To write this script we enter the first sentence into GPT-2 artificial intelligence. Every single word from then on was written by AI. This is how GPT-2's website describes it: While GPT-2 was only trained to predict the next word in a text, it surprisingly learned basic competence in some tasks like translating between languages and answering questions. That's without ever being told that it would be evaluated on those tasks. Below, the actual text we turned into a "movie".

Generative AI language models like OpenAI's GPT-2 produce impressively coherent and grammatical text, but controlling the attributes of this text -- such as the topic or sentiment -- requires architecture modification or tailoring to specific data. That's why a team of scientists at Uber, Caltech, and the Hong Kong University of Science and Technology devised what they call the Plug and Play Language Model (PPLM), which combines a pretrained language model with one or more attribute classifiers that guide novel text generation. Preliminary results in a preprint paper show that PPLM is able to control a "range" of topics and sentiment styles, importantly without sacrificing fluency and while retaining flexibility that in any combination of differentiable models steers text generation. Their research builds on that published by Google and the University of Michigan late last year, which investigated an architecture that could generate sentences from a given sample and change the mood, complexity, tense, or even voice while preserving the original text's meaning meaning. And it could inform work on Plato, Uber's platform for developing and testing conversational AI, which was released in July with connectors that integrate with existing machine learning and model-tuning frameworks.

This article is the first installment of a two-post series on Building a machine reading comprehension system using the latest advances in deep learning for NLP. Stay tuned for the second part, where we'll introduce a pre-trained model called BERT that will take your NLP projects to the next level! In the recent past, if you specialized in natural language processing (NLP), there may have been times when you felt a little jealous of your colleagues working in computer vision. It seemed as if they had all the fun: the annual ImageNet classification challenge, Neural Style Transfer, Generative Adversarial Networks, to name a few. At last, the dry spell is over, and the NLP revolution is well underway!

An updated deep learning introduction using Python, TensorFlow, and Keras. Everything you need to know about TensorFlow 2.0 https://morioh.com/p/2eb14ed72e5e/

Doing research to see where we currently are with faking voice audio with neural networks/deep learning. Learning to create voices from YouTube clips, and trying to see how quickly we can do new voices. In this case, I've used a Deep Convolutional Text to Speech (DCTTS) model to produce pretty darn good results. The voices in the first 2 minutes are all fake. Real Computer Vision for mobile and embedded https://morioh.com/p/974fc441c295/

Natural Language Generation (NLG) is a well studied subject among the NLP community. With the rise of deep learning methods, NLG has become better and better. Recently, OpenAI has pushed the limits, with the release of GPT-2 -- a Transformers based model that predicts the next token at each time space. Nowadays it's quite easy to use these models -- you don't need to implement the code yourself, or train the models using expensive resources. HuggingFace, for instance, has released an API that eases the access to the pretrained GPT-2 OpenAI has published.

In the history of the quest for human-level artificial intelligence, a number of rival paradigms have vied for supremacy. Symbolic artificial intelligence was dominant for much of the 20th century, but currently a connectionist paradigm is in the ascendant, namely machine learning with deep neural networks. However, both paradigms have strengths and weaknesses, and a significant challenge for the field today is to effect a reconciliation. A central tenet of the symbolic paradigm is that intelligence results from the manipulation of abstract compositional representations whose elements stand for objects and relations. If this is correct, then a key objective for deep learning is to develop architectures capable of discovering objects and relations in raw data, and learning how to represent them in ways that are useful for downstream processing.

Generally, in dynamic spectrum access (DSA) networks, co-operations and centralized control are unavailable and DSA users have to carry out wireless transmissions individually. DSA users have to know other users' behaviors by sensing and analyzing wireless environments, so that DSA users can adjust their parameters properly and carry out effective wireless transmissions. In this thesis, machine learning and deep learning technologies are leveraged in DSA network to enable appropriate and intelligent spectrum managements, including both spectrum access and power allocations. Accordingly, a novel spectrum management framework utilizing deep reinforcement learning is proposed, in which deep reinforcement learning is employed to accurately learn wireless environments and generate optimal spectrum management strategies to adapt to the variations of wireless environments. Due to the model-free nature of reinforcement learning, DSA users only need to directly interact with environments to obtain optimal strategies rather than relying on accurate channel estimations.

Data Science refers to a quantitative and qualitative method and process which is used to increase the productivity and business profitability. It is a technique of extracting, acknowledging and analyzing information such as behavioral data, business patterns, and techniques which are dynamic and necessary for business. Every business organization needs to perform Data Science which can provide various benefits such as increased customer satisfaction, enhancing the productivity and performance of the organization and can also provide the companies with the biggest growth opportunities. Data science is also considered an internal function of any business organization which deals with numbers and figures. Intercourse deep knowledge of recording and analyzing along with dissecting information and presenting the findings to make better decisions making for the management.

DeepMind and co-founder Mustafa Suleyman have decided to go their separate ways. Earlier this year there were disputed reports the two were arguing, some even suggested he'd been placed on leave. But now it seems he's actually left the UK-based enterprise. After a wonderful decade at DeepMind, I'm very excited to announce that I'll be joining @Kent_Walker, @JeffDean and the fantastic team at Google to work on opportunities & impacts of applied AI technologies. Can't wait to get going! More in Jan as I start the new job!