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Ada Lovelace: ‘poetical scientist’

Aaron LaiBy Aaron Lai

In secret we met
In silence I grieve,
That thy heart could forget,
Thy spirit deceive.
If I should meet thee
After long years,
How should I greet thee?
With silence and tears.
“When We Two Parted,” Lord Byron

Once upon a time, in a kingdom far, far away, a blue-blooded girl was born with one of the most well-known family names in history. Her mother hated her father so much that her mother decided to nurture this little girl as everything opposite to what her father stood for: passion, romance, poetry. And so the mother taught her daughter science, mathematics and logic. The mother would not even let the girl see any portrait of her father till the age of 20!

Nevertheless, nature gave the daughter a romantic heart, and nurture gave her a logical brain. She became the first female computer scientist, and the Pentagon even named a computer language after her. She is Ada Lovelace, the daughter of poet Lord Byron. She was also the first to question if a machine could think. Alan Turing called it “Lady Lovelace’s Objection.” Coincidentally, in “Second Machine Age” [1], the authors asserted that our future would come from a partnership between human and machine, which was not unlike the marriage of poetry and science. Maybe we can even call her a “poetical scientist,” which could be the next evolution of data scientist because the power of an algorithm could soon hit its marginal diminishing return.

The Ideals of Rationalism and Romanticism

Ada Lovelace’s insights might look obvious to our modern eyes, but to fully appreciate her contribution, we would need to look at it from a historical perspective. People at that time thought that romanticism (imagination or the left brain) and rationalism (reasoning or the right brain) were polar opposites. It is a misconception. True problem-solving requires creativity, and people need to break a paradigm in order to build a paradigm.

Computer science, data scientist, Ada Lovelace, poetical science, machine learning

To fully appreciate Ada Lovelace’s contribution, we would need to look at it from a historical perspective.

Enlightenment brought the role of man to the center and also celebrated the ingenuity of humans as opposed to just a glorification of God. They believed that man with his wisdom could overcome everything and come up with a rational solution. This period witnessed an unprecedented development in science and engineering, and daily lives were fundamentally changed. Rational thinking was considered as the ultimate answering tool to gain knowledge. From knowledge, one could reach Utopia.

Afterward came a new group of people who were idealists in the sense of every meaning of that word – they valued “wholeheartedness, sincerity, the purity of soul, the readiness to dedicate yourself to your ideals, no matter what it was” [2]. To them, conventions were meant to be broken, and rational thinking or just thinking itself was against their souls. The Romantic artists were sensitive, profound, subtle and receptive, and they formed the images in romantic poetry [3].

The two camps were thought to be dichotomous . . . until Ada.

What has Ada Lovelace Done with Her Poetical Science?

Augusta Ada King (née Byron, 1815-1852), Countess of Lovelace, was born with the most famous (or infamous) name of her time or even of all time [4]. Her father was the great romantic poet Lord Byron, but she had been separated from him since birth. Lady Byron was a disciplined lady, and she was very supportive of Ada’s education in mathematics to drive her away from the spirit of her father [5].

The difference between a good and a great scientist is that the great one always has a feeling about the direction of his or her research. Their intuitions might be off or immature, but this is a key point in shaping and crystallizing ideas. Nevertheless, Ada found the division between art and science deterred people from understanding the essence of an idea. Ada believed in the importance of intuition and image in mathematics and science. She called this “Poetical Science.” The key features were observation, interpretation and integration. They were relevant a hundred years ago and are still relevant today.

The first stage is careful observation. She believed that people should pay close attention to details in order to arrive at insights. She often used analogy and metaphor to explain key concepts because she needed to be able to run them through her head first to assure understanding. We should note that her metaphors were both concise and precise, a reflection of deep understanding. This is a skill we urgently need now. We are working in more specialized fields, and very often we communicate in jargon that no one even in related areas could fully understand, not to mention the general public or other learned people. A good metaphor captures the attention of people as well as facilitating discussion.

Poetry has had metaphor since its inception, but it is mostly drawn from one or more parallel features. Romantic poems often project romantic images through various literate means. When Wordsworth wrote, “Till all was tranquil as a dreamless sleep” in “The Prelude,” it did not mean dreamless sleep always happens in tranquility. This is a typical example of how the Romantics used metaphor. However, the metaphors used by Ada were different. She used them to highlight key features that anyone could understand.

For example, people at that time would have no problem picturing a weaving machine. So she called the “Analytical Engine” (the first programmable computing machine proposed by Charles Babbage and with extensive notes and improvements added by Ada) a weaving engine that wove algebraical patterns rather than pictures of flowers and leaves. This was an imaginative yet pragmatic approach to explain abstract situations; this also combined the rich imagination from the romantic side with the precise nature of the rationalist/scientific side. When Babbage constructed the “Difference Machine,” he was thinking about a special-purpose machine to do a specific type of computation. When he started to work on the “Analytical Engine,” he was still considering it as a pure computing device. It was Ada who saw the true potential.

Ada’s most important contribution was not her technical interpretation that became her legacy but her imagination that foretold the arrival of general-purpose computers nearly a hundred years before they were made. Take another example of an imaginative solution – the Fan Chart produced by Florence Nightingale on mortality in the Crimean War. This was the creative use of data in a familiar environment that cemented her reputation as a statistics pioneers (and also the first female Fellow of The Royal Statistical Society). Her charts were so impressively precise that it changed public opinion on the war, as well as a policy change to improve the hygiene as a treatment for the soldiers in her hospital. As a result, modern nursing was born.

The second part of Ada’s poetical science is interpretation. Strong mathematical training gave her the tools to correctly interpret the solutions, whether they were quantitative or qualitative. For instance, she was able to articulate the essence of the dual-properties of wave function using functional analysis after serious study under the famous mathematician Augustus De Morgan, one of the earliest professors of University College London (UCL). Her involvement in science was motivated by her friendship with Mary Somerville, the first female member of Royal Astronomical Society (Somerville College in Oxford was named after her).

Nowadays, it is easy for us to use a software package to calculate, estimate or simulate almost any kind of results, but does the analyst understand the true meaning of the answer? For example, we heard of a senior analytics executive who told people that their results must be correct just because they had a large sample size, without considering the sampling bias. This is a classic example of misinterpretation; we know the price of everything and the value of nothing. When we build quantitative models, it is important to be able to interpret the results properly. It is even more important to build a model that can be interpreted, especially those models built for business in regulated industries such as banking or insurance.

Suppose you have two models for lending decisions: one for Facebook and another one for your website. If you cannot properly interpret the explanatory factors of your models, you might come up with a discrimination lawsuit. It is because someone might accuse you of “knowingly” discriminating against certain groups based on their political belief or race from their Facebook profile!

The last part of Ada’s poetical science is integration. This is another area where Ada’s talent shone. In writing the note for Charles Babbage’s “Analytical Engine,” her approach stated the overall issues and then the defining terms, a very systematic method indeed. She used the same approach to teach mathematics to young students; she invoked the visual elements using colored pens, which were considered vulgar instruments at that time. Nevertheless, this was an ingenious way to integrate abstract models into pragmatic applications. How to integrate conceptual models into implementation is also a big, and probably the most, challenging aspect of analytics.

A few years ago Netflix had a competition to build the best predictive model for movie recommendations. The winning model was not implemented due to its complexity. A great analyst can see the key aspects of a problem and find the best integrated solution, not just the best algorithm or the most elegant model or the latest technology. This is not unlike the current system thinking approach to problem-solving!

How Would That Work?

Ada emphasized using imagination to see connections between subjects that have no apparent connection and then to penetrate the world around us in the world of science. Books on innovation can fill up a whole room, and even a famous writer, Walter Issacson [6], has devoted a chapter on the importance of Ada’s innovative imagination – a critical part of the advances in technology that ultimately led to the founding of Silicon Valley. Coincidentally, in the “Industrializing Analytics: Delivering Analytics at Scale into Core Organizational Processes” seminar hosted by the Department of Management Science & Engineering of Stanford University on April 21, 2016, Professor Blake Johnson suggested a new type of analytics professional – the business scientist – who can design and deploy industrial analytics and can execute rather than pursue a better algorithm. This echoes the view that data scientist could learn from history because technological advance, outsourcing and innovation diffusion could make those with just technical skills obsolete [7].

Integrating the evidence with feasibility is instrumental to any successful program. This is an area where we could use the poetical science framework. We need to open our hearts to listen to other voices and concerns (give empathy), open our minds to different approaches (use metaphor) and open our brain to alternatives (be imaginative). “When we integrate poetry and science it can change our perception of reality” [8]. We often fail to notice that logic and passion can live under the same roof. This may be the future of the analytics profession, a poetical scientist. Even medical schools are incorporating liberal arts education into their training because medical professionals need to be imaginative and go beyond science [9].


Dr. Betty Toole, Ada’s biographer, said Ada could be a derivative trader if she were alive today, as she had a keen interest in betting on horse racing. We beg to differ, as she could probably be a popular writer/presenter/celebrity like Malcolm Gladwell because she was able to articulate abstract concepts in animated terms, as well as having a burning intellectual curiosity. Her poetical science idea was so far ahead of her time that it is still relevant today and probably will stay as relevant in the foreseeable future. We will conclude with the “Stanzas” of Lord Byron: “To do good to mankind is the chivalrous plan. And is always as nobly requited.”

Aaron Lai ( is the senior manager of Analytics for Blue Shield of California. In addition to publications from analytics to risk management, he obtained two patents with five pending across various areas. All opinions expressed here are his own, and they do not necessarily reflect those of his employer and his affiliations.


  1. Brynjolfsson, E. and McAfee, A., 2016, “The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies,” W. W. Norton & Company; 1st edition.
  2. Berlin, I., 2001, “Root of Romanticism, 9.”
  3. Kermode, F., 2001, “Romantic Image 2nd Edition, Routledge Classics, No. 5.
  4. Unless stated otherwise, the information about Ada Lovelace came from Toole, B., 1998, “Ada Lovelace’s Poetical Science” in WSES Conference Proceeding, Athens, Greece, and Toole, B., 2010, “Ada, the Enchantress of Numbers: Poetical Science,” Kindle Edition.
  5. A very entertaining biography of Lord Byron and his relationship with Lady Byron and Ada Lovelace can be found in Woodley, B., 2002, “The Bride of Science: Romance, Reason and Byron’s Daughter,” Macmillan, 1st Edition.
  6. Isaacson, W. 2014, “The Innovators: How a Group of Hackers, Geniuses and Geeks Created the Digital Revolution,” Simon & Schuster, Inc. This book discussed Ada’s detailed understanding (via imagination) of the implication of having a general purpose “thinking” machine.
  7. Lai, A., 2014, “What Data Scientists can learn from History, Annals of Information Systems,” Springer, Special Issue on Real World Data Mining Applications.
  8. Toole, B., 2010, 4578.
  9. Henderson, L. et al., 2016, “Why get a liberal education? It is the life and breath of medicine,” “The Conversation,” Aug. 15, 2016.

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