You may know of us as a service that makes it easy to classify texts (by topic, sentiment or intent) or to extract specific data (such as keywords, names, companies and addresses). Over the last couple years we have been working with clients ranging from well known SaaS companies, to oil and gas businesses. They have all had a common need to automate manual processes around text analysis: whether it be to process support tickets, analyze feedback or reviews, or extract information from contracts and documents. We are now launching a new MonkeyLearn version allowing more people to build text analysis models powered by machine learning. Here is what we are excited to share: - New redesigned GUI and API: a cleaner and simpler to use both for technical and non-technical users.

Artificial intelligence is powering more and more of the things we interact with every day, from our gadgets to our cars. But it's also playing a growing role in how society's undergirding resources -- energy, food, and water -- are sourced, secured, and delivered. In this three-part series, we'll consider how AI is being used to make those resources more environmentally and financially sustainable. AI is not a discrete technology, but rather a school of powerful and widely applicable data science tools, which include machine learning, pattern recognition, and natural language processing. All of these tools can squeeze far more useful information out of data, and more quickly than humans could reasonably do on their own.

The influence of Artificial Intelligence (AI) in smart manufacturing is growing rapidly. Artificial Intelligence, according to the ARC Advisory Group, applies to any device that perceives its environment and takes actions that maximize its chance of success toward some goal. This includes a vast range of technologies, such as traditional logic and rules-based systems, that enable computers to solve problems in ways that at least superficially resemble thinking. According to a recent Accenture Artificial intelligence (AI) research report, corporate profits will increase by an average of 38% by 2035 in large part thanks to a more advanced deployment of Artificial Intelligence into financial, IT and manufacturing applications. But at this early stage of AI implementation, is it still not clear how it will be deployed across many possible use cases.

Ceding control of your home to a remote AI might seem like the stuff of science fiction, but the integration of AI into our appliances is already underway. For example, AI is being used to manage energy use in a device most of us use every day – mobile phones. The latest iteration of Google's Android phone operating system includes a function which studies your app habits to ensure battery is deployed only on the ones you like the most. Meanwhile, rarely used apps, which would previously hum away in the background consuming power, are shut down.

The innovation and upliftment in the UAV industry would have no ends. The British firms named BAE Systems and Prismatic are developing a drone capable of remaining in the sky for an entire year. This solar-powered 150 kgs aircraft with a wingspan of 35 metres will be used for surveillance and to provide communication in remote areas. Basically this Long Endurance UAV is a cheaper alternative to conventional satellite technology, and it makes use of the sun's rays during daytime to charge the battery that would allow it to operate overnight. The tests flights of this aircraft (PHASA-35, Persistent High Altitude Solar Aircraft) are expected next year.

Spider silk transformed into fiber for tissue reconstruction; paper that conducts electricity; renewable diesel fuel; and new techniques for regenerating aging or diseased skin. These are just a handful of examples from a new database of over 1,300 new technologies currently making their way through Israeli Technology Transfer Organizations [TTOs] associated with universities, research institutes, and medical institutions. The new searchable database is designed as a layer in Start-Up Nation Finder, a free-to-use innovation discovery platform from Start-Up Nation Central (SNC), an Israeli non-profit that connects businesses, governments, and organizations around the world to Israeli innovation. The new TTO layer gives users the ability to look over the horizon at emerging technologies in drug discovery, advanced materials science, gene sequencing, robotics, and other fields, through cataloguing the patents, companies, and researchers that are registered at 16 TTOs in Israel. SNC combined data from the TTOs themselves, the Israel Technology Transfer Network, and its own data from Start-Up Nation Finder.

Lt. Col. Stanislav Petrov settled into the commander's chair in a secret bunker outside Moscow. His job that night was simple: Monitor the computers that were sifting through satellite data, watching the United States for any sign of a missile launch. It was just after midnight, Sept. 26, 1983. A single word flashed on the screen in front of him. The fear that computers, by mistake or malice, might lead humanity to the brink of nuclear annihilation has haunted imaginations since the earliest days of the Cold War.

The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread. However, the current justification is intuitive rather than analytical. This work proposes one minor modification to the normal ensembling methodology, which we prove allows the ensemble to perform Bayesian inference, hence converging to the corresponding Gaussian Process as both the total number of NNs, and the size of each, tend to infinity. This working paper provides early-stage results in a reinforcement learning setting, analysing the practicality of the technique for an ensemble of small, finite number. Using the uncertainty estimates they produce to govern the exploration-exploitation process results in steadier, more stable learning.

We develop a new learning-based approach to ill-posed inverse problems. Instead of directly learning the complex mapping from the measured data to the reconstruction, we learn an ensemble of simpler mappings from data to projections of the unknown model into random low-dimensional subspaces. We form the reconstruction by combining the estimated subspace projections. Structured subspaces of piecewise-constant images on random Delaunay triangulations allow us to address inverse problems with extremely sparse data and still get good reconstructions of the unknown geometry. This choice also makes our method robust against arbitrary data corruptions not seen during training. Further, it marginalizes the role of the training dataset which is essential for applications in geophysics where ground-truth datasets are exceptionally scarce.

With the many, many uses of AI, we're seeing an increase in researchers, scientists, organisations and start-ups of all kinds looking at ways we can leverage this technology for good. Whilst'high-technology' has become synonymous with high wages, and high investment, there are loads of projects out there applying this technology to poverty reduction. Harnessing the power of AI to help the most desperate in our society is a fantastic way to use it. So, how is this being done? Recognising the causes of poverty is key in looking at how to tackle the problems using technologies.