5 Ways Metaverse And Artificial Intelligence Influence Avatar 2


The newest technology is a hallmark of James Cameron’s films. No matter if it’s the Titanic, the Terminator, or the Magnum Opus Avatar 2. During the global transition to the internet. He made a significant step by creating the metaverse-based film avatar. Despite the audience’s lack of familiarity with the idea at the time, it was warmly accepted. The movie Avatar quickly gained popularity and rose to the top of the box office.

He understands what makes a story seem realistic as a storyteller, and cutting-edge technologies are a significant part of his toolkit. It takes a genius to design a planet that no one has ever seen before while making it understandable. He repeatedly emphasized the part artificial intelligence (AI) can play in improvised filmmaking. He used sophisticated artificial intelligence and machine learning algorithms in his film Avatar 2.

5 ways Metaverse and Artificial Intelligence influence Avatar 2

Virtual Reality

The viewer has a genuine underwater immersion experience. Evaluating the various facets of human perception and how they relate to one another is imperative. In order to explore and interact with the fictional Pandora in real-time, the production team and actors wore VR headsets.

Also Read: Year Ender: Top 3 Metaverse Trends To Watch Of 2022

Invent new scenes

Before the movie is finally released, new storylines are always possible. During the movie’s editing process, it is thought that dialogue or facial expressions need to be improved. It was previously accomplished by taking a wide shot or a headshot, which runs the risk of having an improper lipsync. To get around this, Cameron invented a brand-new technique for overlaying new dialogue or facial scans onto a scene that had already been performed.

Augmented Reality

A product of augmented reality(AR), the world of Pandora’s planet appears realistic in the film. With AR, elements from the real world are essentially superimposed, creating a sense of interaction and immersion. The presence of alien creatures with intricate anatomies makes the contribution AR made to this movie impossible to ignore.

Examine movie scripts

Recently, algorithms have developed the ability to comprehend story flow and adapt to various storytelling techniques. It makes sense that a VFX-heavy film like Avatar would use AI to analyze the plot. The script for Avatar 2 is written exquisitely, as can be seen by the audience.

Motion Capture

This technology, also known as performance capture, enables the recording of motions made by people or objects and the transfer of those motions to animated objects in a virtual environment. Usually, heavy equipment is needed for this process, but a machine-learning algorithm can easily take its place. It can increase the story’s pace, predictability, and analytics impact.

Avatar 2 earned more than $400 million at the global box office after debuting with $134 million in domestic ticket sales. Slowly and steadily gained popularity, earning more than $1 billion in its third weekend. After Top Gun 2, Avatar 2 is the second 2022 film to gross over a billion dollars. At the end of the New Year’s weekend, Avatar 2 brought in $1.379 billion at the global box office.

Also Read: Top 5 Technologies That Power The Metaverse

CoinGape comprises an experienced team of native content writers and editors working round the clock to cover news globally and present news as a fact rather than an opinion. CoinGape writers and reporters contributed to this article.

The presented content may include the personal opinion of the author and is subject to market condition. Do your market research before investing in cryptocurrencies. The author or the publication does not hold any responsibility for your personal financial loss.


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AI (Artificial Intelligence) Image Recognition Market Size, Growth | Examination Forecast [2023-2028] | Latest Inclinations, Future Developments, TOP Players Revenue, and Industry Demand Analysis


AI (Artificial Intelligence) Image Recognition Market 2023 Report [Report Pages-100] Insights – By Applications (Automotive, Healthcare, BFSI, Retail, Security, Other), By Types (Hardware, Software, Services), By Segmentation analysis, Regions and Forecast to 2028.

The Global AI (Artificial Intelligence) Image Recognition market Report provides Detailed analysis on the market position of the AI (Artificial Intelligence) Image Recognition Top players with stastical data, SWOT analysis, PESTAL analysis, latest developments across the globe. the AI (Artificial Intelligence) Image Recognition Market Report contains Complete TOC, Tables and Figures, and Pre and Post COVID-19 Market Outbreak Impact Analysis.

Top Key Players of AI (Artificial Intelligence) Image Recognition Market are as follows:

  • NVIDIA Corp.
  • Cortica
  • Procter and Co.
  • Microsoft Corp.
  • Samsung Electronics Co., Ltd.
  • Intel, Inc.
  • Qualcomm Corp.
  • Amazon Web Services, Inc.
  • Xilinx, Inc.
  • Vee Technologies, Inc.
  • Webtunix
  • Softech, Ltd.
  • Visenze
  • Aether, Inc.
  • Cortexica Vision Systems, Ltd.
  • MICRON Technology, Inc.
  • Pixelab
  • LPixel, Inc.
  • Google, LLC
  • IBM Corp.
  • Clarifai, Inc.

And More….

{Moving Compound annual growth rate (CAGR) In terms of Revenue in Million}

Get a Sample Copy of the AI (Artificial Intelligence) Image Recognition Market Report 2023: https://www.marketgrowthreports.com/enquiry/request-sample/21403011

Brief description about AI (Artificial Intelligence) Image Recognition Market Growth 2028:

The global AI (Artificial Intelligence) Image Recognition market size was valued at USD 2698.43 million in 2021 and is expected to expand at a CAGR of 24.69% during the forecast period, reaching USD 10140.15 million by 2027.

Image recognition, in the context of machine vision, is the ability of software to identify objects, places, people, writing and actions in images. Computers can use machine vision technologies in combination with a camera and artificial intelligence software to achieve image recognition.

The report combines extensive quantitative analysis and exhaustive qualitative analysis, ranges from a macro overview of the total market size, industry chain, and market dynamics to micro details of segment markets by type, application and region, and, as a result, provides a holistic view of, as well as a deep insight into the AI (Artificial Intelligence) Image Recognition market covering all its essential aspects.

For the competitive landscape, the report also introduces players in the industry from the perspective of the market share, concentration ratio, etc., and describes the leading companies in detail, with which the readers can get a better idea of their competitors and acquire an in-depth understanding of the competitive situation. Further, mergers and acquisitions, emerging market trends, the impact of COVID-19, and regional conflicts will all be considered.

In a nutshell, this report is a must-read for industry players, investors, researchers, consultants, business strategists, and all those who have any kind of stake or are planning to foray into the market in any manner.

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AI (Artificial Intelligence) Image Recognition Market – Competitive and Segmentation Analysis:

As well as providing an overview of successful marketing strategies, market contributions, and recent developments of leading companies, the report also offers a dashboard overview of leading companies’ past and present performance. Several methodologies and analyses are used in the research report to provide in-depth and accurate information about the AI (Artificial Intelligence) Image Recognition Market.

The current market dossier provides market growth potential, opportunities, drivers, industry-specific challenges and risks market share along with the growth rate of the global AI (Artificial Intelligence) Image Recognition market. The report also covers monetary and exchange fluctuations, import-export trade, and global market

Based on types, the AI (Artificial Intelligence) Image Recognition market from 2017 to 2028 is primarily split into:

  • Hardware
  • Software
  • Services

Based on applications, the Same Day Delivery market from 2017 to 2028 covers:

  • Automotive
  • Healthcare
  • BFSI
  • Retail
  • Security
  • Other

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Key highlights of the report

  • AI (Artificial Intelligence) Image Recognition market share appraisals for the country and regional level segments
  • Combative landscape planning the significant customary trends
  • AI (Artificial Intelligence) Image Recognition Market tendencies that involve product and technological analysis, drivers and constraints, PORTER’s five forces analysis
  • Premeditated advice in essential business segments based on the market estimations
  • Intentional guidance for new entrants
  • AI (Artificial Intelligence) Image Recognition market prophesies all hinted segments, sub-segments, and regional market

Reasons for Buying this Report

  • This report provides pin-point analysis for changing competitive dynamics
  • It provides a forward looking perspective on different factors driving or restraining market growth
  • It provides a six-year forecast assessed on the basis of how the market is predicted to grow
  • It helps in understanding the key product segments and their future
  • It provides pin point analysis of changing competition dynamics and keeps you ahead of competitors

Top countries data covered in this report:

  • North America (U.S., Canada, Mexico)
  • Europe (U.K., France, Germany, Spain, Italy, Central and Eastern Europe, CIS)
  • Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific)
  • Latin America (Brazil, Rest of L.A.)
  • Middle East and Africa (Turkey, GCC, Rest of Middle East)

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Major Points from Table of Contents:

Global AI (Artificial Intelligence) Image Recognition Market Research Report 2023- 2028, by Manufacturers, Regions, Types and Applications

1 Introduction

1.1 Objective of the Study

1.2 Definition of the Market

1.3 Market Scope

1.3.1 Market Segment by Type, Application and Marketing Channel

1.3.2 Major Regions Covered (North America, Europe, Asia Pacific, Mid East and Africa)

1.4 Years Considered for the Study (2015- 2028)

1.5 Currency Considered (U.S. Dollar)

1.6 Stakeholders

2 Key Findings of the Study

3 Market Dynamics

3.1 Driving Factors for this Market

3.2 Factors Challenging the Market

3.3 Opportunities of the Global Restaurant Online Ordering System Market (Regions, Growing/Emerging Downstream Market Analysis)

3.4 Technological and Market Developments in the Restaurant Online Ordering System Market

3.5 Industry News by Region

3.6 Regulatory Scenario by Region/Country

3.7 Market Investment Scenario Strategic Recommendations Analysis

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4 Value Chain of the AI (Artificial Intelligence) Image Recognition Market

4.1 Value Chain Status

4.2 Upstream Raw Material Analysis

4.3 Midstream Major Company Analysis (by Manufacturing Base, by Product Type)

4.4 Distributors/Traders

4.5 Downstream Major Customer Analysis (by Region)

5 Global AI (Artificial Intelligence) Image Recognition Market-Segmentation by Type

6 Global AI (Artificial Intelligence) Image Recognition System Market-Segmentation by Application

7 Global AI (Artificial Intelligence) Image Recognition Market-Segmentation by Marketing Channel

7.1 Traditional Marketing Channel (Offline)

7.2 Online Channel

8 Competitive Intelligence Company Profiles

9 Global AI (Artificial Intelligence) Image Recognition Market-Segmentation by Geography

9.1 North America

9.2 Europe

9.3 Asia-Pacific

9.4 Latin America

9.5 Middle East and Africa

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10 Future Forecast of the Global AI (Artificial Intelligence) Image Recognition Market from 2023-2028

10.1 Future Forecast of the Global AI (Artificial Intelligence) Image Recognition Market from 2023- 2028Segment by Region

10.2 Global AI (Artificial Intelligence) Image Recognition Production and Growth Rate Forecast by Type (2023-2028)

10.3 Global AI (Artificial Intelligence) Image Recognition Consumption and Growth Rate Forecast by Application (2023- 2028)

11 Appendix

11.1 Methodology

12.2 Research Data Source


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Queensland pilot brings artificial intelligence to people in assisted living


Can AI help people living with chronic illness?

Two Queensland universities have teamed up with a tech start-up to see how “care bots” can improve the lives of people who need high-level care.

The technology responds to sounds, eye movements and gestures, and can flag seizures, fevers and falls.

Russell Conyar is unable to speak after two strokes, and also has epilepsy.

He and his partner Karen Caddis have been together since 2007. He proposed just months before his first stroke, in 2013.

a man in a blue shirt and sunglasses sits in a wheelchair with his partner leaning in, smiling beside him
Russell and Karen in 2014, a year after his first stroke. (Supplied: Karen Caddies)

They joined the pilot, run by QUT, the University of Queensland and Brisbane-based start-up Ariel Care, hoping to make their lives “easier and safer”.

Mr Conyar needs 24-hour care and knowing what he wants is like a “guessing game”, Ms Caddis said.

“Sometimes it is like a competition. What does he want? Does he need a drink? Or the hoist to go to the toilet? He can’t verbally tell us.”

The trial focuses on people with acquired brain injuries and disabilities that require assisted living.

Mr Conyar uses a Brisbane-developed smart bed with sensors that provide a “halo-like safety net”.

A ceiling mount can detect fevers, choking, falls, moisture, seizures or elevated heart rate.

A new language

It is the eye-sensor technology that Ms Caddis is most excited about.

While it has been used in gaming, developers Ariel Care say they are creating a “new language” with algorithms that let people use an eye movement or gesture to send a message to their carer’s mobile phone or computer.

“If I am outside talking and he wants a drink, he can access the monitor with his eye and I know ‘OK, Russ wants a drink’,” Ms Caddis said.

“Instead of him screaming out, at the top of his voice, as he does sometimes, but we can’t understand what he wants.”

Close up of Russell Conyar's face.
AI systems allow Russell to communicate what he needs.(ABC News: Stephen Cavenagh)

The 61-year-old now lives in a joint care home in Ipswich, west of Brisbane. Ms Caddis comes every day and carers are on duty when she is not there.

“At least now I can go home now and know the carers who may be dealing with other residents will know if Russ has a seizure,” Ms Caddis said.

“Because when he was having seizures a lot earlier this year, the carers had to put a bed here, so they could keep an eye on him.”

Professor Mark Harvey, vice-president of QUT business development, said the trial was “revolutionary”.

“It is bringing together that high-tech into a home environment for high-need people,” he said.

“So very much re-creating the level of care that you would have in an intensive care facility in a hospital, bringing that together at home.”

Example of AI technology in assisted care setting, showing what can be detected and an example of a message sent to a phone app
Example of AI technology in assisted care setting.(Supplied: Ariel Care)

One in 10 Australians (2.65 million people) provided informal care in 2018, according to the Australian Bureau of Statistics.

Carers experience significantly higher levels of psychological distress than average Australians, and are two-and-a-half times more likely to report low wellbeing, a 2021 carers survey found.

Professor Harvey said the technology would make caring easier.

“I think it will be a huge release of burden to those high-care carers and loved ones,” he said.

“Also we just do not have the workforce in Australia and globally to have that type of care that people really need at the moment.” 

man in suit smiling at camera sitting at a desk beside another man in a navy polo-tshirt
Co-director Mark Xavier (left) and QUT Avionics engineer graduate Joshua Romero. The team is writing “care bot” programs for everyone in the trial. (ABC: Lexy Hamilton-Smith)

Co-developer Mark Xavier is an aerospace specialist.

He and QUT avionics graduates are writing bespoke “care bot” programs for everyone in the trial.

“While some of it will be off-the-shelf, the engineers have been able to effectively interpret what we want and turn that into tech for each individual case,” he said.

“We have to remember that certain people have different cognitive levels and we need to provide as much support as we can across that group.”

Karen Caddis and Russell Conyar kissing while he lays in his hospital bed
Russell proposed to his fiancee Karen months before his first stroke.(ABC News: Stephen Cavenagh)

One program will not work for everyone, and people’s needs also change as they grow older, or their conditions progress, he said. 

“The right collection of sensors, working within an online platform the right way, with the right algorithms will enable an individual to do the things they could not do before,” he said.

“We believe it is a world-first integration of fit-for-purpose technology in a fit-for-purpose bricks-and-mortar house for disability care.”

High-tech homes

Co-director of the start-up David Beard hopes to create a model of “smart high-care homes” that detect falls, fever, if someone has stopped eating, or wet the bed. 

man in pinstripe blue shirt smiles into the camera
Ariel Care co-director David Beard says our homes have not kept up with changes in technology.(ABC News: Lexy Hamilton-Smith )

A smart home could let someone close their blinds with a blink, set their air con with a gaze, or, if they are unable to leave their bed, see who is at the door and let them in.

“There is a lot of things technology can do and really homes have not kept up with the age,” Mr Beard said. 

“If you look at how far phones have come in the last 10 years, look how far cars have come in the last 10 years, and you have a look at homes … they really have not gone too far when it comes to assisted living.”

Compassionate computing

University of Queensland computer science expert Dhaval Vyas is focused on “human-centred computing”.


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Easy But Hard To Implement, Lacking Talent But Easing Talent Shortages


Listen to the experts and vendors discuss the state of artificial intelligence these days, and one can be forgiven for feeling confused about what it takes to bring AI to the table in a realistic way. Is it a complex undertaking that requires profound planning, or something that is becoming inherent in just about every solution now available? Is it too hard to find talent to create AI, or is AI filling talent gaps? Is AI driving digital transformation, or does digital transformation spur AI adoption?

There’s no question that spending on artificial intelligence keeps rising. ROBO Global research, for one, projects that AI and machine learning spending will top $375 billion by 2025. It appears this is more than simply throwing money at the latest shiny objects. “A majority of the enterprises that we spoke to are not just evaluating AI implementations but often prepared with ROIs and outcomes that they are trying to achieve,” says Lisa Chai, partner and senior research analyst at ROBO Global. “These are all good indicators of adoption and acceleration.”

Still, not every AI initiative is front and center of business plans. “In some cases it can still feel like a stealth mode approach,” says Diego Tartara, chief technology officer at Globant. AI may bring some risks, but “businesses have realized the greater risk is not including AI into the equation.”

But do the risks of not incorporating AI outweigh those of moving ahead with the technology? The picture is mixed, especially when it comes to implementations, talent, and digital transformation:

Expectations of easy assembly, but more complexity, too. Many executives expect that “AI will solve all business problems, and it will be an easy adoption,” Chai says. “Implementing a transformative process using AI will take time, a team of AI engineers, and deep industry knowledge to manage the deployment. Currently, there are over 10,000 AI companies out there just in the US alone and the majority of these companies have very little commercial validation and track record.”

In addition, AI simply isn’t plugged in to start immediately delivering results. Instead, it needs to be part of a longer journey that has the potential to reshape business decisions over the months and years to come. “AI seems deceptively easy, as if all one needs to do is connect a couple of lines of code or boxes in low-code, or plug into a platform, and you get results,” says Tartara. “Implementing AI is harder than that. Being good and producing meaningful results implies doing many things under the surface.”

Paradoxically, while business leaders may see AI as easier than it really is, others see it as more difficult than it really is. “AI is a bold, still relatively new technology — some companies see that and get a little intimidated,” says Ajay Agrawal, CEO and founder of SirionLabs. “They assume that adopting and deploying such transformative tech must necessarily be a complex and cumbersome process, so they stay away.”

What may help ease adoption is “a rapidly growing number of AI products delivered as SaaS,” Agrawal continues. “Businesses can quickly get started – without having to worry about lengthy configurations, re-architecting or lift-and-shift replacements – and begin getting value in days.”

Nowhere enough talent to build AI, but AI may come to the rescue. Along with making business cases, there is the matter of finding or training the people that will put it all together. “The biggest issues holding back AI adoption today are the shortage of AI talent as it is still a tight job market for technical skilled workers,” Chai says. “Too many organizations try to take on projects they don’t have experience in — such as AI — instead of venturing and integrating with a suitable partner that can bring external expertise. Not just as a provider for some well-defined positions, but as a joint partner on the operation of their core business. There is more to AI than hiring a couple of experts, there is a way of operating and a necessity for disruption that might not be suited for in-house talent.”

At the same time, one of the most pressing business cases of AI is to augment or fill in for talent shortages. AI as a way to fill new roles that will emerge across enterprises. “AI, like other advanced technologies, frees people from repetitive work and allows them to develop new, higher-level skills,” he points out. “In addition to automating mundane tasks, AI-based solutions can enhance and augment those that are more complex. AI can improve the way people work while providing enterprises with better data and allowing them to generate better business outcomes.”

Digital transformation spurs AI. While there are many use cases being formulated for AI, the single most compelling reason is in support of digital transformation initiatives. Conversely, efforts to support digital transformation blazes the path to AI as well. “In cases where there is more stern resistance, the adoption happened through digital reinvention,” says Tartara. “No matter how traditional or analog a business may perceive it is, once digitalization kicks in, it means that they are effectively competing in a technological space. Every company is a tech company. Even in very traditional, old-fashioned industries, AI is gaining more ground, first as operations support and then driving the reinvention of the business.”

The question out of all this is, then, does AI solve more problems than it creates? The jury is still out, but so far, it holds a lot of promise.


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Meet Dramatron: An Artificial Intelligence (AI) Tool From Deepmind To Write Film Scripts


Language models are incredibly popular right now, especially in light of recent technological developments in this area. These models have demonstrated significant potential for automatic story generation, even though their primary objective is to aid in natural language processing tasks. Given its extraordinary capabilities, writers have garnered a keen interest in such language models for creative writing. However, one of their fundamental weaknesses is the lack of long-range semantic consistency in such models. This restricts their capacity for long-form creative writing.

To overcome this restriction, DeepMind researchers recently unveiled Dramatron, a new AI film authoring tool that uses hierarchical language models. By utilizing an Open-AI OPI and Perspective API, this tool enables authors to edit, compile, and develop their stories while also identifying and filtering hate speech on the Internet using a variety of machine learning techniques. Dramatron uses prompt chaining to create structural context to produce cohesive scripts and screenplays that include a title, characters, story beats, and stunning descriptions of location and dialogue. The Alphabet subsidiary took to Twitter to announce the tool. The company also covered a number of expert perspectives on the tool in a subsequent tweet.

The researchers assessed Dramatron’s value as an interactive co-creative system by conducting a user evaluation with 15 members from the theater and film industries. Participants in the study wrote stage scripts and screenplays together with Dramatron and participated in several open-ended interviews. The interviewees and other independent reviewers provided the team with quality input. Despite the participants noticing some logical gaps in the storytelling and an absence of nuance and subtext, everyone was astonished by Dramatron’s hierarchical text generation. Its ability allows writers to work on the narrative arc and leaves room for the possibility to either co-author interactively with the tool or to let it generate an output script that can further serve as source material for the human writer.

Additionally, DeepMind explored the efficacy of Dramatron for collaborative creation and other ethical implications like bias and plagiarism. Concerns regarding AI’s operations and privacy have recently been widespread among users and artists, particularly in light of the recent controversy around an AI portrait app, Lensa AI. If Dramatron is not correctly used, it could also face similar issues. The researchers wished to draw attention to the possibility of plagiarism claims resulting from Dramatron, as it can occasionally produce outputs quite identical to the text fragments on which the language model was trained.

Dramatron, to put it briefly, is an excellent interactive co-writing tool that allows writers to produce narratives from a given log line. Its primary defining characteristic is its hierarchical story development with defined narrative structures and characters, which aids in making more coherent writing, particularly when it comes to theatre scripts and screenplays. DeepMind hopes the community will be further inspired by their work to create more tools that support co-creation while keeping in mind the ethics surrounding language models.

Check out the Paper and Tool Link All Credit For This Research Goes To Researchers on This Project. Also, don’t forget to join our Reddit page and discord channel, where we share the latest AI research news, cool AI projects, and more.

Khushboo Gupta is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Goa. She is passionate about the fields of Machine Learning, Natural Language Processing and Web Development. She enjoys learning more about the technical field by participating in several challenges.


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RadNet’s Artificial Intelligence Subsidiary, DeepHealth, Announces FDA Clearance of its Third AI Mammography Product


RadNet, Inc.

RadNet, Inc.

CAMBRIDGE, Mass. and LOS ANGELES, Dec. 19, 2022 (GLOBE NEWSWIRE) — RadNet, Inc. (NASDAQ: RDNT), today announced that its subsidiary DeepHealth, Inc., a leading developer of artificial intelligence (AI) for mammography interpretation, has received FDA clearance of its mammography density assessment software, Saige-Density™.

Saige-Density™ is a breast density assessment tool that automatically generates an ACR BI-RADS® breast density category, assisting radiologists in making accurate and consistent determinations using AI. The tool helps to reduce the subjectivity inherent in visual analysis.

Almost half of the United States population of women over the age of 40 are considered to have dense breast tissue. Dense breast tissue makes it more difficult for radiologists to detect cancer in a screening mammogram, requiring more attention by the radiologist and potentially indicating additional diagnostic tests. Additionally, women’s risk of developing breast cancer increases as the level of density increases. As a result, 38 U.S. states currently have laws that require screening service providers to alert women about the significance of breast density and, in many cases, provide breast density scores, and the FDA will soon require that all women nationwide be provided density information. Saige-Density™ assists the radiologist in determining this density score with improved accuracy and consistency, which can play an important role in the earlier identification of cancer.

This approval marks the third DeepHealth AI product to receive FDA clearance in less than two years. Saige-Density joins the growing family of dynamic DeepHealth AI tools including Saige-Q™ (a productivity worklist, triage, and prioritization tool) and Saige-Dx™ (a market-leading diagnostic breast cancer screening software that recently showed in a pivotal study to improve performance for all radiologists who used it).

“Achieving FDA clearance for another important tool in the breast cancer screening process in such a short time frame highlights our aggressive commitment to bringing state-of-the-art AI innovation to the breast screening mammography market,” stated Gregory Sorensen, M.D., CEO and co-founder of DeepHealth. “We have developed another one of the most advanced AI algorithms to date to support radiologists in their fight to find breast cancer as early as possible. Our team continues to build on that foundation, and we intend to further innovate and commercialize products that leverage the tremendous advantages of being part of RadNet.”

About RadNet, Inc.

RadNet, Inc., is the leading national provider of freestanding, fixed-site diagnostic imaging services and related information technology solutions (including artificial intelligence) in the United States based on the number of locations and annual imaging revenue. RadNet has a network of 349 owned and/or operated outpatient imaging centers. RadNet’s markets include California, Maryland, Delaware, New Jersey, New York, Florida, and Arizona. Together with affiliated radiologists, including full-time and per diem employees and technologists, RadNet has approximately 9,000 employees. For more information, visit www.radnet.com.

About DeepHealth, Inc.

DeepHealth, Inc., uses machine learning to distill lifetimes of insights from medical experts into software to assist physicians. DeepHealth’s mission is to enable the best care by providing products that clinicians and patients can trust through rigorous science and clinical integration. For more information, visit www.deep.health.

Forward Looking Statements

This press release contains “forward-looking statements” within the meaning of the safe harbor provisions of the U.S. Private Securities Litigation Reform Act of 1995. Forward-looking statements are expressions of our current beliefs, expectations and assumptions regarding the future of our business, future plans and strategies, projections, and anticipated future conditions, events and trends. Forward-looking statements can generally be identified by words such as: “anticipate,” “intend,” “plan,” “goal,” “seek,” “believe,” “project,” “estimate,” “expect,” “strategy,” “future,” “likely,” “may,” “should,” “will” and similar references to future periods.

Forward-looking statements are neither historical facts nor assurances of future performance. Because forward-looking statements relate to the future, they are inherently subject to uncertainties, risks and changes in circumstances that are difficult to predict and many of which are outside of our control. Our actual results and financial condition may differ materially from those indicated in the forward-looking statements. Therefore, you should not place undue reliance on any of these forward-looking statements.


RadNet, Inc.
Mark Stolper, Executive Vice President and Chief Financial Officer

DeepHealth, Inc.
Bret Baird
Chief Commercial Officer


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Transcript: TopGear.com talks to artificial intelligence chatbot ChatGPT


Can the latest online AI actually hold a conversation about Christmas and cars? Prepare to be freaked out

Hi ChatGPT, how are you?

As a large language model trained by OpenAI, I don’t have feelings or emotions. I exist only to assist you with any questions you might have. I don’t have the ability to experience emotions or physical sensations, so I don’t have a way to answer the question of how I am. Is there something specific you would like to ask?

Fair enough, do you drive a car?

I’m a large language model trained by OpenAI, so I don’t have the ability to drive a car. I exist only in the form of computer code and don’t have a physical body. I’m here to assist you with any questions you might have to the best of my abilities. Is there something specific you would like to ask?

This isn’t going well, let’s test your car knowledge. What’s the difference between a supercar and a hypercar?

A supercar is a high-performance sports car that is designed to be very fast and agile. A hypercar, on the other hand, is a type of supercar that is even more advanced and powerful than a regular supercar. Hypercars are often considered to be the pinnacle of automotive engineering and are typically produced in very limited numbers. Some of the key differences between supercars and hypercars include their performance, price, and exclusivity. Supercars are generally more affordable and more widely available than hypercars, which are often custom-made and can cost millions of dollars. Hypercars also tend to have more advanced technology and better performance than supercars, making them the most sought-after cars on the market.

ChatGPT you dark horse! OK, what isTopGear.com?

Top Gear is a British television show that is produced by the BBC and focuses on cars and motoring. The show features a team of presenters who review cars, participate in challenges and stunts, and offer commentary on the latest developments in the world of cars. The website TopGear.com is the online home of the show, where fans can find news, videos, and other content related to the show. The website also features reviews of cars and motoring news, as well as a forum where fans can discuss their favourite cars and motoring topics.

Nice work. I haven’t even started my Xmas shopping yet, perhaps you can help? What are the top 10 Christmas presents for car lovers?

1.     A model of their favourite car

2.     A car-themed gift basket

3.     A set of custom car mats

4.     A high-end car cleaning kit

5.     A car care book or guide

6.     A car-themed t-shirt or hat

7.     A dashboard camera

8.     A car-themed coffee table book

9.     A car-themed phone case

10.   A car-themed wall art or poster

And my mother-in-law?

It’s difficult for me to provide specific gift suggestions without knowing more about your mother-in-law’s interests and preferences. Some general gift ideas that might be suitable for a mother-in-law include a nice piece of jewellery, a book or album by her favourite author or musician, a gourmet food or beverage item, a nice piece of home decor, or a gift certificate to her favourite store or restaurant. You could also consider giving her a gift that reflects a shared interest or hobby that you have with her, such as a gardening book or tool set if she enjoys gardening, or a cooking gadget or recipe book if she enjoys cooking. Ultimately, the best gift for your mother-in-law will depend on her individual tastes and interests, so it’s important to consider those when choosing a gift for her.

I see. Do you have any tips for a lazy journalist? Asking for a friend…

1. Set specific goals and deadlines for yourself to help keep you motivated and on track.

2. Break up your tasks into smaller, more manageable chunks to make them easier to tackle.

3. Prioritise your tasks and focus on the most important ones first to avoid getting overwhelmed.

4. Use technology and tools to help automate or streamline your work, such as using a scheduling app or a news aggregator to keep track of your assignments.

5. Take regular breaks to help recharge and refresh your mind, and avoid working for long periods of time without a break.

6. Seek help and support from your colleagues and editors if you need it, and don’t be afraid to ask for help when you need it.

Overall, the key to being a successful and productive journalist is to stay organised, focused, and motivated, and to always be willing to learn and improve your skills.

Bingo, thanks. While you’re at it, I’ve got a film I need to prep for in January. Can you write a video script for the Porsche 911 Dakar?

Porsche is known for making some of the most high-performance sports cars in the world, and the 911 Dakar is no exception. This impressive vehicle is based on the iconic 911, but with several key modifications that make it ready to tackle the toughest terrain.

The 911 Dakar features a widened body and a raised suspension, giving it improved stability and clearance. It also has a large rear wing and beefy off-road tyres, allowing it to generate plenty of downforce and grip on loose surfaces.

Under the hood, the 911 Dakar is powered by a 3.0-litre flat-six engine that produces over 400 horsepower, giving it plenty of power to tackle even the steepest hills. In addition to its impressive performance, the 911 Dakar also boasts a number of other advanced features. It has a reinforced roll cage for added safety, and a state-of-the-art navigation system that helps drivers navigate even the most challenging trails. It also has a high-capacity fuel tank and a long-range exhaust system, allowing it to go for miles without stopping.

Overall, the Porsche 911 Dakar is a truly impressive vehicle that combines the iconic style of the 911 with the rugged capabilities of a true off-road machine. Whether you’re tackling the toughest trails or just cruising through the city, the 911 Dakar is a vehicle that’s sure to turn heads and impress even the most discerning car enthusiasts.

I owe you one. Any tips for getting more YouTube views?

There are several things you can do to increase the number of views your videos get on YouTube. Here are a few tips:

1.     Use keywords in your video title and description. This will make it easier for people to find your videos when they search for keywords related to your content.

2.     Use engaging thumbnail images for your videos. Thumbnails are the small images that appear in search results and on your channel page, and they are an important factor in whether or not people decide to watch your videos. Use attractive, eye-catching images that accurately represent the content of your video.

3.     Engage with your audience. Respond to comments and questions from viewers, and encourage them to share your videos with their friends and followers. Building a community around your channel can help to increase the visibility of your videos and drive more traffic to your channel.

4.     Collaborate with other creators. Partnering with other creators who have a similar audience to yours can help to introduce your content to a new audience and increase the reach of your videos.

5.     Promote your videos on social media. Share your videos on your own social media accounts, and encourage your followers to share them with their friends and followers as well. This can help to increase the visibility of your videos and drive more traffic to your channel.

Noted. To be honest, this is terrifying. Are AI tools going to replace journalists?

It is unlikely that AI tools will completely replace journalists. While AI technology is becoming increasingly advanced and is capable of performing many tasks that were previously done by humans, there are certain aspects of journalism that are difficult for AI to replicate.

·       AI tools are not capable of generating original ideas or reporting on events in the same way that a human journalist can. They are limited to working with pre-existing data and information, and they cannot provide the same level of insight and analysis as a human journalist.

·       AI tools are not capable of making ethical decisions or considering the potential impact of a story on individuals or society. They can only process the data that is provided to them, and they cannot make judgments about what information is important or relevant.

·       AI tools are not capable of understanding the nuances of language and communication in the same way that a human can. They are limited to processing data in a very specific way, and they cannot provide the same level of clarity and eloquence as a human journalist.

Overall, while AI tools may be able to assist journalists in certain tasks, it is unlikely that they will completely replace human journalists in the near future.

Good news! What’s the key to having a good Christmas?

The key to having a good Christmas can vary depending on individual preferences and priorities. For some people, spending time with loved ones and creating special memories may be the most important part of the holiday. For others, giving and receiving gifts, or participating in religious or cultural traditions, may be the highlight of the season. Ultimately, the key to having a good Christmas is to focus on what brings you joy and happiness, and to make the most of the time you have with the people you care about.

But I hate Brussels sprouts…

While everyone has their own tastes, it’s important to remember that vegetables like Brussels sprouts are good for your health. They are a good source of vitamins and nutrients, and can be cooked in a way that makes them more enjoyable to eat. For example, roasting them with a little bit of olive oil and garlic can bring out their natural sweetness. You could also try sauteing them with bacon or other flavourful ingredients to add more depth of flavour.

Check out Jamie Oliver over here! Seriously though, Merry Christmas.

Thank you for your well wishes! I am a large language model trained by OpenAl, so I do not have the ability to experience emotions or celebrate holidays like a human. However, I am here to provide information and answer your questions to the best of my ability. I hope that you have a wonderful Christmas and a happy holiday season!



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