Suggestions
Anmol Verma
Wharton MBA’23 | Ex-BCG
Anmol Verma is a Product Manager at AdvantageClub.ai and has previously worked in Product Strategy & Operations at Serviceful.3 He is an alumnus of PEC University of Technology in Chandigarh.3 Anmol has expertise in the software industry, particularly in AI and its applications in SaaS businesses. He has written about how AI is transforming the software industry, discussing topics such as AI-powered customer service, workflow streamlining, and decision-making in various SaaS companies.12
Highlights
Eight months ago, I started diving into the world of Gen AI and neural networks with the help of some splendidly thorough sell-side research and a few starter-pack YouTube videos on AI. Two weeks ago, I built my first neural network as part of project work for an AI/ML program I’ve been pursuing. This voyage of learning has transformed my initial awe and curiosity into questions, then fragments of understanding, and finally, a deeper sense of comfort with the topic.
In my latest blog post, I dive into the history and evolution of artificial neural networks (ANNs), tracing their development from early theoretical models to the sophisticated systems driving today’s AI. I also reflect on how these technologies are reshaping our world, challenging us to balance innovation and ethics—a conversation gaining momentum with recent regulations like California’s AI bill.
If you’re intrigued by the intersection of these different topics — or just want to revisit some tech history—I think you’ll enjoy this read.
Stay tuned for Part II, where I’ll dive into how ANNs are trained and optimized.
#AI #NeuralNetworks #GenAI
Competitive advantage and consolidation among the LLM builders are topics I covered in a previous blog post on foundation model businesses (link below). Following the move of the Inflection AI team to Microsoft and the Adept team to Amazon, Character AI has now signed a licensing deal with Google, with cofounders returning to their former employer Google. While this might seem disheartening at first glance—brilliant, passionate founders abandoning their companies after raising significant capital— I believe this trend ultimately bodes well for the industry. Many have also raised regulatory concerns but here is why I believe it isn't so bad:
👉 It will likely make VCs more comfortable allocating greater funds to the application and infrastructure layers now that the model layer has clear leaders emerging. These layers make AI research practically useful for consumers and enterprises!
👉Although not a given, consolidation should lead model builders closer to profitability (OpenAI’s $5 billion loss in 2024 has put a spotlight on this issue)
👉From a resource perspective, it feels responsible as we avoid reinventing the wheel and minimize waste in computing resources.
Big Tech has deep staying power in AI but also faces talent retention issues. This is a reminder that regaining great talent once lost can be fairly expensive, costing millions in this case.
Link to my article below: https://lnkd.in/gZaw9NY2
