Throughout this semester, we have discussed how AI and deep learning can provide companies and organizations with data about individuals and society. Neural networks are used to design algorithms that empower natural language processing, recommendation algorithms and facial recognition. Developments of these technologies have been used to elicit sales and subscriptions from the general public, since the development of them. But with the heightening threats of climate change looming, how can these technologies be used, if only on an individual level, to stem contributions to climate change. As we know, deep learning and AI has not been designed to manage complex problems. It can , however, be used to manage queries with yes and no answers.
Decision making technologies are in the pockets of everyone with a smartphone. Users regularly trust these technologies to guide them through new cities, translate languages and even select which media they should consume. While many may say they do not trust AI or machine learning, their actions demonstrate the opposite. The lack of understanding how AI and machine learning plays a role in daily life and decision making may be keeping users from benefitting from other technologies that can improve life and the world. The balance between technology benefitting users and hurting them is one that is still being developed and understood.
AI and deep learning in itself are not malicious technologies and their benefits, if managed by the right organizations, can outweigh the negative impacts of them. As stated in Marcus’ writings, deep learning presumes a relatively stable world; this we know is not the case, nor has it been. Can accepting the volatility of the world impact the way in which tools like deep learning and AI are designed into technologies? We have seen that AI and deep learning can be used to change individual and even societal behaviors to boost the bottom line of organizations, but can it be designed into technologies that have a different goal? Is technology more likely to be integrated into the lives of individuals if it operates within a familiar institution? Can AI and machine learning be used , in part, to create more stable institutions that allow for the optimization of deep learning?
In my final research projects, I want to analyze the relationships between chatbots and the banking systems. I want to understand how, if anyway, chatbots impact modern banking and how scalable chatbots are across industries.
Gary Marcus, “Deep Learning: A Critical Appraisal,” ArXiv.Org, January 2, 2018