1. Jeopardy is not an excellent way of testing machine intelligence because the problem with jeopardy is that it is comparing machine intelligence to human intelligence. Humans use emotions and intuition in solving a problem whereas Watson is only functioning based on data. Therefore, the comparison is not fair and may cause the problem of comparing apples to oranges. Watson lacks the ability to empathize and make judgment calls like human beings (Hadhazy, 2011). However, there are many benefits of using Watson for data intensive purposes which do not require human touch as it is cost effective and manages to store significant amounts of information.
2. Watson is a close enough example of a computer system demonstrating intelligence that is in close similarity to humans. Human intelligence goes beyond answering questions like Jeopardy using some data that has been fed into the system. Watson has accessibility to 200 million pages of information and it can also process 500 gigabytes of data, but Watson lacks many aspects of human behavior such as thinking, feeling, cognition and language which is why is it not the best example to demonstrate intelligence similar to human intelligence. While Watson uses 80,000 watts, the human mind only uses 20 watts (Laudon & Laudon, 2013). Thus, it is subject to error. However, Watson is a superior product of artificial intelligence which closely replicates the human mind and helps in coming to answers of questions, but one problem with Watson is that it shows deficiencies when it comes to comprehending clues which lead to human beings generating responses at a faster rate as compared to Watson. Thus, Watson is a close similar but not a perfect example as it is hard to replicate human intelligence.
3. The key issue with the case is that Watson is tested on Jeopardy in comparison to human intelligence. An alternative course of action could be testing Watson in other avenues where it is comparable to other machines of artificial intelligence. The best use of Watson would be in customer service systems that require specialized product knowledge as Watson can store large amounts of information without losing it, as opposed to the human mind. (WHY)
Moreover, in data-intensive cases, Watson can come up with solutions quicker than the human mind. However, owning a system like Watson can cost a lot of money, and the company should be able to make such massive investment. In addition to purchasing the system, the business would also have to invest in entering information, training and programming Watson to extract the right meaning from the information and respond to questions asked in human language. Therefore, it is better to use Watson in cases which require a lot of quick processing of data where soft skills such as emotions and intuitiveness are not necessary. Watson has the capability to cater to customer needs by responding quickly to the demands of the customers and ensuring quick information processing to serve their needs (Littleton, 2016).
4. An alternative use for Watson could be in industries which are heavily reliant on information technology and are highly information intensive. Industries which store a large amount of information in human language form can also benefit from using Watson. These industries can include pharmaceutical, call centers and financial services (Rennie, 2013).
Watson can also successfully be used in the process of medical diagnosis where access to a lot of information and data is required. Another useful application of Watson can be expected in science and technology facilities as they are also using big data analytics for complex business problems (Mercer, 2016).
Lastly, Watson can also be introduced in the realm of cognitive applications in the future. These cognitive applications can be helpful in the field of clinical psychology where it can aid in understanding human behavioral patterns and identify their symptoms to be used for diagnosis of any psychological disorders.
Hadhazy, A. (2011). IBM’s Watson: Can a computer outsmart a Jeopardy! Brainiac? Retrieved on 30th March 2017 from http://www.csmonitor.com/Science/2011/0214/IBM-s-Watson-Can-a-computer-outsmart-a-Jeopardy!-braniac
Laudon, K. C., & Laudon, J. P. (2013). Management Information Sytems 13e.
Littleton, T. (2016, May 13). Econusltancy. Retrieved March 26, 2017, from http://www.econsultancy.com
Mercer, C. (2016, October 28). Retrieved March 26, 2017, from http://www.computerworlduk.com
Rennie, J. (2011). Not-So-Elementary Watson: What IBM’s Jeopardy! Computer Means for Turing Tests and the Future of Artificial Intelligence. Retrieved on 30th March 2017 from http://blogs.plos.org/retort/2011/02/15/not-so-elementary-watson-what-ibms-jeopardy-computer-means-for-turing-tests-and-the-future-of-artificial-intelligence/