How to think Non-Likear

How to think NON-Likear PDF

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