Neural Network to be Used For Cooking Receipts

One of the best cooking receipt by an AI is to preheat the oven to 3500 ° C for 8 minutes
06 August 2018   599

The creator of the AIweirdness blog decided to write a cookbook using a neural network. She used the framework from the GitHub resource and published the results.

Despite its early developments, Janelle Shane decided to literally start from scratch and used the possibilities of the textgenrnn framework, transforming them into recipes. The final data for learning the neural network looked like this:

  • framework: textgenrnn, long text mode;
  • memory: 40 characters (default);
  • Duration: about 15 hours with NVIDIA Tesla K80 graphics card (using Google Cloud);
  • temperature: 0,6.

The latest results were better than they were before. But, nevertheless, there are still absurd combinations of ingredients:

  • 1 long granules sugar;
  • 1 Spanish water;
  • 1 cup of cream cheese seeds.

And the names of recipes resemble computer-generated descriptions for products with AliExpress. More details can be found in the AIweirdness blog.

Initially, in order to teach AI to make culinary recipes, Janelle used 30,000 ready-made recipes, which she collected from various sources. However, this experience was not successful. Considering memory of only a few words in length without a certain selection concept, something extraordinary or simply unreal was obtained. For example:

  • 4.5 kg of broccoli dried in a clay pan;
  • half a pint of spicy pieces;
  • 42 cups of milk;
  • Preheat the oven to 3500 ° C for 8 minutes.

And these examples from the cooking tips perfectly illustrate the ineffectiveness of the original method:

  • mix honey, liquid water of the toes, salt and 3 tablespoons of olive oil;
  • throw a frying pan;
  • tear off part of the pan.

In June 2018 AI was taught to create memes. A month later, in July 2018, scientists attempted to improve the model for recognizing speech accents.

Microsoft to Use AI to Create Human Voice

Synthetic voice is nearly indistinguishable from recordings of people
27 September 2018   465

Researchers from Microsoft recorded computer voice, imitating human speech. To overcome the difficulties of the traditional model, they used neural networks for speech synthesis. Microsoft promises to provide support for 49 languages ​​and the ability to create unique voices for the needs of companies in the near future.

Synthesis of speech with the help of neural networks involves comparing the stress and length (so-called prosody) of the speaker's speech units, as well as their synthesis into a computer voice. In systems of traditional speech synthesis, prosody is divided into acoustic and linguistic analysis, controlled by various models. As a result, the speech is noisy and indistinct. Representatives of Microsoft argue that in the model of neural synthesis two stages are combined into one, so the voice sounds like a real one.

The developers are convinced that the synthesis of speech with the help of neural networks will make it more natural to communicate with virtual interlocutors and assistants. Moreover, it will enable you to convert e-books into audiobooks and will allow you to change the scoring of built-in navigators.

Microsoft Neural TTS
Microsoft Neural TTS

Azure computing power is available for real-time use, and Azure Kubernetes is responsible for this. Simultaneous application of neural synthesis of speech together with traditional speaks about expansion and increase of availability of service. At the moment, there are a female voice named Jessa and a man named Guy.

Microsoft is competing in speech recognition and synthesis technologies with Google, which updated its services in late August 2018. Google Cloud announced the release of a stable API for the synthesis of speech Cloud Text-to-Speech with the experimental function of audio profiles and support for several new languages.