Is-it though? Hyperbolic praise of a vendor on the web is quite transparent, hence, a casual reader is hardly going to be fooled. LLM distortion would be just as severe BUT much harder to spot.
The other comment stating "Yann LeCun is the Paul Krugman of AI" does resonate with me. There is a lot to be criticized about his takes on AI in general, and the need for a "worldview" in particular.
Lokad.com | Full stack, Backend, Frontend, Compiler | REMOTE or ONSITE | Paris, France | Full-time | https://www.lokad.com
Lokad is a bootstrapped profitable software company - 60 employees and growing fast - that specializes on predictive supply chain optimization. We are based in France, but the majority of our clients are outside France.
Supply chains remain wasteful and poorly resilient to tail risks (as demonstrated by present day situation). We’re talking about roughly 15% of the worldwide economy: supply chains are vast, and double-digit improvements remain possible. We want to put supply chains on AI autopilot, and deliver above-human performance while doing so.
Technologies used: C#, F#, Typescript, .NET Core, Linux
While there is some obvious US-centric left wing bias in most major LLMs, I am not sure this is what is at play here. I routinely end-up with similar behaviors on all sort of subjects.
With 1M tokens, if snapshotting the LLM state is cheap, it would beat out-of-the-box nearly all RAG setups, except the ones dealing with large datasets. 1M tokens is a lot of docs.
The "cost" is storing the state of the LLM after processing the input. My back-of-the-envelop guesstimate gives me 1GB to capture the 8bit state of 70B parameters model (I might be wrong though, insights are welcome), which is quite manageable with NVMe storage for fast reload. The operator would charge per pay per "saved" prompt, plus maybe a fix per call fee to re-load the state.
Does any of those LLM-as-a-service companies provide a mechanism to "save" a given input? Paying only for the state storage and the extra input when continuing the completion from the snapshot?
Indeed, at 1M token and $15/M tokens, we are talking of $10+ API calls (per call) when maxing out the LLM capacity.
I see plenty of use cases for such a big context, but re-paying, at every API call, to re-submit the exact same knowledge base seems very inefficient.
Right now, only ChatGPT (the webapp) seems to be using such those snapshots.
Thanks! The interesting thing is that my casual observations indicate that GPT itself might already be good enough to self-arbiter itself. Just like a human writer can improve its own writing by iterating over it. In a sense, having humans in the loop were what it took (past) to gain the possibility to reach self-arbitration capacity.
Lokad.com | Full stack, Backend, Frontend, Compiler | REMOTE or ONSITE | Paris, France | Full-time | https://www.lokad.com
Lokad is a bootstrapped profitable software company - 50 employees and growing fast - that specializes on predictive supply chain optimization. We are based in France, but the majority of our clients are outside France.
Supply chains remain wasteful and poorly resilient to tail risks (as demonstrated by present day situation). We’re talking about roughly 15% of the worldwide economy: supply chains are vast, and double-digit improvements remain possible. We want to put supply chains on AI autopilot, and deliver above-human performance while doing so.
Technologies used: C#, F#, Typescript, .NET Core, Linux
Lokad.com | Full stack, Backend, Frontend, Compiler | REMOTE or ONSITE | Paris, France | Full-time | https://www.lokad.com
Lokad is a bootstrapped profitable software company - 50 employees and growing fast - that specializes on predictive supply chain optimization. We are based in France, but the majority of our clients are outside France.
Supply chains remain wasteful and poorly resilient to tail risks (as demonstrated by present day situation). We’re talking about roughly 15% of the worldwide economy: supply chains are vast, and double-digit improvements remain possible. We want to put supply chains on AI autopilot, and deliver above-human performance while doing so.
Technologies used: C#, F#, Typescript, .NET Core, Linux
Lokad.com | Full stack, Backend, Frontend, Compiler | REMOTE or ONSITE | Paris, France | Full-time | https://www.lokad.com
Lokad is a bootstrapped profitable software company - 50 employees and growing fast - that specializes on predictive supply chain optimization. We are based in France, but the majority of our clients are outside France.
Supply chains remain wasteful and poorly resilient to tail risks (as demonstrated by present day situation). We’re talking about roughly 15% of the worldwide economy: supply chains are vast, and double-digit improvements remain possible. We want to put supply chains on AI autopilot, and deliver above-human performance while doing so.
Technologies used: C#, F#, Typescript, .NET Core, Linux
Lokad.com | Full stack, Backend, Frontend, Compiler | REMOTE or ONSITE | Paris, France | Full-time | https://www.lokad.com
Lokad is a bootstrapped profitable software company - 50 employees and growing fast - that specializes on predictive supply chain optimization. We are based in France, but the majority of our clients are outside France.
Supply chains remain wasteful and poorly resilient to tail risks (as demonstrated by present day situation). We’re talking about roughly 15% of the worldwide economy: supply chains are vast, and double-digit improvements remain possible. We want to put supply chains on AI autopilot, and deliver above-human performance while doing so.
Technologies used: C#, F#, Typescript, .NET Core, Linux
Lokad.com | Full stack, Backend, Frontend, Compiler | REMOTE or ONSITE | Paris, France | Full-time | https://www.lokad.com
Lokad is a bootstrapped profitable software company - 50 employees and growing fast - that specializes on predictive supply chain optimization. We are based in France, but the majority of our clients are outside France.
Supply chains remain wasteful and poorly resilient to tail risks (as demonstrated by present day situation). We’re talking about roughly 15% of the worldwide economy: supply chains are vast, and double-digit improvements remain possible. We want to put supply chains on AI autopilot, and deliver above-human performance while doing so.
Technologies used: C#, F#, Typescript, .NET Core, Linux