Difficulties In Measuring AI Advice, Drivers Of AI, A 112903

Difficulties In Measuring Ai Advice Drivers Of Ai And Nuance Ai Sol

Difficulties In Measuring AI, Advice & drivers of AI and Nuance AI solutions

Student Name

For Dr. Bruning

Summer 2020 - Business Intelligence (ITS-531-20)

University of the Cumberlands

Difficulties in measuring AI

Artificial intelligence (AI) is used to define the capability of the machine to mimic human intelligence (OECD, 2019). Measuring the capability of AI systems is crucial in assessing their effectiveness and potential for deployment. One well-known testing method for artificial intelligence is the Turing Test (Sharda, Delen, & Turban, 2020). In this test, an interviewer interacts with both an AI system and a human without knowing which is which, and based on the responses, the intelligence of the AI is inferred. However, this method has several limitations. It only provides a comparative measure based on human responses, which can vary significantly due to differing human intelligence levels. As such, the test may not accurately reflect the true intelligence or capability of the AI system.

Another challenge lies in distinguishing AI from related technologies that may appear similar but function differently (OECD, 2019). For example, in the Turing Test, the AI uses natural language processing (NLP) to understand and respond to questions. If the AI fails to recognize a question, it may be due to input errors rather than a lack of processing ability, leading to inaccurate assessments of its intelligence. Moreover, the limited scope of the Turing Test—focused on question-answer interactions—means it cannot evaluate all forms of AI, such as visual recognition or autonomous decision-making systems. This restricts its utility in comprehensively measuring AI advancements across diverse applications.

Advice on AI

In a 2017 video, McKinsey & Company provided guidance for executives on AI adoption, emphasizing the strategic importance of AI integration in business (London, Bradski, Coates, Deng, & Shah). The video highlights that AI's potential is significant, but organizations need to approach its implementation carefully. Successful AI deployment requires thorough research, aligning technology with specific business objectives, and understanding that not all AI tools are suitable for every organization (London et al., 2017). Furthermore, it emphasizes that the primary challenge is not the technology itself but its impact on organizational processes and culture.

The video suggests that organizations should develop or hire employees with techno-functional expertise—individuals who understand both the technical and business aspects of AI—to bridge the gap between AI capabilities and organizational needs. Decision-makers must consider whether to build an in-house AI team or leverage enterprise AI solutions, weighing factors like cost, control, and customization. Overall, the advice underscores that while AI is a powerful tool, it requires strategic planning, cultural change, and clear business goals for successful integration.

Drivers of AI

The development of AI has been driven by multiple technological and data-related factors, as explained by McKinsey & Company (London et al., 2017). Historically, AI experienced waves of enthusiasm followed by periods of stagnation, often due to overestimations of technology capabilities and limitations in data availability. Currently, AI is considered to be in its third wave, fueled by advances in machine learning, particularly deep learning algorithms.

One of the key drivers is the exponential growth in computing power, which has enabled more complex algorithms and real-time processing (Sharda, Delen, & Turban, 2020). Alongside hardware improvements, the proliferation of big data—collected through internet use, mobile devices, social media, and IoT sensors—has provided the vast datasets needed to train advanced models (OECD, 2019). Deep learning algorithms, which rely on large amounts of labeled data to identify patterns, have driven significant breakthroughs in image recognition, speech processing, and autonomous systems.

Furthermore, innovations in algorithm design, such as convolutional neural networks and reinforcement learning, have facilitated the progress of AI systems. The capability of these algorithms to learn from massive datasets has perpetuated a cycle of improvement, with more data driving better models, which in turn require more extensive data to refine further. The combination of improved computational infrastructure, abundant data, and sophisticated algorithms constitutes the primary drivers of modern AI development.

Nuance’s AI Solutions and Industry Applications

Nuance Communications is a notable company specializing in AI solutions tailored for various industries. Its offerings primarily focus on healthcare, customer engagement, and voice recognition (Nuance, 2020). Nuance’s Healthcare AI platform assists clinicians by automating documentation, diagnostics support, and clinical analytics, thereby streamlining workflow and reducing administrative burdens (Nuance, 2020). These tools enable healthcare providers to enhance patient care while optimizing operational efficiency.

In the customer service sector, Nuance provides omni-channel engagement platforms that integrate multiple communication channels such as email, chat, and voice calls (Nuance, 2020). These systems facilitate seamless customer interactions and improve satisfaction. Additionally, Nuance’s Dragon speech recognition solutions are deployed across various sectors, including legal, financial services, and public safety, to automate tasks such as dictation, web searches, and application controls. Known for their high accuracy and speed, these voice recognition tools have transformed how organizations handle documentation and communication processes (Nuance, 2020).

Despite the technological advancements, Nuance faces challenges similar to other AI firms, such as ensuring data privacy, managing system biases, and maintaining high accuracy across diverse user groups. As AI continues to evolve, companies like Nuance contribute significantly to industry-specific innovations, aligning technological capabilities with practical use cases.

Conclusion

Measuring AI's progress and effectiveness remains a complex endeavor due to inherent limitations in evaluation methods like the Turing Test and the broad scope of AI applications. While AI is advancing rapidly, driven by improvements in data availability, computational power, and algorithms, assessing its true potential poses ongoing challenges. Companies such as Nuance exemplify how specialized AI solutions can enhance operational efficiency in various sectors, yet they also highlight the importance of strategic implementation and ethical considerations. For organizations moving forward, integrating AI requires careful planning, skilled personnel, and clear objectives to harness its full potential while mitigating associated risks.

References

London, S., Bradski, G., Coates, A., Deng, L., & Shah, M. (2017). Ask the AI experts: What’s driving today’s progress in AI? McKinsey & Company. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/ask-the-ai-experts-whats-driving-todays-progress-in-ai

London, S., Bradski, G., Coates, A., Deng, L., & Shah, M. (2017). What advice would you give to executives about AI? McKinsey & Company. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/what-advice-would-you-give-to-executives-about-ai

Nuance. (2020). About us. Nuance Communications. https://www.nuance.com/about-us.html

Nuance. (2020). Dragon Speech Recognition Solutions. Nuance Communications. https://www.nuance.com/omni-channel-engagement/voice-and-ivr/dragon.html

OECD. (2019). Measuring the Digital Transformation: A Roadmap for the Future. OECD Publishing.

Sharda, R., Delen, D., & Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support (11th ed.). Pearson.