If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
For many organizations, achieving digital transformation is not an overnight process. Fortunately, there are tools to help organizations make significant advances and be better positioned to reap the rewards of a digital workplace. For years, business process management (BPM) technologies have been a core part of many organizations' digital transformation strategies. As new innovations in the industry emerge, organizations are finding even more opportunities to become digitally adept. Robotic Process Automation (RPA) has been gaining popularity as the latest automation tool to drive workplace efficiency and productivity – but how is it different from traditional business process management approaches?
We're proud to announce that the IOTA Foundation has partnered on a project initiated by Best Materia and IMC, Japanese maintenance-related companies, and funded by NEDO (New Energy and Industrial Technology Development Organization). NEDO is Japan's largest public management organization promoting research and development as well as the deployment of industrial, energy, and environmental technologies. The goal of the project is to develop technology to strengthen the security, longevity, and durability of critical infrastructure assets in Japan and abroad. By adding artificial intelligence and the IOTA Tangle technology to Risk-Based Maintenance (RBM) Systems deployed in power plants, energy plants, industrial plants, petrochemicals, and oil refining plants, the group hopes to capture a large share of the domestic social infrastructure conservation market, valued at 170 Trillion Yen (1.5 Trillion USD). This type of predictive maintenance system that shares industry data using a distributed database is set to be the first of its kind in the world. While damage prediction assessment based on the current RBM standards exists, most processes are still left up to field workers to do manually.
Artificial Intelligence (AI) is one of the digital technologies progressing rapidly and is a current topic of broad interest. AI has contributed to a more efficient and intelligent way of streamlining business operations, which helps companies achieve cost reduction, improve efficiency and productivity and improve responsiveness to market demand. This article aims to look into the crucial applications of AI in procurement, namely in the area of spend analytics, strategic sourcing and contract management, supplier risk management and robotic process automation (RPA) in procure-to-pay workflow. The chart below shows the applications of AI across Procurement Cycle. Artificial intelligence (AI) is a technology capable of performing tasks that typically require human intelligence by learning, coming to its own conclusions, understanding complex data, engaging in natural dialogues with people, enhancing human cognitive or replacing people on execution of non-routine tasks.
However, as IDTechEx has reported previously in its article'AI in Medical Diagnostics: Current Status & Opportunities for Improvement', image recognition AI's current value proposition remains below the expectations of most radiologists. Over the next decade, AI image recognition companies serving the medical diagnostics space will need to test and implement a multitude of features to increase the value of their technology to stakeholders across the healthcare setting. Radiologists have a range of imaging methods at their disposal and may need to utilise more than one to detect signs of disease. For example, X-ray and CT scanning are both used to detect respiratory diseases. X-rays are cheaper and quicker, but CT scanning provides more detail about lesion pathology due its ability to form 3D images of the chest.
Mining is a traditionally analogue business. After all, the industry's symbol worldwide is a hammer and pick. Yet, despite the sector's antiquated reputation, some major mining companies are taking a progressive stance and proving digitisation and automation can achieve much better operational outcomes. Known as Mine 4.0, the industry is seeing digital transformation creep into everything from trucks, drills and trains to back-office processes, such as procurement and supply chain logistics. Miners have very little control over the revenue side of their business, as the global commodities crash of 2014 to 2015, when prices plunged by more than 30 per cent, and indeed the coronavirus epidemic demonstrate.
On a bright Tuesday afternoon in Paris last fall, Alex Karp was doing tai chi in the Luxembourg Gardens. He wore blue Nike sweatpants, a blue polo shirt, orange socks, charcoal-gray sneakers and white-framed sunglasses with red accents that inevitably drew attention to his most distinctive feature, a tangle of salt-and-pepper hair rising skyward from his head. Under a canopy of chestnut trees, Karp executed a series of elegant tai chi and qigong moves, shifting the pebbles and dirt gently under his feet as he twisted and turned. A group of teenagers watched in amusement. After 10 minutes or so, Karp walked to a nearby bench, where one of his bodyguards had placed a cooler and what looked like an instrument case. The cooler held several bottles of the nonalcoholic German beer that Karp drinks (he would crack one open on the way out of the park). The case contained a wooden sword, which he needed for the next part of his routine. "I brought a real sword the last time I was here, but the police stopped me," he said matter of factly as he began slashing the air with the sword. Those gendarmes evidently didn't know that Karp, far from being a public menace, was the chief executive of an American company whose software has been deployed on behalf of public safety in France. The company, Palantir Technologies, is named after the seeing stones in J.R.R. Tolkien's "The Lord of the Rings." Its two primary software programs, Gotham and Foundry, gather and process vast quantities of data in order to identify connections, patterns and trends that might elude human analysts. The stated goal of all this "data integration" is to help organizations make better decisions, and many of Palantir's customers consider its technology to be transformative. Karp claims a loftier ambition, however. "We built our company to support the West," he says. To that end, Palantir says it does not do business in countries that it considers adversarial to the U.S. and its allies, namely China and Russia. In the company's early days, Palantir employees, invoking Tolkien, described their mission as "saving the shire." The brainchild of Karp's friend and law-school classmate Peter Thiel, Palantir was founded in 2003. It was seeded in part by In-Q-Tel, the C.I.A.'s venture-capital arm, and the C.I.A. remains a client. Palantir's technology is rumored to have been used to track down Osama bin Laden -- a claim that has never been verified but one that has conferred an enduring mystique on the company. These days, Palantir is used for counterterrorism by a number of Western governments.
Computing technology has become pervasive and with it the expectation for its ready availability when needed, thus basically at all times. Dependability is the set of techniques to build, configure, operate, and manage computer systems to ensure that they are reliable, available, safe, and secure.1 But alas, faults seem to be inherent to computer systems. Components can simply crash or produce incorrect output due to hardware or software bugs or can be invaded by impostors that orchestrate their behavior. Fault tolerance is the ability to enable a system as a whole to continue operating correctly and with acceptable performance, even if some of its components are faulty.3 Fault tolerance is not new; von Neumann himself designed techniques for computers to survive faults.4
We live in uncertain times. A global pandemic has disrupted our lives. Our broken economies are rapidly restructuring. Climate change looms, disinformation abounds, and war, as ever, hangs over the lives of millions. And at the heart of every global crisis are the chronically underserved, marginalized, oppressed, and persecuted, who are often the first to befall the tragedies of social, economic, environmental, and technological change.3
Industry 4.0 signifies a seismic shift in the way the modern factories and industrial systems operate. They consist of large-scale integration across an entire ecosystem where data inside and outside the organization converges to create new products, predict market demands and reinvent the value chain. In Industry 4.0, we see the convergence of information technology (IT) and operational technology (OT) at scale. The convergence of IT/OT is pushing the boundaries of conventional corporate security strategies where the focus has always been placed on protecting networks, systems, applications and processed data involving people and information. In the context of manufacturing industries with smart factories and industrial systems, robotics, sensor technology, 3D printing, augmented reality, artificial intelligence, machine learning and big data platforms work in tandem to deliver breakthrough efficiencies.
The widespread use and increasing complexity of mission-critical and safety-critical systems at NASA and in the aerospace industry require advanced techniques that address these systems' specification, design, verification, validation, and certification requirements. The NASA Formal Methods Symposium (NFM) is a forum to foster collaboration between theoreticians and practitioners from NASA, academia, and industry. NFM's goals are to identify challenges and to provide solutions for achieving assurance for such critical systems. New developments and emerging applications like autonomous software for Unmanned Aerial Systems (UAS), UAS Traffic Management (UTM), advanced separation assurance algorithms for aircraft, and the need for system-wide fault detection, diagnosis, and prognostics provide new challenges for system specification, development, and verification approaches. Similar challenges need to be addressed during development and deployment of on-board software for both spacecraft and ground systems.