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Canon Medical expands reach of its MRI artificial intelligence programs – FierceBiotech

Canon Medical is expanding the clinical reach of its artificial intelligence programs designed to improve MRI image quality, saying it can now be used in 96% of all scanning procedures.

The companys Advanced intelligent Clear-IQ Engine, or AiCE, aims to sharpen scans taken by lower-dose, 1.5 Tesla MRIs, to bring their image quality up to par with 3.0 Tesla machines.

The system was previously cleared by the FDA for certain brain- and knee-focused indications, using Canon Medicals Vantage Orian 1.5 Tesla system. Now its applications span all joints, as well as cardiac, abdomen, spine and pelvic scans.

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In todays environment, making images easy to read and acquire is more important than ever, and this is the latest demonstration of our commitment to offering accessible AI that clinicians can use to make the greatest impact on patient care, said Jonathan Furuyama, managing director of Canon Medicals MR business unit.

RELATED: Canon gets FDA nod for high-resolution CT system

The expansion follows two recent FDA clearances for Canon Medical in December and January, including an AI-equipped, large-bore CT scanner and software designed to boost 3D MRI imaging times.

The companys Speeder software, also for its Vantage Orian 1.5 Tesla system, was cleared to help accelerate surgical planning and orthopedic applicationsby reconstructing full resolution images from under-sampled data. This allows technicians to perform a scan at least twice as fast, the company said. The software also includes an application to help clinicians quantify fatty liver disease.

The companys Aquilion Exceed large-bore CT system, meanwhile, uses AiCE technology to provide more distinct images with an opening nearly one meter wide, with an extended field-of-view of 90 centimeters.

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Skinopathy Files Provisional Patent for Artificial Intelligence and Augmented Reality Powered Technology that will Guide Skin Cancer Surgeries – Yahoo…

TORONTO, Feb. 02, 2021 (GLOBE NEWSWIRE) -- Skinopathy, a Canadian medical company founded in 2020, has filed a provisional patent with the United States Patent and Trademark Office (USPTO) for Artificial Intelligence (AI) and Augmented Reality (AR) technology that will help guide surgeons when performing skin cancer excisions.

Healthcare practitioners will be able to use the AI technology which determines, pixel by pixel, the boundaries of cancerous skin tissue by simply taking a picture with their smartphones. The AR technology will then provide surgeons with an overlay of those boundaries through the screen.

Next level healthcare Cancerous skin tissue can sometimes extend beyond the measurable lesion and is typically unseen to even the most eagle-eyed surgeon.

That is why some surgeons choose to be overly cautious and remove more skin than might be necessary to prevent the need for further surgery, which can lead to visible scarring and other disfigurements. Conversely, it is possible that cancerous tissue remains following an excision due to the vagaries of the human body, quality-of-life considerations, or experience of the surgeon. This can potentially lead to continued growth and additional excisions in the future.

Once ready, surgeons will have access to cutting-edge technology that will lead to more informed medical decisions and significantly reduce the hardships felt by the patient and the strain levied on the healthcare system.

This will revolutionize skin cancer treatment, says Alexander Shevchenko, Lead Engineer at Skinopathy. We are providing surgeons with an additional skin cancer fighting tool they can carry in their pockets every day.

Preventing advanced stages of skin cancer Skin cancer is more prevalent than colon, lung, breast, and prostate cancers combined and typically present in very unspectacular ways.

Moles, skin tags, and rashes are rarely viewed as causes for concern and are often dismissed until the discomfort becomes greater than the hassle of seeing a doctor. But it is during that time of latency where dangerous conditions can fester and become deadly. When people finally take action, they are often subject to long wait times or need to travel hundreds (if not thousands) of kilometres to access physicians in a major urban centre.

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Using this technology, people will be able to take pictures of their skin lesion and get an immediate and accurate analysis that will advise on the severity of their condition and provide online access to healthcare practitioners in a matter of days, sometimes even hours.

This is a tremendous milestone for skin cancer, says Dr. Colin Hong, Co-Founder and Chief Medical Officer of Skinopathy. We are using technology to streamline the medical bureaucracy to ensure no one slips through the cracks.

It now takes weeks, sometimes months, to see a skin cancer specialist, and during that time cancerous tissue can grow rapidly and spread to other organs. Making matters worse is the ongoing COVID-19 pandemic which has added more delays and obstacles.

Geographically agnostic technologySkinopathy is using the same kind of technology used for the Re-Captcha security feature found on many websites. However, instead of using AI to determine the difference between a fire hydrant and a bus, Skinopathy is using it to determine the miniscule differences between a mole and a cancerous lesion.

Using real-world images taken by physicians and beta users using their smartphones, the Skinopathy prototypes have yielded 87% accuracy for nine different skin conditions, such as keratosis, and performed even more impressively for basal cell carcinoma, squamous cell carcinoma, and melanoma with accuracy rates ranging from 87% to 96%.

We are very excited about these results, says Dr. Rakesh Joshi, Lead Data Scientist at Skinopathy. There are very subtle nuances on how skin lesions present on the skin, and our models are able to detect the smallest of variances.

Since the technology being developed is geographically agnostic, it can be deployed anywhere in the world and bring needed medical care to under-serviced regions. You can learn more about the technology here.

Skin cancer facts and statsThe Canadian Skin Cancer Foundation states that 1 in 3 cancers diagnosed worldwide is skin cancer and that they outnumber lung, breast, prostate, and colon cancers combined.

Data from the Public Health Agency of Canada suggests the costs associated with skin and subcutaneous tissue diseases was over 2 Billion Dollars in 2010.

Research suggests there is a skin cancer epidemic in the elderly.

About Skinopathy Founded in 2020, Skinopathy is a medical company creating Artificial Intelligence (AI), Augmented Reality (AR), and automation technology that will ensure people and healthcare practitioners receive convenient, reliable, and State-Of-The-Art skin cancer mitigation tools. Its first service, GetSkinHelp.com, is already helping patients connect with specialists through its virtual platform.

ContactKeith LooCo-Founder & Chief Executive Officer(833) 272-7546 x700keith@skinopathy.com

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Using Artificial Intelligence to prevent harm caused by immunotherapy – Hindustan Times

Researchers at Case Western Reserve University, using artificial intelligence (AI) to analyze simple tissue scans, state that they have discovered biomarkers that could tell doctors which lung cancer patients might actually get worse from immunotherapy.

Using Artificial Intelligence to prevent harm caused by immunotherapy

Until recently, researchers and oncologists had placed these lung cancer patients into two broad categories: those who would benefit from immunotherapy, and those who likely would not.

But a third category--patients called hyper-progressors who would actually be harmed by immunotherapy, including a shortened lifespan after treatment--has begun to emerge, said Pranjal Vaidya, a PhD student in biomedical engineering and researcher at the university's Center for Computational Imaging and Personalized Diagnostics (CCIPD).

"This is a significant subset of patients who should potentially avoid immunotherapy entirely," said Vaidya, first author on a 2020 paper announcing the findings in the Journal for Immunotherapy of Cancer.

"Eventually, we would want this to be integrated into clinical settings so that the doctors would have all the information needed to make the call for each individual patient," added Vaidya.

Currently, only about 20 percent of all cancer patients will actually benefit from immunotherapy, a treatment that differs from chemotherapy in that it uses drugs to help the immune system fight cancer, while chemotherapy uses drugs to directly kill cancer cells, according to the National Cancer Institute.

The CCIPD, led by Anant Madabhushi, Donnell Institute Professor of Biomedical Engineering, has become a global leader in the detection, diagnosis, and characterization of various cancers and other diseases by meshing medical imaging, machine learning, and AI.

This new work follows other recent research by CCIPD scientists which has demonstrated that AI and machine learning can be used to predict which lung cancer patients will benefit from immunotherapy.

In this and previous research, scientists from Case Western Reserve and Cleveland Clinic essentially teach computers to seek and identify patterns in CT scans taken when lung cancer is first diagnosed to reveal information that could have been useful if known before treatment.

And while many cancer patients have benefitted from immunotherapy, researchers are seeking a better way to identify who would mostly likely respond to those treatments.

"This is an important finding because it shows that radiomic patterns from routine CT scans are able to discern three kinds of response in lung cancer patients undergoing immunotherapy treatment--responders, non-responders and the hyper-progressors," said Madabhushi, senior author of the study.

"There are currently no validated biomarkers to distinguish this subset of high risk patients that not only don't benefit from immunotherapy but may in fact develop rapid acceleration of disease on treatment," said Pradnya Patil, MD, FACP, associate staff at Taussig Cancer Institute, Cleveland Clinic, and study author.

"Analysis of radiomic features on pre-treatment routinely performed scans could provide a non-invasive means to identify these patients," Patil said.

"This could prove to be an invaluable tool for treating clinicians while determining optimal systemic therapy for their patients with advanced non- small cell lung cancer," added Patil.

As with other previous cancer research at the CCIPD, scientists again found some of the most significant clues to which patients would be harmed by immunotherapy outside the tumor.

"We noticed the radiomic features outside the tumor were more predictive than those inside the tumor, and changes in the blood vessels surrounding the nodule were also more predictive," Vaidya said.

This most recent research was conducted with data collected from 109 patients with non-small cell lung cancer being treated with immunotherapy, she said.

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Beyond the Unknown: Applications of Artificial intelligence In Space – Analytics Insight

Artificial intelligence (AI) is rapidly being explored or adopted by many industries for a wide array of applications. Today it is creating a string of opportunities in space industry use-cases too. As artificial intelligence emerges as a popular theme in space exploration,it is also being deployed for many critical tasks too.

For instance scientists have leveraged artificial intelligence, for charting unmarked galaxies, supernovas, stars, blackholes, and studying cosmic events that would otherwise go unnoticed.One of the recent illustration of this application was when CHIRP (Continuous High-Resolution Image Reconstruction using Patch Priors) Algorithm helped in creating first-ever image of a black hole. CHIRP is a Bayesian algorithm used to perform de-convolution on images created in radio astronomy. It used the image data from the Event Horizon Telescopes to carry further image processing. Even images from the Hubble Space Telescope are used to simulate galaxy formation and further classification using deep learning algorithms.

Artificial intelligence also proves resourceful in classifying heavenly bodies, especially exoplanets. A couple of years ago, a research team developed an artificial neural networks algorithm, to classify planets, based on whether they resemble present-day Earth, early Earth, Mars, Venus or Saturns largest moon, Titan. These five bodies are most potentially habitable objects in our solar system and are therefore associated with acertain probability of life.

In regards to life in outer space, Researchers atNASAs Frontier Development Lab(FDL) employed generative adversarial networks, or GANs, to create 3.5 million possible permutations of alien life based on signals from Kepler and the European Space Agencys Gaia telescope.

Besides, NASA has teamed up with Google to train its artificial intelligence algorithms to sift through the data from the Kepler mission to look for signals from an exoplanet crossing in front of its parent star. With the help of Googles trained model, NASA managed to discover two obscure planets Kepler-90i and Kepler-80g. In 2019, astronomers from the University of Texas at Austin, teamed with Google, to useAI for uncovering two more hidden planets in the Keplerspace telescope archive (Keplers extended mission, called K2).They used an AI algorithm that sifts through Keplers data to ferret out signals that were missed by traditional planet-hunting methods. This helped them discover the planets K2-293b and K2-294b.

Under the Artificial Intelligence Data Analysis (AIDA) project, which is funded under the European Horizons 2020 framework, an intelligent system is being developed that can read and process data from space. The key object of this project is to enable the discovery of new celestial objects, using data from NASA.

AI applications can also found in the field of satellite imagery. Data based on satellite imagery offers insights on several global-scale economic, social and industrial processes, which was previously not possible. Some examples include Earth Observer 1 (EO-1) satellite, SKICAT, ENVISAT. These satellites leverage artificial intelligence to provide actionable insights for agencies, governments and businesses, and help them in making accurate decisions.

While humans are capable ofinterpreting, understanding, and analyzing images collected by satellites, it does cost us time and resources while waiting for a satellite to move back around to the same position to further refine image analysis. Artificial intelligence helps eliminate the necessity for large amounts of communication to and from Earth to analyze photos and helps determine whether a new photo needs to be taken. Moreover, it saves processing power, reduces battery usage, and fast-tracks the image gathering process.

In case of space mining, artificial intelligence will augment mining machinery with intelligence that will empower them to extract minerals and identify any hazards or solve minor issues at hand without the need for immediate support from humans on Earth. Meanwhile, NASA is also developing a companion for astronauts aboard the ISS,called Robonaut, which will work alongside the astronauts or take on tasks that are too risky for them. According to NASAs blog, Robonaut 2 is slowly approaching human dexterity implying tasks like changing out an air filter can be performed without modifications to the existing design.

Artificial intelligence has also helped us develop space humanoids like Kirobo from Japan Aerospace Exploration Agency, Dextre from Canadian Space Agency, and AILA from German Research Center for Artificial Intelligence to help astronauts in space missions. NASAs free-flying robotic system,Astrobee, uses AI to help astronauts reduce their time on routine duties, leaving them to focus more on the things that only humans can do. We also have CIMON or (Crew Interactive Mobile Companion), an AI powered robot that floats through the zero-gravity environment of the space station to research a database of information about the ISS. In addition to the mechanical tasks assigned, CIMON assesses the moods of its human crewmates at the ISS and interacts accordingly with them.

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Artificial Intelligence can predict whether you will die from COVID-19 – Free Press Journal

Copenhagen: Using patient data, artificial intelligence can make a 90 per cent accurate assessment of whether a person will die from COVID-19 or not, according to new research at the University of Copenhagen.

Body mass index (BMI), gender, and high blood pressure are among the most heavily weighted factors. The research can be used to predict the number of patients in hospitals, who will need a respirator and determine who ought to be first in line for a vaccination. The results of the study were published in the journal Scientific Reports -- Nature.

Artificial intelligence is able to predict who is most likely to die from the coronavirus. In doing so, it can also help decide who should be at the front of the line for the precious vaccines now being administered across Denmark.

The result is from a newly published study by researchers at the University of Copenhagen's Department of Computer Science. Since the COVID pandemic's first wave, researchers have been working to develop computer models that can predict, based on disease history and health data, how badly people will be affected by COVID-19.

Based on patient data from the Capital Region of Denmark and Region Zealand, the results of the study demonstrate that artificial intelligence can, with up to 90 percent certainty, determine whether an uninfected person who is not yet infected will die of COVID-19 or not if they are unfortunate enough to become infected. Once admitted to the hospital with COVID-19, the computer can predict with 80 percent accuracy whether the person will need a respirator.

"We began working on the models to assist hospitals, as, during the first wave, they feared that they did not have enough respirators for intensive care patients. Our new findings could also be used to carefully identify who needs a vaccine," explains Professor Mads Nielsen of the University of Copenhagen's Department of Computer Science.

Older men with high blood pressure are highest at risk The researchers fed a computer program with health data from 3,944 Danish COVID-19 patients. This trained the computer to recognise patterns and correlations in both patients' prior illnesses and in their bouts against COVID-19.

"Our results demonstrate, unsurprisingly, that age and BMI are the most decisive parameters for how severely a person will be affected by COVID-19. But the likelihood of dying or ending up on a respirator is also heightened if you are male, have high blood pressure or neurological disease," explains Mads Nielsen.

The diseases and health factors that, according to the study, have the most influence on whether a patient ends up on a respirator after being infected with COVID-19 are in order of priority: BMI, age, high blood pressure, being male, neurological diseases, COPD, asthma, diabetes and heart disease.

"For those affected by one or more of these parameters, we have found that it may make sense to move them up in the vaccine queue, to avoid any risk of them becoming infected and eventually ending up on a respirator," says Nielsen.

Predicting respiratory needs is a must. Researchers are currently working with the Capital Region of Denmark to take advantage of this fresh batch of results in practice. They hope that artificial intelligence will soon be able to help the country's hospitals by continuously predicting the need for respirators.

"We are working towards a goal that we should be able to predict the need for respirators five days ahead by giving the computer access to health data on all COVID positives in the region," says Mads Nielsen, adding: "The computer will never be able to replace a doctor's assessment, but it can help doctors and hospitals see many COVID-19 infected patients at once and set ongoing priorities."

However, technical work is still pending to make health data from the region available for the computer and thereafter to calculate the risk to the infected patients. The research was carried out in collaboration with Rigshospitalet and Bispebjerg and Frederiksberg Hospital.

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How to Build a Modern Workplace with Artificial Intelligence and Internet of Things – BBN Times

1. Automating Tasks

Workplaces have several tasks that are routine and mundane such as scheduling meetings. Usually, employees may send emails back-and-forth to several other employees and enquire about an open slot on their calendar. This process can be increasingly tedious and time-consuming for employees.

Business leaders can adopt AI in the workplace to enhance employee productivity. Organizations can deploy AI-powered personal assistants for scheduling, cancelling, and rescheduling meetings. AI-enabled assistants can analyze an employees schedule and suggest time slots to other employees based on their availability. When a time slot gets decided, an AI assistant will notify all participants of the meeting. Also, AI can be used to automatically transcribe meetings and create a text file. Furthermore, the introduction of AI in the workplace can also automate other tasks such as sorting and categorizing emails.

HR executives usually resolve employee queries related to various workplace policies. Also, HR executives have other core tasks such as managing payroll, recruiting talent, and onboarding new employees. Similarly, IT professionals can be caught up employee queries along with their core tasks. Hence, the productivity of HR and IT department can be severely affected.

The deployment of AI in the workplace can enable organizations to resolve employee queries without interrupting their HR or IT departments.Several organizations are deploying AI-powered chatbotsin the workplace. Similarly, every organization can deploy AI-enabled chatbots that can answer different employee queries accurately. Employees can ask queries using emails, text messages, and online messengers and AI will respond accordingly. If the chatbot is unable to answer a query, then it will assign a request to the concerned personnel who can resolve the query. With this approach, AI-enabled chatbots can also learn how to respond to various queries. Additionally, the deployment of AI in the workplace will allow employees to communicate in various language as AI can translate their queries to English.

The adoption of AI in the workplace can streamline the onboarding process. AI systems can automate various tasks such as generating offer letters, sending documents, and walking new employees through various company-wide policies. Additionally, AI can coach new employees by observing and analyzing how they conduct various tasks. Then, AI tools can suggest ways to improve their efficiency. For instance, AI systems can analyze sales calls of multiple employees and offer tips to improve their performance. For this purpose, AI systems can record sales calls and generate statistics for each employee. Using this approach, AI systems can offer suggestions based on every employees data. Likewise, AI can also train customer service executives to help them deliver better services. With this approach, AI can provide personalized training for each employee.

In the digital age, running a competitive business without data is almost impossible. Businesses collectdifferent types of datasuch as social media data, customer data, and operational data for various applications. However, the obtained cannot be useful if it is not used for generating analytics. Hence, deployment of AI in the workplace can enable business leaders to generate valuable insights from the acquired data. For this purpose, AI systems will optimize data collected from various sources such as social media and personal information of customers and store it in a centralized location. Then, AI systems will analyze the collected data to offer profound insights that can help business leaders predict industry trends, identify anomalies, and generate detailed reports.

The introduction of IoT in the workplace can benefit organizations in the following manner:

Every employee prefers a different temperature on the thermostat and this disagreement can be atopic of conflict in the workplace.Business leaders can install smart thermostats and temperature sensors in the workplace to automate thermostat settings. Smart thermostats learn from employee temperature preferences and set the temperature accordingly. Similarly, business leaders can install several IoT-powered appliances such as smart lights, smart air conditioners, and coffee machines that can be operated using a smartphone.

Organizations can install IoT sensors in the workplace to notify employees about empty conference rooms. These sensors will monitor all conference rooms and display their status as available or busy in a centralized location. With this approach, employees can effortlessly find empty conference rooms.

Business leaders can introduce effective security measures and access control in the workplace with the help of IoT. Conventional keys, badges, and passes can be easily forgotten or duplicated. Hence, organizations can deploy smart locks that can be effortlessly unlocked using a smartphone. Such locks can also enable access control for certain rooms. For instance, only a few employees will have access to rooms that contain crucial paperwork and confidential data. With the help of IoT, business leaders can offer a granular approach to access control in the workplace. Also, smart locks can integrate with existing security systems in an organization.

The US consumes around23% of the worlds energy.Such statistics can be worrying after knowing about depleting energy reserves, overpopulation, and climate change. Energy in the form of electricity is extensively utilized in the workplace for several business procedures. Also, the cause of excessive energy consumption may be the inability to track energy utilization in the workplace. Hence, business leaders can deploy IoT sensors that can monitor energy usage. IoT sensors can monitor energy consumption in real-time and present the data to concerned parties. Concerned personnel can analyze the acquired data and take necessary steps to reduce energy consumption. Also, IoT sensors and smart appliances can help in controlling energy usage. For instance, smart lights have IoT sensors that can detect people in a room. In case a room is empty, lights would shut off and turn back on when someone enters the room. With this approach, organizations can monitor and control energy consumption and conserve energy.

The introduction of IoT and AI in the workplace will help businesses deliver more efficient operations and workflow, leading to a better ROI. Also, IoT and AI can significantly improve employee experience, which can help organizations in attracting and retaining the best talent. Additionally, AI and IoT can work together to make the existing applications more advanced. Hence, business leaders must invest in these modern technologies to reap their benefits and gain a competitive edge.

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Morality Poses the Biggest Risk to Military Integration of Artificial Intelligence – The National Interest

Finding an effective balance between humans and artificial intelligence (AI) in defense systems will be the sticking point for any policy promoting the distancing from humans in the loop. Within this balance, we must accept some deviations when considering concepts such as thekill chain.How would a progression of policy look within a defense application? Addressing the political, technological, and legal boundaries of AI integration would allow the benefits of AI, notably speed, to be incorporated into the kill chain. Recently, former Secretary of Defense Ash Carter stated We all accept that bad things can happen with machinery. What we dont accept is when it happens amorally. Certainly, humans will retain the override ability and accountability without exception. Leaders will be forever bound by the actions of AI guided weapon systems, perhaps no differently than they would be responsible for the actions of a service member in combat and upholding ethical standards of which the AI has yet to grasp.

The future of weapon systems will include AI guiding the selection of targets, information gathering and processing, and ultimately, delivering force as necessary. Domination on the battlefield will not be in traditional means, rather conflicts dominated by AI with competing algorithms. The normalcy of a human-dominated decisionmaking process does provide allowances for AI within the process, however, not in a meaningful way. At no point does artificial intelligence play a significant role in making actual decisions towards the determination of lethal actions. Clearly, the capability and technology supporting integration havefar surpassed the tolerance of our elected officials. We must build confidence with them and the general public with a couple of fundamental steps.

First, information gathering and processing can be controlled primarily by the AI with little to no friction from officials. This integration, although not significant by way of added capability in a research and development (R&D) perspective, will aid in building confidence and can be completed quickly. Developing elementary protocols for the AI to follow for individual systems such as turrets, easy at first then slowly increasing in difficulty, would allow the progression of technology from an R&D standpoint while incrementally building confidence and trust. The inclusion of recognition software into the weapon system would allow specific target selections, be it civilians or terrorists, of which could be presented, prioritized, and then given to the commander for action. Once functioning within a set of defined perimeters confidently, you can increase the number of systems for overlapping coverage. A human can be at the intersection of all the data via a command center supervising these systems with a battlement management system; effectively being a human on the loop with the ability to stop any engagements as required or limiting AI roles based on individual or mission tolerance.

This process must not be encapsulated solely within an R&D environment. Rather, transparency to the public and elected officials alike, must know and be accepting. Yes, these steps seem elementary, however, they are not being done. Focus has been concentrated on capability development without a similar concern for associated policy development when both must progress together. Small concrete steps with sound policy and oversight are crucial. Without such an understanding, decisionmakers cannot in their conscience approve, rather defaulting to the safe and easy answer, no. Waiting to act on AI integration into our weapons systems puts us behind the technological curve required to effectively compete with our foes. It would be foolish to believe our adversaries and their R&D programs are being held up on AI integration due to moral and public support requirements; the Chinese call it intelligentized war and have invested heavily. Having humans on the loop during successful testing and fielding will be the bridge to additionalAIauthorities and public support necessary for the United States to continue to develop these technologies as future warfare will dictate.

John Austerman is an experienced advisor to senior military and civilian leaders focusing on armaments policy primarily within research and development. Experience with 50+ countries and the Levant to include hostile-fire areas and war zones.

Image: Reuters.

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How Artificial intelligence is Transforming the Apparel Industry – BBN Times

Trend Spotting

Taking into account the fast-changing fashion trends, it goes without saying that anticipating fashion trends is not only tricky but also a time-consuming task. Manually researching the previously popular styles, social media fashion trends, and customer preferences, analysts were expected to spot the upcoming trends. The guesswork done by the professionals may or may not be ac

curate. Besides the hassles of manual work, spotting fashion trends can also pose cost issues before fashion brands if not forecasted rightly. Instead, if the brands invest in leveraging AI, they can cut down all the problems quickly.

The AI tool, trained with quality and quantity data, will analyze past fashion data, check out the customer demand and preferences, gauge competitors moves, and identify the market trends. After processing the data, the AI tool will give accurate details on trendy styles and designs within minutes. With AI, fashion brands can bolster their apparel business by tracking the latest fashion trends in just minutes, which would otherwise take days or even months.

Realizing the potential of AI in design, many tech giants are already making big moves by integrating the technology for their benefit. For instance, a group of professionals inAmazon developed an AI toolthat is capable of analyzing and learning the images that are entered, and then generating an altogether new fashion design by itself. Besides, the industry behemoth - Amazon - has developed another AI application that can analyze and process the fed pictures, thereby giving a conclusion on whether a particular style will look trendy or not. Not only Amazon, but there are dozens of other such tech giants who have already embarked on their AI journey, streaming their design creation process completely.IBM, in collaboration with Tommy Hilfiger and The Fashion Institute of Technology (FIT) is using AIto empower designers in boosting the pace of the product development lifecycle.

With customers becoming restless, irritated, and grumpy on not receiving quick assistance or service, fashion retailers are faced with constant pressure to offer what customers want almost instantaneously. Several industry giants have already come up with the newest technology-powered applications that promote enhanced customer experience, one that goes beyond personalized ads, notification alerts on price drops, or chatbot assistance. Using this sophisticated technology, fashion brands strive to put customization at the forefront for customers during their buying journey. There areAI-powered personal stylist appsin the market that allow users to browse clothes online or to click pictures of their clothes. Giving these images as inputs, the app will recommend the best style according to the user's body type, complexion, and preferences while keeping the fashion trends in mind. From providing customers with personalized advertisement notifications to alerting them on price drops to clearing their doubts or queries with chatbots to now being a personal stylist and providing instant outfit suggestions, fashion brands can meet their aim ofelevating customer experience with the help of AI. With AI being able to act as both, design assistants for designers and personal stylist for consumers, it is pretty much clear that the impact of the technology is more than what we ever imagined.

The emergence of trend-setting technology, AI, has changed the way businesses carry out their processes. And, the discussion weve had, is a proof of the fact that the apparel industry is no exception. With a majority of big fashion brands already tapping into the benefits and applications of AI, it is undeniable that soon the technology will become mainstream in medium-sized companies and startups also. So, for garment companies, who haven't planned to adopt AI yet, the right time to plan and kick-start their digital transformation journey is today. After all, no one would want to be left behind in the digital race, isnt it?

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Bitcoin’s wild ride renews worries about its massive carbon footprint – CNBC

Cryptocurrency mining rigs at a crypto mining farm in Romania.

Akos Stiller | Bloomberg via Getty Images

LONDON Bitcoin's price isn't the only thing surging lately the amount of electricity it consumes is also on the rise.

The cryptocurrency has for years alarmed experts due to the sheer level of energy required by so-called miners, which release new coins into circulation.

Bitcoin has a carbon footprint comparable to that of New Zealand, producing 36.95 megatons of CO2 annually, according to Digiconomist's Bitcoin Energy Consumption Index, an online tool created by data scientist Alex de Vries. It consumes as much power as Chile around 77.78 TWh according to Digonomist's estimates.

The Cambridge Bitcoin Electricity Consumption Index, a separate tool from researchers at Cambridge University, shows a much larger figure of 110.53 TWh more than the entire annual energy consumption of the Netherlands.

"That's an unfathomable amount of electricity," said Charles Hoskinson, a cryptocurrency entrepreneur who co-founded Ethereum, the blockchain network underpinning ether, the world's second-most valuable digital coin.

Bitcoin's energy needs are "enormously large," Michel Rauchs, research affiliate at the Cambridge Centre for Alternative Finance, told CNBC. It accounts for around 0.5% of total global electricity consumption, according to the Cambridge researchers' estimates.

"Although we agree the amounts are ludicrous right now, that is still half as much as inactive home appliances in the U.S. consumed," Rauchs said. The amount of energy wasted on idle home devices like phone chargers and microwaves in the U.S. could power the bitcoin network for two years.

Bitcoin isn't controlled by any single authority like a central bank but a disparate network of computers. So-called "miners" run purpose-built computers which compete to solve complex math puzzles in order to make a transaction go through.

The blockchain a digital ledger of all bitcoin transactions is designed this way to ensure that users aren't able to "double spend" funds, a flaw in which the same digital token could be spent more than once. Each block that is added onto the chain carries a hard, cryptographic reference to the previous block. Proponents of bitcoin say this makes it extremely secure.

But bitcoin miners do not run this operation for free. A key incentive of bitcoin's model, known as "proof of work," is the promise of being rewarded in some bitcoin if you manage to solve the complex hashing algorithm.

"The issue is, it can never get better by design," says Hoskinson, who now runs IOHK, a blockchain firm that developed another digital token called cardano.

"The more successful bitcoin gets, the higher the price goes; the higher the price goes, the more competition for bitcoin; and thus the more energy is expended to mine."

Cardano and some other digital coins rely on a "proof of stake" consensus mechanism, where participants buy tokens which allow them to join the network. Hoskinson says the cardano cryptocurrency network consumes only 6 GWh of power, a tiny fraction of bitcoin's energy consumption. Similar proof-of-stake tokens include polkadot and algorand, he added.

Rauchs said bitcoin is only likely to consume more and more electricity over time due to its proof of work mechanism.

"It doesn't really matter whether there are new, more efficient machines on the market," Rauchs said. "You will just use more and more machines but the total electricity consumption won't go down based off of that."

A key measure of bitcoin's mining difficulty hit an all-time high last month. With bitcoin rising in price, revenue to miners is also increasing, incentivizing more participants to mine the cryptocurrency.

Nevertheless, bitcoin believers argue that disputes about its environmental impact are missing the point.

"Energy use in itself is not bad," Meltem Demirors, chief strategy officer of digital asset management firm CoinShares, told CNBC. "Sending and storing emails uses energy. Yet, we don't infer email to be bad because it consumes energy."

"What we have here is people trying to decide what is or is not a good use of energy, and bitcoin is incredibly transparent in its energy use while other industries are much more opaque."

Demirors questioned why the banking industry, for instance, wasn't under more scrutiny for its energy usage. She said bitcoin miners were "incentivized to use renewables" because it's getting cheaper to produce it.

But most bitcoin mining facilities are located in China, which is still heavily reliant on coal-based power. Though the Chinese province of Sichuan is known to attract miners due to its cheap electricity and rich hydropower resources, the level of power generation capacity fluctuates depending on the season.

Then there's the question of how bitcoin is used. Many investors today consider bitcoin to be a form of "digital gold" rather than an efficient payment system Digiconomist estimates that the energy footprint of one bitcoin transaction is equivalent to 100,000 payments on the Visa network.

The cryptocurrency more than quadrupled in value last year and is up another 27% so far this year, according to Coin Metrics, currently trading at about $37,189.

Andrew Hatton, head of IT at Greenpeace U.K., said the larger issue at hand is that "we're largely powering 21st-century technology with 19th-century energy sources."

"Bitcoin's spiralling energy usage is largely down to the huge amount of data-crunching needed to create and maintain this cyber-currency," Hatton told CNBC. "But their fast-growing hunger for electricity is just an early symptom of a much bigger problem to come."

"As online services become bigger and more complex, the demand for computing power is bound to go up over the next few years, and that will require more energy," he added. "The problem is that only about a fifth of the electricity used in the world's data centres comes from renewable sources, and that's not good enough."

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Ether, the world’s second-largest cryptocurrency, hits a record high above $1,700 – CNBC

Ether, the digital token of the Ethereum blockchain, is the second-largest cryptocurrency in the world by market value.

Jaap Arriens | NurPhoto via Getty Images

LONDON The cryptocurrency ether hit a fresh all-time high on Friday, surging past $1,700 for the first time.

Ether, which is the world's second-largest digital coin by market value, climbed 11.2% to a price of $1,743 at around 10:30 a.m. ET, according to data from CoinDesk.

It comes after bitcoin, the most valuable virtual currency, hit a record high close to $42,000 last month.

Bitcoin more than quadrupled in price over the course of 2020, and is up 29% since the start of 2021. Ether has risen about 129% year to date.

Ether has been steadily rising this week as investors await the highly anticipated launch of ether futures contracts from the Chicago Mercantile Exchange next week.

Trading in ether futures is set to start Monday. The CME launched bitcoin futures over three years ago, at the peak of that cryptocurrency's 2017 rally.

Some investors believe that futures and other crypto-focused derivatives products will give institutional investors more confidence to invest in the space.

"Bringing more financial instruments will bring more participants into the market," said SachinPatodia, a partner at Avon Ventures, a venture capital fund affiliated with the parent company of Fidelity. "That probably is positive for the ether price."

But Patodia said a big driver of the price of ether and other smaller digital currencies was the momentum for bitcoin in recent months.

"We've seen this pattern over many crypto cycles that we've gone through, where bitcoin leads the way in price movement and then you see what we call the alt-coins get carried along," he said.

Ethereum, ether's network, was created after bitcoin in 2013. The main difference it has with bitcoin's blockchain is the ability to support applications.

"This move by the CME may spark further buying of ether by new entrants to the market because it provides a way forsophisticatedinvestors to hedge their risk againstpositions that they may be holding on the underlyingasset," Simon Peters, a cryptoasset analyst at online investment platform eToro, told CNBC.

"However, it is worth noting that, like bitcoin, CME ether futures will be cash settled so as not to involve any physicaldelivery, so we shouldn't necessarily expect a major impact on spot prices."

Crypto investors said another factor potentially boosting ether was the start of a major upgrade to the Ethereum blockchain, called Ethereum 2.0. Believers in ether hope the upgrade will make Ethereum faster and more secure.

The total market value of all cryptocurrencies combined hit $1 trillion last month, as bitcoin's price surged to records. Bitcoin bulls say it's gotten a boost from institutional demand, as well as the perception that it is a store of value similar to gold.

Bitcoin was up 4.7% in the last 24 hours, trading at a price $38,151. XRP, the third-largest digital token, climbed 10.7% to 44 cents.

But skeptics like economist Nouriel Roubinisay bitcoin and other cryptocurrencies have no intrinsic value. A recent Deutsche Bank survey found investorsview bitcoin as the most extreme bubblein financial markets.

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