Category Archives: Artificial Intelligence
Looking at 2030: The Future of Artificial Intelligence and Metaverse – Analytics Insight
An analytic predictive study for the future of artificial intelligence and metaverse in 2030
With the pace artificial intelligence is intertwining within our lives, there is no doubt that it will not end anytime soon. Rather, the future looks like a society that would breathe and thrive through artificial intelligence only. Experts believe that specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life. On the other hand, metaverse already has us wrapped in its not so little fingers. From Facebook to Instagram, virtual reality, Whatsapp, and many more, it is quite predictable that by 2030, its empire would only grow further.
A report published from Harvard University presents the eight areas of human activity in which Artificial intelligence technologies are already affecting urban life and will be even more pervasive by 2030: transportation, home/service robots, health care, education, entertainment, low-resource communities, public safety and security, employment, and the workplace will be fully AI-enabled spaces. Some of the biggest challenges in the next 15 years will be creating safe and reliable hardware for autonomous cars and healthcare robots; gaining public trust for Artificial intelligence systems, especially in low-resource communities; and overcoming fears that the technology will marginalize humans in the workplace.
Weve seen a lot of breakthroughs in data analytics. The example of Watson which is an IBM set of algorithms has been very impressive in terms of managing large amounts of data, and ways of structuring the data so that you can see patterns that may have not emerged otherwise. That has been an important leap. But oftentimes, people confuse that leap with machine intelligence and the way that we think about intelligence for humans and its simply not true. So the big leaps that we have had recently in data analytics are important but it also leaves a lot of room for humans to assist these systems. So, it can be said that the wave of the future is the collaboration of humans and these artificial intelligence technologies.
In its fully realized form, the metaverse promises to offer true-to-life sights, sounds, and even smells, whether a tour of ancient Greece or a visit to a Seoul caf can happen from your home. Decked out with full-spectrum VR headsets, smart clothing, and tactile-responsive haptic gloves, the at-home traveler can touch the Parthenon in Athens or taste the rich foam of a Korean dalgona coffee. You wouldnt even have to be you. Members of the metaverse could prowl the Brazilian rainforest as a jaguar or take the court at Madison Square Garden as LeBron James. The only limits are your imagination. It is also expected that using a blend of physical and behavioral biometrics, emotion recognition, sentiment analysis, and personal data, the metaverse will be able to create a customized and enhanced reality for each person.
While the metaverse industry is growing fast, fueled by the pandemic keeping people at home, its an open question as to whether one company will eventually emerge as the dominant force, such as Google, which now has a near-monopoly among search engines. One positive side of this trend is that since it is a virtual platform, the chances of people actually getting physically hurt will lessen, and also it will encourage them to get out of their comfort zone to try new things. The only wondering question left to ask on this matter will be the legal implications of the metaverse. For example, whether a marriage in the metaverse will be legal or if someone is assaulted in the metaverse, how the convict will be penalized. With the virtual avatar trend, there are huge chances of false identity or theft of identity, so recognizing the right person and their physical address can be a difficult job. This should be a major concern for all of the countries and their legislative and crime division.
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Looking at 2030: The Future of Artificial Intelligence and Metaverse - Analytics Insight
TechTank Podcast Episode 39: Civil rights and artificial intelligence: Can the two concepts coexist? – Brookings Institution
Artificial intelligence is now used in virtually all aspects of our lives. Yet unchecked biases within existing algorithmic systems, especially those used in sensitive use cases like financial services, hiring, policing, and housing, have worsened existing societal biases, resulting in the continued systemic discrimination of historically marginalized groups. As banks increase AI usage in loan and appraisal decisions, these populations are subjected to an even greater precision in denials, eroding protections provided by civil rights laws in housing. Meanwhile, the use of facial recognition technologies among law enforcement has resulted in the wrongful arrests of innocent men and women of color through poor data quality and misidentification. These online biases are intrinsically connected to the historical legacies that predate existing and emerging technologies and stand to challenge the policies created to protect historically disadvantaged populations. Can civil rights and algorithmic systems coexist? And, if so, what roles do government agencies and industries play in ensuring fairness, diversity, and inclusion?
On TechTank, Nicol Turner Lee is joined by Renee Cummings, data activist in residence and criminologist at the University of Virginias School of Data Science, and Lisa Rice, president and CEO of the National Fair Housing Alliance. Together, they conduct a deep dive into these difficult questions and offer insight on remedies to this pressing question of equitable AI.
You can listen to the episode and subscribe to theTechTank podcastonApple,Spotify, orAcast.
TechTank is a biweekly podcast from The Brookings Institution exploring the most consequential technology issues of our time. From artificial intelligence and racial bias in algorithms, to Big Tech, the future of work, and the digital divide, TechTank takes abstract ideas and makes them accessible. Moderators Dr. Nicol Turner Lee and Darrell West speak with leading technology experts and policymakers to share new data, ideas, and policy solutions to address the challenges of our new digital world.
All Eye Overlordby Aswin Behera is licensed underCC BY 4.0
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TechTank Podcast Episode 39: Civil rights and artificial intelligence: Can the two concepts coexist? - Brookings Institution
Artificial intelligence innovation among power industry companies has dropped off in the last year – Power Technology
Research and innovation in artificial intelligence (AI) in the power industry operations and technologies sector has declined in the last year. The most recent figures show that the number of AI-related patent applications in the industry stood at 84 in the three months ending January down from 191 over the same period in 2020.
Figures for patent grants related to AI followed a similar pattern to filings shrinking from 64 in the three months ending January 2020 to 10 in the same period in 2021.
The figures are compiled by GlobalData, which tracks patent filings and grants from official offices around the world. Using textual analysis, as well as official patent classifications, these patents are grouped into key thematic areas, and linked to key companies across various industries.
AI is one of the key areas tracked by GlobalData. It has been identified as being a key disruptive force facing companies in the coming years, and is one of the areas that companies investing resources in now are expected to reap rewards from.The figures also provide an insight into the largest innovators in the sector.
Siemens was the top AI innovator in the power industry operations and technologies sector in the latest quarter. The company, which has its headquarters in Germany, filed 51 AI-related patents in the three months ending January. That was down from 125 over the same period in 2020.
It was followed by the US-based Honeywell International with 21 AI patent applications, South Korea-based Korea Electric Power (19 applications), and the US-based 3M (10 applications).
Korea Electric Power has recently ramped up R&D in AI. It saw growth of 68.4% in related patent applications in the three months ending January compared to the same period in 2020 the highest percentage growth out of all companies tracked, with more than 10 quarterly patents in the power industry operations and technologies sector.
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Artificial intelligence innovation among power industry companies has dropped off in the last year - Power Technology
Artificial intelligence (AI) in radiology? : Do we need as many radiologists in the future? – DocWire News
This article was originally published here
Urologe A. 2022 Mar 11. doi: 10.1007/s00120-022-01768-w. Online ahead of print.
ABSTRACT
We are in the middle of a digital revolution in medicine. This raises the question of whether subjects such as radiology, which is superficially concerned with the interpretation of images, will be particularly changed by this revolution. In particular, it should be discussed whether in the future the completion of initially simpler, then more complex image analysis tasks by computer systems may lead to a reduced need for radiologists. What distinguishes radiology in particular is its key position between advanced technology and medical care. This article discusses that not only radiology but every medical discipline will be affected by innovations due to the digital revolution, and that a redefinition of medical specialties focusing on imaging and visual interpretation makes sense and that the arrival of artificial intelligence (AI) in radiology is to be welcomed in the context of ever larger amounts of image data-to at all be able to handle the increasing amount of image data in the future at the current number of radiologists. In this respect, the balance between research and teaching in comparison to patient care is more difficult to maintain in the academic environment. AI can help improve efficiency and balance in the areas mentioned. With regard to specialist training, information technology topics are expected to be integrated into the radiological curriculum. Radiology acts as a pioneer designing the entry of AI into medicine. It is to be expected that by the time radiologists can be substantially replaced by AI, the replacement of human contributions in other medical and non-medical fields will also be well advanced.
PMID:35277758 | DOI:10.1007/s00120-022-01768-w
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Artificial intelligence (AI) in radiology? : Do we need as many radiologists in the future? - DocWire News
Enterprise Artificial Intelligence (AI) Market Size Expected To Reach USD 59.17 Billion CAGR of 45.3%, By 2028 – Digital Journal
The growing demand for AI-based solutions and the need to analyze a complex and large amount of data is driving the market for Enterprise Artificial Intelligence.
Market Size USD 2,879.5 Million in 2020, Market Growth CAGR of 45.3%, Market Trends a Digitalization of enterprises.
The globalenterprise artificial intelligence (AI) marketis forecast to reach USD 59.17 Billion by 2028, according to a new report by Reports and Data. With the advancements in technology, enterprises are taking advantage of intelligent automation, such as machine learning, to improve the operations of business, improve customer experience, and drive innovation.
Artificial Intelligence (AI) is transforming businesses across industries, delivering new opportunities through automated products. Machine learning falls under AI and is used to teach computers how to carry out various range of tasks by analyzing vast amounts of data. Interests in machine learning have increased owing to the breakthroughs in areas such as speech recognition, computer vision, and natural language understanding. Machine learning helps enterprises by automating large areas of work like back-office administration roles, customer contact center queries, and even eventually driving vehicles.
AI is expected to help advance the growth of IoT. With more and more data being produced from technologies like the IoT and virtual reality devices, AI and automation will be crucial in not only managing data but also in supporting the growing pressure on business networks. With businesses becoming more borderless, and far more competitive, AI and machine learning-powered networks are essential in enterprises to reduce complexity and repetition. However, concerns related to data security and privacy are hampering the market growth.
There are various different examples of major enterprises using machine learning: Rolls Royce uses it to analyze data from IoT sensors to spot telltale signs of wear in its engine of the plane and carry out required maintenance. Google uses DeepMinds reducing energy to cool its data centers by about 40%.
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Component Outlook (Revenue: USD Billion; 2018-2028)
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Organization Size Outlook (Revenue: USD Billion; 2018-2028)
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End Users Outlook (Revenue: USD Billion; 2018-2028)
Regional Outlook (Revenue: USD Billion; 2018-2028)
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Enterprise Artificial Intelligence (AI) Market Size Expected To Reach USD 59.17 Billion CAGR of 45.3%, By 2028 - Digital Journal
Top 10 Most-Loved and Most-Hated AI Jobs of 2022 – Analytics Insight
Pursue a career only after knowing about the top most-hated and most-loved AI jobs
A lucrative career in artificial intelligence is a dream of every aspiring AI professional. There is a huge demand for different jobs in AI. Yes, this is a myth that AI is ready to replace all kinds of jobs in the global market across the world. This advanced technology has created a plethora of opportunities to work with tech companies and earn attractive salary packages. It is known that every coin has two sides! Artificial intelligence jobs also can have two sides which manifest as most-loved AI jobs and most-hated AI jobs. It totally depends on the mindset, talent, and technical know-how of any AI professional. Lets explore some of the top ten most-hated AI jobs and most-loved AI jobs in 2022.
AI engineer is one of the top most-loved AI jobs in the fast-growing AI industry. The employee is responsible for programming and developing complex networks of artificial intelligence algorithms to make the machine work like a human brain. The job role involves conducting statistical analysis by automating key infrastructure for data science teams. The average annual salary of this most-loved AI job is US$100,000.
Machine learning engineer is gaining popularity as the most-loved AI job in 2022. The ML engineer is known as a technically proficient programmer to research and design self-running software for automating predictive models. The role is to perform statistical analysis as well as analyze the ML algorithms use cases. The average annual salary of this most-loved AI job is US$130,000.
Business intelligence developer is a well-known most-loved AI job with utmost responsibilities such as leveraging software tools to drive meaningful in-depth insights for driving growth opportunities and earning revenues in the year. The BI developers need to offer quantifiable solutions for complicated problems with data warehouses. The average annual salary of this most-loved AI job is US$100,000.
Data scientist is the hottest job in AI in recent years. An AI professional can transform vast amounts of real-time data into meaningful insights efficiently through multiple data management steps. The main aim is to help organizations make strategic decisions to enhance customer engagement and revenue rates. The average annual salary of this most-loved AI job is US$140,000.
Robotics engineering is one of the thriving most-loved AI jobs across the world. The role involves creating autonomous machines through designing prototypes and maintaining the robotics software. It is needed to test robotics systems and spend the whole day with different robots to enhance productivity. The average annual salary of this most-loved AI job is US$100,000.
Human-Centered Machine Learning designers can feel overwhelmed with the complicated workspace as well as the sheer breadth of ample opportunities for innovation. The job in AI is not as popular as the above-mentioned AI professionals. One expects machine learning to figure out the complicated problems to solve by jumping or skipping steps. This is one of the most-hated AI jobs with an average annual salary of US$115,000.
The database administrator is one of the most-hated AI jobs as it is extremely stressful and one mistake can provide a serious consequence in a company. Any kind of emergency situation related to the database in the existing system, this AI professional should attend, even at the cost of personal life. One must always keep systems updated by maintaining currency to hold on to their job in an organization.
DevOps engineer is an emerging most-hated AI job in 2022 as the development with DevOps is very expensive for the company budget. This job in AI needs to adopt new DevOps technology that can be difficult to manage in a very short period of time because there is very little availability of DevOps engineers.
Field service technician is one of the top most-hated AI jobs among AI professionals because working in the domain of field service management is tough. There are potential chances of running out of copies and forms in the office, not receiving a reliable connection to seamlessly complete any designated work, slow access to real-time data, and many more.
Blockchain UX designer is a popular most-hated AI job in 2022 because there are multiple challenges related to blockchain UX. There is a lack of clear and proper feedback with the rising latency time. It is a time-consuming process for designing the best blockchain UX design for organizations.
That being said, the most common points of intersection for these jobs in AI are the love for technology and the qualification to receive job offers. All aspiring AI professionals must have a Bachelors degree in any technical field from any recognized university with years of practical experience to have a good understanding of the field.
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Juniper research funding to advance artificial intelligence and network innovation – FutureFive New Zealand
Juniper Networks has announced a university funding initiative to fuel strategic research to advance network technologies for the next decade.
Junipers goal is to enable universities, including Dartmouth, Purdue, Stanford and the University of Arizona, to explore next-generation network solutions in the fields of artificial intelligence (AI) and machine learning (ML), intelligent multipath routing and quantum communications.
The company says investing now in these technologies, as organisations encounter new levels of complexity across enterprise, cloud and 5G networks, is critical to replace tedious, manual operations as networks become mission critical for nearly every business. This can be done through automated, closed-loop workflows that use AI and ML-driven operations to scale and cope with the exponential growth of new cloud-based services and applications.
The universities Juniper selected in support of this initiative are now beginning the research that, once completed, will be shared with the networking community. In addition, Juniper joined the Center for Quantum Networking Industrial Partners Program to fund industry research being spearheaded by the University of Arizona.
Cloud services will continue to proliferate in the coming years, increasing network traffic and requiring the industry to push forward on innovation to manage the required scale out architectures," says Raj Yavatkar, chief technology officer at Juniper Networks
"Junipers commitment to deliver better, simpler networks requires us to engage and get ahead of these shifts and work with experts in all areas in order to trailblaze," he says.
"I look forward to collaborating with these leading universities to reach new milestones for the network of the future.
Sonia Fahmy, professor of computer science at Purdue University, adds, With internet traffic continuing to grow and evolve, we must find new ways to ensure the scalability and reliability of networks.
"We look forward to exploring next-generation traffic engineering approaches with Juniper to meet these challenges," she says.
Dartmouth University professor of engineering George Cybenko says, "It is an exciting opportunity to work with a world-class partner like Juniper on cutting edge approaches to next-generation, intelligent multipath routing.
"Dartmouth's close collaboration with Juniper will combine world-class skills and technologies to advance multipath routing performance.
Jure Leskovec, associate professor of computer science at Stanford University says as network technology continues to evolve, so do operational complexities.
"The ability to utilise AI and machine learning will be critical in keeping up with future demands," Leskovec says.
"We look forward to partnering with Juniper on this research initiative and finding new ways to drive AI forward to make the network experience better for end users and network operators.
University of Arizona NSF Center of Quantum Networking director Saikat Guha, adds, The internet of today will be transformed through quantum technology which will enable new industries to sprout and create new innovative ecosystems of quantum devices, service providers and applications.
"With Juniper's strong reputation and its commitment to open networking, this makes them a terrific addition to building this future as part of the Center for Quantum Networks family.
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Juniper research funding to advance artificial intelligence and network innovation - FutureFive New Zealand
The Top 10 Movies to Help You Envision Artificial Intelligence – Inc.
Artificial intelligence has been with us for decades -- just throw on a movie if you don't believe it.
Even though A.I. may feel like a newer phenomenon, the groundwork of these technologies are more dated than you'd think. The English mathematician Alan Turing, considered by some as the father of modern computer science, started questioning machine intelligence in 1950. Those questions resulted in the Turing Test, which gauges a machine's capacity to give the impression of"thinking" like a human.
The concept of A.I. can feel nebulous, but it doesn't fall under just one umbrella. From smart assistants and robotics to self-driving cars, A.I. manifests in different forms...some more clear than others. Spoiler alert! Here are 10 movies in chronological order that can help you visualize A.I.:
1. Metropolis (1927)
German directorFritz Lang's classicMetropolis showcases one of the earliest depictions of A.I. in film, with the robot, Maria, transformed intothe likeness of a woman. The movie takes place in an industrial city called Metropolis that is strikingly divided by class, where Robot Maria wreaks havoc across the city.
2. 2001: A Space Odyssey (1968)
Stanley Kubrick's 2001 is notable for its early depictionof A.I. and is yet another cautionary tale in which technology takes a turn for the worse. A handful of scientists are aboard a spacecraft headed to Jupiter where a supercomputer, HAL(IBM to the cynical), runs most of the spaceship's operations. After HAL makes a mistake and tries to attribute it to human error, the supercomputer fights back when those aboard the ship attempt to disconnect it.
3. Blade Runner (1982)and Blade Runner 2049 (2017)
The original Blade Runner (1982) featured Harrison Ford hunting down "replicants,"or humanoids powered by A.I., which are almost indistinguishable from humans. In Blade Runner2049 (2017), Ryan Gosling's character, Officer K, lives with an A.I. hologram, Joi. So at least we're getting along better with our bots.
4. The Terminator (1984)
The Terminator's plot focuses on a man-made artificial intelligence network referred to as Skynet -- despite Skynet being created for military purposes, the system ends up plotting to kill mankind. Arnold Schwarzenegger launched his acting career out ofhis role as the Terminator, a time-traveling cyborg killer that masquerades as a human. The film probes the question -- and consequences -- of what happens when robots start thinking for themselves.
5. The Matrix Series (1999-2021)
Keanu Reeves stars in this cult classic as Thomas Anderson/Neo, a computer programmer by day and hacker by night who uncovers the truth behind the simulation known as "the Matrix." The simulated reality is a product of artificially intelligent programs that enslaved the human race. Human beings are kept asleep in "pods," where they unwittingly participate in the simulated reality of the Matrix while their bodies are used to harvest energy.
6. I, Robot (2004)
Thissci-fiflickstarring Will Smith takes place in 2035 in a society where robots with human-like featuresserve humankind. An artificial intelligent supercomputer, dubbed VIKI (which stands for Virtual Interactive Kinetic Intelligence), is one to watch, especially once a programming bug goes awry. The defect in VIKI's programming leads the supercomputer to believe that the robots must take charge in order to protect mankind from itself.
7. WALL-E (2008)
Disney Pixar's WALL-E follows a robot of the same namewhose main role is to compact garbage on a trash-ridden Earth. But after spending centuries alone, WALL-E evolves into a sentient piece of machinery who turns out to be very lonely. The movie takes place in2805 and follows WALL-E and another robot, named Eve, who's job is toanalyzeif a planet is habitable for humans.
8. Tron Legacy (2010)
The Tron universe is filled to the brim with A.I. given that it takes place in a virtual world, known as "the Grid." The movie's protagonist, Sam, finds himself accidentally uploaded to the Grid, where he embarks on an adventure that leads him face-to-face with algorithms and computer programs.The Grid is protected by programs such as Tron, but corrupt A.I. programs surface as well throughout the virtual network.
9. Her (2013)
Joaquin Phoenix plays Theodore Twombly, a professional letter writer going through a divorce. To help himself cope, Theodore picks up a new operating system with advanced A.I. features. He selects a female voice for the OS, naming the device Samantha (voiced by Scarlett Johansson), but it proves to have smart capabilities of itsown. Or is it, her own?Theodore spends a lot of time talking with Samantha, eventually falling in love. The film traces their budding relationship and confronts the notion of sentience and A.I.
10. Ex-Machina (2014)
After winning a contest at his workplace, programmer Caleb Smith meets his company's CEO, Nathan Bateman. Nathan reveals to Caleb that he's created a robot with artificial intelligence capabilities. Caleb's task? Assess if the feminine humanoid robot, Ava, is able to show signs of intelligent human-like behavior: in other words, pass the Turing Test. Ava has a human-like face and physique, but her "limbs" are composed of metal and electrical wiring. It's later revealed that other characters aren't exactly human, either.
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The Top 10 Movies to Help You Envision Artificial Intelligence - Inc.
Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography | Scientific Reports -…
This study evaluated if AI could determine the positional relationship between M3 and IAN based on panoramic radiography regarding whether the two structures were in contact or intimate and whether the IAN was positioned lingually or buccally to M3 when two structures were overlapped. In this situation, determining the exact position was limited and unreliable even for the specialist, as shown in previous studies25,26. However, AI could determine both positions more accurately than OMFS specialists.
Until now, if M3 and IAN overlap on panoramic radiograph, specialists could use the known predictive signs of IAN injury to determine the positional relationship whether the two structures were in contact or intimate. Umar et al. compared the positional relationship between IAN and M3 through panoramic radiography and CBCT. Loss of the radiopaque line and diversion of the canal on panoramic radiographs resulted in tooth and nerve contact in 100% of the cases on CBCT. Darkening of the roots were associated with contact on CBCT in 76.9% of the cases studied27. However, another study reported that the sensitivities and specificities ranged from 14.6 to 68.3% and from 85.5 to 96.9%, respectively, for those three predictive signs1. Datta et al. compared those signs with the clinical findings during surgical removal and found that only 12% of patients with positive radiological signs showed clinical evidence of involvement3. In the present study, we adopted CBCT reading results instead of radiological signs on panoramic radiography to determine the positional relationship so that the AI could determine whether the two structures were in contact or intimate, showing an accuracy of 0.55 to 0.72. Compared to another study1, our deep learning model exhibited similar performance (accuracy 0.87, precision 0.90, recall, 0.96, F1 score 0.93, and AUC 0.82) to determine whether M3 is contacting the IAN or not. This could explain the different model performance depending on the characteristics of the data.
To replace CBCT with analysis of panoramas with AI, information about bucco-lingual positioning was necessary to ensure safe surgical outcomes. It has been reported that the lingual position of the nerve to the tooth has a significantly higher risk of IAN injury compared to other positions28. To the best of our knowledge, no studies have evaluated bucco-lingual positioning through panoramic radiograph because there were no methods to predict this position using one radiograph. Two intraoral radiographs with different angle (vertical tube-shift technique) in the third molar area caused patient discomfort and nausea during placement of the film or sensor of the digital intraoral x-ray devices29 and is difficult to use clinically. Since there was no effective method to discern the position, the accuracy of the specialists was low in this study. On the contrary, the AI showed considerably high accuracy ranges from 67.7 to 80.6% despite the small amount of study data. The course of the IAN predominantly is buccal to the tooth28, and our data revealed a similar situation. However, the total number of cases was small to match the numbers in each group evenly for deep learning. In addition, the lack of total number of cases forced the use of a simple deep learning model with a relatively small number of parameters to be optimized. Therefore, training AI with more data could produce more accurate results and be used more widely in clinical settings.
In this study, bucco-lingual determination (Experiment 2) exhibited superior performance for true contact positioning (Experiment 1). The difference in accuracy between the two experiments seems to be a characteristic of the data rather than a special technical difference. There might be a particular advantage for AI to be recognized in bucco-lingual classification, or that some of the contact classification data might have characteristics that are difficult to distinguish.
There are several studies that have developed Al algorithms that have been able to outmatch specialists in terms of performance and accuracy. AI assistance improved the performance of radiologists in distinguishing coronavirus disease 2019 from pneumonia of other origins in chest CT30. Moreover, the AI system outperformed radiologists in clinically relevant tasks of breast cancer identification on mammography31. In the present study, the AI exhibited much higher accuracy and performance compared to those of OMFS specialists. To determine the positional relationship between M3 and IAN, we performed preliminary tests to determine the most suitable AI model using VGG19, DenseNet, EfficientNet, and ResNet-50. ResNet showed higher AUC in Experiment 2 and comparable AUC in Experiment 1 (Supplemental Tables 13). Therefore, it was chosen as the final AI model.
This study has limitations. First, the absolute size of the training dataset was small. Data augmentation by image modification was used to overcome the limitation of a small sized dataset. Nevertheless, as shown in Table 1, there were cases where training did not proceed robustly. Therefore, the performances of the trained models highly depend on the train-test split. This unsoundness of the trained model, which hinders the clinical utility of AI models for primary determination in practice, can be alleviated by collecting more data and using them for training. Also, the size of deep learning models is an important factor in performance and, in general, a large number of instances are required to train a large size deep neural network without overfitting. Thus, not only collecting more data but also exploiting external datasets from multiple dental centers can be considered to increase the performance of AI models. However, this study is meaningful in that the AI model performed better than experts even under these adverse conditions. Second, the images used in this study were cropped without any prior domain knowledge such as proper size or resolution to include sufficient information to determine true contact or bucco-lingual positional relationship between M3 and IAN. If the domain knowledge is reflected to construct a dataset, the performances of AI models can be highly increased. Third, the use of interpretable AI models32, which can explain the reason for the model prediction, can help to identify the weaknesses of the trained models. The identified weaknesses can be overcome by collecting data that the models have difficulty in classifying. Finally, the various techniques developed in the machine learning society, such as ensemble learning33, self-supervised learning34, and contrastive learning35, can be utilized for further improvement of the performance of our models even in situations where the total number of cases is insufficient as well.
Sway AI Announces Its No-Code Artificial Intelligence (AI) Platform to Accelerate AI Adoption in Every Enterprise – Woburn Daily Times
CHELMSFORD, Mass., Feb. 15, 2022 /PRNewswire/ --Sway AI today announced its no-code AI platform for enterprise users and data scientists alike. With Sway AI's platform, enterprises can rapidly build and deploy AI solutions without AI experience or upfront investments.
Sway AI believes that "there's a better way to do AI" by helping enterprises build without expensive upfront investments in AI tools or skillsets. Through its patent-pending technology, Sway AI enables any user to build AI without writing code. Using Sway AI, data scientists and AI experts can collaborate with stakeholders and build prototypes faster, dramatically reducing their time-to-deployment.
AI promises to transform almost every aspect of how we live and work. McKinsey estimates AI can create between $3.5T and $5.8T in value annually across 19 industries. They also report that successful enterprises have realized triple-digit ROIs when implementing AI.
However, Gartner estimates that 85% of AI projects are never deployed. Enterprise AI has long been time-consuming, costly, and risky. It often requires significant upfront investments in skillsets or rapidly evolving tools. Even when enterprises commit to these investments upfront, AI projects can still fail from a lack of stakeholder engagement and collaboration. These risks have long been barriers to AI adoption across industries.
Sway AI addresses these barriers by offering a no-code development platform without the investments that AI often requires. Its collaboration features engage business stakeholders and domain experts throughout the AI development cycle, improving business alignment, reducing risk, and driving increased ROIs.
Sway AI has built a growing pipeline of enterprise customers looking to build AI solutions quickly. For example, it partnered with Trilogy Networks and Veea to provide a turnkey AI solution for precision farming. "Sway AI's platform optimizes models for our real-time low-latency performance needs, which will be critical to the success of our edge AI applications. Through our partnership with Sway AI, we will be able to enhance the edge computing solutions without significant additional investments at the network edge," noted Allen Salmasi, CEO & Chairman of Veea.
"Enterprises faced the daunting task of selecting from a growing and complex marketplace of AI tools, technologies, and models. Sway AI's platform simplifies this everchanging AI ecosystem by offering best-of-class AI tooling through its platform. By using Sway AI enterprises can expect the best AI capabilities available without going through complex evaluation exercises and committing to inflexible technology choices. With Sway AI, an enterprise can reduce development and deployment costs by up to 10x and deployment time from months to hours", said Amir Atai, Sway AI CEO and Co-Founder.
Amir also noted, "This platform accelerates large enterprises' data science teams by helping them rapidly prototype their models. Our platform offers unmatched levels of transparency and collaboration with enterprise stakeholders, which can make all the difference to the success of an AI strategy."
"At Sway AI, we have been carefully listening to our customer's unmet needs. We are the first to offer this combination of enterprise AI capabilities, including a business-user interface, a marketplace of pre-built applications, enterprise collaboration, bring-or-export your models, and an open and extensible architecture. Sway AI offers best-in-class open-source capabilities as a future-proof platform. This minimizes investment and adoption risks, especially as the growth of AI tools accelerate," stated Jitender Arora, Sway AI Chief Product Officer and Co-Founder.
Sway AI is an early no-code innovator founded by several successful entrepreneurs. Co-Founder and Executive Chairman Hassan Ahmed recently served as Chairman and CEO of Affirmed Networks, acquired by Microsoft in 2020. Amir Atai was an executive in three successful startups, the Chief Scientist at Bellcore and Alcatel/Nokia and VP/CTO at Ericsson. Jitender Arora served in many successful startups as engineering/product leader including Acme Packet, acquired by Oracle for $2.1B and led the product management of Amazon Alexa AI/ML Platform.
"AI should no longer be complicated, expensive or hard," said Hassan Ahmed. "AI development often involves consultants, RFPs, engineers, and other costly third-parties that often complicate the problem and add risk. We make sense of the complex and rapidly evolving AI ecosystem to put AI in the hands of business users."
To learn more about Sway AI and its no-code AI solution, visit https://sway-ai.com/
About Sway AI
Headquartered in Chelmsford, MA, Sway AI is a developer of cutting-edge, no-code AI applications and services, offering scalable solutions for enterprises of all sizes. It was founded by proven investors and innovators who started by reinventing how AI projects should be done in a thoughtful, innovative way for all enterprises.
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Sway AI Announces Its No-Code Artificial Intelligence (AI) Platform to Accelerate AI Adoption in Every Enterprise - Woburn Daily Times