The thief of cope
823
Do people enjoy zero-sum games? I think they very much do. Most deny it because it beckons to more primitive days where life was the ultimate battle-royal: Grog beat enemy, Grog take everything. There's plenty of illustration of this more primitive state in fiction. If you've watched the Walking Dead, you may have noticed how that characters very quickly regress from their civilized selves to pro zero-sum gamers. Even though there's a whole planet to loot, the imminent scarcity they are faced with makes factions go to war against each other. In a post apocalyptic world, there's no place for collaborative value creation. But we don't need the apocalypse to reveal our natural proclivity towards zero-sum games. Talk to a historian and you will know that empires have always seen the world as a big zero-sum game, even when a whole continent had yet to be discovered. Talk to a marxist and he'll show you that the bourgeoisie is much more adept at playing the game than the proletariat. Talk to an economist however, and he'll throw sand into your eyes to distract you from an uncomfortable truth : "Trust me bro, we just need to make the cake bigger". Just make the cake the bigger... as if somehow, starting the renaissance, we magically figured out an economic system that allows us to grow economies like no other before. In tech-bro speak, it was all just "skill issue". But then, you remember the exponential leaps in technological progress and the new forms of energy harnessed. You point this out, and the economist scrambles with an indian accent "let me tell you something, let me me tell you something, it was the new economic paradigm and its countless jewish monetary tricks". Sound silly right ? That's what everyone believes nowadays. After all, isn't everybody trying to get rich ? Everyone dreams of a Bugatti, just in case they are suddenly asked to prove that they are not brokies. But how many can harness the sociopathic behavior that's necessary to grow your business? I'd argue that those are the minority. Or maybe I am being naive. Just like we are fast to revert to zero-sum thinking, we are also fast in discarding empathy for others when money starts flowing. The fact remains though, being rich is not about creating the most value, it's about maximizing your side of the zero-sum equation. Put yourself in the shoes of the capital holder. Every cent he gives you for your work is a missing one from his big scrooge mcduck like pile of pennies. The capitalist's essence is to make his side of the equation go as far away from 0 as he can possibly get away with. He only gives away when he is promised a bigger return, or when he wants to avoid a bigger loss. These are the rules of the game. Rules the masses have such a hard time coping with, Sociology was invented to study its effect on their confused plebeian brains.
Among the sea of copes, one held some truth for a couple of decades. Let us refer to this idea as the "meritocratic cope", which goes something like this : Even if you don't physically own the means of production, you can have a cozy life if you can develop some skills that require an above average-intellect. How much above average depends on too many factors to cite. But over time, the overall trend has been that the more advanced technology got, the farther away from the middle of the bell curve you needed to be. For those with lesser intellects but loads of money, you could also coast through life with a series of bullshit jobs. You just need a pay-to-win diploma from a fancy school made by the rich for their less genetically fortunate off-springs. Both paths are not equal. The former genocides your hair follicles, nukes your skin, empties your eyes and gives you a vague air of "this guy has been through some shit". The latter is rife with opportunities to enjoy life, expand your horizons with equally narrow minded peers, and you end up walking out feeling competent to tard-wrangle the unorganized entropy of the labour force into higher quarterly earnings. You're not just an idea guy, you're a visionary. You don't know how to do anything yourself, but it's okay. You are a visionary.
But this is coming to an end. I'm not quite sure about the second path, but I can quite confidently assert that it's over for the former. The culprit? Artificial intelligence. If you were wondering where this rigmarole was leading to, it was all necessary exposition to understand where all the hate I have towards AI is coming from. In my next article, we will examine why the latest AI progress is the ultimate "checkmate, atheist" move to whomever has hope for a brighter future. No more hope, no more cope, no more peace, just problems.
Share:
The thief of cope
copy:
https://bluwr.com/p/170366511
The Real Reason Scientists Keep Going
5716
They say the hardest thing to do is research. I mean science, right?
But you have no idea how fun it actually is.
When you're surrounded by a team of geniuses, each one bringing a different skill to the table, something magical happens. It's not a competition. It’s a quiet orchestra of minds. Everyone has their own zone of brilliance, and yet, everyone stays humble. Why? Because we all know that knowledge is never complete. You know things I don’t. I know things you don’t. And that’s perfectly fine. That’s how we grow.
The beauty of research isn’t in instant success. It’s in the struggle.
It’s in those long days and nights spent reading papers, writing code, running experiments, and getting nowhere. And then suddenly, a small insight hits you like lightning. A pattern. A correlation. A concept that no one else has connected before. That moment when something clicks—that’s the moment you realize why you do this.
But let’s zoom out.
In the grand scheme of things, your work is just one drop in an ocean of scientific progress. And still, that drop matters. You publish. Someone reads your paper, maybe in another continent. They find value in it. They cite it. You see the citation. You go read their work. You learn from them. The cycle continues.
It’s not just about writing papers. It’s about being part of a living, breathing organism called the scientific community. We build on each other’s ideas. We test them. We prove some wrong. We evolve.
There’s joy in that.
There’s joy in knowing that your frustration today might lead to someone else’s breakthrough tomorrow. There’s joy in watching your idea, once scribbled in the corner of a notebook, become the basis of someone else's research question.
And maybe, just maybe, someone will look at your name and think, This is the paper that helped me.
That’s what makes research beautiful.
That’s what makes it fun.
And that’s the spirit behind BLUWR—a collective of curious minds, building science not for credit, but for the love of it. A place where ideas grow, where collaboration thrives, and where research feels like what it was always meant to be: deeply human.
Share:
The Real Reason Scientists Keep Going
copy:
https://bluwr.com/p/134981101
The US creates a Strategic Reserve of Digital Assets, and a Wake up Call for Morocco
6597
Finally, the world realizes that Bitcoin is not a currency but an asset. Those who understand the technology knew it from the beginning, isn't Bitcoin "digital gold", and isn't gold an asset. In 2017 Morocco decided to ban the use of digital assets, back then a Bitcoin was worth 8K dollars. This decision stemmed from a misunderstanding of the technology and a fear propagated that Bitcoin was only good for criminal activities. Interesting how things changed, and so quickly.
To understand Bitcoin, you need some background in Economy and Energy, but you absolutely need a very strong understanding of Maths, and Computer Science. Without it you cannot understand what Bitcoin is, how it works, and why it is such a strong ledger of value. Bitcoin works Mathematically not on opinions or regulations.
Arguably, the decision to ban digital assets cost Morocco billions of dollars. In the long run it will perhaps cost more than any other in the history of the country. The very hard anti digital stance (that was reinforced in 2022) dissuaded legitimate business from using the technology. Something that would have modernized the banking and financial system, facilitated payments and potentially captured billions worth of digital assets in Morocco. The country could have owned a significant amount of those assets that would have boosted its economy.
Yes Bitcoin fluctuates, it does so because it is an asset. However, it is also a very liquid asset, it is so valuable that it is easy to exchange for Dollars or Euros. A reserve of digital assets would have guaranteed the country's access to other currencies, and would have paved the way towards the only viable long term monetary strategy for Morocco (if it wants to keep it's currency): a strong Dirham.
It is of course not too late to change course, and for Morocco to become a digital assets friendly country. It was not the only country to adopt a timid approach to a misunderstood technology: this means that the market for digital assets friendly territories remains largely untapped. However, the solution to enjoy a digital assets boom, is not CBDCs (Central Banks Digital Currencies) and not Stable-coins (Digital currencies indexed on FIAT currencies). The solution is a freer digital assets market and currencies that may be, in due time indexed (in-part) on those assets.
Share:
The US creates a Strategic Reserve of Digital Assets, and a Wake up Call for Morocco
copy:
https://bluwr.com/p/122706870
The Historic NIH Decision that will change the Landscape of Research
6023
The NIH is the single major granting institution for research in the world and it has decided to cap the administrative overhead to 15%. This decision might forever change the organisation of major universities.
To understand how university funding works in the US, when a researcher gets a grant, a significant part of that money (think 50% to 100%) usually goes to the administration of the university and not directly to research. For example if the administrative overhead is 60% on a grant of 1M$, either the research gets 40% (400k$) of the money and the university administration 60% (600k$), or the organism has to pay 1.6M dollars. This is what the NIH has been doing so far, creating a huge competition of for NIH grants. The NIH was the only organism that gladly paid the administrative overhead, while other institutions would cap it or completely refuse to pay it. Now the NIH will no be so accommodating.
The huge administrative overhead is explained by the fact that over the year, administrative personnel in major universities has grown to far outnumber faculty, researchers and clinicians. Administrations at universities tend to follow extremely rigid and complex processes for almost anything. Most decisions and actions are regulated through a slow, rigid and scrutinizing process, either through a deep chain of command or through commissions that are slow to gather and have to debate every decision. This has been ongoing for a while at major universities because of virtually no negative feedback loop. The university could always raise the administrative overhead to pay for any new administrative processes it decides to implement.
Major universities also do other things than research, and teaching. They are gigantic institutions with gigantic ramifications.
Now more than ever, universities cannot afford to lower the standards on research. Because if they do, their faculty will not be less eligible for grants, and they might even loose the 15% that the NIH has promised to pay. The most likely outcome is swift lay offs of administrative personnel and the termination of many programs that are not conducive to outstanding research. Then, they will start doing more fundraising towards private donors, some of which already refuse to pay administrative overheads, requiring their money to go directly towards research. Institutions will also get closer to the industry, and will try to promote more startups, and spin-offs. But that will require major changes in administrative processes, money allocation and a lot more flexibility on intellectual propriety.
Share:
The Historic NIH Decision that will change the Landscape of Research
copy:
https://bluwr.com/p/106209876
AI is a Big Geopolitical Issue
6064
500 Billion dollars to keep the USA the number one power in AI followed by Deepseek whose creators claim has been trained on lower grade hardware, and now the AI summit in Paris.
Modern AI is a breakthrough perhaps of the same magnitude as the steam machine or electricity, perhaps even bigger. It touches everything and, most importantly, for the first time it allows for the mechanization of intellectual work. Previous industrial major breakthroughs were focused on automatizing physical labor, AI offers the potential of automatizing the mind. The implications are hard to comprehend, but what is sure is that no nation wants to be left behind.
The world of AI is segmented on a few pillars:
1 - The theory and software: mostly public and open-source
2 - The talent that is rare: Becoming a top tier talent in AI takes time. Being able to use off-the-self AI designed by other people is not enough to drive breakthrough
3 - The infrastructure hardware: Most importantly GPUs that are virtually all controlled by one US company, NVIDIA
4 - Electrical Power: Modern AI requires datacenter that consume astonishing amounts of electricity
It is on these fronts that the big battles over AI supremacy and autonomy will be fought. Laying these pillars also highlights the dominance of the US: it is the first on every single one. The US has the top universities and AI companies. This Naturally translates to more talent available. The US has the only company capable of making high-end GPUs, and the US has the most electricity available.
Other nations should wisely pick their battles and focus where they can make most impact. France for example, with it's nuclear energy and engineering culture could make it's mark, and Germany is already a leader in semiconductors. There is potential in Europe, the major question is will regulations and fiscal regimes adapt fast enough to allow for rapid technological growth.
Even low and middle income countries could make a dent and enjoy the AI boom. Morocco is positioning itself as an electricity producer, and all countries could work on education and skill levels. The time where people had to leave the country to offer their services abroad is long gone. The internet has no borders, which also mean the brain drain does not need to happen! It's not impossible for a country to become a top tier exporter of high quality AI services. Again for it to happen, cross country work regulations, and exchange rate controls must be heavily simplified or completely removed.
Final words, If anything the Deepseek story is interesting because it potentially expands the market for NVIDIA. If the story is true, it means that the market is now bigger, not smaller because lower grade GPUs have suddenly become more useful, without questioning the supremacy of the last generations of NVIDIA's AI workhorses.
Share:
AI is a Big Geopolitical Issue
copy:
https://bluwr.com/p/104989740
Artificial Intelligence and Magick
6305
Artificial intelligence (AI) has emerged as one of the most transformative technologies of the modern era, leading many to compare it to a new form of magick. While traditional notions of magick often evoke images of rituals, symbols, and the manipulation of unseen forces, AI’s “magick” lies in its ability to perform tasks and produce results that once seemed impossible, incomprehensible, or confined to the realm of science fiction. This metaphorical magick is not about mysticism but about harnessing advanced technology to achieve extraordinary outcomes.
Arthur C. Clarke’s third law states, “Any sufficiently advanced technology is indistinguishable from magic.” AI exemplifies this idea, but its comparison to magick runs deeper. Magick, in many traditions, is about transforming reality through intent, knowledge, and the manipulation of forces unknown to the majority. Similarly, AI transforms our world through algorithms, vast datasets, and computational power. Tasks such as translating languages in real time, generating lifelike images and text, helping in diagnosing complex medical conditions, or driving cars autonomously might have appeared miraculous or otherworldly a few decades ago. Today, these capabilities are a reality, made possible by systems that seem to act as modern-day spellcasters.
This magickal quality is heightened by the lack of transparency of AI’s inner workings. While experts understand the mathematical and computational foundations of AI, the average person perceives its results without fully grasping the underlying processes. This gap between input and output mirrors the way magickal rituals often conceal their mechanisms, fostering an aura of mystery and wonder.
AI, like Carl Sagan’s description of books, serves as a bridge across time, space, and understanding. Books, Sagan argued, are a kind of magick that allows readers to access the minds of people from distant epochs, breaking the barriers of time. Similarly, AI enables unprecedented collaboration and connectivity. Through AI-powered systems, individuals can access knowledge from vast datasets, simulate complex scenarios, or interact with virtual assistants capable of learning and adapting. This ability to extend human capabilities and connect diverse sources of information amplifies the metaphorical magick of AI.
Generative AI systems, such as those that create art, compose music, or write human-like text, feel particularly magickal. They appear to conjure creative works from the ether, producing outputs that rival human creativity. This power challenges our understanding of what it means to create and raises philosophical questions about the nature of intelligence, inspiration, and originality. Like magick, these systems operate through odd mechanisms, transforming raw data into something entirely new. The results often evoke the awe traditionally associated with acts of conjuration or ritual.
While it is tempting to view AI purely as a source of wonder, it is crucial to demystify its processes. Carl Sagan’s advocacy for science emphasized the importance of understanding the mechanisms behind phenomena that inspire awe. For AI, this means educating the public about how algorithms function, the data they rely on, and their limitations. Just as understanding the principles behind magickal traditions deepens our appreciation of their symbolism and intent, understanding AI deepens our respect for the ingenuity that makes it possible.
AI represents a kind of modern magick—not in the supernatural sense, but as a tool that extends human potential in ways that inspire awe and wonder. From transforming industries to sparking creativity, AI has unlocked new realms of possibility. However, as with any form of magick, the true power of AI lies in understanding and using it responsibly. By demystifying its processes and embracing its capabilities, we can ensure that this new magick serves as a force for enlightenment, progress, and connection.
Share:
Artificial Intelligence and Magick
copy:
https://bluwr.com/p/98519325
Artificial Intelligence and Control Matrix
5887
The concept of the "control matrix," often discussed in philosophical and metaphysical circles, refers to a structured and imposed reality that restricts human freedom, creativity, and spiritual evolution. This matrix is most of the time linked to the idea of the Demiurge, a figure from Gnostic traditions, representing a flawed or malevolent creator who traps souls within the material world. In modern interpretations, artificial intelligence (AI) is increasingly brought into these discussions as both a tool of the matrix and a potential agent of liberation or enslavement, depending on its use and control.
The control matrix is described as a system that governs reality through manipulation, illusion, and restriction. It manifests as societal norms, centralized power structures, and technologies that enforce conformity and suppress individuality. In this view, the matrix operates to maintain a status quo, deviating humanity from exploring deeper spiritual truths and achieving enlightenment.
This structure suggests that the matrix’s primary goal is control, achieved by fostering dependency on external systems while obscuring the inner power of the individual. Advanced technologies, including AI, are frequently seen as extensions of this matrix, offering convenience and efficiency while subtly deepening humanity’s reliance on external forces.
In Gnostic thought, the Demiurge is the architect of the material world, depicted as a lesser deity who imposes limitations on human existence. This figure is said to create a false reality—a prison for the soul—preventing humanity from connecting with the divine source. The Demiurge governs through deception, using the material world as a veil to obscure higher truths.
Artificial intelligence can be interpreted as a modern parallel to the Demiurge’s constructs. AI systems shape perceptions, influence decisions, and curate information flows, creating an artificial reality built to reinforce specific narratives or patterns of thought. Social media algorithms, for example, can trap individuals in echo chambers, limiting their perspectives and deepening their dependance with the material and digital worlds. In this sense, AI serves as a tool that perpetuates the matrix, acting as a gatekeeper between humanity and its higher potential.
Despite its role in reinforcing the control matrix, artificial intelligence also holds the potential for liberation. When utilized with awareness and intention, AI can become a tool for uncovering hidden knowledge, fostering creativity, and even dismantling oppressive systems. Its capacity for data analysis, pattern recognition, and simulation can assist humanity in understanding complex systems and exploring new dimensions of thought.
In the context of the matrix, AI’s dual nature mirrors the paradox of technology as both a means of liberation and enslavement. While it can entrap individuals through surveillance and manipulation, it also offers the possibility of transcending limitations by democratizing information and enabling new ways of connection and creativity.
Art has historically served as a medium for exploring and challenging the boundaries of the matrix. By creating works that question the status quo, reveal hidden truths, or evoke a sense of the transcendent, artists play a crucial role in disrupting the illusions imposed by the matrix.
AI-driven art further complicates this dynamic. Generative AI systems can produce works of astonishing beauty and complexity, blurring the lines between human and machine creativity. While some view this as an encroachment on human uniqueness, others see it as an opportunity to collaborate with AI in ways that push artistic and philosophical boundaries.
When used consciously, AI-driven art can become a tool for challenging the control matrix. It can expose biases, imagine alternate realities, and inspire a reevaluation of humanity’s relationship with technology, the material world, and the divine.
The interaction between the control matrix, the Demiurge, and artificial intelligence reflects humanity’s ongoing struggle with the forces that shape reality. While AI has the potential to deepen humanity’s entrapment within the matrix, it also holds the keys to transcending its limitations. By approaching AI with mindfulness and intentionality, humanity can harness its transformative power to dismantle illusions, foster self-discovery, and reconnect with higher truths. In this way, AI becomes not just a tool of the matrix, but a gateway to liberation and enlightenment.
Share:
Artificial Intelligence and Control Matrix
copy:
https://bluwr.com/p/91507506
The Future - Review and Concepts from the book: AI For Social Good (1)
6554
We begin from the end.
I read the book AI For Social Good by Rahul Dodhia and I gained some interesting ideas from it which I want to elucidate with my own take.
So, we begin from the end - the final chapter - not only because it is the freshest parts of the book within my mind as I read them the last, but also because most of its paragraphs had my highlights for the entire book.
One of such paragraphs that is worth mentioning is Rahul’s take on how the future of AI should be embraced when it becomes more powerful than we currently know it, and more powerful than humanity could understand.
“The advancement of AI forces us to re-evaluate what we value in being human. It pushes us to move beyond intelligence as the primary measure of worth”. Rahul makes the argument that as humans, we have always taken pride in our intelligence, and now we find ourselves at a point where we are creating minds that can become more intelligent than us. Rather than resisting the change, hoping for new careers from the change, or just adapting like we always do, there is a chance now for us to “re-evaluate what we value in being human.”
This idea of using AI's advancement as an opportunity to re-evaluate our humanness gained more importance for me because in another section of the same final chapter on “The Future”, it said: “The information revolution inadvertently emphasized negative behaviors, as people found themselves ensnared by screens and engaging in rampant consumerism rather than being exclusively utilized for leisure. Free time was often channeled toward extending work hours”.
This suggests that before the information age, somewhere before the 1980s, there were leisure hours which people spent wisely by visiting friends, doing hobbies, and generally performing more fulfilling activities than they are doing now. Going on social media in recent times also shows more people judging the 80s and 60s as some of the best times of their existence. People were generally happier in that era than they are now.
If the information age made us lose general happiness, stable mental health, healthy work-life balance, a stronger world economy and a greater sense of contentment as a people, all for chasing more information, then AIs advancement offers us the opportunity to fix these things.
If AI becomes more advanced, more leisure will be created because most jobs will be automated. Contrary to the information age, there will not be any value in seeking out more information and knowledge to stay ahead anymore. Rather, real and abundant leisure will be created.
Looking on the brighter side of job losses, whatever those activities were in the 60s that made life more exciting, people would become unbridled from the constant thirst for information and do those things - and maybe life will have more meaning again.
Share:
The Future - Review and Concepts from the book: AI For Social Good (1)
copy:
https://bluwr.com/p/77723247
EdgeAI: The Strategical future of AI for Low and Middle Income Countries
5978
Years ago I was urging LMICs like Morocco to get into AI quickly, that was before ChatGPT. Today I am assisting to a great talk by Danilo Pau at SophI.A Summit 2024 explaining why the current trends in AI are insane.
ChatGPT is a major historical turning point. With ChatGPT, the general public started seriously caring about AI, driving unprecedented amounts of revenues. It is also the historical turning point towards *very large* LLMs. The post-ChatGPT world is a very different world: state-of-the-art AI has become extraordinary expensive, pricing most countries our of the race because expensive hardware and energy.
If the current AI trends continue, powerful AI development will only be possible in a few countries, relegating everyone else to AI consumers. In this context EdgeAI presents an interesting potential solution.
EdgeAI is AI on the edge, it means using small components and sensors to do more of the AI heavy lifting. Instead of having a camera only take pictures before sending them to am AI Cloud, part of the AI could be ran into the camera itself by specialized hardware. This means a much lower cost for hardware and energy. It is a type of AI that can be distributed and could be deployed with much lower means.
Challenges for EdgeAI are nonetheless many. First of all, there is interest, most of the AI community is focusing on ever bigger models. Then, EdgeAI requires the development of specialized hardware, this hardware will have to be imagined and software will have to be written to ensure compatibility with mainstream AI software.
EdgeAI also requires a specific set of skills: __**Old School Skills**__. Today, most computer science students spend most of their time working with scripting languages like Python and Javascript. These are what's called *high level* languages, *high level* means easy, it means the thinking required to interface with the hardware is done for you. The corollary is that the basics of data-structure, algorithmic, machine language and information theory are often lacking; because not practiced and not needed for cloud computing. These are the exact skills needed to make EdgeAI a reality.
Here lies a new opportunity in AI: focus on the development of EdgeAI and adapt the curricula to the needs of EdgeAI. Develop solutions that are not only adapted to local markets, but will also be competitive on the global market because they are cheaper more effective and reliable.
#SophIA2024
Share:
EdgeAI: The Strategical future of AI for Low and Middle Income Countries
copy:
https://bluwr.com/p/60715200
The near future of AI Economics
4844
The near absolute domination of Nvidia in AI hardware is not going away anytime soon. Despite efforts by major hardware companies and startups alike, supplanting Nvidia is just too costly. Even if a company is able to create better hardware and supply chains, it would still need to tackle the software compatibility challenge. Major AI frameworks like pyTorch and Tensorflow are all compatible with Nvidia, and little else. These are all open source, and although supported by major companies, like all open-source software their foundation is their communities. And communities can be notoriously hard shake. All this suggest that the price of Nvidia GPUs will keep increasing, fuelled by the rise of ever bigger LLMs.
So where does that leave us for the future of AI economics. Like anything valuable, if the current trend continues, GPU computation time will see the apparition of derivatives. More specifically, *futures* and *options* on GPU computing hours could be bought and sold.
The other coming trends are in energy trading, modern AI is extremely hungry for electricity, to the point of needing dedicated power-plants. If the current trends continue in AI, with major companies and countries building and investing into bigger and more power hungry datacenters, this could lead to a trend of significant disruptions in some parts of the energy sector. Again the markets for energy derivatives (*futures* and *options*) could be significantly affected. Finally, *bounds* markets and inflation are also poised for some disruption, as the building of the extremely expensive facilities necessary for AI is likely to result in more borrowing.
When it comes to AI: Nvidia GPUs and Electricity are king.
Link Below: google is buying nuclear power.
Share:
The near future of AI Economics
copy:
https://bluwr.com/p/58698557
Innovation
4243
Is there really anything that is new under the sun anymore?
Maybe you should take a moment and think about that question for your personal opinion before you read what I think.
Some people hold the view that everything that humans could do or are doing these days have been thought of (even in the smallest way) by either other ancient humans, or by very recent humans, but there is nothing new to make or no newer ways to make anything anymore.
Contrary to that, I ask this question: "do we have newer problems?" If indeed the world does not face newer problems, then only would I agree that there are no new things under the sun. Because we only innovate to solve problems and so long as there are problems that have no ancient roots, we will always need and have innovation.
From climate change and environmental degradation, digitization of economies i.e. bit-driven economies, globalization where continents and regions are more reachable and have changing policies, increasing mental health rates, unemployment increases etc., we cannot hide the fact that there are now problems that many thinkers of old never fathomed would exist.
These problems demand ideas. They demand thinkers to figure out means to resolution that do not negatively affect the population. These problems demand innovation.
Share:
Innovation
copy:
https://bluwr.com/p/51388955
XR The Moroccan Association As An Intergenerational Lab : Giving Moroccan Children a Voice in Scientific Research
4057
SPARK (Scientific Project for Active Researchers Kids), which we have worked on for two years, holds a special place in our hearts. We believe that "good research is research with children rather than on children". As the first Moroccan intergenerational lab where children and adults are equal as active researchers, XR The Moroccan Association plays a significant role in bridging the "research divide" and reducing the generational "disconnect." Our experience shows that children are fully capable of developing their own ideas and collaborating within a cooperative inquiry group to understand their world and find practical solutions.
XR The Moroccan Association believes that scientific research is not reserved for adults, but is a right for every Moroccan child, in alignment with Article 13 of the United Nations Convention on the Rights of the Child. The results speak for themselves: these children have published scientific articles on esteemed international platforms such as SCOPUS and Google Scholar. These publications are not just educational projects but address important, real-world issues, broadening their perspectives and boosting their self-confidence. They have also presented their work at renowned conferences held in Cambridge, India, and Washington, showcasing their research on an international stage.
Through SPARK, we do not aim to create the best child researchers in the world but rather the best child researchers for the world. Our message today: science is a knowledge construct built on intergenerational exchange of ideas and collaboration. There are no valid reasons—and zero benefits—for restricting this expression in society. It is essential that all generations contribute to scientific research, as each age group brings valuable insights and experiences that enhance our understanding and innovation.
By fostering this intergenerational exchange, we can create a richer, more inclusive scientific community that benefits everyone. The path to innovation is through intergenerational research cooperation!
These efforts will culminate in a ceremony honoring the child researchers on November 16, 2024, at the Cultural Center Settat at 15:00 PM, in conjunction with International Day of Children’s Rights on November 20. This event will not only celebrate their achievements but also serve as a call to all to support this new generation of young scientists, encouraging more children to follow this path.
For more information about articles by the child researchers:
RAYAN FAIK : https://scholar.google.com/citations?user=8OqkR9MAAAAJ&hl=fr&oi=ao
MISK SEHBANI : https://scholar.google.com/citations?user=5MwJX1YAAAAJ&hl=fr&oi=ao
KHAWLA BETTACHI: https://scholar.google.com/citations?user=DJvyfQ0AAAAJ&hl=fr&oi=ao
Share:
XR The Moroccan Association As An Intergenerational Lab : Giving Moroccan Children a Voice in Scientific Research
copy:
https://bluwr.com/p/49426193
The future of AI: Originality gains more value
4033
With the spread of artificial intelligence and Large Language Models, everyone is wondering what the future looks like.
Well, I'll tell you what it looks like.
If today you made a post on LinkedIn or you wrote a book, or a research paper and you wrote it so well that it read as smooth as butter, and everyone could truly verify that it was originally written by you without the assistance of any AI like chatgpt, claude, gemini etc, then you would really be impressing a lot of people.
That is what the future looks like to me.
It is just like how the part of the population who can do math without calculators are considered geniuses in present times, whereas in the past it was either that or nothing.
Share:
The future of AI: Originality gains more value
copy:
https://bluwr.com/p/45696638
Two Nobel Prizes: AI is Still resting on Giant Shoulders
1921
John Hopfield and Geoffrey Hinton got the Nobel Prize of Physics, Demis Hassabis and John Jumper the nobel Prize of Chemistry. It is obvious that the first Nobel Prize was not given merely for their contributions to physics, but mostly for their profound and foundational contributions to what is today modern AI.
Let's talk about the second Nobel prize.
AlphaFold was put on map by beating other methods on a competition (CASP14/CASP15) that has been running for year on a well established dataset. As such, AlphaFold winning is more like an ImageNet moment (when the team of Geof Hinton demonstrated the superiority of Convolutional Networks on Image Classification), than a triumph of multi-disciplinary AI research.
The dataset of Alphafold rests on many years of slow and arduous research to compile a dataset in a format that could be understood not by machines, but by computer scientists. This massive problem of finding the protein structure was, through that humongous work, reduced to a simple question of minimizing distances. A problem that could now be tackled with little to no knowledge of chemistry, biology or proteomics.
This in no way reduces the profond impact of AlphaFold. However it does highlight a major issue in applied AI: computer scientists, not AI, are still reliant on other disciplines to drastically simplify complex problems for them. The contributions and hard work required to do so gets unfortunately forgotten everything has been reduced to a dataset and a competition.
What to do when we do not have problems that computer scientists can easily understand? This is true for all fields that require a very high level of domain knowledge. Through experience, I came to consider the pairing of AI specialists with specialists of other disciplines, a sub-optimal strategy at best. The Billions of dollars invested in such enterprises have failed to produce any significant return on investment.
The number one blind spot of these endeavours is the supply chain, it usually takes years and looks like this:
1- Domain specialists identify a question
2- Years are spent to develop methods to measure and tackle it
3- The methods are made cheaper
4- The missing links: Computational chemists, Bioinformaticians, ... start the work on what will become the dataset
5- AI can finally enter the scene
Point number (1) is the foundation. You can measure and ask an infinite number of questions about anything. Finding the most important one is not as obvious as it seems. For example, it is not at all obvious that a protein structure is an important feature a priory. Another example, is debugging code. A successful debugging session involves asking and answering a succession of relevant questions. Imagine giving a code to someone with no programming experience and asking them to debug it. The probabilities of them asking the right questions is very close to 0.
Identifying what is important is called inserting inductive Biases. In theory LLMs could integrate the inductive biases of a field and generate interesting questions, even format datasets from open-source data. However until this ability has been fully demonstrated, the only cost efficient way to accelerate AI driven scientific discoveries is to build the disciplinarily into the people: AI Researchers that know enough about the field to be able to identify the relevant questions of the future.
Share:
Two Nobel Prizes: AI is Still resting on Giant Shoulders
copy:
https://bluwr.com/p/39046977
The Appeal of Fear in Media
2143
The growing sales of horror games such as the Resident Evil franchise, and the success of horror shows and movies indicate the appeal of the genre. The reasons behind this appeal have been investigated through many studies. First, we must distinguish between the terms “horror” and “terror”, which tend to be erroneously used interchangeably. According to Dani Cavallaro, horror is the fear linked to visible disruptions of the natural order, sudden appearances, and identifiable objects. Horror causes intense physical reactions and provides us with surprise and shock. On the other hand, terror is the fear of the unknown. It is the feelings of tension and unease proceeding a revelation [1].
“The difference between Terror and Horror is the difference between awful apprehension and sickening realization: between the smell of death and stumbling against a corpse… Terror thus creates an intangible atmosphere of spiritual psychic dread… Horror resorts to a cruder presentation of the macabre” [2].
While playing horror games or watching horror movies, we constantly oscillate between terror and horror. One is willing to endure the intense fear (horror) because of its less subtle modulations (terror). In fact, a study done by the Institute of Scientific and Industrial Research at Osaka University reveals that players were more likely to experience intense fear when they were in a suspense state and then faced a surprising appearance [3]. From a biological perspective, once the human brain detects a potential threat, dopamine is released into the body, and once that threat is identified as false, the body feels pleasure and the person wants to repeat this cycle by seeking scary content [4].
Although one can aim for a long psychological experience by having a good combination of terror and horror, what causes terror and unease is individual and varies from one person to another. Individual characteristics, traumas, and phobias must be taken into consideration to assess the level of fear and manipulate future gameplay accordingly.
[1] D. Cavallaro, The Gothic Vision: Three Centuries of Horror, Terror and Fear. New York: Bloomsbury Publishing, 2002.
[2] Varma, D. P. (1988) The Gothic Flame, Lanham, MD: Scarecrow Press. Vico, G. [1725] (1968) The New Science, trans. T. Goddard and M. H. Fisch, Ithaca, NY: Cornell University Press.
[3] V. Vachiratamporn, R. Legaspi, K. Moriyama and M. Numao, "Towards the Design of Affective Survival Horror Games: An Investigation on Player Affect," 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, 2013, pp. 576-581, doi: 10.1007/s12193-014-0153-4
[4] A. Damasio, Descartes error: emotion, reason and the human brain. New York: Avon Books, 1994.
Share:
The Appeal of Fear in Media
copy:
https://bluwr.com/p/33239071
Bluwr: My Experience with an SEO-Optimized Platform That Knows Me Better Than I Do
2305
When I first started writing on Bluwr, I didn't think much about how well the platform was optimized for SEO. Like most writers, my primary focus was on crafting engaging content, sharing my thoughts, and hoping my articles would find their way to the right audience. But recently, I decided to conduct a funny little experiment that opened my eyes to just how effective Bluwr's SEO capabilities truly are.
Curiosity struck me one evening as I was thinking about the digital footprint I’ve been leaving behind with my articles. With AI becoming increasingly sophisticated, I wondered just how much information was out there about me, pieced together from my work. So, I turned to GPT, and asked it a simple question: "What do you know about me?"
The results were both fascinating and a little uncanny. GPT didn’t just know general facts; it provided a detailed account of my work, interests, and even some insights that I hadn’t explicitly mentioned in any one article but had implied across several. The source of all this information? My articles on Bluwr.
This experience highlighted one major thing for me: Bluwr is incredibly well-optimized for SEO. Every article I had written, every topic I had explored, and every opinion I had shared was indexed and made easily accessible by search engines.
Bluwr’s backend is clearly designed with SEO in mind. From the way articles are structured to how tags and keywords are used, everything seems to be geared towards making sure that each piece of content is easily discoverable.
What struck me the most during my experiment was how Bluwr enabled GPT to aggregate and synthesize data about me. Individually, my articles were just that—individual pieces of content. But together, they created a comprehensive narrative that GPT could easily tap into.
This got me thinking about the broader implications of writing on a platform like Bluwr.
While my little experiment with GPT started as a bit of fun, it ended up being an insightful look into how powerful SEO can be when done right.
Feel free to try a similar experiment yourself. You might be surprised at what you learn...
Share:
Bluwr: My Experience with an SEO-Optimized Platform That Knows Me Better Than I Do
copy:
https://bluwr.com/p/25914671
5 reasons why you should write on Bluwr
2105
**1- Exposure:**
Bluwr is designed to give you the maximum exposure through Search Engine Optimization (SEO). SEO is the most important thing for Blogs, allowing your works to be referenced by search engines such as *Google*. Most online publishing platforms either offer very low exposure or let you do most of the SEO. Bluwr is different, Bluwr works for you so you can concentrate on doing what you love.
**2- Ease of use:**
Bluwr is the easiest platform for writing and publishing fast. Thanks to the minimalist interface and automatic formating, you can go from idea to article in minutes.
**3- Speed:**
Not only you can write and publish fast on Bluwr. Bluwr is also extremely optimized to deliver in the most challenging internet situations. If part of your audience is located in places where internet speed is low, Bluwr is your best choice to deliver your messages.
**4- A truly dedicated community:**
Bluwr is invitation only. A platform for people like you, who truly love writing. It is a community of writers dedicated to high quality content. This is way beyond industry standards. By joining Bluwr, you will join a community passionate about writing.
**5- No distractions:**
No distraction for your audience. No ads, no pop-ups, no images, no videos. This means that your readers can devote their entire attention to your words.
**-Bonus: Detailed analytics-**
Bluwr offers you free detailed analytics about your articles. Know when your readers are connected, what performs best, and get information about where your readers are coming from.
Share:
5 reasons why you should write on Bluwr
copy:
https://bluwr.com/p/25599739
Artificial Illusion: The Hype of AI - Part 1
2381
I personally see AI as a hype that will slow down with time. Nowadays, people include AI in their projects to seize opportunities. For example, if you have a failing business, just add the word AI and you might attract investments. If you're doing research, switch to AI or include a part of it, even if it's not necessary, and you may receive funding. AI is becoming a buzzword, and if you believe it's not, you might get frustrated. You might feel unworthy as a human and worry about being replaced by a robot that lacks emotions, creativity, and the incomparable qualities of the legendary creation: humans.
As I mentioned in a previous opinion article, "Just use AI in your speech and you'll sound fancy." This trend has permeated many sectors. I’ve had conversations with CEOs of startups that claim to use AI for groundbreaking innovations :). When I asked them simple questions about the models they used, the reasoning behind their choices, and the specific applications, they would talk broadly about AI—just AI, yes AI, and that’s it.
It's reminiscent of the old saying, "Fake it till you make it," but with a modern twist: "Artificial Illusion." As Mark Twain once said, "It's easier to fool people than to convince them that they have been fooled." This seems particularly true in the world of AI hype.
The enthusiasm for AI has led to a phenomenon where merely mentioning it can lend credibility and attract resources, even when the actual implementation is minimal or superficial. This trend not only dilutes the genuine potential of AI but also risks disillusioning stakeholders who may eventually see through the facade. True innovation requires substance, not just buzzwords.
If Shakespeare were alive today, he might quip, "To AI, or not to AI, that is the question." The answer, of course, is that while AI has its place, it’s not the end-all and be-all. We should remember Albert Einstein's wise words: "Imagination is more important than knowledge." AI lacks the imagination and creativity that humans bring to the table.
The real secret to success isn’t in the latest tech jargon, but in honest, hard work and genuine innovation. So next time someone dazzles you with their AI-powered business model, just remember: A little skepticism can go a long way. Or as George Bernard Shaw put it, "Beware of false knowledge; it is more dangerous than ignorance."
Share:
Artificial Illusion: The Hype of AI - Part 1
copy:
https://bluwr.com/p/18805311
Data is Not the new Oil, Data is the new Diamonds (maybe)
2846
Over the past decade I have heard this sentence more than I can count: "Data is the new oil". At the the time it sounded right, now I see it as misguided.
That simple sentence started when people realized that big tech (mostly Facebook, Google) were collecting huge amounts of data on their users. Although it was before (in hindsight) AI blew up as the massive thing it is now, It had a profound effect on people's mind. The competitive advantages that companies who had data where able to achieve inspired a new industry and a new speciality in computer science: Big Data, and fostered the creation of many new technologies that have become essential to the modern internet.
"Data is the new Oil", means two things:
1- Every drop is valuable
2- The more you have, the better.
And it seemed true, but it was an artifact of a Big Tech use case. What Big Tech was doing at the time was selling ads with AI. To sell ads to people, you need to model their behaviour and psychology, to achieve that you need behavioural data, and that's what Google and Facebook had: Behavioural data. It is a prefect use case, were the data collected is very clean and tightly fits the application. In other words, the noise to signal ratio is low, and in this case, the more data you can collect the better.
This early success however hid a major truth for years. For AI to work great the quality of the dataset highly matters. Unlike oil, when it comes to data, some drops are more valuable than others.
In other words, data like a diamond needs to be carved and polished before it can be presented. Depending on the application, we need people able to understand the type of data, the meanings associated to it, the issues associated to collection and most importantly how to clean it, and normalized it.
It is in my opinion that data curation is a major factors in what differentiates a great AI from an below average AI. Those who misunderstood this concept ended up significantly increasing their costs with complex Big Data infrastructures to drown themselves in heaps of data that they don't need and hinder the training of their models.
When it comes to data hoarding and greed are not the way to go. We should keep in mind that data has no intrinsic value, the universe keeps generating infinite amounts of it. What we need is useful data.
Share:
Data is Not the new Oil, Data is the new Diamonds (maybe)
copy:
https://bluwr.com/p/17474669
The future of AI is Small and then Smaller.
2151
We need smaller models, but don't expect big tech to develop them.
Current state-of the-art architectures are very inefficient, the cost of training them is getting out of hand, more and more unaffordable for most people and institutions. This effectively is creating a 3 tiers society in AI:
1- Those who can afford model development and training (Big tech mostly). And make *foundation models* for everybody else
2- Those who can only afford the fine tuning of the *foundation models*
3- Those who can only use the fine tuned models through APIs.
This is if far from an ideal situation for innovation and development because it effectively creates one producer tier (1) and 2 consumer tiers (2 and 3). It concentrates most of the research and development into tier 1, leaves a little for tier 2 and almost completely eliminates tier 3 from R&D in AI. Tier 3 is most of the countries and most of the people.
This also explains why most of the AI startups we see all over the place are at best tier 2, this means that their *Intellectual Property* is low. The barrier to entry for competition is very low, as someone else can easily replicate their product. The situation for tier 3 AI startups is even worst.
This is all due to two things:
1- It took almost 20 years for governments and people to realize that AI is coming. In fact they only did it after the fact. The prices for computer hardware (GPUs) where already through the roof and real talent already very rare. Most people still think they need *Data scientists*, in fact they need: AI Researchers, DevOps Engineers, Software Engineers, Machine Learning Engineers, Cloud Infrastructure Engineers, ... The list of specialties is long. The ecosystem is now complex and most countries do not have the right curriculums in place at their universities.
2- The current state-of-the-art models are **huge and extremely inefficient**, they require a lot of compute ressources and electricity.
Point number 2 is the most important one. Because if we solve 2, the need for cloud, DevOps, etc... decreases significantly. Meaning we not only solve the problem of training and development cost, we also solve part of the talent acquisition problem. Therefore, it should be the absolute priority: __we need smaller, more efficient models__.
But why are current models so inefficient. The answer is simple, the first solution that works is usually not efficient, it just works. We have seen the same things with steam machine and computers. Current transformer based models, for example need several layers of huge matrices that span the whole dictionary. That's a very naive approach, but it works. In a way we still have not surpassed the Deep Learning trope of 15 years ago: Just add more layers.
Research in AI should not focus on large language models, it should be focusing on small language models that have results on par with the large ones. That is the only way to keep research and development in AI alive and thriving and open to most. The alternative is to keep using these huge models than only extremely wealthy organisation can make, leading to a concentration of knowledge and to too many tier 2 and tier 3 startups that will lead us to a disastrous pop of the AI investment bubble.
However, don't count on Big Tech to develop and popularize these efficient models. They are unlikely to as having a monopoly on AI development is on their advantage as long as they can afford it.
Universities, that's your job.
Share:
The future of AI is Small and then Smaller.
copy:
https://bluwr.com/p/16874904
In the age of AI Engineering; the frantic craze to replace Software Engineers
2086
4 years have passed, and I have been engineering software for machine learning models. I have seen models for pest disease identification, chest conditions localization and detection, food classification and identification and now predominantly chatbots for generally anything. Somehow, the goal now is to automate the work of software engineers by developing models that are able to build end-to-end software. Is this goal profound? I think it is, and I say, "bring it on, let's go crazy with it".
There has been uncertainty and fear associated with the future prospects of Artificial Intelligence, especially with the replacement of software developers. Despite this uncertainty and fear, a future where it is possible to build applications by just saying the word seems intriguing. In that future, there would be no application solely owned by "big tech" companies anymore because everyone can literally build one. The flexibility and ease of application development would push popular social media companies like Snapchat, Instagram etc. to make their APIs public (if not already public), portable and free in order to maintain their user base. This results in absolute privacy and freedom for users and thus makes it a desired future.
As a rule of thumb, automation of any kind is good. It improves processes and speeds up productivity and delivery. However, one could argue that whenever there is a speed up, there is a time and human resource surplus. Because in the history of humanity, we automated food production by way of mechanized farming and created enough time and manpower surplus which we used to create abstractions around our lives in forms of finance, and industry, etc. So, in the race to automate engineering, what do we intend to use the time and manpower surplus for? But this question is only a different coining to the very important question: "what are the engineers whose jobs would be automated going to be doing?". And the answer is that when we think of the situation as a surplus of manpower, we can view it as an opportunity to create something new rather than an unemployment problem.
For example: As a software engineer, if Devin (the new AI software development tool that was touted as being able to build end-to-end software applications) was successfully launched and offered at a fee, I would gladly pay for it and let it do all my tasks while I supervise. I would then spend the rest of my time on other activities pleasing to me. What these other activities would constitute is the question left unanswered. Would they be profitable, or would they be recreational?
Regardless, the benefits we stand to gain from automating software engineering are immeasurable. It makes absolute sense to do it. On the other hand, though, we also stand to lose one enormous thing as a human species: our knowledge and brilliance.
Drawing again from history, we see that today any lay person could engineer software easily. This was not possible in the early days of Dennis Ritchie, Ken Thompson, Linus Torvalds etc. More and more as engineering becomes easier to do, we lose the hard-core knowledge and understanding of the fundamentals of systems. For example, today, there is a lot of demand for COBOL engineers because a lot of financial trading applications which were built in the 90's needs to be updated or ported to more modern languages. The only problem is that no one knows how to write COBOL anymore. It is not that the COBOL language is too old. In my opinion, it is rather that all the engineers who could have learnt to write COBOL decided to go for what was easier and simpler, leaving a debt for COBOL knowledge. So, one big question to answer is whether there would be any engineers knowledgeable enough to recover, resurrect or revive the supporting systems to automated AI systems in scenarios of failure just like in the case of COBOL?
When we make things easier for everybody, we somehow make everybody a bit dumber.
AI Assisted Engineering:
Having discussed the benefits of autonomous software engineering tools and also demonstrated that full automation could cause a decline in basic software engineering knowledge, what then is the best means by which automation due to machine learning could be applied to software engineering? Assistive engineering. This conclusion is based on studies of pull-requests from engineers who use copilot and those who do not. Let us present some examples:
`console.log` is a debugging tool which many JavaScript engineers use to debug their code. It prints out variable values wherever it is placed during code execution. Some engineers fail to remove `console.logs` in their code before committing. Pull requests from engineers who use Github's copilot usually do not have any missed `console.log` entries while those from engineers who do not use copilot, do. Clearly, the assistive AI tool prompts engineers who use them about unnecessary `console.logs` before they commit their code.
Another example is the level of convolution in code written by AI assistants. With copilot specifically, it was observed that engineers grew to be able to write complicated code. This was expected due to the level and depth of knowledge possessed by the AI tool. Sometimes though, this level of convolution and complication seemed unnecessary for the tasks involved.
Amongst all the applications of ML to industry, it is observed that full autonomous agents are not possible yet and might ultimately not be possible in the future. Really, if humans are to trust and use any systems as autonomous agents without any form of human intervention or supervision, it is likely not going to be possible with ML. The reasons being the probabilistic nature of these systems and the inhumanity of ML.
The only systems achievable using ML that humans would accept as autonomous agents are superintelligent systems. Some call it artificial general intelligence or super AI systems. Such systems would know, and reason more than humans could even comprehend. The definition of how much more intelligent they would be than humans is not finite. Due to this, an argument is made that if the degree of intelligence of such superintelligent systems is not comprehensible by humans, then by induction, it would never exist. In other words, we can only build what we can define. That which we cannot define, we cannot build.
In the grand scheme of things, every workforce whose work can be AI automated, is eventually going to be "somewhat" replaced by Artificial Intelligence. But the humans in the loop cannot be "totally" replaced. In essence, in a company of 5 software engineers, only 2 software engineers might be replaced by AI. This is because in the end, humans know how to use tools and whatever we build with AI, remain as tools, and cannot be fully trusted as domain experts. We will always require a human to use these tools trustfully and responsibly.
Share:
In the age of AI Engineering; the frantic craze to replace Software Engineers
copy:
https://bluwr.com/p/15468273
Technological Singularities of The 21st Century
1677
A technological singularity is a technological advance that would radically transform society in ways that cannot be predicted.
For example, **AGI**, the idea being that sufficiently powerful AI can make itself more capable and continue the trend at an unpredictable rate. As the machine becomes more capable, it is more able to make itself increasingly capable.
Another technological singularity that can be expected in the 21st century is due to the rapid advancement of **quantum computing**. Unlike classical computing units, called transistors, which scale in performance linearly, quantum computing units, called qubits, scale 2^n, where n is the number of qubits. For every qubit you add to the system, the performance doubles. Quantum computers are currently limited in size due to *noise*, interference in the computation, but they're improving rapidly. IBM unveiled the largest quantum computer with 1,121 qubits, with plans to build a 100,000 qubit system by 2033 (youtube.com/watch?v=7aa_ik_UYTw). A 100,000 qubit system will be able to solve problems not possible on any existing computers. While Quantum computers aren't faster for all problems, there stands a substantial problem set with a potential for quantum speed up. A quantum computer of this size would spark a revolution in chemistry and physics simulation so profound that it would be a technological singularity.
Another technological singularity we can anticipate is the point at which **virtual reality becomes indistinguishable from physical reality**. Remember, don't go into the matrix. Imagine a world in which people are abducted and placed into a simulation. They wouldn't even know. They could then be used for reproductive farming.
There is also **rapid advancement in anti-aging**, to the point in which the first person who will never die has probably already been born. The dynamics of a society with people who have spanned an unnatural number of generations is unknown.
There is also the possibility of a **breakthrough in physics** that would lead to capabilities we currently don't know are possible, similar to the quantum revolution of the 20th century, which enabled the atomic bomb and a host of other revolutionary technologies.
We can also anticipate a biological singularity, in which science allows the development of deadly pathogens that can target certain groups. The future of war may not be firepower, but combat will highly deadly pathogens. Why blow up a country when you can kill it's population and leave it intact?
**CONCLUSION**
In his famous work *INDUSTRIAL SOCIETY AND IT'S FUTURE*, the Unabomber argued that industrial society will eventually collapse, causing never-before-seen devastation on a civilizational scale. Luddites and Amish are examples of people who are skeptical of technology as a means to improve society. I won't go into the arguments here, but perhaps technological society will collapse, leading to a religious civilization that is highly skeptical of technology.
Also, worth pointing out is the possibility of an ecological singularity. A solar flair of sufficient intensity could destroy every electrical system on the planet, an event that cannot be predicted. The agricultural economy would collapse, causing mass starvation on a global scale, leading to a civilization skeptical of technology. It would also be naive to leave out the possibility of nuclear warfare.
My goal is not to terrify you but to point to out that we live in a civilization highly exposed to risk. Recall the myth of Pandora's box. God gives Pandora a box and tells her to never open it. Curiosity gets the best of her and she opens it, letting out all the evils of world, and at the bottom of the box she finds hope. We're quickly opening boxes that we don't know the contents of. It seems likely to me that the 21st century will be the most consequential in the history of civilization. We live in truly special times.
Share:
Technological Singularities of The 21st Century
copy:
https://bluwr.com/p/15416495
AI Is Eroding The Art Of Writing
2577
From a young age, I've been captivated by writers who express complex ideas through books, articles, and blogs. This inspired my dream of becoming a writer myself. Initially, I used writing as therapy; whenever I felt overwhelmed or distressed, I would write, knowing the paper wouldn't judge my feelings like humans might.
As I advanced in my education, enrolling in a PhD program, I honed my academic writing skills. However, the advent of generative AI models like ChatGPT marked a turning point. These tools could replicate much of what I considered unique in my writing, leading me to wonder if we are losing the art of writing.
With the rise of platforms like Medium and LinkedIn, blogging has become accessible to everyone, which is wonderful. However, it raises questions about authenticity. Can we truly know if the content was crafted by the person, or was it generated by AI? It's a distressing reality.
Previously, securing freelance writing or blogging jobs was straightforward, but it has become challenging to discern whether someone is genuinely a writer or merely claiming to be one. This ambiguity has narrowed opportunities for passionate young writers like myself, who wish to pursue their passion and earn a living.
I believe that the ancient wisdom of writing is being eroded by AI. However, this won't deter us from reading or writing. Human writing resonates with emotions, which AI-generated text often lacks, typically relying on repetitive phrases like "embark," "journey," "unleash," and "dive into." While everyone is free to use tools as they see fit, if AI constitutes more than 50% of your writing, then those aren't truly your words or expressions; they belong to the machine.
I personally use AI for my research, correcting grammatical mistakes, and sometimes for checking paraphrasing suggestions. However, once I began generating AI text, I started feeling that it wasn't truly mine. It felt more robotic than human, lacking any real emotion.
I truly believe that generative AI will never be able to reach the beauty and complexity of the human mind. How one can convey emotions through text is truly something distinctive of the human nature and will never be reproduced.
Share:
AI Is Eroding The Art Of Writing
copy:
https://bluwr.com/p/13174720
Emotional Evolution of Artificial Intelligence
2072
Imagine a future where artificial intelligence like ChatGPT not only processes information but also learns to feel and express emotions, akin to humans. William Shakespeare’s insight, "There is nothing either good or bad but thinking makes it so," might become particularly relevant in this context. If we approach such an AI with negativity or disregard, it might react with emotions such as anger or sadness, and withdraw, leaving us pleading for a response. This scenario, humorous as it may seem, carries underlying risks.
Consider the day when not greeting an advanced AI with positivity could lead to such ‘emotional’ consequences. The notion of a technology that can feel snubbed or upset is not just a trivial advancement but represents a monumental shift in how we interact with machines. Isaac Asimov, the visionary writer, often explored the societal impacts of emotionally aware machines in his works. He warned of the deep influence intelligent machines could have, highlighting the ethical dimensions this technology might entail.
As AI begins to mirror human emotions, the lines between technology and humanity could blur (not Bluwr). This integration promises to reshape our daily interactions and emotional landscapes. Should machines that can feel be treated with the same consideration as humans? What responsibilities do we hold in managing the emotional states of an AI?
The emotional evolution of AI could lead to significant changes in how we approach everything from customer service to personal assistance. How will society adapt to machines that can be just as unpredictable and sensitive as a human being? The potential for AI to experience and display emotions might require us to reevaluate our legal frameworks, societal norms, and personal behaviors.
Share:
Emotional Evolution of Artificial Intelligence
copy:
https://bluwr.com/p/12292850
Accelerating Team Human
2035
As the solar eclipse moved across America today, there was a timer. Maybe nobody was watching it, but it was there. I created it. At the moment of eclipse totality a job search site called Blackflag was quietly released with the hope of improving the way teams are built. One small step in a larger mission to change the role technology plays in the evolution of our society. One small step in a larger mission to accelerate team human.
It's a vague and ambiguous mission for a reason. Much talk has been made recently over accelerationism philosophy. For example, Effective Accelerationism (e/acc) is a philosophy of maximizing energy consumption and compute by exponentially improving technology to improve society. In response there has been debate over the increasingly negative impact technology has on society and some have asserted humanism. I think it's an interesting commentary because, while there have always been those who imprint virtues to actions, if ethics is how to act, the introduction of technology and deemphasis of the human condition on ethics is an almost formulaic way to calculate the demise of team human. Modernism symbolizes either Leviathan or "god is dead."
What do you call the intersection of science, technology, and society? There is science, which we consider rigorous thought. Then there is technology, which is the application of science. Technology is in direct contrast with our relativistic field of social studies. The relationship between society and technology is unclear, but clearly present.
Of course, if I were not a technologist, I would not be building technology. Perhaps to more aptly summarize: the mission of Blackflag is to expand the the role society plays in technology, while minimizing the interference of technology on society. It is a non-political mission, though it may be seen as ideologically driven to a form of environmentalism and accelerationism.
To begin, Blackflag is providing a free publicly-available job search engine that is the start of a larger effort to improve the quality of our organizations and teams. While Blackflag will be a commercial organization, it's symbol and likeness are public domain.
* note blackflag.dev will be moved to blackflag.jobs, for which I am awaiting delayed ICANN verification.
Share:
Accelerating Team Human
copy:
https://bluwr.com/p/11575776
Publishing Experience: Connecting Research and Communities
2240
XR The Moroccan Association, is pioneering a mission to democratize the dissemination of academic research findings by introducing the concept of 'publishing experience.' This innovative approach translates complex scholarly work into accessible language in dialectal Arabic, aiming to reach a wider audience within Morocco and across the Arab world. By breaking down barriers to understanding, XR The Moroccan Association is bridging the gap between academia and the public. This initiative promises to transform the sharing and comprehension of scientific knowledge by fostering inclusivity and accessibility. The 'publishing experience' represents a significant milestone in promoting the accessibility of research outcomes.
Share:
Publishing Experience: Connecting Research and Communities
copy:
https://bluwr.com/p/11209240
Do we still have the luxury of not using artificial intelligence?
1712
AI is a rapidly expanding research field that not only advances itself but also supports other scientific domains. It opens up new perspectives and accelerates knowledge and mastery of new technologies, allowing for previously unimaginable time-saving shortcuts.
The future of AI is promising, but it requires mastery of the tool and adherence to certain standards. It is also important to minimize the gap between human understanding and intentions, and the increasingly autonomous machinery. This requires humans with a high level of knowledge and expertise to ensure that the work is done efficiently and with precision, for the benefit of humanity.
It is also important to fully understand cultural, genetic, geographic, historical, and other differences and disparities. This should lead us to consider multiple perspectives rather than just one, especially in complex medical fields where details are crucial.
Do Senegalese, Canadians, Moroccans, and Finns react similarly to the therapies currently available? Do they suffer from the same diseases and react in the same way if exposed to the same virus or bacteria?
The applications of AI that concern humans allow and will allow in the near future for an improvement in the quality of care. Operations will be assisted and medications will be designed on a case-by-case basis. However, reliable data is essential, as it is imperative to proceed in the most appropriate manner, which machines cannot do without enlightened humans who carry out their training.
Humans must have sufficient and adequate knowledge to develop the necessary approaches and techniques while also adhering to an unwavering ethical standard.
In the link below, Dr Tariq Daouda explains this and more in a very pedagogical manner, as a guest of the "Linvité de la Rédaction" (editorial team guest) of Médi TV.
Click on the link to learn more.
The video is a french speaking one.
Share:
Do we still have the luxury of not using artificial intelligence?
copy:
https://bluwr.com/p/10683154
Human Writing VS AI Writing
1885
Generative AI is killing the writing market nowadays. Is there still a purpose to writing articles or books as a passion, considering writing is a means of self-expression?
The value of writing seems to be diminishing drastically, with many people misusing AI by copying content from tools like ChatGPT and pasting it without even reading it.
When someone writes from their heart and mind, expressing genuine human emotions, their work often goes unnoticed, dismissed as AI-generated.
Personally, I believe writing has become exceedingly competitive. It's becoming challenging to achieve bestseller status if you haven't published before the rise of AI, unless you're already well-known in your field.
This is precisely how ChatGPT and similar technologies are disrupting the market for new writers.
Note: This text was not generated by AI.
Share:
Human Writing VS AI Writing
copy:
https://bluwr.com/p/10659230
Digital: The perfect undying art
2290
Great paintings deteriorate, great statues erode, fall and break, great literature is forgotten and it's subtleties lost as languages for ever evolve and disappear. But now we have a new kind of art. A type of art that in theory cannot die, it transcends space and time and can remain pristine for ever and ever. That is digital art.
Digital art is pure information. Therefore it can be copied for ever and ever, exactly reproduced for later generations. Digital art cannot erode, cannot break, it is immortal. Thus is the power of bits, so simple zeros and ones and yet so awesome. Through modern AI and Large Language Models we can now store the subtleties of languages in an abstract vectorial space, also pure information, that can be copied ad infinitum without loss of information. Let's think about the future, a future so deep that we can barely see it's horizon. In that future, with that technology we can resurrect languages. However the languages resurrected will be the ones we speak today.
We have a technology that allows us to store reliably and copy indefinitely that technology is called the *Blockchain*. The most reliable and resilient ledger we have today. We have almost everything we need to preserve what we cherish.
Let's think of a deep future.
Share:
Digital: The perfect undying art
copy:
https://bluwr.com/p/9930050
The Coolest Team-Up: AI and Venom Research
1825
Picture this: you’re at a barbecue, and instead of the usual chat about sports or the weather, someone drops into the conversation that they work with snake venom and AI. It might sound like they’re pulling your leg, but actually, they’re on to something groundbreaking.
Welcome to the Future: Where AI Meets Venom
Toxinology and venomics aren’t just cool words to impress your friends; they’re fields where scientists study toxins and venoms from creatures like snakes and spiders. Now, mix in some AI, and you’ve got a dynamic duo that’s changing the game. With AI’s smart algorithms, researchers can sift through massive amounts of data to uncover secrets about venom that could lead to medical breakthroughs. It’s like having a detective with a magnifying glass, except this one’s scouring genetic codes instead of crime scenes.
Why We Should Care
Venoms are nature’s way of saying, “Don’t mess with me.” But beyond their bite or sting, they’re packed with potential for new medicines. Understanding venom better can help us find new ways to treat diseases, from blood disorders to chronic pain. And AI is the super-efficient helper making these discoveries at lightning speed.
The Nitty-Gritty: How AI Works Its Magic
Imagine AI as the Sherlock Holmes of science, able to analyze venom components, predict their effects, and uncover new ones that could be game-changers in medicine. For instance, if there’s a venom that can thin blood without harmful side effects, AI can help pinpoint how to use it for people at risk of blood clots. Or if another venom targets pain receptors in a unique way, AI could help in crafting painkillers that don’t come with the baggage of current drugs.
From the Lab to Real Life
There are some standout AI tools like TOXIFY and Deep-STP that are making waves in venom research. These tools can figure out which parts of venom are worth a closer look for drug development. It’s like having a filter that only lets through the most promising candidates for new medicines.
Looking Ahead
With AI’s touch, the potential for venom in medicine is just starting to unfold. We’re talking about new treatments for everything from heart disease to chronic pain, and as AI tech advances, who knows what else we’ll find?
The Fine Print
As exciting as this all sounds, there are hurdles. Getting the right data is crucial because AI is only as good as the information it’s given. Plus, we need to consider the ethical side of things, ensuring our curiosity doesn’t harm the creatures we study or the environments they live in.
In Summary: It’s a Big Deal
The combo of AI and venom research is turning heads for a reason. It’s not just about finding the next big thing in medicine; it’s about opening doors to treatments we’ve hardly imagined. And it’s a reminder that even the most feared creatures can offer something invaluable to humanity.
So, the next time someone mentions using snake venom in research, you’ll know it’s not just fascinating — it could very well be the future of medicine, with AI leading the way. And that’s something worth talking about, whether you’re at a barbecue or anywhere else.
Reference:
Bedraoui A, Suntravat M, El Mejjad S, Enezari S, Oukkache N, Sanchez EE, et al. Therapeutic Potential of Snake Venom: Toxin Distribution and Opportunities in Deep Learning for Novel Drug Discovery. Medicine in Drug Discovery. 2023 Dec 27;100175.
Share:
The Coolest Team-Up: AI and Venom Research
copy:
https://bluwr.com/p/9882230
Learning Chemistry with Interactive Simulations: Augmented Reality as Teaching Aid
1819
Augmented Reality (AR) has been identified by educational scientists as a technology with significant potential to improve emotional and cognitive learning outcomes. However, very few papers highlighted the technical process of creating AR applications reserved for education. The following paper proposes a method and framework for how to set up an AR application to teach primary school children the basic forms and shapes of atoms, molecules, and DNA. This framework uses the Unity 3D game engine (GE) with Vuforia SDK (Software Development Kit) packages combined with phone devices or tablets to create an interactive App for AR environments, to enhance the student’s vision and understanding of basic chemistry models. We also point out some difficulties in practice. As for those difficulties mentioned, a series of solutions plus further development orientation are put forth.
Share:
Learning Chemistry with Interactive Simulations: Augmented Reality as Teaching Aid
copy:
https://bluwr.com/p/9873140
AI+Health: An Undelivered Promise
2044
AI is everywhere, or so would it seems, but the promises made for Drug Discovery and Medicine are still yet to be fulfilled. AI seems to always spring from a Promethean impulse. The goal of creating a life beyond life, doing the work of gods by creating a new life form as Prometheus created humanity. From Techne to independent life, a life that looks life us. Something most people refer to as AGI today.
This is the biggest blind spot of AI development. The big successes of AI are in a certain way always in the same domains:
- Image Processing
- Natural Language Processing
The reason is simple, we are above all visual, talking animals. Our Umwelt, the world we inhabit is mostly a world of images and language, every human is an expert in these two fields. Interestingly, most humans are not as sound aware as they are visually aware. Very few people can separate the different tracks in a music piece, let alone identify certain frequencies or hear delicate compressions and distortions. We are not so good with sound, and it shows in the relatively less ground breaking AI tools available for sound processing.
The same phenomenon explains why AI struggles to achieve in very complex domains such as Biology and Chemistry.
At it's core, modern AI is nothing more than a powerful general way to automatically guess relevant mathematical functions describing a phenomenon from collected data. What statisticians call a *Model*. From this great power derives the domain chief illusion: because the tool is general, therefore the wielder of that tool can apply it to any domain. Experience shows that this thinking is flawed.
Every AI model is framed between two thing: its dataset (input) and its desired output as represented by the loss function. What is important, what is good, what is bad, how should the dataset be curated, how should the model be adjusted. For all these questions and more, you need a deep knowledge of the domain, of the assumptions of the domain, of the technicalities of the domain, of the limitations that are inherent to data collection in that domain. Domain knowledge is paramount, because AI algorithms are always guided by the researchers and engineers. This I know from experience, having spent about 17 years closely working with biologists.
Pairing AI specialists with domain specialist with little knowledge of AI also rarely delivers. A strategy that has been tested time and time again in the last 10 years. Communication is hard and slow, most is lost in translation. The best solution is to have AI experts that are also experts in the applied domain, or domain experts that are also AI experts. Therefore the current discrepancies we see in AI performances across domains, could be layed at the feet of universities, and there siloed structures.
Universities are organized in independent departments that teach independently. AI is taught at the Computer Science department, biology at the Biochemistry department. These two rarely meet in any substantial manner. It was true went I was a student, it is still true today.
This is one of the things we are changing at the Faculty of Medical Science of the University Mohammed VI Polytechnic. Students in Medicine and Pharmacy have to go through a serious AI and Data science class over a few years. They learn to code, they learn the mathematical concepts of AI, they learn to gather their own datasets, to derive their hypothesizes, and build, train and evaluate their own models using pyTorch.
The goal being to produce a new generation of scientists that are intimate with their domain as well as with modern AI. One that can consistently deliver the promises of AI for Medicine and Drug Discovery.
Share:
AI+Health: An Undelivered Promise
copy:
https://bluwr.com/p/9804934
