If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Written on 17 November 2017. About 30 percent of local institutions see AI playing an important role in their innovation plans, according to GFT Technologies' Digital Banking Expert Survey. By comparison, 23 percent of sector firms in the UK and Mexico see AI as crucial in their strategy, while only 17 percent of US banks perceive the technology as an important aspect of their overall plans, the study from the financial services vendor says. The survey covered 285 professionals from small to large retail banks based in Brazil, Germany, Italy, Mexico, Spain, Switzerland, the UK and the US. Brazilian firms may be enthusiastic about the potential of artificial intelligence for tasks such as automating customer service and achieving greater customer engagement, but the country still struggles with issues ranging from infrastructure, lack of qualified manpower and effective partnerships with AI vendors and fintechs - that means the number of real initiatives is still small.
Google uses a machine-learning artificial intelligence system called "RankBrain" to help sort through its search results. Wondering how that works and fits in with Google's overall ranking system? Here's what we know about RankBrain. The information covered below comes from three original sources and has been updated over time, with notes where updates have happened. First is the Bloomberg story that broke the news about RankBrain (See also our write-up of it).
Arthur C. Clarke famously stated that "any sufficiently advanced technology is indistinguishable from magic." No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people's hearts. One known property of artificial neural networks (ANNs) is that they are universal function approximators. This means that any mathematical function can be represented by a neural network.
Researchers from our group at QUT and the Australian Centre for Robotic Vision have had six papers accepted to the upcoming Australasian Conference on Robotics and Automation to be held at The University of Technology Sydney. This year the conference trialed a dual submission process with the IEEE International Conference on Robotics and Automation, meaning work can be presented at both conferences but only published in the proceedings of one. The papers cover ongoing research in our lab spanning topics including robotics, positioning and AI for applications in mining, construction safety and autonomous vehicles. I'll give an overview here of the research we're doing, and a wrap up at the end. Despite very high safety standards, work sites of all varieties around Australia still cause large numbers of injuries and occasional fatalities.
Abstract: Automatic Chemical Design leverages recent advances in deep generative modelling to provide a framework for performing continuous optimization of molecular properties. Although the provision of a continuous representation for prospective lead drug candidates has opened the door to hitherto inaccessible tools of mathematical optimization, some challenges remain for the design process. One known pathology is the model's tendency to decode invalid molecular structures. The goal of this thesis is to test the hypothesis that the origin of this pathology is rooted in the current formulation of Bayesian optimization. Recasting the optimization procedure as a constrained Bayesian optimization problem results in novel drug compounds produced by the model consistently ranking in the 100th percentile of the distribution over training set scores.
A Guide to AI Accelerators and Incubators I. Rationale for the post Well, let's be completely honest: the current startups landscape is incredibly messy. There are plenty of ways to get funded to start your own company--but how many of them are not simply'dumb money'? How many of them give you some additional value and really help you scale your business? This problem is particularly relevant for emerging exponential technologies such as artificial intelligence, machine learning and robotics. For those specific fields, highly specialized investors/advisors are essential for the success of the venture.
Before AI systems can be deployed in healthcare applications, they need to be'trained' through data that are generated from clinical activities, such as screening, diagnosis, treatment assignment and so on, so that they can learn similar groups of subjects, associations between subject features and outcomes of interest. These clinical data often exist in but not limited to the form of demographics, medical notes, electronic recordings from medical devices, physical examinations and clinical laboratory and images.12 For example, Jha and Topol urged radiologists to adopt AI technologies when analysing diagnostic images that contain vast data information.13 Li et al studied the uses of abnormal genetic expression in long non-coding RNAs to diagnose gastric cancer.14 Shin et al developed an electrodiagnosis support system for localising neural injury.15
Given the interesting recent article on "The Emergence of a Fovea while Learning to Attend", I decide to make a review of the paper written by Luo, Wenjie et al. called "Understanding the Effective Receptive Field in Deep Convolutional Neural Networks" where they introduced the idea of the "Effective Receptive Field" (ERF) and the surprising relationship with the foveal vision that arises naturally on Convolutional Neural Networks. The receptive field in Convolutional Neural Networks (CNN) is the region of the input space that affects a particular unit of the network. Note that this input region can be not only the input of the network but also output from other units in the network, therefore this receptive field can be calculated relative to the input that we consider and also relative the unit that we are taking into consideration as the "receiver" of this input region. Usually, when the receptive field term is mentioned, it is taking into consideration the final output unit of the network (i.e. a single unit on a binary classification task) in relation to the network input (i.e. It is easy to see that on a CNN, the receptive field can be increased using different methods such as: stacking more layers (depth), subsampling (pooling, striding), filter dilation (dilated convolutions), etc.
This article was written by Saurav Kaushik. Saurav is a Data Science enthusiast, currently in the final year of his graduation at MAIT, New Delhi. He loves to use machine learning and analytics to solve complex data problems. Have you come across a situation when a Chief Marketing Officer of a company tells you – "Help me understand our customers better so that we can market our products to them in a better manner!" I did and the analyst in me was completely clueless what to do!
In a post-competition interview competition's winners noted the value of focusing on feature generation, also called feature engineering. Data scientists spend a significant portion of their time, effort, and creativity working on engineering good features; in contrast, they spend relatively little time running machine learning algorithms. A simple example of an engineered feature would involve subtracting two columns and including this new number as an additional descriptor of your data. In the case of the whales, the winning team represented each sound clip in its spectrogram form and built features based on how well the spectrogram matched some example templates. After that, they then subsequently iterated new features that would help them correctly classify examples that they got wrong through the use of a previous set of features.