AI CEO Argues for UBI through AI, But Experts Disagree

Table of Contents

1. Introduction
2. Piaggio Fast Forward and Trimble Collaboration
3. Robots Following Construction Workers
   • Pilot Tests in Colorado
   • Platooning: Group Travel
   • Assessing Optimal Number of Robots
4. Negotiating Global Agreements for AI in Warfare
   • Importance of Early Negotiations
   • Key Principles for Agreements
5. Bakery Scan AI and Cancer Detection
   • Bakery Scan for Medical Use
   • Scanning Cancerous Cells
6. Flaws in the Idea of Mind as Computation
   • Meaning and Intentionality of Mental States
   • Limitations of Symbolic Computation
7. Argument for Strict AI Regulations
   • Overhyped AI Capabilities
   • Current Limitations of AI Systems
8. OpenAI's GPT-3 and Its Applications
   • Use in 300 Apps
   • Output Quality Concerns
9. Criticism of Universal Basic Income from AI
   • Sam Altman's Concept
   • Potential Harms and Criticisms
10. Possible Deepfake in Myanmar's Military

Robots That Follow Construction Workers

Piaggio Fast Forward, in collaboration with industrial technology services provider Trimble, has made advancements in using the Boston Dynamics API to Create robots that can follow construction workers. The pilot tests for these robots took place at an office building under construction in Colorado. The robots are capable of traveling in groups, known as "platooning," with one Spot Mini and two Guitar robots. However, it is important to assess human attitudes towards the number of robots that can safely follow a human on a construction site. The concern is not only about the robot's safety but also about the optimal number of robots before the situation becomes unusual or overwhelming. According to Piaggio Fast Forward CEO Greg Lynn, the company believes it could support a convoy of 50 to 100 robots, but implementing such a large number would be impractical.

Piaggio Fast Forward and Trimble Collaboration

Piaggio Fast Forward, a robotics company, has collaborated with Trimble, an industrial technology services provider, to develop robots that can follow construction workers. By utilizing the Boston Dynamics API, these robots have been given the ability to navigate construction sites and mimic the movements of humans. This collaboration aims to improve efficiency and safety in the construction industry by leveraging the capabilities of both companies.

Robots Following Construction Workers

The pilot tests for the robots took place at an office building under construction in Colorado. The robots, including one Spot Mini and two Guitar robots, demonstrated their ability to follow construction workers. This functionality is referred to as "platooning," where multiple robots can travel together in a group. By working in harmony with humans, these robots have the potential to streamline construction processes and reduce the risk of accidents.

Pilot Tests in Colorado

The pilot tests conducted in Colorado were successful in showcasing the robots' capabilities. They were able to navigate the construction site and follow the paths of human workers effectively. The tests also provided valuable insights into the practicality and scalability of using robots in construction environments.

Platooning: Group Travel

One fascinating feature of these robots is their ability to travel in groups, known as platooning. In this configuration, one Spot Mini robot leads the group, being closely followed by two Guitar robots. By traveling in this formation, the robots can work together to assist construction workers and optimize efficiency.

Assessing Optimal Number of Robots

While robots have the potential to enhance productivity in the construction industry, it is crucial to consider the optimal number of robots that can safely and effectively follow a human worker. Besides the concern for the robots' safety in proximity to heavy machinery operated by humans, there is also a question of how many robots can be involved before the situation becomes overwhelming or unnatural. Piaggio Fast Forward CEO Greg Lynn acknowledges that the company could support a convoy of 50 to 100 robots. However, he also recognizes the challenges of implementing such a large number of robots and the potential impracticality of doing so.

Negotiating Global Agreements for AI in Warfare

As AI capabilities Continue to advance rapidly, there is a growing need for global agreements that govern the use of AI during warfare. Brooking argues that the time is ripe to negotiate these agreements to ensure ethical and responsible use of AI in the Context of military operations. Similar to early treaties on nuclear, biological, and chemical weapons after World War II, these agreements should focus on several key principles.

Importance of Early Negotiations

Early adoption and adaptation of AI and emerging technologies in warfare necessitate the establishment of global agreements to govern their conduct. It is crucial to initiate these discussions and reach Consensus before AI capabilities become fully deployed and embedded in military planning. By acting proactively, there is an opportunity to Shape the future use of AI in warfare and safeguard against potential abuses or unintended consequences.

Key Principles for Agreements

The agreements governing AI in warfare should incorporate ethical principles that prioritize human rights, accountability, and civilian protection. It is vital to keep humans in the loop when it comes to autonomous weapons systems to ensure a level of control and responsibility. Additionally, adopting a norm of not having AI algorithms within nuclear operational command and control systems can help mitigate the risks associated with AI's potential to cause catastrophic damage. Enhancing transparency on the safety of AI-Based weapons systems and developing effective oversight mechanisms can further ensure compliance with international agreements.

Bakery Scan AI and Cancer Detection

The Bakery Scan AI, developed by Brain Company, originally designed for assisting bakeries, has been adapted for medical purposes. While Baker Scan was initially designed to recognize different pastries, it has proven effective in scanning small microscope slides for cancerous cells. The system utilizes deep learning for object recognition and measures cell nuclei to identify potential cancer cells accurately.

Bakery Scan for Medical Use

The Bakery Scan AI system, equipped with deep learning algorithms, has been repurposed for medical use. Instead of scanning pastries, it can now analyze microscope slides to help identify cancerous cells. This adaptation demonstrates the versatility of AI and its potential in various fields, including healthcare.

Scanning Cancerous Cells

The system scans microscope slides and identifies cells, specifically focusing on the measurement of the cell nucleus. By analyzing individual cells and the arrangement of their nuclei, the Bakery Scan AI can identify potential cancer cells. This technology can aid in the early detection and diagnosis of cancer, improving patient outcomes and potentially saving lives.

Flaws in the Idea of Mind as Computation

The idea that the mind can be explained solely as a form of computation is flawed, according to an argument put forth by Mind Matters. The author highlights the inherent limitations of understanding mental states as symbolic computations.

Meaning and Intentionality of Mental States

All mental states have meaning; they are intentional and carry significance. Computation, understood as the manipulation of symbols, inherently lacks intentionality. A computer can match one set of electron configurations to another, but this does not provide intrinsic meaning. Similarly, a map of a city presupposes the existence of the city itself, making a map an inadequate representation of the city. The same applies to the pattern of electrons on a screen, which presupposes thought but does not encompass its essence.

Limitations of Symbolic Computation

The author argues that because thoughts cannot be reduced to symbols, thinking cannot be equated to computation. While computers can perform algorithmic decision-making tasks, they lack the capacity for genuine thought. The inherent limitations of symbolic computation prevent it from capturing the depth and complexity of human thought and consciousness.

Argument for Strict AI Regulations

Artificial intelligence researchers argue that strict regulations should be imposed on AI development at this stage due to the technology's limitations and potential risks. CNBC reports on the perspective of Neil Lawrence, an AI professor at the University of Cambridge and former Amazon machine learning executive.

Overhyped AI Capabilities

Many areas of AI have been overhyped, and the current capabilities of AI are limited to narrow tasks. The development of artificial general intelligence (AGI) that matches human intelligence is still far from realization. Lawrence emphasizes the need for a realistic assessment of AI's current capabilities to avoid inflated expectations and misplaced regulatory decisions.

Current Limitations of AI Systems

AI systems are currently limited to performing specific algorithmic decision-making tasks. While they excel in these designated areas, they lack the versatility and adaptability of human intelligence. Strict regulations can prevent premature deployment of AI systems with capabilities beyond their current limitations, reducing the risks associated with overdependence on AI.

OpenAI's GPT-3 and Its Applications

OpenAI's natural language processing model, GPT-3, has gained popularity and widespread adoption in various applications. The Verge reports on the extensive use of GPT-3 across 300 apps, highlighting its significant impact in generating text content.

Use in 300 Apps

GPT-3 has found applications in approximately 300 different apps. Its powerful natural language processing capabilities make it a valuable tool for generating human-like text, assisting with tasks such as writing articles, creating conversational chatbots, and more. The versatility of GPT-3 contributes to its popularity among developers and users alike.

Output Quality Concerns

While GPT-3 has revolutionized text generation, there are concerns about the quality and reliability of its output. Just like any algorithm, text generators have the potential to absorb and amplify harmful biases. Furthermore, the open-ended nature of GPT-3 can produce erroneous or misleading content if not carefully monitored and guided. It is essential to ensure mechanisms for quality control and preventing the dissemination of misinformation or harmful narratives.

Criticism of Universal Basic Income from AI

Sam Altman's concept of a universal basic income (UBI) funded by the worth generated by AI machines is facing criticism from industry sources. CNBC interviews several experts who question the feasibility and potential harms of Altman's proposal.

Sam Altman's Concept

Altman envisions a future where AI technologies generate enough wealth to provide each individual with $13,500 per year. The idea involves governments collecting and redistributing the monetary value created by AI machines as they replace human jobs. This concept aligns with similar proposals made by figures like Stephen Hawking. However, not everyone agrees with this approach.

Potential Harms and Criticisms

Critics argue that Altman's concept of UBI could be unreasonable, misleading, or even cause more harm than good. The redistribution of wealth through AI taxation raises concerns about the unintended consequences on the economy and job market. Additionally, the idea of an all-powerful AI entity running every non-AI company out of business and leading to widespread unemployment raises questions about the concentration of power and potential social implications.

Possible Deepfake in Myanmar's Military

There are speculations and concerns about the authenticity of a video released by Myanmar's military. The video features Theo Minge, a detained former chief minister, seemingly confessing to offering bribes. Experts and observers familiar with Theo's appearance question the integrity of the video, suggesting that deepfake technology may have been employed to alter the images.

The video's low-quality resolution makes it difficult to definitively determine whether deepfake technology was used. Some speculate that the military deliberately released a low-quality version to evade detection. The utilization of deepfakes in this manner raises concerns about the potential manipulation of facts and the erosion of trust in digital media.

Highlights

• Piaggio Fast Forward and Trimble collaborate on creating robots that can follow construction workers, enhancing productivity and safety.
• Global agreements are needed to govern the ethical use of AI in warfare, incorporating principles of human rights and accountability.
• Bakery Scan AI, repurposed for medical use, shows promise in detecting cancerous cells on microscope slides.
• Symbolic computation falls short in explaining the complexities of the human mind and consciousness.
• Strict regulations on AI development at this stage are crucial to mitigate risks and manage inflated expectations.
• OpenAI's GPT-3 is widely used in various applications but raises concerns about output quality and bias amplification.
• Criticisms of universal basic income funded by AI-generated wealth include feasibility issues and potential social impacts.
• Speculations about a deepfake video in Myanmar's military highlight concerns about the manipulation of digital media.