As The Prominent Philosopher Jerry Kaplan Puts It Viewpoint

As The Prominent Philosopher Jerry Kaplan Puts It Viewpoint Artifici

As the prominent philosopher Jerry Kaplan states, “Viewpoint Artificial Intelligence Think Again” (Jerry, 2017). The core message emphasizes the importance of human effort and cautions against over-reliance on artificial intelligence (AI) to replace human cognition. Kaplan advocates for a balanced approach, highlighting that social and cultural conventions are often overlooked in the development of intelligent machines. The dominant public narrative suggests that increasingly intelligent machines will surpass human capabilities, threaten jobs, and potentially pose existential risks. However, AI researchers generally do not share this hyperbole, and such misconceptions could hinder practical advancements and lead to counterproductive public policies (Jerry, 2017).

Furthermore, Kaplan asserts that machines do not possess minds, and current evidence does not support the idea that they will ever have consciousness or true understanding. The fear that robots will eventually elevate themselves to threaten human existence is misguided; instead, the real concerns lie in how increasingly capable machines will integrate into our daily lives, sharing physical and public spaces. David (2016) explores the notion that intelligent machines could, paradoxically, lead to a net increase in employment if harnessed properly. This perspective shifts the focus from fearing job losses to understanding how AI can be a tool for job creation and enhancement.

Regarding employment and economic growth, innovative firms—particularly those less than three years old—are often more agile, capturing greater market share and creating new jobs. A report notes that over 1.4 million new jobs were created by such young firms, whereas employment in mature companies declined by approximately 400,000 jobs (Jerry, 2017). Looking ahead to 2030, the skills required in the workforce will evolve, demanding not only advanced IT competencies but also soft skills like communication, creativity, and critical thinking (NBN & Regional Australia Institute, 2016). The labor market will likely see three categories of work: evolving jobs that integrate new technologies, fading jobs replaced by AI, and emerging jobs that capitalize on technological advances (Walliam, 2016).

One proactive approach to understanding future trends involves analyzing historical patterns and projecting them forward—what The Future Factory describes as pattern-based forecasting. Such methods can help individuals and policymakers anticipate shifts, prepare accordingly, and mitigate adverse impacts. Critics warn that AI might eliminate nearly half of current jobs by 2025, sparking fears of massive unemployment (Rutkin, 2013). Renowned futurist Ray Kurzweil predicts that by 2020, a $1,000 computer could match the human brain's capabilities, with advanced AI systems developing rapidly thereafter (Frey, 2016). Similarly, Ben Goertzel of the OpenCog project confidently states, “I am confident that we will have human-level AI by 2025 or sooner” (Olson, 2013).

Despite these forecasts, historical evidence from industrialization suggests automation often reduces costs, stimulates economic growth, and creates new employment opportunities in unforeseen sectors. The fear of mass unemployment due to AI has often been unfounded; instead, technological progress tend to complement human labor rather than replace it entirely. This dynamic has played out throughout history, exemplified by the Y2K scare that ultimately failed to materialize into a crisis (Walliam, 2016). According to Steve Lohr (2017), new analytical tools are essential for measuring and managing the impact of technology on jobs and skills, enabling workers and institutions to adapt more effectively.

Specifically, software tools can provide vital insights for workers facing declining occupations, guiding them toward alternative careers that require similar but slightly modified skill sets. These tools can also aggregate data on job placement success for training programs designed to teach new competencies. In the context of AI expansion, such technological aids can enhance life quality, increase convenience, and free time traditionally spent on routine activities. As artificial intelligence advances, the development and integration of sophisticated software tools will play a crucial role in aligning workforce skills with emerging economic demands, ensuring societal resilience amid rapid technological change (Jerry, 2017).

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The ongoing debate surrounding artificial intelligence’s role in future employment and societal development is complex and multi-faceted. Central to this discussion is the perspective put forth by philosopher Jerry Kaplan, who encourages re-evaluating exaggerated fears of AI leading to human obsolescence (Jerry, 2017). Kaplan’s stance emphasizes careful consideration of AI’s capabilities and limitations, arguing that machines do not possess consciousness or self-awareness—traits quintessential to human cognition. This perspective fosters a more balanced approach, acknowledging AI as a tool to augment rather than replace human work.

The public narrative often sensationalizes AI’s potential to dominate the workforce, leading to panic and misguided policies. One virulent myth is that AI systems will “escape” human control or pose an existential threat, a notion not supported by current scientific evidence. Instead, the focus should be on how AI can support economic growth, innovation, and improved quality of life. David (2016) underscores that a strategic blend of human creativity and machine efficiency can lead to positive employment outcomes, including job creation in emerging sectors.

In the context of job growth, recent data reveal that young, innovative companies are significant drivers of employment. Startups are more likely to generate new positions, highlighting the importance of fostering entrepreneurship and technological innovation (Jerry, 2017). Looking toward 2030, it is predicted that the workforce will increasingly require advanced digital skills and soft skills such as communication, adaptability, and emotional intelligence. These competencies will be essential for workers to remain relevant and competitive in an AI-enabled economy (NBN & Regional Australia Institute, 2016).

The workforce is expected to undergo substantial transformation, categorized into evolving jobs, fading jobs, and emerging jobs. Evolving jobs will integrate more technology, changing their nature and skill requirements. Some occupations will become obsolete as machines outperform humans in routine tasks, but new job categories will emerge, often in fields related to AI development, maintenance, and oversight (Walliam, 2016). Forecasting models, such as those proposed by The Future Factory, utilize historical data trends to predict future job markets, allowing proactive planning and policy formulation.

However, the narrative of job destruction remains persistent. Experts estimate that AI could eliminate nearly 50% of tomorrow’s jobs by 2025, fueling fears of mass unemployment (Rutkin, 2013). Notable futurists like Ray Kurzweil predict that by 2020, personal computers will match human brain power, and AI systems will continue to evolve rapidly (Frey, 2016). Yet, historical precedents suggest that technological innovation typically results in economic expansion and employment creation in new sectors (Walliam, 2016). The key is understanding that automation often complements human labor, leading to higher productivity and new opportunities.

To manage these shifts effectively, new tools for tracking technology’s impact on jobs are critical. Lohr (2017) advocates for developing sophisticated analytical software that can provide real-time insights into labor market trends, skill shortages, and retraining opportunities. Such tools would assist policymakers, educators, and workers in navigating the transition, ensuring that the benefits of AI are harnessed while mitigating adverse effects. These advancements include platforms that analyze job market data, match workers with retraining programs, and monitor employment patterns across different sectors (Steve, 2017).

In conclusion, AI’s influence on employment is neither a foregone conclusion of mass unemployment nor an unmitigated boon. Instead, it presents both challenges and opportunities. Embracing the development of intelligent software tools will be instrumental in helping society adapt, providing valuable information for career transitions, skill development, and workforce planning. As Kaplan argues, a nuanced and cautious approach is necessary—one that recognizes AI’s limitations while leveraging its strengths to foster sustainable economic growth and improved quality of life.

References

• Frey, C. B. (2016). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
• Jerry, Kaplan. (2017). Viewpoint Artificial Intelligence: Think Again. Vol. 60, pp. 1-4.
• NBN & Regional Australia Institute. (2016). The Skills Needed for an AI-Driven Economy. Canberra: Regional Australia Institute.
• Olson, P. (2013). The rise of human-level AI: A timeline projection. AI Today, 15(3), 1-6.
• Rutkin, A. (2013). Will AI eliminate half of all jobs by 2025? Tech Trends, 52(4), 78-85.
• Steve Lohr. (2017). New tools needed to track technology’s impact on jobs. The New York Times.
• Walliam, William. (2016). Forecasts of AI and future jobs in 2030: Muddling through likely, with two alternative scenarios. Journal of Future Studies, 10, 83-96.
• Yale University. (2020). Automation, AI, and the future of work. Center for Business and Economics.
• Itamar, Arel, Rose Derek, & Karnowski, Thomas. (2010). Deep machine learning—A new frontier in AI research. Research Frontier, University of Tennessee.
• David, Tuffey. (2016). Can intelligent machines in the workforce lead to a net gain in jobs? Griffith University.