After decades of struggle and disappointing results, Artificial Intelligence (AI) is finally coming into its own. Recent advances in computational power, mathematical refinements enabling the creation of much deeper neural networks, and dramatic improvements in techniques used to train machine learning systems have all combined to create applications with real practical value. But what is Artificial Intelligence? In the broadest terms, AI is the attempt to create human level intelligence in machines. This is not something we've achieved and some argue we never will (though I wouldn't bet against human innovation).
AVORA, a London-based company that delivers next-generation Business Intelligence (BI) and machine learning as a service, announces that it has raised €1.7 million in funding from institutional and angel investors. New investor Crane Venture Partners joins angel investors Peter Simon, founder of retailer Monsoon, and Steve Garnett, former chairman of Salesforce EMEA. According to analyst firm Gartner, through 2017, a full 60% of big data projects will fail to go beyond piloting and experimentation, and will be abandoned. AVORA was founded by serial entrepreneur Ricky Thomas, who previously established and sold two online companies – DatingUK and PetMeds. After experiencing the data challenges when running businesses firsthand, Thomas developed AVORA, offering a Software as a Service solution that redefines how companies get value from their data.
Any sufficiently complicated machine learning system contains an ad-hoc, informally-specified, bug-ridden, slow implementation of half of a programming language.1 As programming languages (PL) people, we have watched with great interest as machine learning (ML) has exploded – and with it, the complexity of ML models and the frameworks people are using to build them. State-of-the-art models are increasingly programs, with support for programming constructs like loops and recursion, and this brings out many interesting issues in the tools we use to create them – that is, programming languages. While machine learning does not yet have a dedicated language, several efforts are effectively creating hidden new languages underneath a Python API (like TensorFlow) while others are reusing Python as a modelling language (like PyTorch). We'd like to ask – are new ML-tailored languages required, and if so, why?
AMONG THE MANY EMERGING TRENDS IN THE technology sector, the rise of artificial intelligence (AI) is likely to be one of the most significant over the coming years. AI refers to the ability of machines to perform tasks that would typically be associated with human cognition such as responding to questions, recognizing faces, playing video games or describing objects. Over recent years, AI capability has improved to such an extent that a range of commercial applications are now possible in areas like consumer electronics, industrial automation and online retail. Technology companies of all sizes and in locations all around the world are developing AI-driven products aimed at reducing operating costs, improving decision-making and enhancing consumer services across a range of client industries. And despite a decline in venture capital funding across industries overall in 2016, AI startups raised a record $5 billion globally last year – a 71% annualized growth rate and near-tenfold rise over the 2012 level (see EXHIBIT 1).
Apache Spark is a general-purpose cluster computing framework, with native support for distributed SQL, streaming, graph processing, and machine learning. Now, the Spark ecosystem also has an Spark Natural Language Processing library. Get it on GitHub or begin with the quickstart tutorial. The John Snow Labs NLP Library is under the Apache 2.0 license, written in Scala with no dependencies on other NLP or ML libraries. It natively extends the Spark ML Pipeline API.
The Raspberry Pi 3 Model B is the latest version of the $35 Raspberry Pi computer. The Pi isn't like your typical machine, in its cheapest form it doesn't have a case, and is simply a credit-card sized electronic board -- of the type you might find inside a PC or laptop but much smaller. See also: Raspberry Pi: The smart person's guide As you can see below you can use the Pi 3 as a budget desktop, media center, retro games console, or router for starters. However that is just the tip of the iceberg. There are hundreds of projects out there, where people have used the Pi to build tablets, laptops, phones, robots, smart mirrors, to take pictures on the edge of space, to run experiments on the International Space Station -- and that's without mentioning the wackier creations -- self-driving goldfish anyone?
Marketing has reached a tipping point This year businesses and brands will spend more on digital advertising than traditional forms. Total digital ad spending in 2017 will equal $77.37 billion, or 38.4% of total ad spending, while TV ad spending will total $72.01 billion, or 35.8% of total media ad spending, according to eMarketer. The fastest growing component of internet ad spend will be social media, which will grow at an average rate of 20% a year globally to 2019 when it will hit $55 billion, according to Zenith. So although programmatic advertising on digital media sites has long been heralded the future, the smart businesses are looking to social. And together with artificial intelligence (AI) it appears set to transform how companies connect with their target markets.
We're pleased to announce the next step towards deep learning for every device and platform. Today Vertex.AI is releasing PlaidML, our open source portable deep learning engine. Our mission is to make deep learning accessible to every person on every device, and we're building PlaidML to help make that a reality. We're starting by supporting the most popular hardware and software already in the hands of developers, researchers, and students. The initial version of PlaidML runs on most existing PC hardware with OpenCL-capable GPUs from NVIDIA, AMD, or Intel.
In the context of machine learning, tensor refers to the multidimensional array used in the mathematical models that describe neural networks. In other words, a tensor is usually a higher-dimension generalization of a matrix or a vector. Through a simple notation that uses a rank to show the number of dimensions, tensors allow the representation of complex n-dimensional vectors and hyper-shapes as n-dimensional arrays. Tensors have two properties: a datatype and a shape. TensorFlow is an open source deep learning framework that was released in late 2015 under the Apache 2.0 license.