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AI, RPA, and machine learning, you must have heard these words echoing in the tech industry. Be it blogs, websites, videos, or even product descriptions, disruptive technologies have made their presence bold. The fact that we all have AI-powered devices in our homes is a sign that the technology has come so far.

If you are under the impression that AI, robotic process automation, and machine learning have nothing in common, then heres what you need to know, they are all related concepts. Oftentimes, people use these names interchangeably and incorrectly which causes confusion among businesses that are looking for the latest technological solutions.

Understanding the differences between AI, ML, and RPA tools will help you identify and understand where the best opportunities are for your business to make the right technological investment.

According to IBM, Robotic process automation (RPA), also known as software robotics, uses automation technologies to mimic back-office tasks of human workers, such as extracting data, filling in forms, moving files, etc. It combines APIs and user interface (UI) interactions to integrate and perform repetitive tasks between enterprise and productivity applications. By deploying scripts which emulate human processes, RPA tools complete autonomous execution of various activities and transactions across unrelated software systems.

In that sense, RPA tools enable highly logical tasks that dont require human understanding or human interference. For example, if your work revolves around inputting account numbers on a spreadsheet to run a report with a filter category, you can use RPA to fill the numbers on the sheet. Automation will mimic your actions of setting up the filter and generate the report on its own.

With a clear set of instructions, RPA can perform any task. But theres one thing to remember, RPA systems dont have the capabilities to learn as they go. If there is a change in your task, (for example if the filter has changed in the spreadsheet report), you will have to manually input the new set of instructions.

The highest adopters of this technology are banking firms, financial services, insurance, and telecom industries. Federal agencies like NASA have also started using RPA to automate repetitive tasks.

According to Microsoft, Artificial Intelligence is the ability of a computer system to deal with ambiguity, by making predictions using previously gathered data, and learning from errors in those predictions in order to generate newer, more accurate predictions about how to behave in the future.

In that sense, the major difference between RPA and AI is intelligence. While these technologies efficiently perform tasks, only AI can do it with similar capabilities to human intelligence.

Chatbots and virtual assistants are two popular uses of AI in the business world. In the tax industry, AI is making tax forecasting increasingly accurate with its predictive analytics capabilities. AI can also perform thorough data analysis which makes identifying tax deductions and tax credits easier than before.

According to Gartner, Advanced machine learning algorithms are composed of many technologies (such as deep learning, neural networks, and natural language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.

Machine learning is a part of AI, so the two terms cannot be used interchangeably. And thats the difference between RPA and ML, machine learnings intelligence comes from AI but RPA lacks all intelligence.

To understand better, let us apply these technologies in a property tax scenario. First, you can create an ML model based on a hundred tax bills. The more bills you feed the model, the more accurately it will make predictions for the future bills. But if you want to use the same machine learning model to address an assessment notice, the model will be of no use. You would then have to build a new machine learning model that knows how to work with assessment notices. This is where machine learnings intelligence capabilities draw a line. Where ML fails to recognize the similarities of the document, an AI application would recognize it, thanks to its human-like interpretation skills.

The healthcare industry uses ML to accurately diagnose and treat patients, retailers use ML to make the right products available at the right stores at the right time, and pharmaceutical companies use machine learning to develop new medications. These are just a few use cases of this technology.

No, but they can work together. The combination of AI and RPA is called smart process automation, or SPA.

Also known as intelligent process automation or IPA, this duo facilitates an automated workflow with advanced capabilities than RPA using machine learning. The RPA part of the system works on doing the tasks while the machine learning part focuses on learning. In short, SPA solutions can learn to perform a specific task with the help of patterns.

The three technologies, AI, RPA, and ML, and the duet, SPA hold exciting possibilities for the future. But only when companies make the right choice, the rewards can be reaped. Now that you have an understanding of the various capabilities of these technologies, adapt and innovate.

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AI, RPA, and Machine Learning How are they Similar & Different? - Analytics Insight

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While there have been strides taken in filling up the gender gap across fields, especially engineering and technology-based, there are still miles to go. At times, though, it is due to part societal misconceptions and part lack of knowledge about different fields that we have a gap to fill. That said, what we need is information on all available career and educational prospects that help with choosing the path forward. One such option is machine learning. Machine learning (ML), for the uninitiated like me, is the science of getting computers ie the machines to study and behave like humans, and improve their learning over time automatically, from the fed information and data that comes in the form of observations and real-world interactions. It is a subset of artificial intelligence (AI).

Photo: Vaishali Kasture

With digitisation and AI being a huge part of the future, a career in ML could be successful and rewarding, as Vaishali Kasture, Leader Strategic Projects, AISPL, Amazon Web Services (AWS) India and South Asia, can attest to. Machine learning is one of the most disruptive technologies we will encounter in our generation. Were seeing ML adopted across all industries, verticals, and businesses. For example, Zomato uses machine learning for menu digitisation and enabling consumers to run advanced searches for dishes, and RedBus uses ML to improve click-through rates on their website by 25% and conversion rates by 5%.

Importance Of Machine Learning For The Future

In her over two-decade-old career, one thingKasture has realised is that technology is one of the most important driving factors in any business, be it banking where she started her career or the Knowledge Process Outsourcing (KPO) industry. Even when working at one of Indias prominent credit bureaus, she saw that technology was the key differentiator. There she used the cloud, machine learning and artificial intelligence to drive faster and better outcomes for our banking customers. This really opened my eyes to the power of the cloud and new emerging technologies, she notes, I am convinced that every business will be reimagined using new and emerging technologies, and only those that adapt and embrace this change will survive. She joined AWS in 2019 on the back of this conviction.

The AWS DeepRacer Womens League India 2021 is intentionally designed to create awareness of ML among women students in India, enable them to explore ML, learn collaboratively, and inspire them to take up careers in ML. We were delighted that over 17,000 women students from all corners of India showed interest to participate in the competition, she smiles. DeepRacer as the AWS website states is an autonomous 1/18th scale race car designed to test real-life models by racing them on a physical track. Using cameras to view the track and a reinforcement model to control throttle and steering, the car shows how a model trained in a simulated environment can be transferred to the real-world.

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ML proved to be useful in the current pandemic too! It is playing a key role in better understanding and addressing the COVID-19 pandemic. In the fight against the pandemic, organisations have been quick to apply their machine learning expertise in several areas including scaling customer communications, understanding how COVID-19 spreads and speeding up research and treatment.

Overcoming The Gender Disparity In Technology

Despitethe strides women have made in engineering, IT and beyond, there is still a gender gap in the field. Kasture gives a clear idea on what can be and should be done: At the grassroots level, there is a strong gender stereotype about women in STEM in general. We need to remove this stereotype. Encourage girls from a very young age in schools and colleges to opt for STEM programmes. Once women join the workforce, encourage them to actively raise their hands and ask for roles in hot technologies areas like ML, AI, analytics, augmented and virtual reality, blockchain, and quantum computing. Organisations need to partner with women, support, and reward them for working in new and emerging technologies. A mentoring programme to encourage women to participate in enhancing their knowledge and giving them an edge is also very useful. A knowledge series designed to give women deeper learning in a safe environment will go a long way.

Also read: 5 Indian Women Making Waves In The Field Of Science And Technology

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Future Calling: Machine Learning Is The Next Big Thing! - Femina

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Neurosurg Focus. 2021 May;50(5):E5. doi: 10.3171/2021.2.FOCUS201113.

ABSTRACT

OBJECTIVE: Frailty is recognized as an important consideration in patients with cancer who are undergoing therapies, including spine surgery. The definition of frailty in the context of spinal metastases is unclear, and few have studied such markers and their association with postoperative outcomes and survival. Using national databases, the metastatic spinal tumor frailty index (MSTFI) was developed as a tool to predict outcomes in this specific patient population and has not been tested with external data. The purpose of this study was to test the performance of the MSTFI with institutional data and determine whether machine learning methods could better identify measures of frailty as predictors of outcomes.

METHODS: Electronic health record data from 479 adult patients admitted to the Massachusetts General Hospital for metastatic spinal tumor surgery from 2010 to 2019 formed a validation cohort for the MSTFI to predict major complications, in-hospital mortality, and length of stay (LOS). The 9 parameters of the MSTFI were modeled in 3 machine learning algorithms (lasso regularization logistic regression, random forest, and gradient-boosted decision tree) to assess clinical outcome prediction and determine variable importance. Prediction performance of the models was measured by computing areas under the receiver operating characteristic curve (AUROCs), calibration, and confusion matrix metrics (positive predictive value, sensitivity, and specificity) and was subjected to internal bootstrap validation.

RESULTS: Of 479 patients (median age 64 years [IQR 55-71 years]; 58.7% male), 28.4% had complications after spine surgery. The in-hospital mortality rate was 1.9%, and the mean LOS was 7.8 days. The MSTFI demonstrated poor discrimination for predicting complications (AUROC 0.56, 95% CI 0.50-0.62) and in-hospital mortality (AUROC 0.69, 95% CI 0.54-0.85) in the validation cohort. For postoperative complications, machine learning approaches showed a greater advantage over the logistic regression model used to develop the MSTFI (AUROC 0.62, 95% CI 0.56-0.68 for random forest vs AUROC 0.56, 95% CI 0.50-0.62 for logistic regression). The random forest model had the highest positive predictive value (0.53, 95% CI 0.43-0.64) and the highest negative predictive value (0.77, 95% CI 0.72-0.81), with chronic lung disease, coagulopathy, anemia, and malnutrition identified as the most important predictors of postoperative complications.

CONCLUSIONS: This study highlights the challenges of defining and quantifying frailty in the metastatic spine tumor population. Further study is required to improve the determination of surgical frailty in this specific cohort.

PMID:33932935 | DOI:10.3171/2021.2.FOCUS201113

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Performance assessment of the metastatic spinal tumor frailty index using machine learning algorithms: limitations and future directions - DocWire...

Alzheimer's Disease remains one of the most common brain disorders affecting people, especially the elderly, worldwide - and a new study reports that there's not only one, but there are four different types of these progressive brain disorders.

The currently irreversible brain condition has been characterized by slowly declining memory, cognitive capabilities, which eventually lead to the incapability to perform even the simplest type. As mankind learns more about this disease, the better we can address this condition and hopefully in the near future, develop a cure for it. This makes the new discovery particularly important progress in the study of the disease.

A report appearing in the latest Nature Medicine, published last April 29, presents findings from an international team of researchers - including those from the McGill University in Canada, the King's College London in the UK, Skne University Hospital in Sweden, Yonsei University College of Medicine in South Korea, as well as members of AVID Radiopharmaceuticals and the Alzheimer's Disease Neuroimaging Initiative.

(Photo: ADEAR via Wikimedia Commons)Diagram of the brain of a person with Alzheimer's Disease

ALSO READ: Recent Study Shows Link Between Alzheimer's Disease and Major Surgery

In the study titled "Four distinct trajectories of tau deposition identified in Alzheimer's disease," researchers explain how Alzheimer's disease is "characterized by the spread of tau pathology throughout the cerebral cortex."

The brain has a member of the microtubule-associated family called the "Tau protein," which is involved in a number of neurodegenerative diseases like Parkinson's and Alzheimer's disease.

Tau pathology refers to the existence of a pathological aggregation of these proteins in the neurofibrillary tangles (NFTs). These misshapen proteins and the pattern of how they get tangled have long been previously believed to be more or less similar to people having neurodegenerative disease.

This particular phenomenon, which develops in cases of Alzheimer's disease, was examined by the researchers with help from specially-developed machine learning algorithms. The machine learning tool was trained to analyze brain scans of 1,143 people - a mixed data set of healthy brains and those diagnosed with Alzheimer's disease.

"We identified four clear patterns of tau pathology that became distinct over time," said Oskar Hansson, co-author of the study and a neurologist from the Clinical Memory Unit at the Lund University, in a press release from the Swedish university.

Hansson additionally explains that the prevalence of the subgroups was anywhere from 18 to 30 percent of the cases in the study. This means that all of the subtypes of the disease appear to be almost equally common, with no single subtype dominating over the others.

The first variant, Subtype 1: Limbic, was found in 33 percent of the cases. It was characterized by pathologic tau spread mostly within the brain's temporal lobe and is affecting patient memory. It is followed by the Subtype, MTL-Sparing, which was present in 18 percent of the cases and spreads across other sections of the cerebral cortex. Under these cases, memory problems become less common but are dominated by difficulties in planning and performing actions.

The third, Subtype 3: Posterior, was found in 30 percent of the cases - tau proteins spreading in the visual cortex, which is the brain's region for processing eyesight. In this case, patients experience difficulties in orientation, depth and distance perception, and processing shapes. The last one, Subtype 4: L Temporal, was only detected in 19 percent of cases and is asymmetrically spread in the left hemisphere, affecting speech and language.

"We now have reason to reevaluate the concept of typical Alzheimer's, and in the long run also the methods we use to assess the progression of the disease," commented Jacob Vogel, co-author of the study from McGill University.

RELATED ARTICLE: Study Shows Alzheimer's Could Be Predicted Through Writing Tests

Check out more news and information on Alzheimer's Disease in Science Times.

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Four Types of Alzheimer's Disease and How Machine Learning Helped Identify Them - Science Times

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.

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Can machine learning help save the whales? How PNW researchers use tech tools to monitor orcas - GeekWire