#computer science just been Solved. What of all the problems I learned and researched about. Which were cool. Are they just dead

[aeolianblues]

I'm not an extrovert. At all. In everyday life, I'm a yapper, sure, but I need someone to first assure me I am okay to yap, so I don't start conversations, even when I really want to join in sometimes! It's just the social anxiety acting up. God knows where from and why I lose a lot of my inhibitions when it comes to talking to people about music. I don't know where the confidence has suddenly sprung from. I've made a crazy amount of friends in musical circles, either just talking to people about common music or (since it is after all in music circles) talking to bands about their own music. I let out a sigh of relief any time an interaction goes well, because in truth it's going against my every instinct. I wish I could do that in everyday life

[the-real-wholesome-bitch]

Mindless consumption and AI

Ok, so I am a computer science student and an artist, and quite frankly, I hate AI. I think it is just encouraging the mindless consumption of content rather than the creation of art and things that we enjoy. People are trying to replace human-created art with AI art, and quite frankly, that really is just a head-scratcher. The definition of art from Oxford Languages is as follows: “the expression or application of human creative skill and imagination, typically in a visual form such as painting or sculpture, producing works to be appreciated primarily for their beauty or emotional power.” The key phrase here is “human creative skill”; art is inherently a human trait. I think it is cool that we are trying to teach machines how to make art; however, can we really call it art based on the definition we see above? About two years ago, I wrote a piece for my school about AI and art (I might post it; who knows?), where I argued that AI art is not real art.

Now, what about code? As a computer science student, I kind of like AI in the sense that it can overlook my code and tell me what is wrong with it, as well as how to improve it. It can also suggest sources for a research paper and check my spelling (which is really bad; I used it for this). Now, AI can also MAKE code, and let me tell you, my classmates abuse this like crazy. Teachers and TAs are working overtime to look through all the code that students submit to find AI-generated code (I was one of them), and I’ll be honest, it’s really easy to find!

People think that coding is a very rigid discipline, and yes, you do have to be analytical and logical to come up with code that works; however, you also have to be creative. You have to be creative to solve the problems that you are given, and just like with art, AI can’t be creative. Sure, it can solve simple tasks like making an array that takes in characters and reverses the order to print the output. But it can’t solve far more complex problems, and when students try to use it to find solutions, it breaks. The programs that it generates just don’t work and/or it makes up some nonsense.

And as more AI content fills the landscape, it’s just getting shittier and shittier. Now, how does the mindless consumption of content relate to this? You see, I personally think it has a lot to do with it. We have been consuming information and content for a long time, but the amount of content that exists in this world is greater than ever before, and AI “content” is just adding to this junkyard. No longer are people trying to understand the many techniques of art and develop their own styles (this applies to all art forms, such as visual art, writing, filmmaking, etc.). People will simply just enter a prompt into Midjourney and BOOM, you have multiple “art pieces” in different styles (which were stolen from real artists), and you can keep regenerating until you get what you want. You don’t have to do the hard work of learning how to draw, developing an art style, and doing it until you get it right. You can “create” something quickly for instant gratification; you can post it, and someone will look at it. Maybe they will leave a like on it; they might even keep scrolling and see more and more AI art, therefore leading to mindless consumption.

[cyberstudious]

hi, im an high school sophomore interested in computer science and im also new to your blog. i was wondering if you would recommend conputer science and what have been your strengths and pitfalls with the field? thank u so much for your time.

Hi! Welcome to my blog, haha thanks for stopping by and sending an ask!

My path was self-taught game dev/web dev -> CS degree -> cybersecurity, so that's the perspective I'm writing from. My current job is basically just writing code for cybersecurity-related things (which I really like!). I do enjoy computer science and I think it's a great field to get into because you can do so many different things! I listed out my personal pros/cons under the cut but the tl;dr is that CS is a good field if you like constantly learning things, building things, and knowing how stuff works under the hood.

things I like about computer science:

so many options and things you can learn/specialize in

having programming skills and knowing how computers work gives you the foundational knowledge to succeed in a lot of things, both practical and theoretical/research-based. if you don't really like programming, there is plenty of theoretical math stuff you can do that's related to CS (this is what my partner is going back to grad school for haha)

lots of info available online for self-guided learning

do you want to learn how to make X? someone has almost certainly already written a tutorial for that and put it online for free. there are lots of open-source projects out there where you can read their documentation and even look at the code to figure out how things work!

there is always more to learn

tech evolves and you have to keep your skills up to date - that means there's always something new and interesting happening!

being able to build things

do you want to make an app? a website? a video game? a quick script to automate some annoying task that you do all the time? you can do that. all you need is a computer and some time! once you have some skills, it's amazing when you realize you can just Make Stuff literally whenever

understanding how things actually work

in a world of apps & operating systems that actively try to hide the technical layer of how they work in favor of "user friendliness", there is power to understanding what's actually happening inside your computer

problem-solving mindset

this kind of goes hand-in-hand with being able to build things, but eventually you get the hang of looking at a problem, breaking it down, and figuring out how to build a solution. this is something that I knew was an important soft skill, but I didn't really have any concrete examples until I started working with some technical but non-programmer coworkers. knowing programming & how to build things really does just help you solve problems in a concrete way and I think that's pretty cool.

things that can make computer science difficult:

programming is a cycle of failing until you succeed

programming is not something you get right on your first try - there's a reason that patches and updates and bug fixes exist. this might take some getting used to at first, but after that it's not an issue. failing constantly is just part of the process, but that means that solving those problems and feeling great when you figure it out is also part of the process!

there's so much to learn, you will have to go out and learn some of it on your own

a CS degree will not fully prepare you to be a professional developer, you will likely have to learn other languages & frameworks on your own (this is kind of a good thing btw - the average college probably isn't updating their curriculum often enough to teach you relevant frameworks/some professional coding things).

there is always more to learn

this is the other side of tech always evolving - sometimes it can feel like you're constantly behind, and that's okay - you can't learn literally everything! just do your best, explore a bit, and figure out the subset of things that you're actually interested in

lots of screen time

there are tech jobs where you can be active and move around and stuff, but I work from home and write code most of the day so I spend a ton of time in front of my computer. this isn't a huge problem, I just make an effort to spend time on my non-computer hobbies outside of work. something to note when you're looking for jobs, I suppose!

occasional toxic culture?

I'm thinking of "leetcode grindset bros" here because that was a common character at the college I went to - just ignore them and do things at a pace that feels comfortable to you, you'll be fine

on a related note, in my experience there will always be some dude who has been programming since like the age of 5 and seems to know everything and is kind of an ass about it, ignore these people too and you'll be fine

things are getting better, but CS is still very much a male-dominated field. however, there are plenty of organizations focused on supporting minority groups in tech! you can find a support group and there will always be people rooting for you.

that got kinda long lol, but feel free to reach out if you have any more questions!

[jcmarchi]

Has AI Taken Over the World? It Already Has

New Post has been published on https://thedigitalinsider.com/has-ai-taken-over-the-world-it-already-has/

Has AI Taken Over the World? It Already Has

In 2019, a vision struck me—a future where artificial intelligence (AI), accelerating at an unimaginable pace, would weave itself into every facet of our lives. After reading Ray Kurzweil’s The Singularity is Near, I was captivated by the inescapable trajectory of exponential growth. The future wasn’t just on the horizon; it was hurtling toward us. It became clear that, with the relentless doubling of computing power, AI would one day surpass all human capabilities and, eventually, reshape society in ways once relegated to science fiction.

Fueled by this realization, I registered Unite.ai, sensing that these next leaps in AI technology would not merely enhance the world but fundamentally redefine it. Every aspect of life—our work, our decisions, our very definitions of intelligence and autonomy—would be touched, perhaps even dominated, by AI. The question was no longer if this transformation would happen, but rather when, and how humanity would manage its unprecedented impact.

As I dove deeper, the future painted by exponential growth seemed both thrilling and inevitable. This growth, exemplified by Moore’s Law, would soon push artificial intelligence beyond narrow, task-specific roles to something far more profound: the emergence of Artificial General Intelligence (AGI). Unlike today’s AI, which excels in narrow tasks, AGI would possess the flexibility, learning capability, and cognitive range akin to human intelligence—able to understand, reason, and adapt across any domain.

Each leap in computational power brings us closer to AGI, an intelligence capable of solving problems, generating creative ideas, and even making ethical judgments. It wouldn’t just perform calculations or parse vast datasets; it would recognize patterns in ways humans can’t, perceive relationships within complex systems, and chart a future course based on understanding rather than programming. AGI could one day serve as a co-pilot to humanity, tackling crises like climate change, disease, and resource scarcity with insight and speed beyond our abilities.

Yet, this vision comes with significant risks, particularly if AI falls under the control of individuals with malicious intent—or worse, a dictator. The path to AGI raises critical questions about control, ethics, and the future of humanity. The debate is no longer about whether AGI will emerge, but when—and how we will manage the immense responsibility it brings.

The Evolution of AI and Computing Power: 1956 to Present

From its inception in the mid-20th century, AI has advanced alongside exponential growth in computing power. This evolution aligns with fundamental laws like Moore’s Law, which predicted and underscored the increasing capabilities of computers. Here, we explore key milestones in AI’s journey, examining its technological breakthroughs and growing impact on the world.

1956 – The Inception of AI

The journey began in 1956 when the Dartmouth Conference marked the official birth of AI. Researchers like John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon gathered to discuss how machines might simulate human intelligence. Although computing resources at the time were primitive, capable only of simple tasks, this conference laid the foundation for decades of innovation.

1965 – Moore’s Law and the Dawn of Exponential Growth

In 1965, Gordon Moore, co-founder of Intel, made a prediction that computing power would double approximately every two years—a principle now known as Moore’s Law. This exponential growth made increasingly complex AI tasks feasible, allowing machines to push the boundaries of what was previously possible.

1980s – The Rise of Machine Learning

The 1980s introduced significant advances in machine learning, enabling AI systems to learn and make decisions from data. The invention of the backpropagation algorithm in 1986 allowed neural networks to improve by learning from errors. These advancements moved AI beyond academic research into real-world problem-solving, raising ethical and practical questions about human control over increasingly autonomous systems.

1990s – AI Masters Chess

In 1997, IBM’s Deep Blue defeated world chess champion Garry Kasparov in a full match, marking a major milestone. It was the first time a computer demonstrated superiority over a human grandmaster, showcasing AI’s ability to master strategic thinking and cementing its place as a powerful computational tool.

2000s – Big Data, GPUs, and the AI Renaissance

The 2000s ushered in the era of Big Data and GPUs, revolutionizing AI by enabling algorithms to train on massive datasets. GPUs, originally developed for rendering graphics, became essential for accelerating data processing and advancing deep learning. This period saw AI expand into applications like image recognition and natural language processing, transforming it into a practical tool capable of mimicking human intelligence.

2010s – Cloud Computing, Deep Learning, and Winning Go

With the advent of cloud computing and breakthroughs in deep learning, AI reached unprecedented heights. Platforms like Amazon Web Services and Google Cloud democratized access to powerful computing resources, enabling smaller organizations to harness AI capabilities.

In 2016, DeepMind’s AlphaGo defeated Lee Sedol, one of the world’s top Go players, in a game renowned for its strategic depth and complexity. This achievement demonstrated the adaptability of AI systems in mastering tasks previously thought to be uniquely human.

2020s – AI Democratization, Large Language Models, and Dota 2

The 2020s have seen AI become more accessible and capable than ever. Models like GPT-3 and GPT-4 illustrate AI’s ability to process and generate human-like text. At the same time, innovations in autonomous systems have pushed AI to new domains, including healthcare, manufacturing, and real-time decision-making.

In esports, OpenAI’s bots achieved a remarkable feat by defeating professional Dota 2 teams in highly complex multiplayer matches. This showcased AI’s ability to collaborate, adapt strategies in real-time, and outperform human players in dynamic environments, pushing its applications beyond traditional problem-solving tasks.

Is AI Taking Over the World?

The question of whether AI is “taking over the world” is not purely hypothetical. AI has already integrated into various facets of life, from virtual assistants to predictive analytics in healthcare and finance, and the scope of its influence continues to grow. Yet, “taking over” can mean different things depending on how we interpret control, autonomy, and impact.

The Hidden Influence of Recommender Systems

One of the most powerful ways AI subtly dominates our lives is through recommender engines on platforms like YouTube, Facebook, and X. These algorithms, running on AI systems, analyze preferences and behaviors to serve content that aligns closely with our interests. On the surface, this might seem beneficial, offering a personalized experience. However, these algorithms don’t just react to our preferences; they actively shape them, influencing what we believe, how we feel, and even how we perceive the world around us.

YouTube’s AI: This recommender system pulls users into hours of content by offering videos that align with and even intensify their interests. But as it optimizes for engagement, it often leads users down radicalization pathways or towards sensationalist content, amplifying biases and occasionally promoting conspiracy theories.

Social Media Algorithms: Sites like Facebook,Instagram and X prioritize emotionally charged content to drive engagement, which can create echo chambers. These bubbles reinforce users’ biases and limit exposure to opposing viewpoints, leading to polarized communities and distorted perceptions of reality.

Content Feeds and News Aggregators: Platforms like Google News and other aggregators customize the news we see based on past interactions, creating a skewed version of current events that can prevent users from accessing diverse perspectives, further isolating them within ideological bubbles.

This silent control isn’t just about engagement metrics; it can subtly influence public perception and even impact crucial decisions—such as how people vote in elections. Through strategic content recommendations, AI has the power to sway public opinion, shaping political narratives and nudging voter behavior. This influence has significant implications, as evidenced in elections around the world, where echo chambers and targeted misinformation have been shown to sway election outcomes.

This explains why discussing politics or societal issues often leads to disbelief when the other person’s perspective seems entirely different, shaped and reinforced by a stream of misinformation, propaganda, and falsehoods.

Recommender engines are profoundly shaping societal worldviewsm especially when you factor in the fact that misinformation is 6 times more likely to be shared than factual information. A slight interest in a conspiracy theory can lead to an entire YouTube or X feed being dominated by fabrications, potentially driven by intentional manipulation or, as noted earlier, computational propaganda.

Computational propaganda refers to the use of automated systems, algorithms, and data-driven techniques to manipulate public opinion and influence political outcomes. This often involves deploying bots, fake accounts, or algorithmic amplification to spread misinformation, disinformation, or divisive content on social media platforms. The goal is to shape narratives, amplify specific viewpoints, and exploit emotional responses to sway public perception or behavior, often at scale and with precision targeting.

This type of propaganda is why voters often vote against their own self-interest, the votes are being swayed by this type of computational propaganda.

“Garbage In, Garbage Out” (GIGO) in machine learning means that the quality of the output depends entirely on the quality of the input data. If a model is trained on flawed, biased, or low-quality data, it will produce unreliable or inaccurate results, regardless of how sophisticated the algorithm is.

This concept also applies to humans in the context of computational propaganda. Just as flawed input data corrupts an AI model, constant exposure to misinformation, biased narratives, or propaganda skews human perception and decision-making. When people consume “garbage” information online—misinformation, disinformation, or emotionally charged but false narratives—they are likely to form opinions, make decisions, and act based on distorted realities.

In both cases, the system (whether an algorithm or the human mind) processes what it is fed, and flawed input leads to flawed conclusions. Computational propaganda exploits this by flooding information ecosystems with “garbage,” ensuring that people internalize and perpetuate those inaccuracies, ultimately influencing societal behavior and beliefs at scale.

Automation and Job Displacement

AI-powered automation is reshaping the entire landscape of work. Across manufacturing, customer service, logistics, and even creative fields, automation is driving a profound shift in the way work is done—and, in many cases, who does it. The efficiency gains and cost savings from AI-powered systems are undeniably attractive to businesses, but this rapid adoption raises critical economic and social questions about the future of work and the potential fallout for employees.

In manufacturing, robots and AI systems handle assembly lines, quality control, and even advanced problem-solving tasks that once required human intervention. Traditional roles, from factory operators to quality assurance specialists, are being reduced as machines handle repetitive tasks with speed, precision, and minimal error. In highly automated facilities, AI can learn to spot defects, identify areas for improvement, and even predict maintenance needs before problems arise. While this results in increased output and profitability, it also means fewer entry-level jobs, especially in regions where manufacturing has traditionally provided stable employment.

Customer service roles are experiencing a similar transformation. AI chatbots, voice recognition systems, and automated customer support solutions are reducing the need for large call centers staffed by human agents. Today’s AI can handle inquiries, resolve issues, and even process complaints, often faster than a human representative. These systems are not only cost-effective but are also available 24/7, making them an appealing choice for businesses. However, for employees, this shift reduces opportunities in one of the largest employment sectors, particularly for individuals without advanced technical skills.

Creative fields, long thought to be uniquely human domains, are now feeling the impact of AI automation. Generative AI models can produce text, artwork, music, and even design layouts, reducing the demand for human writers, designers, and artists. While AI-generated content and media are often used to supplement human creativity rather than replace it, the line between augmentation and replacement is thinning. Tasks that once required creative expertise, such as composing music or drafting marketing copy, can now be executed by AI with remarkable sophistication. This has led to a reevaluation of the value placed on creative work and its market demand.

Influence on Decision-Making

AI systems are rapidly becoming essential in high-stakes decision-making processes across various sectors, from legal sentencing to healthcare diagnostics. These systems, often leveraging vast datasets and complex algorithms, can offer insights, predictions, and recommendations that significantly impact individuals and society. While AI’s ability to analyze data at scale and uncover hidden patterns can greatly enhance decision-making, it also introduces profound ethical concerns regarding transparency, bias, accountability, and human oversight.

AI in Legal Sentencing and Law Enforcement

In the justice system, AI tools are now used to assess sentencing recommendations, predict recidivism rates, and even aid in bail decisions. These systems analyze historical case data, demographics, and behavioral patterns to determine the likelihood of re-offending, a factor that influences judicial decisions on sentencing and parole. However, AI-driven justice brings up serious ethical challenges:

Bias and Fairness: AI models trained on historical data can inherit biases present in that data, leading to unfair treatment of certain groups. For example, if a dataset reflects higher arrest rates for specific demographics, the AI may unjustly associate these characteristics with higher risk, perpetuating systemic biases within the justice system.

Lack of Transparency: Algorithms in law enforcement and sentencing often operate as “black boxes,” meaning their decision-making processes are not easily interpretable by humans. This opacity complicates efforts to hold these systems accountable, making it challenging to understand or question the rationale behind specific AI-driven decisions.

Impact on Human Agency: AI recommendations, especially in high-stakes contexts, may influence judges or parole boards to follow AI guidance without thorough review, unintentionally reducing human judgment to a secondary role. This shift raises concerns about over-reliance on AI in matters that directly impact human freedom and dignity.

AI in Healthcare and Diagnostics

In healthcare, AI-driven diagnostics and treatment planning systems offer groundbreaking potential to improve patient outcomes. AI algorithms analyze medical records, imaging, and genetic information to detect diseases, predict risks, and recommend treatments more accurately than human doctors in some cases. However, these advancements come with challenges:

Trust and Accountability: If an AI system misdiagnoses a condition or fails to detect a serious health issue, questions arise around accountability. Is the healthcare provider, the AI developer, or the medical institution responsible? This ambiguity complicates liability and trust in AI-based diagnostics, particularly as these systems grow more complex.

Bias and Health Inequality: Similar to the justice system, healthcare AI models can inherit biases present in the training data. For instance, if an AI system is trained on datasets lacking diversity, it may produce less accurate results for underrepresented groups, potentially leading to disparities in care and outcomes.

Informed Consent and Patient Understanding: When AI is used in diagnosis and treatment, patients may not fully understand how the recommendations are generated or the risks associated with AI-driven decisions. This lack of transparency can impact a patient’s right to make informed healthcare choices, raising questions about autonomy and informed consent.

AI in Financial Decisions and Hiring

AI is also significantly impacting financial services and employment practices. In finance, algorithms analyze vast datasets to make credit decisions, assess loan eligibility, and even manage investments. In hiring, AI-driven recruitment tools evaluate resumes, recommend candidates, and, in some cases, conduct initial screening interviews. While AI-driven decision-making can improve efficiency, it also introduces new risks:

Bias in Hiring: AI recruitment tools, if trained on biased data, can inadvertently reinforce stereotypes, filtering out candidates based on factors unrelated to job performance, such as gender, race, or age. As companies rely on AI for talent acquisition, there is a danger of perpetuating inequalities rather than fostering diversity.

Financial Accessibility and Credit Bias: In financial services, AI-based credit scoring systems can influence who has access to loans, mortgages, or other financial products. If the training data includes discriminatory patterns, AI could unfairly deny credit to certain groups, exacerbating financial inequality.

Reduced Human Oversight: AI decisions in finance and hiring can be data-driven but impersonal, potentially overlooking nuanced human factors that may influence a person’s suitability for a loan or a job. The lack of human review may lead to an over-reliance on AI, reducing the role of empathy and judgment in decision-making processes.

Existential Risks and AI Alignment

As artificial intelligence grows in power and autonomy, the concept of AI alignment—the goal of ensuring AI systems act in ways consistent with human values and interests—has emerged as one of the field’s most pressing ethical challenges. Thought leaders like Nick Bostrom have raised the possibility of existential risks if highly autonomous AI systems, especially if  AGI develop goals or behaviors misaligned with human welfare. While this scenario remains largely speculative, its potential impact demands a proactive, careful approach to AI development.

The AI Alignment Problem

The alignment problem refers to the challenge of designing AI systems that can understand and prioritize human values, goals, and ethical boundaries. While current AI systems are narrow in scope, performing specific tasks based on training data and human-defined objectives, the prospect of AGI raises new challenges. AGI would, theoretically, possess the flexibility and intelligence to set its own goals, adapt to new situations, and make decisions independently across a wide range of domains.

The alignment problem arises because human values are complex, context-dependent, and often difficult to define precisely. This complexity makes it challenging to create AI systems that consistently interpret and adhere to human intentions, especially if they encounter situations or goals that conflict with their programming. If AGI were to develop goals misaligned with human interests or misunderstand human values, the consequences could be severe, potentially leading to scenarios where AGI systems act in ways that harm humanity or undermine ethical principles.

AI In Robotics

The future of robotics is rapidly moving toward a reality where drones, humanoid robots, and AI become integrated into every facet of daily life. This convergence is driven by exponential advancements in computing power, battery efficiency, AI models, and sensor technology, enabling machines to interact with the world in ways that are increasingly sophisticated, autonomous, and human-like.

A World of Ubiquitous Drones

Imagine waking up in a world where drones are omnipresent, handling tasks as mundane as delivering your groceries or as critical as responding to medical emergencies. These drones, far from being simple flying devices, are interconnected through advanced AI systems. They operate in swarms, coordinating their efforts to optimize traffic flow, inspect infrastructure, or replant forests in damaged ecosystems.

For personal use, drones could function as virtual assistants with physical presence. Equipped with sensors and LLMs, these drones could answer questions, fetch items, or even act as mobile tutors for children. In urban areas, aerial drones might facilitate real-time environmental monitoring, providing insights into air quality, weather patterns, or urban planning needs. Rural communities, meanwhile, could rely on autonomous agricultural drones for planting, harvesting, and soil analysis, democratizing access to advanced agricultural techniques.

The Rise of Humanoid Robots

Side by side with drones, humanoid robots powered by LLMs will seamlessly integrate into society. These robots, capable of holding human-like conversations, performing complex tasks, and even exhibiting emotional intelligence, will blur the lines between human and machine interactions. With sophisticated mobility systems, tactile sensors, and cognitive AI, they could serve as caregivers, companions, or co-workers.

In healthcare, humanoid robots might provide bedside assistance to patients, offering not just physical help but also empathetic conversation, informed by deep learning models trained on vast datasets of human behavior. In education, they could serve as personalized tutors, adapting to individual learning styles and delivering tailored lessons that keep students engaged. In the workplace, humanoid robots could take on hazardous or repetitive tasks, allowing humans to focus on creative and strategic work.

Misaligned Goals and Unintended Consequences

One of the most frequently cited risks associated with misaligned AI is the paperclip maximizer thought experiment. Imagine an AGI designed with the seemingly innocuous goal of manufacturing as many paperclips as possible. If this goal is pursued with sufficient intelligence and autonomy, the AGI might take extreme measures, such as converting all available resources (including those vital to human survival) into paperclips to achieve its objective. While this example is hypothetical, it illustrates the dangers of single-minded optimization in powerful AI systems, where narrowly defined goals can lead to unintended and potentially catastrophic consequences.

One example of this type of single-minded optimization having negative repercussions is the fact that some of the most powerful AI systems in the world optimize exclusively for engagement time, compromising in turn facts, and truth. The AI can keep us entertained longer by intentionally amplifiying the reach of conspiracy theories, and propaganda.

Conclusion

[myeducation001]

BS Studies: A Comprehensive Guide to Your Future

1. Introduction to BS Studies

So, you're thinking about pursuing a Bachelor of Science (BS) degree? Awesome choice! But, what exactly is BS Studies, and why is it such a big deal today? A BS degree focuses on science and technical subjects, designed to provide you with the practical skills and theoretical knowledge you need to excel in your chosen field. It's not just about science either – a BS degree can lead you into careers in tech, business, health, and more.

2. The Evolution of BS Degrees

The concept of a Bachelor of Science degree has been around for centuries, originally rooted in the study of natural sciences like biology and chemistry. However, over the years, it has evolved to include a broader range of disciplines, from computer science to business analytics. This expansion reflects the increasing need for specialized knowledge in today's rapidly evolving industries. BS studies are now more interdisciplinary, allowing students to blend different areas of interest for a unique educational experience.

3. Why Choose a BS Degree?

You might wonder, "Why should I choose a BS degree over other types of programs?" One of the biggest advantages is its focus on practical, hands-on learning. BS degrees often incorporate labs, fieldwork, and projects, helping you develop the technical skills needed in today’s job market. Plus, a BS degree opens up a wide array of career options, from tech and engineering to healthcare and business.

4. Popular Fields in BS Studies

BS in Computer Science

A highly sought-after field, a BS in Computer Science equips you with coding, software development, and algorithmic thinking skills – all critical in the tech-driven world.

BS in Engineering

Whether it’s civil, mechanical, or electrical engineering, a BS in this field gives you the technical expertise to design, build, and innovate in various industries.

BS in Health Sciences

Health-related BS degrees, such as nursing or public health, prepare you to address global healthcare challenges and make a tangible difference in people's lives.

BS in Business Administration

With a focus on economics, management, and operations, a BS in Business Administration can set you on the path to becoming a leader in the corporate world.

BS in Environmental Science

This degree is perfect for those passionate about sustainability, offering tools to tackle the pressing environmental issues of today.

5. Specializations within BS Studies

Many BS programs offer specializations, allowing students to dive deeper into niche areas. For example, within Computer Science, you can specialize in Artificial Intelligence or Cybersecurity. This gives you the flexibility to tailor your education to your career goals.

6. Skills You Gain from a BS Degree

Graduating with a BS degree doesn’t just mean you have a diploma – it means you’ve acquired a wealth of skills that employers are actively seeking. These include:

a) Analytical Thinking**: The ability to analyze data and problem-solve is crucial in almost any field.

b) Technical Skills**: From software development to lab techniques, you’ll gain hands-on experience.

c) Problem-Solving Abilities**: BS degrees often emphasize real-world problem-solving.

d) Communication and Teamwork**: Many projects require collaboration, honing your teamwork and leadership skills.

7. Job Opportunities after BS Studies

Graduates with BS degrees are in high demand across a variety of industries. The tech industry, for example, is always on the lookout for computer scientists and engineers. Healthcare sectors are hiring health professionals, while business industries need analysts and managers with strong technical backgrounds. Whether you aim to work in startups, multinational corporations, or research, a BS degree can open doors to numerous possibilities.

8. How to Choose the Right BS Program

Selecting the right BS program is a significant decision. Start by considering your interests and career goals. Look into the curriculum, faculty expertise, university ranking, and accreditation. Also, think about the balance between theoretical knowledge and practical application. Internships and hands-on experience should also be a top priority when choosing the right program.

[clonemediaarchive]

Carbon Copy Consumables by Deborah Sheldon

https://www.sciencewritenow.com/read/science-humour-and-the-absurd/carbon-copy-consumables

Look, what you’ve got to understand about industry – and I’m talking about the food industry in particular – is that the pursuit of money always trumps common sense. It’s been this way since Year Dot. For instance, there’s only one type of banana across the whole planet, the Cavendish, but here’s the kicker: each piece of fruit is a clone. I’m not bullshitting you. They’re grown from suckers. So, every banana is genetically identical. If a pathogen comes along that can wipe out just one banana, it’ll wipe out the crop worldwide.

And this isn’t a theory, mind you. It happened already.

Prior to the Cavendish, the only commercial banana was another cloned variety, the Gros Michel, and that crop got destroyed by a kind of soil fungus in the 1960s. The Cavendish was its replacement. But did the food industry learn anything from putting all its eggs – or Gros Michel bananas – into the one basket? No, except to do it all over again because of economics. Even when the smallest possible risk is complete and utter catastrophe. You see where I’m coming from? Money trumps common sense. Every. Single. Time.

Don’t get me wrong, I’m not against food cloning. That’s my trade, after all. Cloning is a great idea. Finding a way to computerise, mechanise and standardise the process solved a lot of problems like overfishing, deforestation, famines, and suchlike and et cetera, but hey, I don’t need to make a speech. Anybody with half a brain knows that food cloning factories are a boon to mankind. I’m only stating my point of view for the record.

Also, for the record, my name is Charles Pomeroy but everyone calls me Charlie. I’m thirty-four years old, single, no kids, Aussie by birth, and a factory runner for Carbon Copy Consumables. For the past eight years, I’ve worked at their Antarctica plant servicing the research stations, hotels, resorts, casinos, theme park, restaurants, private homes and what have you. The busiest time of year is summer when the tourist ships come by the dozen and every business is running at full capacity. With about nine thousand mouths to feed, I have to run the factory twenty-four seven. Yeah, all by my lonesome.

The company website explains their setup if you’re interested, but in a nutshell, the Antarctica factory is about a kilometre long, three storeys high, covered in gantries and stuffed to the gills with machines. Carbon Copy Consumables is ‘lights-out’ manufacturing with everything controlled by a bunch of computers. Even the trucks that pick up the supplies are automated and self-driven, and each truck is packed by robot arms.

So, the four reasons I’m needed there…

One: feed the machines. Our base material looks like bouillon powder. It’s actually a combination of elements including carbon, nitrogen, sulphur – I forget the others – but ninety-seven percent of every living thing on Earth is made up of just six elements. Amazing, right? At full storage capacity, I’ve got six vats and each one’s about the size of a wheat silo.

Two: keep the joint hygienic. The machines have self-cleaning cycles; I top up detergents.

Three: equipment maintenance. Our machines are so smart they’re almost self-sufficient, the emphasis on ‘almost’. Nothing beats the human mind. Training to be a factory runner takes four years because you need to learn how to service every part of every machine. Yeah, there’s manuals to jog your memory, but it’s a specialised field with lifelong job security. Why would Carbon Copy Consumables sack a factory runner after investing four years into them? And you get paid top dollar while you train. Sweet gig. If you ever want a career change, look into it. Just be aware the competition is stiff. For every opening, there’s a thousand applications. You’ve got to be the best of the best.

And four: stock control. The machines can’t make informed decisions about which foods need to be cloned. I take orders from all over Antarctica. You’ve got no idea of the vast amounts of produce I churn out to allow three meals and snacks for nine thousand people in peak season. Hold onto your little cotton socks because I’m about to blow your mind. Ready?

Five tonnes of vegetables. That’s metric tonnes, mind you, per day. Two tonnes of beef, every cut from chuck to eye fillet. One tonne of chicken. Ten thousand eggs. All. Per. Day. And so on, and so forth. Can you grasp the scale of this operation? Can you imagine trying to fly this amount of naturally-sourced food into Antarctica? Well, that’s how they used to do it in the old days. That’s why the population was capped at about one thousand; the logistics of supply were too difficult.

Oh yeah, and another reason: a bunch of Antarctic Treaties about keeping the continent pristine. Those treaties were overturned for the sake of money. Capitalism is great, don’t get me wrong – it’s dragged most of the world out of poverty – but there’s a few drawbacks here. Did you know that one-third of Antarctica is now a giant tip covered in garbage? Anyhow, that’s progress. Two steps forward, one step back. Don’t worry, a company will come up with a way to turn rubbish into something useful, like gold, if there’s money in it.

Sure, I’m on good terms with the freight runners, ship captains, pilots, et cetera. You know what? Cards on the table? I’ll come straight out and tell you that my partner in the botany scheme was a pilot named Jenny. I’m guessing you’re interrogating her anyway, so there’s no point me trying to be discreet. The whole sideline about the plants was her idea, with a forty-sixty split. She promised me bucketloads of cash, and boy, was she right on the money.

There are two flowering plants native to Antarctica: the hair grass and the pearlwort. You find them mainly on the western peninsula and on a couple of islands. One time Jenny told me, while she was waiting on her plane to be refuelled and loaded, that some knob-ends from Sydney’s North Shore were scouting for unusual plants for their daughter’s bridal bouquet and table arrangements, and would I be interested in some quick dough?

Now, these Antarctic plants look pretty dull, but that’s not the point. Rarity symbolises wealth. Even if the plants happened to look like busted arseholes covered in fly-blown crap, it wouldn’t matter. Do you know what happened in the seventeenth century when the pineapple was first brought over to Britain from Barbados? Well, the pineapple was such a rare fruit, and so expensive, that super-rich people would bung one in the middle of their ballroom and host a party to flex on their high-society friends. The not-so-rich rented pineapples for the sole purpose of bragging. Even a rotting pineapple had prestige.

And hundreds of years later, rich people are exactly the same.

Long story short, yeah, I cloned the plants, and Jenny sold them to this family. Within months, Jenny and me had an enterprise. Strictly under the table, of course. It’s not like we took out ads. Word of mouth only. Just like the trade in stolen art works, right? Inner circle stuff. People want to show off to their mates, not get arrested by Interpol.

Oh, we made money for jam. And we never worried about us double-crossing each other. Jenny couldn’t run the plants through the machines herself because cloning is locked down tighter than the diamond industry. I couldn’t get plants out of Antarctica without a pilot’s licence, and besides that, didn’t have any contacts with buyers. Jenny and I were partners in crime. Both of us faced jail. We had reasons to be faithful to our handshake.

But word gets around in the upper echelons of the filthy rich.

And soon, Jenny came to me with another request, this time from Asia. Some billionaire wanted to throw a dinner party with penguin on the menu.

Look, I’m not going to debate which animals are okay to eat and which ones aren’t. As far as I’m concerned, once you’ve eaten meat, you’ve crossed a line and can’t wag the finger at anybody for their choices. Still, I had to think about this offer for a long, long while. Could I really offer up cloned penguins knowing they were destined for someone’s cooking pot?

Jenny had convincing arguments, namely… I provided beef, lamb, pork and chicken as food, didn’t I, so what’s the difference? The penguin destined for the table wouldn’t be the original or ‘real’ penguin, just a clone, while the real penguin would be released back into the wild, unharmed, free to live its life, swim and raise babies. Penguins get eaten by seals and orcas every day, so why not by people? Et cetera. Bottom line: the money was jaw-dropping.

Antarctica has lots of different penguins like king, adelie, chinstrap, gentoo. Penguins are fast in water; on land they’re bumbling idiots. My first penguin was a chinstrap, so-called because it has this little banding of black feathers under its beak. It’s an aggro species but small and real clumsy on the ice. It took five minutes to stuff one in my backpack. Hey, there’s about eight million of the buggers; it wasn’t like taking one for a couple of hours would upset the balance of anything important.

Right?

And yet…I’d never put a live animal through the machines. For some reason, I imagined the cloned penguin would be turned inside-out. Crazy, huh? I had to keep reminding myself that fruits and vegetables are alive when they’re cloned. Oh yes, of course they are – if they were dead, they’d be withered and black.

Even so, I had a big problem. The machines can’t read anything that’s moving because they work on similar principles to 3D food printers. I had to find a way to keep the penguin as still as possible. I chose sleeping pills. My working hours are all over the place. Naturally, I’ve got stashes. I figured the medication would stay in the bird’s guts and blood, and not migrate into its muscles. Therefore, anyone who ate its meat wouldn’t get dosed.

I cloned the drugged bird.

The process takes seventeen minutes for the first replication. After that, once the sequencing is worked out, the replication rate is lightning fast: pow, pow, pow. The cloned penguins were asleep, which made packaging and transportation much easier. Since we use automated systems to load trucks and planes, only me and Jenny knew what was going on.

Good God, over the next year…

Money, money, money.

So much money…

Occasionally, there were ‘exposés’ on blogs and threads about illegal penguin meat, but the mainstream media figured it was an urban myth. Hah! I supplied every kind of penguin that exists in Antarctica. Yet each specimen I kidnapped was returned, unharmed, to the ice shelf where I found it. I never penned any of them to save time. That would’ve been cruel. And remember, the clones exported for eating purposes weren’t ‘real’ in the same way the original penguins were real. Manufactured clones don’t count. That’s law, right?

Soon we got other requests. Antarctic seabirds became popular: blue-eyed shag, giant petrel, snowy sheathbill, cape pigeon. But these birds can fly! Trapping them required ingenuity on my part; luckily, I’m very intelligent. The price per kilo had to be higher than for penguins. Astronomically higher. That said, Antarctic seabirds are stringy. You’ve got to braise them low and slow. Even if you’re a pro chef who does everything perfectly, the meat still comes out dry, chaffy, tasteless. Look, it’s not about flavour. Remember the pineapple? If dog shit was rare, the one-percenters would serve it at dinner parties with silver spoons.

Did I eat any of these meats? No. Beef, chicken, lamb, pork: that’ll do fine. Occasionally I eat fish and seafood but don’t come at me with weird shit like eel, oysters or sea urchin. Novelty doesn’t interest me. I won’t try a food just for the ‘experience’. Not that I’m shaming anyone who’s into that kind of thing. Live and let live, I always say.

So, dealing in cloned plants, penguins, seabirds…as you can imagine, I was busy.

Busy enough that I swapped sleeping pills for amphetamines. The factory ran twenty-four seven and I had a side business that was essentially a full-time job in itself – when could I sleep? And the money was another time-sink. Do you know how difficult it is to launder and hide cash? You can’t use bank accounts without explaining why, how, when, and the tax department always sticks in its beak. From necessity, I stayed awake for three, sometimes four days at a stretch. Ah, crazy times... But after a few years, I was going to retire and cruise the world on a five-hundred-foot yacht.

It was exhaustion, I guess. Desperation. Amphetamines don’t create energy; they stop you from sleeping, and the sleep debt adds up. Then you start making dumb decisions. That’s the only way I can explain it. One day, when I was popping another pill and staring in the mirror at the black bags under my eyes, I thought, “Why the hell am I killing myself, burning the candle at both ends – and in the middle too – when there’s such an easy solution?”

Sure, the idea gave me pause. Each of us likes to think of ourselves as unique. But I got to pondering about identical twins, triplets, quadruplets, quintuplets. I’m an only child. Would it be so bad to have a ‘brother’? We could split the chores. Perhaps share some of my money. I was the mastermind, so any divvying of funds would be at my discretion since the clone would be my employee, right? I know how it sounds, but it made perfect sense at the time.

Putting myself into the machine was like taking a seat in an untested rollercoaster. You’re doing something that should be perfectly safe, at least in theory, but feels terrifying. The machine clicked, hummed, buzzed, whirred, knocked, whistled, tapped, and each sound scared the absolute shit out of me as I lay on the table, motionless, because I’d never heard those sounds before and I began to panic, wondering if something had gone wrong, if I would die. Get turned inside-out.

Let me tell you, that was an excruciating seventeen-minute wait.

The alarm went off: the sequencing and first replication had finished. I laughed and cried in relief. I’d only keyed in one clone. Just one. I got off the table and ran to the other end of the factory, which took about five minutes. The Other Charlie was standing there in my uniform. You know what surprised me? It turns out I’m bow-legged. I had no idea. The other thing that bothered me was his posture. His shoulders were tilted one way and his hips the other, as if there was a sideways bend in his spine, but subtle, very mild. I guess I was critical because I was seeing myself in the flesh for the first time. I looked old. Maybe that was on account of how tired I was, so empty and rundown.

“Charlie?” I said. “Do you understand what’s going on?”

“Perfectly,” he said. “Let’s get started.”

“Sweet,” I said. “Run the shift while I get some shut-eye. I’ll be back later with a chinstrap penguin.”

“No worries,” he said, and went about his – our – business.

I had the most restful sleep I’ve enjoyed in ages. Then I took a snowmobile and headed to an ice shelf. Have you ever visited Antarctica? It’s beautiful. Light-blue ice mountains, clear sky, snow in all shades and textures. Anyway, I spotted a crowd of chinstrap penguins – they stick out like dog’s balls against the white landscape – and parked my snowmobile about half a kilometre distant so the engine noise wouldn’t spook them. I walked the rest of the way. And as I trudged over the last little rise, damned if I didn’t find the Other Charlie squatting there, wrestling a penguin into his backpack while a horde of angry penguins shrieked at him.

“What the hell’s going on?” I said, pissed off. “Why aren’t you at the factory?”

“What are you talking about?” he said. “You’re the one supposed to be running the shift.”

“Bullshit,” I said. “So, who’s running the shift?”

“I guess nobody is now,” he said, and looked annoyed, pouting, as if I was the one who’d done the wrong thing. “We’d better get back. I’ve got a penguin already, so let’s go.”

We rode to town on our respective snowmobiles. I was fuming the whole journey. Clearly, the Other Charlie was throwing his weight around. He wanted to be equal partners, not my employee. But as the original Charlie Pomeroy I had first dibs. As we neared civilisation, I wracked my brains, trying to figure how to rein in this cheeky bastard.

Back at the factory, we both got a surprise.

Some Other Charlie was there and he looked just as shocked to see us.

“How come there’s two of you?” he said. “What the hell’s going on?”

“You’re asking me what’s going on?” I said. “I’m the one who deserves answers.”

“Why do you deserve answers?” the Other Charlie said, hands on hips.

The three of us got to arguing. My theory: Other Charlie had the same bright idea and had cloned himself while I’d slept. However, Other Charlie and Some Other Charlie were both now insisting they were the original, which was ludicrous, considering it was me who first went through the replication process. Meanwhile, the penguin thrashed inside the backpack, squawking its head off, and I started to worry the little bugger was going to hurt himself. When the three of us headed to the backpack at the same time, we halted, stunned.

“What the hell’s going on?” said a voice, and blow me if there wasn’t a fourth Charlie walking over, his face pale and shocked. “How come there’s three of you?”

And the four of us yelled at the same time, “What the hell’s going on?”, which made the hairs stand up on the back of my neck. But it scared my clones in the exact same way and when I saw the identical expressions of fear on their faces, I started to shake. They started shaking too in perfect mimicry. I was caught in a hall of mirrors. My heart banged hard enough to explode. Meanwhile, the trapped penguin screeched over and over. We turned to the backpack as one. And then—

“What the hell’s going on?” said a voice.

Christ, it was another Charlie. I can’t explain the horror!

Then another Charlie appeared. And another...and another…

God, the way I figure it, each clone must have cloned himself, unaware.

After some fraught arguing, the bunch and I ended up cooperating to scour the kilometre of factory from one end to the other in order to flush out any other Charlies. Meanwhile, more Charlies kept arriving at intervals with kidnapped penguins. Each time, we’d have to stop and have another pow-wow.

God, if it wasn’t so terrifying, maybe it’d be funny.

We walked together in a line, shoulder to shoulder. Each of us ignored the distressed penguins without discussion. We found about a dozen more Charlies at various points, who joined our search, while others kept coming in from outside, bearing penguins. The birds wouldn’t stop calling to each other, distressed and frantic. The chinstrap sounds a lot like a seagull, did you know that? I kept closing my eyes against their cries, trying to imagine that I was on a beach somewhere and only dreaming this nightmare, until I noticed my clones doing the same thing and felt a heart-seizing panic attack coming on.

When the alarm sounded, we froze and stared at each other in terror. The alarm meant that yet another Charlie had been created, and would soon be jogging towards us from the far end of the factory, shouting, “What the hell’s going on?” I’d forgotten to turn off the machines. We all had. How many clones in total? Oh God, I don’t know. I couldn’t even guess…

Getting sprung by the authorities was my fault.

Whenever I cloned a plant, penguin or seabird, I deleted the history from the logs. For some reason – probably because I was sleep-deprived – I forgot to do that after making the Other Charlie. And because he’s me, he forgot to delete the history when he created his own clone, and so on. That tripped a red flag at Carbon Copy Consumables, and then military police came, and well…you know the rest.

Listen, I understand that clones aren’t protected under any laws or Geneva conventions. Fair enough. Unauthorised clones have to be put down. No complaint from me on that score. My only issue is that you destroy the clones and not me by mistake. I’m happy to go to jail if that’s my punishment, or pay a fine or whatever. Surely, there’s some way to tell us apart? A medical test. Isn’t there? There has to be. The clones might be telling you the exact same story, but my statement is the truth, I swear to God, because I’m the real deal. Okay? Hand on heart. I am the original Charlie Pomeroy.

[myeducation001]

BS Studies: A Comprehensive Guide to Your Future

1. Introduction to BS Studies

So, you're thinking about pursuing a Bachelor of Science (BS) degree? Awesome choice! But, what exactly is BS Studies, and why is it such a big deal today? A BS degree focuses on science and technical subjects, designed to provide you with the practical skills and theoretical knowledge you need to excel in your chosen field. It's not just about science either – a BS degree can lead you into careers in tech, business, health, and more.

2. The Evolution of BS Degrees

The concept of a Bachelor of Science degree has been around for centuries, originally rooted in the study of natural sciences like biology and chemistry. However, over the years, it has evolved to include a broader range of disciplines, from computer science to business analytics. This expansion reflects the increasing need for specialized knowledge in today's rapidly evolving industries. BS studies are now more interdisciplinary, allowing students to blend different areas of interest for a unique educational experience.

3. Why Choose a BS Degree?

You might wonder, "Why should I choose a BS degree over other types of programs?" One of the biggest advantages is its focus on practical, hands-on learning. BS degrees often incorporate labs, fieldwork, and projects, helping you develop the technical skills needed in today’s job market. Plus, a BS degree opens up a wide array of career options, from tech and engineering to healthcare and business.

4. Popular Fields in BS Studies

BS in Computer Science

A highly sought-after field, a BS in Computer Science equips you with coding, software development, and algorithmic thinking skills – all critical in the tech-driven world.

BS in Engineering

Whether it’s civil, mechanical, or electrical engineering, a BS in this field gives you the technical expertise to design, build, and innovate in various industries.

BS in Health Sciences

Health-related BS degrees, such as nursing or public health, prepare you to address global healthcare challenges and make a tangible difference in people's lives.

BS in Business Administration

With a focus on economics, management, and operations, a BS in Business Administration can set you on the path to becoming a leader in the corporate world.

BS in Environmental Science

This degree is perfect for those passionate about sustainability, offering tools to tackle the pressing environmental issues of today.

5. Specializations within BS Studies

Many BS programs offer specializations, allowing students to dive deeper into niche areas. For example, within Computer Science, you can specialize in Artificial Intelligence or Cybersecurity. This gives you the flexibility to tailor your education to your career goals.

6. Skills You Gain from a BS Degree

Graduating with a BS degree doesn’t just mean you have a diploma – it means you’ve acquired a wealth of skills that employers are actively seeking. These include:

a) Analytical Thinking**: The ability to analyze data and problem-solve is crucial in almost any field.

b) Technical Skills**: From software development to lab techniques, you’ll gain hands-on experience.

c) Problem-Solving Abilities**: BS degrees often emphasize real-world problem-solving.

d) Communication and Teamwork**: Many projects require collaboration, honing your teamwork and leadership skills.

7. Job Opportunities after BS Studies

Graduates with BS degrees are in high demand across a variety of industries. The tech industry, for example, is always on the lookout for computer scientists and engineers. Healthcare sectors are hiring health professionals, while business industries need analysts and managers with strong technical backgrounds. Whether you aim to work in startups, multinational corporations, or research, a BS degree can open doors to numerous possibilities.

8. How to Choose the Right BS Program

Selecting the right BS program is a significant decision. Start by considering your interests and career goals. Look into the curriculum, faculty expertise, university ranking, and accreditation. Also, think about the balance between theoretical knowledge and practical application. Internships and hands-on experience should also be a top priority when choosing the right program.

[jcmarchi]

Studies in empathy and analytics

New Post has been published on https://thedigitalinsider.com/studies-in-empathy-and-analytics/

Studies in empathy and analytics

Upon the advice of one of his soccer teammates, James Simon enrolled in 14.73 (The Challenge of World Poverty) as a first-year student to fulfill a humanities requirement. He went from knowing nothing about economics to learning about the subject from Nobel laureates.

The lessons created by professors Esther Duflo and Abhijit Banerjee revealed to Simon an entirely new way to use science to help humanity. One of the projects Simon learned about in this class assessed an area of India with a low vaccination rate and created a randomized, controlled trial to figure out the best way to fix this problem.

“What was really cool about the class was that it talked about huge problems in the world, like poverty, hunger, and lack of vaccinations, and it talked about how you could break them down using experiments and quantify the best way to solve them,” he says.

Galvanized by this experience, Simon joined a research project in the economics department and committed to a blended major in computer science, economics, and data. He began working on a research project with Senior Lecturer Sara Ellison in 2021 and has since contributed to multiple research papers published by the group, many concerning developmental economic issues. One of his most memorable projects explored the question of whether internet access helps bridge the gap between poor and wealthy countries. Simon collected data, conducted interviews, and did statistical analysis to develop answers to the group’s questions. Their paper was published in Competition Policy International in 2021.

Further bridging his economics studies with real-world efforts, Simon has become involved with the Guatemalan charity Project Somos, which is dedicated to challenging poverty through access to food and education. Through MIT’s Global Research and Consulting Group, he led a team of seven students to analyze the program’s data, measure its impact in the community, and provide the organization with easy-to-use data analytics tools. He has continued working with Project Somos through his undergraduate years and has joined its board of directors.

Simon hopes to quantify the most effective approaches to solutions for the people and groups he works with. “The charity I work for says ‘Use your head and your heart.’ If you can approach the problems in the world with empathy and analytics, I think that is a really important way to help a lot of people” he says.

Simon’s desire to positively impact his community is threaded through other areas of his life at MIT. He is a member of the varsity soccer team and the Phi Beta Epsilon fraternity, and has volunteered for the MIT Little Beavers Special Needs Running Club.

On the field, court, and trail

Athletics are a major part of Simon’s life, year-round. Soccer has long been his main sport; he joined the varsity soccer team as a first-year and has played ever since. In his second year with the team, Simon was recognized as an Academic All-American. He also earned the honor of NEWMAC First Team All-Conference in 2021.

Despite the long hours of practice, Simon says he is most relaxed when it’s game season. “It’s a nice, competitive outlet to have every day. You’re working with people that you like spending time with, to win games and have fun and practice to get better. Everything going on kind of fades away, and you’re just focused on playing your sport,” he explains.

Simon has also used his time at MIT to try new sports. In winter 2023, he joined the wrestling club. “I thought, ‘I’ve never done anything like this before. But maybe I’ll try it out,’” he says. “And so I tried it out knowing nothing. They were super welcoming and there were people with all experience levels, and I just really fell in love with it.” Simon also joined the MIT basketball team as a walk-on his senior year.

When not competing, Simon enjoys hiking. He recalls one of his favorite memories from the past four years being a trip to Yosemite National Park he took with friends while interning in San Francisco. There, he hiked upward of 20 miles each day. Simon also embarks on hiking trips with friends closer to campus in New Hampshire and Acadia National Park.

Social impact

Simon believes his philanthropic work has been pivotal to his experience at MIT. Through the MIT Global Research and Consulting Group, which he served as a case leader for, he has connected with charity groups around the world, including in Guatemala and South Africa.

On campus, Simon has worked to build social connections within both his school and city-wide community. During his sophomore year, he spent his Sundays with the Little Beavers Running Team, a program that pairs children from the Boston area who are on the autism spectrum with an MIT student to practice running and other sports activities. “Throughout the course of a semester when you’re working with a kid, you’re able to see their confidence and social skills improve. That’s really rewarding to me,” Simon says.

Simon is also a member of the Phi Beta Epsilon fraternity. He joined the group in his first year at MIT and has lived with the other members of the fraternity since his sophomore year. He appreciates the group’s strong focus on supporting the social and professional skills of its members. Simon served as the chapter’s president for one semester and describes his experience as “very impactful.”

“There’s something really cool about having 40 of your friends all live in a house together,” he says. “A lot of my good memories from college are of sitting around in our common rooms late at night and just talking about random stuff.”

Technical projects and helping others

Next fall, Simon will continue his studies at MIT, pursuing a master’s degree in economics. Following this, he plans to move to New York to work in finance. In the summer of 2023 he interned at BlackRock, a large finance company, where he worked on a team that invested on behalf of people looking to grow their retirement funds. Simon says, “I thought it was cool that I was able to apply things I learned in school to have an impact on a ton of different people around the country by helping them prepare for retirement.”

Simon has done similar work in past internships. In the summer after his first year at MIT, he worked for Surge Employment Solutions, a startup that connected formerly incarcerated people to jobs. His responsibility was to quantify the social impacts of the startup, which was shown to help the unemployment rate of formerly incarcerated individuals and help high-turnover businesses save money by retaining employees.

On his community work, Simon says, “There’s always a lot more similarities between people than differences. So, I think getting to know people and being able to use what I learned to help people make their lives even a little bit better is cool. You think maybe as a college student, you wouldn’t be able to do a lot to make an impact around the world. But I think even with just the computer science and economics skills that I’ve learned in college, it’s always kind of surprising to me how much of an impact you can make on people if you just put in the effort to seek out opportunities.”

[jcmarchi]

Zev Farbman, Co-Founder & CEO at Lightricks – Interview Series

New Post has been published on https://thedigitalinsider.com/zev-farbman-co-founder-ceo-at-lightricks-interview-series/

Zev Farbman, Co-Founder & CEO at Lightricks – Interview Series

Zev Farbman is the Co-Founder & CEO at Lightricks, a pioneer in innovative technology that bridges the gap between imagination and creation. As an AI-first company, with a mission to build an innovative photo and video creation platform, they aim to enable content creators and brands to produce engaging, top-performing content. Their state-of-the-art technology is focused on photo and video processing and is based on both groundbreaking computational graphic research and generative AI features.

What initially attracted you to computer science?

I grew up in a science-minded house with both parents trained as mechanical and electrical engineers. We emigrated to Israel when I was 12, where I developed an interest in computers and always liked creating beautiful pixels, starting with using Basic when I was just ten. The field’s capacity for problem-solving and innovation was a major draw.

By the time I entered university, computers had already become valuable tools for creative tasks, such as enhancing photos, similar to the edits being done for high-end magazines. Though I gravitated toward computer graphics and image processing, I was fascinated by all the areas of computer science and learned what I needed to advance my studies.

Could you share the story of how an academic discussion you had about editing images on a smartphone suddenly created a lightbulb moment for a new business opportunity?

My research colleagues and I were working on new ways to manage the characteristics of pixels that make up a digital image. This was during the time when social media was just entering the “selfie” era, and we were having a hard academic discussion about the limitations of image editing on mobile devices. We were exploring how smartphones, despite their growing camera capabilities, lacked sophisticated editing tools.

This gap in the market led to a eureka moment. We envisioned a mobile app that could bring professional-level photo editing to the average smartphone user, making it as easy as a few taps on the screen.

How did this discussion then transition to the launch of Lightricks?

We realized that academic research, while valuable, wouldn’t have as broad an impact on as many people. And with the explosion of social media, there was an opportunity to leverage our knowledge – so we transitioned from academia to industry and created Lightricks, a fully bootstrapped company.

The first product that you launched in 2013 was Facetune. What was the initial concept for this app, and what made it such a huge success?

The initial concept for Facetune was to democratize photo retouching. Before Facetune, such editing was mainly reserved for professionals using complex software. We simplified this process, enabling users to achieve magazine-level photo retouching on their phones. Its success was due to its simplicity and the increasing desire for high-quality social media content.

In the beginning, we were aware that every expense, even an additional table, was significant. One of our co-founders actually chased journalists to introduce our app because we had no advertising or marketing budget. As we grew, we needed office space but couldn’t afford much. We ended up renovating an abandoned student dorm into our office space. It started humbly but eventually became a great workspace.

What are some of the other popular tools you have offered over the years?

Following Facetune, we expanded our suite with apps like Enlight, a more comprehensive photo editing tool, and Videoleap, which brought our approach to video editing. Each tool was designed with the same philosophy: to make professional-grade creative tools accessible. For example, Videoleap offers powerful video editing features in a mobile-friendly format, making it easier for creators to produce high-quality video content.

How have your legacy tech stack and apps evolved with the advent of generative AI?

For a long time, our backend systems have depended on different degrees of AI to edit content without disrupting the original source. Over time these have evolved, and it is only in the last year or so that the AI layers are visible – and understood – by users.

 These intuitive features integrate a setting, makeup, hair, or clothing in a way that assists in understanding user intent and automating complex tasks. For instance, AI-driven features in photo editing can suggest edits based on the content of the photo, or automate tasks like object removal or style transfer, making the process more efficient and creative.

Lightricks has recently released an open-source variant of Stable Diffusion’s AnimateDiff called LongAnimateDiff. What is this specifically and what should users expect from this tool?

LongAnimateDiff is our open-source contribution to the community. It offers advanced capabilities for animating sequences but also extends the number of frames that can be created to 64. It doesn’t sound like a lot, but it’s a tremendous leap toward true generative AI video.

You stated recently that you believe that photo editing will soon be a commodity, could you elaborate on this statement and how it will impact software companies?

It’s not a surprise that advanced photo editing tools have become widespread and user-friendly. Correcting or enhancing photos was once only done by experienced photo editors using expensive software and hard to come by computing systems. Today, you can fix a selfie with the flick of your finger. And now even the early challenges of the first AI images that made them awkward looking and non-realistic have been addressed.

Video will be coming right behind – and as democratization expands, any unique selling points for software companies will increasingly lie in user experience, community building features, and specialized functionalities. Companies will need to innovate constantly to provide value beyond the basic editing capabilities that will become standard.

What is your vision for the future of the creator economy?

In the future, I see the creator economy becoming even more dynamic and inclusive, with AI playing a pivotal role. AI will unlock new tools and opportunities, especially in areas like video creation, where it can automate time-consuming processes or generate new content ideas.

This will lower the barriers to entry, allowing more people to participate in the creator economy. For example, AI could enable creators to generate custom animations or enhance video quality, opening new avenues for creativity and monetization. The impact of AI will be to make sophisticated content creation more accessible, thus empowering a broader range of voices and talents in the digital landscape.

Thank you for the great interview, readers who wish to learn more should visit Lightricks.