Optical components play a key role in the optimal performance of today's cobots, allowing them to see and respond to their environment in real-time and ensuring that they operate safely and effectively. As we reflect on history, we can observe numerous systems with resemblances to robots. In fact, some concepts of robotics and artificial intelligence (AI) are speculated to date back to ancient times. The resourceful Ancient Greeks, for instance, created sophisticated automated puppet theatres1, while the Ancient Egyptians devised ingenious mechanical operating systems embodied in figurines2. Fast-forwarding to the present era, significant progress has been made since the days of Ancient Greece and Egypt.

Artificial Intelligence (AI) surged in popularity last year, as both businesses and the public saw first-hand examples of its potential applications. Companies like OpenAI released a wave of public demos, such as the advanced chatbot ChatGPT that has drawn the attention of Microsoft. Text-to-image generators such as Dall-E 2, Midjourney and Stable Diffusion took the limelight, as millions of users began to create their own AI-generated art, to the anger of artists and companies such as Getty Images. In its tech predictions for 2023, Dell Technologies Ireland said AI could become the "main engine of innovation" for the year, as more organisations adopt the technology to harness the full potential of data and support teams across a business. The sector has shown no sign of slowing down so far this year, with OpenAI reportedly in talks to raise funds at a $29bn valuation, Apple rolling out an AI audio narration tool and Microsoft researching an AI model that can simulate anyone's voice from only three seconds of audio.

The leaps AI made last year are expected to boost the digital transformation of businesses, while disrupting various sectors such as cybersecurity and autotech. Artificial intelligence (AI) surged in popularity last year, as both businesses and the public saw first-hand examples of its potential applications. Companies such as OpenAI released a wave of public demos, including the advanced chatbot ChatGPT which has drawn the attention of Microsoft. Text-to-image generators such as Dall-E 2, Midjourney and Stable Diffusion took the limelight as millions of users began to create their own AI-generated art, much to the anger of artists and companies such as Getty Images. In its tech predictions for 2023, Dell Technologies Ireland said AI could become the "main engine of innovation" for the year, as more organisations adopt the technology to harness the full potential of data and support teams across a business.

But how will AI and 5G most affect our everyday business lives? What are 5G and AI use cases? Convergence makes 5G and AI use cases exciting: 5G could unleash the artificial intelligence revolution, moving it into a different league and creating new AI use cases. When Apple launched the iPhone, few people understood its significance. There was a reason for this.

Machine vision is increasingly important for many applications, such as object classification. However, relying on conventional RGB imaging is sometimes insufficient – the input images are just too similar, regardless of algorithmic sophistication. Hyperspectral imaging adds the extra dimension of wavelength to conventional images, providing a much richer data set. Rather than expressing an image using red, green, and blue (RGB) values at each pixel location, hyperspectral cameras instead record a complete spectrum at each point to create a 3D data set, sometimes referred to as a hyperspectral data cube. The additional spectral dimension facilitates supervised learning algorithms that can characterize visually indistinguishable objects – capabilities that are highly desirable across multiple application sectors.

Local Motors with its Olli robot shuttle is progressing at a furious pace making others look - well - pedestrian. This genuinely symmetrical (no U-turns) multipurpose machine has the essential large sliding doors for the disabled and fast entry/exit. Designed to purpose, envisaging easy configuration for hospital patients on beds, etc. 3D printed, its in-wheel motors from Protean Electric provide vectored traction for bad weather and Local Motors is even progressing air-less tires. In an industry first, The Goodyear Tire & Rubber Company has developed a non-pneumatic (airless) tire (NPT) and wheel assembly to support autonomous vehicle transportation in a city setting. It trials soon with Jacksonville Transportation Authority on Local Motors Olli 2.0 AV in collaboration with Beep, Inc. Roaring ahead, Local Motors just announced a partnership with Liftango to build software for on-demand routing and passenger pick up/drop off for Olli 2.0. IDTechEx reports predicted these things and the latest one, "Robot Shuttles for Smart Cities 2021-2041" roadmaps a lot more that will come from the Teslas of this industry.

The COVID-19 pandemic has forced the closure of non-essential radiology services, with over 850,000 MRI and CT scans still outstanding in the UK, meaning patients waiting over two months for a consultation and upwards of 30 days to receive results. Independent market researcher IDTechEx has published a recent report exploring how image recognition in Artificial Intelligence (AI) represents a potential solution to the problem. The report highlights that AI technology can help to compensate for the lack of qualified staff by extending the responsibility of diagnosis beyond just experts and enables trained radiologists of all levels to approach the analysis process with the same tools and skillset so that more resources can be utilised simultaneously. In addition, AI accelerates the detection of abnormalities from medical scans by greatly diminishing reading times – sometimes by over 90% – which allows doctors to examine cases faster and see patients sooner. The key issue is that as of November 2020, AI may not be widely implemented enough throughout hospitals to make a difference.

The market research report compiles information from over 120 hardware and software companies to identify the key trends in the drone industry. The major players of the drone industry are compared within the areas of drone industry such as software, hardware, and analytics. This provides you with the knowledge to make informed decisions and understanding of this disruptive and fast-growing market area. These use cases include Search and Rescue, Agriculture, Delivery, Security, Mapping and Localisation.

Details on the roles that emerging technologies have played in the face of the COVID-19 pandemic throughout 2020 have been released by IdTechEx. With results needed at an unprecedented scale in a limited time, diagnostic approaches were explored to quickly diagnose COVID-19 patients. The lungs of patients with COVID-19 have certain visual hallmarks such as ground-glass opacities and areas of increased density, both of which can be detected using CT and x-ray imaging. To further speed up this process, companies developing artificial intelligence solutions for the detection of respiratory diseases quickly tailored their software to differentiate COVID-19 from other respiratory infections, decreasing image analysis time to the matter of seconds. In order to prepare for the oncoming surge in COVID-19 patients and to prevent the spread of COVID-19 between patients in healthcare settings, healthcare systems around the world halted provision of "non-urgent" doctor visits, which extends to everything from cardiac patients to cancer treatments. This, combined with the public's concern and confusion around COVID-19 diagnosis, caused a huge increase in demand for digital health services.

The use of image visualization and limited recognition software in medical diagnostics started over 20 years ago. This technology had however nearly reached its performance limits when deep learning (DL) and convolutional neural networks (CNNs) were developed, heralding a step-change in the capability and performance of machine vision. This progress demonstrates that image recognition AI technology can match or even exceed human-level performance (in terms of accuracy, sensitivity, and specificity) in many disease areas and on many imaging modalities. The technical threshold for the automation of these diagnostic tasks has already been reached, laying the groundwork for commercial growth in the short and long term. This is shown in the market projections below.