AI: Past, Present, and Future

8 min readApr 25, 2023

In this article, I provide my casual perspective on the growth of AI, its current impact on society, and where AI is headed in the future.

Fun fact (no offense) 🙂

A data scientist and a software engineer were walking in a park, discussing their work. The data scientist said, “Did you know that 99% of the world’s data has been generated in the last two years?” The software engineer replied, “Yeah, and I’m pretty sure I wrote most of the code that generated it!”

Legend has it

Once upon a time: Artificial intelligence has been around since ancient times, with myths and legends describing the creation of artificial beings with human-like characteristics. On the other hand, the modern history of AI begins in the mid-twentieth century with the emergence of computer science and the development of electronic digital computers.
1770 — The First Encounter: The mechanical chess-playing automaton known as “The Turk,” built in 1770 by the Hungarian engineer Wolfgang von Kempelen, is one of the earliest recorded examples of a machine capable of “thinking” like a human being. When playing chess against human opponents, the Turk could defeat some of the game’s top players. However, it was discovered later that the machine was, in fact, controlled by a human operator hidden inside it, 🙂.
1950 — The Imitation Game: Alan Turing, a British mathematician, and logician, published a paper titled “Computing Machinery and Intelligence” in 1950, another early example of AI. Turing proposed the Turing Test in this paper, which is still used today to measure a machine’s ability to exhibit intelligent behavior comparable to, or indistinguishable from, that of a human. In addition, Turing proposes that instead of determining if the machine is intelligent, we ask if the computer can win a game known as the “Imitation Game.” Check out the fantastic Hollywood film, The Imitation Games, loosely based on Turing’s idea.
1956 — AI Baptized: John McCarthy, a computer scientist, first used the phrase “artificial intelligence” (AI) in 1956 when he convened the Dartmouth Conference, a two-month brainstorming session aimed at examining how to make machines “think” like humans. Given its role in establishing artificial intelligence as an independent field of study and development, the Dartmouth Conference is often recognized as a pivotal moment in the history of AI.
1959 — Terminator Arrives: Arthur Samuel coined “machine learning” in 1959. Samuel was a computer scientist and artificial intelligence pioneer known for his work on early computer games and machine learning approaches. Machine learning, according to Samuel, is “a field of study that gives computers the ability to learn without being explicitly programmed.”
1962 — Elixir Unearthed: in 1962, John Tukey published “The Future of Data Analysis”, in which he described Data Analysis as a shift in the realm of statistics, writing, “… as I have watched mathematical statistics evolve, I have had cause to worry and to mistrust…”I’ve come to believe that my primary interest is data analysis…” Tukey refers to the concoction of statistics and computers, a novel idea at that time to turn raw data into gold!
1974s — Leto, The Mother: The term “Data Science,” as “the science of dealing with data,” was coined in 1974 by a Danish computer science pioneer and Turing Award winner named Peter Naur. Data Science is commonly regarded as an umbrella term that includes all things data.. Naur used the phrase “Data Science” several times in his book “Concise Survey of Computer Methods”. He offered his convoluted description of the new concept: “The usefulness of data and data processes derives from their application in building and handling models of reality.” 🤔? However, the area did not take off until the 1990s, when the internet began to generate vast amounts of data (check the fun fact above 🙂).
1977 — Holmes, The Detective: Tukey authored a second paper (finally turned into a path-breaking book that’s still regarded as the EDA bible), Exploratory Data Analysis (EDA), in which he argued that data should be used to decide “which” hypotheses to test and that confirmatory data analysis and exploratory data analysis should be used in tandem. Tukey emphasizes that EDA is like detective work; one must be curious and imaginative to uncover hidden mysteries concealed beneath the data.
2008 — Frodo, The Final Ring Bearer: Jeff Hammerbacher, a researcher at Facebook, created the term “Data Scientist” in 2008, concerned with the lack of a fitting title for his role. “The best minds of my generation are thinking about how to make people click ads,” he famously declared. That stinks.” Since then, the area has expanded significantly, and data scientists are now among the most in-demand professions in the technology business.
2010 and Beyond — The Renaissance: Some of the significant milestones include…
– Big Data Tools, NoSQL, Cloud Computing, and Massive Data Generation (2010–2011).
– Geoffrey Hinton and colleagues made a breakthrough with ImageNet by utilizing a convolutional neural network [CNN] (2012).
– Ian Goodfellow and colleagues (2014) introduced Generative Adversarial Networks (GANs).
– Tensorflow (2015) and PyTorch (2016) are examples of open-source deep-learning frameworks.
– The seminal Transformers paper “Attention Is All You Need” was published in 2017.
- Release of groundbreaking Large Language Models (LLMs) based on transformers, such as GPT and BERT (2018). GPT and BERT have made significant contributions to the field of NLP and are widely utilized in applications such as text classification, sentiment analysis, language translation, and question-answering.
- ChatGPT, an artificial intelligence language model created by OpenAI, was released in 2020.
- GitHub Co-Pilot, an AI-powered code-generating tool, released in 2021.

The Present

Please remember that I am using the phrases Data Science, AI, and Machine Learning loosely and interchangeably; they are all related but have subtle differences. I intend to publish a separate article to analyze their relationship and distinctions soon.

Fun Fact (Data Humor) 😀

While data science is a serious and vital field, some amusing anecdotes have arisen. One such case involves a data scientist working on a grocery store chain project to evaluate customer data. After crunching the numbers and analyzing the outcomes, the data scientist discovered a link between the sale of diapers and beer. The data scientist was initially perplexed by this correlation but soon understood it made sense. Many new parents would come into the store to buy diapers and frequently pick up a six-pack of beer. The data scientist provided the findings to the grocery store chain, which cleverly placed beer displays near the diaper aisle, resulting in higher alcohol sales.

While the story may not be super hilarious, it shows the unexpected insights derived from data analysis and the potential for data science to lead to innovative and novel solutions.

Practical Applications

The development of AI technologies such as machine learning, deep learning, computer vision, and natural language processing advanced significantly in the last ten years. Today, AI is a rapidly developing field with many applications, including self-driving cars, virtual assistants, tailored recommendations on e-commerce websites, voice recognition on our smartphones, and scientific research. In addition, data science is utilized to extract insights from data and make smarter decisions in practically every business, from healthcare and banking to sports, entertainment, transportation, and education. Here are some of the practical applications of AI that are genuinely mind-blowing:

1. Autonomous vehicles: Thanks to developments in artificial intelligence, self-driving cars, trucks, and drones are becoming a reality. These vehicles are outfitted with sensors, cameras, and other technology to navigate roadways and situations without human intervention.

Check out this demo of Google’s Waymo project’s fully autonomous driving technology. Waymo One is a taxi service already available to the public in specific locations in the USA.

https://www.youtube.com/watch?v=aaOB-ErYq6Y

2. Healthcare: Artificial intelligence (AI) is being applied in healthcare to enhance patient outcomes, save costs, and streamline operations. AI systems, for example, can analyze medical imagery to discover disease symptoms or assist clinicians in making more accurate diagnoses.

The modality.ai (https://modality.ai) is a platform that involves a patient in discussion with a virtual agent who conducts interviews and speaking exercises as if the patient were conversing with a speech pathologist or neurologist, or psychiatrist. Their face and limb movements and speech responses are streamed into the cloud and speech biomarkers that correlate with and predict their progression are extracted.

3. Crime and Fraud Detection: AI is being used by banks and other financial organizations to detect and prevent fraud. AI systems can mine massive volumes of data for suspicious trends and detect potential fraud before it happens. AI is also helping law enforcement solve various cases.

The Indian government maintains a website called www.trackthemissingchild.gov.in that contains information on all reported missing children and those housed at various childcare institutions nationwide. To match the photos, it employs facial recognition technology. Using this, Delhi Police reunited 2930 lost children with their families in just five days!

https://www.thebetterindia.com/138862/face-recognition-software-helps-find-missing-children-in-delhi/

4. Agriculture: AI can optimize crop yields, minimize waste, and improve agricultural sustainability. Farmers, for example, can employ AI-powered sensors to monitor soil moisture, temperature, and other factors to make data-driven irrigation and fertilizer decisions.

Blue River Technology created a “See & Spray” equipment that sprays fertilizer on crops and herbicide on weeds with pinpoint accuracy. It is stated that it saves 95% of the surplus fertilizer/herbicide that would otherwise be utilized. See & Spray includes multi-camera arrays (front and rear-facing sensors) and dedicated NVIDIA GPUs for each individual row-unit, which provide computer vision and deep learning (training a computer to act on what it sees) power. At the same time, custom-designed nozzles (that convey in a showerhead-like fashion) only activate when directly over the target.

Check out a quick intro of See & Spray here…

https://www.youtube.com/watch?v=-YCa8RntsRE

Similarly, several examples and success stories of AI/ML/DS are being applied in various industries, including customer service, cybersecurity, banking, education, etc.

The Future

Several promising future AI advancements are now being explored and developed. Here are a few examples:

1. Explainable AI: One of the most challenging aspects of artificial intelligence is determining how decisions are made. Explainable AI addresses this by developing models that can explain their decision-making process transparently and understandably. Many tech companies have already made headway in this subject, but there is still a long way to go; here are a few examples:

https://www.ibm.com/watson/explainable-ai

https://cloud.google.com/explainable-ai

2. Human-like AI: Researchers are developing AI systems to understand and interpret human emotions and mimic human behavior and decision-making. This has the potential to revolutionize industries such as customer service and healthcare.

Neon, Samsung’s AI-powered avatar, is the world’s first ‘Artificial Human’:

https://www.youtube.com/watch?v=2UlBFiL6noU

Japan Releases Fully Functional Female Robots:

https://www.youtube.com/watch?v=PLBAbmETEmo

3. Quantum computing: Quantum computing has the potential to dramatically increase the speed and power of AI systems, allowing for more complex and sophisticated analysis and decision-making.

https://www.ibm.com/topics/quantum-computing

4. Edge computing: Edge computing involves processing data on devices closer to the source of the data, such as smartphones or IoT devices. This can significantly increase the speed and efficiency of AI systems, allowing for real-time decision-making and analysis.

https://www.ibm.com/cloud/what-is-edge-computing

AI has the potential to transform industries and improve our daily lives in ways we can only imagine. However, they also raise important ethical and societal considerations that must be carefully considered and addressed.