Over the past few centuries, healthcare technology has come a long way--from the invention of the stethoscope in 1816 to robots performing surgery in 2020. As computers became more common starting in the 1960s and 1970s, researchers began to explore how they might enhance healthcare, and the first electronic health record (EHR) systems appeared by 1965 in the U.S. But it wasn't until the 1980s and 1990s that clinicians began to rely on computers for data management. Internet connectivity paved the way for much better data management, and EHRs became far more common in the 2000s. On the clinical side, healthcare technology improved greatly between the 1950s and the turn of the twenty-first century.
The onset of next-generation Artificial Intelligence (AI) applications in Europe presents new regulatory challenges with respect to technologies deemed to present risks to the existing legal framework, rights, and ethics. The scope of potential challenges is broad, with many AI technologies already featuring prominently in our everyday life: algorithms deciding on the fate of our loan applications, recognising faces on public streets, flagging potentially illegal content online, targeting adverts to individual profiles online, estimating the outcome of elections, and even being employed across warzones the world over, as a means to highlight areas of potential hazard and risk. The use of algorithms to supplement human intelligence received a boost at the turn of the Millenium, with the onset of machine learning devices and the realization among technologists that'Big Data' could be taken advantage of in predictive mechanisms, effectively providing machines with the vast intelligence required to conduct complex real-time decisions. In this vein, of vital importance to the operation of AI technologies, is access to and utilisation of data streams. While the EU has strict safeguards on the use of personal data for this cause, as part of its General Data Protection Regulation, it is now seeking to harness the power of industrial data sharing as a means to boost competitiveness with other global players in the field of data-driven innovation.
Imagine this scenario: You have an app that uses machine learning and you want the app to learn from your user's data in real-time. That means as new user data is generated, your app is able to make predictions and perform training on the incoming data-stream to improve itself automatically. How would you go about building this? Take some time to stare at this chart, it's an example of this pipeline. That text data is being streamed in real-time using a software product called "Apache Kafka" to a model.
Hewlett Packard Enterprise is teaming up with NASA to launch its Spaceborne Computer-2 into orbit on February 20, touting the move as accelerating space exploration and enabling real-time data processing, with the ultimate aim of building computing resources that can serve on board a mission to Mars. Spaceborne Computer-2 will be installed on the International Space Station (ISS) for the next two to three years. It's hoped the edge computing system will enable astronauts to eliminate latency associated with sending data to and from Earth to tackle research and gain insights immediately for projects such as real-time analysis of satellite images with artificial intelligence. HPE expects the kit to be used for experiments such as processing medical imaging and DNA sequencing to unlocking key insights from volumes of remote sensors and satellites. The first Spaceborne Computer was sent into space in August 2017.
The AI market for the transportation industry is big and getting a lot bigger. In fact, it's projected to grow at a compound annual rate of nearly 18% from 2017 to 2030, with its size increasing to $10.3 billion by 2030. Commercial trucking, beset with labor shortages and safety concerns, stands to benefit enormously, but the adoption curve is steep. While the buzz around autonomous trucks has made headlines, the reality is that AI will have a much larger impact for the foreseeable future, but it's implementation will also present challenges -- challenges that have echoes in just about any industry or sector confronting a major digital transformation. I caught up with Avi Geller, CEO of fleet management company Maven (Machines) and MIT alum, who believes that AI is central to solving some of the logistics industry's most pressing problems and creating efficiencies not possible before.
Warnings about misinformation are now regularly posted on Twitter, Facebook, and other social media platforms, but not all of these cautions are created equal. New research from Rensselaer Polytechnic Institute shows that artificial intelligence can help form accurate news assessments--but only when a news story is first emerging. These findings were recently published in Computers in Human Behavior Reports by an interdisciplinary team of Rensselaer researchers. They found that AI-driven interventions are generally ineffective when used to flag issues with stories on frequently covered topics about which people have established beliefs, such as climate change and vaccinations. However, when a topic is so new that people have not had time to form an opinion, tailored AI-generated advice can lead readers to make better judgments regarding the legitimacy of news articles.
Distributing the COVID-19 vaccine is a logistical puzzle that teeters on a delicate structure of chemists, data scientists, freight drivers, healthcare professionals, distributors, state health departments, and policy makers. When even one of these pieces in the structure is imbalanced, the whole vaccine distribution tower could tumble. The U.S. Federal Drug Administration (FDA) has authorized two vaccinations based on data findings from extensive clinical trials and manufacturers, which have been deemed safe for distribution and use under Emergency Use Authorizations (EUA). However, supply limitations and other pressing challenges have compounded the logistical complexities and contributed to the slow rollout and incomplete shipment of doses. With a projected 600 million vaccination doses required in the U.S and demand currently outweighing supply, a vaccination effort of this scale comes with risks and challenges across end-to-end vaccine distribution management.
One of the biggest lessons Australia and New Zealand business leaders can take from the past 12 months is that a climate of uncertainty is now the new normal. The shift in customer behaviour brought about by the COVID-19 pandemic, coupled with rapid information technology changes, has already presented significant challenges. As a result, many organisations have had to bring forward their digital transformation plans and complete projects in weeks or months rather than years. During 2021, CIOs will have to work throughout their organisations and apply digital technologies and data to unlock new business opportunities. They must also work to promote a growth mindset that will help to unlock fresh innovation and agility. Adopting such a growth mindset will require CIOs and IT teams to embrace six key trends during the coming 12 months.
Edge computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data processing and content sharing among vehicles and terrestrial edge computing (TEC) infrastructures (e.g., 5G base stations and roadside units) with little or no human intervention, plays a key role in the intelligent transportation systems. However, EC-IoV is heavily dependent on the connections and interactions between vehicles and TEC infrastructures, thus will break down in some remote areas where TEC infrastructures are unavailable (e.g., desert, isolated islands and disaster-stricken areas). Driven by the ubiquitous connections and global-area coverage, space-air-ground integrated networks (SAGINs) efficiently support seamless coverage and efficient resource management, represent the next frontier for edge computing. In light of this, we first review the state-of-the-art edge computing research for SAGINs in this article. After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas. The main objective of the framework is to minimize the task completion time and satellite resource usage. To this end, a pre-classification scheme is presented to reduce the size of action space, and a deep imitation learning (DIL) driven offloading and caching algorithm is proposed to achieve real-time decision making. Simulation results show the effectiveness of our proposed scheme. At last, we also discuss some technology challenges and future directions.
Real-time ridesharing systems such as UberPool, Lyft Line and GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the “right” requests to travel together in the “right” available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible combinations of requests (with respect to the available delay for customers) as possible in real-time; and then (ii) optimizing assignment of the feasible request combinations to vehicles. Since the number of request combinations increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such approaches have to employ ad hoc heuristics to identify a subset of request combinations for assignment. Our key contribution is in developing approaches that employ zone (abstraction of individual locations) paths instead of request combinations. Zone paths allow for generation of significantly more “relevant” combinations (in comparison to ad hoc heuristics) in real-time than competing approaches due to two reasons: (i) Each zone path can typically represent multiple request combinations; (ii) Zone paths are generated using a combination of offline and online methods. Specifically, we contribute both myopic (ridesharing assignment focussed on current requests only) and non-myopic (ridesharing assignment considers impact on expected future requests) approaches that employ zone paths. In our experimental results, we demonstrate that our myopic approach outperforms the current best myopic approach for ridesharing on both real-world and synthetic datasets (with respect to both objective and runtime). We also show that our non-myopic approach obtains 14.7% improvement over existing myopic approach. Our non-myopic approach gets improvements of up to 12.48% over a recent non-myopic approach, NeurADP. Even when NeurADP is allowed to optimize learning over test settings, results largely remain comparable except in a couple of cases, where NeurADP performs better.