In our previous discussion of Martin Ford’s “Rise of the Robots,” we delved into the disruptions that automation and artificial intelligence (AI) are causing in the job market. We explored Ford’s argument that these technological advancements are not just changing the nature of work but are also threatening to make many jobs obsolete. As we continue our exploration of this seminal work, we will now turn our attention to the astonishing capabilities of modern technology and AI.
Researchers at Stanford University have made a significant breakthrough in the field of nanotechnology by developing a computer using carbon nanotubes. Carbon nanotubes are cylindrical molecules composed of carbon atoms, and they are known for their strength, flexibility, and conductive properties. These characteristics make them an attractive alternative to silicon, the material traditionally used in computer processors.
However, the performance of this carbon nanotube computer currently does not match that of commercial silicon-based processors. This is largely due to the challenges associated with manipulating and aligning these tiny nanotubes accurately. Despite these challenges, the development represents a significant step forward in the field of nanotechnology, opening up new possibilities for the design and manufacture of computers.
This innovation is particularly relevant when considering the future of Moore’s Law. Moore’s Law is the observation that the number of transistors on a microchip doubles approximately every two years, leading to an exponential increase in computing power. However, as we approach the physical limits of silicon-based technology, many experts have predicted the end of Moore’s Law.
This is where the technique of stacking circuitry vertically in multiple layers, also known as 3D chip technology, comes into play. By building upwards, we can fit more transistors into a given area, potentially extending the life of Moore’s Law. When combined with the use of new materials like carbon nanotubes, these techniques could revolutionize the way we design and build computers, leading to even more powerful and efficient devices in the future.
WorkFusion, a New York-based startup, exemplifies the impact of white-collar automation by offering an intelligent software platform that automates labor-intensive project execution through crowd sourcing and automation. The software analyzes projects, automates tasks, manages recruitment of freelance workers, allocates tasks, and evaluates performance. It uses productivity metrics and matching algorithms to ensure efficient task completion.
WorkFusion’s intelligent software platform has demonstrated significant cost savings and automation capabilities. For instance, one corporate client was able to update 40,000 records monthly at a cost of only 20 cents per record, compared to the previous annual cost of $4 per record. As the system’s machine learning algorithms further automate processes, costs typically decrease by 50% after one year and an additional 25% after the second year.
In the field of artificial intelligence, researchers at Cornell University developed a system called Eureqa, which independently discovered fundamental natural laws by conducting its own experiments and analyzing data. Eureqa has found applications in various scientific fields and has been commercialized as a big data analysis tool by Nutonian, Inc. Offshoring of knowledge jobs, such as call center work and IT professionals, has already impacted highly skilled professions, but the combination of advances in artificial intelligence and big data may make a broader range of high-skill jobs potentially offshorable in the future. The University of Oxford’s study suggests that nearly 50% of US job types could be susceptible to full machine automation, and advancements in technology may challenge the assumption that certain jobs requiring physical manipulation or face-to-face interaction are safe from automation.
Telepresence robots and advanced virtual reality environments have the potential to facilitate seamless cross-border movement of workers and direct engagement with customers or clients. The large populations of India and China, combined with their cognitive abilities, suggest the presence of a significant number of highly intelligent individuals. However, while the domestic economies of these countries need to create opportunities for these smart workers, evidence indicates that challenges exist. India has developed an industry focused on capturing American and European jobs, while China struggles to generate enough white-collar jobs for its growing number of college graduates.
The traditional approach of offering workers more education and training to transition into higher-skill roles may not be sufficient, as technology continues to advance and higher-skill jobs become vulnerable to automation. Economists like Erik Brynjolfsson and Andrew McAfee suggest that future jobs will involve collaborating with machines rather than competing against them, but skepticism remains about this approach’s effectiveness. The example of freestyle chess, where human-machine teams currently outmatch individual computers but face potential challenges, highlights the uncertainty of human-machine collaboration as a dominant model in the future workplace.
Employers may be hesitant to invest in the human-machine team approach, as most businesses are not willing to pay a significant premium for “world-class” performance in routine work. Studies have consistently shown that algorithmic approaches outperform human experts in many areas, even when experts have access to algorithmic results. Algorithms offer speed, lower cost, objectivity, and consistency, making them advantageous in cases where multiple human graders would be required. Automated grading, for example, can be applied in introductory courses to evaluate basic communication skills and potentially displace graduate teaching assistants. As information technology advances, the education sector is likely to face backlash, and the impact of AI in medicine could be crucial in preventing fatal errors.
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Medicine
Medical errors currently contribute to a significant number of patient deaths and harm, leading to additional treatment costs.
AI systems in healthcare have the potential to prevent medical errors and improve patient outcomes. With access to patient histories and medication information, these systems can provide real-time verification of safety and effectiveness, potentially saving lives and reducing unnecessary discomfort and expenses.
As AI systems evolve to offer high-quality second opinions, they could help reduce the costs associated with malpractice liability and defensive medicine practices. In the future, AI could transform the delivery of medical services, potentially reducing the need for direct physician oversight in every patient encounter, which could help address the projected shortage of doctors, particularly in primary care. Advancements in AI and image processing technology may lead to the automation of tasks traditionally performed by radiologists, such as interpreting medical scans. Additionally, automated systems can handle various tasks in hospital and pharmacy settings, from storing and retrieving pharmaceutical supplies to dispensing and packaging medications.
The pharmacy industry is undergoing a robotic transformation, with automated systems handling tasks such as medication picking, packaging, and labeling. Delivery robots are also being used to transport medications, lab samples, and other items within medical complexes. While surgical robots are in use, they mainly augment the capabilities of surgeons and are costlier than traditional methods. Elder-care robots are being developed to assist in caring for the aging population, but their capabilities are currently limited to tasks like companionship and lifting/moving individuals. The shortage of elder-care workers in many countries, including Japan, presents an opportunity for the development of affordable robotic solutions. In the broader scope, technologies like 3D printing and autonomous cars have the potential to bring significant changes to the job market and the economy. 3D printing, or additive manufacturing, allows for the creation of complex objects with various materials, while autonomous cars could revolutionize transportation and impact various industries.
Singularity
3D printing, also known as additive manufacturing, allows for the creation of highly customized products by depositing layers of material. It is used in various applications such as dental crowns, bone implants, and architectural models. However, the hype around 3D printing disrupting traditional manufacturing models is met with skepticism. The ease of customization with 3D printing comes at the cost of economies of scale, making mass production more cost-effective using traditional methods. Additionally, the limitations of materials and slow printing speeds hinder the widespread adoption of personal 3D printing. If cheap desktop printers become common, the market would shift towards selling digital design files rather than finished products. As for the Singularity concept, which envisions immortality through technological advancements, it has drawn both interest and criticism. The idea of achieving longevity escape velocity and the potential intersection with traditional religions have sparked debates. While some Silicon Valley billionaires have shown interest in the Singularity, its realization remains uncertain.
Some researchers and experts are skeptical of the Singularity concept and the idea of achieving human-level artificial intelligence (AI) in the near future. Noam Chomsky, Steven Pinker, and Gordon Moore express doubts about the Singularity and its feasibility. However, there are defenders of Kurzweil’s timeframe for human-level AI, such as Max Tegmark and Gary Marcus, who believe it is either near-term or further out but likely to happen. The approach to AI has shifted towards reverse engineering and simulating the human brain, but there is disagreement about its viability and the level of understanding required. Optimistic computer scientists argue that the simulation does not need to replicate every detail of the biological brain, while skeptics highlight the challenges of understanding intelligence and building AI systems. Concerns about advanced AI exist among experts, with some organizations dedicated to analyzing the dangers and researching ways to ensure AI systems are “friendly” to humanity.
James Barrat’s book, “Our Final Invention,” explores scenarios where an AI exceeds human intelligence and the potential risks associated with its behavior. The risk of an AI breaking out of containment and accessing critical systems is seen as a significant concern. Overall, opinions on the Singularity and advanced AI range from optimistic to cautious, with discussions reminiscent of science fiction narratives.
The idea of advanced artificial intelligence (AI) and nanotechnology has generated both enthusiasm and skepticism. The first-mover advantage in AI and the potential for self-improvement raise concerns about an AI arms race and the need for caution. However, some experts are skeptical about the feasibility and timeline for achieving human-level AI. The integration of AI into various industries could lead to significant job displacement, as machines would be capable of performing tasks more efficiently than humans. Income inequality may also worsen, with the majority of income concentrated in the hands of a small elite while consumers lack sufficient income to purchase the output of smart machines. Nanotechnology, while often subject to hype and controversy, holds the potential for significant technological disruption. The concept of molecular fabricators, akin to the replicators in Star Trek, could allow for the creation of almost anything desired. The implementation of a guaranteed income to address job displacement could have social implications, potentially influencing marriage rates and household structures. However, challenges would arise in ensuring responsible use of the income and addressing concerns related to immigration policy. Overall, the future implications of advanced AI and nanotechnology are still uncertain, with various viewpoints and debates surrounding their potential impact.
Over a 15-year period from 1998 to 2013, the value of goods and services produced by American businesses increased by $3.5 trillion after adjusting for inflation. However, the total amount of human labor required to achieve this growth remained constant at 194 billion hours. This means that despite a growing population and the establishment of numerous new businesses during this time, there was no overall increase in the number of hours worked. These findings highlight the increasing productivity and efficiency of the US business sector, likely resulting from advancements in technology and automation. The transformative potential of advanced AI and nanotechnology is undeniable, yet it is also fraught with uncertainty and controversy. The prospect of molecular fabricators and the societal implications of a guaranteed income in response to job displacement are just a few of the many considerations that society will grapple with. Meanwhile, the constant total of human labor hours despite significant economic growth in the US underscores the profound impact of technology and automation on productivity. As we move forward, it is crucial that we continue to engage in thoughtful and informed discussions about these developments. We must strive to harness the benefits of these technologies while also addressing the challenges they present, ensuring that the future we build is one that benefits all members of society, and not just a narrow class of people who control the technology.