You have a DSLR camera with you and you went on a trip to a beautiful island. Of course, you would want to click some pictures. Now, you upload those memories on to your desktop and your computer automatically understands what's in those pictures! That's where Computer Vision comes in! Now you want to categorize your pictures according to the objects (beaches, trees, animals) in them.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Frequent data collection on the size of individual animals can provide important information for evaluating growth rates and size distributions, which provide insights into productivity, conditions of the pond and potential yield. This information can help the farm manager predict and avoid unwanted situations. Prawn farm technicians pull up feed trays as part of their daily workflow to understand feed consumption and adjust feed rates. The tray typically captures a good number of prawns because feed is added to the tray to attract the prawns. We aim to take advantage of this practice as this process is more frequent (once/twice daily) than the casting of a net (once every week or fortnight).

CSE graduate student Yu-Wei Chao has been selected to receive a 2016 Google PhD Fellowship to support his work in the area of computer vision and machine learning. This year, Google awarded 39 fellowships to top PhD students in the US and Canada who are doing exceptional work in computer science, related disciplines, or promising research areas. Yu-Wei is a third year PhD student working with Prof. Jia Deng. His research focuses on computer vision and machine learning. He was awarded the Google PhD Fellowship based on his recent work on large-scale visual recognition of human actions.

Understanding the growth and distribution of the prawns is critical for optimising the feed and harvest strategies. An inadequate understanding of prawn growth can lead to reduced financial gain, for example, crops are harvested too early. The key to maintaining a good understanding of prawn growth is frequent sampling. However, the most commonly adopted sampling practice, the cast net approach, is unable to sample the prawns at a high frequency as it is expensive and laborious. An alternative approach is to sample prawns from feed trays that farm workers inspect each day. This will allow growth data collection at a high frequency (each day). But measuring prawns manually each day is a laborious task. In this article, we propose a new approach that utilises smart glasses, depth camera, computer vision and machine learning to detect prawn distribution and growth from feed trays. A smart headset was built to allow farmers to collect prawn data while performing daily feed tray checks. A computer vision + machine learning pipeline was developed and demonstrated to detect the growth trends of prawns in 4 prawn ponds over a growing season.

We are looking for a technological leader who will manage the young but innovative team and lead the technical projects. Huawei's vision is to enrich life through communication. We are a fast growing and leading global information and communications technology solutions provider. With our three business units Carrier, Enterprise and Consumer, we offer network infrastructure, cloud computing solutions and devices such as smartphones and tablet PCs.Among our customers are 45 of the world's top 50 telecom operators, and one third of the world's population uses Huawei technologies. Huawei is active in more than 170 countries and has over 180,000 employees of which more than 80,000 are engaged in research and development (R&D).

Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy.

This Japanese IoT Company Uses Computer Vision And Machine Learning To Automate Daily Visual Inspections In Locations With No Power Nor Network …

How did farming affect your day today? If you live in a city, you might feel disconnected from the farms and fields that produce your food. Agriculture is a core piece of our lives, but we often take it for granted. The world's population is expected to grow to nearly 10 billion by 2050, increasing the global food demand by 50%. As this demand for food grows, land, water, and other resources will come under even more pressure. The variability inherent in farming, like changing weather conditions, and threats like weeds and pests also have consequential effects on a farmer's ability to produce food.

Artificial Intelligence is moving at the speed of light, with multiple companies creating software, products and services in not just a vertical way – more of a horizontal disruption. Form Healthcare to Security, from Real Estate to Telecom, here is a look into 28 companies powering the disruption with AI – 1st Edition. Sherpa.ai was founded in 2012 after deep research into Artificial Intelligence, with the conviction of creating a personal assistant that would be not just useful, but indispensable for users. In order to do this, Sherpa brought together a team of experts in Artificial Intelligence who, coupled with a fantastic design, have been able to create the next generation of Digital Assistants which will help users make their life not just more exciting, but also more enjoyable. WellSaid Labs has developed state of the art text-to-speech technology that creates life-like synthetic voice, from the voices of real people.