Buzzwords like Artificial Intelligence (AI) and machine learning are commonly heard at conferences and industry events and they often conjure up images of robots or killing machines from the Terminator. However, panelists from the Innofest Unbound conference in Singapore all felt that technologies such as AI should not replace humans as it is commonly imagined – rather, they should augment the work of clinicians and hopefully, even enhance the patients' interactions with their doctors. A medical doctor by training and also the founder of MEDGIC, a startup which utilises AI to detect skin conditions, Dr Reid Lim feels that the use of AI should always involve doctors and not replace them. "Healthcare systems are becoming unsustainable and we need AI to help automate some things and to help alleviate the burden on doctors. AI is not new and it seems strange that some people are only beginning to grasp the use AI." "A lot of radiologists are already using Computer Aided Diagnosis (CAD) for mammography and it has been happening for some time. So the idea is for us as a tech startup to pursue what we call human-centric AI. We try to make AI as explainable as possible and we always want humans to be involved in the whole process," he added.

By now, every second company worldwide uses at least one application powered by artificial intelligence. But large barriers for adoption remain. What are those blockers and how can we innovate to remove them? SAP reveals the priorities for its new AI-focused SAP Innovation Center in Newport Beach and kicks off an innovation campaign with HeroX to accelerate the impact of enterprise AI. AI has entered our daily lives.

It's a frightening and far too common scenario: One in four Americans age 65 and older falls each year, according to the Centers for Disease Control and Prevention. These accidents, which comprise 2.8 million injuries, account for an emergency room visit every 11 seconds. Falls are the leading cause of fatal injury for this population, numbering more than 27,000 deaths every year. No family wants to envision the scenario, even a minor one. After all, older adults often face a tough recovery, and they may avoid social engagements or exercise due to fear of falling again.

Businesses that rely on storage and warehousing are benefitting a lot since IoT happened. It can aid in effectively tracking and managing inventory as it gives you automatically-controlled options. You simply have to install IoT software and devices in your storage units and warehouses. They will assist you in managing inventory changes. In retail, businesses also link AI with RFID and cloud technology to track inventory.

Researchers from VA and the artificial intelligence company DeepMind, part of Google, have developed technology that has the potential to predict a life-threatening kidney condition up to two days before it happens. The findings appeared July 31 in the journal Nature. Using VA data, the researchers developed an artificial intelligence model to detect acute kidney injury in patients up to 48 hours before it would otherwise be identified. Acute kidney injury can progress to kidney failure that requires dialysis. It can also affect other organs and cause death.

Z Advanced Computing, Inc. (ZAC) of Potomac, MD announced on August 27 that it is funded by the US Air Force, to use ZAC's detailed 3D image recognition technology, based on Explainable-AI, for drones (unmanned aerial vehicle or UAV) for aerial image/object recognition. ZAC is the first to demonstrate Explainable-AI, where various attributes and details of 3D (three dimensional) objects can be recognized from any view or angle. "With our superior approach, complex 3D objects can be recognized from any direction, using only a small number of training samples," said Dr. Saied Tadayon, CTO of ZAC. "For complex tasks, such as drone vision, you need ZAC's superior technology to handle detailed 3D image recognition." "You cannot do this with the other techniques, such as Deep Convolutional Neural Networks, even with an extremely large number of training samples. That's basically hitting the limits of the CNNs," continued Dr. Bijan Tadayon, CEO of ZAC.

WITH the cost of bringing a new drug to market now an average US$2.6bn1 and one-in-ten drug candidates failing to make it to market despite successfully completing Phase I trials2, it is no wonder that pharmaceutical companies have seized on the unparalleled data-processing potential of artificial intelligence (AI) systems. Their use in identifying compounds, some of which may have completed clinical trials already, that could be re-purposed to treat alternative diseases quickly and comparatively cheaply, is well documented. But as research scientists are beginning to find, AI systems are capable of achieving so much more. The potential applications of AI in drug discovery are almost endless, but one of the main areas of focus to date has been repurposing existing drugs. Typically, this involves finding new uses for drugs that have already attained market and regulatory approvals for the treatment of a specific disease.

Software star-up, Z Advanced Computing, Inc. (ZAC), has received funding from the U.S. Air Force to incorporate the company's 3D image recognition technology into unmanned aerial vehicles (UAVs) and drones for aerial image and object recognition. ZAC's in-house image recognition software is based on Explainable-AI (XAI), where computer-generated image results can be understood by human experts. ZAC – based in Potomac, Maryland – is the first to demonstrate XAI, where various attributes and details of 3D objects can be recognized from any view or angle. "With our superior approach, complex 3D objects can be recognized from any direction, using only a small number of training samples," says Dr. Saied Tadayon, CTO of ZAC. "You cannot do this with the other techniques, such as deep Convolutional Neural Networks (CNNs), even with an extremely large number of training samples. That's basically hitting the limits of the CNNs," adds Dr. Bijan Tadayon, CEO of ZAC.

When Lei Wang became a data annotator two years ago her job was fairly simple: Identifying people's gender in images. But since then Wang has noticed increasing complexity in the tasks she is assigned: from labeling gender to labeling age, from framing 2D objects to 3D bounding boxes, from daylight images to late night and foggy scenes, and the list goes on. Wang is 25 years old. She used to be a receptionist, but when her company shut down in 2017 an algorithm engineer friend suggested she explore a new career path in data annotation -- the essential process of labeling data to make it usable for artificial intelligence systems, particularly those using supervised machine learning. Being out of a job, she decided to give it a try.

The burgeoning sector is seen as a key component in China's tech and trade war against the United States.