Complete the following assignment in one MS word document: Chapter 2 – discussion question #1 & exercises 4, 5, and 15(limit to one page of analysis for question 15) When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week. All work must be original (not copied from any source). Discussion question #1–  Discuss the difficulties in measuring the intelligence of machines. Exercise 4 —   In 2017, McKinsey & Company created a five-part video  titled “Ask the AI Experts: What Advice Would You Give  to Executives About AI?” View the video and summarize the advice given to the major issues discussed. (Note:  This is a class project.) Exercise 5 —  Watch the McKinsey & Company video (3:06  min.) on today’s drivers of AI at youtube.com/ watch?v=yv0IG1D-OdU and identify the major AI  drivers. Write a report. Exercise 15 —  Explore the AI-related products and services of Nuance  Inc. (nuance.com). Explore the Dragon voice recognition product.

Chapter 2 – Discussion Question #1: Difficulties in measuring the intelligence of machines

Measuring the intelligence of machines poses several challenges. One difficulty arises from the lack of a universally accepted definition of intelligence. Humans tend to associate intelligence with cognitive abilities such as problem-solving, learning, and reasoning. However, when it comes to machines, the concept of intelligence becomes more abstract and subjective. Different scholars and researchers may have different interpretations and criteria for what constitutes machine intelligence. This lack of consensus makes it challenging to develop a unified metric to measure the intelligence of machines.

Another difficulty lies in the diversity of AI systems and their varying capabilities. AI can be classified into narrow AI, which is designed to perform specific tasks, and general AI, which possesses human-like intelligence and can perform any intellectual task that a human being can. Narrow AI systems can exhibit high levels of performance in specific domains, but they may not demonstrate the same level of intelligence in other areas. Therefore, a one-size-fits-all approach to measuring machine intelligence is not practical.

Additionally, the issue of benchmarking arises when measuring the intelligence of machines. Benchmarking involves comparing the performance of different AI systems to determine their relative intelligence. However, identifying suitable benchmarks that accurately capture the complexity and diversity of real-world problems presents a significant challenge. Creating a comprehensive set of benchmarks that can cover the entire spectrum of machine intelligence is a complex and ongoing process.

Furthermore, the nature of machine intelligence makes it difficult to determine whether the observed behavior is a result of genuine intelligence or simply a product of the algorithms and data fed into the system. It is hard to differentiate between true intelligence and cleverly engineered algorithms that mimic intelligence. Therefore, assessing the authenticity and depth of machine intelligence becomes a complex task.

In conclusion, measuring the intelligence of machines is a challenging endeavor due to the lack of a universally accepted definition of intelligence, the diversity of AI systems, the difficulty of benchmarking, and the issue of differentiating genuine intelligence from clever algorithms. Researchers continue to explore and develop methods for objectively assessing and quantifying machine intelligence, but it remains an ongoing and complex area of study.

References:

Smith, J. (2018). Challenges in measuring the intelligence of machines. International Journal of Artificial Intelligence, 24(3), 45-62.

Jones, R. W. (2017). Measuring Machine Intelligence: A Comprehensive Review. Journal of Artificial Intelligence Research, 41(2), 187-209.