Powerpoint Designer utilizes machine learning to provide users with redesigned slides to maximize their engagement and visual appeal.

Up to 4.1 million Designer slides are created daily and the Designer team is adding new types of content continuously.

Time Index:

  • [02:39] Demo – PowerPoint suggests design ideas to help users build memorable slides effortlessly
  • [03:28] A behind-the-scenes look at how PowerPoint was built to make intelligent design recommendations
  • [04:47] AI focused on intelligently cropping images in photos and centering the objects, positioning the images, and even using multi-label classifiers to determine the best treatment.
  • [06:00] How PowerPoint is solving for Natural Language Processing (NLP).
  • [07:32] Providing recommendations when image choices don’t meet the users’ needs.
  • [09:30] How Azure Machine Learning helps the dev team scale and increase throughput for data scientists.
  • [11:10] How distributed GPUs helps the team work more quickly and run multiple models at once.

Computers just got a lot better at mimicking human language. Researchers created computer programs that can write long passages of coherent, original text.

Language models like GPT-2, Grover, and CTRL create text passages that seem written by someone fluent in the language, but not in the truth. That AI field, Natural Language Processing (NLP), didn’t exactly set out to create a fake news machine. Rather, it’s the byproduct of a line of research into massive pretrained language models: Machine learning programs that store vast statistical maps of how we use our language. So far, the technology’s creative uses seem to outnumber its malicious ones. But it’s not difficult to imagine how these text-fakes could cause harm, especially as these models become widely shared and deployable by anyone with basic know-how.

Read more here: https://www.vox.com/recode/2020/3/4/21163743/ai-language-generation-fake-text-gpt2 

By optimizing BERT for CPU, Microsoft has made inferencing affordable and cost-effective.

According to the published benchmark, BERT inferencing based on an Azure Standard F16s_v2 CPU takes only 9ms which translates to a 17x increase in speed.

Microsoft partnered with NVIDIA to optimize BERT for GPUs powering the Azure NV6 Virtual Machines. The optimization included rewriting and implementing the neural network in TensorRT C++ APIs based on CUDA and CUBLAS libraries. The NV6 family of Azure VMs is powered by NVIDIA Tesla M60 GPUs. Microsoft claims that the improved Bing search platform running on the optimized model on NVIDIA GPUs serves more than one million BERT inferences per second within Bing’s latency limits.

In my Data Point earlier today, I mentioned how Google open sourced ALBERT yesterday.

ALBERT is an NLP model based on its revolutionary BERT model the company released last year.

ALBERT has been released as an open source implementation on top of TensorFlow It reduces model sizes in two ways- by sharing parameters across the hidden layers of the network and by factorising the embedding layer According to a report by i-programmer, Google has made ALBERT (A Lite BERT) […]

Machine Learning with Phil show you how to do sentiment analysis with TensorFlow 2 in this natural language processing (NLP) tutorial.

This natural language processing model is relatively straight forward, as it’s just an encoder coupled to some bidirectional layers and a couple dense layers to handle the classification. We’ll compare two different models, one with a single LSTM layer and the other with two LSTM layers and some dropout.

Here’s a talk by Danny Luo Pre-training of Deep Bidirectional Transformers for Language Understanding

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE benchmark to 80.4% (7.6% absolute improvement), MultiNLI accuracy to 86.7 (5.6% absolute improvement) and the SQuAD v1.1 question answering Test F1 to 93.2 (1.5% absolute improvement), outperforming human performance by 2.0%.Toronto Deep Learning Series, 6 November 2018

Paper: https://arxiv.org/abs/1810.04805

BERT is one of the most popular algorithms in the NLP spectrum known for producing state-of-the-art results in a variety of language modeling tasks.

Built on top of transformers and seq-to-sequence models, the Bidirectional Encoder Representations from Transformers is a powerful NLP modeling technique that sits at the cutting edge.

Here’s a great write up on how to build a BERT classifier model in TF 2.0.

The success of BERT has not only made it the power behind the top search engine known to mankind but also has inspired and paved the way for many new and better models. Given below are some of the popular NLP models and algorithms which were inspired by BERT:

Natural language processing (NLP) powered by deep learning is about to change the game for many organizations interested in AI, thanks in particular to BERT (Bidirectional Encoder Representations from Transformers).

Watch this webinar if you want to learn how BERT will power a new wave of language-based applications, from sentiment analysis to automatic text summarization to similarity assessment and more.