With the lightening speed that technology is changing, and the desire for tech companies to distinguish themselves by offering the most innovative product, Artificial Intelligence (AI) is taking center stage as a highly debated and deliberated concept and a potential reality.
Excitement surrounding AI is also tempered by warnings regarding the foreseeable dangers; from risking the replacement of U.S. workers to science fiction narratives about automated devices slaughtering the world population. No matter the individual beliefs, the creators behind the scenes are moving forward with AI type systems such as Apple’s Siri, Microsoft’s Cortana, and Google’s Deep Learning.
A true understanding of Artificial Intelligence, especially its deep network within the burgeoning technology age, is a key aspect of moving into the next age of development.
What is Artificial Intelligence?
There is no one definition for the different types of Artificial Intelligence currently imagined. Many innovative technology developers look to create humanoid AI machines. This type of AI is meant to mirror a human’s body and mind. On the other end of the spectrum, there are developers who “just want to get the job done and don’t care if the computation has anything to do with human thought.”
In an article published by Computer World, Kris Hammond, Chief Scientist and co-founder of Narrative Scientist and professor at Northwestern University, explains that “artificial intelligence is a sub-field of computer science. Its goal is to enable the development of computers that are able to do things normally done by people — in particular, things associated with people acting intelligently.”
Aki Ito goes on to explain further about the infiltration of AI in an article published on Bloomberg, “artificial intelligence has arrived in the American workplace, spawning tools that replicate human judgments that were too complicated and subtle to distil into instructions for a computer. Algorithms that “learn” from past examples relieve engineers of the need to write out every command.”
Therefore, understanding Artificial Intelligence comes down to reviewing the individual intentions and projects of each innovative mind in the laboratory.
Understanding Big Data
Big data is a simple terming spanning a broad topic. Generally, big data is used in reference to the data collected by businesses due to the fact that it can be either refined (structured) or raw (unstructured). The difference between “data” and “big data” comes down to the three “V’s”: volume – terabytes, petabytes, or exabytes – variety – SQL, document files, or streaming data – and velocity – the rate in which the data is analyzed.
While volume and variety are important aspects of big data, one of the most revolutionizing aspects comes down to velocity. Per TechTarget, “velocity is also meaningful, as big data analysis expands into fields like machine learning and artificial intelligence, where analytical processes mimic perception by finding and using patterns in the collected data.”
Putting The Pieces Together: Artificial Intelligence & Big Data
Artificial Intelligence and big data meet at volume. Simply put, big data is quickly superseding the human potential for analyzing.
Per H.O. Maycotte, contributing writer to Forbes, “there’s so much data being created — 44 zettabytes by 2020, according to IDC. The teams of data analysts that companies rely on today to uncover meaning simply can’t keep pace with the growth. In a prescient report issued several years ago, McKinsey Global Institute predicted a shortage of just this kind of talent by 2018.”
Sooner than anticipated, the amount of big data collected by organizations and enterprises will outgrow the ability for data scientists, analysts, and current computer programs to process it. The potential uses of Artificial Intelligence invite exciting possibilities for reinvention, especially for the organization of manpower and distribution of workloads. With that said, there is fevered debate regarding the projected displacement of workers due to AI’s ability to process data faster and more efficiently.
An Oxford University study published in 2013 went into further detail regarding the relationship between AI’s influence on big data analyzing:
“The advances, coupled with mobile robots wired with this intelligence, make it likely that occupations employing almost half of today’s U.S. workers … become possible to automate in the next decade or two …. ‘These transitions have happened before,’ said Carl Benedikt Frey, co-author of the study and a research fellow at the Oxford Martin Programme on the Impacts of Future Technology. ‘What’s different this time is that technological change is happening even faster, and it may affect a greater variety of jobs.'”
The future is at odds with itself. Big data is growing faster than humans can manage it, yet the development of Artificial Intelligence could take the place of jobs instead of work side-by-side those workers.