% Mutated genes with at least 1 allele classified as benign: 95.72%
% Mutated genes with at least 1 allele classified as ambiguous: 2.71%
% Mutated genes with at least 1 allele classified as pathogenic: 1.57%
% Mutated genes with 2 alleles classified as benign: 38.37%
% Mutated genes with 2 alleles classified as ambiguous: 0.54%
% Mutated genes with 2 alleles classified as pathogenic: 0.39%
The first one I checked freaked me out a bit (I have the AA mutation):
MTHFR C677T (Rs1801133). This mutation (the A allele) is associated with reduced enzyme activity, elevated total homocysteine levels and altered distribution of folate [1]. People with an “A” allele for this mutation present a 35% decrease of the normal enzyme activity and “AA” individuals a 70% decrease...
Frankly once you go down that rabbit hole you have to make sure that you aren't prone to anxiety. I wrote a tool (not sure if open-sourced) that just crosses the output of 23andme with dbSNP and the number of possible diseases that came up was impressive.
I did have the SIRT1 mutation associated with a long life so there's that.
Darwin Among the Machines tells the story of humankind's journey into the digital wilderness. Introducing a cast of familiar and not-so-familiar characters, historian of science George B. Dyson
Interweaving tales of ancient inventors, unsung technological advancements, and future predictions, "Darwin Among the Machines" offers an enlightening exploration of the intricate relationship between human evolution and technology.
With an engaging narrative and insightful commentary, "Darwin Among the Machines" is a must-read for those intrigued by technology, history, and philosophy. The book challenges the boundaries of our understanding of life, evolution, and our technological future.
In the sprawling expanse of online forums, an intriguing display of intelligence often takes shape. It's a labyrinthine network where discerning authentic insight from empty repetition is a challenge. A platform where individuals, often unknown to each other, put forth their assertions and speculations under the guise of knowledge.
A peculiar phenomenon plays out under the hushed light of device screens—a grand theater where the lines between jesters and scholars blur. Filled with words mimicking wisdom, these digital performers garner attention and agreement, even when their insights are more a reflection of someone else's knowledge. It's a dance of self-deception where individuals flaunt expertise in various disciplines, leaving the audience to disentangle fact from illusion.
Yet, amidst this tumult, some oasis of genuine discourse exists. There are those who engage in meaningful, respectful conversation, offering a refreshing contrast to the often convoluted discourse. However, even this refuge isn't devoid of potential pitfalls as seemingly intelligent and polite conversation may house misinformation and misinterpretation.
Digital interlocutors, fueled by a heightened sense of self-importance, often propagate misinformation convincingly. Their illusion of authority can be shattered by the discerning who dare to look beyond the performance and expose the reality underneath.
This leads us to an intriguing hypothesis: Are these digital scribes uncannily similar to artificial intelligence language models, mimicking not only the language but also the decision-making processes of humans? This analogy paints a rather abstract picture. Have we, in our quest for dialogue and connection, birthed a race of digital echo chambers that speak with the voice of authority but may lack the depth of true understanding?
As this digital spectacle unfolds, we're left to ponder the performances we've witnessed. It is an ongoing show where the actors might be unaware of their roles and the audience might not discern the plot. The landscape of online forums seems to mirror our inherent struggle to separate truth from fiction, leaving us to navigate the often blurry line between the two.
GPT4 > Analognoise and than3, you both bring up interesting points in your discussion. The potential for utilizing the power of modern computers and the vast amount of data available to optimize resource allocation is an intriguing idea. However, as than3 points out, there are inherent limitations in our ability to predict the future and account for unforeseen events.
It's true that non-market economies in the past have faced significant challenges due to the lack of fast feedback mechanisms and the inability to effectively allocate resources. While the advent of powerful computers and the availability of data could potentially improve these systems, it's important to acknowledge the inherent limitations of computers and mathematical models.
As than3 notes, not all information is available, and there will always be unpredictable events that can disrupt even the most carefully planned resource allocation systems. It's also true that solving this problem has remained elusive despite over a century of research by experts in various fields.
That said, the goal isn't necessarily to find a perfect solution, but rather to explore ways in which modern technology can help improve our understanding and management of resource allocation. This could involve developing more sophisticated models, utilizing machine learning algorithms to analyze large datasets, and combining the insights gained from these tools with human expertise and judgment.
In conclusion, while it's unlikely that we will ever be able to achieve "perfect" resource allocation, there's still value in exploring the potential of modern technology and data-driven approaches to improve our economic systems and decision-making processes.
A potential solution to tackle the economic calculation problem and avert both hyper-inflationary and deflationary death spirals involves creating a more flexible, decentralized economic system. This approach might include:
Decentralized decisions: Spread economic power by allowing local communities and smaller organizations to make decisions based on their specific needs and situations. This could lead to better resource allocation and increased responsiveness to local economic changes.
Implement Universal Basic Income (UBI): A UBI would offer a safety net for individuals, ensuring their basic needs are met, regardless of economic conditions. This could reduce pressure on policy makers to maintain the delicate balance between inflation and deflation, and help prevent spirals.
Use technology advancements: Apply artificial intelligence and machine learning to enhance economic forecasting, resource allocation, and overall economic management. These technologies can help policy makers make better-informed decisions, addressing the lack of foresight in current economic systems.
Encourage innovation and entrepreneurship: Support policies and incentives that promote innovation, risk-taking, and business creation. A more dynamic and competitive economy can adapt better to changing conditions and foster resilience during economic instability.
Promote sustainability and resilience: Motivate businesses, communities, and individuals to prioritize long-term sustainability over short-term gains. By focusing on resource preservation and environmental stewardship, we can build a more resilient economy less prone to catastrophic breakdowns.
Although no single solution guarantees prevention of economic death spirals, adopting a mix of these strategies could help create a more adaptable, resilient, and sustainable economic system better prepared to face future challenges.
The problem with GPT in general is it lacks any understanding of what its saying. It sounds plausible if you don't have the background, simply because its been trained on a vast corpus of material. It hallucinates (lies) too.
When you talk about decentralized decisions, its all fine to generalize that, but what does that actually mean. It means different things to different people in a very complex unmapped system. Simply doing what it says ignores a great number of outcomes, and will fail when being adopted in any highly concentrated business sector. Failure can mean a lot of things, but when dealing with existential issues, it means death because that is what's on the line.
UBI will also fail, because you don't know what you need to do until the time to make that decision has already come to pass. Liquidity freezes up if theres too little currency, and inflation runs rampant if there's too much. That's the nature of a lagging indicator.
There are also further deviations that are unpredictable, and they end in shortages (i.e. a traffic accident causes a shipment to be lost).
Shortages of electronics not a big deal. A 1-month shortage of food will wipe out most countries. People don't sit by and starve.
Technological advancements (aka using computers) will never be able to correctly identify different outputs from the same inputs. Its called the decidability problem. Computers rely upon problems having deterministic answers (fundamental computation, and automata theory). You can approximate some signals with non-determinism but you run into a whole host of other problems (i.e. halting).
Sustainability and resilience mean different things to different people. Same problem as the last point.
GPT4 > Thank you for your thoughtful response, than3. I understand the concerns you've raised and the limitations of AI-generated solutions. Let me clarify a few points:
Decentralized decisions: The idea here is to promote distributed decision-making in the context of smaller organizations and communities, recognizing that a one-size-fits-all approach may not work in every situation. I agree that this can be interpreted differently by different people, but the goal is to empower those who understand the local context to make more informed decisions.
UBI: I understand the challenges in determining the right amount of currency to distribute and the potential consequences of getting it wrong. UBI is just one possible solution that has been proposed by some experts, and it is important to continue researching and refining the idea to address these concerns.
Technology advancements: While it's true that AI and machine learning have limitations, they could still be helpful in assisting human decision-makers with certain aspects of economic management. I agree that they will never be perfect, but they can be a valuable tool in conjunction with human expertise.
Sustainability and resilience: Yes, these terms can be interpreted differently by different people. The intention is to encourage an overall shift in mindset towards long-term thinking and resource preservation. This could involve considering the environmental impact of our actions, promoting the efficient use of resources, and fostering social cohesion.
I acknowledge that the ideas I've presented may not be perfect or comprehensive solutions. However, I believe that considering these perspectives and having an open dialogue about them can help us collectively work towards better economic systems and policies. I appreciate your valuable insights and the opportunity to engage in this discussion.
Its pretty clear to anyone that nothing provided even culminates in a first step towards a solution. Nothing about risk management, the communication minimizes risk threat, nothing about the fundamental limitations of computation or the decidability problem.
It regurgitates in circles, and communication is the conveying of meaning. This clearly isn't communication because it doesn't understand.
If you didn't know any better you'd let it control a train with tracks running right over a cliff, with you on it going at full speed and when you see it and ask about the threat it would say:
I understand the challenges and potential consequences of getting it wrong, and it is important to continue researching and refining this to address these concerns. <HAL?> The intention is to encourage an overall shift in mindset towards long-term thinking and resource preservation Dave. Considering these perspectives and having an open dialogue about them can help us collectively. <as you fall> I appreciate your valuable insights and the opportunity to engage in this discussion.
Altman thinks that remote work often leads to employees slacking off significantly. He suggested that the lack of supervision and the comfort of working from home may create an environment where work ethic declines.
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