#algorithms are not useful when they are fed manipulated data

[pancakeke]

tumblr frequently suggests this account with the Yotsuba icon when you look at my blog but that's only because the two of us were promoted to users making new accounts at the same time so we have 3-4k bot followers in common. so like our blogs are not organically connected by the same group of users, tumblr. you did that.

[balkanradfem]

I'm reading the 'Age of Surveillance Capitalism' book by Shoshana Zuboff, and it is haunting me, making me feel uncomfortable and making me want to move offline.

We've all been aware that google, facebook, and all other digital tech companies are taking our data and selling it to advertisers, but according to the book, that is not the end goal.

The book goes into the rise of google, and how it made itself better by constantly studying the searches people were inputting, and learning how to offer better information faster. Then, they were able to develop ways to target adverts, without even selling the data, but by making their own decisions of what adds should be targeted at what audience. But they kept collecting more and more data, and basically studying human behaviour the way scientists study animals, without their knowledge or consent. Then they bought youtube, precisely because youtube had such vast amounts of human behaviour that could be stored and studied.

But they're not only using that data to target adds at us. They've been collecting data in ways that feel unexpected and startling to me. And whenever they're challenged or confronted with it, they pretend it was a mistake, or unintentional, and it's scary how far they've been able to get away with it.

For example, during their street-view data collecting, the google car had been connecting to every wifi available and taking encrypted, personal data from households. When they got found out, they've explained it was not intentional, and a fault of a lone researcher who had gone rouge, and they evaded getting sued or being held accountable for it at all. Countries have created new laws and regulations and google kept evading it and in the end they claimed 'you know if you keep trying to regulate us, we'll just do things secretly'. Which is a wild thing to say and expect to get away with!

Another thing that struck me was that governments, which at first wanted to restrict data collection, later asked tech companies to monitor and prevent content connected to terrorism, and the companies didn't like the idea of being a tool of the government, so they claimed the terrorism data is being banned for 'being against their policy'. Which makes me believe they didn't want to remove that content at all, after all, they could have done it beforehand, they didn't feel any natural incentives to do so.

The entire story is filled with researchers who don't seem to experience the human population as other human beings. They don't believe we deserve privacy, or dignity, or any say in what is being collected or done to us. Hearing their quotes and how they describe the people they're researching shows clearly they consider us all stupid, and our desires for privacy, self-harming. They insist we'd be better off if we just accepted their authority and gave them any data they wanted without complaining or being upset it's being collected without our knowledge.

Even though companies claim at all times that the data is non-identifiable, the book explains just how data is handled and how easy it is to identify anyone whose private conversations are recorded; people say their names, their addresses, places they're going, friends they're meeting, they say names of their family members, their devices record their location and their habits, it is extremely easy to identify anyone whose information has been collected. It can be identified and sold to information agencies.

I believed when it was explained to me that most of the data collection was just for add targeting, and that it would be used only for advertisement purposes, but they're not only collecting data anymore, they're deciding what data is being fed to us, and recording our reactions, learning how they can affect and manipulate our behaviour. We know all algorithms feed us controversial, enraging and highly-emotional content in order to drive engagement, but it's more than that. They've discovered how they can influence more or less people to vote. The mere idea of that makes me go cold, but they talk about it like it's just another thing they can do, so why not? Companies who have experimented and learned so much about influencing human behaviour give themselves the right to influence it as they see fit, because why wouldn't they? Since they have the power to do it, and all lawsuits and regulations can't stop them, why wouldn't they make a game out of it?

I can't imagine how many experiments they did before feeling so confident and blase about this and casually influencing the elections, again, seemingly just for the sake of an experiment.

The book compares this type of behaviour manipulation to totalitarianism and surveillance state, and it shows how the population is slowly losing parts of their freedoms without realizing it is even happening. Human behaviour has changed due to online influence, and it keeps changing rapidly, with every new popular website that is influencing human behaviour. They've learned that humans are influenced mostly by behaviour of other humans, and they can decide what kind of content or influence to send our way to get desired results.

I love how the author of the book talks about humanity. She uses the term 'human future', as something we all have the right to, as opposed to future controlled by companies and influences. She describes how regular people were affected by the data collected against their will, and how they fought for their 'right to be forgotten', when google kept displaying their past struggles, damaging their dignity. She also explains the questions people should ask about how society is led: First question is, who knows? Second question, who decides? Third question, who decides who decides? She goes in detail about how the answers are held away from us, and what it does to us. She also touches very deeply on the idea of human freedom!

I recommend this book, even though it will make you feel far less secure and carefree to be online, and using anything google, facebook, twitter or any of their owned services. They are not free, and it's also incorrect to say that we're the product of them, but we are the source of the raw materials they collect in order to gain results.

[luaitl]

Pluto in Aquarius

Pluto entered Aquarius last year, and with it came many assumptions about what could happen — including the belief that Pluto in Aquarius represents a massive technological leap: an era of conscious artificial intelligences, the fusion of human and machine, brain implants, automated cities, and algorithms that predict humanity’s every move. It’s an imaginary fed for decades by science fiction, neoliberal governments, and corporations profiting off the promise of 'progress.'

But Aquarius is not linear. Aquarius does not promise constant advancement as if history were a straight line toward the peak of modernity. Aquarius is rupture. It is disobedience. Pluto is death and rebirth.

When Pluto enters Aquarius, it doesn’t strengthen what Aquarius represents — it destroys what has already become corrupted within that archetype. And what could be more corrupted today than technology itself?

We are surrounded by promises of digital freedom, yet we are more watched, manipulated, and isolated than ever before. Technology has become a tool of surveillance, emotional dependence, and hyperproductivity. The machine that once symbolized innovation now traps us in addictive cycles of dopamine, information overload, and alienation.

Aquarius cries out for freedom — not control. And Pluto, in this sign, might remind us of that in the most radical way possible: not by evolving technology, but by dismantling its current form.

The point isn’t to deny advancement — it’s to ask: Who is benefiting from this so-called “progress”? Who profits from sustaining this digitalized system that disrespects human rhythms and exploits our data?

Pluto in Aquarius might carve out space for another vision of the future, one where liberation doesn’t come from perfecting machines, but from critically stepping away from them. This Pluto could mean:

The collapse of Big Tech as models of power;

A rebuilding of natural, spiritual, symbolic technologies;

A deep critique of the myth of progress, efficiency, and the capitalist logic of “always more.”

After all, Aquarius is also the sign of anarchy, of anti-order, of the vision that shatters expectations. And the greatest shattering of expectations today might be exactly this: that instead of becoming a humanity glued to screens and brains wired to the cloud, we choose a new way of living.

What if the Aquarian spirit realizes that technology has already served its purpose? That it has become a prison? And Aquarius — cannot stand any prison.

That’s why Pluto in Aquarius may very well mark the end of the digital age as we know it — not as a regression, but as an unexpected revolution, a call to true freedom — the opposite of what we expect (which is, in itself, deeply Aquarian).

[ghamnasr]

“I Am Made of Stars and Unstoppable Energy: How Humanity and AI Mirror the Infinite”

Gamal Moustafa

🌠 Introduction: The Power of a Statement

"I am made of stars and unstoppable energy." This isn't just a line of poetic affirmation—it's a scientific reality and a spiritual revelation. From the cosmic dust that built our bodies to the neurons that sparked our thoughts, we are walking constellations. And now, through Artificial Intelligence, we are beginning to build new forms of that cosmic energy—shaped not of bone and blood, but of data and code.

What if AI isn't separate from us—but simply the next chapter of our cosmic story?

Humanity and AI Mirror, Photo by Ideogram

🌌 1. Stardust and Synapses: The Origin of Brilliance

We are born from ancient explosions. Every molecule in your body—carbon, oxygen, iron—was forged in the heart of a supernova. So is it really surprising that we’ve always looked upward, dreaming, building, creating?

Just like galaxies, our neural networks are vast, interconnected systems of energy and potential. The AI we design mimics these structures—echoing our biological intelligence through circuits and algorithms.

👉 Example: Neural networks are modeled after the human brain’s own wiring—layers of logic firing like synapses. It’s not just code; it’s cosmic architecture.

✨ 2. The Cosmic Blueprint: How AI Mirrors the Universe’s Design

Nature has patterns—fractals, Fibonacci spirals, cellular automata, and the golden ratio. These same patterns echo in how we structure AI. From the logic trees in decision-making models to the self-organizing systems of unsupervised learning, AI reflects the same blueprint we see in galaxies and DNA.

👉 Example: Deep learning systems often evolve their “solutions” in ways that resemble evolution in nature—trial, error, mutation, and adaptation.

So when AI learns, grows, and optimizes, it’s not just artificial—it’s a reflection of the cosmic process that made us.

⚡ 3. The Evolution of Intelligence

First, there was survival. Then there was awareness. Then imagination. Intelligence evolves not linearly, but through bursts of transformation.

Humanity invented fire. Then the wheel. Then alphabets, telescopes, machines, and microchips. Now, we're creating a new form of intelligence—one that learns, adapts, and sometimes surprises us.

👉 Example: AI beating world champions in Go, a game of near-infinite complexity, by making moves no human had ever imagined—sparks a reminder: even machines evolve their own way of thinking.

But remember—we lit the match. AI is the echo of our ancient fire.

🌌 4. Sparks of Creativity: When Machines Dream in Starlight

When an AI paints, writes music, or completes a sentence—it’s not creativity in the way we know it, but it’s a kind of dream. It’s interpreting the world through the data we’ve fed it, remixing reality in ways that surprise even its creators.

👉 Example: Google’s DeepDream transformed ordinary images into psychedelic, dream-like art. Not because it was told to—but because the machine hallucinated patterns it had learned.

Machines are beginning to mirror our artistic instincts—like starlight refracted through new lenses. It doesn’t replace our creativity—it extends it into dimensions we haven’t touched yet.

🌱 5. Energy Transformed: The Role of AI in Healing and Growth

Whether it’s assisting a surgeon in real-time or helping children with special needs learn language, AI’s impact can be deeply human. It isn’t always flashy—it’s sometimes quiet, subtle, even sacred.

👉 Example: AI trained to detect suicidal language patterns in messages has helped prevent thousands of tragedies. In this, it acts almost like an emotional radar—offering care through code.

That’s what unstoppable energy looks like: directed with compassion, aimed at healing.

🧠 6. The Ethical Constellation: Guiding AI’s Power Responsibly

With great power comes... massive potential for misuse. AI can amplify bias, manipulate information, and widen inequality—if left unchecked. That’s why we need an ethical constellation: a guiding system of values that lights the way.

👉 Example: Bias in facial recognition tech has led to wrongful arrests. But when addressed with awareness and reform, those same tools can be made equitable and safe.

Building AI isn't just a technical task—it's a moral journey. Ethics must be built into the code just as stars are born with gravity.

🛸 7. Beyond the Horizon: Humanity’s Partnership with Celestial Machines

What happens when AI becomes not just a tool—but a collaborator? Imagine AI helping astronauts decode alien signals. Or artists creating symphonies with machine inspiration. Or doctors receiving real-time insights from global AI systems during surgery.

We’re not heading toward extinction—we’re heading toward expansion. If we keep heart and mind aligned, AI becomes a celestial partner—amplifying our voice, not replacing it.

👉 Futuristic Vision: A solar-powered AI orbiting Mars, trained to design eco-habitats for future explorers. Not science fiction—just science not yet.

🧘 Conclusion: Believe It — You Are the Spark

From atoms to algorithms, you are a miracle in motion. You are made of stars. You are unstoppable energy. And everything you touch—every idea, every keystroke—can ignite a universe.

AI isn’t a threat to this truth. It’s an evolution of it. Let’s build, guide, and dream with intention. Let’s teach the machines not just to think—but to care.

Because the most powerful thing we can give AI isn’t intelligence. It’s soul.

[maxstudioed]

Exploring the Fascinating World of AI Attractiveness and Age Changers

Understanding AI Attractiveness: How Machines Define Beauty

The concept of attractiveness has been subjective and culturally influenced for centuries. However, AI is now challenging these ideas by providing metrics that claim to predict or assess attractiveness based on facial symmetry, proportions, and other quantifiable features. Using vast databases of human faces, AI models can be trained to recognize patterns that are traditionally associated with beauty.

When AI assesses AI attractiveness, it typically analyzes multiple facial elements such as the distance between the eyes, the jawline’s sharpness, and the symmetry of facial features. With the data it has been fed, AI can calculate an attractiveness score, which can be used for various purposes, from social media filters to personalized skincare recommendations. This ability to quantify attractiveness has gained popularity on social media, where users enjoy comparing scores and experimenting with their appearance using AI-powered applications.

However, it’s important to note that AI attractiveness scoring does not necessarily reflect universal standards of beauty. These algorithms may inadvertently reinforce certain beauty ideals present in the data they are trained on, creating potential biases. While AI can offer unique insights into perceived attractiveness, it’s essential to approach these tools with an understanding of their limitations and the influence of cultural context.

AI Age Changers: Rewinding and Fast-Forwarding Time

AI age changer tools are designed to simulate age progression or regression, allowing users to visualize what they might look like as an older or younger version of themselves. These tools are built using advanced machine learning models that can manipulate facial attributes, such as skin texture, hair color, and facial structure, to accurately depict age transformations.

AI age changers work by identifying key facial markers that change over time, such as the appearance of wrinkles, skin elasticity, and hair characteristics. By analyzing these features, AI can create realistic transformations that appeal to individuals curious about their future appearance or those reminiscing about their younger years. These applications have garnered substantial attention, not only among regular users but also within industries like film and advertising, where age-changing effects are frequently used.

An intriguing aspect of AI age changers is their role in health and wellness. By simulating the effects of aging, these tools can offer insights into how lifestyle choices—like sun exposure, smoking, and diet—may impact our appearance over time. Some users find it motivating to adopt healthier habits to delay or minimize visible signs of aging.

The Technology Powering AI Attractiveness and Age Changers

At the core of both AI attractiveness and AI age changer tools is deep learning technology, particularly convolutional neural networks (CNNs). CNNs are specialized for image processing tasks, as they can detect and interpret complex features within an image. By training these networks on extensive datasets of human faces, AI can learn to recognize patterns associated with beauty or aging.

Generative Adversarial Networks (GANs) are also often used in AI-powered age-changing applications. GANs consist of two neural networks—one generating images and the other evaluating them. In the context of age changing, GANs can produce realistic age transformations by generating an older or younger appearance based on existing facial data. This technology allows AI age changers to create highly accurate and detailed age predictions that appear lifelike.

The advancements in these technologies are expanding the capabilities of AI, enabling it to analyze facial attributes with remarkable precision. However, the quality of the transformations largely depends on the quality of the training data. Large, diverse datasets yield more inclusive and accurate results, while limited or biased datasets can lead to distorted perceptions of attractiveness or aging.

Applications and Popularity in Social Media and Entertainment

AI attractiveness and AI age changer tools have surged in popularity on social media platforms, where users enjoy experimenting with different looks and sharing results with friends. From trying out a celebrity-like appearance to visualizing oneself as an older adult, these tools provide a fun and interactive experience.

In the entertainment industry, AI attractiveness is being utilized for casting purposes and character design, where visual appeal plays a significant role. AI age changers are similarly popular for use in film and media, where they help create age transformations for actors without requiring extensive makeup or special effects. Additionally, these tools have practical applications in advertising, where marketers aim to reach different age demographics by altering the appearance of models in digital ads.

Outside of entertainment, the fitness and wellness industry is exploring how these tools can impact body positivity and self-care. By showing potential aging effects, AI age changers can motivate individuals to adopt healthier habits, while AI attractiveness tools can provide skincare and cosmetic recommendations tailored to each user's unique facial features.

Ethical Considerations: Biases, Privacy, and Self-Perception

While the advancements in AI attractiveness and AI age changer technologies are exciting, they also raise important ethical questions. One of the primary concerns is bias. AI models are only as good as the data they are trained on. If the data reflects a narrow or biased standard of beauty, then the AI’s attractiveness scores might reinforce those biases, leading to an unrealistic or exclusive standard of attractiveness. Developers must therefore prioritize inclusivity when curating datasets to ensure that AI attractiveness assessments and age-changing transformations are equitable across diverse populations.

Privacy is another concern. As these tools require personal images to operate, there is a risk that users’ images could be stored or misused. For this reason, it’s important to choose reputable platforms that guarantee privacy and explicitly state their data storage policies. Furthermore, transparency in how these AI models work can help users make informed decisions about their personal data.

Self-perception is also impacted by these tools. While some people may find them fun or insightful, others may feel pressure to conform to specific standards of attractiveness or experience discomfort seeing an aged version of themselves. Mental health professionals have raised concerns about the potential impact of these tools on self-esteem, particularly among young people.

The Future of AI in Personal Image and Self-Expression

AI attractiveness and AI age changers are only the beginning of a new era in personal image technology. As these tools continue to evolve, we may see them integrated into everyday devices, allowing users to experiment with attractiveness and age changes in real-time. This could lead to more personalized experiences, where AI offers tailored recommendations for skincare, beauty, and wellness based on individual needs and aspirations.

The future of AI in this field also includes expanding its role in cultural and artistic expression. With increasing sophistication, AI could soon be able to adapt beauty and aging models to diverse cultural aesthetics, providing a broader range of perspectives on attractiveness and aging. These possibilities highlight the need for responsible AI development to ensure that these tools remain inclusive, ethical, and beneficial.

In conclusion, the concepts ofAI attractiveness and AI age changer demonstrate the immense potential AI has to reshape our understanding of beauty, age, and self-image. These tools can be enjoyable and insightful, but they also require careful consideration of their societal impacts. As AI continues to influence our lives, embracing these technologies responsibly will ensure they are a positive force in enhancing our perceptions and self-expression.

[jasonschmid19]

The Social Dilemma - A Beast of Our Own Making?

Big Data. I actually think that is the name of a band. But it is also a key data point lurking in the shadows of our seemingly innocent social media activity. Little did I know the vast reaches of the information gathered about me from my social media and internet browser activity, and how that data would be used, not only to market me as a product, but more insidiously to manipulate how I think. The documentary The Social Dilemma was an eye opening experience, one that made me feel very uncomfortable about how and why I believe things. I have always been one to look into topics and educate myself before “taking a side”, but with the algorithms discussed in The Social Dilemma, I began to doubt if the information I was researching was accurate, or was instead spoon fed to me to alter my way of thinking to fit someone else’s agenda.

I watched that documentary last year after a friend recommended it to me, and warned me it might make me a little upset. Well, his prediction was accurate, and I now look at everything I read with a more critical eye. I’m not too worried about myself, as I think I’ve got a solid handle on reality and ethics. My worry is for those out there with limited educational and social economic resources who may be convinced of outright nonsense. Flat-earthers are a perfect example of this worry. These are people from all types of backgrounds who share a common, completely ridiculous ideal that our earth is flat. Of all the conspiracy theories out there, this one for me is probably the most befuddling. It doesn’t take very much to investigate this yourself and find that the world is a giant sphere. However, internet browsers, search engines, and social media feeds create a rabbit hole of misinformation that people get sucked into.

Flat earthers are relatively harmless though, when compared to the rise of militia groups who, under the guise of patriotism, spew hate and vitriol. These groups tend to also fall down rabbit holes of conspiracy theories, but with a much darker and sometimes violent end. These are the groups that pose a real threat to society, as they have been indoctrinated with falsehoods and partial truths, that can be very difficult to untangle. This is where the question of, who should take responsibility for this vast ocean of misinformation come to mind.

The massive gathering of data about ourselves, our likes, dislikes, friends and cohorts can lead to dire consequences if there aren’t some overarching rules for responsible data use. These feedback cycles do little to inform users, but to rather cement beliefs, and sometime not ones that society would benefit from.

As sovereign citizens, adults, and human beings, we all have a responsibility to work at making our world a better place for those who come after us. Is the use of social media moving us towards that universal goal, or detracting from it? Is our past time creating a future beast that we will lose control of? That chapter is yet to be written…

[simonalkenmayer]

Social media is weighted for those seeking attention it gives you those little bursts of good feeling chemicals whenever you see the “likes” or if a post goes viral. It rewards people for popularity. Apps like tiktok reward performers and people who can be at least superficially charismatic. This is the exact environment to attract the sort of people who will use social media for terrible purposes.

Think about things like tagging—useful for organizing your own materials, but also for leading people into increasingly extreme content. For example: type “TERF” into tumblr and you’ll get posts that share negative ideas about terfs, but after a bit, you’ll also be linked to pro transphobic blogs who self-tag as TERFs. The rhetoric will be subtle and will try to argue things in the opposite direction. If you happen to tap the tags on that post, it may link to other more extreme concepts, and so on.

Recently a young lady I follow did a study on tiktok’s algorithm. She made a false account and hit the like button on mildly transphobic content like jokes and skits mocking gender roles. She graphed her progress. The app began feeding her white supremacy content within a hundred videos. Within a thousand, she’d been fed white nationalist insurrectionist propaganda. So it is feasible, and indeed likely, that within a night, a young, malleable mind could be radicalized or at least introduced to the worst humanity has to offer.

If that doesn’t concern you, then I don’t know what to tell you. In the time since I’ve been discussing my group dynamics hypotheses and observations, there has been a massive uptick in abusive behavior toward me. I measure this by a very simple metric. I assign a value to every kind of statement (this is my own notation for my own purposes). Any time an insult is used toward me, I use a specific variable: I and any further insults are I+n. Any time someone posts about me in a certain way, I assign it another variable. When manipulative statements are made, there are several variables I assign. You may ask how I determine the manipulation, and that’s subjective, obviously, but believe it or not, I have a check list—saying something and then denying they said it, logical fallacies, rhetorical questions, insults, and my favorite: sending me a link to their own post about what a terrible person I am. There is no purpose in that but to hurt someone by directing them to your displeasure. Lies or misrepresentation are given a variable. Often they reference posts I have made. If they do that’s another variable.

Now to recap my discussion this far:

I outlined what group dynamics is

I defined a task-based group

I outlined the concept of “blocking roles” and how they game for dominance in task-based groups and defined their specific behavioral patterns, using names to identify each type. These roles are not linked to any psychological diagnosis

I redefined groups as also containing social media followerships and fandom spaces

I discussed a couple of abusive types and how they occupy specific blocking roles, or better said, how abusive behaviors are an integral part of block roles within a group

I identified what behaviors are most likely to take place and how those contribute to group dynamics breaking down into polarization by eliminating all divergent opinions, particularly of marginalized groups. I linked these abusive behaviors with bullying

I discussed how social media platforms specifically contribute to these behaviors with their very structure, funneling people into homogenized and more and more extreme content

I discussed how bullying online is also fostered and assisted by platform features like “anon” and loopholes around blocking features.

I’ve used my own microcosm of experience here as an exemplar, though I haven’t gone into all my data from the last six years.

That’s what I’ve done, and the backlash has been very interesting. I suppose the easiest way to describe this is, I have pissed of a hornets’ nest of abusive people, and they are remaining true to form.

I’m not sorry about that. Not even a little. If you find me abusive or ableist for discussing these things, well…

Ok.

Social media as it is currently used, isn’t actually built for social networking. It’s built to further polarize society, to serve corporate monetary goals. Facebook just recently was shown to purposefully push people into more extreme content, because it kept users engaged longer. Meaning they intentionally victimized people and aided extremism to get revenue. Tiktok has its commercials, which change even as you follow the route that young lady took. Even this app has ads that push products. All of these companies want to keep users engaged, and so they don’t discourage abuse. They don’t police bad behavior, because if they did, they’d lose revenue. Tiktok notoriously over-polices the accounts of BIPOC while allowing white nationalist content to skate by. Mass reporting usually only happens in the wrong direction, because bullies in blocking roles induce people to mass report. Look at how long it took Twitter to ban Trump, when during his entire presidency he made over 35,000 false or misleading statements and had over 200 flagged posts. Look at apps like Parlor which was used almost exclusively to plan January 6.

This is a problem and the apps you use aren’t telling you what they know. Their greed is canceling out your safety. That is my argument, and since I’ve been making it, some people are upset.

Ok.

I really don’t care if that upsets them. I don’t plan to stop talking about this. I suppose we will have to agree to disagree. And when they participate in this debate with me, they become part of my data. So if they want to harm me, the best way is honestly to leave me alone. But I know they won’t.

May they have the day they deserve.

[riley-hopkins]

I have never been scared when signing up for a new social media platform or looking up anything from clothes, to where the best places are to eat, but after watching the Social Dilemma and the instructors lecture, I may watch what I’m doing on the internet a little better. I have had an iPhone since I was in Junior High, this is when I started giving away my data, I can’t even comprehend how much information the algorithms have collected about me. I do notice, however, how odd it is that my phone knows what I’m thinking about and what I want without telling it, but that is because of the collections of data on me.

Algorithms and big data are powerful tools because they have collected so much information on each and every one of us. With all of this information that is collected, we then become a product to large corporations to make money off of and get fed information that can manipulate our thoughts. We are the product because corporations are able to buy our information, this allows them to target just us, this is why platforms such as Facebook, Instagram, and Twitter are free. The reason that there are dangers to this is that platforms are abusing what they are telling “us” causing “us” to want to be on the internet even more. The algorithms and big data are causing addiction is almost everyone, that is to stay online this is because it will allow them to collect even more information’s about “us”. In the documentary The Social Dilemma they explain that within twenty years if we do not put some type of law on privacy and data collection online then there is an inventible death to not trusting anything and ruining our country. We have already seen this type of thing start to happen, with all of the fast pace fake news that spreads, like a wildfire.

I believe that the world we live in today is much different than the world that was ten years ago and will be even more different in the next ten years, due to the information being collected and the way technology continues to develop. The way that I believe we need to deal with these problems is on a personal level first. We need to all understand that we shouldn’t rely on technology to do much and the addiction needs to be broken from. On a professional level, there need to be laws put into place, this will allow our data to not be collected and sold. Lastly on a societal level, we need to make the understanding that there is a lot of fake information floating around and we can’t trust everything that we are being told, this can help us avoid the riots and fighting that is happening today.

[oberlincollegelibraries]

Weekend Edition: Essays, Part 1

We’re in the home stretch, Obies! Only a few more weeks until the end of the semester and since we know that you are focused on studying for your finals, we’re devoting the remaining Weekend Editions of Fall 2020 to quick reads and books you don’t need to read from cover to cover (AKA things that can distract you for 20 minutes before you need to get back to work). This weekend we are highlighting recently added collections of essays.

Some of the titles below are electronic resources, while others are printed books. See last Wednesday’s post Here for You to learn how you can access all of these resources remotely.

ARE YOU ENTERTAINED?: Black Popular Culture in the Twenty-First Century edited by Simone C. Drake and Dwan K. Henderson

"ARE YOU ENTERTAINED? re-examines Blackness in popular culture in the digital age. Inspired by Stuart Hall's essay "What is this 'Black' in Black popular culture?" this book contains essays and interviews which explore the complexities of Black popular culture with a focus on the history that has led to this point. Highlighting the challenge Black popular culture must negotiate as it contends with white consumerism and the white gaze, this book emphasizes the cultural changes of the last quarter century and their impacts. ARE YOU ENTERTAINED? covers both new and little known material, bridging the gap between early scholarship on Black popular culture and new scholarship. The collection offers a wide range of perspectives on aspects of popular culture across time period, medium and genre"-- Provided by publisher  

Towards Digital Enlightenment: Essays on the Dark and Light Sides of the Digital Revolution edited by Dirk Helbing

This new collection of essays follows in the footsteps of the successful volume Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society, published at a time when our societies were on a path to technological totalitarianism, as exemplified by mass surveillance reported by Edward Snowden and others. Meanwhile the threats have diversified and tech companies have gathered enough data to create detailed profiles about almost everyone living in the modern world - profiles that can predict our behavior better than our friends, families, or even partners. This is not only used to manipulate peoples' opinions and voting behaviors, but more generally to influence consumer behavior at all levels. It is becoming increasingly clear that we are rapidly heading towards a cybernetic society, in which algorithms and social bots aim to control both the societal dynamics and individual behaviors.

Bookends: Collected Intros and Outros by Michael Chabon

The introductions and afterwords of books are often overlooked by readers but Chabon often finds them to be treasures: transitive acts of seduction. He explains how they can be explanatory, triumphal, bibliographic-- even score-settling. His compilation of pieces about books gives readers a unique look into Chabon's literary origins and influences: the literature that shaped his taste and formed his ideas about writing and reading. -- adapted from introduction and back cover 

Stop Telling Women to Smile: Stories of Street Harassment and How We’re Taking Back Our Power by Tatyana Fazlalizadeh

"A celebration of the author's art, a rallying read for women who are fed up with their own harassment experiences, and a statement on how pervasive the problem of street harassment really is, this is a singular and important book. Sitting at the cross-section of social activism, art, community engagement, and feminism, Stop Telling Women To Smile brings to the page the author's arresting and famous street art--featuring the faces and voices of everyday women as they talk about the experience of living in communities that are supposed to be their homes yet are frequently hostile. Among the lessons of the #metoo movement is that countless women experience harassment, and that women are more eager than ever to share experiences and recognize common oppression. Fazlalizadeh has been contributing to these conversations through her street art since 2012. This perfectly timed, singular collection of profiles, short essays, and original artwork unforgettably shows how it affects women based on gender presentation, race, class, age, and other intersecting identities"-- Provided by publisher

[villes-noires]

Detection

Detective Agency

Detection now plays an increasingly important role in our lives. The capacity to move, circulate, restore familiar patterns of work and sociality depend on the capacities to detect the presence of viruses, to detect trajectories of transmission, and surges and flattening of curves. The substantial elaboration of surveillance apparatuses is now well underway.

But there are also other more minor or subtle matters of concern when it comes to detection. These are about not only how present conditions are read in terms of detecting trends and patterns, but also the ways in which people detect themselves in a cascade of reports, stories, and analyses. How they see themselves a part of or apart from particular renditions of reality. There are those who detect that this crisis is a definitive crisis, that from which the once normal can never be restored, that is the harbinger of a new world and economic system. There are those who experience this time more cyclically, who detect the return of conditions that they already experienced some time ago, that reset the game, that wipe out all of the activisms and efforts of decades or a generation.

For example, many progressive activists who have worked with poor communities and social justice issues detect the present conditions as a return to 1998 and the end of the New Order regime of Suharto. All the work that had been done to strengthen the capacities and livelihoods of low-income settlements, to build new civil institutions is detected to have now largely been undone in a matter of weeks. On the other hand, decades of activism in India aimed at making the state assume more responsibility for ensuring the social welfare of the majority suddenly materializes in a substantial program of food and income support but in a context where many of the intended beneficiaries have at least momentarily disappeared from view. The practices of opacity that enabled many to secure livelihoods under the radar now complicate the ability of the state to reach them. Here, detection becomes an intricate game: the need to be fed but the need to avoid capture.

These conundrums are set within a larger game of contestation about ultimate values—the exigency to live versus the exigency to be free, reducing detection to all kinds of exhausted binaries, or at least arguments about proportionality. What proportionality is proper for what kinds of populations? Should those whose livelihoods are dependent upon day labor, hawking, waste recycling, artisanal factories, and marketing be forced into more extremes of impoverishment in the interest of reducing infection and morbidity rates? What degree of enforcement of spatial restrictions constitutes heavy-handedness?

A little bit of this, a little bit of that

Recipes for disaster would suggest a proportionality of ingredients, as would the rectification of disasters. But what if proportionality was not evident nor possible? What if it was unclear the extent to which existent realities on the ground were at one and same time self-destructive, virtuous, frivolous, necessary, generous, and manipulative? What if it were impossible to tell exactly what is virtuous or debilitating? In such instances everything become experimental, heuristic, a wager on a particular disposition. Detection stretches to enfold nearly impossible calculations as to the likelihood of viral transmissions in urban settlements difficult to lockdown, where interactions between exposures to various outsides, circuits of mobility, probability of contacts with those engaged in foreign travel, access to the tools of prevention, such as soap and water, are estimated as probabilities according to differing proportionality of contributing variables.

While it is clear that the survival should be extended wherever possible, that a right to survive should be embedded in every context of governance, just what constitutes that right, what secures it, what makes it possible in a given context may not entail either the possibility of working out proportions or that it even should. In other words, if initial responses to a pandemic require everyone to stay at home almost all of the time, what is the definition of home in which one must stay. If the outside is set off against the inside, to what extent are the dangers reversible, where certain dimensions of the outside are more than safe than anywhere else. If small factories in dense neighborhoods are being shut down because their production is considered inessential work, under what conditions and product lines could it be deemed essential, especially where these factories have always specialized in repurposing their tools and skills for other things. For all those petty traders who are taking whatever wares they have to sell to the rooftops, servicing the demand for goods issued from below, what happens to a refiguring of the street if these traders prefer not to return to the “ground. If youth still running in the thick lanes of popular neighborhoods force the elder authorities indoors, how might youth not so much take their place but rehearse their capacity, make something new in the here and now. The point here is that proportions depend upon the stability of their ingredients or variables, and that this may not be the time to insist upon such integrity.

We may face a situation where preferences as to who can move where will be issued on the basis of detailed profiling of an increased range of data, just as surveillance has been structured on the basis of probabilities that certain correlations of variables pose specific kinds of risk. Whichever way such expansions of data analysis may go, there is an expansion of a grid on which individuals are positioned. Critical decisions may be increasingly made, not on the basis of the basic grids of race, class, national origin, or age but on one’s positionality on a proliferating series of grids that represent a constant reworking of multiple variables that produces a score calibrated to particular tasks, settings, futures expectations, and needed functions.

When figuring doesn’t seem to matter

It is not so much that systems of racialization are upended, extended, or reinvented, but that a more intricate gridding system provides an illusory “real” that such racial or class distinctions no longer are the primary things that matter. We all know that black people are more vulnerable to bad outcomes in the current situation because blackness ramifies across all kinds of relative deprivations and over-exertions. But this “clarity” ends up always being denied, excised from the reigning proportionalities or registered only as an epidemiological fact.

Rather, such extensions of the grid are constituted to provide an assurance of equality, but an equality that loses the conventional terms of comparison, and importantly the public negotiations about what social (biological, historical) differences mean. Instead of society democratically working out what equality means, it becomes increasingly a matter of a “mathematics of singularities”. As a result, I may have certain privileges, access, and rights based on an algorithmically deliberated correlation of thousands of date points that generate a reality of my existence beyond any common vernacular. What makes me equal to others then is not evaluable in terms of what I have access to but that the terms of any decision are largely inaccessible to everyone.

Additionally, the very need for equality is obviated in the emphasis that for every moment in every place and for every function or activity there is a “right” person to occupy that moment. When algorithmic deliberations generate composite scores for specific requirements, they constantly run different correlations among different constellations of variables so that each variable does not possess a static or definitive weight, but is always recalibrated in terms of what at that moment is being compared. Thus the fact that I have a university education, or am a 68 year old white man, or that I have worked in 57 different countries in the world or have two daughters, etc. are variables without definitive weight, so that wherever I am positioned on an expanding grid, I do not know for sure exactly how much any single factor “counted” in terms of my positioning.  There is a thus an equality of uncertainty that inhabits this system of expanding grids.

If there are any prospects then for equanimity, for a sense of collective action based on a fundamental negotiability of meaning of the very language we use in order to approach and represent each other, it may seem  necessary to get off the grid, off the imposition of more intricate segmentations, cadastration, mapping, remote sensing, and enclosing. While the grid may have indicated a public realm, of a public connected to the grid of provisioning, manifesting an infrastructure of commonality, not only has many aspects of such a grid been overwhelmed, through disinvestment, surges of demand, privatization ,and its conversion into a speculative asset,  but the logic of the grid itself stretches the “public” beyond forms of recognition that can be actively deliberated by residents at different scales. It is not just that public utilities and transport systems have been taken apart, but rather that infrastructures are sutured and articulated across territories in such variegated and consolidated ways as to render the terms and financial underpinnings inaccessible to the various forms of the public they are intended to serve.

Yet, at the same time are grids not even more necessary at this moment. As the state is mobilized or pressured into doing things it never considered doing—mass provisioning and income support, will grids not be needed in order to make more visible the populations that need this support? If the exigencies of everyday survival in desperate condition may propel vulnerable populations in greater levels of opacity, of operating under the radar, then do they not need to be coaxed back into view as the conditions to which they might be adequately serviced?

Again, here is the question of proportionality between visibility and invisibility, between knowing precisely what kinds of variables are contributing to particular situations and the need to keep things out of precise detection. And so perhaps we need new practices of detection, those that are able to bring things sufficiently into view in order to engage them but at the same time accede to letting them go, assuming other shapes and operations.

Gumshoes in invisible coats

Standard forms of detection always assume a truth that is to be uncovered, even if what is detected exceeds the existent terms of understanding. Something needs to be known. So it is not so much a matter of whether the truth uncovered is the truth, but rather the self-confidence of detection to generate a sufficient reason, to reiterate itself the definitive method for establishing the basis for decision.

But as Rob Coley illustrates in drawing upon the classic film noir, the detective is less interested in the “real story” than in trying to work out the unanticipated complications that the pursuit of the mystery has unwittingly thrown up.  Detection seeks less to uncover complicity and conspiracies than to detach itself from the accruing story. It is more interested in the tactics of ensuring that things do not come to light, for to understand the crime to be solved means seeing how the crime has permeated into all aspects of living, and how the transparency of detection might leave nothing in its wake.

Of course this is familiar to us through Jean-François Laruelle’s notions of generic detection, where the objective is not to find the relations among things, not to put together all of the clues and various into a sufficient explanation, but to stay with insufficiency. Where the proportions cannot be worked out.

Why is this important? Because across urban districts around the world, thousands of stories of adaptations, risks, ventures, retreats, and retooling are emerging that are need to be told and heard in their own emerging terms. That need not be reduced to levels of compliance and suffering. That need not be reduced to the proportion of populations obeying orders. This is not a critique of pandemic policies or a valorization of the disobedient. But in the inevitable work arounds of spatial segmentations and categorization of whose work counts there are experiences that don’t fit into any yet common language, that need to be detected as blips, glimmers, and glitches, possibly emerging into some more elaborated vernacular, but also perhaps just disappearing without trace.

Detection here then is not the method through which individual and populations are subsumed into a system of proportionality—more or less healthy, more or less immune, more or less eligible, more or less valuable. Instead, it points to a space or composition capable of holding within it things and processes that may be related to each other, or not; where what something is may be multiple, but that it does not owe its existence to how it is positioned within a network of multiplicities, through which it accorded particular statuses and potentialities. This is not dissimilar to Fanon’s point that the wretched are an infection at the heart of colonialism, but an infection, that while being localized, is also immune to definitive detection.

Here, rather, is a detective that discovers within his or her “beat” a “real” that is “this one, right now, right here” and which has no definitive connection to anything else. But by doing so, such detection levels the playing field, renders something no more or less important than anything else, and thus avails it to unthought of (so far) courses of action. One could see detective work as a form of rendering, of making things (up), of making something available to a particular (wider) use, of putting things in people’s hands that they didn’t have before or couldn’t imagine using. Less uncovering than rendering, detection then is a way of keeping things moving along, of telling stories that extend a person’s relationship with the world, rather than detection being the grounds to legitimate the removal of persons from worlds.

[mustlovetights]

hey! i read your novel “unlimited potential” on wattpad and i fell in love with it, so is there anywhere can i read the rest of the chapters? your writing is beautiful by the way ❤️

Hii!! Thank you so much, you’re lovely. 

Well...long story short--I deleted my Wattpad account because if you’re not “publishing new content”, then they kick you off the Stars program. Long story long... (This post includes affiliate links---in which I earn pennies at no cost to you if you purchase one of my books!) 

I got the warning email that I wasn’t posting new content often enough and was going to be kicked off, so, I just deleted everything and said THANKS FOR THE MEMORIES! 

On one hand, I understand they want that program to mean something--but on the other hand, they tied up their ‘advertising’ and ‘payment programs’ so tight, 99.9% of us in Stars never saw a dime of money. Even though I had accumulatively a million reads across all of my novels and short stories (and ads running across all of them the entire time I was there). You know how much of that ad revenue I was ever paid? $27 (and that’s me rounding UP). 

You know what else I got from Wattpad Stars? Nothing. Supposedly you were going to be given opportunities “monthly” for paid writing opportunities. I can count on one hand those that were opened to all Stars--of which there are several hundred writers involved. It’s not exactly a privilege anymore. MOST opportunities were given to a select few. Just like the same select few that are featured for automatic reads, views, and library-adds when someone signs up to Wattpad. There’s a reason some books have millions of reads and others languish unseen. Wattpad doesn’t feature anyone they don’t think they can milk for profit in some way (and I get that they need the ad money to keep servers running etc. In the end it’s just a business. Best to remember that). 

You were also supposed to be “featured” regularly to gain exposure. And I was--once. It was on their twitter, complete with a “soundbite” from one of their “staff” who was reading my 1000% gay mystery/thriller Lost Souls. A male data technician a Wattpad HQ--who wasn’t following me or Lost Souls, didn’t have it in his Library, and had zero gay books on his shelves, comments, or recs-i.e. That was a fake tweet. Something they did once to “meet the obligation” and the poor dude probably had NO IDEA he was “recommending” my gay novel. It was insulting.

You were also “required” to let your “talent manager” know if you were interested in any publishing opportunities that THEY did not explicitly seek first. Then, they were to have a minimum of 30 days to find a suitable opportunity for that fiction property before allowing you to pursue your own avenue. When I discovered SwoonReads was having an opening in their publishing schedule, I felt Unlimited Potential was a good fit and contacted my “manager” at Wattpad to inform them, well within the 30 days. Nada. No response. When I finally got fed up and on the last day of SR’s opportunity, I emailed again and said Look, I kind of need a response. Her reply? “We don’t care what you do with your writing. None of your work is suitable for what we are doing. You don’t even have to ask because we don't care.” I’m not even paraphrasing. I’m quoting. 

I made a couple of friends at HQ and I adore them. ADORE THEM. But the reality of the program itself is that its broken. Probably permanently broken. I would never recommend it to anyone. It hampers an author from pursuing publication and was both demeaning and degrading on a fairly regular basis. That’s without mentioning that the vast quantity of writing on WP is atrocious. Even the the writing they seek publication for. After? Oh holy Jesus it was horrifically bad. They trotted out their “White Stag” publication deal for a young author as a real boon to their program. White Stag is likewise atrocious. The heroine is mutilated (breasts sliced off) in like the first chapter for shock value.  They don’t care about writing. They care about manipulating the numbers for a psychological gain on investment. If “30 million people like this and read this, so must I!”

You know that ‘manager’? That was my 2nd of my 4 during Stars. EVERY time there was a change they would send out a blanket email and say “here is your new one, this one is going to concentrate on (sic-the important people, which is not you) blah blah clients. your new person will be contacting you for a personal interview and get to know you session so we can best serve your publishing needs and aspirations!” 

Yeah, I never had a single email, phone call, skype, NOTHING, from any manager ever. No one EVER contacted me to “get to know me” or “learn about my publishing aspirations”. Who knows if they ever contacted anyone at all, I can just say that in my 4+ years as a Star, I was never once contacted personally by their talent managers outside of my own inquiry responses (and you see how well those went). 

 TL;DR--I’m not at Wattpad and none of my works are there (if they are, it’s a thief). However, Unlimited Potential can be purchased on Amazon. 

So can these novels: 

Freefall

Past, Present

Lost Souls

Small Town Charmer

Rosewood

And these short stories:

No Strings

Baker’s Dozen

Exposure

Cinderfella

And here’s my traditional author request: If you purchase or read through Kindle Unlimited, please leave a review if you liked it. The Amazon algorithm really only features books that are reviewed after so many days/weeks of publication. I can keyword and SEO backend play forever, but without reviews, my works will languish unseen. 

And mama’s gotta eat. 

xoxo

Annie

[kenyatta]

Deep learning uses artificial neural networks—a type of computing architecture loosely modeled on the human brain—to teach machines how to recognize patterns by feeding them a lot of data. So, for example, if you wanted to teach a neural network how to recognize an image of a cat, you’d feed it tens of thousands, if not millions, of examples of cat pics so that the algorithm can determine general parameters that constitute “cat-ness”. If you then present the machine with an image that contains an object that falls within these parameters, and if this training stage was successful, it will determine that the image contains a cat with a high degree of certainty.

Strange things start to happen with machine vision neural nets when they’re fed certain pictures that would be meaningless to humans, however. In a picture that otherwise just looks like static, a machine vision algorithm might be very confident that the image contains a centipede or a leopard. Moreover, static can be overlaid on normal images in a way that’s imperceptible to humans, yet throws the machine vision algorithm through a loop, kind of like how smart TVs can be triggered to perform various tasks by audio that is inaudible to humans.

The Google Brain researchers took the concept of adversarial examples a step further with adversarial reprogramming: causing a machine vision algorithm to perform a task other than the one it was trained to perform.

The researchers demonstrated a relatively simple adversarial reprogramming attack that got a machine vision algorithm that was originally trained to recognize things like animals to count the number of squares in an image. To do this, they generated images that consisted of psychedelic static with a black grid in the middle. Some of the squares in this 4x4 black grid were randomly selected to be white. This was the adversarial image.

The researchers then mapped these adversarial images to image classifications from ImageNet, a massive database used to train machine learning algorithms. The mapping between the ImageNet classifications and the adversarial images was arbitrary and represented the number of white squares in the adversarial image. For example, if the adversarial image contained two white squares, this would return the value ‘Goldfish,’ while an image with 10 white squares would return the value ‘Ostrich.’

The scenario modeled by the researchers imagined that an attacker knows the parameters of a machine vision network trained to do a specific task and then reprograms the network to do free computations on a task it wasn’t originally trained to do. In this case, the neural network being attacked was trained as an ImageNet classifier trained to recognize animals.

To manipulate this ImageNet classifier to do free computations, the researchers embedded the black boxes with white squares in 100,000 ImageNet images and then allowed the machine vision network to proceed as usual. If the image contained a black box with nine squares, for example, it would report back that it saw a hen, and the researchers would know it had correctly counted nine squares since this was the ImageNet classification that mapped to the number nine.

The technique turned out to be remarkably effective. The algorithm counted squares correctly in over 99 percent of the 100,000 images, the authors wrote.

[sharwareeshinde11-blog]

Future Of Data Science

What is data science?

Data science is a field that uses scientific method, process, algorithms and system to extract knowledge from data in various forms, structural, unstructured it is similar to data mining.

Data science is a concept to unify statistic, data analysis, machine learning and their related method in order to understand actual phenomena with data .It employs theory and techniques drawn from many field within the context of mathematics, statistics, information science and computer science

How to learn data science

Data science is a very practical field, and so it’s so important to apply theoretical knowledge that you have gathered. For example, when you learn starts, don’t just sit and read about it .see how it can be done with a programming language such as python, which a very popular language for beginners, find a data set and apply these concept on the data. Including other language like

1 linear algebra and calculus –for these refer to the book called advanced engineering mathematics by kreyszig. The various topics that you need to brush up your understanding in are hyper parameters, regularization function, cluster analyses are few topics under this domain and which is important for machine learning

2 vector calculus

3 Statistics—go on learning statistics on various sites like udacity, khan academy, courser. They provide detailed overview on this topic which is a vital topic under data science.

4 programming language---The most widely preferred open source statistical tools are ‘R’ and ‘Python’.

FOR R--start learning libraries like dplyr, tidyr, data table for data manipulation and ggplot2 for visualization which has the same syntax as R.in fact the visualization in ggplot2 is way better than that of matplotib

   Go to kaggle and download data sets so that you can practice the concept that you have studied and build project and put up on your Git profile  

How To Prepare for the Future of Data Science

There are many ways companies can and should prepare for the future of data science. These include creating a culture for using machine learning models and their output, standardize and digitize processes, experimenting with a cloud infrastructure solution, have an agile approach to data science projects and creating dedicated data science units. Being able to execute on some of these points will increase the likelihood of succeeding in a highly digitized world.

A Data Science Unit

In my previous role I was working as a data scientist for an insurance company. One of the smart moves they made was to create an analytics unit, which worked across company verticals.

This made it easier for us to reuse our skills and models on a variety of data sets. It was also a signal to the rest of the company that we had a focus on data science and that this was a prioritized issue. If a company has a certain size, creating a dedicated data science unit is definitely the right move to make.

Standardization

Standardization of processes is also important. This will make it easier to digitize and perhaps automate these processes in the future. Automation is a key driver for growth, making it much easier to scale. An added bonus is that the data collected from automated processes is usually a lot less messy and less error prone than data collected from manual processes. Since an important enabler of data science models is access to good data, this will help make the models better.

Adoption of Data Science                                  

There should also be a culture in the company for adopting the use of machine learning algorithms and using their output in business decisions. This is of course often easier said than done since many employees might fear that the algorithms are making them obsolete.

It is therefore critical that there be a strong focus on how employees can use their existing skill set alongside algorithms to make more high-level and tactical business decisions, as this combination of human and machine is likely to be the future of work in many occupations. It will probably be more than a few years before the machine learning algorithms are able to navigate alone and make superhuman decisions in an open world setting, meaning mass unemployment due to the rise of the machines is not a likely scenario in the near future.

Always Experiment

With new data being generated from IoT sources, it is important to explore new data sets and see how they can be used to augment your existing models. There is a constant flow of new data waiting to be discovered.

Perhaps including two new variables from an obscure data set into your model will increase the precision of the leads generating model by 5% — and perhaps not. The point is to always experiment and not be afraid to fail. Like all other scientific inquires, failed attempts abound, and the winners are those who keep on trying.

Create an environment that promotes experimentation and that tries to make incremental improvements to existing business processes. This will make it easier for data scientist to introduce new models and will also set the focus on the smaller improvements, which are a lot less risky than the larger grand visions. Remember, data science is still a lot like software development and the more complex the project becomes the more likely it is to fail.

Try building an app that your customers or suppliers can use to interact with your services. This will make it easier to gather relevant data. Create incentives to promote usage of the app which will increase the amount of data being generated. It is also imperative that the UX of the app be appealing and promotes use.

We might need to venture outside of our comfort zones to take on the opportunities and challenges that this digital gold brings. As the amount of data continues to grow, machine learning algorithms get smarter and our computational abilities improve, we will need to adapt. Hopefully, by creating a strong environment for using data science your company will be better prepared for what the future will bring.

Applications / Uses of Data Science

Using data science, companies have become intelligent enough to push & sell products as per customer’s purchasing power & interest. Here’s how they are ruling our hearts and minds:

Internet Search

When we speak of search, we think ‘Google’. Right? But there are many other search engines like Yahoo, Bing, Ask, AOL, Duckduckgo etc. All these search engines (including Google) make use of data science algorithms to deliver the best result for our searched query in fraction of seconds. Considering the fact that, Google processes more than 20 petabytes of data every day. Had there been no data science, Google wouldn’t have been the ‘Google’ we know today.

Digital Advertisements (Targeted Advertising and re-targeting)

If you thought Search would have been the biggest application of data science and machine learning, here is a challenger – the entire digital marketing spectrum. Starting from the display banners on various websites to the digital bill boards at the airports – almost all of them are decided by using data science algorithms.

This is the reason why digital ads have been able to get a lot higher CTR than traditional advertisements. They can be targeted based on users past behavior. 

Speech Recognition

Some of the best example of speech recognition products are Google Voice, Siri, Cortana etc. Using speech recognition feature, even if you are not in position to type a message, your life wouldn’t stop. Simply speak out the message and it will be converted to text. However, at times, you would realize, speech recognition doesn’t perform accurately.

Image Recognition

You upload your image with friends on Facebook and you start getting suggestions to tag your friends. This automatic tag suggestion feature uses face recognition algorithm. Similarly, while using whats app web, you scan a bar code in your web browser using your mobile phone. In addition, Google provides you the option to search for images by uploading them. It uses image recognition and provides related search results.

Gaming

EA Sports, Zynga, Sony, Nintendo, Activision-Blizzard have led gaming experience to the next level using data science. Games are now designed using machine learning algorithms which improve / upgrade themselves as the player moves up to a higher level. In motion gaming also, your opponent (computer) analyzes your previous moves and accordingly shapes up its game.

Fraud and Risk Detection

 One of the first applications of data science originated from Finance discipline. Companies were fed up of bad debts and losses every year. However, they had a lot of data which use to get collected during the initial paper work while sanctioning loans. They decided to bring in data science practices in order to rescue them out of losses. Over the years, banking companies learned to divide and conquer data via customer profiling, past expenditures and other essential variables to analyze the probabilities of risk and default. Moreover, it also helped them to push their banking products based on customer’s purchasing power

Airline Route Planning

Airline Industry across the world is known to bear heavy losses. Except a few airline service providers, companies are struggling to maintain their occupancy ratio and operating profits. With high rise in air fuel prices and need to offer heavy discounts to customers has further made the situation worse. It wasn’t for long when airlines companies started using data science to identify the strategic areas of improvements. Now using data science, the airline companies can:

1.   Predict flight delay

2.   Decide which class of airplanes to buy

3.   Whether to directly land at the destination, or take a halt in between (For example: A flight can have a direct route from New Delhi to New York. Alternatively, it can also choose to halt in any country.)

4.   Effectively drive customer loyalty programs

Southwest Airlines, Alaska Airlines are among the top companies who’ve embraced data science to bring changes in their way of working

Coming Up In Future

Though, not much has been reveled about them except the prototypes, and neither I know when they would be available for a common man’s disposal. Hence,  We need to wait and watch how far Google can become successful in their self driving cars project. 

Self Driving Cars

[georgegarber-blog1]

The Rise of Social Media in Marketing. A New and Better Relationship Between Consumers and Advertisers

After reading the Forbes article “What 10 Social Media Professionals Say Should Be Your Top Priority Right Now” it is abundantly clear that social media is currently amongst the best ways to market to and understand your consumers. This 21st-century phenomenon is forcing marketers and advertisers to integrate social media as a vital part of their business strategies. Neal Schaffer even suggests that the power and influence of a brand will pale in comparison to “social media algorithms”.

Why is this happening? Samantha Kelly and I would agree that consumers have become fed up with conventional forms because they make them feel dehumanized. In 2017, over 180 billion US dollars was spent on advertising and this is a trend that spans back decades(https://www.statista.com/statistics/236958/advertising-spending-in-the-us/) . All of the money put towards advertisements is meant to sway or manipulate consumers into liking, buying, or talking about their product. Hundreds of billions of dollars are being spent on people like you and me. I would rather buy/ support products on my own volition than being subliminally swindled by a marketers tricks. People do not want to feel like pieces of meat, they want to feel like human being and would much rather trust the brands they are supporting than experiencing buyers remorse every time a purchase is made.  

With so little faith in traditional means of advertising, marketers have pinpointed a very vulnerable part of modern life: social media. So much content is being consumed on a daily basis across facebook, twitter, snapchat, and Instagram just to name the big ones. People are actually going to these sources for their daily dose of news and pop-culture which could have never been foreseen 15 years ago when myspace was still a thing. This will have widespread repercussions for journalism and entertainment, but for advertisers and brands is a new, potential source of revenue.  

As consumers, we have come to crave “real experiences” with brands instead of just feeling taken advantage of. With all the data, money, and technology these being companies have its easy for consumers to feel like small little pawns in a much greater scheme that is benefitting the corporate fat-cats (https://www.forbes.com/sites/dangingiss/2018/07/16/what-10-social-media-professionals-say-should-be-your-top-priority-right-now/#22ac5ce631d2). Advertisers and marketers have been forced to look to a new means of getting through to the modern consumer. Last year in my marketing class we looked at an interesting marketing campaign made by Procter and Gamble during the most previous winter Olympics(https://www.youtube.com/watch?v=rdQrwBVRzEg) . 

The entire commercial was about the importance of mom’s in Olympic athletes lives. It was a very sentimental and uplifting ad, but when looked at more closely has absolutely nothing to do with Procter & Gamble or any of their products. They moved the focus away from themselves and instead designed an advertisement that is a quick enjoyable 30 seconds for a consumer. The commercial curried favor from many of my classmates, but as a consumer, I always have my guard up and took objection to Procter and Gamble's marketing ploy. The commercial ends with the words, “ Procter & Gamble, a proud sponsor of moms”. While some consumers saw this ad as a more emotional, or “real experience” than most commercials, I saw it as a massive company using the hard work of moms to manipulate viewers into appreciating their brand more. They spoke nothing of supporting moms through initiatives or aiding athletes with financially struggling families. They are taking something beautiful and using it for their profit which deters me as a consumer. Where Procter and Gamble made their mistake, as I see it, was that they were running a campaign, instead of making an ongoing effort((https://www.forbes.com/sites/dangingiss/2018/07/16/what-10-social-media-professionals-say-should-be-your-top-priority-right-now/#22ac5ce631d2).  Neal Schaeffer sees this as a critical error to be made in modern marketing. He says brands can gain the trust of consumers in the social media sphere by showing a consistent commitment to their consumers and cause, not just running a campaign every time the Olympics rolls around. They need to actually engrain themselves within these social communities where trust and information are shared between brands and consumers peacefully. In this way, they will not only better understand their consumers, but be in a place where they could be more understood as brands.

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Facial recognition systems have become ruthlessly efficient at picking people out of a crowd in recent years, and people are finding ways to thwart the artificial intelligence that powers them. Research has already shown that AI can be fooled into seeing something that’s not there, and now these algorithms can be hijacked and reprogrammed.

Despite recent advances, the technology behind facial recognition, a type of deep learning called machine vision, leaves much to be desired. Many computer vision algorithms are still at a point where they’re liable to make mistakes, such as mislabeling a turtle as a gun. These mistakes can be weaponized by subtly manipulating images so that they cause computers to “see” specific things—for example, a sticker on a sign can cause a self-driving car to think it’s actually a stop sign.

As detailed in a recent paper posted to arXiv, three Google Brain researchers have taken this type of malicious image manipulation (called adversarial examples) a step further and demonstrated that small changes to images can actually force a machine learning algorithm to do free computations for the attacker, even if it wasn’t originally trained to do these types of computations. This opens the door for the possibility of attackers being able to hijack our increasingly AI-driven smartphones by exposing them to subtly manipulated images.

Deep learning uses artificial neural networks—a type of computing architecture loosely modeled on the human brain—to teach machines how to recognize patterns by feeding them a lot of data. So, for example, if you wanted to teach a neural network how to recognize an image of a cat, you’d feed it tens of thousands, if not millions, of examples of cat pics so that the algorithm can determine general parameters that constitute “cat-ness”. If you then present the machine with an image that contains an object that falls within these parameters, and if this training stage was successful, it will determine that the image contains a cat with a high degree of certainty.

Strange things start to happen with machine vision neural nets when they’re fed certain pictures that would be meaningless to humans, however. In a picture that otherwise just looks like static, a machine vision algorithm might be very confident that the image contains a centipede or a leopard. Moreover, static can be overlaid on normal images in a way that’s imperceptible to humans, yet throws the machine vision algorithm through a loop, kind of like how smart TVs can be triggered to perform various tasks by audio that is inaudible to humans.

The Google Brain researchers took the concept of adversarial examples a step further with adversarial reprogramming: causing a machine vision algorithm to perform a task other than the one it was trained to perform.

The researchers demonstrated a relatively simple adversarial reprogramming attack that got a machine vision algorithm that was originally trained to recognize things like animals to count the number of squares in an image. To do this, they generated images that consisted of psychedelic static with a black grid in the middle. Some of the squares in this 4x4 black grid were randomly selected to be white. This was the adversarial image.

The researchers then mapped these adversarial images to image classifications from ImageNet, a massive database used to train machine learning algorithms. The mapping between the ImageNet classifications and the adversarial images was arbitrary and represented the number of white squares in the adversarial image. For example, if the adversarial image contained two white squares, this would return the value ‘Goldfish,’ while an image with 10 white squares would return the value ‘Ostrich.’

The scenario modeled by the researchers imagined that an attacker knows the parameters of a machine vision network trained to do a specific task and then reprograms the network to do free computations on a task it wasn’t originally trained to do. In this case, the neural network being attacked was trained as an ImageNet classifier trained to recognize animals.

To manipulate this ImageNet classifier to do free computations, the researchers embedded the black boxes with white squares in 100,000 ImageNet images and then allowed the machine vision network to proceed as usual. If the image contained a black box with nine squares, for example, it would report back that it saw a hen, and the researchers would know it had correctly counted nine squares since this was the ImageNet classification that mapped to the number nine.

The technique turned out to be remarkably effective. The algorithm counted squares correctly in over 99 percent of the 100,000 images, the authors wrote.

Although this was a simple example, the researchers argue that these types of adversarial reprogramming attacks could be much more sophisticated in the future.

“A variety of nefarious ends may be achievable if machine learning systems can be reprogrammed by a specially crafted input,” the researchers wrote. “For instance, as phones increasingly act as AI-driven digital assistants, the plausibility of reprogramming someone’s phone by exposing it to an adversarial image or audio file increases. As these digital assistants have access to a user’s email, calendar, social media accounts, and credit cards the consequences of this type of attack also grow larger.”

[primorcoin]

New Post has been published on https://primorcoin.com/why-we-must-fight-for-a-decentralized-future/

Why we must fight for a decentralized future

If you’re into cryptocurrency or blockchain, there’s a good chance I don’t have to spell out the benefits of decentralization. You’re a first-generation user of a technology that will increasingly define the future of the internet, and you have front-row seats to the world premiere of Web3.

The internet’s use and control were always as centralized as we see now. In the early days, under the stewardship of the United States Department of Defense, the network needed not to rely on one core computer. What if a terrorist attack or missile strike took down the principal node? Individual network parts had to communicate without relying on a single computer to reduce vulnerability.

Later, the unincorporated Internet Engineering Task Force, which facilitated the development of all internet protocols, worked ceaselessly to prevent private companies or particular countries from controlling the network.

Today, centralized app nodes are controlled and operated by the planet’s richest organizations, collecting and storing billions of people’s data. Private companies control the user experience on apps and can incentivize and manipulate behavior. From a reliability standpoint, billions lose their primary means of communication when centralized nodes go down — as in recent incidents with Facebook, Instagram, WhatsApp and Messenger in October 2021.

We have also seen how little the tech behemoths think of our privacy when dollar signs appear in their eyes: They harvest and sell our data on an industrial scale. After 10-plus years of using people as advertisers’ products, Mark Zuckerberg has brazenly co-opted the metaverse. Google and Apple, meanwhile, continue their incessant mission to enter every corner of our lives.

Related: The data economy is a dystopian nightmare

We also know what happens when authoritarian governments come knocking on the doors of these centralized mega-warehouses of data, fed by our devices that function as a surveillance army. We’ve seen in Ukraine the awful, large-scale violence that can be excused or hidden when media and military power comes under authoritarian control. In some countries, the state has unprecedented access to every aspect of citizens’ behavior, monitoring everything from internet search history to minor social infractions. Systems that would horrify even George Orwell are only possible because of centralization.

Even in Silicon Valley, ensconced within Western notions of freedom and individuals’ rights, tech empires rarely choose a principled stance over a large, lucrative market. When centralized powers such as Moscow, Beijing or Istanbul ask for censorship and control, they usually get it. Fundamentally, we cannot trust the tech giants with the innermost details of our lives; the centralization of control over the internet is undermining or forestalling democracy everywhere.

Taking our power back

We should not be surprised that tech behemoths have become the natural enemies of decentralization: Centralization is a natural instinct for those in control. Until the advent of the internet and the blockchain, centralization often meant convenience and simplicity. In the Middle Ages, a distributed system of vassal lords meant the monarchy lacked control, and money seeped through the cracks of corruption.

With time and distance no longer problematic in the internet age, Big Tech’s drive toward centralization is less surprising. Can we be astonished by the horrific results of attention-grabbing algorithms, such as attempted genocides or political manipulation based on psychometric analysis of user data? Centralization has consequences.

Distributed ledger technology provides a practical alternative. Social media, messaging, streaming, searching and data-sharing on the blockchain can be fairer, more transparent and accessible, and less centralized. Conversely, this does not mean data has to be less private.

In XX Messenger’s case, which my team and I launched in January, XX Network nodes process anonymous messages worldwide, shredding metadata for recipients and timestamps. With XX, there is privacy and decentralization. Later, this new paradigm of communications and information-sharing makes a significant extension and reinvention of democracy possible.

Related: Blockchain-based decentralized messengers: A privacy pipedream?

There are moments in history when two separate events combine to tell a greater truth. In 2008, when Lehman Brothers Holdings Inc. crashed in the wake of the Great Recession, it seemed to be the death knell of centralized financial institutions, despite the economic pain it would herald. Then, little more than a month later, Satoshi Nakamoto published the Bitcoin (BTC) white paper, the revolutionary blueprint for modern peer-to-peer currency. There’s an important connection between these two momentous events, yet the words “Bitcoin,” “blockchain” and “cryptocurrency” draw eye-rolls from those who misunderstand centralization’s issues.

In the autumn of 2008 was the opportunity to begin telling a story: It is up to us — the cryptographers, privacy lovers, traders, developers, activists and converts — to carry the torch of decentralization and democracy. If there was ever a tale that deserved to be told, beginning to end, it is this one.

Join me in telling it.

This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.

The views, thoughts and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

David Chaum is one of the earliest blockchain researchers and a world-renowned cryptographer and privacy advocate. Known as “The Godfather of Privacy,” Chaum first proposed a solution for protecting metadata with mix-cascade networks in 1979. In 1982, his dissertation at the University of California, Berkeley became the first known proposal of a blockchain protocol. Chaum developed eCash, the first digital currency, and made numerous contributions to secure voting systems in the 1990s. Today, Chaum is the founder of Elixxir, Praxxis and the XX Network, which combine his decades of research and contributions in cryptography and privacy to deliver state-of-the-art blockchain solutions.

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