1,949 karma · joined April 26, 2018
contact at paulw dot tokyo
blog: https://paulw.tokyo
twitter: @PaulWTokyo
> Working with AI feels more like leadership than coding
Original article on IBM research
Hugging face weights: https://huggingface.co/collections/ibm-granite/granite-41-la...
The strange part is that those car can be sold in the EU markets already. They just have to comply with the same pollution and safety standards as other cars. What would justify an exception?
I would politely disagree. Torch started in Lua, and switched to Python because of its already soaring popularity. Whatever drove Python's growth predates modern AI frameworks
> my feeling is that I’d still reach for it if my hands are really physically hurting, and I need to keep working. Usually once I reach the point where I’ve got blisters on my fingers I think it’s better to just take a break
I'm dumbfounded, and impressed in an unhealthy way. Do some of you regularly type so much that you develop blisters?
On your job board I'm seeing slightly different infos about the location requirement. Can you clarify if the role is globally remote or US only?
Location: Europe or Japan
Remote: Flexible
Willing to relocate: Yes
Skills: international marketing professional (7 years). Decided to specialize further, I obtained a master in globalization, business and development from the University of Sussex. Speak English and Japanese, intermediate in French.
Résumé/CV: https://www.linkedin.com/in/yukie-soeda-319499196 (email me for a pdf)
Email: 901stb {@t} gmail.com
(independent from m3at's account)
https://github.com/m3at/hn_jobs_gpt_etl
Only using the plain OpenAI api. This was on GPT-3.5, but it should be easy to move to 4o and make use of the json mode. I might try a quick update this weekend
I'm not as sure for keyboards in VR! There has been a lot of research on non-invasive brain computer interface (BCI), including predictive systems that guess what you want to type, instead of where you're actually typing (example from 8y ago [1]).
Simpler gesture recognition is already on the market (like this one, from 2020, for the apple watch [2]). And now bigger VR players are investing in the tech [3]. I expect useful brain interface to be integrated in common VR devices in a couple of generations.
[1] Air Keyboard: Mid-Air Text Input Using Wearable EMG Sensors and a Predictive Text Modeland a Predictive Text Model; https://digitalcommons.dartmouth.edu/cgi/viewcontent.cgi?art... [2] https://mudra-band.com/ [3] https://www.androidcentral.com/gaming/virtual-reality/zucker...
For the differences, looking at the technical report [1] on selected benchmarks, rounded score in %:
Dataset | Gemini Ultra | Gemini Pro | GPT-4
MMLU | 90 | 79 | 87
BIG-Bench-Hard | 84 | 75 | 83
HellaSwag | 88 | 85 | 95
Natural2Code | 75 | 70 | 74
WMT23 | 74 | 72 | 74
[1] https://storage.googleapis.com/deepmind-media/gemini/gemini_...
Looking at the technical report [1], on selected benchmarks, rounded score in %:
Dataset | Gemini Ultra | Gemini Pro | GPT-4
MMLU | 90 | 79 | 87
BIG-Bench-Hard | 84 | 75 | 83
HellaSwag | 88 | 85 | 95
Natural2Code | 75 | 70 | 74
WMT23 | 74 | 72 | 74
[1] https://storage.googleapis.com/deepmind-media/gemini/gemini_...
The parallel can be made with model weights being static assets delivered in their completed state.
(I favor the full process being released especially for scientific reproducibility, but this is an other point)
Alternatively vespa cloud [3] offer both but… not the easiest to work with, it's tailored for businesses where search is a primary component.
Feel free to shoot me an email (in profile) with your context if you have more questions, in case I can help
[1] This model is a solid baseline if you're working with English text: https://huggingface.co/sentence-transformers/all-mpnet-base-...
[2] OpenAI's embeddings is probably the easiest to get started, and the API is straightforward. It's not the best performing embeddings for retrieval but good enough in some cases: https://platform.openai.com/docs/guides/embeddings/use-cases
SageMaker and VertexAI are the AI services of AWS and GCP respectively, and they both offer embedding generation and vector databases (the two key pieces necessary for embedding search).
There are a bunch of smaller companies offering vector search as a service too, example pinecone to name just one: https://www.pinecone.io/
While I could find some excuses to exclude ARIMA, notably that in practice you need to input some important priors about your time series (periodicity, refinements for turning points, etc) for it to work decently, "prohibitive compute and extensive training time" are just not applicable.
That part is a bit wanky, but the rest of the paper, notably the zero shot capability, is very interesting if confirmed. I look forward for it to be more accessible than a "contact us" api to compare to ARIMA and others myself
If yes I'll reach out, or feel free to do so (email in profile)
Though imo the killer feature of FF address bar is simply that it's tied to a proper search history. Unlike chrome (which I sadly have to use at work), that only keep 90 days of history (!), making the address bar useless for anything but tabs, recent searches and as a link to a search engine. I really can't see an excuse for that behavior, the sqlite used by chrome is a few mb at worst.