Do you think AI can do what you are doing now in the next 5 years?
Final Results
52%
Yes
17%
No
11%
Definitely Noo
13%
Probably
7%
I don't really know
#EthiopianAcademics
I met people from different countries around the world doing Bsc/MS/PhD and say my professor/advisor helped me get something, helped me do this cool thing, mentored me etc, but Ethiopian counterparts(not all but almost all) are just a disgrace here.
I met people from different countries around the world doing Bsc/MS/PhD and say my professor/advisor helped me get something, helped me do this cool thing, mentored me etc, but Ethiopian counterparts(not all but almost all) are just a disgrace here.
π’5π2π1
In the spirit of Microsoft's service outages, i'll show you around their office for today. The food is the best thing π
π₯11π2π1
Vision + Language
I wanted to share a very good practical on vision + language. I worked with Yuki Asano to develop this notebook. If you got any interest in this, it could help a lot.
Basically, in this vision+lang models you will have a joint image and text embedding model which means that it maps both text and images to the same embedding space.
Notebookπ
I wanted to share a very good practical on vision + language. I worked with Yuki Asano to develop this notebook. If you got any interest in this, it could help a lot.
Basically, in this vision+lang models you will have a joint image and text embedding model which means that it maps both text and images to the same embedding space.
Notebookπ
π₯5π2
Analysis for Llama 3.1.
1. 15.6T tokens, Tools & Multilingual
2. Llama arch + new RoPE
3. fp16 & static fp8 quant for 405b
4. Dedicated pad token
5. <|python_tag|><|eom_id|> for tools?
6. Roberta to classify good quality data
7. 6 staged 800B tokens long context expansion
Data mixture
- 50% general knowledge
- 25% maths & reasoning
- 17% code data and tasks
- 8% multilingual data
Source
1. 15.6T tokens, Tools & Multilingual
2. Llama arch + new RoPE
3. fp16 & static fp8 quant for 405b
4. Dedicated pad token
5. <|python_tag|><|eom_id|> for tools?
6. Roberta to classify good quality data
7. 6 staged 800B tokens long context expansion
Data mixture
- 50% general knowledge
- 25% maths & reasoning
- 17% code data and tasks
- 8% multilingual data
Source
π2
Forwarded from Techα’α΅ (Hilina)
It's been an incredibly exciting week in the world of AI:
- OpenAI launched a new search tool called SearchGPT
- Meta updated its Llama language model to version 3.1
- Mistral AI released a new and improved Mistral Large 2 model
- DeepMind's AI achieved a silver medal at the International Math Olympiad
- Elon Musk announced plans to develop Grok 2 and 3
- OpenAI launched a new search tool called SearchGPT
- Meta updated its Llama language model to version 3.1
- Mistral AI released a new and improved Mistral Large 2 model
- DeepMind's AI achieved a silver medal at the International Math Olympiad
- Elon Musk announced plans to develop Grok 2 and 3
β‘5
Btw it's depressing that almost allπ of them are almost inaccessible, either beta version or needs high gpus.
π’6
I expect
1. bank rates will rapidly converge to the current parallel market rate, maybe a bit lower.
2. market rate will not go down but it will rise more slowly, over the next 6 months, % change of ETB/USD will be less than the last 6 months.
3. longer term, ETB will strengthen
Source from Nemo Semret
1. bank rates will rapidly converge to the current parallel market rate, maybe a bit lower.
2. market rate will not go down but it will rise more slowly, over the next 6 months, % change of ETB/USD will be less than the last 6 months.
3. longer term, ETB will strengthen
Source from Nemo Semret
πRanking Programming Languages by Energy Efficiency
Compiled languages βtend to beβ the most energy-efficient and fastest-running.
...the five slowest languages were all interpreted: Lua, Python, Perl, Ruby and Typescript. And the five languages which consumed the most energy were also interpreted ones.
Paper
Compiled languages βtend to beβ the most energy-efficient and fastest-running.
...the five slowest languages were all interpreted: Lua, Python, Perl, Ruby and Typescript. And the five languages which consumed the most energy were also interpreted ones.
Paper
β‘7π1