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Aerial image of endangered Southern Resident killer whales in K pod. The image was obtained using a remotely piloted octocopter drone that was flown during health research by Dr. John Durban and Dr. Holly Fearnbach. (Vulcan Image)

Being an orca isnt easy. Despite a lack of natural predators, these amazing mammals face many serious threats most of them brought about by their human neighbors. Understanding the pressures we put on killer whale populations is critical to the environmental policy decisions that will hopefully contribute to their ongoing survival.

Fortunately, marine mammal researchers like Holly Fearnbach of Sealife Response + Rehab + Research (SR3) and John Durban of Oregon State University are working hard to regularly monitor the condition of the Salish Seas southern resident killer whale population (SKRW). Identified as J pod, K pod and L pod, these orca communities have migrated through the Salish Sea for millennia. Unfortunately, in recent years their numbers have dwindled to only 75 whales, with one new calf born in 2021. This is the lowest population figure for the SRKW in 30 years.

For more than a decade, Fearnbach and Durban have flown photographic surveys to capture aerial images of the orcas. Starting in 2008, image surveys were performed using manned helicopter flights. Then beginning in 2014, the team transitioned to unmanned drones.

As the remote-controlled drone flies 100 feet or more above the whales, images are captured of each of the pod members, either individually or in groups. Since the drone is also equipped with a laser altimeter, the exact distance is known making calculations of the whales dimensions very accurate. The images are then analyzed in whats called a photogrammetric health assessment. This assessment helps determine each whales physical condition, including any evidence of pregnancy or significant weight loss due to malnourishment.

As a research tool, the drone is very cost effective and it allows us to do our research very noninvasively, Fearnbach said. When we do detect health declines in individuals, were able to provide management agencies with these quantitative health metrics.

But while the image collection stage is relatively inexpensive, processing the data has been costly and time-consuming. Each flight can capture 2,000 images with tens of thousands of images captured for each survey. Following the drone work, it typically takes about six months to manually complete the analysis on each seasons batch of images.

Obviously, half a year is a very long time if youre starving or pregnant, which is one reason why SR3s new partnership with Vulcan is so important. Working together, the organizations developed a new approach to process the data more rapidly. The Aquatic Mammal Photogrammetry Tool (AMPT) uses machine learning and an end-user tool to accelerate the laborious process, dramatically shortening the time needed to analyze, identify and categorize all of the images.

Applying machine learning techniques to the problem has already yielded huge results, reducing a six-month process to just six weeks with room for further improvements. Machine learning is a branch of computing that can improve its performance through experience and use of data. The faster turnaround time will make it possible to more quickly identify whales of concern and provide health metrics to management groups to allow for adaptive decision making, according to Vulcan.

Were trying to make and leave the world a better place, primarily through ocean health and conservation, said Sam McKennoch, machine learning team manager at Vulcan. We got connected with SR3 and realized this was a great use case, where they have a large amount of existing data and needed help automating their workflows.

AMPT is based on four different machine learning models. First, the orca detector identifies those images that have orcas in them and places a box around each whale. The next ML model fully outlines the orcas body, a process known in the machine learning field as semantic segmentation. After that comes the landmark detector which detects the rostrum (or snout) of the whale, the dorsal fins, blowhole, shape of the eye patches, fluke notch and so forth. This allows the software to measure and calculate the shape and proportions of various parts of the body.

Of particular interest is whether the whales facial fat deposits are so low they result in indentations of the head that marine biologists refer to as peanut head. This only appears when the orca has lost a significant amount of body fat and is in danger of starvation.

Finally, the fourth machine learning model is the identifier. The shape of the gray saddle patch behind the whales dorsal fin is as unique as a fingerprint, allowing each of the individuals in the pod to be identified.

There are a lot of different kinds of information needed for this kind of automation. Fortunately, Vulcan has been able to leverage some of SR3s prior manual work to bootstrap their machine learning models.

We really wanted to understand their pain points and how we could provide them the tools they needed, rather than the tools we might want to give them, McKennoch said.

As successful as AMPT has been, theres a lot of knowledge and information that has yet to be incorporated into its machine learning models. As a result, theres still the need to have users in-the-loop in a semi-supervised way for some of the ML processing. The interface speeds up user input and standardizes measurements made by different users.

McKennoch believes there will be gains with each batch they process for several cycles to come. Because of this, they hope to continue to improve performance in terms of accuracy, workflow and compute time to the point that the entire process eventually takes days, instead of weeks or months.

This is very important because AMPT will provide information that guides policy decisions at many levels. Human impact on the orcas environment is not diminishing and if anything, is increasing. Overfishing is reducing food sources, particularly chinook salmon, the orcas preferred meal. Commercial shipping and recreational boats continue to cause injury and their excessive noise interferes with the orcas ability to hunt salmon. Toxic chemicals from stormwater runoff and other pollution damage the marine mammals health. Ongoing monitoring of each individual whale will be critical to maintaining their wellbeing and the health of the local marine ecosystem.

Vulcan plans to open-source AMPT, giving it a life of its own in the marine mammal research community. McKennoch said they hope to extend the tool so it can be used for other killer whale populations, different large whales, and in time, possibly smaller dolphins and harbor seals.

Read more from the original source:
Can machine learning help save the whales? How PNW researchers use tech tools to monitor orcas - GeekWire

What is artificial intelligence?Artificial intelligence (AI) is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

Simply put, AIs goal is to make computers/computer programs smart enough to imitate the human mind behaviour.Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings. AI must have access to properties, categories, objects and relations between all of them to implement knowledge engineering. AI initiates common sense, problem-solving and analytical reasoning power in machines, which is much difficult and a tedious job.

What is machine learning?Artificial Intelligence and Machine Learning are much trending and also confused terms nowadays. Machine Learning (ML) is a subset of Artificial Intelligence. ML is a science of designing and applying algorithms that are able to learn things from past cases. If some behaviour exists in past, then you may predict if or it can happen again. Means if there are no past cases then there is no prediction.

ML can be applied to solve tough issues like credit card fraud detection, enable self-driving cars and face detection and recognition. ML uses complex algorithms that constantly iterate over large data sets, analysing the patterns in data and facilitating machines to respond different situations for which they have not been explicitly programmed. The machines learn from the history to produce reliable results. The ML algorithms use Computer Science and Statistics to predict rational outputs.

Safety & quality:Artificial intelligence frameworks deliver more secure, more precise production lines results with more prominent speed and more consistency than human workers. Whats more, on the processing plant floor, AI-based detection can be utilised to keep employees and equipment safer, recognising likely dangers, for example, a worker who has forgotten to wear the appropriate safety gear.

Waste reduction and transparency:

Obviously, the beverage industrys waste angle is a profoundly discussed and scrutinised part of the business. All through the worlds supply chains, AI is being utilised to follow each and every stage of the manufacturing and supply chain process, for example, tracking costs, overseeing stock levels, and even nations of origin.

Improving food safety standards:Regardless of where you go on the planet, food safety standards are consistently imperative to follow, and guidelines appear to be turning out to be stricter constantly. Particularly with Covid-19, and nations become more mindful of how contaminated food can be.

Luckily, robots that utilise AI and machine learning can handle and process food, fundamentally disposing of the odds that contamination can happen through touch. Robots and machinery cant communicate infections and such that people can, accordingly limiting the risk of it turning into an issue.

PackagingArtificial intelligence-driven robotics are proving key to meeting the pressing and picking demands quickened by customers expanding utilisation of e-commerce. The complex and work escalated nature of the process offers remarkable potential for intelligent automation.

Supply chain managementAI is essentially programming computers so they can receive data, evaluate it, make a decision based on the evaluation, and then perform a given task based on the decision. This emerging technology helps the beverage industry with Supply Chain Management through logistics, predictive analytics, and transparency. The dairy industry is using AI to improve quality assurance, provide better forecasting models, and keep up with consumer trends.

Rising technologies based on artificial intelligence & machine learning in beverage industry

RoboticsRobots refer to machines that can perform tasks or operations by themselves after being programmed using a computer. Those tasks may be either simple and repetitive, or adaptive and more complex, in which the latter requires the integration of other AI methods, such as CV and ML, to continually retrain and learn to carry out more advanced operations.In tea and coffee, robots have been developed for brewing and dispensing purposes. a tea brewing machine is able to make cups of tea in specific times with adequate water temperature and record the consumption patterns. Another recent development was a robot named Teforia; it consists of an in-home tea maker in which the user is able to add any combination of tea leaves and water, then the robot is controlled using a smartphone application to start the process; it claims to be able to brew the beverage to achieve the optimal flavour profile for each consumer. In the case of coffee, a robotic coffee maker, Mugsy, was developed using Raspberry Pi and it is possible to integrate it with different applications such as text messages, Twitter or an Alexa device, which are used to indicate to the robot to start brewing the coffee.

A commercial robotic pourer has been developed, which consists of two arms and a screen; the authors integrated it with a camera, which is able to detect the level of the liquid to predict when to stop serving. A robotic arm was designed to pick a cup and serve the beverage ordered (orange juice, apple juice or iced tea), a second robot was programmed to pick the bottled soft drinks and take them to the clients table, while a third machine was intended to have a conversation with clients while waiting for the order.

Computer vision techniquesThe CV technique refers to a subdivision of AI, which consists of automatic information extraction from either images or videos by imitating the human eye functions. It can be coupled with robotics, specific equations or algorithms, basic statistics, and ML algorithms, to fully automate the technique as an AI system; this may allow the procedure to be stand-alone and to classify or predict the quality parameters of the product. Some advantages include that it is non-destructive, non-contact, may be replicated, is automatic, and therefore, considered as a rapid method, which is more accurate and reliable than some traditional procedures such as visual inspection and sensory analysis, which include human error as a possible drawback.

Machine learningMachine learning (ML) is a branch of artificial intelligence, which refers to a computer-based system that may be trained to find patterns among a dataset to classify or predict specific parameters, and it is able to improve its performance by feeding new data. Machine learning may be divided into supervised and unsupervised algorithms, which, at the same time, may be classified into different subtypes.The use of ML has been increasing in recent years in the food and beverage industry due to its ability to improve production and assess the quality in a faster, more accurate, objective, and cost-effective way.

1. Machine learning in hot beveragesCompared to other types of beverages, the application of ML has been less explored in hot drinks. By using machine learning, the quality of green or black tea can be predicted with the help of different inputs. Green teas can be classified according to the quality grade using both back propagation neural networks and probabilistic neural networks with the outputs of an e-nose as inputs of the models.

A model has been developed using the flavonoids, catechins, and total methyl-xanthines content to predict the antioxidant activity. CIELab colour parameters of coffee beans are used to classify them according to their colour through ANN with a 100% accuracy; however, that perfect classification is due to the direct relationship of the categories and the inputs, which makes the model senseless and useless. A model is developed to predict the roasting degree of coffee using results from hyperspectral images (8741734 nm) through support vector machine with a 90% accuracy.

2. Machine learning in non-alcoholic beveragesThere are several studies using ML in non-alcoholic beverages; however, it has not been applied extensively for water quality assessment, and nothing has been done in bottled water. An ANN model is used to assess the quality of drinking water when entering the distribution system, using microbiological and chemical data as inputs with 100% accuracy. No recent studies have been published regarding the application of ML in soft drinks; however, there are some papers in fruit juices. The use of e-nose outputs as inputs to model fruit juices quality has been popular. ML has been used to classify strawberry juice samples according to the processing treatment with 100% accuracy.

Conclusion:Despite the increasing trend in the applications of emerging technologies, which involve the use of AL, robotics, ML and CV in the beverage industry, there are still several gaps to be covered. Robotics science needs to be more explored in beverages as a tool to aid other AI components, which would maximise the use of some emerging methods. Regarding CV, most approaches developed mainly for the assessment of hot drinks and non-alcoholic beverages are based on the analysis of colour; however, more research needs to be conducted to apply this technology to measure other parameters related to the quality traits specific to each product.

The main issue with ML is that there is still a lack of knowledge among researchers concerning the proper development techniques, usage, and interpretation of the algorithms and modelling, as well as the way to select the best models to avoid over- or under-fitting, which are common problems within the existing publications. Furthermore, the combination of two or more of the aforementioned methods should be considered to be implemented as an approach to AI in the different beverage categories, especially for hot and other non-alcoholic drinks in which these technologies have not been very popular among the companies.

(Sanjay is head, SafeFoodz Solutions, a food safety-regulatory advisor-trainer. The article is co-authored by Arya and supported by Saisha Raut, They can be reached at safefoodz@gmail.com)

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How beverage industry is affected by machine learning & AI | FNB News - fnbnews.com

Thanks to advanced technology, consumers can now access, spend, and invest their money in safer ways. Lenders looking to win new business should apply technology to make processes faster and more efficient.

Artificial intelligence has transformed the way we handle money by giving the financial industry a smarter, more convenient way to meet customer demands.

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Machine learning helps financial institutions develop systems that improve user experiences by adjusting parameters automatically. It's become easier to handle the extensive amount of data related to daily financial transactions.

Machine learning and AI are changing how the financial industry does business in these ways:

Fraud Detection

The need to enhance fraud detection and cybersecurity is no longer an option. People pay bills, transfer money, trade stocks, and deposit checks through smartphone applications or online accounts.

Many businesses store their information online, increasing the risk of security breaches. Fraud is a major concern for companies that offer financial services--including banks--which lose billions of dollars yearly.

Machine learning and artificial intelligence technologies improve online finance security by scanning data and identifying unique activities. They then highlight these activities for further investigation. This technology can also prevent credential stuffing and credit application fraud.

Cognito is a cyber-threat detection and hunting software impacting the financial space positively. Its built by a company called Vectra. Besides detecting threats automatically, it can expose hidden attackers that target financial institutions and also pinpoint compromised information.

Making Credit Decisions

Having good credit can help you rent an apartment of your choice, land a great job, and explore different financing options. Now more than ever, many things depend on your credit history, even taking loans and credit cards.

Lenders and banks now use artificial intelligence to make smarter decisions. They use AI to accurately assess borrowers, simplifying the underwriting process. This helps save time and financial resources that would have been spent on humans.

Data--such as income, age, and credit behavior--can be used to determine if customers qualify for loans or insurance. Machine learning accurately calculates credit scores using several factors, making loan approval quick and easy.

AI software like ZestFinance can help you to easily find online lenders, all you do is type title loans near me. Its automated machine learning platform (ZAML) works with companies to assess borrowers without credit history and little to no credit information. The transparent platform helps lenders to better evaluate borrowers who are considered high risk.

Algorithmic Trading

Many businesses depend on accurate forecasts for their continued existence. In the finance industry, time is money. Financial markets are now using machine learning to develop faster, more exact mathematical models. These are better at identifying risks, showing trends, and providing advanced information in real time.

Financial institutions and hedge fund managers are applying artificial intelligence in quantitative or algorithmic trading. This trading captures patterns from large data sets to identify factors that may cause security prices to rise or fall, making trading strategic.

Tools like Kavout combine quantitative analysis with machine learning to simultaneously process large, complex, unstructured data faster and more efficiently. The Kai Score ranks stocks using AI to generate numbers. A higher Kai Score means the stock is likely to outperform the market.

Online lenders and other financial institutions can now streamline processes thanks to faster, more efficient tools. Consumers no longer have to worry about unnecessary delays and the safety of their transactions.

About The Author:

Aqib Ijaz is a content writingguru at Eyes on Solution. He is adept in IT as well. He loves to write on different topics. In his free time, he likes to travel and explore different parts of the world.

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3 Applications of Machine Learning and AI in Finance - TAPinto.net