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Having an A.I. assistant: The Good, The Bad, and the Ugly.

"Hey, S voice, where can I get some free food nearby?"

I don't know how many times I wanted to ask my phone these things. Part of me wants to hear an answer that goes: "Hey, you're in luck, there's a small diner right across the corner. They're giving away free burgers for single desperate lonely guys like you."

It hurts. But this burn is something that I can handle if it means that I get to have free food.

And yet, this never really happens. It's always a generic answer that goes as "Hey, here is a list of eateries nearby."

My lazy, good-for-nothing butt doesn't want this. Oh, if only we had an A.I. that was actually worth it...

Yes, you know where this is going. If you had any confusion now, I'll clear it.

Personal assistant A.I. has been all the rage, well, since as long as we have learned about the concept of A.I.

Since the advent of smartphones, the idea of a virtual Artificial Intelligent assistant has taken the world by storm.

Whether you have Apple's Siri, or Samsung's S Voice, or Cortana from Microsoft, A.I. assistants are now everywhere.

In today's article, we will be focusing on some of the great aspects of A.I. and its impact on human lives. We will also talk about what A.I. in its current form needs to improve.

We will also discuss what is the scope for improvement in all the aspects of A.I. where it is utter garbage.

So, let's get going now, shall we?

The Good:

Even though the A.I. personal Assistant isn't nearly at the level where we can compare it to the likes of J.A.R.V.I.S., it's a close second.Despite being rudimentary when compared to fictional digital butlers, current A.I. can do a lot of things.

This Includes:

It doesn't matter if you're the CEO of a 500-million-dollar company or a student. You can always trust that an A.I. personal assistant will take care of mundane low-value tasks.

Some of the best A.I. personal assistants can actually replace full-fledged assistants. Take the case for SIRI here. Apple's Magnum Opus iPhone is nothing without its operating software and voice.

Not only Siri can act as a friend and share jokes, but it can also make calls, give messages, give recommendations on the basis of web search results.

And this is just one of over a dozen Personal A.I. assistants applications. Cortana, which is from Microsoft can schedule email, create and write notes, and even schedule meetings.

Now that's something, isnt it?

There are hours upon hours of content online that is just waiting for you. All this optimization can happen via a Personal A.I. assistant. All you need to do is search for it.

This is only the best thing about having a full-fledged personal A.I. assistant. Let's see the other side of the coin.

The Bad:

No matter what we do, for now, there are certain limitations for personal A.I. Assistants.When it comes to mundane tasks, then you can rely on personal assistants, but anything more than that means you're asking for trouble.

For example,As of writing this article, your personal A.I. assistant will not be able to inform you when your apps need to be updated.

Nor will it be able to delete an app or change anything from the notification settings.Not so neat.

There are other things as well which include not being able to see your health. Unless and until you have enough dough to grab an i-watch or something, yeah, your personal A.I. assistant will not be able to inform you how many steps you have to take.

As you can see, anything that is dependent on voice commands that includes altering anything, cannot be done by these personal A.I. Assistants.

And we haven't even reached the Ugly Part yet.

The Ugly:

The saddest part, and I mean the saddest part for most people out there, is that they feel that they are substituting human emotions with an A.I.

Asking emotional questions to Artificial Intelligence at first seemed a normal thing to do.

After all, it's just for fun. But as we see in societies where A.I. becomes too mainstream, emotional connections between humans seem to be the first thing to go.

Now is this a symptom of a deeper problem or it's just a result of something different, no one really knows. But this goes to show that as humans, we still haven't been able to adapt clearly to the advance of A.I.

Conclusion:

When it comes to A.I. then the highs can be very high, and the lows can be very low.

Personal A.I. can be great for business owners, and students, especially when it comes to moderating simple tasks.

With the current advance in the field and its integration to human platforms, only time can tell where things are headed.

It's also important to remember, that the integration of A.I. right now is still in its nascent stage and there is a lot of room for growth.

We will see you at the next one.

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Artificial Intelligence Assistant: The Good, The Bad, and The Ugly. - Finextra

March 2, 2022 Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In aspecial issueofCancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases.

The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all, explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases.

Promising applications of AI, DL, and ML presented in this issue include identifying early-stage cancers, inferring the site of the specific cancer, aiding in the assignment of appropriate therapeutic options for each patient, characterizing the tumor microenvironment, and predicting the response to immunotherapy.

A comprehensive overview of the literature regarding the use of AI approaches to identify biomarkers for ovarian and pancreatic cancer illustrates underlying principles and looks at the gaps and challenges that face the field as a whole. Ovarian and pancreatic cancers are rare, but lethal because they lack early symptoms and detection. Lead investigator Juergen A. Klenk, PhD, Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA, and colleagues describe studies using AI and ML to analyze images for the early detection of disease, and models that can be built to predict likely outcomes for the patient. Some of the challenges, such as the difficulty of gathering large enough datasets, are discussed.

Algorithms develop biases and produce prejudiced responses when the data they are trained on are non-representative or incomplete, Dr. Klenk said. The investigators suggest that the development of larger and more diverse image databases for rare cancers across institutions, standardized reporting methods, and easier-to-understand interfaces that increase user trust are needed to make a true impact on biomarker discovery.

Lead investigator Debiao Li, PhD, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA, and colleagues developed a model to identify individuals at risk for pancreatic ductal adenocarcinoma (PDAC). PDAC is associated with many preconditional abnormalities that can be visible on a computerized tomography (CT) scan, but these are difficult to comprehend by visual assessment. In their study, the investigators used CT scans from patients with confirmed PDAC and CT scans from the same patients who had had a CT scan six months to three years before diagnosis to identify a set of CT features that were potentially predictive of PDAC. The model was 86% accurate in classifying the patients and the healthy controls, using the identified CT features.

The challenge of AI for the advancement of pancreatic cancer research is the scarcity of data due to low prevalence. The purpose of this proof-of concept model Is to encourage researchers to establish a larger dataset for extensive training and validation of the model, said Dr. Li.

Radiomics is an emerging field where features are extracted from medical imaging using various techniques. Radiomic features can quantify tumor intensity, shape, and heterogeneity and have been applied to oncologic detection, diagnosis, therapeutic response, and prognosis. Lead investigators Shaoli Song, PhD, Shanghai Medical College and Fudan University, Shanghai, China, and Lisheng Wang, PhD, Shanghai Jiao Tong University, Shanghai, China, and colleagues combined radiomic data from preoperative positron emission tomography (PET) and CT images in patients with early stage uterine cervical squamous cell carcinoma. They used algorithms to develop a prognostic signature capable of predicting disease-free survival.

This model could provide more accurate information about potential relapse and metastasis, and could be helpful in decision-making, they observed.

Other papers in the special issue focus on the development of new computational tools to facilitate the application of AI to biomarker identification; the use of whole cell imaging and immunofluorescence to identify immune features in pancreatic tumors to provide prognostic information; the use of microRNAs and applied machine learning to identify a miRNA profile associated with gastrointestinal stromal tumors; and the use of hierarchical clustering of combined multi-omic datasets to identify an antitumor immune signature in patients with colon cancer.

Dr. Rodland added that the articles in this special issue are only a small sampling of the various approaches to using AI, DL, and ML in biomarker research. There is a continuing urgent need for more effective strategies for improving the early detection of cancers. Cutting-edge AI systems have been shown to improve sensitivity and specificity in the interpretation of both imaging and non-imaging data for breast, lung, prostate, and cervical cancers, she stated.

For more information: http://www.iospresscom

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Artificial Intelligence and Machine Learning Show Promise in Cancer Diagnosis and Treatment - Imaging Technology News

PARIS, March 2, 2022 /PRNewswire/ -- GLEAMER, a French medtech company pioneering the use of artificial intelligence technology in the practice of radiology, announced today that the United States Food and Drug Administration has cleared its BoneView AI software for use by U.S. healthcare specialists to aid in diagnosing fractures and traumatic injuries on X-rays. In a U.S. study recently published by Boston University School of Medicine, BoneView was shown to help detect and localize fractures over the entire appendicular skeleton, rib cage, thoracic and lumbar spine, improving sensitivity and specificity, while reducing reading time.BoneView received the CE mark class 2a certification in the European Union in March 2020 and has been widely adopted in more than 300 institutions across 13 countries.

GLEAMER developed BoneView to aid radiologists, orthopedic surgeons, emergency physicians, rheumatologists, family physicians and physician assistants, all of whom read X-rays in clinical practice to diagnose fractures in their patients. BoneView detects fractures in X-ray images and submits them to radiologists for final validation, providing healthcare professionals with a safe, reliable, time-saving and user-friendly tool. The BoneView AI algorithm is cleared as a CADe/CADx (computer assisted detection and diagnosis) by the FDA and highlights regions of interest with bounding boxes around areas where fractures are suspected so radiologists can prioritize reading those X-rays.

The Study conducted between July 2020 and January 2021, used images acquired in the US from multiple centers on instruments from a wide variety of manufacturers and involved readers from Boston University School of Medicine (MA), Stony Brook University Renaissance School of Medicine (NY), and Massachusetts General Hospital - Harvard Medical School (MA). Results showed that BoneView AI assistance provided a 10.4 percent improvement of fracture detection sensitivity and shortened the radiograph reading time by 6.3 seconds per patient. The BoneView AI algorithm's standalone performance for fracture detection had an AUC of .97.

Across the six types of specialists participating in the Study, the combination of AI and health professionals' interpretations lowered the false negative rate (undetected fractures) on X-rays by 29 percent, while reducing reading time by 15 percent on exams specifically selected for their difficulty. BoneView also improved the specificity of fracture detection by radiologists and non-radiologists involving many anatomical locations, including foot/ankle, knee/leg, hip/pelvis, hand/wrist, elbow/arm, shoulder/clavicle, rib cage and thoracolumbar spine.

"BoneView can change everything about the way X-ray reading is done today," said Christian Allouche, CEO and co-founder of GLEAMER. "In the value-based U.S. health care system, providers tell us they want to improve the radiographic diagnostic process which accounts for a huge part of their workload and optimize patient management. We are delighted and proud to offer clinicians and patients BoneView AI for this state-of-the-art advancement in radiology and patient care."

Traumatic skeletal injuries are aleading source of consultation in emergency departments, representing one-third of annual visits. Fracture interpretation errors can represent up to 24 percent of harmful diagnostic errors seen in the ER and are more common during the evening and overnight hours, most likely related to non-expert reading and fatigue.

"Radiologists' workload has doubled in the past two decades, and despite technological progress, they must analyze hundreds more images every day, requiring the readings to be highly reliable," explained Ali Guermazi, MD, PhD, Chief of Radiology at VA Boston Healthcare System and Professor of Radiology and medicine at Boston University School of Medicine, and leader of the U.S. study. "The assistance of AI should allow usto improve the specificity of the complementary exams prescribed after the radiography, to avoid delays in care, and to direct patients into the right therapeutic pathway. Our study was focused on fracture diagnosis, and a similar concept can be applied to other diseases and disorders."

To date, BoneView has analyzed more than three million images around the world and is deployed in more than 13 countries across Europe, the Middle East, Asia-Pacific and North America. More than 3,500 radiologists and emergency physicians now relyon BoneView in their clinical routines.The solution is now available in the U.S. directly via GLEAMER and through other platforms including Fujifilm, Aidoc, Ferrum Health, Blackford Analysis.

About GLEAMER

GLEAMER's first globally available AI software, BoneView, recently received clearance by the U.S. Federal Food and Drug Administration and CE mark class 2a certification in Europe. Studies by world-leading radiologists and academic medical doctors have shown that BoneView improves detection of fractures in X-ray images, providing healthcare professionals with a safe, reliable, time-saving second reading. GLEAMER develops a suite of AI solutions for Radiology that encapsulate medical-grade expertise. The company wants to support imaging users to secure diagnoses for all patients and at all times, while improving efficiency. GLEAMER's AI Companions are directly integrated in the users' usual reading environment and act as an automated and transparent second reading to improve diagnostic accuracy in X-ray imaging. GLEAMER's solutions are currently being used across 13 countries in more than 300 institutions.

For more information: http://www.gleamer.ai

Media ContactIvy CohenIvy Cohen Corporate Communications[emailprotected](212) 399-0026

SOURCE Gleamer

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GLEAMER Receives FDA Clearance for Its Artificial Intelligence Software to Help Detect and Localize Fractures - PR Newswire

This article features various ways AI can be applied to audit carbon credits

The total quantity of carbon dioxide (CO2) and other greenhouse gases (GHG) emitted in the lifecycle of the product or service, or in any specific financial year, is referred to as a carbon footprint. The measurement is commonly represented in kilos of CO2 equivalents, accounting for the impacts of various greenhouse gases on global warming.

A carbon credit is a marketable permit or certification that entitles the holder to emit one tonne of carbon dioxide or the equivalent of some other greenhouse gas it is effectively a carbon offset for greenhouse gas producers. The primary purpose of carbon credits is to help reduce greenhouse gas emissions from industrial activity in order to mitigate the impacts of global warming. They can also sell excess carbon credits.

Companies are thus motivated to cut greenhouse emissions on two levels: first, they will be penalized if they exceed the quota, and second, they may profit by preserving and reselling part of their emission permits.

a. A carbon offset that is traded in the voluntary market for credits is known as a voluntary emissions reduction (VER).

b. Emission units (or credits) produced within a legal framework with the goal of offsetting a projects emissions are known as certified emissions reductions (CERs).

Emerging IoT-powered devices can assist businesses in tracking and monitoring emissions throughout their whole carbon footprint. These IoT devices may help businesses gather and organise data regarding their activities and operations, as well as from every component of their supply chain, including materials.

Embodied carbon measurement is difficult because it necessitates tracing materials via complex manufacturing supply networks. AI can aid in the calculation of overall materials embodied carbon emissions, which can be difficult to track for big work sites.

Carbon offset monitoring necessitates meticulous documentation of all the many sorts of operations carried out by a corporation to offset carbon emissions. AI, object recognition, cloud computing, and other technologies can assist businesses in automatically recording and analysing data with minimum human intervention.

Artificial intelligence (AI) can assist companies in measuring and forecasting air quality and pollution levels, as well as tracking and predicting the increase and decrease of air pollution on job sites.

AI can learn to enforce on-site sorting and prevent illegal disposal of the wastes, which will aid in the reduction of carbon emissions and pollution in general.

AI & predictive analytics can assist businesses in conducting hassle-free carbon credit trading, therefore empowering the whole carbon credit trading industry.

Manual asset management becomes inefficient when the number of machines employed on job sites grows, as it is impossible for humans to monitor each and every machine at all times. AI technology may be utilised to continuously monitor operation hours, fuel use, and instances of wasteful equipment utilisation without missing a beat, assisting in the optimization of machinery usage.

AI, IoT, & cloud computing can all work together to maintain track of a companys carbon credits in an automated manner.

Predictive AI can assist businesses in estimating future emissions throughout their carbon footprint, taking into account current efforts, new carbon reduction strategies, and future demand.

AI, as well as other technologies such as IoT, may be used to track carbon pollution from various sources on job sites. This can assist businesses in identifying high-emitting & low-emitting fuels and, as a result, setting objectives, making decisions about their use, and reducing emissions.

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Applications of Artificial Intelligence in Carbon Credit Auditing - Analytics Insight

Telecommunications giant Bell is entering a partnership with Vector Institute to advance research and applications relating to artificial intelligence.

The institute is dedicated to studying AI and works with various companies and organizations to drive research and development.

Bell notes this partnership will help the company continue innovating in the telecom sector and be a part of emerging AI technologies in Canada and across Bell.

Bell is thrilled to collaborate with Vector and the work theyre doing in developing new research and expertise in artificial intelligence inCanada, John Watson, group president of customer experience, said in a statement.

Fostering the development of new technologies within our borders helps Canadian industry, and in turn, benefits Canadians. We are proud to help accelerate innovation in this field so that we can harness AI for applications at Bell.

The company says its currently using AI in all lines of business and will continue to do so to identify areas to improve its operations and customer experience.

Source: Bell

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Bell to work with Vector Institute on artificial intelligence research - MobileSyrup