## Huzaifa Anas’s Word Expanded Definition of Algorithm

Huzaifa Anas Expanded Definition of Algorithm

TO: Prof. Jason Ellis

FROM: Huzaifa Anas

DATE: 10/2015/2019

SUBJECT: Expanded Definition of Algorithm

## Introduction

Algorithms play a pivotal role in computer science and this paper by examining the etymology, definition, context, a working definition will be formed through analysis of different sources like dictionaries, journals, newspaper articles, and encyclopedias, blogs, and social media. Multiple sources are used because they all cater to different audiences. We first focus on the modern definition and its etymology to provide a basis to understand the term and then look at how the term is used practically for a holistic understanding of the term and finally attempt to formulate a definition based on these two factors to make a definition which works for Computer Science (CS).

## Definitions

The word algorithm originates as a homage to the famous al-Khwarizmi, who was a famous polymath remembered for his pioneering Algebra. The word algorithm was algorism in Middle English, which traces its roots to French words algorithme â€ś meaning “Arabic system of computation,” originating (under mistaken connection with Greek arithmos “number”) from Old French algorisme meaning “the Arabic numeral system “(etymoline.com). The progenitor of these was algorismus which was an archaic Latin translation of al-Khwarizmi. Eventually, during the mid 20th century, the word expanded to any method of computation.

The Oxford English Dictionary (OED) defines algorithms for Mathematics and Computing as â€śA procedure or set of rules used in calculation and problem-solving; (in later use spec.) a precisely defined set of mathematical or logical operations for the performance of a particular taskâ€ť, and this concept is basically translated over to psychological use of the term (algorithm n.2 & 3., 2019). While looking at the OEDâ€™s historical definitions we see the definition limited to mathematical theorems, but then expanding. Overall the idea remained mostly static of having a systematic method to tackle some issues. Britannica also similarly defines an algorithm as a â€śsystematic procedure that producesâ€”in a finite number of stepsâ€”the answer to a question or the solution of a problemâ€ť (algorithm, 2019). The Gale Encyclopedia of Science defines it slightly differently as â€śa set of instructions for accomplishing a task that can be couched in mathematical terms. If followed correctly, an algorithm guarantees successful completion of the taskâ€ť(Algorithm, 2014). Overall in these definitions, we see the trend of having a specific modus operandi to solve specific problems. The only real difference is the scope of this concept being applied from like computer science (CS), CS & math, or this concept in general.

## Working Definition

Algorithms now have become dynamic nature, possibly inconsistent, adaptable, and more independent through the Ai in computer science they should be defined as a set of instructions or guidelines, that may self regulate, which can be used to attempt to solve specific or group of problems with a large degree of success.

## References

A Genetic Algorithm to Pilot Pod Racers. (2018, August 16). Retrieved October 14, 2019, from https://www.codingame.com/blog/genetic-algorithms-coders-strike-back-game/.

algorithm (n.). (n.d.). Retrieved October 15, 2019, from https://www.etymonline.com/word/algorithm.

Algorithm. (2014). In K. L. Lerner & B. W. Lerner (Eds.), The Gale Encyclopedia of Science (5th ed., Vol. 1, p. 131). Farmington Hills, MI: Gale. Retrieved from https://link-gale-com.citytech.ezproxy.cuny.edu/apps/doc/CX3727800076/GVRL?u=cuny_nytc&sid=GVRL&xid=7b6e67ce (This one wonâ€™t work without the citytech.ezproxy.cuny.edu part)

Angwin, J. (2016, August 1). Make Algorithms Accountable. Retrieved October 14, 2019, from https://www.nytimes.com/2016/08/01/opinion/make-algorithms-accountable.html?searchResultPosition=3.

Hutson, M. (2018). Artificial intelligence faces reproducibility crisis. Science (New York, N.Y.), 359(6377), 725-726. https://science.sciencemag.org/content/359/6377/725

Shams, A. (2017, November 27). Facebook post by Alex Shams on the gender bias of google translates algorithm for Turkish to English. Retrieved October 15, 2019, from https://www.facebook.com/alexrezashams/posts/10105798694396775.

Silver, D. davidsilver@google. co., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., â€¦ Hassabis, D. dhcontact@google. co. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140â€“1144. https://doi-org/10.1126/science.aar6404

## Huzaifa Anas 500 word article summary

TO: Prof. Jason W. Ellis

FROM: Huzaifa Anas

DATE: September 17

SUBJECT: 500-Word Summary of Hassabis et al â€śNeuroscience-Inspired Artificial Intelligenceâ€ť

Hassabis et al in Neuron argues that the field of neuroscience and AI (artificial intelligence) have a symbiotic relationship, but itâ€™s in jeopardy, because of decreasing communication and collaboration. The contention states neuroscience provides a productive source of inspiration for algorithms and architecture, which is â€śindependent of and complementary to the mathematical and logic-based methods and ideas that have largely dominated traditional approaches to AIâ€ť and â€śneuroscience can provide validation of AI techniques that already exist.â€ť (Hassabis et al, 2017, p. 1). Moreover, they believe the progress in AI will eventually pay dividends to neuroscience by being a good test field. Within this article, past breakthroughs are examined to support this argument, while looking at how continued collaboration and communication can benefit both fields.

Two of AIâ€™s backbones originate from neuroscience, whichâ€™s deep learning and reinforcement learning. Deep learning has revolutionized AI through dramatic advances in its neural and capable networks of learning freely from unstructured or unlabeled data. Reinforcement learning, the second pillar of modern AI, is a powerful tool enabling AI researchers to create software agents that act in an environment maximizing some sort of reward. In the 1940s artificial neural networks were developed, which could compute logical functions and ultimately â€ślearn incrementally via supervisory feedback (Rosenblatt, 1958) or efficiently encode environmental statistics in an unsupervised fashionâ€ť (Hasabis, 2017, p. 2). This is the foundation for deep learning. Soon after backpropagation algorithms were made, which allowed learning to occur in networks of multiple layers whose value was recognized in 1986 by cognitive and neuroscientists working on Parallel distributed processing or PDP, which better-represented human-like behavior than serial logical processing, which AI researchers were focusing on. PDP has been applied to machine translation through the idea that â€śwords and sentences can be represented in a distributed fashion (i.e., as vectors)â€ť (Hasabis, 2017, p. 2). Deep learning ultimately became a field independent of PDP. Reinforcement learning comes from animal learning research, which Pavlov and Skinner pioneered. Reinforcement learning is used in robotic control, skillful play in backgammon and go.

If someone looks closely, AI research is still heavily inspired and guided by Neuroscience through AI work on attention, while eventually pivoting towards efficient learning and more independent behavior like transfer learning and imagination. The goal of AI is to form human-like behavior, and itâ€™s practical an accurate biological framework as a reference. Attention is a critical issue currently because not all information is equal and therefore unlike before where all information was treated equally in neuroscience now information is being given different values, which allows for more efficient computing power usage. For the future, we want to decrease the computing power and a large amount of data needed for AI as currently. Humans can learn from a few examples, which AI canâ€™t, and researchers are trying to apply developmental psychology ideas here. For imagination and transfer, learning neuroscience is still pioneering this part, but in the future, itâ€™ll hopefully provide practical insights for AI work. All things considered, both fields can provide feedback to each other by having neuroscience provide ideas, and AI proves as a testing ground for these ideas. This isnâ€™t compulsory, but just an effective and logical symbiotic relationship.

Article Cited APA format

Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.

Iâ€™m not sure if restructuring definitions is considered plagiarism.