Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.

AdaBoost [9] is considered a successful Ensemble method and is commonly used in combination with traditional Machine Learning algorithms, especially Boosted Decision Trees [3]. One of the main principles behind it is the additional emphasis given to the so-called hard to classify examples from a training set. Deep Neural Networks have also had great success on many visual problems, and there are a number of benchmark datasets in this area where the state-of-the-art results are held by some Deep Learning algorithm [12, 4]. Ideas from Transfer of Learning have found applications in Deep Learning; for example, in Convolutional Neural Networks (CNNs), when sub-features learned early in the training process can be carried forward to a new CNN in order to improve generalisation on a new problem of the same domain [13]. It has also been shown that these Transfer of Learning methods reduce the "warm-up" phase of the training, where a randomly-initialised CNN would have to relearn basic feature selectors from scratch.

Google uses continuous streams of data from industry partners in order to deliver accurate results to users. Unexpected drops in traffic can be an indication of an underlying issue and may be an early warning that remedial action may be necessary. Detecting such drops is non-trivial because streams are variable and noisy, with roughly regular spikes (in many different shapes) in traffic data. We investigated the question of whether or not we can predict anomalies in these data streams. Our goal is to utilize Machine Learning and statistical approaches to classify anomalous drops in periodic, but noisy, traffic patterns. Since we do not have a large body of labeled examples to directly apply supervised learning for anomaly classification, we approached the problem in two parts. First we used TensorFlow to train our various models including DNNs, RNNs, and LSTMs to perform regression and predict the expected value in the time series. Secondly we created anomaly detection rules that compared the actual values to predicted values. Since the problem requires finding sustained anomalies, rather than just short delays or momentary inactivity in the data, our two detection methods focused on continuous sections of activity rather than just single points. We tried multiple combinations of our models and rules and found that using the intersection of our two anomaly detection methods proved to be an effective method of detecting anomalies on almost all of our models. In the process we also found that not all data fell within our experimental assumptions, as one data stream had no periodicity, and therefore no time based model could predict it.

The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting. Augmentor is a software package, available in both Python and Julia versions, that provides a high level API for the expansion of image data using a stochastic, pipeline-based approach which effectively allows for images to be sampled from a distribution of augmented images at runtime. Augmentor provides methods for most standard augmentation practices as well as several advanced features such as label-preserving, randomised elastic distortions, and provides many helper functions for typical augmentation tasks used in machine learning.

Deriving an effective facial expression recognition component is important for a successful human-computer interaction system. Nonetheless, recognizing facial expression remains a challenging task. This paper describes a novel approach towards facial expression recognition task. The proposed method is motivated by the success of Convolutional Neural Networks (CNN) on the face recognition problem. Unlike other works, we focus on achieving good accuracy while requiring only a small sample data for training. Scale Invariant Feature Transform (SIFT) features are used to increase the performance on small data as SIFT does not require extensive training data to generate useful features. In this paper, both Dense SIFT and regular SIFT are studied and compared when merged with CNN features. Moreover, an aggregator of the models is developed. The proposed approach is tested on the FER-2013 and CK+ datasets. Results demonstrate the superiority of CNN with Dense SIFT over conventional CNN and CNN with SIFT. The accuracy even increased when all the models are aggregated which generates state-of-art results on FER-2013 and CK+ datasets, where it achieved 73.4% on FER-2013 and 99.1% on CK+.

StarCraft and StarCraft II are among the biggest and most successful games of all time, with players competing in tournaments for more than 20 years. The original game is also already used by AI and ML researchers, who compete annually in the AIIDE bot competition. Part of StarCraft's longevity is down to the rich, multi-layered gameplay, which also makes it an ideal environment for AI research. For example, while the objective of the game is to beat the opponent, the player must also carry out and balance a number of sub-goals, such as gathering resources or building structures. In addition, a game can take from a few minutes to one hour to complete, meaning actions taken early in the game may not pay-off for a long time.

There's been quite a bit of buzz and investment in cognitive computing, and all its associated pieces – machine learning, natural language processing and deep learning. It's good to see that the AI concept has finally gained traction, as it has been the subject of excitement on and off for three decades now. Each AI wave has ended in some disappointment as enterprises found it difficult to apply the technology to everyday business problems and opportunities. Maybe this time, things will be different, and we will see AI progress to new levels. With machine and deep learning the wind in its sails, AI may offer ways for systems and applications to serve businesses and customers with a minimum of human blood, sweat and tears trying to make it all flow.

Google's DeepMind AI has mastered Atari arcade classics and beaten human world champions at board games, and now it's set to take on a much bigger challenge - StarCraft II. The research lab has teamed up with video game company Blizzard Entertainment to open StarCraft II as an AI research environment the firms hope will give insight into the most complex problems related to artificial intelligence. Together, they are releasing a set of tools to accelerate AI research in the strategy game their algorithm can eventually beat it. Google's DeepMind research lab has teamed up with video game company Blizzard Entertainment to open StarCraft II as an AI research environment the firms hope will give insight into the most complex problems related to artificial intelligence DeepMind has tackled games like Atari Breakout, but StarCraft II presents new challenges in how it contains multiple layers and sub-goals. Players must accomplish smaller goals along the way, such as gathering resources or building structures.

Google has been pretty far ahead of the curve when it comes to its artificial intelligence research. The world was shocked when its AI beat a top human player at the game of Go. More recently the company taught AI to use imagination and make predictions. Google is making its AI more human -- to a startling degree. At first glance, it might seem counter-intuitive to build an artificial agent that needs to'sleep' – after all, they are supposed to grind away at a computational problem long after their programmers have gone to bed.

To an untrained AI, the world is a blur of confusing data streams. Most humans have no problem making sense of the sights and sounds around them, but algorithms tend only to acquire this skill if those sights and sounds are explicitly labelled for them. Now DeepMind has developed an AI that teaches itself to recognise a range of visual and audio concepts just by watching tiny snippets of video. This AI can grasp the concept of lawn mowing or tickling, for example, but it hasn't been taught the words to describe what it's hearing or seeing. "We want to build machines that continuously learn about their environment in an autonomous manner," says Pulkit Agrawal at the University of California, Berkeley.