I know both countries, and in Algeria the Law No. 18-07, effective since August 10, 2023, establishes personal data protection requirements with severe penalties. The text is somewhat more strict than Tunisia.
The most surprising observation for me is that the well known Wu’s Method for proving geometric theorems solved 10 problems ! AlphaGeometry solved 25 problems.
I have switched to using Perplexity, as I found it to be more accurate than to Bard. The last time I used Bard, it hallucinated an entire nonvexistent module of a Java NLP library. I asked about: Pair-HMM Learning and Inference in Java in Mallet ... and it provided incorrect information about a module that does not actually exist.
I believe there is maybe a little contradiction with the NSA's own 'Rule of Two,' as outlined in their Commercial Solutions for Classified Program (CSfC). It specifies two completely independent layers of cryptography to protect data :)
This is a very nice observation that I've found in the dancing links paper :
My trusty old SPARCstation 2 computer, vintage 1992, is able to perform approxi- mately 0.39 mega-updates per second when working on large problems and maintaining the S fields. The 120 MHz Pentium I computer that Stanford computer science faculty were given in 1996 did 1.21 mega-updates per second, and my new 500 MHz Pentium III does 5.94. Thus the running time decreases as technology advances; but it remains essentially proportional to the number of updates, which is the number of times the links do their dance. Therefore I prefer to measure the performance of algorithm DLX by counting the number of updates, not by counting the number of elapsed seconds.
I have a question for which I haven't found a definitive answer yet: is how can one effectively manage typos and Out-of-Vocabulary (OOV) words in RAG systems?
For instance, if I search for a specific product name but accidentally mistype it, the resulting encoded vector might not be similar to the vector for the correctly spelled product name ?
I'm still using old-school maximum-entropy models and HMMs for NLP and integrating them into our products. The model size is less than 1MB, and the inference time is really negligible. These models are used in batch processing for billions of entries and with no access to internet. Not sure if LLMs can fit this task?
One of the problems with sentences provided to LLMs is that they may refer to specific subjects, and could potentially be part of the training set. For example the following is considered extremely difficult :
> oJn amRh wno het 2023 Meatsrs ermtnoTuna no duySan ta atgsuAu ntaaNloi Gflo bClu, gnclcinhi ish ifsrt nereg ecatkj nad ncedos raecer jroam
When you perform a google search for just 2023 Meatsrs, you can find a very similar sentence, and you could decipher the sentence very quickly …
The idea is quite interesting, but it would be more useful if we could perform an action when a threat is detected. For example, in the network module, we can use the eBPF-provided actions: drop, forward, or log.
Regrettably, while we are discussing the author's motives, we may inadvertently overlook the miscalculation by NIST. The crux of NIST's primary error lies in improperly multiplying two costs when they should have been added.
If this assertion holds true, it would be prudent to at least revisit and revise their draft !
From my understanding, Spring Boot takes care of providing the JSON metadata for many libraries, including Jackson. So in practice, it can be more straightforward to use it in a Spring Boot project.
They dropped this company in anticipation of a future version of the CoC and sponsorship rules. Certainly, they have the right to define their rules, but they should revise the rules beforehand.
Another possibility for the period of relatively long peace is the modern equivalent of The Pax Romana. For reference Pax Romana, describes a 200-year era in the Roman Empire from 27 B.C.E. to 180 C.E. This timeframe marked a significant phase of peace and remarkable economic growth achieved through hegemony.
The US has the biggest economy and the largest military spending by far.
Personally, I would find it easier to be less cynical if the European Union's charging standard extended to include everyday items such as my German Braun shaver. As it currently stands, the standard appears to be limited to the likes of handheld mobile devices, tablets, digital cameras, and similar technologies produced mainly by non EU corporations.
This is actually exactly what happened with us. After creating a new account with the intention of exploring the ARM64 services, our account was unexpectedly suspended. I contacted them to have information on the specific concerns regarding my customer information and the reason for deactivating my account. Unfortunately, we did not receive any response to our inquiries.
Very interesting as usual from Fabrice Bellard, but I'm a little bit disappointed this time, because libnc is a closed source DLL. Nevertheless it will be interesting to compare it to the amazing work of Georgi Gerganov: GGML Tensor Library. Both are heavily optimized, supports AVX intrinsics and are plain C/C++ implementation without dependencies.
My understanding about the date of training from ChatGPT page: ChatGPT is fine-tuned from a model in the GPT-3.5 series, which finished training in early 2022. So it was based on a model from early 2022 but it was partially trained (fine-tuned) and published in November 30, 2022. But I can't find conclusive evidence about the exact date. I hope if an OpenAI engineer would enlighten me ...
The problem with many of the tasks that people are trying is: the answers are already available on the internet for those very popular crosswords. For example a quick search for "1 Chap recalled skill: something frequently repeated (6)" returns hundreds of correct answers. It’s highly probable that it has already encountered the questions and answers for this crosswords in the training phase.