Both fruits have been a staple of agriculture for 10,000 years. Breakthroughs, discoveries, and DIY tips sent every weekday. Grapes and olives have remained two of the most consistently documented crops since the Middle Eastern dawn of agriculture around 10,000 years ago. But when times got hard across the Levant and northern Mesopotamia, early farmers often went to great lengths to maintain one of these fruity staples compared to the other. According to a study published on September 17 in the journal, cultures consistently ensured that their wine continued to flow, even when the olive oil didn't.

This paper proposes an innovative approach to improving quality control of olive oil manufacturing and preventing damage to the machinery caused by foreign objects. We developed a computer-vision-based system that monitors the input of an olive grinder and promptly alerts operators if a foreign object is detected, indicating it by using guided lasers, audio, and visual cues.

Modern robotics has enabled the advancement in yield estimation for precision agriculture. However, when applied to the olive industry, the high variation of olive colors and their similarity to the background leaf canopy presents a challenge. Labeling several thousands of very dense olive grove images for segmentation is a labor-intensive task. This paper presents a novel approach to detecting olives without the need to manually label data. In this work, we present the world's first olive detection dataset comprised of synthetic and real olive tree images. This is accomplished by generating an auto-labeled photorealistic 3D model of an olive tree. Its geometry is then simplified for lightweight rendering purposes. In addition, experiments are conducted with a mix of synthetically generated and real images, yielding an improvement of up to 66% compared to when only using a small sample of real data. When access to real, human-labeled data is limited, a combination of mostly synthetic data and a small amount of real data can enhance olive detection.

Pests destroy up to 40% of the world's crops each year, causing $220 billion in economic losses, according to the UN Food and Agriculture Organization (FAO). Trapview is harnessing the power of AI to help tackle the problem. The Slovenian company has developed a device that traps and identifies pests, and acts as an advance warning system by predicting how they will spread. "We've built the biggest database of pictures of insects in the world, which allows us to really use modern AI-based computing vision in the most optimal way," says Matej Štefančič, CEO of Trapview and parent company EFOS. As climate change causes species to spread, and disrupts the migration patterns of highly destructive pests, such as desert locusts, Štefančič hopes to help farmers save their crops with quicker, smarter interventions.

The annual meeting of the North American chapter of the Association for Computational Linguistics (NAACL) introduced an industry track in 2018, and at this year's conference, which begins next week, one of the industry track chairs is Amazon principal research scientist Rashmi Gangadharaiah. "The NAACL industry track inspired industry tracks at other conferences such as COLING and EMNLP," Gangadharaiah says. "The industry track provides a forum for researchers in the industry to exchange ideas and discuss successful deployments of ML [machine learning] and NLP [natural-language processing] technologies, as well as share challenges that arise in deploying such systems in real-world settings." For instance, Gangadharaiah explains, "academic research is often done in very controlled settings. It's not a negative thing: people have to do research, and it's useful to start in a controlled setting. But when we put such systems in real-world situations, we typically have to worry about latency, memory, and space. It's not always accuracy that we go for. So I think it makes it more interesting that way."

Olive is healthcare's first intelligent digital workforce and has been successfully deployed at numerous healthcare systems across the country. Olive helps streamline and automate the most high-volume, repetitive tasks so healthcare professionals can concentrate on their patients and solve healthcare's most challenging problems. Olive's promise to her customers is that she finds out where she can make an impact, onboards quickly, shows up to work everyday, does her job extremely well, and gets smarter over time. This role can be 100% remote. As part of the Payer Product Analytics team, you have proven strengths in advanced data management techniques and tools and a keen desire to build and document a clean and scalable data model that will serve us well as we build out our product and analytics offerings.

Olive is changing healthcare by bringing solutions to all members of the healthcare space: patients, providers, and payers. Olive drives connections across the three sides of the market, shining a new light on the broken processes that stand between providers and patient care. Olive leverages AI and direct-from-source data to reveal life-changing insights that make healthcare more efficient, affordable and effective. Olive is connecting the entire healthcare system, so we can all have a healthier tomorrow. Olive is empowering patients to take control of their healthcare.

Width-based planning has shown promising results on Atari 2600 games using pixel input, while using substantially fewer environment interactions than reinforcement learning. Recent width-based approaches have computed feature vectors for each screen using a hand designed feature set or a variational autoencoder (VAE) trained on game screens, and prune screens that do not have novel features during the search. In this paper, we explore consideration of uncertainty in features generated by a VAE during width-based planning. Our primary contribution is the introduction of active learning to maximize the utility of screens observed during planning. Experimental results demonstrate that use of active learning strategies increases gameplay scores compared to alternative width-based approaches with equal numbers of environment interactions.

The connected economy has tremendous untapped potential in the healthcare industry. A case in point can be seen in one of the key pain points uncovered by the pandemic: the lack of connectivity that exists between various points of care. When data flow is interrupted, time and money are wasted. In an interview with PYMNTS, Dr. YiDing Yu, chief medical officer of healthcare-centric AI platform Olive, told PYMNTS that the health care system, marked by manual processes and paper flows, can be dramatically improved through the use of advanced technologies, notably artificial intelligence (AI). The company has maintained that it has been building the first healthcare "AI workforce" that automates workflows and streamlines manual tasks through its AI-underpinned software as a service.

Healthcare software startup Olive is expanding its artificial intelligence capabilities to assist healthcare workers with time-consuming tasks like prior authorizations and patient verifications. The company's new Olive Helps platform, unveiled Monday as part of the HLTH VRTL conference, is designed to work "hand-in-hand" with healthcare employees to provide real-time intelligence throughout their workday. The goal is to turn healthcare employees into "super" human workers who can work faster, smarter and more efficiently, Olive CEO Sean Lane told Fierce Healthcare in an exclusive interview. "Olive Helps is like an ever-present companion that's always sensing the needs of human workers and delivering valuable information tailored for the individual user and their environment," he said. Olive Helps minimizes the time it takes a healthcare worker to carry out critical activities such as billing and patient verification by using AI technology to anticipate what the user will need and providing the information and tools necessary to do their jobs, according to the company.