This new model converts code from a programming language to another without any supervision. It can take a Python function and translate it into a C++ function, and vice-versa, without any prior examples. It understands the syntax of each language and can thus generalize to any programming language! Watch the video This week my interest was directed towards TransCoder. A new model by the AI team at Facebook Research. Ask any questions or remarks you have in the comments, I will gladly answer everything! Subscribe to not miss any AI news and terms clearly vulgarized! Share this to someone who needs to learn more about Artificial Intelligence! Spread knowledge, not germs! The TransCoder Paper: https://arxiv.org/abs/2006.03511 Video transcript 00:00 this new model converts code from a 00:02 programming language to another without 00:04 any supervision 00:05 it can take a python function and 00:07 translate it into a c 00:08 function and vice versa without any 00:10 prior examples 00:12 it understands the syntax of each 00:14 language and can thus generalize 00:16 to any programming languages let's see 00:18 how they did that 00:21 [Music] 00:25 this is what's ai and i share artificial 00:27 intelligence news every week 00:29 if you are new to the channel and want 00:31 to stay up to date please consider 00:32 subscribing to not miss 00:34 any further news to understand this new 00:36 model by facebook 00:38 called the transcoder we first need to 00:40 introduce what it is 00:41 in short it's a trans compiler meaning 00:44 that it is a source to source translator 00:46 which converts a source code from a high 00:48 level programming language 00:50 such as c plus or python to another one 00:54 currently this type of translation 00:55 requires manual modifications 00:58 in order to work properly because of the 01:00 complexity of the task 01:01 it is hard to respect the target 01:03 language conventions 01:04 when changing from a programming 01:06 language a to a programming language b 01:09 plus since it is a complex task it 01:12 requires a lot of knowledge 01:13 in both target and source programming 01:15 languages to work 01:16 which means a lot of examples and 01:19 computation time 01:20 this is why facebook tried this new 01:22 approach using unsupervised machine 01:24 translation to train this fully 01:26 unsupervised 01:27 neural trans compiler the model was 01:29 trained on open source github projects 01:32 and mainly trained to translate 01:33 functions between three programming 01:35 languages 01:36 c plus plus java and python they 01:38 achieved that using a sequence to 01:40 sequence model with attention 01:42 composed of an encoder and a decoder 01:44 with a transformer architecture 01:46 trained in an unsupervised way 01:48 specifically on functions 01:50 at first they initializes the model with 01:53 cross-lingual mask language 01:55 model pre-training this is done by 01:58 randomly masking 01:59 some of the tokens and training the 02:01 transcoder to predict these mask tokens 02:04 based on the context allowing the model 02:06 to create high quality 02:08 sequence representations regardless of 02:10 the programming language 02:12 once the pre-training is done the 02:13 decoder is then trained 02:15 using the pre-trained transcoder as 02:17 input to always generate 02:19 valid code sequences even when fed with 02:21 noisy data 02:22 increasing the encoder robustness to 02:24 input noise 02:26 in order to translate functions from one 02:28 language to another 02:29 we need to add this last part the back 02:31 translation 02:33 which is composed of two models as you 02:35 can see in this image 02:36 the target to source model which is used 02:39 to translate the target code into the 02:41 source code 02:42 producing a noisy source sequence then 02:45 this sequence is used in the source to 02:47 target model to reconstruct the target 02:49 sequence 02:50 from this noisy input if this doesn't 02:53 tell you anything 02:54 i invite you to check out the videos i 02:56 made on attention 02:57 and transformers which are linked in the 02:59 description below 03:01 as the results show there are still a 03:03 lot of progress to be done in the field 03:05 but the best translation shows over 90 03:08 success 03:08 which is a huge jump in accuracy here 03:10 are some examples of functions 03:12 translated using the transcoder 03:15 i invite you to pause the video and take 03:17 a deeper look at these examples 03:35 programmers will be able to easily 03:37 improve and build 03:38 robust cross-platform software with 03:41 these improvements in translations 03:43 with little or no modifications to make 03:45 to their code 03:46 using a single code base the code and 03:49 pre-training models will be publicly 03:51 available soon as they said in the paper 03:54 of course this was just a simple 03:56 overview of this unsupervised code 03:58 translator 03:58 i strongly recommend to read the paper 04:00 link to the description to learn more 04:02 about it 04:02 and try out the code whenever it's 04:04 available leave a like if you went this 04:06 far in the video 04:08 and since there are over 90 of you guys 04:10 watching that are not subscribed yet 04:12 please consider subscribing to the 04:14 channel to not miss any further news 04:16 clearly explained if you want to support 04:18 the channel i now have a patreon 04:20 linked in the description where you can

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The Facebook TransCoder Explained: Converting Coding Languages with AI

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