In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research.[1] The term was coined by analogy to the idea of a nuclear winter.[2] The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later. The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the "American Association of Artificial Intelligence"). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research.[2] At the meeting, Roger Schank and Marvin Minsky--two leading AI researchers who had survived the "winter" of the 1970s--warned the business community that enthusiasm for AI had spiraled out of control in the 1980s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.[2] Hype is common in many emerging technologies, such as the railway mania or the dot-com bubble. The AI winter is primarily a collapse in the perception of AI by government bureaucrats and venture capitalists.

Working in partnership with the Scottish Government (SG) and with the assistance of the broader public sector and innovation community, Scottish Enterprise (SE) wishes to procure R&D services to create new products and services that will help in the Government's efforts to address the Climate Emergency. In doing so, we are specifically looking for solutions that exploit Artificial Intelligence (AI) techniques to deliver these solutions. This builds upon the proposition that Scotland has excellent capabilities in the application of AI. Working in partnership with the Scottish Government (SG) and with the assistance of the broader public sector and innovation community, Scottish Enterprise (SE) wishes to procure R&D services to create new products and services that will help in the Government's efforts to address the Climate Emergency. In doing so, we are specifically looking for solutions that exploit Artificial Intelligence (AI) techniques to deliver these solutions.

The purpose of this article is two-fold, first to help organisations better understand the challenges of starting AI-projects and second how to systematically approach them. The intended target audience are C-level executives and managers who are considering to invest in AI-solutions for their business. During the last year we at Webstep have worked on developing a system and a process to facilitate the start-up of machine learning projects. We loosely follow the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology: starting with business understanding, assessing the AI-readiness within the organisation, identifying promising areas for AI and finally finishing with deployment or evaluation depending on the project goal. In this article we describe an example of how we applied the methods and system in a project for an organization that works with forecasts.

Looking ahead, innovation leaders need to take a look at these tech trends that will drive business operations. FREMONT, CA: Technology is an essential part of our daily life. From the moment one wakes up until going back to sleep, everyone today relies heavily on technology. With the introduction of machine learning and artificial intelligence, enterprises have been able to automate several business activities. With the evolution of digitalization, the technology trends change, the choices change, and the demands change.

Salesforce is hoping to make AI an indispensable part of every business with a range of smart tools and platforms. At its Dreamforce 2019 event in San Francisco this week, the software giant unveiled a host of enterprise-focused services and tools it hopes will spur on AI adoption in companies of all sizes. Salesforce also unveiled its own-brand smart speaker (pictured above) modelled on its Einstein AI mascot, although whether it will go on general sale is still unclear. This growth in business-focused AI is set to be powered by a major expansion to Einstein Voice Assistant, which allows Salesforce users to communicate directly to the platform and get answers instantly. Einstein Voice Assistant is now able to be tailored to any particular industry, allowing for custom-built skills that can cover tasks from checking order history to sentiment tracking.

Previously, research groups were able to analyze brain activity using electroencephalogram (EEG) tests from which they could use the data to develop predictive models. I was with a friend who had a seizure, and it was incredibly scary. We were sitting at a bar in Brooklyn watching a Mets game, nothing out of the ordinary, when suddenly he just stood up and fell backwards, knocked his head into a chair and went into convulsions. I had no idea he suffered from periodic epileptic seizures. And it's much, much more horrible if you're the one who suffers from seizures.

The Oil & Gas sector is ripe for innovation, particularly when it comes to Artificial Intelligence (AI). A recent report conducted by Markets & Markets suggested that the value of AI within the Oil and Gas industry could reach a monumental $2.85 billion by 2022 – with an astonishing compound annual growth rate (CAGR) of 13%. Right now, the potential application of AI in Oil and Gas is broad and diverse, from process efficiencies and facilities management and safety, to forecasting, planning and surveying. We recently explored how augmented reality (AR) is already revolutionising the oil and gas sector and AR in the new enterprise. One fantastic example of how AI is impacting the Oil and Gas industry is a recent initiative conducted by ExxonMobil.

Ride-sourcing services are becoming an increasingly popular transportation mode in cities all over the world. With real-time information from both drivers and passengers, the ride-sourcing platform can reduce matching frictions and improve efficiencies by surge pricing, optimal vehicle-trip assignment, and proactive ridesplitting strategies. An important foundation of these strategies is the short-term supply-demand forecasting. In this paper, we tackle the problem of predicting the short-term supply-demand gap of ride-sourcing services. In contrast to the previous studies that partitioned a city area into numerous square lattices, we partition the city area into various regular hexagon lattices, which is motivated by the fact that hexagonal segmentation has an unambiguous neighborhood definition, smaller edge-to-area ratio, and isotropy. To capture the spatio-temporal characteristics in a hexagonal manner, we propose three hexagon-based convolutional neural networks (H-CNN), both the input and output of which are numerous local hexagon maps.

A large number of firms are still reporting a 50% failure rate for AI projects, pointing to the lack of unrealistic expectations and AI skills as the two main roadblocks. In such a rapidly growing market, the adoption of AI needs a clear plan to overcome these challenges. Whether it's through the growing AI investment – which is expected to skyrocket to $98bn by 2023 – or the fact that AI projects are set to double over the upcoming year, organizations are improving efficiency, performance, and analytics capabilities with the support of AI to solve real-world problems faster. McKinsey found that in two-thirds of the use cases, AI – specifically deep learning – improved performance beyond that provided by other technologies. Below are the steps to ensure the smooth and successful implementation of AI.

Artificial intelligence and automation are responsible for a growing number of decisions by pubic authorities in areas like criminal justice, security and policing and public administration, despite having proven flaws and biases. Facial recognition systems are entering public spaces without any clear accountability or oversight. Lawyers must play a greater role in ensuring the safety and accountability of advanced data and analytics technologies, says Karen Yeung at the University of Birmingham. The dream of artificial intelligence stretches back seven decades, to a seminal paper by Alan Turing. But only recently has AI been commercialized and industrialized at scale, weaving its way into every nook and cranny of our lives.