5 interesting things (22/07/2022)

I analyzed 1835 hospital price lists so you didn’t have to
 – this post had a few interesting things. First, learning about CMS’s price transparency law. In Israel this is a non-issue since the healthcare system works differently, and most of the procedures are covered by the HMOs so there is no such concern. I would be interested in further analysis about the missing or non-missing prices. I.e., for which CPT codes most hospitals have prices, for which CPT codes most hospitals don’t have prices, can we cluster them (e.g. cardio codes? women’s health? procedures usually done on elder people?). This dataset has great potential, and I agree with most of the points in the “Dead On Arrival: What the CMS law got wrong” section.


How to design better APIs – there are several things I liked in this post – first, it is written very clearly and gives both positive and negative examples. Second, it is language agnostic. That last tip – “Allow expanding resources” was mind-blowing to me, so simple to think of and I never thought of adding such an argument. Now I miss a cookie-cutter template to implement all that good advice.


min(DALL-E) – “This is a fast, minimal port of Boris Dayma’s DALL·E Mega. It has been stripped down for inference and converted to PyTorch. The only third-party dependencies are NumPy, requests, pillow, and torch”. Now you can easily generate images using min-dalle on your machine (but it might take a while),


Bonus – https://openai.com/blog/dall-e-2-pre-training-mitigations/

4 Things I Learned From Analyzing Menopause Apps Reviews – Dalya Gartzman, She Knows Health CEO, writes about 4 lessons she learned from analyzing Menopause Apps Reviews. I think it is interesting in 2 ways – app reviews are first, as a product-market fit strategy, to see what users are telling, asking, or complaining about in related.


Inconsistent thoughts on database consistency – this post discusses the many aspects and definitions of consistency and how it is used in different contexts. I absolutely love those topics. Having said that, I wonder if people hold those discussions in real life and not just use common cloud-managed solutions encapsulating some of those concerns.


Playing with DALL·E mini

DALL·E 2 is a multimodal AI system that generates images from text. OpenAI announced the model in April 2022. OpenAI is known for GPT-3, an autoregressive language model with 175 billion parameters. DALL·E 2 uses a smaller version of GPT-3. Read more herehere, and here (the last one also slightly discusses Google’s image).

While the results look impressive at first sight, there are some caveats and limitations, including word order and compositionality issues, e.g., “A yellow book and a red vase” from “A red book and a yellow vase” are indistinguishable. Moreover, as one can see in the “A yellow book and a red vase” example below the images or more of the same, another drawback is that the system cannot handle negation, e.g., “A room without an elephant” will create, well, see below. Read more here.

Since I don’t have access to DALL·E 2, I used DALL·E mini via Hugging Face for all the examples in this post. However, the two models experience the same issues.

A yellow book and a red vase
A room without an elephant

The model might have biases for example check all those software developers who write code, all men (also note that the face are very blurry in contrast to other surfaces in the images) –

software developer writing code
A CTO giving a talk

I decided to troll that a bit to find more limitations or point-out blind spots. Check out the following examples –

Object Oriented Programming
Object Disoriented Programming
Exploratory Data Analysis

The examples above demonstrate that model does not handle abbreviations well. I can think of several reasons for that, but that emphasizes the need to use precise wording and might need to try several times to get the desired result.

Trying negation again (in this case, the abbreviation worked okish) –

Structured Query Language

Which of course reminds all of us of this one –

And a few more –

SOLID principles
Clean Code
Computer Vision

To conclude, I cannot see a straightforward production-grade usage of this model (and it is anyhow not publically available yet) but maybe one use it for brainstorming and ideation. For me it feels like NLP in the days of TF-IDF there is yet a lot to come. Going forward I would love to have some more tunning possibilities like a color scheme or control the similarity between different results (mainly allow more diversity rather than more of the same).