With every successive year, the lines between medical devices and consumer electronics become increasingly blurred - medtech companies seek to expand their reach beyond patients, and consumer electronics companies have targeted medical applications as a key focus for their wearable products.

AI chips can empower the intelligence of robotics, with future potential for smarter and more independent cars and robots. Alongside the uses of Chat GPT and chatting with robots at home, the potential for this technology to enhance working environments and reinvent socializing is promising.

Materials informatics applies data-driven strategies to materials R&D. Long before generative AI technology reached peak hype, it had a long history of success in this field. A common approach is to use machine learning models trained on databases of material structures and properties, which then capture the underlying structure-property relationship.

Software reading scans and online management of mental health are examples of digital healthcare that keep up with the modernization of the world, where efficiency and patient satisfaction are two key benefits. The weight of increasing demand for services could be lifted as more options become available for patients to seek treatment, and communication across the medical field can be redesigned.

Materials informatics represents the data revolution's impact on materials R&D. Data-centric approaches, including AI and machine learning, are transforming the way materials scientists and engineers work, getting materials to market faster and driving development in new directions. A range of strategic approaches have emerged, with leading players and commercialized materials developed via data-driven methods emerging.

Thursday 21 March 2024 - Materials Informatics: The Data Revolution and AI Boom come to Materials R&D - The latest commercial developments in the field, including the impact of new industry interest in the age of AI; Assessment of the different strategic approaches being adopted; The key technology pillars required for a MI approach; Industry pain points; Application case studies

Healthcare systems around the world are being put under increasing strain every year. Key challenges they face include overburdened medical staff and a steady rise in patients with chronic health conditions that require high levels of care.

Ross and Rachel's relationship in the TV series Friends was characterized by its on-again, off-again nature, with the inevitability that they end up together in the end. Tesla started its full self-driving hardware with radar in 2016, it began the break-up with radar in 2021, and radar was removed from production entirely in 2021.

Artificial Intelligence is transforming the world as we know it; from the success of DeepMind over Go world champion Lee Sedol in 2016 to the robust predictive abilities of OpenAI's ChatGPT, the complexity of AI training algorithms is growing at a startlingly fast pace, where the amount of compute necessary to run newly-developed training algorithms appears to be doubling roughly every four months.

IDTechEx have launched a new monthly podcast, 'Tomorrow's Tech by IDTechEx', bringing you up to date technology trends. In each episode our host will interview an industry expert from IDTechEx, offering insights into a range of emerging technologies.

IDTechEx's market research report, "AI Chips for Edge Applications 2024-2034: Artificial Intelligence at the Edge", projects that the global AI chips market for edge devices will grow to US$22 billion by 2034, with the consumer electronics industry vertical taking the largest chunk of this revenue figure, at just over 45% by this date.

Due to rising labor costs and labor shortages, coupled with the surge in e-commerce and logistics demands, the mobile robotics market is rapidly expanding. IDTechEx's forecast indicates that the annual market size for mobile robotics in logistics and delivery is set to approach US$150 billion, marked by a robust double-digit compound annual growth rate (CAGR) over the next two decades.

Radar has been one of the most significant additions to vehicles in the past two decades. It provides luxury advanced driver assistance system (ADAS) features like adaptive cruise control (ACC), as well as critical safety features like automatic emergency braking and blind spot detection.

Quantum technologies are evolving at a rapid pace. Spurred by the promise of exponentially faster computation and classically unachievable sensitivity, billions of dollars are being poured into the development of quantum computers and quantum sensors.

Tuesday 28 November - What is the state of the quantum technology market in 2023? Quantum Computing: Benchmarking friends and foes; Quantum Sensing: Sensitivity vs. SWAP-C; Quantum Communications: Trading on trust

Radars are already cemented into the automotive market as they provide popular ADAS features such as adaptive cruise control (ACC) and blind spot detection (BSD). Despite their near ubiquitous presence in vehicles, IDTechEx's new report "Automotive Radar 2024-2044: Forecast, Technologies, Applications" finds that there is still large growth potential for this sensor, mostly driven by new opportunities for short-range radars.

As reported by Nvidia's Q2 financial statement, its second-quarter data center's revenue was a record US$10.32 billion, up 141% from the previous quarter and up 171% from a year ago. With the increasing demand for high-performance servers in data centers, IDTechEx has seen challenges in data center thermal management and tremendous opportunities in this industry.

The road to fully autonomous vehicles is, by necessity, a long and winding one; systems that implement new technologies that increase the driving level of vehicles (driving levels being discussed further below) must be rigorously tested for safety and longevity before they can make it to vehicles that are bound for public streets.

Wednesday 27 September 2023 - AI capabilities, and why these matter; Market revenue forecasts for AI chips at the edge up to and including 2034; Analyses relating to multiple key industry verticals, such as consumer electronics and automotive; Discussion of trends within these industry verticals.