In the past 2 years, 5 EV companies have lost 800 billion dollars of their market cap!
Today, the aggregate market cap of the top 60 companies is $2.229 T.
Long-term investors in TSLA, NIO, RIVN, LCID, and XPEV have lost money equal to Toyota, Porsche, BYD, Mercedes-Benz, BMW, Volkswagen, Ford, and Ferrari combined!
The market always punishes people for FOMO..!
The AI hype will have the same future, I have no doubt!
Compare the NVDA balance sheet with TSLA and you will see what you should visit!
If you have doubts check how many SHORT Analyses I have published in the past 2 yeas for these tickers!
I have published 122 short analyses in the past 30 months for the top-mentioned EV makers!
Today, the aggregate market cap of the top 60 companies is $2.229 T.
Long-term investors in TSLA, NIO, RIVN, LCID, and XPEV have lost money equal to Toyota, Porsche, BYD, Mercedes-Benz, BMW, Volkswagen, Ford, and Ferrari combined!
The market always punishes people for FOMO..!
The AI hype will have the same future, I have no doubt!
Compare the NVDA balance sheet with TSLA and you will see what you should visit!
If you have doubts check how many SHORT Analyses I have published in the past 2 yeas for these tickers!
I have published 122 short analyses in the past 30 months for the top-mentioned EV makers!
Comment:
A comparison between TSLA and NVDA
Comment:
I read all the comments and most people believe there is a big difference between AI and EV, which is true. But my main point is not the difference between the two but the similarities between their price patterns!
George Soros says:
Stock market bubbles don't grow out of thin air. They have a solid basis in reality, but reality as distorted by a misconception.
George Soros says:
Stock market bubbles don't grow out of thin air. They have a solid basis in reality, but reality as distorted by a misconception.
Comment:
AI (Artificial Intelligence) and AGI (Artificial General Intelligence) are related terms but have distinct meanings.
AI refers to the broad field of creating computer systems or software that can perform tasks that would typically require human intelligence. AI systems are designed to simulate intelligent behavior, make decisions, and solve problems. However, AI can be further divided into two categories: Narrow AI and General AI.
Narrow AI, also known as Weak AI, is designed to perform specific tasks within a limited domain. Examples include voice assistants like Siri and Alexa, recommendation systems, and image recognition algorithms. Narrow AI systems excel at specific tasks but lack a broader understanding or capability beyond their designated functions.
On the other hand, AGI, or Artificial General Intelligence, refers to highly autonomous systems that possess the ability to understand, learn, and apply knowledge across various domains, similar to human intelligence. AGI would have the capacity to perform any intellectual task that a human being can do. It would exhibit general problem-solving skills, reasoning, learning, creativity, and even self-awareness.
The key distinction between AI and AGI is that AI focuses on building systems that excel at specific tasks, whereas AGI aims to develop systems that possess a level of intelligence and versatility comparable to human beings. AGI represents a significant leap forward, as it requires machines to exhibit a more comprehensive understanding of the world and the ability to transfer knowledge and skills from one domain to another.
While AI is already widely employed in various applications today, AGI remains a theoretical concept. Building AGI is a complex and ambitious endeavor, as it involves not only replicating human-like intelligence but also addressing challenges related to consciousness, common-sense reasoning, and ethical considerations.
In summary, AI encompasses a broad range of computer systems that can perform tasks requiring human intelligence, while AGI specifically refers to the development of machines capable of human-level intelligence and versatility across multiple domains.
AI refers to the broad field of creating computer systems or software that can perform tasks that would typically require human intelligence. AI systems are designed to simulate intelligent behavior, make decisions, and solve problems. However, AI can be further divided into two categories: Narrow AI and General AI.
Narrow AI, also known as Weak AI, is designed to perform specific tasks within a limited domain. Examples include voice assistants like Siri and Alexa, recommendation systems, and image recognition algorithms. Narrow AI systems excel at specific tasks but lack a broader understanding or capability beyond their designated functions.
On the other hand, AGI, or Artificial General Intelligence, refers to highly autonomous systems that possess the ability to understand, learn, and apply knowledge across various domains, similar to human intelligence. AGI would have the capacity to perform any intellectual task that a human being can do. It would exhibit general problem-solving skills, reasoning, learning, creativity, and even self-awareness.
The key distinction between AI and AGI is that AI focuses on building systems that excel at specific tasks, whereas AGI aims to develop systems that possess a level of intelligence and versatility comparable to human beings. AGI represents a significant leap forward, as it requires machines to exhibit a more comprehensive understanding of the world and the ability to transfer knowledge and skills from one domain to another.
While AI is already widely employed in various applications today, AGI remains a theoretical concept. Building AGI is a complex and ambitious endeavor, as it involves not only replicating human-like intelligence but also addressing challenges related to consciousness, common-sense reasoning, and ethical considerations.
In summary, AI encompasses a broad range of computer systems that can perform tasks requiring human intelligence, while AGI specifically refers to the development of machines capable of human-level intelligence and versatility across multiple domains.
Comment:
The history of AI and AGI can be traced back to different periods, each with its own milestones and advancements. Here is a brief overview of their histories:
AI:
- 1940s-1950s: The foundations of AI were laid during this period. Early pioneers like Alan Turing and John McCarthy proposed ideas related to machine intelligence and the possibility of creating machines that could exhibit human-like intelligence.
- 1956: The term "Artificial Intelligence" was coined, and the field of AI was established as a distinct research discipline. The Dartmouth Conference, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, brought together leading researchers to explore AI.
- 1950s-1960s: Early AI research focused on symbolic or rule-based AI systems. Programs were developed to solve problems using logical rules and symbols, such as the Logic Theorist and General Problem Solver.
- 1980s-1990s: AI experienced a period of significant growth, with advancements in areas such as expert systems, natural language processing, and machine learning. Expert systems attempted to capture human expertise in specific domains, while machine learning algorithms were developed to enable computers to learn from data.
- 2000s-2010s: AI technologies started gaining widespread adoption in various domains, including voice recognition, image recognition, recommendation systems, and autonomous vehicles. Machine learning, particularly deep learning algorithms, revolutionized AI by enabling the training of neural networks with large datasets.
- Present: AI continues to advance rapidly, with breakthroughs in areas like reinforcement learning, natural language processing, computer vision, and robotics. AI is being used in diverse applications, ranging from healthcare and finance to entertainment and smart home devices.
AGI:
- The concept of AGI emerged as a distinct idea within the broader field of AI.
- Early discussions about AGI date back to the 1960s when researchers like Marvin Minsky envisioned machines with general intelligence.
- However, developing AGI has proven to be an immensely challenging task due to the complexity and breadth of human intelligence.
- While significant progress has been made in specific AI domains, achieving AGI remains an ongoing and ambitious goal. There is ongoing research and exploration in areas like cognitive architectures, computational neuroscience, and integrative AI approaches that aim to move closer to AGI.
It's important to note that AGI, as a fully autonomous and versatile form of intelligence, has not been achieved yet, and there is ongoing debate and speculation about the timeline and feasibility of achieving AGI.
AI:
- 1940s-1950s: The foundations of AI were laid during this period. Early pioneers like Alan Turing and John McCarthy proposed ideas related to machine intelligence and the possibility of creating machines that could exhibit human-like intelligence.
- 1956: The term "Artificial Intelligence" was coined, and the field of AI was established as a distinct research discipline. The Dartmouth Conference, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, brought together leading researchers to explore AI.
- 1950s-1960s: Early AI research focused on symbolic or rule-based AI systems. Programs were developed to solve problems using logical rules and symbols, such as the Logic Theorist and General Problem Solver.
- 1980s-1990s: AI experienced a period of significant growth, with advancements in areas such as expert systems, natural language processing, and machine learning. Expert systems attempted to capture human expertise in specific domains, while machine learning algorithms were developed to enable computers to learn from data.
- 2000s-2010s: AI technologies started gaining widespread adoption in various domains, including voice recognition, image recognition, recommendation systems, and autonomous vehicles. Machine learning, particularly deep learning algorithms, revolutionized AI by enabling the training of neural networks with large datasets.
- Present: AI continues to advance rapidly, with breakthroughs in areas like reinforcement learning, natural language processing, computer vision, and robotics. AI is being used in diverse applications, ranging from healthcare and finance to entertainment and smart home devices.
AGI:
- The concept of AGI emerged as a distinct idea within the broader field of AI.
- Early discussions about AGI date back to the 1960s when researchers like Marvin Minsky envisioned machines with general intelligence.
- However, developing AGI has proven to be an immensely challenging task due to the complexity and breadth of human intelligence.
- While significant progress has been made in specific AI domains, achieving AGI remains an ongoing and ambitious goal. There is ongoing research and exploration in areas like cognitive architectures, computational neuroscience, and integrative AI approaches that aim to move closer to AGI.
It's important to note that AGI, as a fully autonomous and versatile form of intelligence, has not been achieved yet, and there is ongoing debate and speculation about the timeline and feasibility of achieving AGI.
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Comment: