This year, we saw a dazzling application of machine learning. The OpenAI GPT-2 exhibited impressive ability of writing coherent and passionate essays that exceed what we anticipated current language models are able to produce. The GPT-2 wasn't a particularly novel architecture – it's architecture is very similar to the decoder-only transformer. The GPT2 was, however, a very large, transformer-based language model trained on a massive dataset. In this post, we'll look at the architecture that enabled the model to produce its results. We will go into the depths of its self-attention layer. My goal here is to also supplement my earlier post, The Illustrated Transformer, with more visuals explaining the inner-workings of transformers, and how they've evolved since the original paper. My hope is that this visual language will hopefully make it easier to explain later Transformer-based models as their inner-workings continue to evolve.

LOS ANGELES, CA, Oct. 20, 2019 (GLOBE NEWSWIRE) -- via NEWMEDIAWIRE – iPR Software, the leader in Online Newsrooms, Digital Publishing, Digital Asset Management (DAM) solutions, and customized integrated solutions, announced its largest technology rollout to date at Public Relations Society of America's International Conference in San Diego, California. With the launch of "Metatron," iPR Software's new application empowers Artificial Intelligence (AI) cloud capabilities as well as integrating the power of machine learning into DAM and customized software platforms to increase productivity and corporate asset sharing across multiple customer ecosystems. This latest software release further advances the company's vision for clients to publish their news and information to Traditional and Social media channels and better engage their B2B & B2C audiences while increasing traffic to their branded media and corporate assets. Leading organization's today are utilizing cloud applications to access the latest technology with encryption algorithms they can securely manage, publish, and share rich branded media content. Metatron introduces core, cloud-based software features that enable customers to securely publish and share key digital media and corporate assets, target practical enterprise use cases, increase workflow efficiencies, and automate mundane tasks to reduce data and storage errors.

Since our recent release of Transformers (previously known as pytorch-pretrained-BERT and pytorch-transformers), we've been working on a comparison between the implementation of our models in PyTorch and in TensorFlow. We've released a detailed report where we benchmark each of the architectures hosted on our repository (BERT, GPT-2, DistilBERT, ...) in PyTorch with and without TorchScript, and in TensorFlow with and without XLA. We benchmark them for inference and the results are visible in the following spreadsheet. We would love to hear your thoughts on the process.

AI has been talked about since the very early days of computing and has attained mainstream use in recent years with the likes of Amazon's Alexa and Apple's Siri. "Just as in the last 40 years, computation has enabled us to change the way we do business and create new products, AI will help us to make better decisions," Carlos Kuchovsky, chief of technology and R&D at BBVA, tells Finextra. "We are now looking at the ways in which it can help us change the way we operate and bring value." The Bank of England has recently reported that machine learning tools are in use at two thirds of UK financial firms, with the average company using it two business areas, which is expected to double in the next three years. It may be through interoperation with cloud and blockchain technology that AI's capabilities will be fully harnessed. AI Utilisation of machine learning and artificial intelligence has become commonplace in everyday life, whether it be in search engines, music streaming services or internet shopping.

The product, which leverages ByteDance's artificial intelligence capabilities, will be a "robotic learning companion" for K-12 students to use at home.

Since time immemorial, sound has been a key source of communication within nature. Birds, bees, whistle of the foliage, thunder, the whisper of air, dolphins speaking underwater and many more such forms of sound are some of the visible examples created by nature, with well-defined purposes. If one were to meander through a dense, human uninhabited forestland, and tip toe in silence, the various forms of the above sounds can be experienced in its raw form, and is a fascinating raw, musical experience! A music that does not have a well-defined structure, but nevertheless, an experience worth savouring! We all know that sound and its associated emotions has been a primary source of human communication.

I think implementing papers is a good approach. Depending on the paper, this might involve just constructing a network of standard layers in the proposed architecture, or it could involve creating custom layers / operations / training loops. Most papers will claim results on standard / openly available datasets, so you can see if you can reproduce their claimed accuracy. Check out Papers with Code: Image Classification subtasks dataset ideas.