As the experience economy gathers pace, diversity of thought and authenticity are now playing a crucial role in improving a business's bottom line. Ensuring that your workforce reflects the audience it serves is a huge step forward in the name of progress. But can tech help us shake our bad habits? Despite our best attempts to create a more productive work environment where innovative ideas will flourish, it appears some human traits have been holding us back. For example, the financial cost of a bad hire is estimated at more than US$18,700.

Throughout his career, Milosz has been consulting and devising growth strategies for small and start-up businesses, particularly within financial services. His focus areas include search, conversion, user experience and technical developments. Prior to the acquisition of Chilli Fruit Web Consulting, Milosz has been involved in Plus Guidance (an early-stage UK tech start-up, now acquired) and Sigma Digital Marketing Agency based in Oxfordshire.

Date: Tuesday 15th October Time: 18:30-21:00 Location: RAI Exhibition Center *** Registration is free but you must do so in advance through Meetup. The event will be in English and is open to all.

Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R2 of 0.76 and RMSE of 125 g/m2 for shrub biomass and a pseudo R2 of 0.74 and RMSE of 141 g/m2 for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77–79% of the variance, with RMSE ranging from 120 to 129 g/m2 for shrub and total biomass, respectively.

Machine learning in finance has become more prominent recently due to the availability of vast amounts of data and more affordable computing power. Machine learning in finance is reshaping the financial services industry like never before. Leading banks and financial services companies are deploying AI technology, including machine learning (ML), to streamline their processes, optimise portfolios, decrease risk and underwrite loans amongst other things. Here in this article, we will explore some important ways machine learning is transforming the financial services sector and examples of real applications of machine learning in finance. To answer this question and understand the role of machine learning in finance, we must first understand why machine learning is suitable for finance. Machine learning is about digesting large amounts of data and learning from that data in how to carry out a specific task, such as distinguishing fraudulent legal documents from authentic documents. Machine learning in finance is the utilization a variety of techniques to intelligently handle large and complex volumes of information. ML excels at handling large and complex volumes of data, something the finance industry has in excess of. Due to the high volume of historical financial data generated in the industry, ML has found many useful applications in finance. The technology has come to play an integral role in many phases of the financial ecosystem, from approving loans and carrying out credit scores, to managing assets and assessing risk.

Canada's busiest airport will soon be using artificial intelligence-powered technology to detect weapons. The operator of Toronto's Pearson International Airport says it has agreed to test the new system developed at an Ivy League American university and marketed by a B.C. company. Vancouver-based Liberty Defense Holdings Ltd. says the technology, known as Hexwave, can detect both metallic and non-metallic weapons ranging from guns and knives to explosives. It operates by capturing radar images, then using artificial intelligence to analyze those images for signs of a weapon concealed in bags or under clothing. Liberty says the technology is not able to recognize facial features and therefore does not pose a privacy risk, a position experts in the field view with some skepticism.

Lead a team and help develop next-generation AI technologies used by millions. Duolingo AI Research is a nimble and rapidly-growing team that has already revolutionized language learning for more than 300 million people around the world.

An outbreak of a highly contagious mosquito-borne virus in the U.S. has spread quickly to major cities around the world. It's all hands on deck to stop the disease from spreading–and that includes the deployment of artificial intelligence (AI) systems, which scour online news and social media for relevant data and patterns. Working with these results, and data gathered from numerous hospitals around the world, scientists discover an interesting link to a rare neurological condition and a treatment is developed. Within days, the disease is under control. It's not hard to imagine this scenario*--but whether future AI systems will be competent enough to do the job depends in large part on how we tackle AI development today.

CAPE TOWN – One of the major drivers of the Fourth Industrial Revolution (4IR) or the age of intelligentisation is the major advances in Artificial Intelligence (AI) that are supporting and even taking over from humans in many situations. AI is increasingly replacing humans where knowledge could be learned or the decision-making formula is known. It is in particular the AI abilities of machine learning and deep learning that makes AI so powerful in numerous fields. Machine learning refers to the ability of computer systems to learn by itself and to adapt accordingly, allowing them to perform a specific task without explicit instructions. In the Business Report of last Friday I illustrated that AI even transforms the disciplines based on "human touch" such as social work and is used to predict successful youth influencers in an HIV campaign; match homeless people with the best-suited housing and most effective social interventions; and select vulnerable families and children in need of intervention.

The founders of Vise AI met when they were 13, a couple of teenagers more interested in applied artificial intelligence than English class. Fast-forward several years and the pair has relocated from the Midwest to San Francisco to raise money for a financial technology business they've been self-funding since 2016. As teenagers with an inordinate amount of AI knowledge, Samir Vasavada and Runik Mehrotra proved to be quite useful to large businesses, investment bankers and other financiers. Leveraging their AI know-how, they were paid $700 per hour by a consulting firm to teach financial "experts" about AI. Mehrotra, according to Vasavada, is a mathematical prodigy: "And that translates extremely well to AI, right, because what underlies AI is math," Vasavada, co-founder and chief executive officer of Vise AI, tells TechCrunch.