aiappreciator

It is true that every major DB ventor, SQL or not, is smashing the AI/vector keyword on their front pages. In Elastic for example, their vector capabilities have gone from laughable to respectable in a year. Its a lot simpler to just use one DB instead of many.

But a question for true DB experts here:

1. Is there any real advantage to building a dedicated vector DB from scratch?

2. Is vector DB something that can be just 'tacked on' to a normal DB with no major performance penalties?

We know from history, that data warehouses are genuinely different from databases, and cloud data warehouses are overwhelmingly superior to on-prem ones. So that emerged as a distinct, enduring category with Snowflake/Databricks/Bigquery.

Don't worry.

GPT written code gets committed to github (After human review, so its working code only). Github data gets shared with OpenAI (Microsoft). So GPT continues to improve.

The recent deterioration is due to reduction in parameter count for GPT-4 to save money.

The customers want to donate money to local taxi operators and drivers? The taxi drivers who barely understand what open source is?

This can work, but definitely not through crowdfunding. This better works through say business associations, say a national taxi association (composed of all small/regional taxi operators). That association can allocate a small portion of the funds to build this open taxi framework.

I think that is extremely obvious, given their interface looks 90% like github.

They are trying to branch out into training AI models and running inference themselves. But their costs for inferencing look extremely uncompetitive compared to GPU providers like coreweave.

H100s consume like 350W. 3000 H100s = 350kW 350*0.2h = 70kWh, about $20 at $0.3/kWh (lets assume data centers have expensive electrical costs).

H100s are intended to be retailed piecemeal. The big enterprise model is DGX GH200, at $10 mil each.

Its just that current supply is extremely short, so the H100s end up only available to big buyers. But that will be resolved in time. Nvidia wants every university lab to have a H100 so no competitor sneaks in there.

You really, really don't want to use inferior models where accuracy is a life or death issue.

This seems like a very promising product, a ChatGPT interface, enterprise version, is a large gap in the market.

However, this part in your advertising, sounds very dubious:"Credal can be deployed fully on-premise for Large Enterprises, including the large language models themselves"

What do you mean the LLMs themselves? Open source I can understand, how are you going to move GPT-4 to on prem? OpenAI is not giving you the weights.

The cutting edge for graphics is all raytracing, and Nvidia still dominates. DLSS3, pathtracing, etc, these are for 'graphics', but heavily dependent on AI post processing, so Nvidia still rules.

So in the gaming market, Nvidia still commands a huge premium. No AMD card can play Cyberpunk on overdrive 4k.

The stock market reacted quite positively to this acquisition (Basically confirmed a few days ago). Buying a soon-bankrupt startup is very cheap, and getting a strong tech stack and already formed team with strategic synergies with the main business is going to be valuable.

This is contrasted with panic acquisitions like say Adobe & Figma.

Amazon only has AWS, their retail business isn't worth that much (maybe $200 bil max, given low margins). Online2Offline has mostly a disappointment, as the margins of tech just could not be replicated in offline businesses.

Microsoft has O365 (Dominant business tool), Github/Vscode (Dominant coding tools, also perfect datasource for AI coding), Gaming (Windows is still overwhelmingly dominant in gaming, and also Xbox exists), AI (They own 50% of OpenAI), and finally Azure.

Alphabet has the consumer google suite (Youtube/gmail/google) that just prints out extremely high margin cash. They also have android/chrome as defensive moats for the core suite.

Hence Amazon only beats out Meta in stock valuation

Meta made the dumb decision to invest in VR, rather than AI. Those giant and expensive VR teams (Or the devs who made horizon worlds...) aren't going to easily transition into AI.

AI generated content is the real core of metaverses, not VR goggles. Hence Nvidia is actually making the right bet on its 'omniverse' infrastructure.

Nvidia has made no layoffs, and I don't expect any within the next 5 years.

Old people do not like radical new technology. That's not a new observation.

It doesn't benefit you to adapt to the new workflow, with all its initial inefficiencies and errors, since you don't get the long term benefit.

Prompt engineering is merely the entry-drug to AI-wrangling.

SD has gotten to the point that someone can fine tune a model (LORAs) with 2 days of time and $2 of GPU time.

There'll be roles for AI wranglers in every large company, where you'll be gathering the dataset and building LORA plugins for the AI to adapt specifically for your codebase/customerbase/documentation etc.

There's also processes involved in building APIs for the AI (AIPI?) to use and interface with your documentation and systems, setting up vector databases, monitoring AI output etc.

People who think there won't be job for expert AI users are just coping. Thinking "haha AI will kill your job too". The steam engine was more powerful than 100 men. In the end it required like 30 people up and down the value chain to support the engines, from coal mining, to coal shoving, to maintenance, to manufacturing.

Stop linking paywalled articles. Also, can anyone summarize the differences between milvus and pinecone? And will vector databases remain separate from mainstream normal databases for long? What architecturally is blocking such a merge?