Mind Foundry, an artificial intelligence startup vying to help insurers decide which drivers should be covered, has raised $22 million in funding, the latest sign of growing demand to deploy AI in critical sectors where there's little room for error. The startup's AI tools are being used to detect cognitive decline in older drivers in Japan to aid Asian insurance giant, Aioi Nissay Dowa Insurance Co., in predicting and preventing accidents. Aioi invested in the funding round along with Parkwalk Advisors and the University of Oxford, said Brian Mullins, Mind Foundry's chief executive officer. The Series B round brings the startup's total funding to $44 million. Traditionally, insurers have relied heavily on details such as the type of car and the driver's age to predict who is more likely to be involved in serious accidents -- and to set insurance premiums.

Mind Foundry has been a pioneer in the development and use of'humble and honest' algorithms from the very beginning of its applications development. As Davide Zilli, Client Services Director at Mind Foundry explains, 'baked in' transparency and explainability will be vital in winning the fight against biased algorithms and inspiring greater trust in AI and ML solutions. Today in so many industries, from manufacturing and life sciences to financial services and retail, we rely on algorithms to conduct large-scale machine learning analysis. They are hugely effective for problem-solving and beneficial for augmenting human expertise within an organisation. But they are now under the spotlight for many reasons – and regulation is on the horizon, with Gartner projecting four of the G7 countries will establish dedicated associations to oversee AI and ML design by 2023.

Mind Foundry, a technology spin-out from the University of Oxford's Machine Learning Research Group (MLRG), announced the commercial launch of a revolutionary humanized machine learning platform. For the first time the new cloud-based platform allows anyone, of any technical ability and in any size of organization, to swiftly unlock the full value of ever increasing volumes of data to make decisions on complex business issues without the need for data scientists. The platform was developed through work with some of the world's largest investment firms, telecommunications providers, manufacturers and heavy industry companies. Organizations can proactively solve business problems by easily leveraging the predictive power of their existing data. The platform automatically builds appropriate machine learning solutions for business problems in minutes or hours, rather than weeks or months, and provides full transparency and auditability of solutions.

In the heart of Oxford's Ashmolean Museum, surrounded by countless monuments to human achievement, the founders of Mind Foundry regaled the audience with their firsthand account of the inception of the company. In a world that so often fearmongers the advent of democratised artificial intelligence (AI), Professor Stephen Roberts and Professor Michael Osborne pose a more optimistic vision for the future of human and machine, not one of AIs, but rather of augmented human intelligence. Since 2015, the AI for Business report has been a staple of the Raconteur publishing calendar, positioning us to narrate the future of this nascent, but rapidly growing, industry. Over that period, we have watched avidly as AI has transitioned from technology to a category on the Gartner Hype Cycle, spawning more emergent technologies than perhaps any other transitive category since big data. Much to this accord, Mind Foundry has debuted as one of the first-mover commercial machine-learning platform-as-a-service (ML PaaS) or automated machine-learning (AutoML) offerings in the market, presenting a solution which promises to keep humans relevant in the age of machine-learning. Mind Foundry has entered the market as the world leader and category-defining product that will turn the much hyped potential of creating and empowering'citizen data scientists' into reality With the vast delta in demand and supply for data science talent, this is an important addition to the marketplace.

Mind Foundry, a technology spin-out from the University of Oxford's Machine Learning Research Group (MLRG), today announced the commercial launch of a revolutionary humanised machine learning platform. For the first time the new cloud-based platform allows anyone, of any technical ability and in any size of organisation, to swiftly unlock the full value of ever increasing volumes of data to make decisions on complex business issues without the need for data scientists. The platform was developed through work with some of the world's largest investment firms, telecommunications providers, manufacturers and heavy industry companies. Organisations can proactively solve business problems by easily leveraging the predictive power of their existing data. The platform automatically builds appropriate machine learning solutions for business problems in minutes or hours, rather than weeks or months, and provides full transparency and auditability of solutions.

After a few more iterations, AuDaS was able to find a model with a 92% classification accuracy. This means that AuDaS was able to learn a relationship in the data that is able to predict Infosys' score 30 days ahead with a 92%! This accuracy can be increased by infusing the Analysts' awareness of context which could affect the score. That is why Machine Learning should augment Analysts, not automate them! Please don't hesitate to reach out with your feedback if you are an Analyst or wish to see a live demo of AuDaS!

In certain applications the objective function is expensive or difficult to evaluate. In these situations, a general approach consists in creating a simpler surrogate model of the objective function which is cheaper to evaluate and will be used instead to solve the optimization problem. Moreover, due to the high cost of evaluating the objective function, an iterative approach is often recommended. Iterative optimizers work by iteratively requesting evaluations of the function at a sequence of points in the domain. Bayesian Optimization adds a Bayesian methodology to the iterative optimizer paradigm by incorporating a prior model on the space of possible target functions.

Mind Foundry has raised 1.2m in seed funding from private investors and investment company Oxford Sciences Innovation to commercialise the technology. The company's technology is based on advanced algorithms and techniques developed by Professors Stephen Roberts and Michael Osborne (pictured here), who lead Machine Learning research at Oxford's Department of Engineering Science. Mind Foundry will build on over 25 years of academic research and applied commercial consulting engagements within the Machine Learning Research Group, a specialist research team within Information Engineering at the University's Department of Engineering Science. Stephen Roberts commented: "We live in an era in which the success of organisations and advances in science and technology are increasingly reliant on the fast and accurate analysis of data. But driving these advances is a deluge of data which is outstripping the computational ability to process it, let alone act upon it. We live in an age of big data – but not necessarily big insight. Mind Foundry has the capability to turn this data into actionable insight."