The lighting requirements are subjective and one light setting cannot work for all. However, there is little work on developing smart lighting algorithms that can adapt to user preferences. To address this gap, this paper uses fuzzy logic and reinforcement learning to develop an adaptive lighting algorithm. In particular, we develop a baseline fuzzy inference system (FIS) using the domain knowledge. We use the existing literature to create a FIS that generates lighting setting recommendations based on environmental conditions i.e. daily glare index, and user information including age, activity, and chronotype. Through a feedback mechanism, the user interacts with the algorithm, correcting the algorithm output to their preferences. We interpret these corrections as rewards to a Q-learning agent, which tunes the FIS parameters online to match the user preferences. We implement the algorithm in an aircraft cabin mockup and conduct an extensive user study to evaluate the effectiveness of the algorithm and understand its learning behavior. Our implementation results demonstrate that the developed algorithm possesses the capability to learn user preferences while successfully adapting to a wide range of environmental conditions and user characteristics. and can deal with a diverse spectrum of environmental conditions and user characteristics. This underscores its viability as a potent solution for intelligent light management, featuring advanced learning capabilities.

In recent years, the intelligence of various parts of the home has become one of the essential features of any modern home. One of these parts is the intelligence lighting system that personalizes the light for each person. This paper proposes an intelligent system based on machine learning that personalizes lighting in the instant future location of a recognized user, inferred by trajectory prediction. Our proposed system consists of the following modules: (I) human detection to detect and localize the person in each given video frame, (II) face recognition to identify the detected person, (III) human tracking to track the person in the sequence of video frames and (IV) trajectory prediction to forecast the future location of the user in the environment using Inverse Reinforcement Learning. The proposed method provides a unique profile for each person, including specifications, face images, and custom lighting settings. This profile is used in the lighting adjustment process. Unlike other methods that consider constant lighting for every person, our system can apply each 'person's desired lighting in terms of color and light intensity without direct user intervention. Therefore, the lighting is adjusted with higher speed and better efficiency. In addition, the predicted trajectory path makes the proposed system apply the desired lighting, creating more pleasant and comfortable conditions for the home residents. In the experimental results, the system applied the desired lighting in an average time of 1.4 seconds from the moment of entry, as well as a performance of 22.1mAp in human detection, 95.12% accuracy in face recognition, 93.3% MDP in human tracking, and 10.80 MinADE20, 18.55 MinFDE20, 15.8 MinADE5 and 30.50 MinFDE5 in trajectory prediction.

Hazardous Lighting Market size was valued at $1.8 billion in 2020 and it is estimated to grow at a CAGR of 2.29% during 2021-2026. The growth is mainly attributed to the increasing investment on various industries, high penetration of internet of things (IoT), increasing demand for efficient advanced lighting solutions across industries and rapid industrialization in emerging economies. Furthermore, the constant innovation in advanced technologies such as artificial intelligence (AI), machine learning (ML), radio-frequency identification (RFID) along with other wireless technologies, which are being used for producing advanced connected hazardous lighting system; and awareness regarding energy conservation boost the growth of hazardous lighting market. Furthermore, government's initiatives for greener strategies to support sustainable development across the world, is one of the major driving factors of hazardous lighting industry. Hence, the above mentioned factors will drive the adoption rate of various hazardous lighting solutions such as industrial LED lighting, fluorescent lighting, high-intensity discharge lamps and others, during the forecast period 2021-2026.

Recommender systems attempt to identify and recommend the most preferable item (product-service) to individual users. These systems predict user interest in items based on related items, users, and the interactions between items and users. We aim to build an auto-routine and color scheme recommender system for home-based smart lighting that leverages a wealth of historical data and machine learning methods. We utilize an unsupervised method to recommend a routine for smart lighting. Moreover, by analyzing users’ daily logs, geographical location, temporal and usage information, we understand user preferences and predict their preferred light colors. To do so, users are clustered based on their geographical information and usage distribution. We then build and train a predictive model within each cluster and aggregate the results. Results indicate that models based on similar users increases the prediction accuracy, with and without prior knowledge about user preferences.

All the sessions from Transform 2021 are available on-demand now. Thanks to AI advancements and applications, edge computing is already seeing widespread interest from industries ranging from manufacturing to healthcare and retail. Leveraging the growing power and ubiquity of CPUs and neural processing units, edge AI can process growing haystacks of data right where they're being created, finding their needles quickly for local or remote processing. Edge AI is an enabler for early networked autonomous cars -- instantly recognizing and sharing details on accidents, weather conditions, and traffic from vehicle sensors and smart infrastructures in real-time. Similarly, edge AI has empowered wearables to actively monitor seniors for chronic health conditions, alerting remote caregivers within seconds of detecting abnormalities in their biometric data. It's clear that edge AI has the ability to open up a whole new world of insights and opportunities across multiple industries, but connecting the distributed data processors to usefully aggregate their discoveries is a higher-level task.

Artificial Intelligence used to be a fictive narrative from Isaac Asimov's novels and futuristic films. Today it's 2019, and AI is no longer just a product of imagination. Let's take a look at the world's favorite futuristic movies. We already have them in every household. These can be easily found on Amazon and even your local Home Depot.

The accumulation of data in the college setting, plus better hardware and software for putting it to work, means the education community can now make better decisions, retain and educate students with greater success and position themselves for growth in a world of changing demographics, economics and environmental concerns. Here's a look at digital transformation in higher education, and how it is improving it. Kicking off Information Age's Digital Transformation month, we look at everything you need to know about what is digital transformation in business; the challenges, the technologies and above all, how to succeed There are two very big (and very old) problems with the communal learning environment -- the classroom -- that technology is slowly helping us solve. Artificial intelligence is already helping to bring both of these shortcomings of traditional education back into balance. Because colleges and universities increasingly use digital platforms to present course materials and facilitate exams, AI services could easily interface with these dashboards to provide professors and other faculty with detailed and highly useful information.

The gadgets we think of as home essentials have changed beyond recognition in the past few decades. In 1970, just 35 per cent of homes had a landline telephone and there were no mobiles. Now, technologies that were once the stuff of science fiction are part of our daily lives: take Amazon's Alexa – inspired by the talking computer on the Starship Enterprise in Star Trek. Today, millions of British households own these'smart speakers' that allow voice commands to control everything from TVs to lighting systems. Indeed, as of this year, one in five households will be'smart', with devices that are wirelessly connected to the internet, and controlled via smartphone or a hub.

Technology is a wonderful thing; with those small glimpses of the future from sci-fi films are now realities. We have Artificial Intelligence (AI) managing our homes and businesses and automation software streamlining every process. In fact, there is a gadget out there that can help with almost every bit of our daily lives. However, a lot of technology runs on electricity, a need which presents endless issues for those concerned with global warming, climate change and all things green. This is where the use of smart tech or green tech comes in.

Ikea, the major furniture retailer, has been dabbling in augmented and virtual reality. Recently the company launched a survey to gauge how their consumers really feel about artificial intelligence. This indicated that Ikea is looking to AI and virtual assistant capabilities, to improve customer experience. The survey, called "Do You Speak Human?," was created by Ikea's Space10 innovation and design lab. Questions included asking respondents whether consumers want AI to be human-like, male, female or gender-neutral, and even if it should be religious, among other questions.