For senior IT people, 2019 may not look to be the happiest of new years. Many experienced technologists are finding their roles outsourced, with other employers looking for only younger (read: cheaper) employees. "I had three jobs in three years," Mike, a 50-something New York-based IT specialist, told me a year ago. "They've all ended with even new hires being let go and the work outsourced. I had to go before a judge to explain my financial situation, and he said I should take a class to update my skills. As if that would fix it."

Earlier this month the thermal imagery manufacturer FLIR bought the UAV developer Aeryon Labs for $200 million, beating their previous record in publicly disclosed drone investments of $134M. This has been yet another signal that even though the drone industry suffered some hard hits in 2018, the period of consolidation, larger investments and serious R&D advances is ahead. In fact, if one were to look at merely the investment figures for 2018, it wouldn't even be that easy to tell that the drone industry struggled. Records were set, partnerships formed, and accelerators continued to support exceptional start-ups. A total of $702 million was invested into the drone industry in 2018 (up from $625M in 2017), $483 million of which was funnelled into the top 20 drone deals.

The normal life cycle of a machine learning model includes several stages, see Figure 1. There are countless online courses and articles about preparing the data and building models but there is much less material about model deployment. Yet, it is precisely at this stage where all the hard work of data preparation and model building starts to pay off. This is where models are used to score (or get predictions for) new cases and extract the benefits. My intent here is to fill this gap, so that you will be fully prepared to deploy your model using time tested resources.

Machine learning technology is poised to be huge thing in financial services. In fact, two-thirds of UK-based firms are already using it. That is according to two of the UK's top financial regulators. The Financial Conduct Authority (FCA) and the Bank of England have taken a deep dive into how the financial services industry in the country is using machine learning. The research is based on a survey sent out to 300 firms, including banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms.

Understand the fundamental assumptions of time series data and how to take advantage of them. Transforming a data set into a time-series. Start coding in Python and learn how to use it for statistical analysis. Carry out time-series analysis in Python and interpreting the results, based on the data in question. Examine the crucial differences between related series like prices and returns.

The vast majority of work within formal methods (the area of computer science that reasons about hardware and software as mathematical objects in order to prove they have certain properties) has involved analysing models that are fully specified by the user. More and more, however, critical parts of algorithmic pipelines are constituted by models that are instead learnt from data using artificial intelligence (AI). The task of analysing these kinds of models presents fresh challenges for the formal methods community and has seen exciting progress in recent years. While scalability is still an important, open research problem -- with state-of-the-art machine learning (ML) models often having millions of parameters --in this post we give an introduction to the paradigm by analysing two simple yet powerful learnt models using Imandra, a cloud-native automated reasoning engine bringing formal methods to the masses! Verifying properties of learnt models is a difficult task, but is becoming increasingly important in order to make sure that the AI systems using such models are safe, robust, and explainable.

GUWAHATI: A Karnataka-born NRI's company will use Artificial Intelligence (AI) to improve the quality of tea and the health of tea workers in tea-rich …

Square's merchant products have become popular among small business owners. Machine learning is everywhere these days, and finance is no different. Both large banking institutions and scrappy startups are attempting to "disrupt" money with artificial intelligence (AI), collectively helping make AI an integral part of the industry's future. As the head of the commerce platform machine learning team at Square Inc., Marsal Gavalda helps lead the technical aspects of the Jack Dorsey-founded payments platform. Gavalda and his team apply machine learning and automation to Square's payment products, which in recent years have aimed to revolutionize the way merchants conduct transactions.

The summer of 2019 gave us some of the clearest examples yet of how climate change is transforming our world. The hottest June ever was followed up with the hottest July ever -- which also turned out to be the hottest month in recorded history. Scientists memorialized the first Icelandic glacier to lose glacier status and predicted the country would be glacier-free in 200 years. And unprecedented wildfires raged in the normally frozen Arctic, throwing up a smoke cloud nearly the size of Europe. The 2018 report from the International Panel on Climate Change gives us a stark time horizon: 20 years.