What does the future of artificial intelligence in the workplace look like for employee experience?

Over last few years, artificial intelligence (AI) has become a very significant part of business operations across all industries. Its already making an impact as part of our daily lives, from appliances, voice assistants, search, surveillance, marketing, autonomous vehicles, video games, TVs, to large sporting events.

AI is the result of applying cognitive science techniques to emulate human intellect and artificially create something that performs tasks that only humans can perform, like reasoning, natural communication and problem-solving. It does this by leveraging machine learning technique by reading and analyzing large data sets to identify patterns, detect anomalies and make decisions with no human intervention.

In this ever-evolving market, AI has become super crucial for businesses to upscale workplace infrastructure and improve employee experience. According to Precedence Research, the AI market size is projected to surpass around $1,597.1 billion by 2030, and is expanding growth at a CAGR of 38.1% from 2022 to 2030.

Currently, AI is being used in the workplace to automate jobs that are repetitive or require a high degree of precision, like data entry or analysis. AI can also be used to make predictions about customer behavior or market trends.

In the future, AI is expected to increasingly be used to augment human workers, providing them with recommendations or suggestions based on the data that it has been programmed to analyze.

Todays websites are capable of using AI to quickly detect potential customer intent in real-time based on interactions by the online visitor, and to show more engaging and personalized content to enhance the possibility of converting customers. As AI continues to develop, its capabilities in the workplace are expected to increase, making it an essential tool for businesses looking to stay ahead of the competition.

Kai-Fu Lee, a famous computer scientist, businessman and writer, said in a 2019 interview with CBS News, that he believes 40% of the worlds jobs will be replaced by robots capable of automating tasks.

AI has a potential to replace many types of jobs that involve mechanical or structured tasks that are repetitive in nature. Some opportunities we are seeing now are robotic vehicles, drones, surgical devices, logistics, call centers, administrative tasks like housekeeping, data entry and proofreading. Even armies of robots for security and defense are being discussed.

That said, AI is going to be a huge disruption worldwide over the next decade or so. Most innovations come from disruptions; take COVID-19 pandemic as an example, it dramatically changed how we work now.

While AI takes some jobs, it is also creates many opportunities. When it comes to strategic thinking, creativity, emotions and empathy, humans will always win over machines. This rings the bell to adapt with the change and grow human factors in workplace in all possible dimensions. Nokia and Blackberry mobile phones, Kodak cameras are the living examples of failing by not acknowledging the digital disruption. Timely market research, using the right technology and enabling the workforce to adapt for change can bring success to businesses through digital transformation.

Related Article:What's Next for Artificial Intelligence in Customer Experience?

There will be changes in the traditional means of doing things, and more jobs will be generated. AI has the potential to revolutionize the workplace, transforming how we do everything from customer service to driving cars in one of the busiest places like downtown San Francisco. However, there are still several challenges that need to be overcome before AI can be widely implemented in the workplace.

One of the biggest challenges is developing algorithms that can reliably replicate human tasks. This is often difficult because human tasks often involve common sense and reasoning, which are difficult for computers to understand. We should also ensure that AI systems are fair and unbiased. This is important because AI systems are often used to make decisions about things like hiring and promotions, and if they are biased then this can lead to discrimination. We live in the world of diversity, equity, and inclusion (DEI), and mistakes with AI can be costly for businesses. It may take a very long time to develop a customer-centric model that is completely dependent on AI, one that is reliable and trustworthy.

The future of AI is hard to predict, but there are a few key trends that are likely to shape its development. The increasing availability of data will allow AI systems to become more accurate and efficient, and as businesses and individuals rely on AI more and more, a need for new types of AI applications means more work and jobs. As these trends continue, AI is likely to have a significant impact on the workforce. It can very well lead to the automation of many cognitive tasks, including those that are currently performed by human workers.

This could result in a reduction in the overall demand for labor as well as an increase in the need for workers with skills that complement the AI systems. AI is the future of work; there's no doubt about that, but how it will shape the future of human workforce remains to be seen.

Many are worried that AI will remove many jobs, while others see it as an opportunity to increase efficiency and accuracy in the workforce. No matter which side you're on, it's important to understand how AI is changing the way we work and what that means for the future.

Related Article: 8 Examples of Artificial Intelligence in the Workplace

Let's look at few real-world examples that are already changing the way of work:

All above implementations look great. However, it is important to note that AI should be used as a supplement to human intelligence, not a replacement for it. When used properly, AI can help businesses thrive. The role of AI in the workplace is ever evolving, and it will be interesting to see how businesses adopt these technologies and improve the overall work environment to provide the best employee experience.

AnOctober 2020 Gallup pollfound that 51% of workers are not engaged they are psychologically unattached to their work and company.

Here are some employee experience aspects that AI could improve:

Employees need to know and trust that you have their best interests in mind. The value of AI in human resources is going to be critical to deliver employee experiences along with human connection and values.

Read more here:
What's Your Future of Work Path With Artificial Intelligence? - CMSWire

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence (AI) in Drug Discovery Market by Component (Software, Service), Technology (ML, DL), Application (Neurodegenerative Diseases, Immuno-Oncology, CVD), End User (Pharmaceutical & Biotechnology, CRO), Region - Global forecast to 2024" report has been added to ResearchAndMarkets.com's offering.

The Artificial intelligence/AI in drug discovery Market is projected to reach USD 4.0 billion by 2027 from USD 0.6 billion in 2022, at a CAGR of 45.7% during the forecast period. The growth of this market is primarily driven by factors such as the need to control drug discovery & development costs and reduce the overall time taken in this process, the rising adoption of cloud-based applications and services. On the other hand, the inadequate availability of skilled labor is key factor restraining the market growth at certain extent over the forecast period.

Services segment is estimated to hold the major share in 2022 and also expected to grow at the highest over the forecast period

On the basis of offering, the AI in drug discovery market is bifurcated into software and services. the services segment expected to account for the largest market share of the global AI in drug discovery services market in 2022, and expected to grow fastest CAGR during the forecast period. The advantages and benefits associated with these services and the strong demand for AI services among end users are the key factors for the growth of this segment.

Machine learning technology segment accounted for the largest share of the global AI in drug discovery market

On the basis of technology, the AI in drug discovery market is segmented into machine learning and other technologies. The machine learning segment accounted for the largest share of the global market in 2021 and expected to grow at the highest CAGR during the forecast period. High adoption of machine learning technology among CRO, pharmaceutical and biotechnology companies and capability of these technologies to extract insights from data sets, which helps accelerate the drug discovery process are some of the factors supporting the market growth of this segment.

Pharmaceutical & biotechnology companies segment expected to hold the largest share of the market in 2022

On the basis of end user, the AI in drug discovery market is divided into pharmaceutical & biotechnology companies, CROs, and research centers and academic & government institutes. In 2021, the pharmaceutical & biotechnology companies segment accounted for the largest share of the AI in drug discovery market. On the other hand, research centers and academic & government institutes are expected to witness the highest CAGR during the forecast period. The strong demand for AI-based tools in making the entire drug discovery process more time and cost-efficient is the key growth factor of pharmaceutical and biotechnology end-user segment.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights

4.1 Growing Need to Control Drug Discovery & Development Costs is a Key Factor Driving the Adoption of AI in Drug Discovery Solutions

4.2 Services Segment to Witness the Highest Growth During the Forecast Period

4.3 Deep Learning Segment Accounted for the Largest Market Share in 2021

4.4 North America is the Fastest-Growing Regional Market for AI in Drug Discovery

5 Market Overview

5.1 Introduction

5.2 Market Dynamics

5.2.1 Market Drivers

5.2.1.1 Growing Number of Cross-Industry Collaborations and Partnerships

5.2.1.2 Growing Need to Control Drug Discovery & Development Costs and Reduce Time Involved in Drug Development

5.2.1.3 Patent Expiry of Several Drugs

5.2.2 Market Restraints

5.2.2.1 Shortage of AI Workforce and Ambiguous Regulatory Guidelines for Medical Software

5.2.3 Market Opportunities

5.2.3.1 Growing Biotechnology Industry

5.2.3.2 Emerging Markets

5.2.3.3 Focus on Developing Human-Aware AI Systems

5.2.3.4 Growth in the Drugs and Biologics Market Despite the COVID-19 Pandemic

5.2.4 Market Challenges

5.2.4.1 Limited Availability of Data Sets

5.3 Value Chain Analysis

5.4 Porter's Five Forces Analysiss

5.5 Ecosystem

5.6 Technology Analysis

5.7 Pricing Analysis

5.8 Business Models

5.9 Regulations

5.10 Conferences and Webinars

5.11 Case Study Analysis

6 Artificial Intelligence in Drug Discovery Market, by Offering

7 Artificial Intelligence in Drug Discovery Market, by Technology

8 Artificial Intelligence in Drug Discovery Market, by Application

9 Artificial Intelligence in Drug Discovery Market, by End-user

10 Artificial Intelligence in Drug Discovery Market, by Region

11 Competitive Landscape

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/q5pvns

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Worldwide Artificial Intelligence (AI) in Drug Discovery Market to reach $ 4.0 billion by 2027 at a CAGR of 45.7% - ResearchAndMarkets.com - Business...

After describing various ways in which AI and big data are involved already in our daily lives, ranging from the food we eat, the cars we drive and the things we buy, he concludes that it is leading to the Fourth Industrial Revolution, a phrase coined by Klaus Schwab, the head of the World Economic Forum. All aspects of life will be transformed in a way analogous to the prior industrial revolutions (first the use of steam and waterpower, second the expansion of electricity and telegraph cables, and third, the digital revolution of the end of the 20th century).

At the heart of the book are the chapters in which he explains what data and AI have already accomplished for our health and what they can do in the future. The ever-expanding amount of personal data available combined with advances in AI allows for increasing accuracy of diagnoses, treatments and better sensors and software. Glorikian notes that today there are over 350,000 different healthcare apps and the mobile health market is expected to approach $290 billion in revenue by 2025.

Glorikian employs a light, informal style of writing, with references to pop culture such as Star Trek. He asks the reader questions and intersperses each chapter with what he calls sidebars. They are short illustrative stories or sets of examples. For example, AI Saved My Life: The Watch That Called 911 for a Fallen Cyclist (p. 68) starts with a man who lost consciousness after falling off his bike, and then lists other ways current phones can save lives. Other sidebars explain basic concepts like the meaning of genes and DNA; or about gene editing with CRISPR.

Present and Future Advances

Before getting into more complex issues, Glorikian describes what be most familiar to readers: the use of AI-enabled smartphone apps which guide individuals towards optimal diets and exercising as well as allow for group activities through remote communication and virtual reality. There are already countless AI-enabled smartphone apps and sensors allowing us to track our movements and exercise, as well as our diets, sleep and even stress levels. In the future, their approach will become more tailored to individual needs and data, including genomics, environment, lifestyle and molecular biology, with specific recommendations.

He speculates as to what innovations the near future may bring, remarking: What isnt clear is just how long it will take us to move from this point of collecting and finding patterns in the data, to one where we (and our healthcare providers are actively using those patterns to make accurate predications about our health. He gives the example of having an app to track migraine headaches, which can find and analyze patterns in the data (do they occur on nights when you have eaten a particular kind of food or traveled on a plane, for example). Eventually, at a more advanced stage, it might suggest you take an earlier flight or eat in a different restaurant that does not use ingredients that might be migraine triggers for you.

Healthcare will become more decentralized, Glorikian predicts, with people no longer forced to wait hours in hospital emergency rooms. Instead, some issues can be determined through phone apps and remote specialists, and others can be handled at rapid care facilities or pharmacies. Hospitals themselves will become more efficient with command centers monitoring the usage of various resources and using AI to monitor various aspects of patient health. Telerobotics will allow access to specialized surgeons located in major urban centers even if there are none in the local hospital.

In the chapter on genetics, Glorikian presents three ways in which unlocking the secrets of an individuals genome can have practical health consequences right now. The first is the prevention of bad drug reactions through pharmacogenomics, or learning how genes affect response to drugs. Second are enhanced screening and preventative treatment for hereditary cancer syndromes. One major advancement just starting to be used more, notes Glorikian, is liquid biopsy, in which a blood sample allows identification of tumor cells as opposed to standard physical biopsies. It is less invasive and sometimes more accurate for detecting cancers prior to the appearance of symptoms. The third way is DNA sequencing at birth to screen for many disorders which are treatable when caught early. The future may see corrections of various mutations through gene editing.

He points out the various benefits in the health field of collecting large sets of data. For example, it allows the use of AI or machine learning to better read mammogram results and to better predict which patients would see benefit from various procedures like cardiac resynchronization therapy or who had greater risk for cardiovascular disease. There is hope that this approach can help detect the start and the progression of diseases like Alzheimers or diabetic retinopathy. Ultimately it may even be able to predict fairly reliably when individuals would die.

At present, AI accessing sufficient data is helping identify new drugs, saving time and money by using statistical models to predict whether the new drugs will work even before trials. AI can determine which variables or dimensions to remove when making complex computations of models in order to speed up computational processes. This is important when there are large numbers of variables and vast amounts of data.

Glorikian does not miss the opportunity to use the current Covid-19 crisis as a teaching moment. In a chapter called Solving the Pandemic Problem, Glorikian discusses the role AI, machine learning and big data played in the fight against the coronavirus pandemic, in spotting it early on, predicting where it might travel next, sequencing its genome in days, and developing diagnostic tests, vaccines and treatments. Vaccine development, like drug development, is much faster today than even 20 years ago, thanks to computational modeling and virtual clinical trials and studies.

Potential Problems

Glorikian does not shy away from raising some of the potential problems associated with the wide use of AI in medicine, such as the threat to patient privacy and ethical questions about what machines should be allowed to do. Should genetic editing be allowed in humans for looks, intelligence or various types of talents? Should AI predictions of lifespan and dates of death be used? What types of decisions should machines be allowed to make in healthcare? And what sort of triage should be allowed in case of limited medical resources (if AI predicts one patient is for example ten times more likely to die than another despite medical intervention)? There are grave dangers if hackers access databanks or medical machines.

There are also potential operational problems with using data as a basis for AI, such as outdated information, biased data, missing data (and how it is handled), misanalyzed or differently analyzed data.

Despite all these issues, Glorikian is optimistic about the value of AI. He concludes, But despite the risk, for the most part, the benefits outweigh the potential downsidesThe data we willingly give up makes our lives better.

Armenian Connection

When asked at the end of June, 2022 how Armenia compares with the US and other parts of the world in the use of AI in healthcare, he made the distinction between the Armenian healthcare system and Armenian technology that is directed at the world healthcare system.

On the one hand, he said, I dont know of a lot that is being incorporated into the healthcare system, although we do have a national electronic medical record system that they have really been improving on a consistent basis. Having a call management record system throughout the country will provide data for the next step in use of AI, and that, he said is very exciting.

On the other hand, for technology companies involved in healthcare and biotechnology in Armenia, he said, I would always like to see more, but there are some really interesting companies that have sprouted up over the last five years. Also, with the tech giant NVDIA opening up a research center in Armenia, Glorikian said he hoped there will be interesting synergies since this company does invest in the healthcare area.Harry Glorikian, second from left, next to Acting Prime Minister Nikol Pashinyan, in a December 19, 2018 Yerevan meeting

At the end of 2018, Glorikian met with then Acting Prime Minister Nikol Pashinyan to discuss launching the Armenian Genome project to expand the scope of genetic studies in the field of healthcare. He said that this undertaking was halted for reasons beyond his understanding. He said, My lesson learned was you can move a lot faster and have significant impact by focusing on the private sector.

Indeed, this is what he does, as an individual investor and as a member of the Angel Investor Club of Armenia. While the group looks at a broad range of companies, mainly technology driven, he and a few other people in it take a look at those which are involved in healthcare. In fact, he is going to California at the very end of June to learn more about a robot companion for children called Moxie, prepared by Embodied, Inc., a company founded by veteran roboticist Paolo Pirjanian. Pirjanian, who was a guest on Glorikians podcast several weeks ago, lives in California, but Glorikian said that the back end of his companys work is done in Armenia.

Glorikian added that he is always finding out about or running into Armenians in the diaspora doing work with AI.

Changes

When asked what has changed since the publication of the book last year, he replied, Things are getting better! While hardware does not change overnight, he said that there have been incremental improvements to software during the period of time it took to write the book and then have it published. He said, For someone reading the book now, you are probably saying, I had no idea that this was even available. For someone like me, you already feel a little behind.

Readers of the book have already begun to contact Glorikian with anecdotes about what it led them to find out and do. He hopes the book will continue to reach more people. He said, The biggest thing I get out of it is when someone says I learned this and I did something about it. When individuals have access to more quantifiable data, not only can they manage their own health better, but they also provide their doctors with more data longitudinally that helps the doctor to be more effective. Glorikian said this should have a corollary effect of deflating healthcare costs in the long run.

One minor criticism of the book, at least of the paperback version that fell into the hands of this reviewer, is the poor quality of some of the images used. The text which is part of those illustrations is very hard to read. Otherwise, this is a very accessible read for an audience of varying backgrounds seeking basic information on the ongoing transformations in healthcare through AI.

Read the rest here:
Glorikian's New Book Sheds Light on Artificial Intelligence Advances in the Healthcare Field - The Armenian Mirror-Spectator

When you picture a hospital radiologist, you might think of a specialist who sits in a dark room and spends hours poring over X-rays to make diagnoses. Contrast that with your dentist, who in addition to interpreting X-rays must also perform surgery, manage staff, communicate with patients, and run their business. When dentists analyze X-rays, they do so in bright rooms and on computers that arent specialized for radiology, often with the patient sitting right next to them.

Is it any wonder, then, that dentists given the same X-ray might propose different treatments?

Dentists are doing a great job given all the things they have to deal with, says Wardah Inam SM 13, PhD 16.

Inam is the co-founder of Overjet, a company using artificial intelligence to analyze and annotate X-rays for dentists and insurance providers. Overjet seeks to take the subjectivity out of X-ray interpretations to improve patient care.

Its about moving toward more precision medicine, where we have the right treatments at the right time, says Inam, who co-founded the company with Alexander Jelicich 13. Thats where technology can help. Once we quantify the disease, we can make it very easy to recommend the right treatment.

Overjet has been cleared by the Food and Drug Administration to detect and outline cavities and to quantify bone levels to aid in the diagnosis of periodontal disease, a common but preventable gum infection that causes the jawbone and other tissues supporting the teeth to deteriorate.

In addition to helping dentists detect and treat diseases, Overjets software is also designed to help dentists show patients the problems theyre seeing and explain why theyre recommending certain treatments.

The company has already analyzed tens of millions of X-rays, is used by dental practices nationwide, and is currently working with insurance companies that represent more than 75 million patients in the U.S. Inam is hoping the data Overjet is analyzing can be used to further streamline operations while improving care for patients.

Our mission at Overjet is to improve oral health by creating a future that is clinically precise, efficient, and patient-centric, says Inam.

Its been a whirlwind journey for Inam, who knew nothing about the dental industry until a bad experience piqued her interest in 2018.

Getting to the root of the problem

Inam came to MIT in 2010, first for her masters and then her PhD in electrical engineering and computer science, and says she caught the bug for entrepreneurship early on.

For me, MIT was a sandbox where you could learn different things and find out what you like and what you don't like, Inam says. Plus, if you are curious about a problem, you can really dive into it.

While taking entrepreneurship classes at the Sloan School of Management, Inam eventually started a number of new ventures with classmates.

I didn't know I wanted to start a company when I came to MIT, Inam says. I knew I wanted to solve important problems. I went through this journey of deciding between academia and industry, but I like to see things happen faster and I like to make an impact in my lifetime, and that's what drew me to entrepreneurship.

During her postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL), Inam and a group of researchers applied machine learning to wireless signals to create biomedical sensors that could track a persons movements, detect falls, and monitor respiratory rate.

She didnt get interested in dentistry until after leaving MIT, when she changed dentists and received an entirely new treatment plan. Confused by the change, she asked for her X-rays and asked other dentists to have a look, only to receive still another variation in diagnosis and treatment recommendations.

At that point, Inam decided to dive into dentistry for herself, reading books on the subject, watching YouTube videos, and eventually interviewing dentists. Before she knew it, she was spending more time learning about dentistry than she was at her job.

The same week Inam quit her job, she learned about MITs Hacking Medicine competition and decided to participate. Thats where she started building her team and getting connections. Overjets first funding came from the Media Lab-affiliated investment group the E14 Fund.

The E14 fund wrote the first check, and I don't think we would've existed if it wasn't for them taking a chance on us, she says.

Inam learned that a big reason for variation in treatment recommendations among dentists is the sheer number of potential treatment options for each disease. A cavity, for instance, can be treated with a filling, a crown, a root canal, a bridge, and more.

When it comes to periodontal disease, dentists must make millimeter-level assessments to determine disease severity and progression. The extent and progression of the disease determines the best treatment.

I felt technology could play a big role in not only enhancing the diagnosis but also to communicate with the patients more effectively so they understand and don't have to go through the confusing process I did of wondering who's right, Inam says.

Overjet began as a tool to help insurance companies streamline dental claims before the company began integrating its tool directly into dentists offices. Every day, some of the largest dental organizations nationwide are using Overjet, including Guardian Insurance, Delta Dental, Dental Care Alliance, and Jefferson Dental and Orthodontics.

Today, as a dental X-ray is imported into a computer, Overjets software analyzes and annotates the images automatically. By the time the image appears on the computer screen, it has information on the type of X-ray taken, how a tooth may be impacted, the exact level of bone loss with color overlays, the location and severity of cavities, and more.

The analysis gives dentists more information to talk to patients about treatment options.

Now the dentist or hygienist just has to synthesize that information, and they use the software to communicate with you, Inam says. So, they'll show you the X-rays with Overjet's annotations and say, 'You have 4 millimeters of bone loss, it's in red, that's higher than the 3 millimeters you had last time you came, so I'm recommending this treatment.

Overjet also incorporates historical information about each patient, tracking bone loss on every tooth and helping dentists detect cases where disease is progressing more quickly.

Weve seen cases where a cancer patient with dry mouth goes from nothing to something extremely bad in six months between visits, so those patients should probably come to the dentist more often, Inam says. Its all about using data to change how we practice care, think about plans, and offer services to different types of patients.

The operating system of dentistry

Overjets FDA clearances account for two highly prevalent diseases. They also put the company in a position to conduct industry-level analysis and help dental practices compare themselves to peers.

We use the same tech to help practices understand clinical performance and improve operations, Inam says. We can look at every patient at every practice and identify how practices can use the software to improve the care they're providing.

Moving forward, Inam sees Overjet playing an integral role in virtually every aspect of dental operations.

These radiographs have been digitized for a while, but they've never been utilized because the computers couldn't read them, Inam says. Overjet is turning unstructured data into data that we can analyze. Right now, we're building the basic infrastructure. Eventually we want to grow the platform to improve any service the practice can provide, basically becoming the operating system of the practice to help providers do their job more effectively.

Read more:
Taking the guesswork out of dental care with artificial intelligence - MIT News

Over the last several decades, genetic research has seen incredible advances in gene sequencing technologies. In 2004, scientists completed the Human Genome Project, an ambitious project to sequence the human genome, which cost $3 billion and took 10 years. Now, a person can get their genome sequenced for less than $1,000 and within about 24 hours.

Scientists capitalized on these advances by sequencing everything from the elusive giant squid to the Ethiopian eggplant. With this technology came promises of miraculous breakthroughs: all diseases would be cured and world hunger would be a thing of the past.

So, where are these miracles?

We need about 60 to 70% more food production by 2050.

In 2015, a group of researchers founded Yield10 Bioscience, an agriculture biotech company that aimed to use artificial intelligence to start making those promises into reality.

Two things drove the development of Yield10 Bioscience.

One, obviously, [the need for] global food security: we need about 60 to 70% more food production by 2050, explained Dr. Oliver Peoples, CEO of Yield10 Bioscience, in an interview with Freethink. And then, of course, CRISPR.

It turns out that having the tools to sequence DNA is only step one of manufacturing the miracles we were promised.

The second step is figuring out what a sequence of DNA actually does. In other words, its one thing to discover a gene, and it is another thing entirely to discover a genes role in a specific organism.

In order to do this, scientists manipulate the gene: delete it from an organism and see what functions are lost, or add it to an organism and see what is gained. During the early genetics revolution, although scientists had tools to easily and accurately sequence DNA, their tools to manipulate DNA were labor-intensive and cumbersome.

Its one thing to discover a gene, and it is another thing entirely to discover a genes role in a specific organism.

Around 2012, CRISPR technology burst onto the scene, and it changed everything. Scientists had been investigating CRISPR a system that evolved in bacteria to fight off viruses since the 80s, but it took 30 years for them to finally understand how they could use it to edit genes in any organism.

Suddenly, scientists had a powerful tool that could easily manipulate genomes. Equipped with DNA sequencing and editing tools, scientists could complete studies that once took years or even decades in mere months.

Promises of miracles poured back in, with renewed vigor: CRISPR would eliminate genetic disorders and feed the world! But of course, there is yet another step: figuring out which genes to edit.

Over the last couple of decades, researchers have compiled databases of millions of genes. For example, GenBank, the National Institute of Healths (NIH) genetic sequence database, contains 38,086,233 genes, of which only tens of thousands have some functional information.

For example, ARGOS is a gene involved in plant growth. Consequently, it is a very well-studied gene. Scientists found that genetically engineering Arabidopsis, a fast-growing plant commonly used to study plant biology, to express lots of ARGOS made the plant grow faster.

Dozens of other plants have ARGOS (or at least genes very similar to it), such as pineapple, radish, and winter squash. Those plants, however, are hard to genetically manipulate compared to Arabidopsis. Thus, ARGOSs function in crops in general hasnt been as well studied.

The big crop companies are struggling to figure out what to do with CRISPR.

CRISPR suddenly changed the landscape for small groups of researchers hoping to innovate in agriculture. It was an affordable technology that anyone could use but no one knew what to do with it. Even the largest research corporations in the world dont have the resources to test all the genes that have been identified.

I think if you talk to all the big crop companies, theyve all got big investments in CRISPR. And I think theyre all struggling with the same question, which is, This is a great tool. What do I do with it? said Dr. Peoples.

The algorithm can identify genes that act at a fundamental level in crop metabolism.

The holy grail of crop science, according to Dr. Peoples, would be a tool that could identify three or four genetic changes that would double crop production for whatever youre growing.

With CRISPR, those changes could be made right now. However, there needs to be a way to identify those changes, and that information is buried in the massive databases.

To develop the tool that can dig them out, Dr. Peoples team merged artificial intelligence with synthetic biology, a field of science that involves redesigning organisms to have useful new abilities, such as increasing crop yield or bioplastic production.

This union created Gene Ranking Artificial Intelligence Network (GRAIN), an algorithm that evaluates scientific databases like GenBank and identifies genes that act at a fundamental level in crop metabolism.

That fundamental level aspect is one of the keys to GRAINs long-term success. It identifies genes that are common across multiple crop types, so when a powerful gene is identified, it can be used across multiple crop types.

For example, using the GRAIN platform, Dr. Peoples and his team identified four genes that may significantly impact seed oil content in Camelina, a plant similar to rapeseed (true canola oil). When the researchers increased the activity of just one of those genes via CRISPR, the plants had a 10% increase in seed oil content.

Its not quite a miracle yet, but with more advances in gene editing and AI happening all the time, the promises of the genetic revolution are finally starting to pay off.

Wed love to hear from you! If you have a comment about this article or if you have a tip for a future Freethink story, please email us attips@freethink.com.

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How artificial intelligence is boosting crop yield to feed the world - Freethink

DUBLIN--(BUSINESS WIRE)--The "Growth Opportunities in Cloud, Artificial Intelligence, and Edge Computing" report has been added to ResearchAndMarkets.com's offering.

This edition of IT, Computing and Communications (ITCC) Technology Opportunity Engine (TOE) provides a snapshot of the emerging ICT led innovations in Cloud, Artificial Intelligence and Edge Computing.

This issue focuses on the application of information and communication technologies in alleviating the challenges faced across industry sectors in areas such as retail, industrial, BFSI, and automotive.

ITCC TOE's mission is to investigate emerging wireless communication and computing technology areas including 3G, 4G, Wi-Fi, Bluetooth, Big Data, cloud computing, augmented reality, virtual reality, artificial intelligence, virtualization and the Internet of Things and their new applications; unearth new products and service offerings; highlight trends in the wireless networking, data management and computing spaces; provide updates on technology funding; evaluate intellectual property; follow technology transfer and solution deployment/integration; track development of standards and software; and report on legislative and policy issues and many more.

Innovations in Cloud, Artificial Intelligence, and Edge Computing

For more information about this report visit https://www.researchandmarkets.com/r/37mrl6

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IT, Computing and Communications (ITCC) Technology Innovations/Growth Opportunities Report 2022 with Focus on Cloud, Artificial Intelligence, and Edge...

New Jersey, United States This Artificial Intelligence in Education Market research examines the state and future prospects of the Artificial Intelligence in Education market from the perspectives of competitors, regions, products, and end Applications/industries. The Worldwide Artificial Intelligence in Education market is segmented by product and Application/end industries in this analysis, which also analyses the different players in the global and key regions.

The analysis for the Artificial Intelligence in Education market is included in this report in its entirety. The in-depth secondary research, primary interviews, and internal expert reviews went into the Artificial Intelligence in Education reports market estimates. These market estimates were taken into account by researching the effects of different social, political, and economic aspects, as well as the present market dynamics, on the growth of the Artificial Intelligence in Education market.

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Key Players Mentioned in the Artificial Intelligence in Education Market Research Report:

Google Inc., Microsoft Corporation, eGain Corporation, QlikTech International AB, Cognii Next IT Corporation, Nuance Communication Inc., Quantum Adaptive Learning LLC. IntelliResponse System Inc., IBM Corporation.

The Porters Five Forces analysis, which explains the five forces: customers bargaining power, distributors bargaining power, the threat of substitute products, and degree of competition in the Artificial Intelligence in Education Market, is included in the report along with the market overview, which includes the market dynamics. It describes the different players who make up the market ecosystem, including system integrators, middlemen, and end-users. The competitive environment of the Artificial Intelligence in Education marketis another major topic of the report. For enhanced decision-making, the research also provides in-depth details regarding the COVID-19 scenario and its influence on the market.

Artificial Intelligence in EducationMarket Segmentation:

Global Artificial Intelligence in Education Market, By Educational Model

Domain Model Learner Model Pedagogical Model

Global Artificial Intelligence in Education Market, By Application

Content Delivery Systems Intelligent Tutoring Systems Interactive Websites Learning Platform and Virtual Facilitators Smart Content

Global Artificial Intelligence in Education Market, By End-User

Higher Education Primary and Secondary Education

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Artificial Intelligence in Education Market Report Scope

Key questions answered in the report:

1. Which are the five top players of the Artificial Intelligence in Education market?

2. How will the Artificial Intelligence in Education market change in the next five years?

3. Which product and application will take a lions share of the Artificial Intelligence in Education market?

4. What are the drivers and restraints of the Artificial Intelligence in Education market?

5. Which regional market will show the highest growth?

6. What will be the CAGR and size of the Artificial Intelligence in Education market throughout the forecast period?

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Artificial Intelligence in Education Market Size, Scope and Forecast | Google Inc., Microsoft Corporation, eGain Corporation, QlikTech International...

MarqVisions technology comes at a time when the global counterfeit market is exploding, as it is projected to grow another 50% this year to reach nearly $3 trillion in 2023.

LOS ANGELES and PARIS (PRWEB) June 27, 2022

MarqVision, a next-generation, AI-powered IP protection platform, today announced that it is the recipient of a coveted 2022 Innovation Award from LVMH Mot Hennessy Louis Vuitton (LVMH). The company was recognized in the Data and Artificial Intelligence Special Mention category at this years Viva Technology show in Paris, which took place June 15-18. As a winner, MarqVision has been invited to join the LVMH accelerator program, La Maison des Startups, at the Station F incubator.

For the past six years, the LVMH Innovation Awards program has been one of the highlights of the Viva Technology show, which has itself become a key event for the worlds innovation ecosystem. Through its participation, LVMH recognizes the need to support entrepreneurial spirit and innovation in order to build a better future for everyone. It also demonstrates how its own success is due in part to the ongoing dialogue between its 75 Maisons and the world of startups, a constant source of creativity.

MarqVisions technology comes at a time when the global counterfeit market is exploding, as it is projected to grow another 50% this year to reach nearly $3 trillion in 2023. The companys technology enables efficient removal of counterfeits end-to-end by automating the traditional anti-counterfeiting process. Its proprietary AI models detect counterfeits with 95%+ accuracy and remove counterfeit sales at scale.

MarqVision was one of more than 950 startups to apply for the 2022 Innovation Awards, and applications were received from 75 countries. A total of 21 startups from 10 different countries were selected as finalists, notably reflecting their ability to enhance the customer experience through different dimensions.

It is such an honor to receive an Innovation Award in the Data & Artificial Intelligence category, considering the amazing companies that participated this year, said DK Lee, co-founder and CBO of MarqVision. We are thrilled that MarqVision has been singled-out for developing first-of-its-kind technology to address the massive global counterfeit problem and theft of intellectual property. Our platform uniquely exists to protect human creativity and innovation in todays digital world, which is perfectly aligned with LVMHs vision for the Innovation Awards.Three of the LVMH Maisons have already selected MarqVision as their brand protection provider.

At LVMH, Innovation is our lifeblood. Its what allows us to continually increase the desirability of our Maisons products and services. The finalists of the 2022 Innovation Award will bring us their capacity to nourish the encounter between luxury and technology even more as their entrepreneurial spirit joins and inspires our own, says Bernard Arnault, CEO and Chairman, LVMH.

About the LVMH Innovation AwardThe LVMH Innovation Award was introduced in 2017 to recognize promising start-ups from around the world. The award affirms the importance of new ideas resonating with the groups core values of excellence, creativity, innovation, and entrepreneurial spirit. Each year, hundreds of startups submit to be chosen as finalists and be invited to be part of the LVMH Lab during the Viva Technology Show in Paris which brings together the game changers driving the digital transformation around the world.

About MarqVisionMarqVision helps global brands identify and remove counterfeits from more than 1,500 online marketplaces across the world. Counterfeiting is a massive and growing threat worldwide, and MarqVision is on a mission to protect creativity and innovation with technology that allows brands to automatically monitor and protect their IPs. Harnessing image recognition and natural language processing, this AI-powered SaaS makes it faster than ever before to take down counterfeits. Founded in 2020 by Harvard Law graduates and backed by Softbank and Y Combinator, MarqVision is bringing forth the next evolution of brand protection for businesses everywhere. Learn more: http://www.marqvision.com.

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MarqVision Wins Prestigious LVMH 2022 Innovation Award for Data and Artificial Intelligence - PR Web

Artificial Intelligence in Ultrasound Imaging market research report comprises of the end to end research solutions created using effective methodology. The business report provides an opportunity for success by eliminating all of the guess work and by understanding client needs and expectations. Few more features of this report are cost-effective, detail oriented, multi-geo data capabilities, on-time delivery, and last but not the least, best-in-class market research. Systematic research, collection, and analysis have been carried out while formulating such world class marketing report. In addition, Artificial Intelligence in Ultrasound Imaging report contains noteworthy and insightful information gathered from in-depth interviews.

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Leading Key Players Operating in the Artificial Intelligence in Ultrasound Imaging Market Includes:

NVIDIA Corporation, Intel Corporation, IBM, EchoNous, Inc., Microsoft, General Vision Inc., GENERAL ELECTRIC COMPANY, Johnson & Johnson Services, Inc., Siemens Healthcare Private Limited, Medtronic, CloudMedx Inc., Agfa-Gevaert Group

Market Analysis and Insights: Global Artificial Intelligence in Ultrasound Imaging Market:

This Artificial Intelligence in Ultrasound Imaging market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research Artificial Intelligence in Ultrasound Imaging market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Global Artificial Intelligence in Ultrasound Imaging Market Scope and Size:

Artificial Intelligence in Ultrasound Imaging market is segmented on the basis of type and application. The growth amongst these segments will help you analyse meagre growth segments in the industries, and provide the users with valuable market overview and market insights to help them in making strategic decisions for identification of core market applications.

Artificial Intelligence in Ultrasound Imaging market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, global presence, production sites and facilities, production capacities, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies focus related to Artificial Intelligence in Ultrasound Imaging market.

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Artificial Intelligence in Ultrasound Imaging Market, By Region:

Artificial Intelligence in Ultrasound Imaging market is analysed and market size insights and trends are provided by country, type, application and end-user as referenced above.

The countries covered in the Artificial Intelligence in Ultrasound Imaging market report are U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.

North America dominates the Artificial Intelligence in Ultrasound Imaging market due to rise in the surgical procedures, increase in the R&D activities initiated by government and rise in the geriatric population in this region. Europe is the expected region in terms of growth in Artificial Intelligence in Ultrasound Imaging market due to also rise in the surgical procedures, increase in the R&D activities initiated by government and rise in the geriatric population in this region.

Table of Contents

Global Artificial Intelligence in Ultrasound Imaging Market Size, status and Forecast

1 Market summary2 Manufacturers Profile3 Global Artificial Intelligence in Ultrasound Imaging Sales, Overall Revenue, Market Share and Competition by Manufacturer4 Global Artificial Intelligence in Ultrasound Imaging market analysis by numerous Regions5 North America Artificial Intelligence in Ultrasound Imaging by Countries6 Europe Artificial Intelligence in Ultrasound Imaging by Countries7 Asia-Pacific Artificial Intelligence in Ultrasound Imaging by Countries8 South America Artificial Intelligence in Ultrasound Imaging by Countries9 Middle east and Africas Artificial Intelligence in Ultrasound Imaging by Countries10 Global Artificial Intelligence in Ultrasound Imaging Market phase by varieties11 Global Artificial Intelligence in Ultrasound Imaging Market phase by Applications12 Artificial Intelligence in Ultrasound Imaging Market Forecast13 Sales Channel, Distributors, Traders and Dealers14 Analysis Findings and Conclusion15 Appendix

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What are the market opportunities, market risks, and market overviews of the Artificial Intelligence in Ultrasound Imaging Market?

Research Methodology: GlobalArtificial Intelligence in Ultrasound Imaging Market

Data collection and base year analysis is done using data collection modules with large sample sizes. The market data is analyzed and estimated using market statistical and coherent models. Also market share analysis and key trend analysis are the major success factors in the market report. To know more please request an analyst call or can drop down your enquiry.

The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Global versus Regional and Vendor Share Analysis.

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Artificial Intelligence in Ultrasound Imaging Market to Garner to USD 1314.24 million by 2028 Designer Women - Designer Women

Actionable insights and market data provided in the high-qualityArtificial Intelligence in Medical Imaging Market Reporthelp build business growth strategy by astute and authoritative DBMR team.The document focuses on smaller, singular topics, issues, or populations, rather than an overall market sample.This industry analysis document sheds light on finer details about the exact company.The marketing report contains valuable information about the buyer personas, target audience, and customers of the business to determine the viability and success of the product or service.An international healthcare report provides an in-depth understanding of who the buyers are, the specific market, and what influences the purchasing decisions and behavior of members of the target audience.

A better understanding of the market gained through the use of a world-class healthcare report will help in developing the products and advertising campaigns to more specifically address the target market.The market research report not only saves time and money, but also reduces business risks.To advance the companys industry knowledge, to create new advertising and marketing campaigns, as well as to identify demographic needs to target, this industry report will be very useful.Whether companies are researching new product trends or analyzing the competition of an existing or emerging market,

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Growing capability and realization of personalized treatment, improvement of procedures and treatment of patients is the vital factor driving the escalating growth of the market, increase in the number of diagnostic procedures, the rising disease prevalence, rising favorable reimbursement policies, growing presence of key players and favorable government regulations, rapidly changing healthcare infrastructure in Asian countries such as China , Indonesia and India and rising prevalence of various lifestyle related chronic diseases such as cancer and cardiovascular diseases are the major factors among others driving artificial intelligence in imaging market medical.Furthermore,HealthcareThe growing industry and emerging markets with a growing geriatric population base will create more new opportunities for artificial intelligence in the medical imaging market during the forecast period 2021-2028.

Artificial Intelligence in Medical Imaging Market Scope and Market Size

Artificial Intelligence in Medical Imaging Market is segmented on the basis of technology, offering, type of deployment, application, clinical applications and end user.The growth among these segments will help you analyze low growth segments within the industries and provide users with valuable market insight and market insights to help them make strategic decisions for identification of major market applications.

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Country Level Analysis of Artificial Intelligence in Medical Imaging Market

Artificial Intelligence in Medical Imaging Market is analyzed and market size insights and trends are provided by country, technology, offering, deployment type, application, clinical applications and end-user as listed above . The countries covered in the Artificial Intelligence in Medical Imaging market report are US, Canada & Mexico North America, Germany, France, UK, Netherlands- Bas, Switzerland, Belgium, Russia, Italy, Spain, Turkey, the rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in Asia-Pacific (APAC), Saudi Arabia, United Arab Emirates, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) in the Middle East and Africa (MEA) frame, Brazil, Argentina and Rest of South America in the South America frame.

North America and Europe dominate the artificial intelligence in medical imaging market owing to the increase in technologically advanced healthcare infrastructure and high disposable income in this region.Asia-Pacific is the expected region in terms of growth of artificial intelligence in medical imaging market owing to rapidly changing healthcare infrastructure in countries such as China, Indonesia and the United States. India.

The country section of the Artificial Intelligence in Medical Imaging market report also provides individual market impacting factors and regulatory changes in the country market that are impacting current and future trends of the market.Data points such as consumption volumes, production sites and volumes, import and export analysis, price trend analysis, raw material cost, value chain analysis Downstream and Upstream are some of the major indicators used to forecast the market scenario for each country.In addition, the presence and availability of global brands and the challenges they face due to significant or rare competition from local and national brands,

Competitive Landscape and Market Share Analysis of Artificial Intelligence in Medical Imaging

Artificial Intelligence in Medical Imaging market competitive landscape provides details by competitor.Details included are company overview, company financials, revenue generated, market potential, research and development investment, new market initiatives, global presence, locations and production facilities, production capacities, company strengths and weaknesses, product launch, product breadth and breadth, application dominance.The data points provided above are only related to the companies emphasis on artificial intelligence in the medical imaging market.

The key players covered in the Artificial Intelligence in Medical Imaging market report are BenevolentAI, OrCam, Babylon, Freenome Inc., Clarify Health Solutions, BioXcel Therapeutics, Ada Health GmbH, GNS Healthcare, Zebra Medical Vision Inc. , Qventus Inc, IDx Technologies Inc., K Health, Prognos, Medopad Ltd., Viz.ai Inc., Voxel Technology, Renalytix AI plc, Beijing Pushing Technology Co. Ltd., PAIGE, mPulse Mobile, Suki AI Inc., BERG LLC, Zealth Inc., OWKIN INC., and Your.MD Ltd.UK among other national and global players.Market share data is available separately for Global, North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America .

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Artificial Intelligence in Medical Imaging Market Analysis by Trends, Demand, Products and Technology Forecast to 2028 Designer Women - Designer...