Category Archives: Artificial Intelligence
Artificial intelligence in healthcare? ‘Don’t focus solely on technology’ – Innovation Origins
Tech expert Jarno Duursma sees both advantages and disadvantages when it comes to using AI in healthcare. First the advantages: Scientists at Life Lines, a large-scale study into the onset of chronic diseases among 165 thousand people in the northern Netherlands, make use of artificially intelligent software. Duursma: This research has been going on since 2006. A huge database is being compiled from all those studies and questionnaires. With the help of AI, doctors are able to identify connections that they would otherwise never have spotted, like improving the diagnosis of depression or the prediction of cancer.
Or what about research into medicines? At Leiden University in the Netherlands, researchers are working on a model that is based on 3.8 million measurements that have been published on drug candidates since the 1970s. This acts as a kind of library that helps scientists search in the right direction. The system also predicts interactions between a chemical and a protein based on 5.5 billion data points. Using the softwares predictions, a chemist can get to work testing whether the potential drug will work in actual practice. In this regard, the use of artificial intelligence saves a lot of time and money. These are very fine applications that allow you to develop a drug that works faster or to use an existing drug for other diseases. These are great developments that get me fired up, Duursma adds.
Something else that gets Duursma enthused: Avatars in healthcare. For example, in the form of a digital doctor who conducts a simple intake or summarizes complicated and lengthy pieces of text in a short video for patients. By letting artificial intelligence carry out an intake, a doctor has more time to spare. You can also use this digital doctor to explain patient leaflets using a video, which sometimes works better than long texts.
Your weekly innovation overviewEvery sunday the best articles of the week in your inbox.
Despite his enthusiasm, Duursma also warns against using AI in healthcare. Our healthcare is becoming more and more expensive and is putting more pressure on society. We need to do something about this, but we shouldnt be focusing solely on technology. We still need to keep a critical eye on the dangers of AI.
In his view, we tend to overestimate the merits of software. To illustrate this point, he points to an algorithm that predicts whether a mole is malignant or not. The software was perfectly capable of picking out bad birthmarks, but what transpired when the scientists started probing into how it did that? The system did not reach its conclusions by looking at the moles themselves, but saw the ruler that dermatologists use to track the growth of suspicious moles as an important signal. This shows that an algorithm trained with different pictures of birthmarks comes up with an assessment based on something completely different than what you might expect.
Duursma sees the same thing in a host of initiatives that were designed to detect Covid-19 on lung photos with the use of AI. These lung photos are all different qualities and there are a lot of nuances in them. So, in any event, the data is very messy. A specific AI system once again drew a conclusion on the basis of something weird. The algorithm based its diagnosis on a font on the x-ray images of certain hospitals where there were a lot of corona patients. This black box is one danger that AI poses that we need to be aware of.
According to Duursma, another disadvantage of using artificial intelligence is that we want to capture all problems as data. By this datafication of the problem, you might be needlessly diminishing the problem. This creates a techno-solutionism, whereby you only focus on where data can be collected. Whereas when you zoom out, not everything can be captured as data. These problems are then excluded from it.
Nor should we be blind, Duursma believes, to any unintended long-term consequences that technology or artificial intelligence may cause. As an example, he cites the selfie cameras in iPhones: The selfie camera has contributed to making the individual even more of a focal point. Young people now visit a plastic surgeon with their favorite Snapchat filter: This is how I want to look. Thats an unintended consequence of this technology, but no Apple developer had ever considered that before.
Duursma goes on to say that we need to pay more attention to the talents and qualities that we lose along the way as a result of technology, especially in healthcare. I used to be very good at remembering phone numbers. Now my phone does that for me. The same goes for navigating or doing math in your head. These are skills that we are losing through the use of technology. Especially in healthcare, it is important that we treat this very carefully. Look at this from the perspective of a moral compass. Imagine that we will soon have an infallible algorithm for checking moles. Are radiologists then allowed to unlearn this skill? Or do we teach students not to look at photos because the software does that? I dont have answers to these questions, but we should continue to critically examine this aspect.
Tech philosopher at Fontys University of Applied Sciences, Rens van der Vorst, also offers much the same critical examples when talking about AI in healthcare. Generally speaking, you see that the diagnostic results of algorithms are quite disappointing. Following the outbreak of corona, all sorts of claims were made. For example, about an algorithm that could predict whether someone had corona based on the sound of someones cough. All those initiatives turned out not to be so successful after all. We tend to overestimate the impact of technology in the short term but underestimate it in the long term. Maybe the same kind of thing is happening with AI.
Van der Vorst sees mainly advantages to the use of AI in logistics operations in hospitals. Technology often serves as an amplifier. So if you start using AI to help a supermarket operate more efficiently, a supermarket will operate more efficiently. The same is true for a hospital. Weve seen that software is not yet good enough at making diagnoses, but artificial intelligence is capable of planning more efficiently. AI can also play a role in preventive care right now. With measurements taken in the home and advice on healthy living, to name a few things.
Originally posted here:
Artificial intelligence in healthcare? 'Don't focus solely on technology' - Innovation Origins
How Will Health Care Regulators Address Artificial Intelligence? – The Regulatory Review
Policymakers around the world are developing guidelines for use of artificial intelligence in health care.
Baymax, the robotic health aide and unlikely hero from the movie Big Hero 6, is an adorable cartoon character, an outlandish vision of a high-tech future. But underlying Baymaxs character is the very realistic concept of an artificial intelligence (AI) system that can be applied to health care.
As AI technology advances, how will regulators encourage innovation while protecting patient safety?
AI does not have a precise definition, but the term generally describes machines that have the capacity to process and respond to stimulation in a manner similar to human thought processes. Many industriessuch as the military, academia, and health carerely on AI today.
For decades, health care professionals have used AI to increase efficiency and enhance the quality of patient care. For example, radiologists employ AI to identify signs of certain diseases in medical imaging. Tech companies are also partnering with health care providers to develop AI-based predictive models to increase the accuracy of diagnoses. A recent study applied AI to predict COVID-19 based on self-reported symptoms.
In the wake of the COVID-19 pandemic and the rise of telemedicine, experts predict that AI technology will continue to be used to prevent and treat illness and will become more prevalent in the health care industry.
The use of AI in health care may improve patient care, but it also raises issues of data privacy and health equity. Although the health care sector is heavily regulated, no regulations target the use of AI in health care settings. Several countries and organizations, including the United States, have proposed regulations addressing the use of AI in health care, but no regulations have been adopted.
Even beyond the context of health care, policymakers have only begun to develop rules for the use of AI. Some existing data privacy laws and industry-specific regulations do apply to the use of AI, but no country has enacted AI-specific regulations. In January 2021, the European Union released its proposal for the first regulatory framework for the use of AI. The proposal establishes a procedure for new AI products entering the market and imposes heightened standards for applications of AI that are considered high risk.
The EUs suggested framework provides some examples of high-risk applications of AI that are related to health care such as the use of AI to triage emergency aid. Although the EUs proposal does not focus on the health care industry in particular, experts predict that the EU regulations will serve as a framework for future, more specific guidelines.
The EUs proposal strikes a balance between ensuring the safety and security of the AI market, while also continuing to promote innovation and investment in AI. These conflicting values also appear in U.S. proposals to address AI in health care. Both the U.S. Food and Drug Administration (FDA) and the U.S. Department of Health and Human Services (HHS) more broadly have begun to develop guidelines on the use of AI in the health industry.
In 2019, FDA published a discussion paper outlining a proposed regulatory framework for modifications to AI-based software as a medical device (SaMD). FDA defines AI-based SaMD as software intended to treat, diagnose, cure, mitigate, or prevent disease. In the agencys discussion paper, FDA asserts its commitment to ensure that AI-based SaMD will deliver safe and effective software functionality that improves the quality of care that patients receive. FDA outlines the regulatory approval cycle for AI-based SaMD, which requires a holistic evaluation of the product and the maker of the product.
Earlier this year, FDA released an action plan for the regulation of AI-based SaMD that reaffirmed its commitment to encourage the development of AI best practices. HHS has also announced its strategy for the regulation of AI applied in health care settings. As with FDA and the EU, HHS balances the health and well-being of patients with the continued innovation of AI technology.
The United States is not alone in its attempt to monitor and govern the use of AI in health care. Countries such as China, Japan, and South Korea have also released guidelines and proposals seeking to ensure patient safety. In June 2021, the World Health Organization (WHO) issued a report on the use of AI in health care and offered six guiding principles for AI regulation: protecting autonomy; promoting safety; ensuring transparency; fostering responsibility; ensuring equity; and promoting sustainable AI.
Scholars are also discussing the use of AI in health care. Some experts have urged policymakers to develop AI systems designed to advance health equity. Others warn that algorithmic bias and unequal data collection in AI can exacerbate existing health inequalities. Experts argue that, to mitigate the risk of discriminatory AI practices, policymakers should consider the unintended consequences of the use of AI.
For example, AI systems must be trained to recognize patterns in data, and the training data may reflect historical discrimination. One study showed that women are less likely to receive certain treatments than men even though they are more likely to need them. Similarly biased data would train an AI system to perpetuate this pattern of discrimination. Health care regulators must address the need to protect patients from potential inequalities without discouraging the development of life-saving innovation in AI.
As the use of AI becomes more prominent in health care, regulators in the United States and elsewhere find themselves considering more robust regulations to ensure quality of care.
The rest is here:
How Will Health Care Regulators Address Artificial Intelligence? - The Regulatory Review
Global Artificial Intelligence Market Is Expected To Set A New Benchmark With A CAGR Of 40.2% By 2028 | Up Market Research – PRNewswire
PUNE, India, Oct. 19, 2021 /PRNewswire/ -- According to a recent market study published by Up Market Research titled, "Global Artificial Intelligence Marketby Technology (Machine Learning, Deep Learning, Machine Vision, Natural Language Processing), by Solution (Services, Hardware, Software), by End Use (BFSI, Automotive & Transportation, Advertising & Media, Agriculture, Manufacturing, Retail, Healthcare, Law) and Region: Size, Share, Trends and Opportunity Analysis, 2018-2028", As per the study the market value was USD 62.35 million in 2020. It is expected to grow at a compound annual rate (CAGR) of 40.2% between 2021 and 2028. Tech giants have been directing continuous research and innovation to drive the adoption of new technologies across a variety of industries, including automotive, healthcare, finance, and manufacturing. Technology has been an integral part of these industries for centuries, but Artificial Intelligence has put technology at the heart of many organizations. AI is now being integrated into almost every program and apparatus, from autonomous vehicles to life-saving medical equipment. AI has been proven to be the key element of the digital revolution.
The report covers comprehensive data on emerging trends, market drivers, growth opportunities, and restraints that can change the market dynamics of the industry. It provides an in-depth analysis of the market segments which include products, applications, and competitor analysis.
Key Market Players Profiled in the Report
Download PDF Sample Report Here:https://www.upmarketresearch.com/request-sample/69858
This report also includes a complete analysis of industry players that cover their latest developments, product portfolio, pricing, mergers, acquisitions, and collaborations. Moreover, it provides crucial strategies that are helping them to expand their market share.
Highlights on the segments of the Artificial Intelligence Market
Based on Solution, the market is divided into Hardware, Software, and Services. Software solutions dominated the artificial intelligence market, accounting for over 38.0% of global revenue in 2020. This is due to prudent improvements in information storage capacity and high computing power. Parallel processing capabilities are used to deliver high-end AI software for dynamic end-use verticals. Services in artificial intelligence include integration, maintenance, and support. This segment is expected to grow at an impressive rate during the forecast period. AI hardware comprises chipsets like Graphics Processing Unit (GPU), CPU and application-specific integrated circuits.
On the basis of Technology,the market is divided intoDeep Learning, Machine Learning, Natural Language Processing, and Machine Vision. Deep learning dominated the market, accounting for 38.0% of global revenue in 2020. Its complex data-driven applications such as speech recognition and text/content are responsible for the market's high share. This technology allows for the resolution of data volume challenges and offers attractive investment opportunities. Deep learning and machine learning are important investments in AI. This includes AI platforms as well as cognitive applications. These include tagging and clustering, categorization and hypothesis generation. Alerting, filtering and navigation are all part of the AI platform. They allow for the creation of intelligent, advisory and cognitively-enabled solutions.
Based on End Use, the market is divided into Healthcare, BFSI, Law, Retail, Advertising & Media, Automotive & Transportation, Agriculture, Manufacturing, and Others. Advertising and media dominated the market, accounting for over 18.0% of global revenue in 2020. The growing popularity of AI marketing applications is responsible for this high share. The healthcare sector will continue to hold a significant share of the market by 2028. BFSI includes financial analysis, risk assessment and investment/portfolio solicitations. Due to the high demand in this sector for compliance and risk applications, artificial intelligence has seen a significant increase in the BFSI. Retail, law, transportation, agriculture and other verticals are also possible for artificial intelligence systems. Conversational AI platforms are the most popular in each vertical.
On the basis of Regions,the market is categorized as Asia Pacific, North America, Latin America, Europe, and Middle East & Africa. North America was the dominant market, accounting for more than 40.0% of global revenue in 2020. This is due to government initiatives that encourage adoption of AI across different industries. As the United States' strategy to promote leadership in artificial intelligence, the American AI Initiative was launched by President Donald J. Trump in February 2019. Also, In the coming years, significant growth is expected in Asia Pacific. The significant increase in investments in artificial intelligence is responsible for this growth.
To Buy the Complete Report: https://www.upmarketresearch.com/report/artificial-intelligence-market-global-industry-analysis
Key Benefits for Industry Participants & Stakeholders:
Read 210 Pages Research Report with Detailed ToC on "Global Artificial Intelligence Market by Technology (Machine Learning, Deep Learning, Machine Vision, Natural Language Processing), by Solution (Services, Hardware, Software), by End Use (BFSI, Automotive & Transportation, Advertising & Media, Agriculture, Manufacturing, Retail, Healthcare, Law) and Region (North America, Latin America, Europe, Asia Pacific and Middle East & Africa) - Industry Analysis, Growth, Share, Size, Trends, and Forecast 2021 2028"
For Any Questions on This Report: https://www.upmarketresearch.com/enquiry-before-buying/69858
Segments Covered in the Report
The global Artificial Intelligence market has been segmented based on
By Technology
By Solution
By End Use
Regions
Other Trending Reports:
About Up Market Research:
Up Market Research provides global enterprises as well as medium and small businesses with unmatched quality of "Market Research Reports" and "Industry Intelligence Solutions". Up Market Research has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domain.
Our key analysis segments, though not restricted to the same, include market entry strategies, market size estimations, market trend analysis, market opportunity analysis, market threat analysis, market growth/fall forecasting, primary interviews, and secondary research & consumer surveys.
Contact:
Alex Mathews1st Floor, Kalpavruksha Office No 1,GK Lane Number 3,Ingawale Nagar, Pimple Nilakh, Pune,Maharashtra 411027Phone: +1 909 414 1393Email: [emailprotected]Web: https://www.upmarketresearch.com
SOURCE Up Market Research
Artificial Intelligence to Boost the Global Wound Care Market by 2026 with Minimal Intervention Solutions – inForney.com
The global wound care solutions market is estimated to garner $30.5 billion in revenue by 2026 at a compound annual growth rate of 6.7%, finds Frost & Sullivan
SAN ANTONIO, Oct. 18, 2021 /CNW/ --Frost & Sullivan's recent analysis, Global Wound Care Solutions and New-age Technology Growth Opportunities, finds that participants in the wound care industry are investing heavily in technologies and solutions that require minimal/no medical intervention and can be used by patients, family and care providers. Primarily contributed by basic and advanced wound care solutions product types, the global wound care solutions market is estimated to garner $30.5 billion in revenue by 2026 from $20 billion in 2020, an uptick at a compound annual growth rate (CAGR) of 6.7%.
With technological advancements and a diverse array of traditional and advanced wound care solutions comprising apps, software, services, devices, and wearables, North America will dominate the wound care market by 2026. Also, the European wound care market will witness stable growth as the market becomes saturated due to technological advancements. Asia-Pacific will see a maximum growth rate as countries across the region adopt wound care solutions rapidly. Similarly, a surge in demand for faster wound recovery and advanced wound dressings in the Middle East and Latin America, respectively, will drive the wound care solutions market in the rest of the world over the forecast period.
For further information on this analysis, please visit: https://frost.ly/6eh
"The requirement for faster, less-invasive wound healing is boosting the demand for advanced wound care solutions," said Suchismita Das, Healthcare & Life Sciences Research Analyst at Frost & Sullivan. "Additionally, the resumption of elective surgeries that were placed on hold during the pandemic will further boost the post-pandemic demand for surgical wound care solutions."
Das added: "As end-users increasingly prefer 'at-home' solutions, simple and effective wound monitoring devices and solutions that require less intervention from clinicians are gaining traction. Further, the artificial intelligence (AI)-enabled solutions, sensor-based devices/wearables, and wound assessment devices aid care providers with clinical decision support (CDS) for faster diagnosis of complex wounds, leading to effective care pathways."
Government and corporate funding for developing next-gen wound care solutions that primarily enable early wound detection and prevention is set to increase, presenting the following growth opportunities for market participants:
Global Wound Care Solutions and New-age Technology Growth Opportunitiesis the latest addition to Frost & Sullivan's Healthcare & Life Sciences research and analyses available through the Frost & Sullivan Leadership Council, which helps organizations identify a continuous flow of growth opportunities to succeed in an unpredictable future.
About Frost & Sullivan
For six decades, Frost & Sullivan has been world-renowned for its role in helping investors, corporate leaders and governments navigate economic changes and identify disruptive technologies, Mega Trends, new business models, and companies to action, resulting in a continuous flow of growth opportunities to drive future success. Contact us: Start the discussion.
Global Wound Care Solutions and New-age Technology Growth Opportunities
MG30
Contact:
Mariana Fernandez
Corporate Communications
E: Mariana.Fernandez@frost.com
View original content to download multimedia:https://www.prnewswire.com/news-releases/artificial-intelligence-to-boost-the-global-wound-care-market-by-2026-with-minimal-intervention-solutions-301401695.html
SOURCE Frost & Sullivan
Originally posted here:
Artificial Intelligence to Boost the Global Wound Care Market by 2026 with Minimal Intervention Solutions - inForney.com
Artificial Intelligence Technology Solutions, Inc. Reports Revenue Increases of Over 400% Over Same Period Prior Year as Shown in 2nd Quarter SEC…
HENDERSON, Nev., October 19, 2021--(BUSINESS WIRE)--Artificial Intelligence Technology Solutions, Inc., (OTCPK:AITX), a global leader in AI-driven security and productivity solutions for enterprise clients, filed its quarterly report on Form 10-Q with the Securities and Exchange Commission for the period ended August 31, 2021. AITX is a full SEC reporting company that files detailed annual and quarterly reports as prepared by a PCAOB registered firm and reviewed by an independent auditor.
This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20211019005507/en/
A sampling of the many AITX, its subsidiaries RAD, RAD-G and RAD-M, developments in the 2nd quarter of FY 2022. Included are two new apps, RAD Light My Way and RAD AR (Augmented Reality), plus the announcement of the RAD 3.0 product line. (Graphic: Business Wire)
"The first half of our fiscal year saw continued progress, development, plus exponential sales growth," said Steve Reinharz, President and CEO of AITX. "Both subscription revenues and sales revenues saw dramatic increases year over year."
Key Takeaways from the 10-Q Filing
AITX Financials
The Company completed actions to eliminate almost all of its dilutive financial instruments as follows: 1. Substantially all of the convertible debt has been paid or converted; 2. The number of Series F convertible preferred Shares were reduced; 3. An agreement was reached amongst all Series F shareholders not to convert their shares prior to August 2023, unless there is an uplisting of the Companys stock or an asset sale.
Unless subsequent events reinstate a dilutive financial instrument, none of which are under consideration, the number of outstanding shares of the company will only grow as a result of actions related to the effective and current S-3.
Device parts inventory (on hand) as of August 31, 2021 at $488K, up from $25K as of August 31, 2020, an increase of 1,852%. This increase supports both organic growth, inventory for fast delivery, and support for an expanding sales pipeline.
Story continues
Research and Development spending fiscal YTD August 31, 2021 at $1,334K, up from the previous FY period of $190K, a 602% increase. The increase supports investments in new project development, increased engineering, programming resources, and other R&D initiatives.
Robotic Assistance Devices (RAD) Sales Growth
The Company reports that for the quarter ended August 31, 2021, AITXs second quarter of fiscal year 2022, device subscription revenues, referred to as Recurring Monthly Revenue (RMR), increased 69% over the same period of the prior year. Six month total revenues, including all sales and subscriptions, increased 404% over the prior fiscal years period.
Sales Funnel Development
The Company reports that its sales funnel continues its solid growth as its sales team continues to produce significant activity and results. RAD President and COO, Mark Folmer commented, "We expect to close the month of October with an additional RMR of nearly $14,000. This will bring RADs total RMR to just over $80,000. Were expected to cross the $1 million annualized RMR run rate in the current fiscal quarter, ending November 30."
New Products Announced RAD 3.0
On Wednesday, October 13, 2021, the Company announced its new lineup of RAD 3.0 devices. RAD 3.0 marks a complete design and re-engineering of nearly all RAD solutions. "The entire RAD team worked feverishly hard throughout Q2 so that we could preview all of the improvements in design and performance that we showcased earlier this week," Reinharz added. "The response to our RAD 3.0 announcements has been overwhelmingly positive. Im sure that we have a hit on our hands and we cannot wait for our customers to see these in person," Reinharz commented.
The AITX Investors Open House and RAD 3.0 Reveal video is available for viewing at https://tinyurl.com/hkp5ds
"Fiscal year 2022 continues to confirm RADs inevitable progress," Reinharz added. "These second quarter results reveal our constant grind, whether its inventing new products, penetrating new markets, or solidifying our financial position. There is so much more on the immediate horizon that we expect to conquer, making FY 2022 an incredibly big year for us in sales, team, tech and industry stature," Reinharz concluded.
The Company recommends that interested parties examine the published 10-Q to review all details, and reminds readers that this release is limited to the applicable highlights of the quarter.
Follow Steve Reinharz on Twitter @SteveReinharz for future AITX and RAD updates.
AITX through its subsidiary, Robotic Assistance Devices, Inc. (RAD), is redefining the $25 billion (US) security and guarding services industry through its broad lineup of innovative, AI-driven Solutions-as-a-Service business model. RAD solutions are specifically designed to provide a cost savings to businesses of between 35%-80% when compared to the industrys existing and costly manned security guarding and monitoring model. RAD delivers this tremendous costs savings via a suite of stationary and mobile robotic solutions that complement, and at times, directly replace the need for human personnel in environments better suited for machines. All RAD technologies, AI-based analytics and software platforms are developed in-house.
CAUTIONARY DISCLOSURE ABOUT FORWARD-LOOKING STATEMENTS
This release contains "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E the Securities Exchange Act of 1934, as amended and such forward-looking statements are made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. Statements in this news release other than statements of historical fact are "forward-looking statements" that are based on current expectations and assumptions. Forward-looking statements involve risks and uncertainties that could cause actual results to differ materially from those expressed or implied by the statements, including, but not limited to, the following: the ability of Artificial Intelligence Technology Solutions to provide for its obligations, to provide working capital needs from operating revenues, to obtain additional financing needed for any future acquisitions, to meet competitive challenges and technological changes, to meet business and financial goals including projections and forecasts, and other risks. Artificial Intelligence Technology Solutions undertakes no duty to update any forward-looking statement(s) and/or to confirm the statement(s) to actual results or changes in Artificial Intelligence Technology Solutions expectations.
About Artificial Intelligence Technology Solutions (AITX)
AITX is an innovator in the delivery of artificial intelligence-based solutions that empower organizations to gain new insight, solve complex challenges and fuel new business ideas. Through its next-generation robotic product offerings, AITXs RAD and RAD-M companies help organizations streamline operations, increase ROI and strengthen business. AITX technology improves the simplicity and economics of patrolling and guard services, and allows experienced personnel to focus on more strategic tasks. Customers augment the capabilities of existing staffs and gain higher levels of situational awareness, all at drastically reduced cost. AITX solutions are well suited for use in multiple industries such as enterprises, government, transportation, critical infrastructure, education and healthcare. To learn more, visit http://www.aitx.ai and http://www.roboticassistancedevices.com, or follow Steve Reinharz on Twitter @SteveReinharz.
View source version on businesswire.com: https://www.businesswire.com/news/home/20211019005507/en/
Contacts
Steve Reinharz949-636-7060@SteveReinharz
Potential of Artificial Intelligence Replacing Animal Testing in the Future – Analytics Insight
Animal testing is considered to be one of the worst cruelties towards any animal in this world over 100 million animals such as mice, frogs, dogs, rabbits, monkeys, cats, and many others are killed in animal experimentation. Meanwhile, cutting-edge technologies like artificial intelligence, machine learning, etc. are helping in boosting productivity while reducing workloads from human employees efficiently through AI models. Thus, artificial intelligence holds the potential to replace animal testing in the future. Artificial intelligence replacing animal testing can be a new approach to save these animals from undergoing lab experiments that hurt and kill them. Lets explore how AI models can save these animals from going through harmful animal testing.
Animal testing has saved millions of human lives at a cost of the precious lives of animals for a long time. It has helped the world with unbelievable medical innovations like vaccines, antibiotics, and many more drugs. But, this is not fair to these animals who sacrifice their lives for these experiments days after days for multiple years. This has been observed that some animal testing is not reliable enough to predict the behavior of drugs in human bodies. There is a huge wastage of time, money, animal lives, and many more. Thus, AI models are suitable and favorable for those experiments. Artificial intelligence and machine learning are known for generating reliable outcomes efficiently and effectively throughout the year if the training data is accurate.
AI models can save the lives of millions of animals with computer vision and accurate datasets. This is one of the true alternatives to animal models that holds huge potential to generate reliable and safe outcomes for drug discoveries. The emergence of quantum computing is creating a massive way with breakthroughs and experiments. Thus, there is no need for utilizing different animals for not-so-reliable animal testing.
In 2016, Thomas Hartung led some researchers from Johns Hopkins University to successfully develop an artificial intelligence algorithm that can determine substance toxicity after comparing it to similar databases and predictions from previously conducted animal testing. This software project showed this group of researchers that testing on animals showed inconsistencies and different animals can show different results to the same experiment. There is a concern that laboratories cannot use animals for these experiments but all kinds of testing are not possible to be completed by computers.
Start-ups like Verisim Life have started utilizing the power of artificial intelligence and machine learning in biosimulation to replace animal testing in the nearby future. It is a San Francisco biotechnology start-up focused on building digital animal simulations to reduce animal testing for drug discoveries. When animal testing is slow and unreliable, this AI model can eradicate the cruelty as well as boost the process of drug discovery to supply at a faster rate.
Share This ArticleDo the sharing thingy
About AuthorMore info about author
Read more here:
Potential of Artificial Intelligence Replacing Animal Testing in the Future - Analytics Insight
WVU Researchers Using Artificial Intelligence To Help Diagnose Those With Autism – West Virginia Public Broadcasting
West Virginia University researchers are using artificial intelligence and other advanced technologies to help diagnose people with autism.
The program is aimed at more easily identifying phenotypes related to Autism Spectrum Disorder. These phenotypes are noticeable traits or characteristics a person with ASD might have.
Autism phenotyping is something we are still in the dark ages with. We have no clue how many different types of autism we are dealing with, said WVU professor Xin Li, one of the projects head researchers.
Technology like neural imaging and behavior imaging, along with eye-tracking data will help identify these specific traits. Li says he hopes this data will find different types of ASD and help reduce the gap between a childs birth and their diagnosis. The average age of a child newly diagnosed with ASD is 4 years old -- Li says part of the goal of this research is to reduce that age in half, aiming for diagnoses at 2 years old. The earlier the diagnosis, Li says, the more effective the treatment.
Li says this research is important because of how little is known about ASD compared to other disorders. The better the technology available to diagnose those with ASD, the better phenotypes can be successfully grouped into ASD subtypes.
If we think about something were familiar with for example, a butterfly a butterfly can have different wings, have different patterns, colors Those are the easy traits for laymen to tell a different species from one butterfly to another one, Li said.
Recent data from the Centers for Disease Control and Prevention says 1 in 54 children in the U.S. are diagnosed with ASD.
Read this article:
WVU Researchers Using Artificial Intelligence To Help Diagnose Those With Autism - West Virginia Public Broadcasting
What is Artificial Intelligence as a Service (AIaaS)? | ITBE – IT Business Edge
Software as a Service, or SaaS, is a concept that is familiar to many. Long-time Photoshop users will recall when Adobe stopped selling its product and instead shifted to a subscriber model. Netflix and Disney+ are essentially Movies as a Service, particularly at a time when ownership of physical media is losing ground to media streaming. Artificial Intelligence as a Service (AIaaS) has been growing in market adoption in recent years, but the uninitiated might be asking: what exactly is it?
In a nutshell, AIaaS is what happens when a company develops and licenses use of an AI to another company, most often to solve a very specific problem. For example, Bill owns a company that sells hotdogs through his e-commerce site. While Bill offers a free returns policy for dissatisfied customers, he lacks the time to provide decent customer support, and rarely replies to emails. Separately, a software developer has created a chatbot that can handle most customer inquiries using natural language processing, and often solve the issue or answer a question before human intervention is even required. For a monthly fee, the chatbot is licensed to the hotdog vendor, and implemented on his website. Now, the bot is solving 80% of customer issues, leaving Bill with the time to respond to the remaining 20%. But Bill is still too preoccupied making hotdogs, so he subscribes to a service like Flowrite, that uses AI to intelligently write his emails on the fly.
AI is also being put in service to analyze large sets of data and make predictions, streamline information storage, or even detect fraudulent activity. Amazons personal recommendation engine, an AI powered by machine learning, is now available as a licensed service to other retailers, video stream platforms, and even the finance industry. Googles suite of AI services range from natural language processing, handwriting recognition, to real-time captioning and translation. IBMs groundbreaking AI, Watson, is now being deployed to fight financial crimes, target advertisements based on real-time weather analysis, and analyze data to help hospitals make treatment judgements.
Also read: AI-Enabled Payments: A Q&A with Tradeshift
Also read: How Quantum Computing Will Transform AI
Machine learning AIs improve with time, usage, and development. Some, like YouTubes recommendation engine, have become so sophisticated that it sometimes feels like we have entire television stations tailored perfectly to our interests. Others, like language model AI GPT-3, produce entire volumes of text that are nearly indistinguishable from an authentic human source.
Microsoft has even put GPT-3 to use to translate conversational language into a working computer code, potentially opening up a new frontier in how software can be written in the future, and giving coding novices a fighting chance. Microsoft has also partnered with NVIDIA to create a new natural language generation model, three times as powerful as GPT-3. Improvements in language recognition and generation have obvious carryover benefits for the future development of chatbots, home assistants, and document generation as well.
Industrial giant Siemens has announced they are integrating Googles AIaaS solutions to streamline and analyze data, and predict, for instance, the rate of wear-and-tear of machinery on their factory floor. This could reduce maintenance costs, improve the scheduling of routine inspections, and prevent unexpected equipment failures.
AIaaS is a rapidly growing field, and there will be many more niches discovered that it can fill for years to come.
Read next: Top 5 Benefits of AI in Banking and Finance
The rest is here:
What is Artificial Intelligence as a Service (AIaaS)? | ITBE - IT Business Edge
Understanding the UK Artificial Intelligence commercialisation – GOV.UK
The government is undertaking research to explore how AI R&D is successfully commercialised and brought to market.
The Department for Digital, Culture, Media and Sport (DCMS), along with the Office for Artificial Intelligence and Digital Standards and Internet Governance (DSIG), are leading the research project.
Research consultants Oxford Insights and Cambridge Econometrics have been commissioned with exploring the ways technology transfer happens for AI, and are seeking to conduct interviews with those with knowledge of the industry.
The research aims to increase understanding of the following topics:
Oxford Insights and Cambridge Econometrics would like to speak individuals with experience and knowledge of the AI development ecosystem, Innovate UK and other funding programmes, Standards Developing Organisations (SDOs), AI patents, AI R&D in the public and private sectors, AI funding and Venture Capital, and AI policy.
Our interviews will take approximately 45 mins -1 hour; however, we are happy to accommodate if time doesnt permit this length of interview. We may request your approval to follow up on specific points and themes identified across all our interactions.
Please get in touch with either aisha.naz@dcms.gov.uk or sam.hainsworth@dcms.gov.uk if you have any clarifications or questions. We look forward to working with you.
Read more here:
Understanding the UK Artificial Intelligence commercialisation - GOV.UK
Beethoven’s Unfinished 10th Symphony Brought to Life by Artificial Intelligence – Scientific American
Teresa Carey: This is Scientific Americans 60-Second Science. I'm Teresa Carey.
Every morning at five oclock, composer Walter Werzowa would sit down at his computer to anticipate a particular daily e-mail. It came from six time zones away, where a team had been working all night (or day, rather) to draft Beethovens unfinished 10th Symphonyalmost two centuries after his death.
The e-mail contained hundreds of variations, and Werzowa listened to them all.
Werzowa: So by nine, 10 oclock in the morning, its likeIm already in heaven.
Carey: Werzowa was listening for the perfect tunea sound that was unmistakably Beethoven.
But the phrases he was listening to werent composed by Beethoven. They were created by artificial intelligencea computer simulation of Beethovens creative process.
Werzowa: There werehundreds of options, and some are better than others. But then there is that one which grabs you, and that was just a beautiful process.
Carey: Ludwig van Beethoven was one of the most renowned composers in Western music history. When he died in 1827, he left behind musical sketches and notes that hinted at a masterpiece. There was barely enough to make out a phrase, let alone a whole symphony. But that didnt stop people from trying.
In 1988 musicologist Barry Cooper attempted. But he didnt get beyond the first movement. Beethovens handwritten notes on the second and third movements are meagernot enough to compose a symphony.
Werzowa: A movement of a symphony can have up to 40,000 notes. And some of his themes were three bars, like 20 notes. Its very little information.
Carey: Werzowa and a group of music experts and computer scientists teamed up to use machine learning to create the symphony. AhmedElgammal, the director of the Art and Artificial Intelligence Laboratory at Rutgers University, led the AI side of the team.
Elgammal: When you listen to music read by AIto continue a theme of music, usually its a very short few seconds, and then they start diverging and becoming boring and not interesting. They cannot really take that and compose a full movement of a symphony.
Carey: The teams first task was to teach the AI to think like Beethoven. To do that, they gave it Beethovens complete works, his sketchesand notes. They taught it Beethoven's processlike how he went from those iconic four notes to his entire Fifth Symphony.
[CLIP: Notes from Symphony no. 5]
Carey: Then they taught it to harmonize with a melody, compose a bridge between two sectionsand assign instrumentation. With all that knowledge, the AI came as close to thinking like Beethoven as possible. But it still wasnt enough.
Elgammal: The way music generation using AI works is very similar to the way, when you write an e-mail, you find that the e-mail thread predicts whats the next word for you or what the rest of the sentence is for you.
Carey: Butlet the computer predict your words long enough, and eventually, the text will sound like gibberish.
Elgammal: It doesnt really generate something that can continue for a long time and be consistent. So that was the main challenge in dealing with this project: How can you take a motif or a short phrase of music that Beethoven wrote in his sketchand continue it into a segment of music?
Carey: Thats where Werzowas daily e-mails came in. On those early mornings, he was selecting what he thought was Beethovens best. And, piece by piece, the team built a symphony.
Matthew Guzdial researches creativity and machine learning at the University of Alberta. He didnt work on the Beethoven project, but he says that AI is overhyped.
Guzdial: Modern AI, modern machine learning, is all about just taking small local patterns and replicating them. And its up to a human to then take what the AI outputs and find the genius. The genius wasnt there. The genius wasnt in the AI. The genius was in the human who was doing the selection.
Carey: Elgammal wants to make the AI tool available to help other artists overcome writers block or boost their performance. But both Elgammal and Werzowa say that the AI shouldnt replace the role of an artist. Insteadit should enhance their work and process.
Werzowa: Like every tool, you can use a knife to kill somebody or to save somebodys life, like with a scalpel in a surgery. So it can go any way. If you look at the kids, like kids are born creative.Its like everything is about being creative, creative and having fun. And somehow were losing this. I think if we could sit back on a Saturday afternoon in our kitchen, and because maybe were a little bit scared to make mistakes, ask the AI to help us to write us a sonata, song or whateverin teamwork, life will be so much more beautiful.
Carey: The team released the 10th Symphony over the weekend. When asked who gets credit for writing it Beethoven, the AIor the team behind itWerzowa insists it is a collaborative effort. But, suspending disbelief for a moment, it isnt hard to imagine that were listening to Beethoven once again.
Werzowa: I dare to say that nobody knows Beethovenas well as the AI, didas well as the algorithm. I think music, when you hear it, when you feel it, when you close your eyes, it does something to your body. Close your eyes, sit back and be open for it, and I would love to hear what you felt after.
Carey: Thanks for listening. For Scientific Americans60-Second Science, Im Teresa Carey.
[The above text is a transcript of this podcast.]
Originally posted here:
Beethoven's Unfinished 10th Symphony Brought to Life by Artificial Intelligence - Scientific American