Here Comes AI

It’s May 25, 2022. Two days ago

on May 23, 2022, Monday at 17:42:53 UTC Google Brain published their text-to-image diffusion model implementation: Imagen.

Imagen is comparable to Dall-E (from OpenAI) which was initially launched on January 5, 2021 and then greatly improved on via Dall-E 2 on April 6, 2022. Creatively, Dall-E derives its name from a combination of Salvador Dali and Wall-E.

Dall-e 2 had great improvements on Dall-E and the image outcomes were just much more photorealistic and impressive.

Dall-E vs Dall-E 2 results

Imagen from Google Brain

Now, almost a year later, we have Google’s Imagen published which makes Dall-e 2 results look primitive. Imagen’s image outcomes are so impressive that, it looks beyond the artistic capabilities of human designers and illustrators. Of course art and design are subjective terms but every single Imagen image published is truly mind-blowingly accurate, well-designed and photorealistic.

Samples produced by Imagen have higher image quality and better text-image alignment.

I think it’s a tie since OpenAI pioneered this implementation and Google Brain took even Dall-E 2’s achievement to a complete next level. Scoreboard shows OpenAI 1 – 1 Google.

Both Imagen and Dall-E 2 have specific AI characteristics. They are,

  • Generative: Generative vs Discriminative Machine Learning Models.
  • Transformative: Transformative models contribute to the outcomes with nuances and perspectives similar to the humans such as generating a visually pleasant and photorealistic image rather than just combining values. [1]
  • Diffusion Models: Diffusion models denoise images. For example a low resolution or pixelated area can be improved with diffusion models. The image with noise (particularly Gaussian noise) is converted to higher quality samples hence the pixels (and noise) are diffused resulting in a higher quality image. Diffusion models yield great quality results and are computationally more efficient than alternative methods such as autoregressive models. UC Berkeley’s Ho et al. have a fantastic research paper on denoising with diffusion models.
Youtuber Marques Brownlee has a pretty informative and entertaining video on Dall-E 2 posted in May 2022. It’s great and shows a sneak peak to some unofficial text-to-image experiments since Marques was granted permission by OpenAI to make some tests with the model. There aren’t even many videos on Imagen yet but expect crazy amount of discussions and content on Imagen in the next couple of years.
Imagen’s academic paper is also a gem for Artifical Intelligence enthusiasts.

OpenAI states “We recognize that work involving generative models has the potential for significant, broad societal impacts. “

Google Imagen Samples: Images created based on text descriptions

Digital Image Basics

Pretty much all of these incredibly exciting advancements are based on digital image fundamentals. If it seems too confusing to you, you can take a look into how images are represented by numbers at each pixel level and how they can be represented and manipulated numerically through these tutorials:

Once you are able to see images as matrices of numerical values (usually a combination of RBG, red, blue and green and sometimes RGBA with alpha transparency value) covering the resolution array of the image, your whole perspective about how AI and ML can be implemented through computer vision models changes.

Societal Impacts

It’s starting to feel like we are there. AI’s real-world successes are being increasingly felt. Every new applied-AI milestone leaves your mouth open. It’s hard not to imagine the societal impacts. Millions of humans who derive lots of self-worth and satisfaction from their work suddenly becoming unemployed or even worse irrelevant.

At this rate of AI innovation, it’s not hard to imagine the next decade being truly disrupted by AI implementations. By 2032 we would probably have an army of trained AI algorithms that can walk, talk, drive, write better than humans. And the way AI works is, we are not talking about slightly better, they will be crushingly better and the gap will exponentially widen.

Here comes AI and the world is definitely not ready..

Economically, emotionally, physically, socially nor theologically.


[1] Forecasting Transformative AI: An Expert Survey:

French Vocabulary

Seine from Notre-Dame de Paris

Image: Seine from Notre-Dame de Paris

Language studies keep your memory and cognition sharp, provide a meaningful way to socialize and just straight out boost the feel-good hormones. [1].

In a study funded by the Spanish government (Ministry of Economy and Competitiveness [MINECO], learning the meaning of new words showed significant fMRI activation in the ventral striatum (VS), a region associated with reward processing in the brain. [2]

Also, seemingly learning a language can increase the size of your brain. Hopefully, not continuously! [3]

In 2020, I did extensive amount of computer science studies, not to mention there were curfews and travel restrictions everywhere. In retrospective I think my brain really needed some balancing activity and French practice was a fantastic way for that healthy stimulation (Big thanks to my dad for the idea). I was tidying up some of my notes from the pandemic 2020 the other day and I came across lots of French material. Pages of French words that I used to know and didn’t want to forget.

So, I created this wordcloud of French vocabulary from my worn-out notebook. It’s a personalized list of words I noted during my Zoom meetings with Pascal Poirson who’s a friend, businessperson, former CTO and an amazing mentor/coach. (There might be a couple of typos.)

Python word cloud created for personal French vocabulary collection

Now I don’t have to worry about saving some old physical notes and also this word cloud is a pleasure to look at. I would think it also provides a stronger memory connection than just creating a text document since it caters to the visual memory as well.

Python Code ( Wordcloud)

And here is the Python code I’ve used for the visualization above, in case you’d like to create your own word cloud.

I love this Python library called Wordcloud. (Back to computer science) It provides a programmatic and aesthetic way to represent notes, labels, tags, summaries etc. which is perfect!


stopwords = {"le", "je", "ca", "en", "un", "les", "tu", "sois", "son", "ils", "bon", "se", "la", "soi", "suis"}
wc = WordCloud(background_color="white", max_words=1500, mask=None, min_font_size=8,
               width=800, height=500, colormap="viridis", stopwords=stopwords)

The original Python tutorial can be found here: Python word cloud tutorial.

Thank You

French language, culture, and the people made my life richer and more meaningful throughout the years and I’m thankful for that.

Pascal particularly has been incredibly pleasant and resourceful as a friend, language exchange partner and business mentor/coach. His consultations were subtle yet very impactful long term. If you’d like to enquire for his coaching & consultation services in your navigation of business growth, talent management, tech implementations, strategic planning and change related topics, you can reach out to him and his team through Group DT-CF’s website.

Voici un petit post de remerciement!


[1] Emotions and Instructed Language Learning: Proposing a Second Language Emotions and Positive Psychology Model:

[2] The Role of Reward in Word Learning and Its Implications for Language Acquisition:

[3] What happens in the brain when you learn a language?:

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