TheDataScienceBlog

Artificial Intelligence (AI) is the process of using algorithms, a method for solving a problem, to replicate human intelligence. To dwell deeper on the implications of AI, it becomes critical to elaborate on the current thinking on the types of AI. In the process of delineating the types of AI, we also would understand what has been accomplished in AI and what is the promise of AI. 

Reactive Machine

Reactive machines represent the most basic type of AI. A+s the name implies, all it can do is react to the input. Such machines have no recollection of prior encounters, and as a result the way they make decisions is purely based on the computational power of the underlying technology. Some famous examples of these machines include: spam filters, recommendation engines, and chess engines such as IBM’s Deep Blue.  

Limited Memory

The most common type of AI is limited memory AI. This type of AI uses its ability to store existing data, and then it uses these data to improve prediction accuracy. Limited memory AI finds application in voice assistants, self driving-cars, from companies like Tesla and Waymo One.

Theory of Mind

Our first theoretical type of AI is ‘Theory of Mind’ AI. Till date, Theory of Mind AI remains a theoretical concept that has not been implemented yet. Theory of Mind AI seeks to understand emotional intelligence, and hence this kind of AI pertains greatly to incorporating human psychology in machines.

Self Aware AI

Finally, we have the self-aware AI, which, similar to Theory of the Mind AI, is also theoretical currently. Self-aware AI is seen as the end product and goal for AI as it intends to build a human-like consciousness and empathy in machines.

Personally I do not think that the Theory of Mind AI would change things immensely as we have barely scraped the surface of the capabilities of this AI. I believe it will revolutionize voice assistants and change machines forever. In my opinion, the promise of the AI lies in creating inanimate objects that are able to understand emotion, which is something the world has never experienced. And as a result, I can imagine it drastically changing the relationship between man and machine.

It is obvious that we are inundated with data. The only near certainty suggests that this inundation would continue for the foreseeable and distant future. 

It is well recognized that we live in a world that possesses a massive volume of data and continues to generate data at an immense rate. For example, estimates suggest that there were 44 zettabytes of existing data at the beginning of 2020 and in 2018 daily rate of data creation was at 2.5 quintillion bytes per day (see https://seedscientific.com/how-much-data-is-created-every-day/). 

Further, these data appear in many forms including numerical, text, images, audio, and video data. As a result, in addition to storing and accessing these data, there is a need for methods that can capture unique features of these data. For example, a transcript of an audio interview, which is a textual representation of these data, fails to capture features such pitch and tone that can substantially change the meaning and sentiment being conveyed. 

By rhythm of data, I mean issues that relate to volume, rate of increase, and diversity of today’s data. In this blog series, I intend to document the challenges that current data present, discuss the potential solutions, and present my thoughts. As I learn and document my learning, I am hoping it will benefit others that follow.

Hello! 

I’m Kabir Grewal, and I am a senior at East Chapel Hill High School in Chapel Hill, North Carolina. 

My fascination with data science was firstborn when I realized the endless possibilities and scenarios in which it could be applied and would be immensely impactful. From diagnosing diseases and developing treatment regimens, to creating algorithms to predict stock market movements, to models of climate changes and weather patterns, and to detecting and managing social trends, among others. In fact, today and in the future, one can easily predict that data science will play a prominent and perhaps a dominant role in every aspect of modern day lives. 

What is data science? Data science is the field of academia which pertains to using technology and computing to detect patterns. As technology and modeling processes improve, so does the importance and effectiveness of data science on every aspect of the society – whether it relates to science, business, politics, and social issues. Data science itself contains a blend of unique disciplines, such as, statistics, mathematics, computer science, economics, industrial engineering among others. 

As I expand my knowledge on the emerging field data science, I thought a nice way to document my understanding and share my thoughts. So, this blog… Hopefully other beginners will find this to be helpful!