The days of task-specific algorithms are gone. Advanced Machine learning should continuously update and retrain our models to keep up with the rapidly evolving threat landscape. It's time organizations adopt deep learning that can accurately and autonomously identify relevant patterns and concepts within continually growing amounts of telemetry. The Webroot channel features educational and informative videos from our subject matter experts, the first to harness the cloud and artificial intelligence to protect businesses and individuals against cyber threats. Look for videos on machine learning, artificial intelligence, threat intelligence, home and mobile security, endpoint protection, DNS Protection, and security awareness training--plus global threat news, life at Webroot, and more.

I'm new to RNNs and LSTM and would like some direction with a problem I have. I have a data set containing system metrics (like CPU utilization, disk operations, memory use) of an AWS EC2 instance with a total of 7 columns and around 8000 rows. Each row represent 5 minutes of system performance. I want to build a LSTM model to forecast the features for let's say the next half hour based on previous time steps. What would be the best approach for solving a problem like this?

Last month OpenAI rather dramatically withheld the release of its newest language model, GPT-2, because it feared it could be used to automate the mass production of misinformation. The decision also accelerated the AI community's ongoing discussion about how to detect this kind of fake news. In a new experiment, researchers at the MIT-IBM Watson AI Lab and HarvardNLP considered whether the same language models that can write such convincing prose can also spot other model-generated passages. The idea behind this hypothesis is simple: language models produce sentences by predicting the next word in a sequence of text. So if they can easily predict most of the words in a given passage, it's likely it was written by one of their own.

IMAGINE having to solve a jigsaw puzzle with 1 million pieces, without knowing what the final picture is supposed to look like. It is a challenge that computer designers and logistics planners grapple with every day. Now a version of DeepMind's game-playing artificial intelligence can come up with a more efficient solution. The method might have applications in networking problems including routing traffic through cities, couriering deliveries across a country and designing more efficient computer chips.

As with all emerging technological trends, some elements of artificial intelligence are hyped out of proportion, some elements are ahead of their time, and some even incite fear. However, there remains some truth beneath the hype, cycles and buzzwords. Advancements in AI stand to benefit the energy sector but come with own limitations and practical concerns. Currently, AI, Machine Learning, and their other counterparts Deep Learning, Reinforcement Learning etc, have seen wide coverage in a variety of industries. But what do all these terms mean?

The site is a community resource that connects deep learning research papers with code implementations. It also enables us to take a birds-eye view of the field as a whole. We can see what the research trends are, which frameworks are being adopted by the community, and which techniques are gaining favour. In this post we summarise some of the key developments in deep learning in the second half of 2018 using the data we have from the site. We then briefly discuss the road ahead for the deep learning community.

In part 1 we introduced Q-learning as a concept with a pen and paper example. In part 2 we implemented the example in code and demonstrated how to execute it in the cloud. In this third part, we will move our Q-learning approach from a Q-table to a deep neural net. With Q-table, your memory requirement is an array of states x actions. For the state-space of 5 and action-space of 2, the total memory consumption is 2 x 5 10.

Artificial Intelligence (AI) and machine learning algorithms such as Deep Learning have become part of our daily lives. The technology is used in everything from translation services to improving medical diagnostics and banking to creating computer models in climate science. Our current machines have been very successful at solving hard application problems, displaying seemingly intelligent behavior. Based on the ever-increasing amount of data and more complicated computers available today, learning algorithms appear to reach human capabilities. But the question still remains - Users still don't know exactly how AI systems reach their conclusions. Researchers from TU Berlin, Fraunhofer Heinrich Hertz Institute HHI and Singapore University of Technology and Design (SUTD) wanted to know if AI systems were truly intelligent or did they make some lucky guesses.

Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood. In particular, it is commonly believed that Gaussian encoder/decoder assumptions reduce the effectiveness of VAEs in generating realistic samples. In this regard, we rigorously analyze the VAE objective, differentiating situations where this belief is and is not actually true. We then leverage the corresponding insights to develop a simple VAE enhancement that requires no additional hyperparameters or sensitive tuning. Quantitatively, this proposal produces crisp samples and stable FID scores that are actually competitive with a variety of GAN models, all while retaining desirable attributes of the original VAE architecture. A shorter version of this work will appear in the ICLR 2019 conference proceedings (Dai and Wipf, 2019). The code for our model is available at https://github.com/daib13/