AI focusses on the creation of intelligent machines that can perform functions like speech recognition, learning & problem solving.

We cannot deny that this technology has made its place in many people's heart. But still, we will tell you a brief introduction to machine learning. Machine learning or ML usually defines as an application of Artificial Intelligence. It has the ability to make computers predict something automatically. There are two major types of machine learning algorithms.

The AI/machine learning acquisitions by the Big Four totaled around 70% of the major acquisitions made in the history of the companies.

As you read this essay, you understand each word based on your understanding of previous words. You don't throw everything away and start thinking from scratch again. Traditional neural networks can't do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It's unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones.

The goal here is similar, make the rest of the network learn a common representation, while making the normalization parameters learn language specific semantics. The One-to-Many and Many-to-One models are trained for English to French, German, Italian and Spanish Translation and Vice Versa. The Many to Many model is trained on English-French, French-English, English-German and German-English. The image stylization paper specifies how a N-style network can pick up an N 1th style through fine-tuning an existing model. Similarly, I fine-tune my Many-to-Many model to pick up Portuguese.

Synthetic biology is a rapidly accelerating market, with an estimated conservative global valuation of around US$14 billion.1 To polymath hacker and explorer JJ Hastings, the ability to produce fast, efficient and customisable materials enabled by machine learning and computer-aided design will change the world with an array of entirely new, advanced materials. The extraordinary is now possible: producing spider silk without spiders, egg proteins without chickens and fragrances without flowers. These materials can now be produced by renewable feedstock, reducing the need for large scale agricultural bases or energy-intensive manufacturing. The predicted impact of biomanufacturing drew US$1.7 billion in 2017 investment alone.2 Synthetic biology companies are partnering with fashion designers, heavily backed by VC dollars, as well as forming'organism foundries'.

I have a Variational autoencoder model created in Keras.Encoder is built with three 3D Convolutional layers Flatten Dense layer. Decoder is built with three 3D Transposed Convolutional layers to reconstruct the input 3D images. My goal is to replace Flatten and Dense layer in Encoder with 1x1x1 Convolutional layer. Any ideas how to do that?

In February 2019, OpenAI released a paper describing GPT-2, a AI-based text-generation model based on the Transformer architecture and trained on massive amounts of text all around the internet. From a text-generation perspective, the included demos were very impressive: the text is coherent over a long horizon, and grammatical syntax and punctuation are near-perfect. At the same time, the Python code which allowed anyone to download the model (albeit smaller versions out of concern the full model can be abused to mass-generate fake news) and the TensorFlow code to load the downloaded model and generate predictions was open-sourced on GitHub. Neil Shepperd created a fork of OpenAI's repo which contains additional code to allow finetuning the existing OpenAI model on custom datasets. A notebook was created soon after, which can be copied into Google Colaboratory and clones Shepperd's repo to finetune GPT-2 backed by a free GPU.