This is a 40 minute talk by Christoph Windheuser, Machine Learning projects builder at ThoughtWorks, from GOTO Berlin 2018.

https://youtu.be/Na012w9N36M?list=PLEx5khR4g7PJW7u0GKxRPIQddtu69boT3

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Please give the talk abstract a read below before giving it a watch:

Bringing Artificial Intelligence applications to life is much more than running an AI framework on an artificial data set. It starts with data gathering, consolidation, cleaning and continues with data science and model building. It goes all the way until deployment, DevOps and lifecycle management. In this talk you’ll learn on how AI will change the today software development and deployment processes.

The text data can have a lot of random word deletion/insertion, missing spaces, wrong characters, … due to error during the OCR process.

The data looks something like this: pastebin

Now I’m thinking of an auto-correction method to clear those spelling errors. Character-level convolution seems pretty good in this case.

Text might also miss some spaces: “I am here today” -> “I am heretoday”. So it would need a way to detect when to add spacing.

However, I wonder if are there any existing data structures / machine learning methods that can correct words based on the context as well as their current spelling. For example, “ch_mpionchip” already contains most of the correct characters, and only a few more characters need to be added. Combining context + spelling information will make it much more easier to predict the word than just using the spelling information.

Google BERT provides great sentence-level embedding, but doesn’t work too well when words are misspell. Glove or word2vec is even worse and can only recognize correct-spelled words. In this case, the best option would be a type of embedding that can retain most of the word information, even when part of it is misspell. For example, humans can easily understand “enviroment” as “environment”, or “chmpionship” as “championship”, “heretoday” as “here today”, …

What do you think is a good way to combine both context/word semantic and word spelling for auto-correction ? Please give your thoughts below.

Thanks for reading!

CALL FOR ABSTRACTS

Computational Creativity and Deep Generative Design:

Bridging the gap

CC-DeepGen-19

June 17th or 18th, Charlotte, North Carolina, USA

Submission date for Extended Abstracts: May 5th

Over the last few years, various models that use deep learning for generation and creation have become increasingly popular, e.g., GANs, VAEs. These deep generative design models create new media across images, text and music. Computational creativity has explored and continues to explore how surprise, curiosity, novelty, and other evaluative criteria, can be formalised for use by creative software in the sciences, the arts, literature, gaming and elsewhere.

This one-day workshop explores issues in the application of evaluation metrics from computational creativity to these deep generative models. The intent is to explore both how deep generative models can be more effectively used in computational creativity, and how evaluation metrics from computational creativity might contribute to deep generative models more generally. By bringing together researchers from both fields the workshop will explore the potential to improve deep generative design.

Areas of interest

GANs, VAEs & variants · Incorporating computational creativity metrics · Increasing diversity in generated artefacts · Adding more autonomy to models · Computational creativity in latent spaces · Generating more targeted artefacts · Innovation engines

Attendees will be able to access all accepted abstracts before attending the workshop to aid in an open atmosphere of discussion.

Submission Guidelines

Attendees will submit extended abstracts (~400wds, about one page) that they are willing to present and discuss.

Extended Abstracts: abstracts should cover radical ideas on how deep generative design can use concepts and ideas from computational creativity, e.g., various metrics of creativity. These abstracts can cover works-in-progress or new directions for deep generative design. Abstracts should be focussed on ideas that can generate discussion and ideation.

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CfA: https://easychair.org/cfp/CC-DEEPGEN-19

Submission link: https://easychair.org/conferences/?conf=ccdeepgen19

Venue

The workshop will be part of the Tenth International Conference on Computational Creativity (ICCC’2019) which will be held in Charlotte, North Carolina, USA.

Contact

All questions about submissions should be emailed to: [jeremiah.hayes@accenture.com](mailto:jeremiah.hayes@accenture.com)

The tradeoff:

Jupyter Notebooks are great for visual output. You can immediately see your output and save it for later. You can easily show it to your colleagues. However, you cannot check them into version control. The json structure is just unreadable.

Version control saves our life because it gives us control over the mighty powers of coding. We can easily see changes and focus on whats important.

Until now, those two worlds were separate. There were some trials to merge the two worlds but none of the projects really felt seamless. The developer experience just was not great.

Introducing Jupytext:

https://github.com/mwouts/jupytext

Jupytext saves two (synced) versions of your notebook. A .ipynb file and a .py file. (Other formats are possible as well.) You check the .py file into your git repo and track your changes but you work in the Jupyter notebook and make your changes there. (If you need some fancy editor commands like refactoring or multicursor, you can just edit the .py file with PyCharm, save the file, refresh your notebook and keep working).

Also, the creator and maintainer, Marc is really helpful and kind and he works really long to make jupytext work for the community. Please try out jupytext and show him some love via starring his github repo. https://github.com/mwouts/jupytext

I’m new to ML scene and looking to build a relatively simple ML web scraper. There are potentially hundreds of pages i plan on scraping, data on them are pretty much the same but formatted differently. Is it possible to manually type up some excel files with the data I need and train the ML build with that?

Hi everyone. I wrote an article about OpenAI’s GPT-2 language model, which recently got published on the FloydHub blog. In it, I explain most of the NLP breakthroughs that led to the creation of what media outlets are referring to as “the AI that’s too dangerous to release.” You can read the article here.

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Like in my previous article, I included a jupyter notebook that can be run with just a few clicks, so that you see an actual, live, demo running in real time. The demo includes a pretrained GPT-2 (courtesy of hugging face) that can generate text based on a prompt that you provide.

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I’ve stayed away from any opinions on the decision not to release the full model since I feel that there’s been plenty of that on twitter already. Here, I aim to provide a clear and straightforward explanation of how exactly the algorithm works, so that you can make your own informed decisions.

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Here’s a link to the article: https://blog.floydhub.com/gpt2/

I’ll share an interesting undergraduate project we’ve done through only pre-trained BERT model.

It can solve TOEIC blank problem(Part 5) better than me!

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There are two types of problems: finding more contextual or grammatical sentence(word)

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1. Selecting Correct Grammar Type.

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Q) The teacher had me _________ scales several times a day. 1. play (Answer) 2. to play 3. played 4. playing 

1. Selecting Correct Vocabulary Type.

   Q) The wet weather _________ her from going shopping. 1. interrupted 2. obstructed 3. impeded 4. discouraged (Answer)

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####Result

Total 7067 datasets

bert-base-uncased 5192( 73.46%)

bert-base-cased 5398(76.38%)

bert-large-uncased 5321( 75.29%)

bert-large-cased 5148( 72.84%)

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My github repository is here:

https://github.com/graykode/toeicbert

Hi all,

I want to know about things an AI has produced that shocked or scared you or generally made you say wtf. I know the sub /r/uncannyvalley exists, but I am trying to start a subreddit specifically for uncanny things generated by an AI. It’s called /r/AIfreakout and I have so far compiled all AI generated media that has made me uneasy. I figure redditors here would know best about such content because you interact with it on a daily basis. Please feel free to post in the comments or in the sub and subscribe. Thanks!