In our RADAR’s ceaseless journey to explore the intersections of art and technology, we at RED-EYE often find ourselves enamored by AI artists. Throughout this article, as we unpack our reflections on weekly events, the evocative works of AI artists that have captivated us will accompany our thoughts, providing a visual stimulation that mirrors our contemplations.
These avant-garde creators, in collaboration with intricate AI algorithms, co-craft visuals that are not only stunning but also profoundly reflective of our times. Some sort of zeitgeist, the spirit of the age, seems to be encapsulated in these human-machine co-creations, surreal storytelling of what can be dubbed as a 'collective subconscious.’
It's essential to clarify that we're not attributing machines with having a subconscious of their own. Instead, we suggest that these machines, through their impartial processing, act as reflective surfaces, capturing and presenting an unbiased view of a collective human biased subconscious. This concept can be traced back to the age-old adage that 'beauty lies in the eye of the beholder,' implying that the perceptions and biases we hold influence the reality we perceive. Somehow machines can act as mirrors to the subjectivity inherent in human experience without possessing it themselves.
Machines act as "reflective surfaces", therefore we shall acknowledge once for all their passive, non-judgmental role in processing data, they merely reflect back what they're given, without adding any bias of their own. While the machines themselves are impartial, the data they process may carry the biases of the collective human subconscious. So, we need to reflect on this complex interplay between machine impartiality and the potential biases present in the data (that we humans create).
In today's rapidly digitizing world, information isn't merely something we actively seek and consume. More often than not, it is thrust upon us—curated snippets in endless scrolls, tailored by algorithms based on our online behaviours, presuming what we might find interesting or engaging. It is mostly a passive reception of information, influenced heavily by algorithmic determinations.
The unprecedented volume of data, coupled with advanced computational capabilities, has enabled insights and patterns previously unimaginable. Machine Learning and Artificial Intelligence sift through mountains of information, curating content that aligns with our preferences, anticipates our needs, and even predicts our behaviours.
A paradigm shift has occurred in the way we engage with, understand, and interact with information. This shift is largely attributed to the colossal volumes of data generated daily. From climatic sensors and social media posts to transaction records and cell phone GPS signals, data sources are diverse and ubiquitous. This omnipresent data is often referred to as 'big data.' Lately, big data has gained significant attention, not just within scientific communities but also in boardrooms, political debates, and newsrooms.
So we might state that this era is characterized by personalized information. News articles, advertisements, social media feeds – everything seems tailor-made, echoing our beliefs, preferences, and inclinations. Such hyper-personalization can sometimes be advantageous. It ensures relevance, enhances user engagement, and fosters efficiency. However, it's not without its pitfalls.
Modern journalism has evolved from documenting and storytelling to a data-driven enterprise. Newspapers and digital platforms leverage analytics to discern readers' preferences. The backend algorithms coldly analyze which articles are most read or shared, and which headlines captivate audiences the most. While this data-centric approach can enhance user engagement, it can also compromise journalistic integrity, leading to sensationalism and an undue emphasis on viral content over substantive journalism.
With the rapid proliferation of data, surveillance – both governmental and corporate – has intensified. Every digital footprint, from our online searches to our purchase histories, is potentially monitored. While some argue that this surveillance enhances security and user experience, it undeniably poses significant ethical dilemmas.
As we race towards an increasingly data-structured society, the ramifications are profound. The choices we make today – whether to share information, whom to trust with our data, and how to regulate data usage – will indelibly shape our future. It's imperative to approach them with a discerning eye, constantly questioning, analyzing, and understanding their broader societal implications.