‘A Recipe’ For Hot Streaks And Success In Careers and Business

Artificial Intelligence (AI) and ‘A Recipe’ For Hot Streaks and Success In Careers and Business.

I really like the 2011 movie; Moneyball.?

It’s based upon the true story about the 2002 Oakland Athletics Baseball team’s experience in identifying and assembling player talent. Brad Pitt does an exceptional job of portraying Billy Beane, the GM of the team at that point in time, now the EVP. The film nicely conveys what took place by using a little data science, to assemble and build, a remarkable and exceptional team.?

The story is also a really interesting focus on different algorithmic constructs and the aggregation of pools of talent. Players were taken with entirely different performance levels, prior success achieved, motivations, salaries, timing [age], and within certain financial / budgetary constraints. All of which literally resulted in The Streak during the 2002 season.

The results portrayed in Moneyball also appear to be applicable across many different professions throughout the sciences, arts, culture, and business.?

In a most recent research project headed by C. Lee Giles at College of Information Sciences and Technology, Pennsylvania State University, and?Dashun Wang at The Center for Science of Science and Innovation at Northwestern University, researchers used Artificial Intelligence (AI) driven analysis, neural networks, and modeling analytics to create and analyze large, and varying data sets. Thereby also creating new ‘knowledge bases’. These ‘knowledge bases’ are rapidly becoming a part of our ever increasing algorithmic ‘Dataome’.

Samples were taken and examined including top performers from within a variety of disparate fields, avocations, or “creative domains” such as;?

Artists - Vincent Van Gough,?

Directors - Peter Jackson,?

Scientists - Albert Einstein.

The authors then did something really interesting, the group;

“ …. developed a deep neural network for the three domains of focus: paintings, film, and science.?For example, the AI systems used visual-recognition technology to analyze the types of subjects and brushstrokes in paintings, and identified film genre and style by analyzing plot summaries, cast, and other information available online. They used this innovative method to examine the careers of 2,128 artists, 4,337 directors, and 20,040 scientists—millions of works in total.”

The researchers continued comments and observations are critical;

“We’ve found one of the first identifiable regularities related to the onset of a hot streak,” says coauthor Jillian Chown, a Kellogg associate professor of management and organizations.”?

“It can help individuals and organizations understand the types of activities to engage in, and the optimal sequence to use for bigger impact.”

“It has to be the combination of exploration followed by exploitation: experimenting in different areas, learning different domains and approaches, then really hunkering down and developing that body of high-impact work.”

“The results make clear the “recipe” for a hot streak: exploration of creative options followed by exploitation of a specific “lane” of work ultimately leading to greater success. This held true across all three domains studied.”

“Our work shows that people experiment and likely gain new skills from work in different subfields, and then help find the best one to exploit, which seem crucial for hot streak.”

Most significantly, this activity is meaningful and transferable into many real world applications.?For example, one area is patents; “A firm could look at how concentrated their patents are in certain areas to understand their pattern of exploration and exploitation related to innovation.”?Another niche opportunity is a ‘deep dive’ into new product or market development; “Similarly, a pharmaceutical or biotech business could examine how it works within and across products for different therapeutic areas—cancer, diabetes, etc.—as it seeks to explore and exploit.”

Interesting implications and decisions for us all moving into the future. From the C-Suite, to C Level Leadership, throughout The Board Level, and to all sources of funding and revenue.?And, cascading and passing through all levels of management in a clear, consistent, and understandable way.

There is an abundance of choice and availability as to where and how to apply; “A Recipe For Success” as the graphics and descriptive text reproduced below in Figure 1 and Figure 3 clearly show.?The following graphic images, descriptive and explanatory text, are reproduced as appearing in reference #3 below. Thank you to the authors for permitting access and such use.

Of particular note, and most appropriately, is the algorithmic interconnect illustrated in Figure 1 among scientists with related subject matter publications and shared topics of common interest, research, and patterns of 'Papers and references', ‘Co-citing networking’, and ‘Community detection’.

Fig. 1: Quantifying individual creative trajectories using high-dimensional representation technique.

a?The architecture of the deep neural network to build high-dimensional representation of artworks. We connect a pre-trained VGGNet with three fully connected layers and fine-tune the model with art style labels. The blue box indicates the convolutional layer and the yellow box the max pooling layer. The green bar shows the top styles predicted by the model for the input image (Image reproduced under Creative Commons Attribution 3.0 Unported license). We construct the high-dimensional representation of artworks by combining the output from the first and third convolutional layer (blue arrows) and the second fully connected layer (red arrow).?b?An illustration of the 64 filters in the first convolutional layer. We highlight the first filter, the original image, and the output after the image passing through the filter. The red box represents the size of the filter (3 × 3 pixel box).?c?The activation of four layers in VGGNet and the saliency map of the post-impressionism class. The saliency map visualizes the important pixels for predicting the post-impressionism. Layers close to the input capture?low-level features, such as brush strokes, whereas the layers close to the output?capture?high-level features such as the shape of objects.?d?Word embedding for film plots. Target words are encoded as a binary vector and passed to the neural network. We use the hidden layer to represent the embedding of words and plots.?e?Node embedding for the co-casting network. We apply DeepWalk to the co-casting network of 79 K films, to capture the co-occurrence of nodes from the trajectories of random walkers. We use the hidden layer of the model to represent the cast information. We concatenate the word embedding from plots and the node embedding from casts to construct a 200-dimensional vector to represent each film.?f?An illustration of the co-citing network among papers published by a scientist. Two papers are connected if they have at least one common reference, with link weight measuring the total number of references they share. Following prior work16, we apply a community detection algorithm to the co-citing network and identify the topic of each paper as the community it belongs to.

Fig. 3: Exploration, exploitation and career hot streaks.

a–c Career histories of a Jackson Pollock, b Peter Jackson, and c John Fenn illustrate the topics they worked on before and during their hot streak and the impacts of the work. Color of the dots is consistent with the dots shown in Fig. 2b, f, j. d–f The distribution of entropy ?? ( ????) before a hot streak for 1000 realizations of the randomized careers for all individuals analyzed in our datasets. The vertical line indicates ???? measured in real careers, showing that it is significantly larger than expected (z-scores are 4.24 for artists, 2.94 for directors, and 13.90 for scientists). g–i Same as (d–f), but for the entropy of work produced during hot streak. ???? in real careers (vertical line) is significantly smaller than expected (z-scores are ?2.42 for artists, ?8.54 for directors, and ?22.71 for scientists). j–l The dynamics of topic entropy ?? surrounding the onset of hot streak for real and randomized careers, measured through a sliding window of six artworks, five films or five scientific papers. Error bars represent the standard error of the mean. m–o Cumulative entropy distribution ?? ≤ (??) before and during hot streak in real careers across the three domains. P values of the KS-test are 3.7×10?6 for artists, 1.5× 10 ?51.5 ×10?5 for directors, and 1.1×10?64 for scientists. p–r Cumulative entropy distribution ??≤(??) before and during hot streak for the null model. P- values are 0.23 for artists, 0.77 for directors, and 0.06 for scientists. s–u The probability to observe the onset of a hot streak at the end of an exploration episode alone (not followed by exploitation), or at the beginning of an exploitation episode alone (not proceeded by exploration), or at the transition from exploration to exploitation, or from exploitation to exploration. We then compare with the baseline probability of having a hot streak. Here we calculate entropy with a sliding window of two years for artists and scientists, and five works for directors, and define exploration and exploitation episodes as entropy above or below one’s average.

References :

1. The 2011 movie; Moneyball. The story of the 2002 season for the Oakland Athletics Baseball Team.?Based upon the book; Moneyball: The Art of Winning An Unfair Game, by Michael Lewis, 2003.

2. Scharf, C. A., The Ascent of Information: Books, Bits, Genes, Machines, and Life's Unending Algorithm, Riverhead Books, 2021.

3. Liu, L., et. al., Understanding the onset of hot streaks across artistic, cultural, and scientific careers., Nature, Nature Communications,12, Article?number:?5392., September 13, 2021. [Altmetric score: 99th percentile / ranked 1st., 10.May.2022].

4. Article in Northwestern University’s Kellogg Insight, Careers., Dashun Wang, What Triggers a Career Hot Streak?, New research reveals a recipe for success., October 4, 2021.

5. Carroll, S.M., The Big Picture: On the Origins of Life, Meaning, and the Universe Itself, Dutton, 2016.

6. West, G., Scale: The Universal Laws of Life and Death in Organisms, Cities and Companies, Penguin Press, 2017.

Copyright William A. Paradise 2022