ecerri's Profile

I love this! I’m too far removed by now from my mechanical engineering days, but even though I enjoyed playing with Solidworks and the feeling of modernity using simulations versus hand drafting, I can’t help but feeling that generative design is the next step in the evolution and maybe it’s time for some engineers to cede some of the “control” in design criteria. I think the ability of the computer to come up with an optimal design for weigh-reduction/load-bearing trade-off would not only be faster but beyond some typical engineering creativity (just like nature has interesting structures evolved over a very long period of time). In my opinion, this should go beyond the convenience tool for existing engineers and rather democratize product development the way no-code is doing for software.
My hope for nTopology is that they pursue the end-to-end solution, ideally acquire or partner for the right 3D-printing technology (bypassing manufacturability concerns) and making a great UX (I never enjoyed ANSYS learning curve)

This tool sounds awesome! I’ve been curious about more professional uses of GPT-3 since its release; I didn’t expect this would have so much traction already including at large companies. I like how it has very well defined use cases (blogs, website copy) and multiple languages in which they can iterate and continue to improve the model, particularly if this is used in professional settings (versus playing around with fictional stories with other open language models). I agree with you that this may not be best suited for truly original content but rather as an augmentation and convenience tool for existing creative humans, probably similar to the roll of DALL-E 2 today. One concern I get from AI-generated text is about our long-term prospects: if the majority of content in the internet becomes AI-generated, we will be flooded with information that gets reinforced by a self-perpetuating cycle of web crawlers capturing and feeding AI-text to generate further AI-text in the future — it does not sound very appealing.

Funny enough, my next suggested Youtube video from CopyAi was “Write a 1000 Word blog in minutes with AI” — Carlos, do you have something to confess for this assignment? Perhaps it’s not there yet, but future HBS generations may be taking note.

Vielen Dank, dass Sie dies mit uns teilen!
As an international and language-enthusiast, I have experienced the long evolution of online translators from not really useful / too literal (nonsense word by word) to the current stage that seems pretty good basic functioning. I’m curious how this performs against Google Translate (my default) in a head-to-head match on multiple scenarios of conversational/informal text and more technical/industry-context content. The way it was designed and trained to prioritize quality makes me think that the focus on enterprise customers seems like a great opportunity, but I wonder how competitive and sustainable the freemium model could be against giants like Google and Microsoft that benefit from deep pockets, massive data and embedded/convenience in many daily products.

That’s a really unfortunate result! As an international myself, this made me curious and decided to give Craiyon another shot — this is what I learned:
*For both of my home countries, as well as Romania, it just gave me “creative” variations of the national flag.
*For home cities, and Bucharest, it gave me a city building that looks sort of like it belongs but it’s not really the actual one there.

So I guess the final result is that the tool was under too much traffic before, and we both get underwhelming (but something?) to reminisce home 🙂

That’s a very surprising result, how it appears only one idea for the representation of religion and just do slight variations. It would be interesting to know what their data sources are for training the algorithm and if it’s presumable very western-focused, how can we make sure the percent representation of religion is reflecting the global diversity — since this one is obviously very Christian.

It also made me think, if AI eventually gets out of control and rule our lives, will it pretend to be God as interpreted by the Christian world? There’s been so much speculation how AI could be a neutral overlord at the face of religious conflict, but maybe it’s training biases will show at the end?

That image definitely nails the sentiment but did you find a lot of variety between the proposed panels?
I had a similar feeling from my prompt that it’s capturing the essence of what I was trying to get, but I can’t help but feel it doesn’t provide enough variations or creative explorations that could also represent the prompt (i.e. someone in pajamas still at home instead of someone already at work?)

That’s amazing on the quality difference between Craiyon and Dall-E 2 rendering people! My attempt provided quite disappointing human representations (though I give them the benefit of the doubt it being an expensive “free” resource), but Dall-E 2 seems pretty much stock photos to me — I wonder if it’s able to capture other nuance in the statement (like reading papers while drinking coffee) so that it’s not just plugging in a stock image and has to draw something different.

Interesting enough that my result for Craiyon also included women and I would be curious if their dataset is simply more diverse or what’s going on with the potential biases in Dall-E 2 results.

Thanks for sharing, I’ve never heard of this and looks really interesting! I like the concept of being hyperlocal neighborhood market, and also got the vibe of Facebook Marketplace; however, it seems like this app and community carries a stronger reputation and implicit trust. I share your concerns with it’s chances of success in the US given the less densely populated and city layouts, though I wonder if they may get some traction in specific immigrant neighborhoods that may be more tight-knit. Do you have a sense of where they are operating in the US?

Thanks for sharing, that’s an interesting a application to the shared resource model (Airbnb, uber, etc) I hadn’t heard before!
My concern would be about matching the right location density for minimal disruptions / decent economic cost-benefit on transport/logistic and the ability to integrate with the process flow reliably, but seems like a good opportunity to serve SMB for e-commerce variable capacity demand.

As a non-user, I hear about how addictive, accurate and sometimes niche their algorithm is, so it’s good to get a glimpse from the outside how the algorithm is doing the magic!

Great pun on the penguin waddle!

On a more serious note, it seems like PRH is making the right investments to get back into more control of their future versus being fully dependent on digital behemoths like Amazon — I am concerned though if it might be too late. While initiatives like “ensuring online visibility” (essentially a SEO approach to e-retailers) and “Today’s Top Books” (mining data across the web) may help them improve sales, I am not sure how they can position themselves to retrain the customer to bypass the convenience of Amazon and create a strong 1st party data source for most of their volume. Nonetheless, this new data-driven approach should still help them against competitive publishers at understanding and catering to their customer base.

This is such a cool product; I remember about 5 years ago when I was taking care of a dog, I came across some articles about Furbo being a successfully crowdfunded product. It seems that with time they have been able to grow strongly into the niche of pet cams and use AI to solve other customer pain points like remote training and safety monitoring. While the “pandemic pups” is the perfect wave for them to grow today, I wonder where their next step is going to be (either hardware or new services) and how would the “pet specialization” allow them to survive competition with major players in the indoor camera segment that can recognize pets, like Google Nest or Amazon Ring.

I love that Samsara has become an IoT success story after years of industry hype about the technology innovation. I think they made a great call in becoming a fleet-management software platform versus being a sensors company, since they are able to create and capture a lot more value from the data insights, as you shared about driver coaching and accident prevention. However, I also wonder if their key reliance in transportation and logistics may become a challenge, not only short term in a recessionary environment, but also longer term with autonomous vehicles initiatives for the trucking industry.

Thanks for sharing Nthato. As a former mechanical engineer, I appreciate the use of technology to further push the engineering excellence in Formula 1. While they partnered with the F1 organization for the racing insights, I wonder why they decided to limit their computing capabilities for CFD analysis only to change the design regulations (versus being a tool that R&D engineering teams can use to improve their own design). Also, I would love to hear more about your take as a fan of the AWS F1 insights on the sport (which you mentioned are now being censored) — I think that those cars fully loaded with sensors and the ability to process real-time data in the cloud would make an interesting new layer of fan engagement (like a premium service for an inside view of your team race-time decisions and the effects on a digital twin of the asset in the race).