exist in a new temporary social structure that destabilises the previously enforced, even if just from observing and not partaking. „they were breaking fown a conventional distinction between public and private in order to establish new relations of equality; in this sense they were incorporating into the very social form of resistance the principles they were struggling to realise in broader political forms.”

•Chants of silmiyya „a gentle exhortation: peaceful, peaceful” – „the collective chant was a way of encouraging people to resist the mimetic pull of military aggression.” à a space previously governed by regime, thus incentivised for violence, being reestablished and furthermore pushed towards nonviolence in order to sever theatre of legitimacy. „language worked not to incite inaction, but to restrain one”

•On media revolution Butler poses two initial questions: How does media revolution make actual bodies less central to the political action? How important was the locatedness of bodies in the events that took place?

•„Will the Palestinians have their Tahrir Square?” -> Butler did not mention this without reason: the Palestinian question being a timeless territorial battle with annexation, theft of land and colonisation narrowing the field of resistance. I would write that the relevance has increased but it has always been relevant since the Nakba, the land has been thieved along with places of cultural and political significance (‚Tahrir Squares’ of Palestine). 2021 saw expulsion of whole neighborhoods in Sheikh Jarrah to make room for Israeli settlers. Where is the Tahrir Square in a colonial/settler conflict as opposed to the civil conflict? The rules of the game change when you step into territory that is not ‚yours’ on paper.

•„The street scenes become politically potent only when and if we have a visual and audible version of the scene communicated in live or proximate time, so that the media does not merely report the scene, but is part of the scene and action” – If a tree falls in a forest allegory. -> Aaron Bushnell

•„the freedom of the media to broadcast from these sites is itself an exercise of freedom and so a mode of exercising [...] This is doubtless why both Hosni Mubarak and David Cameron, eight months apart, both argued for the censorship of social media networks. At least in some instances, the media not only report on social and political movements that are laying claim to freedom and justice in various ways; the media also are exxercising one of those freedoms” „[...] that Twitter and other virtual technologies have led to disembodiment of the public sphere, I disagree. [...] But under conditions when those with cameras or Internet capacities are imprisoned or tortured or deported, the use of the technology effectively implicates the body.”

•Rob Nixon’s ‚slow violence’: a measure of violence that is not summarised by a casualty count or immediate damage but by the chemical and structural violence committed à e.g. long term effects of white phosphorous bombs on Gaza poisoning the sea, environmental effects of war. Slow violence also focuses on „narrative ways in which to make this slow violence visible and accountable.”

• „what lies beyond the first-hand sensory, or even the time-span of human perception” à Effects of nuclear warfare on Hiroshima/Nagasaki, Vietnam

[finance007]

Navigating the Cryptocurrency Frontier: Contrasting Bitcoin and Ripple in the Dynamic Landscape

Within the ever-evolving realm of cryptocurrency, Bitcoin and Ripple emerge as two captivating figures. Bitcoin, recognized as the progenitor of digital currency, and Ripple, with its innovative approach to global financial transactions, represent distinct facets of the crypto revolution. Gaining insights into the key differences between these two titans is essential for investors and enthusiasts looking to navigate the intricate terrain of cryptocurrencies.

Body:

Bitcoin: Pioneer of Digital Currency: Originating in 2008, Bitcoin stands as the original cryptocurrency, operating as a decentralized form of digital cash. Powered by blockchain technology and utilizing a proof-of-work consensus mechanism, Bitcoin's primary focus is on facilitating peer-to-peer electronic cash transactions. It enables secure and transparent financial interactions without the need for intermediaries.

Ripple: Transforming Global Financial Transactions: Introduced in 2012, Ripple takes a unique approach by targeting the global financial industry. Its core objective is to streamline cross-border transactions for banks and financial institutions. Employing a distinct consensus algorithm and a native digital asset, XRP, Ripple seeks to enhance the efficiency of international money transfers, positioning itself prominently in the fintech landscape.

Blockchain Technology: While both Bitcoin and Ripple leverage blockchain technology, their applications diverge significantly. Bitcoin's blockchain focuses on maintaining a decentralized ledger for financial transactions, prioritizing security and immutability. In contrast, Ripple's blockchain technology is tailored to expedite cross-border payments by creating a network that connects various financial institutions.

Cryptocurrency Supply: Bitcoin has a capped supply of 21 million coins, a deliberate design choice mirroring the scarcity and value proposition of precious metals. In contrast, Ripple's XRP has a fixed total supply of 100 billion tokens, with a portion held by the Ripple company. The distribution model of XRP aims to incentivize liquidity and foster market stability.

Community and Philosophy: Bitcoin is often linked with a decentralized ethos, advocating for financial independence and autonomy from traditional banking systems. Ripple, conversely, collaborates with established financial institutions, adopting a more centralized approach to drive efficiency in global transactions.

Conclusion:

In the dynamic landscape of cryptocurrency, Bitcoin and Ripple play distinct roles. Bitcoin, as the pioneer, emphasizes decentralized peer-to-peer transactions, while Ripple focuses on revolutionizing global financial transactions through collaboration with traditional institutions. Grasping the key differences between these two cryptocurrencies is crucial for investors and enthusiasts navigating the multifaceted realm of digital assets.

[tanmaygamesartdesign]

Digital Media Sources and Significance

Readers:-

gamestudies.org. (n.d.). Game Studies - Social Realism in Gaming. [online] Available at: https://gamestudies.org/0401/galloway/.

The review looks at the short lived and spatial parts of gaming, restricting ways to deal with acting that incorporate diegesis, algorithmic acting, and detecting. As per Galloway, these four models give a careful, substantial structure for understanding the subtleties of player connections in computer games. Having perused Galloway's work, I found it hard to understand the few features of gamic development. By giving a reasoning to dealing with the breakdown of player interests, the proceeding with legitimate arrangement gives a huge commitment to the area of game assessments. By zeroing in on the brief and land partitions, Galloway's review gives a pivotal protect to researchers and experts who need to acquire a more profound comprehension of the parts impacted via mechanized gaming correspondences.

2. Kocurek, C.A. (2012). The Agony and the Exidy: A History of Video Game Violence and the Legacy of Death Race. Game Studies, [online] 12(1). Available at: https://gamestudies.org/1201/articles/carly_kocurek.

‌Carly A. Kocurek, the maker, investigates the true and social significance of PC game arcades in forming American culture's impression of life as a youngster during the 1980s. Kocurek looks at how computer game arcades laid out and keep up with orientation standards, especially comparable to the encounters of young men, as they advanced. At last, the investigation includes, the mix of advancement, socialization, and masculinity in the arcade shapes the social record enveloping PC games. As a result of Kocurek's savvy survey, I obtained a more critical handle of the social repercussions significantly covered all through the whole presence of PC game arcades. The article incites perusers to contemplate the habits where that gaming settings add to the course of action of characters and direction norms. Kocurek's work will be helpful to scholastics who are enthused about the intermingling of direction studies, social history, and gaming society.

3. Koskimaa, R. (2021). Book Review: How Pac-Man Eats. Game Studies, [online] 21(4). Available at: https://gamestudies.org/2104/articles/koskimaa_review [Accessed 9 Jan. 2024].

‌A fundamental assessment of Engraving W. Roosa is presented in Frans Mäyrä's book review, "Book Review: Pac-Man's Eating regimen". The survey digs into Roosa's examination of Pac-Man as a social and social peculiarity, looking at the book's commitment to game investigations and support in more extensive conversations about gaming and society. Mäyrä researches Roosa's assessment of the social effect, certain establishment, and meaningful meaning of Pac-Man, giving a more significant look at the book's advantages and obstructions. I learned a lot about the breadth and depth of Roosa's Pac-Man research thanks to Mäyrä's critical eye. For scholastics who are keen on the nexus between social examinations and gaming, this article gives direction, featuring the significance of Roosa's work in putting Pac-Man in the point of view of bigger socio-social stories.

4. Woods, S. (2011). Congenial by Design: A Review of A Casual Revolution. Game Studies, [online] 11(2). Available at: https://gamestudies.org/1102/articles/woods [Accessed 9 Jan. 2024].

‌The piece analyzes all of Juul's cases in detail, focusing on how casual gaming has changed video games and their player bases. The inspector underlines Juul's assessment concerning the effects of extra loose, open games, course of action basics, and the greater gaming neighborhood. I gleaned some significant knowledge about the advancement of the computer game business and its effect on culture from this survey. Juul's assessment of relaxed gaming as a progressive power underlines that it is so basic to comprehend the numerous manners by which clients communicate with computerized data. The report gives a nuanced perspective on the solid transaction between player decisions, the bigger socio-social base, and the game methodology for the two researchers and specialists. It advances thoughtfulness also.

5. Gazzard, A. (2011). Unlocking the Gameworld: The Rewards of Space and Time in Videogames. Game Studies, [online] 11(1). Available at: https://gamestudies.org/1101/articles/gazzard_alison.

‌Alison Gazzard investigates the significance of fleeting and spatial angles in computer games in her book taking a gander at how players receive rewards from the virtual universes they investigate. The article reveals insight into the perplexing connection between game plan and player association by utilizing contextual analyses and client tributes to feature how reality add to the entire gaming experience. Gazzard's examination of the advantages covered by fleeting and spatial parts extends our insight into game elements. The paper stresses that it is so vital to consider these elements while planning games, showing how they affect player drenching and satisfaction. Researchers and fashioners might benefit enormously from Gazzard's review, which has provoked a basic reassessment of how spatial and transient variables shape the prizes that players seek after in the perplexing game universes they investigate.

6. Schulzke, M. (2009). Moral Decision Making in Fallout. Game Studies, [online] 9(2). Available at: https://gamestudies.org/0902/articles/schulzke.

‌In the article Matthias Schulzke investigates the ethical ramifications of choices made by players in the Aftermath computer game establishment. The paper analyzes how the game's ethical dynamic situations are complicatedly planned and how player office, outcomes, and account setting influence players' capacity to go with moral choices. As per Schulzke, these virtual moral conditions offer players a particular chance to investigate and foster their ethical characters while likewise testing customary thoughts of profound quality and reflecting moral difficulties found in the genuine world. I acquired a comprehension of the complicated connection between player organization and account plan in computer games and how it shapes moral dynamic cycles through Schulzke's work. By highlighting the capacity of virtual environments to elicit significant moral considerations, the paper contributes to the expanding debate regarding the connection between interactive storytelling in digital media and ethics.

7. Sicart, M. (2008). Defining Game Mechanics. Game Studies, [online] 8(2). Available at: https://gamestudies.org/0802/articles/sicart.

‌Miguel Sicart offers a fundamental evaluation of the possibility of game mechanics to offer a careful handle of its definition and use in the discipline of game evaluations. Sicart watches out for the term's multidisciplinary character, its diverse nature and nuances, and the challenges in depicting it in a way that is generally relevant. The article analyzes the various parts that make up game mechanics to exhibit how dynamic and setting subordinate they are through inside and out research. Sicart's post featured the way that game parts are dynamic and continually changing, which stunningly expanded how I could translate them. Sicart's evaluation of the multidisciplinary parts of game mechanics rouses scholastics to see the point in a general sense and see the extent of ways it could show up in different sorts and media. This article can be a critical asset for educated authorities and game fashioners who need to get an additional huge information on the complex relationship between game mechanics and the more prominent electronic gaming scene.

8. Wood, E.S., Jamie (2021). ‘Actual history doesn’t take place’: Digital Gaming, Accuracy and Authenticity. Game Studies, [online] 21(1). Available at: https://gamestudies.org/2101/articles/stirling_wood.

‌In her article, Stephanie A. Stirling-Wood investigates the intricate connection that exists between authenticity, historical correctness, and digital gaming. The review questions regular thoughts of verifiable story truth by fundamentally looking at how intelligent gaming portrayals of the past stray from the real world. Stirling-Wood causes to notice how, with regards to gaming, player organization, mechanics of games, and story restrictions influence authentic exactness. In the wake of perusing Stirling-Wood's paper, I gained tons of useful knowledge about the complicated connections affecting verifiable portrayals in computer games. The examination of legitimacy that goes past straightforward exactness features the numerous troubles that both gamers and game makers should survive. By testing assumptions about verifiable precision in games, Stirling-Wood's work propels a more complicated perception of the connection between player activity, computerized media, and history.

9. Leino, O.T. (2012). Death Loop as a Feature. Game Studies, [online] 12(2). Available at: https://gamestudies.org/1202/articles/death_loop_as_a_feature.

‌The creator of this piece dives into the inventive idea of integrating passing circles into PC games. The story and interactivity system investigates the outcomes of death to increase player participation, challenge, and drenching. The essential spotlight is on how passing circles improve computer game ongoing interaction and story structure, giving a clever point of view on the meaning of disappointment and reiteration in gaming. In the wake of perusing the post, I gained some useful knowledge about how to decisively incorporate passing cycles in game plans. The examination challenges assumptions about game plan shows while additionally enlightening their consequences for player organization and account progression. For the two scholastics and game planners, the paper is a provocative asset that challenges ordinary reasoning on the spot of disappointment in the gaming system.

10. Hammar, E.L. (2020). Playing Virtual Jim Crow in Mafia III - Prosthetic Memory via Historical Digital Games and the Limits of Mass Culture. Game Studies, [online] 20(1). Available at: https://gamestudies.org/2001/articles/hammar [Accessed 9 Jan. 2024].

‌Hanna B. Gerdes investigates the capability of authentic computer games in focusing on how racial bias is depicted in Mafia III during the 1960s. Gerdes utilizes the expression "prosthetic memory" to examine how computerized games go about as social memory and authentic story delegates, permitting clients to modify their own related involvements, particularly those associated with institutional prejudice. I had the option to figure out the unpredictable connection between player organization, authentic portrayal, and social memory in computerized games thanks to Gerdes' review. The piece encourages critical reflection on the ways in which popular media can and cannot convey intricate historical narratives. Gerdes' examination of Mafia III raises significant issues about the commitments and impediments put on game makers while portraying fragile subjects in their works, as well as enlightening the nuanced manners by which computerized games could advance our attention to authentic treacheries.