AI News, Machine Learning & AI for Healthcare 2019

Inside The Machine Learning that Google Used to Build Meena: A Chatbot that Can Chat About Anything

It seems that every year Google plans to shock the artificial intelligence(AI) world with new astonishing progress in natural language understanding(NLU) systems.

Just a few weeks into 2020, Google Research published a new paper introducing Meena, a new deep learning model that can power chatbots that can engage in conversations about any domain.

However, despite all the progress, most conversational systems remain highly constrained to a specific domain which contrasts with our ability as humans to naturally converse about different topics.

The alternative is an emerging area of research known as open-domain chatbots that focuses on building conversational agents that chat about virtually anything a user wants.

Despite the excitement around open-domain chatbots, the current implementation attempts still have weaknesses that prevent them from being generally useful: they often respond to open-ended input in ways that do not make sense, or with replies that are vague and generic.

SSA →f(Sensibleness, Specificity) The actual mathematical formulation of the SSA metric is pretty sophisticated but the initial experiments conducted by Google showed a strong correlation with the human-likeness of a chatbot.

The most interesting of those is convolutional layers at the bottom of both its encoder and decoder modules that were added in a similar branching pattern in both places.

This optimization is particularly interesting because the encoder and decoder architectures are not shared during the NAS, so this architecture was independently discovered as being useful in both the encoder and decoder, speaking to the strength of this design.

For decades the AI research community has been debating whether in order to reach a point where a model can carry out high-quality, multi-turn conversations with humans, we could simply take an end-to-end model and make it bigger — by adding more training data and increasing its parameter count — or is it necessary to combine such a model with other components?

Well, the first version of Meena reportedly has 2.6 billion parameters and is trained on 341 GB of text, filtered from public domain social media conversations.

To put that in context, compared to an existing state-of-the-art generative model, OpenAI GPT-2, Meena has 1.7x greater model capacity and was trained on 8.5x more data.

The initial tests with Meena showed that the chatbot was able to engage in conversations across a large variety of topics achieving high levels of SSA.

Python for Machine Learning bootcamp

It is structured the following way: 01 - Python basic 02 - Object-Oriented Programming 03 - Numpy 04 - Matplotlib 05 - Panda 06 - Data Visualization

10 - Linear Regression 11 - Logistic Regression Titanic Dataset 12 - KNN algorithm 13 -SVM 14 - Decision Tree Classifier and Regressor 15 - Random Forest Classifier and Regressor 16 - K-Mean Clustering 17 - Principal Component Analysis (PCA) 18 - Ensemble Learning 19 - Learning Curve 20 - Python Interview Questions Moreover, the course is packed with practical exercises which are based on real-life examples.

OpenAI goes all-in on Facebook’s Pytorch machine learning framework

In what might only be perceived as a win for Facebook, OpenAI today announced that it will migrate to the social network’s PyTorch machine learning framework in future projects, eschewing Google’s long-in-the-tooth TensorFlow platform.

Additionally, the company says it plans to make available its Spinning Up in Deep RL educational resource on PyTorch in early 2020, after which point it intends to investigate scaling AI systems with data parallel training, visualizing those systems with model interpretability, and building general-purpose robotics frameworks.

(OpenAI is in the process of writing PyTorch bindings for its highly optimized blocksparse kernels, and it says it’ll open-source those bindings in the coming months.) PyTorch, which Facebook publicly released in October 2016, is an open source machine learning library based on Torch, a scientific computing framework and script language that’s in turn based on the Lua programming language.


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