Category: Uncategorized

Pierre-Simon Laplace in Théorie Analytique Des Probabilités

We need more decision / data scientists to ask “What is the probability of sun rising tomorrow?” yet we don’t seem to put a strong emphasis on probability theory for several reasons.

Probability is not thoroughly covered (ideally as a standalone course) in most data science / business analytics programs. In predictive analytics, common packages/libraries for ensemble methods focus on classifications, almost hiding the probability calculations (which are distorted in some anyway). Most frequentist reporting are limited to point estimates and errors, again hiding the underlying probabilistic assumptions. Et Cetera.

In search of a short reading to share with my students, I’ve come across a recent book (updated in May, 2022) by Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav (Slava) Vaisman Ph.D. The book is open access and the appendices serve as a nice refresher. Appendix C is a little primer on probability for example.

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In this latest collaboration with Duygu Dagli, we shed some light on it and soon other topics. We plan to bridge conceptual and applied takes on statistical modeling and machine learning methods and the business problems they may help solve. We’ll likely touch on causal inference, predictive analytics, and how firms can organize to be more data centric. Check it out and stay tuned for the upcoming pieces on experiments and the analysis of experimental data.

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Jeremy Siegel almost loses it here for good reason. The Federal Reserve Board called it transitory inflation when prices were skyrocketing last Fall. When the same Fed (except a couple members) now argues that inflation is not yet slowing at a reasonable pace despite the price contractions recent data shows, questions arise. Fed gives the impression that it is now overcorrecting what it overcorrected earlier by loosening the monetary policy too much and for too long.

In case after case, our data modeling and inference practices are tested against lags in data. Lack of a high predictive power, we resort to overcorrections. Overcorrecting is doing more than enough (vs. not doing enough) and sounds better than coming short. But then, the pendulum swings back a little harder.

What do we learn from such swings? Well, one rather obvious takeaway is to put more emphasis on correctly understanding and modeling lags in time series. Another one is to be content with coming short occasionally, especially when the cost of overcorrecting is much higher than the cost of coming short.

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The commercial refrigerator/shelf looking device in the picture is an ikejime machine. Ikejime is considered the fastest and most humane method of killing fish. The method also leads to the best taste of fish because fish are killed instantly before their bodies go into distress and produce lactic acid and ammonia into their muscles. Ikejime involves the insertion of a spike quickly and directly into the hindbrain, causing immediate brain death.

That is, if fishermen know where the hindbrain exactly is for each species, and can insert the spike quickly and precisely within minutes of catching a fish, and have time to do so repeatedly. Well, that’s what robots are for.

Shinkei Systems’ machine is a combination of hardware and an edge detection algorithm, the engine behind object recognition in convolutional neural networks. Challenges abound. The machine operates on a fishing boat that tilts around even at zero speed. Apparently, “even in the same species, even with the same contour, the brain can be in a different location” as well.

Working with fishermen in Maine, New Hampshire and Cape Cod, Shinkei Systems seems to have been accomplishing the task on fresh-caught fish at a rate of one every 10-15 seconds. Moving forward, accuracy should increase and time to complete the task should decrease, leading to further opportunities.

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“We wanted to make sure people with proximity to the data, their country, the language they speak, had a hand in choosing what language came into the model’s training,” says Jernite.

#BLOOM, the first truly open-science, open-access, and multilingual (46 languages) large language model with 176B parameters (slightly larger than GPT-3) will soon be released as a complete pretrained model. Behind the project is #BigScience, a wide-scale collaboration of over 1,000 researchers.

The project is quite impressive overall, both for the extent of collaboration and outcome. It’s also an engineering delight to watch. The model has been trained using 384 A100 GPUs (with 80 GB of memory each) since March 11, 2022.

BigScience provides updates on training every day (having hit its initial target earlier than planned, the model is currently being trained for “a few more days”). See the links in the comments to follow the updates and download the model. The full model will be released on #HuggingFace (also a partner of the project).

This is a significant step forward for at least two reasons: the way the training data was collected and the core values behind the initiative. BigScience seems to have prioritized data quality by hand crafting the training data. In a world of models that favor kitchen sink approaches (because they can!), this is a progress. More obviously, BLOOM paves the way for a true democratization by removing the strings that have been attached to the use of such models by #OpenScience, #Google, and #Facebook (apply for API access, accredited researcher only etc.).

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While attending the Symposium on Data Science & Statistics to present our study in Improving Algorithms for Big Data session two weeks ago, I learned about useful new methods (and met the great people behind them).

One of my favorites is Sparsity Ranked Lasso (SRL) by Ryan Peterson. The paper mainly focuses on lasso but the idea is also extended to other regularization approaches such as elastic net.

Takeaway: Use SRL over ordinary lasso especially if your model has interaction terms and polynomials. On average, SRL’s predictive performance is better than lasso in the 112 datasets from the Penn Machine Learning Benchmark database. Ryan goes on to show also that SRL overperforms a Random Forest (RF) in a case study both in accuracy and efficiency. Even if SRL performs on par with a RF, why not use SRL as it is both interpretable and explainable!

The part I loved about SRL is the simple yet important challenge, which the authors call “covariate equipoise”: the prior belief that all covariates are equally likely to enter into a model. Basically, a model’s simplicity is usually defined by its parsimony: the number of parameters. This is no matter whether a parameter is an interaction (or a polynomial form) of the other terms in the model. This is problematic for obvious reasons and SRL solves it by treating covariate groups differently based on their type.

And yes, there is a package for that: sparseR. Link to the R package and nicely written paper are in the comments.

R package – Paper

A seemingly persistent effect of the pandemic on #Uber is a 50% decrease in the mobility’s share of revenue (a decrease from a 80% share to less than a 40% share of rides in total revenue). Based on revenue, Uber is now more a delivery company than a mobility company.

This is data centricity extrapolated: a shift from carrying people to carrying objects while solving pretty much the same data-driven optimization problem. The article is from last year but the effect persists as of Q1 2022: Only 37% of the revenue is from carrying people.

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Now that object detection is almost a solved problem, work on the next frontier, text-to-image generation, began to thrive. #Google Research’s most recent work on generative models, #Imagen, uses text embeddings from a large language model called #T5 (similar to #GPT3 and #OPT175B) to encode text for image synthesis.

Interestingly, the study finds that increasing the size of the language model improves performance more than increasing the size of the image diffusion model. Imagen achieves exceptional similarity between real and synthetic images (measured by the distance metric FID, Imagen achieves a score of 7.27 on the COCO dataset). Human raters confirm the performance of the model.

The paper is nicely written with a much-needed ethics discussion at the end, and full of colorful images. Apparently, Imagen does not perform as well when generating images that portray humans.

Synthetic data generation and image restoration are two common use cases of #GANs. I will post a link to one such study on medical images in the comments. #Arts and #crafts is obvious. I can also think of use cases for #fashion and potentially #personalization of products in #retail. What are some other business use cases?

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Interesting read and perspective on modeling the learning in hippocampus and potentially applying the model structure to the design and development of algorithms. Clone-structured cognitive graph (CSCG) uses Markov chains and dynamic Markov compression. So CSCGs form a probabilistic sequence model.

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Models remain interpretable to the extent that the components of the original function are retained. The authors claim 55% to 92% improvement in accuracy in short to medium-term forecasts, which is impressive if generalizable. Model training time increases 4-fold but prediction time improves 14-fold. Developed on PyTorch so it can be parallelized and deployed on GPUs, potentially to reduce training time. Ported to R but using a Python environment.

Looks promising especially for “AI on the edge” type mobile applications.

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