Twitter announced on Monday it has acquired Fabula AI, a London-based machine learning research company. Fabula AI's team will join Twitter and work alongside Sandeep Pandey as part of Twitter's research group focused on natural language processing, reinforcement learning, machine learning ethics, recommendation systems and graph deep learning. Fabula's graph deep learning research is used to detect network manipulation. With this acquisition, Twitter aims to use Fabula's capabilities to better identify bad actors and malicious behavior on the platform, in addition to enhancing its recommendations processes. While the Fabula research team will initially focus on improving the health of conversations happening on Twitter, the company said the team's efforts will expand in the future, aiming to help stop spam and abuse, as well as improve recommendations, the explore tab and the onboarding experience.

Nowadays, the words machine learning and deep learning are on the lips of everyone participating in forums worldwide. In order to distinguish between machine learning and deep learning, below is a brief but detailed description of both concepts. Machine learning involves data-trained algorithms designed to make predictions for the future or to optimize processes and systems. The automation of these algorithms is what is known as artificial intelligence, and more specifically deep learning. In machine learning, there are different types of algorithms with either supervised or unsupervised learning.

While O'Reilly has identified several trends among enterprise companies for adopting artificial intelligence, we decided to drill down further to learn just how businesses worldwide are planning and prioritizing this work. In a recent survey, we asked respondents about revenue-bearing AI projects their organizations have in production. How might their AI adoption patterns change over the course of the next year? In this report, data science experts Ben Lorica and Paco Nathan examine the survey's results to reveal how (and how many) respondents are ramping up AI projects. You'll also learn how the scope of AI use among companies is quickly expanding into deep learning, human in the loop, knowledge graphs, and reinforcement learning.

We've created MuseNet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files. MuseNet uses the same general-purpose unsupervised technology as GPT-2, a large-scale transformer model trained to predict the next token in a sequence, whether audio or text. Since MuseNet knows many different styles, we can blend generations in novel ways[1]. Here the model is given the first 6 notes of a Chopin Nocturne, but is asked to generate a piece in a pop style with piano, drums, bass, and guitar.

I love being a data scientist working in Natural Language Processing (NLP) right now. The breakthroughs and developments are occurring at an unprecedented pace. From the super-efficient ULMFiT framework to Google's BERT, NLP is truly in the midst of a golden era. And at the heart of this revolution is the concept of the Transformer. This has transformed the way we data scientists work with text data – and you'll soon see how in this article.

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) have expanded greatly in the past few years, and are predicted to keep growing in reach. However, despite their prominence in the news and their expansion into mainstream enterprise, many people still don't fully understand the difference between these three areas of computer science. Are they complete different fields? Are they subsets of one another? Are the same algorithms used for each?

Machine learning is a complex discipline. But implementing machine learning models is far less daunting and difficult than it used to be, thanks to machine learning frameworks--such as Google's TensorFlow--that ease the process of acquiring data, training models, serving predictions, and refining future results. Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles together a slew of machine learning and deep learning (aka neural networking) models and algorithms and makes them useful by way of a common metaphor. It uses Python to provide a convenient front-end API for building applications with the framework, while executing those applications in high-performance C .

Exchanging gradients is a widely used method in modern multi-node machine learning system (e.g., distributed training, collaborative learning). For a long time, people believed that gradients are safe to share: i.e., the training data will not be leaked by gradient exchange. However, we show that it is possible to obtain the private training data from the publicly shared gradients. We name this leakage as Deep Leakage from Gradient and empirically validate the effectiveness on both computer vision and natural language processing tasks. Experimental results show that our attack is much stronger than previous approaches: the recovery is pixel-wise accurate for images and token-wise matching for texts. We want to raise people's awareness to rethink the gradient's safety. Finally, we discuss several possible strategies to prevent such deep leakage. The most effective defense method is gradient pruning.

Artificial Intelligence is an excellent tool to improve efficiency and lower cost in many quantitative real world applications, but what if the task is not easily defined? What if the task is generating creativity? Poetry is a creative endeavor that is highly difficult to both grasp and achieve with any level of competence. As Rita Dove, a famous American poet and author states, "Poetry is language at its most distilled and most powerful." Taking Doves quote as an inspiration, our task was to generate high quality haikus using artificial intelligence and deep learning.

In this paper, we study how to learn an appropriate lane changing strategy for autonomous vehicles by using deep reinforcement learning. We show that the reward of the system should consider the overall traffic efficiency instead of the travel efficiency of an individual vehicle. In summary, cooperation leads to a more harmonic and efficient traffic system rather than competition