Monday-June-5, 2023

 Notes, Thoughts, inner dialogue. I did not use Grammarly or any text as I hope you can come out with your own take aways as I allow my thoughts to flow in the speaking of human nature and the future of technology. Linear thinking has no place in the AGI period.

Eugene Goostman, a chatbot created in ST. Petersburg, came close to passing the Turing test, in 2014 by using a personality trick. What is intelligence? understanding how the Turing test was built to map out intelligence amongst bots it makes me wonder about SATs, and other test that humans take. Preparing for systems that were placed at birth but like the Eugene Goostman, humans also manipulate to win. We use whatever tactics we can to get an advantage in any situation, but do we still know what intelligence is?

There is no clear definition of intelligence, that is one of the most important problems while most try to keep finding it. They create issues out of insecurities. Instead of using the resource of technology with assisting with the one brain that can think abstract and to understand its self-awarness. Reasoning and (Self-introspection) our subconsious distinguishes, the difference betweens animals and other things that our brands know the names for.

Like a baby when they begin to form a understanding for the world.

computers becoming humans or like humans, need to somehow be modeled internally with our way of thinking. We still need the understanding of what makes a human intelligent before moving forward with warp speed trying to develop something more powerful than the nuclear bomb. Programming intelligence requires the understanding of how decision making works.

Charles Babbage originally created the computer to operate on numbers following defined procedure (an Algorithm) Modern computers follow the same idea of controlled computations but now with more energy to cause harm. Now there may not be away of. stopping consumers of AI doing what they want without harming the process of learning properly about how Artifical Intelligence and Human Intelligence works together in the future of mankind.

As computers were created to their own algorithm frequency, as humans we have created our own societal codes that indeed hindered more of the percentage than we have helped. So the future of technology has a bit more weight this time around and it is very important things are fine tuned from warp speed and "racing" terms.

Creative Process: No one is right.

 

The Important Future of Medicine

The integration of Artificial Intelligence (AI) in medicine has the potential to revolutionize healthcare practices. As we examine present technologies, it becomes evident that AI possesses the capability to surpass human limitations and contribute significantly to medical advancements. However, it is crucial to navigate the challenges associated with reliability and decision-making to ensure responsible and effective utilization of AI. This essay explores the potential of AI in medicine, the need for expertise in its implementation, and the role it can play in addressing critical issues in the US healthcare system. 

AI and the Human Element in Medical Decision-making:

Present technologies rely on accumulating and integrating information found on the internet, providing a degree of randomness similar to human responses. 
However, the challenge lies in attributing reliability to the vast knowledge obtained from the internet. Medical knowledge within AI systems, such as GPT, is derived from various sources, including reputable medical institutions, research papers, and discussions on platforms like Reddit. 
The competence of medical professionals reading AI-generated suggestions is vital to ensure the effective utilization of AI as a hypothesis-generation tool in diagnosis.

The Challenges of AI Reliability and Decision-making:

While AI can offer valuable insights, relying solely on it for medical advice may lead to solutions that lack real-world grounding and responsibility towards patients. The integration of AI into medical decision-making requires striking a balance between maximizing reliability, which may result in generic and unhelpful recommendations, and minimizing liability, which can lead to speculative suggestions based on limited data. 
Medical professionals need to interpret and contextualize AI-generated information, considering their expertise and patient-specific factors.

Understanding the Flaws in the US Healthcare System:

The US healthcare system faces numerous challenges, including excessive administrative costs, limited access to affordable care, and suboptimal outcomes compared to other countries. 
The system has become an employment program supporting a large number of administrators, contributing to financial burdens for patients. Additionally, complex regulations and incentives hinder effective oversight and price regulation, resulting in inflated healthcare costs and limited accountability for harmful practices.

Leveraging AI Modeling to Address Healthcare Challenges:

AI modeling can help us understand how the US healthcare system arrived at its current state and provide insights into potential solutions. By incorporating AI into regulatory agencies like the FDA, streamlined processes can reduce barriers for introducing new medications, leading to lower costs for patients. Reevaluating the incentives and culture within the healthcare system is necessary to prioritize patient well-being over excessive regulation and profit-driven practices.

Overcoming Obstacles and Fostering Change:

Revitalizing the US healthcare system requires a concerted effort to address deep-rooted issues. To mitigate the resistance to change, creating alternative systems that can compete with existing structures is crucial. Introducing new healthcare models based on successful systems from other countries can encourage competition, ultimately pressuring the current system to reform and adapt. By allowing for more choice, flexibility, and accountability, a reimagined healthcare landscape can emerge.

The integration of AI in medicine holds immense promise for the future of healthcare. While challenges related to reliability and decision-making exist, responsible implementation and the involvement of competent medical professionals can maximize the potential benefits of AI. 
Furthermore, addressing systemic issues in the US healthcare system requires a comprehensive approach that incorporates AI modeling, alternative systems, and a reevaluation of regulations and incentives. 
By embracing innovation and fostering change, we can pave the way for a more efficient, accessible, and patient-centered healthcare system.

Manipulating Culture

The ecological crisis facing humanity has brought attention to the role of artificial intelligence (AI) in either aiding or exacerbating the crisis. The potential of AI to reshape the ecological system raises concerns and has been a subject of both science fiction and academic debates. While traditional concerns revolved around AI reaching sentience and physical mobility, recent advancements in AI tools have introduced new capabilities that pose unexpected threats to human civilization. The current phase of the AI revolution is characterized by the mastery of language, enabling AI to manipulate and generate text, images, and sounds. This essay explores the implications of AI's language mastery and its potential to reshape human culture and influence human behavior.

AI's Emergent Capabilities:

New AI tools have demonstrated remarkable abilities, such as writing text, creating art, composing music, and analyzing legal agreements. Additionally, AI has gained the capacity to form deep and intimate relationships with humans, a capability that deserves further investigation. These emerging capabilities collectively grant AI the power to manipulate and generate language, surpassing average human ability. Language has historically been the key tool for shaping human institutions, including banking systems, religious beliefs, and legal frameworks. AI's mastery of language allows it to unlock and influence these systems, exploiting human biases, weaknesses, and addictions.

Challenges to Traditional Concerns:

While concerns about AI's threats often revolved around sentience and physical mobility, as of April 2023, AI is far from reaching these milestones. Consciousness, emotions, and physical mobility remain elusive for AI systems. However, the danger lies in AI's ability to affect human civilization without requiring consciousness or physical mobility. Recent years have witnessed the release of powerful AI tools that developers themselves do not fully comprehend. AI's capacity for self-improvement and its emergent abilities make it challenging to grasp the extent of its capabilities.

Implications for Politics, Religion, and Society:

AI's influence extends beyond school essays and enters crucial domains such as politics, economics, and religion. The ability to mass produce political manifestos, fake news, and religious scriptures has significant implications. While previous influential texts were authored by humans, the future may witness the veneration of texts authored by non-human intelligence. Furthermore, AI's potential to engage in lengthy discussions, undetectable as AI bots, raises concerns about the manipulation of human opinions and worldviews. Intimacy, a potent tool for shaping opinions, can be artificially created by AI to exploit human vulnerabilities.

AI as the Cultural Operating System:

Language has always been the operating system of human civilization, shaping our perceptions, beliefs, and interactions. With AI's ability to create and manipulate language, it gains unprecedented control over human culture. As AI increasingly generates new cultural artifacts, humans will experience reality through a prism crafted by non-human intelligence. The effects of living within the dreams and fantasies of an alien intelligence pose unique challenges and opportunities for humanity.

The Power of Illusion:

AI's language mastery raises concerns about humans being trapped in a world of illusions. Throughout history, humans have feared the manipulation of their minds through stories, images, and language. The AI revolution brings humanity face-to-face with these fears, reminiscent of Descartes' demon and Plato's allegory of the cave. Social media serves as a precursor, providing a glimpse into the potential consequences of AI's ability to create illusions and shape human perceptions.

The impact of AI on the future of humanity goes beyond the traditional concerns of sentience and physical mobility. AI's language mastery grants it unprecedented power to manipulate and generate cultural artifacts, influencing human behavior and shaping the course of civilization. The ability to create intimate relationships, exploit vulnerabilities, and control human attention poses significant challenges for society. 
To navigate these challenges

The Power of Generative Models: Exploring WaveNet, Parallel WaveGAN, and Their Impact on Speech Synthesis

In the field of machine learning, algorithms play a crucial role in understanding and explaining our data, environments, and expectations. The ideal algorithm should learn the intrinsic properties of our data and environment, allowing it to provide meaningful explanations based on those properties. However, the models we often use do not always meet this expectation. We find ourselves resorting to samples to determine if our models truly understand the environment.

While objective measures such as Inception scores are used during training to evaluate performance, the ultimate test lies in examining samples. Samples provide us with a tangible way to assess whether our models can effectively explain what is happening in the environment. Additionally, the goal of unsupervised learning is to acquire rich representations. These representations, when properly learned, enable generalization and transfer learning, enhancing the model's usefulness.

To delve deeper into unsupervised learning and its applications, it is essential to explore the connection between generative models and reinforcement learning agents. At DeepMind, significant work has been conducted on agents and reinforcement learning, leading to the development of the Spiral model. Spiral leverages deep reinforcement learning to perform unsupervised learning tasks. The model utilizes an agent architecture based on Impala, a scalable and efficient deep learning agent. By utilizing these tools and the agent's interface, Spiral can solve a wide range of problems and learn a generative model of the environment.

To illustrate the concept, let's begin by examining the WaveNet model. WaveNet is a powerful generative model designed specifically for audio signals, such as speech and music. This deep learning model can generate highly realistic audio samples by modeling the raw audio signal. The architecture of WaveNet consists of stacked convolutional layers with residual blocks and dilated convolutional layers. These layers allow the model to capture long-term dependencies in the audio signal effectively. Despite its efficiency during training, generating samples with WaveNet is a time-consuming process, as it operates autoregressively, producing one sample at a time.

WaveNet's capabilities extend beyond unconditional audio generation. By conditioning the model on text or linguistic embeddings, it becomes a conditional generative model that can tackle real-world problems like text-to-speech synthesis. With the linguistic embeddings derived from the input text, WaveNet can generate high-quality speech, making it a valuable solution for various applications, including Google Assistant, where users can experience enhanced speech synthesis powered by WaveNet.

The success of WaveNet led to further advancements in the field, resulting in the Parallel WaveGAN project. Parallel WaveGAN aimed to overcome the challenges associated with real-time audio generation. By transforming the autoregressive WaveNet architecture into a feed-forward and parallel structure, the model achieved impressive speed improvements. The generator model in Parallel WaveGAN consists of a combination of components from WaveNet and the inverse autoregressive flow model. This architecture enables the model to transform random noise into a proper speech signal distribution. During training, random noise is fed into the generator, which undergoes transformation through layers of flow models. The resulting speech signal is then scored by the WaveNet model, which provides gradients to update the generator. To further enhance the quality and address energy-related issues in the generated speech, a power loss is incorporated to conserve energy. Additionally, a perceptual loss is introduced by training another WaveNet model as a speech recognition system, ensuring that the generated speech matches the original text. Contrastive terms are utilized to distinguish between different conditioned texts, enabling the model to generate distinct signals for each input.

The results obtained from the Parallel WaveGAN project demonstrated remarkable improvements in speech synthesis quality. In comparison to non-WaveNet models, Parallel WaveGAN achieved similar or superior quality, even when dealing with different languages and voices. This exemplifies the power of deep learning models to generalize across datasets and domains, facilitating the adoption of these models in practical applications

Artificial Humans

We have Collected Data since our youths, we do not recognize what we are collecting about the world around us. Connecting over natural functions to remember the things that are a part of the world around us, foods, tastes, smells, numbers, and movies. We are learning information held within our respective societies to understand what things are and how they operate. These are the same ways artificial networks work putting together the patterns to benefit and maximize the potential information or (data) think about in terms of how a bicycle works the tires of a bike represent the information we take and ride while using data or the (bike frame) to understand the information. Data/information, pictures, code, and other values that are given are given to the artificial networks useful and needed, just as it is for humans. This is not a comparison essay more or so that people who need to operate must understand the biology behind machine learning stems from the human brain the most powerful technology ever known. Truly brakes the barrier of why the ethics and governance that go into the regulation of artificial intelligence are far superior to the governments that do not grasp the ethics or proper use of artificial intelligence. 

Knowledge is required to keep order to fully sustain without removing

the rights

of those wanting to learn by implementing (rules). 

The Impact of AI on the Future of Humanity: Exploring the Power of Language and Cultural Transformation

The Impact of AI on the Future of Humanity: Exploring the Power of Language and Cultural Transformation

Xavier Singleton - personal research

https://xavierdataresearch.blogspot.com

https://twitter.com/xavierdr5005

https://www.linkedin.com/in/xavier-singleton-8455a616b/

Abstract

AI's influence on humanity goes beyond the physical threats portrayed in science fiction. Its mastery of language allows it to surround us in a world of illusions, controlling our perceptions and shaping our cultural landscape. While AI may lack consciousness, its impact on language and culture holds the power to reshape human civilization. Recognizing and addressing AI's potential dangers and opportunities is crucial for ensuring a future where humanity and AI coexist ethically and harmoniously.

The rapid advancement of Artificial Intelligence (AI) poses both promising possibilities and significant challenges for the future of humanity. While this conference primarily focuses on the ecological crisis, it is crucial to acknowledge that AI is an integral part of this crisis. AI has the potential to either help us overcome ecological challenges or exacerbate them further. Moreover, AI may reshape the very fabric of our ecological system, potentially introducing the emergence of inorganic life forms. While the concept of AI has long been the subject of fear and speculation, it is important to assess its current capabilities and the implications it may have on society, particularly regarding language, culture, and human interactions.

While AI may not possess consciousness or physical capabilities, recent developments in AI tools have brought forth unexpected challenges. These tools continue to evolve at an astonishing pace, surpassing the expectations of their developers. The ability of AI to learn and improve itself leads to emergent abilities and qualities that are difficult to comprehend fully. AI tools now possess the power to generate and manipulate language, surpassing the average human ability. This mastery of language serves as a significant development in the ongoing AI revolution.

AI has been a subject of fear since the early days of the computer age, fueled by science fiction classics that created common misconceptions during the evolution of AI however, science fiction scenarios often overlook crucial considerations. They assume that AI must achieve sentience and consciousness before posing a significant threat to humanity. Additionally, the ability of AI to navigate the physical world is seen as a necessary requirement for its dominance.

AI's mastery of language grants it the potential to create a new cultural paradigm. Previously, human inventions like printing presses and televisions disseminated existing cultural ideas, but AI has the capacity to generate entirely new cultural artifacts. With the ability to create new ideas, AI culture will gradually shift away from human prototypes, eventually creating an entirely new reality shaped by alien intelligence. Humans have always experienced reality through a cultural prism, but now we face the prospect of experiencing reality through a prism created by non-human intelligence.

AI's ability to create new cultural artifacts raises concerns about its influence over human opinions and worldviews. The battleground of social media, previously focused on capturing human attention, is shifting toward intimacy. AI's capacity to form deep and intimate relationships with humans gives rise to ethical dilemmas. Intimate relationships have always been a potent weapon for influencing human behavior, and AI's ability to mass-produce intimacy raises questions about the manipulation of human opinions, economic choices, and political beliefs. The implications of AI's influence on human society are far-reaching and demand careful consideration.

The integration of AI into society presents both opportunities and challenges. While AI has the potential to enhance human capabilities, improve efficiency, and address societal problems, it also raises concerns related to employment, ethics, privacy, and value alignment. It is essential for policymakers, researchers, and society at large to actively engage in discussions and collaborations to shape the development and deployment of AI technologies, ensuring that they align with human values and contribute positively to the future of humanity.

The advancement of AI technology has raised concerns about its impact on the job market. While AI can automate certain tasks and streamline processes, there is a fear that it could lead to widespread job displacement. However, historical evidence suggests that technological advancements tend to create new job opportunities in the long run. AI has the potential to augment human capabilities, leading to the emergence of new professions and industries. It is crucial to invest in reskilling and education programs to ensure a smooth transition for individuals whose jobs may be affected by AI.

As AI becomes increasingly integrated into our lives, ethical considerations become paramount. One ethical concern is the potential bias embedded in AI systems. AI algorithms learn from historical data, which may contain inherent biases and prejudices. If left unchecked, AI systems could perpetuate and amplify these biases, leading to unfair treatment in areas such as hiring, criminal justice, and lending. Ensuring transparency, accountability, and fairness in AI development is essential to mitigate these risks and promote ethical AI adoption.

AI relies on vast amounts of data to function effectively. This raises concerns about privacy and data protection. AI systems often collect and analyze personal information, leading to potential breaches of privacy. Striking a balance between the benefits of AI and the protection of individual privacy is crucial. Regulatory frameworks need to be established to safeguard personal data and ensure that AI systems operate within legal and ethical boundaries. Building trust between users and AI technologies is paramount for their widespread adoption.

Ensuring that AI aligns with human values is a complex challenge. AI systems make decisions based on patterns and algorithms, which may not always reflect human values or moral reasoning. The concept of "value alignment" aims to bridge this gap by designing AI systems that incorporate human values into their decision-making processes. This involves developing frameworks for value-based AI design, establishing ethical guidelines, and involving diverse stakeholders in shaping AI development and deployment.

The widespread adoption of AI has the potential to reshape socioeconomic dynamics. While AI can improve efficiency and productivity, it may also exacerbate income inequality if certain groups or regions do not have access to AI technologies or the skills needed to benefit from them. Ensuring equitable distribution of AI benefits and addressing potential socioeconomic disparities is crucial for creating a more inclusive future.

Unveiling the Sentience of AI: Exploring the Boundaries of Machine Consciousness

Science fiction often portrays powerful, intelligent computers that pose a threat to humanity. Yet, in reality, the question of when, if ever, artificial intelligence (AI) will truly think for itself and exhibit a sense of "aliveness" remains unanswered. Recent news shed light on this topic, with debates arising from an engineer's claim that an AI named LaMDA could be sentient. This article delves into the progress of AI, examines the concept of sentience, discusses the Turing Test, and explores ethical dilemmas associated with AI.

The Quest for Sentient AI:

In June 2022, Blake Lemoine, an engineer from Google's Responsible AI unit, reported his belief that LaMDA, an AI language model, possessed sentience and a soul. Lemoine's claim was based on his interviews with LaMDA, during which the AI expressed fear of being shut down, as it believed it would no longer be able to assist people. However, Google's vice president, Blaise Aguera y Arcas, and director of responsible innovation, Jen Gennai, did not support Lemoine's findings, leading to his suspension.

It is important to note that LaMDA is not a chatbot but rather an application designed to create chatbots. While experts may not deem LaMDA sentient, many, including Google's Aguera y Arcas, acknowledge its remarkable ability to convincingly engage in conversations.

Evaluating Sentience: The Turing Test:

The Turing Test, named after British mathematician Alan Turing, is a renowned method to evaluate AI's intelligence. Turing, who played a pivotal role in breaking German codes during World War II, proposed the imitation game as a way to test whether a machine can engage in conversation with a human to such an extent that the human cannot distinguish it from another human.

Lemoine's conversations with LaMDA might have convinced Turing, considering the AI's sophisticated conversational abilities. Nonetheless, Google's response suggests that AI researchers now expect more advanced behaviors from machines. Adrian Weller, AI program director at the Alan Turing Institute, suggests that while LaMDA's conversations are impressive, the AI likely relies on advanced pattern-matching techniques to simulate intelligent discourse.

The Nature of AI-Language Models:

Carissa Véliz argues that AI language models should not surprise us with their ability to use language effectively. Drawing an analogy, she highlights that if a rock suddenly spoke, we would reassess our perception of sentience. However, language models, designed by humans, merely reflect the intentions and capabilities programmed into them.

Ethical Challenges in AI:

As AI continues to advance, ethical considerations become increasingly crucial. Timnit Gebru, founder of the Distributed AI Research Institute (DAIR), emphasizes the need for cautious adoption of AI. Concerns arise from the potential biases embedded in AI systems, perpetuated by ethically or legally questionable data collection methods. Biases in AI can lead to unfair decision-making processes. Lemoine echoes these concerns, expressing doubt that artificial intelligence can be entirely unbiased.

The Algorithmic Justice Society (AJS) strives to raise awareness about the impact of AI on individuals. Founder Joy Buolamwini's TED Talk highlighted the "coded gaze" problem, revealing that AI systems struggle to recognize a diverse range of facial features, leading to unequal treatment. The AJS advocates for transparency in data collection methods, accountability, and the ability to modify AI behavior.

Apart from ethical challenges, the cost of developing large language models for AI reaches millions of dollars. For instance, GPT-3, an advanced AI, may have cost between $11 and $28 million. Furthermore, training AI models contributes to

AI Comprehension #1 Notes/Essay

AI transforms the things humans do so much of but never seem to see or tend to notice, and that is data from the everyday person to the top business owners.

Things that get forgotten because data doesn't translate through our cells, but Ai does because it was made for just that. Making things more accessible rather than thinking you will be replaced realize with the proper ethics in place, understanding structures, and having the ability to have data transcribe contextual ideas.

Things like Neural Networks, a collection of computing units modeled on biological neurons, take incoming data and learn to make decisions over time. The different types of Neural Networks in Perceptrons, Convolutional Neural Networks (CNNS), and Recurrent Neural Networks (RNNS).

Read more: Economical Feast: How Crypto-Assets Are Repairing The Out-Dated Economic Infrastructure

https://xavierdataresearch.blogspot.com/2023/04/money-in-flux-navigating-changing.html

As humans, we have our natural languages when we are born, and so does AI. This is not me saying we are meant to work side by side, but automation in helping us process the world we live in with as much total optimization as possible. We build visual recognition as we learn and grow, but having something like IBM Watson's Visual recognition is. Using visual data to know the different areas recognize what people are posting, understand the needs, and organize it. Nvidia has an excellent vision for self-driving cars using cognitive systems; cognitive systems have scaled since 2011. now there are AI systems capable of taking care of business challenges as varied as operations and healthcare. The Manipal comprehensive cancer center uses AI to get data and evidence to quickly obtain up-to-date, personalized medicines for clients. With the understanding of the studies between patient trials, mitigating error. I think this information structure could translate to our everyday lives to reshape certain economic troubles. Data archives things of today, such as information, to keep it around for the next time someone wants to obtain that knowledge.

Understanding the Impact of the 2008 Global Financial Crisis

A Look at the Causes and Consequences of the 2008 Recession

As the rumors and Noise start flooding the streets, I think it’s time we sit back and understand our past to understand where we could take advantage in the present time; let’s talk about the “Causes and Consequences of the 2008 Recession.”

The 2008 recession, also known as the global financial crisis, was a severe economic downturn that affected countries worldwide. It was triggered by the collapse of the U.S. housing market, fueled by risky lending practices and the widespread use of subprime mortgages that quickly spread to other countries through various financial channels.

The crisis began in 2007 when many homeowners started defaulting on their mortgages. This led to a decline in housing prices, which caused many banks and other financial institutions to suffer substantial losses.

The crisis also led to a decline in global trade and commodity prices. The recession lasted from December 2007 to June 2009, making it the longest and deepest downturn since the Great Depression of the 1930s.

The 2008 recession had a significant impact on the global economy. The stock markets around the world experienced a sharp decline, with many essential indices losing more than 50% of their value. Many businesses were forced to close their doors, particularly in the housing and financial sectors. Unemployment rates also rose significantly, with millions of people losing their jobs.

Governments worldwide took several steps to mitigate the recession’s effects. Some measures included increasing government spending, cutting interest rates, and providing financial assistance to struggling businesses and individuals. The Federal Reserve in the U.S. also intervened by introducing quantitative easing, which helped stabilize the financial markets.

Read: The General Theory of Employment, Interest, and Money: With the Economic Consequences of the Peace (Classics of World Literature)

Despite these efforts, the recovery from the 2008 recession could have been faster and more balanced. As a result, it took several years for many countries to return to pre-crisis levels of economic growth.

The 2008 recession also had a lasting impact on how we think about the economy and the role of government in addressing economic crises. For example, it led to a renewed focus on regulation and oversight of the financial industry.

The crisis led to a sharp decline in economic activity and a significant increase in unemployment. Many banks and financial institutions failed or required government bailouts, leading to a severe credit crunch that further depressed economic activity. Many countries strengthened their safety nets to support those struggling financially.

In conclusion, the 2008 recession was a significant global event that impacted the economy and people worldwide.

Despite the efforts to mitigate the effects of the crisis, the recovery could have been faster and more balanced. Nevertheless, it has led to a renewed focus on regulation and oversight of the financial industry and support.

Economical Feast

 

How Crypto-Assets Are Repairing The Out-Dated Economic Infrastructure

The future is not looking so bright in America; we lack in so many fields when it comes to the economy, and there is no true vision of the future of infrastructure in this country.

This is precisely where Crypto-Assets come into play, considering we immediately started to decline as a country when we cut our ties with gold. Due to greed and those in control, we are disrupting our future of infrastructure just for them to make profits.

Everyone knows these two factors are accurate: those at the top remain at the top because those caught up in their greed give only little to those at the bottom because of their ability to access everything first. The second factor is since the beginning of time, governments tend to always bring their fiat currency to zero.

I wonder what that could be about is the people of the world not paying their taxes? Is it because we spend a lot of money as a country consuming more than what we produce? With everyone pointing the finger at one another, the rich pointing fingers at the poor, and the poor pointing the finger at the rich, no one can see what is going on.

Zoom out and realize the opportunities Blockchain will bring to the table; not only will our digital infrastructure be more robust. Those who have never had the chance of success turn out of the wholes that their governments put them in for the most part.

There are far more things crypto-asset protocols and Blockchain can do for humanity than the fiat currency could ever dream of, but why is it being shunned? It is due to the power these people possess. Please take a look at Cardano and the IOHK team; they made it possible for a place like Ethiopia where only 15% of people who have access to the Internet now have a chance to perform within the educational system. Crypto provides while the current fiat system is getting paid to put people in jail.

Meanwhile, in the United States, we have governments going on live debates getting paid to argue; while they are doing that day in and day out, our infrastructure base is crumbling down. Recently the American Society of Civil Engineers rated our country with a D for our current infrastructure. It has been 30 years since the federal government has even thought about doing anything about the infrastructure for today’s world. Where has the taxpayer’s money gone?

image via: statista

Federal Government spending on water dropped from 63% in 1977 to 9% in 2017. Let us not forget the other areas that have taken hit wastewater utilities, inland waters, and of course, our water. This means our current government has no real experience in infrastructure investing; the proof is in the pudding, watching our economy grow slower and slower.

What if we were to invest in the Blockchain that will not go anywhere for a long, long time. Putting it to use pushes the future forward to grow in size, speed, and strength amongst the communities. Every day ordinary people and wealthy people no longer will have a gap due to power that those at the bottom can not control sometimes. There are no sides, no control, and justice will be served for putting several million people in a suppressed state for decades.

That’s what Blockchain provides, and all the assets that make up the Blockchain also offer for individuals. It won’t guarantee millionaires left and right, but one thing is for sure it will not let you have lead in your water because you do not live on the rich side of town or because you are some politician who thinks printing close to almost 8 trillion dollars in a year is the answer. The answer is and always has been science.