[March 24] Interesting Things I Learnt This Week

 1. Ultrasound becoming Utra small - How MEMS technology has miniaturized ultrasound imaging. It discusses the limitations of traditional ultrasound machines. These machines are bulky and require multiple probes. MEMS technology allows for the creation of a single probe that can image the entire body. This probe is small enough to fit in a lab coat pocket.  The article also details the technical aspects of MEMS transducers.

My Take: Any and all technology is bound to get cheap and miniaturized over time with progress in science and technology. Utrasound is something which can be thought of a speaker and a mic in very layman terms. These are bound to get smaller over time and be more effective. But like any other tool, this can be misused and the reason for skewed male/female ratio in India can also be attributed to ultrasound. My worry is if this tech is available more widely and portable, it will further skew the gender ratios in India(and probably a lot of other countries). This particular tech has to be introduced with great care and all the different social impacts be studied carefully so that proper safeguards are introduced before it becomes widely available. 

 

2. Reverse Engineering Perplexity -  Details about how perplexity is probably working. Not a definitive one but a very good speculation. It seems like perplexity basically summarizes the content from the top 5-10 results of google search. If you search for the exact same thing on google and perplexity and compare the sources, they match 1:1. 

My Take: This is a very interesting take. Some others have speculated that they used Bing instead of Google. We can probably build a browser extension which can do this for any search engine. You don't really need billions of dollars except for the indexing those 4 links. I may attempt it sometime, will let you know.


3. OpenAI GPT-4 vs Groq Mixtral-8x7b - This is an article about comparing OpenAI GPT-4 and Groq Mixtral-8x7b. It discusses parsing speed and accuracy. OpenAI GPT-4 is more accurate but slower. Groq Mixtral-8x7b is faster but makes some mistakes. The author thinks Mixtral-8x7b could be improved with better prompts.

My Take: I think this article is more worrying for NVIDIA than its for OpenAI. Their hardware specialty got a very good challenge from Groq LPU. Models will eventually reach the statis like state which the databases have and with so many open source models out there, it will be a challenge to keep these closed sources models relevant especially if you have to deploy them on laptops, mobile phones etc. Groq is very promising and I hope they can break into the hardware world. Then there is TPUs from Google which have remained deployed only in GCP. I hope Groq's LPUs does not end up like that.

 

Comments

Post a Comment

Popular posts from this blog

[17th November 2024] Interesting Things I Learnt This Week

Image
 1. Booleans are a trap : Booleans, while seemingly simple, can introduce complexity and limitations into domain modeling. When used to represent complex states, booleans can lead to unclear code and potential errors. To avoid these pitfalls, consider using enums or enum sets. These approaches provide a more expressive and maintainable way to model complex states in your applications. My Take : I wish I had learned this earlier in life. When working with Google Docs, I realized the limitations of booleans. Expanding boolean-based behaviors often led to adding more fields, making the system complex. Enums would have provided a simpler solution. The problem wasn't just the booleans themselves, but also the need to pass them through multiple layers of the stack, often requiring additional parameters. Another important aspect of enums is the inclusion of an " enum not specified " state. This state explicitly indicates that an enum value hasn't been set, which can be usef...
Read more

[10th November 2024] Interesting Things I Learnt This Week

Image
1. One Company A/B Tested Hybrid Work. Here’s What They Found. - Recent research has revealed that a hybrid work model, where employees work from home three days a week, can significantly boost employee satisfaction and reduce turnover rates. This not only improves employee morale but also saves companies millions of dollars in recruitment and training costs. To successfully implement a hybrid model, businesses should prioritize rigorous performance management systems, coordinate team and company-wide schedules, and secure leadership support. Moreover, executives should experiment with different management practices to optimize their approach and maximize the benefits of hybrid work. My Take : There have been too many debates about remote work versus office work. I would have loved to see data comparing the effectiveness of remote, hybrid, and office-based work. I've worked at companies that mandated office work, like Tata Elxsi, where we didn't have laptops and had to rely on...
Read more

[24th November 2024] Interesting Things I Learnt This Week

Image
1. AI Makes Tech Debt More Expensive - The core argument is that generative AI significantly widens the gap between codebases with low and high tech debt. Companies with clean code can leverage generative AI to write code much faster, while companies with messy legacy codebases struggle to adopt these new tools. This widens the gap between the two codebases, making tech debt even more expensive for companies with outdated systems. My Take : Legacy systems inevitably accumulate technical debt. A common analogy for this is the state of urinals over time: missed targets lead to increasing mess, keeps pushing people away from urinals and leading to more mess in turn. AI-powered coding tools could potentially accelerate the accumulation of tech debt. However, skilled engineers can leverage AI to reduce and even eliminate it. Ultimately, AI is just a tool, and human judgment remains crucial for making informed decisions. I hope AI will be used responsibly to drive positive outcomes. 2. Make...
Read more