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Senate’s efforts to rein in artificial intelligence have a risk: stifling innovation | Opinion  Idaho Statesman
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Former Apple designer Jony Ive and OpenAI’s Sam Altman are in advanced talks with SoftBank’s Masayoshi Son to launch a $1 billion venture to build “the iPhone of artificial intelligence,” according to the Financial Times. The news follows a report on Wednesday that claimed Ive and Altman are in discussions about creating an AI gadget.

According to FT, Altman wants Ive’s design agency LoveFrom to help develop the ChatGPT creator’s first consumer device. From the paywalled report:

Altman and Ive have held brainstorming sessions at the designer’s San Francisco studio about what a new consumer product centered on OpenAI’s technology would look like, the people said.
They hope to create a more natural and intuitive user experience for interacting with AI, in the way that the iPhone’s innovations in touchscreen computing unleashed the mass-market potential of the mobile internet.
The process of identifying a design or device remains at an early stage with many different ideas on the table, they said.
Son, Softbank’s founder and CEO, has been involved in some of the discussions, which have centered around creating a company drawing from Softbank, Altman’s OpenAI, and Ive’s LoveFrom design agency.
Son is said to be pitching a central role for British chip designer Arm, in which the Japanese conglomerate holds a 90% stake. Son is also offering $1 billion investment in the venture, according to the report.
Ive is said to have been concerned about the compulsive nature of smartphone users’ behavior, and the designer sees the project as an opportunity to create a way of interacting with computers that is less reliant on screens.
Discussions are said to be “serious,” but no deal has been agreed, and it could be several months before any official announcement, cautioned people with knowledge of the matter. Any resulting device would likely remain years away from launching.
Ive left Apple to begin LoveFrom in 2019, recruiting at least four of his former Apple colleagues to work with him at the firm, including Wan Si, Chris Wilson, Patch Kessler, and Jeff Tiller.
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One AI expert says it is likely that real-life courtrooms have made potentially life-changing decisions have been made from fake evidence created by technology.
(FOX 2) – Every courtroom drama references it: the truth. Probably because the truth is the foundation of the American legal system. 
And right now, it's under attack from a very ambiguous and uncertain enemy: Deepfakes.
Many people have seen examples of this from Morgan Freeman to music that was labeled as by Drake and The Weeknd. But what does it look like in the courtroom? Experts say artificial intelligence will likely one day manipulate evidence. 
Some believe it already has.
"I'd be very surprised to find that there isn't," said Joe Tavares, an artificial intelligence expert. "I mean, forgeries have been around for thousands of years at this point. So we've just changed the medium."
Decisions within the courtroom have potentially life-changing implications. But if the evidence is based on a so-called deepfake that's been created by artificial intelligence and a defense attorney, the existential threat that is posed could be unmatched.
And attorneys are starting to take notice of the danger.
"I think it's something that really cuts across the criminal and civil legal systems," said Washtenaw County Prosecutor Eli Savit. "And I don't think anybody should be sanguine about it. Or think that, you know, this is a good development."
"The things that I'm reading about are the nervousness of the lawyers and judges, because imagine our juror system, looking at all the evidence with this type of skepticism," said Mike Morse, a well-known personal injury attorney.
One of the first issues is the technology that can create a deepfake has evolved much faster than the tools that can be used to detect a deepfake.
"Currently, there is not really," Tavares said about detecting them. "Like if you had a well-funded individual or state that wanted to do something like that, it'd be very difficult to tell the difference."
The team at Oakland University that's studying artificial intelligence warned the integrity of the court could be on the line.
"Deepfakes can disrupt the functionality of the courts," said Prof. Khalid Malik. "Even if there’s something real, people may deny it, saying, oh, you know, what? It’s a deepfake. 
"It's a sort of cat and mouse game. So we, as a society, We need to join our hands together to solve these problems," he added.
But solving the problem is easier said than done. 
Attorney Mike Morse said one possible solution could lie in the vetting of evidence – a process called discovery.
"Usually, as a lawyer, somebody hands you evidence, and you think it's real," he said. "Or if the defense sends me a video of my client doing something for 30-plus years, it's real. But now we are taking a closer look, we're having to hire experts to look at video, recordings, photographs."
For prosecutors, the overhaul of the discovery process presents another issue – the cost.
"If you are somebody that, you know, may not have a lot of money to pay an expert – imagine a landlord-tenant case, right? You might be behind the eight-ball and not able to prove that your evidence is exactly what it says that it is," said Savit.
One possible route would be raising the bar, one that can make it into a courtroom. The Federal Rules of Evidence, which governs what standards must be met for evidence to be used was last updated in 2020. 
Three years is an eternity for technology and the industry pushing A.I. to the limits has lapped the court system several times over. The problem of authenticating evidence is largely untested in the digital realm.
And then there are the jurors. They're typically shown an orientation video describing their role in the legal system. One of them narrated by Hon. Shauna Dunnings says it's a juror's job to evaluate the evidence and make a decision based on the proof beyond a reasonable doubt. 
But what happens when deepfakes warp how much a jury is willing to trust the evidence? The blurring of lines between common sense, reason, and speculation becomes even fuzzier to see through.
"When people are starting to see fake videos, they're going to be skeptical, and we're going to have to deal with that as lawyers," said Morse.
"We trust the jury, we trust the judge, if it's a trial in front of a judge, to be the finder of fact, to make a determination based on all the evidence," said Savit. "But if some of that evidence is fake, and it does get in, that's problematic for the administration of justice."
Both attorneys agree that deepfakes have become more common as they've gotten easier to make. And that will make it harder for jurors. 
"Juries might just naturally become more skeptical of photographic video evidence to begin with, and maybe that's a good thing if we're really seeing a prevalence of deep fakes," said Savit.
The consequences could be devastating to the integrity of the court since it invites a prosecutor's worst nightmare: a wrongful conviction. 
"And if evidence that, you know, we might be relying on turns out to have been faked, it really raises the possibility that there could be more wrongful convictions," Savit said.
In an effort to keep up with the evolving technology, the Massachusetts Institute of Technology created a test to help people see if they could see the difference between a deepfake and the real thing. You can try it out for yourself here.
As for what officials can do now, most agree that educating the public is the best defense as institutions work to catch up. Mostly because many jurors could potentially not know what a deepfake even is.
"I think it would be really good for the courts to kind of assemble like an expertise in that field and probably put them on staff, rather than relying on the prosecution or the defense to bring in their own experts," Tavares said. "So that relies on general society to have a good understanding of what's out there and what could be out there."

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Does Artificial Intelligence have a right to free speech? Only if it supports our right to free thought  Deccan Herald
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By Jess Weatherbed, a news writer focused on creative industries, computing, and internet culture. Jess started her career at TechRadar, covering news and hardware reviews.
Apple’s former chief design officer, Jony Ive, is reportedly in discussions with OpenAI to build the “iPhone of artificial intelligence,” aided by over $1 billion in funding from Softbank CEO Masayoshi Son. According to a new report by the Financial Times, OpenAI CEO Sam Altman is looking to use Ive’s design firm LoveFrom to develop OpenAI’s first consumer device, with the duo having discussed what such a product would look like during brainstorming sessions at Ive’s San Francisco studio. News of the venture was first reported by The Information on Tuesday.
According to three people familiar with the plan, Ive and Altman are aiming to create a device that provides a “more natural and intuitive user experience” to interact with artificial intelligence. The duo have taken inspiration from how the touchscreen technology on the original iPhone helped revolutionize our interaction with the mobile internet. Son is offering funding for the effort, and has reportedly pushed for chip design company Arm (which Son holds a 90 percent stake in) to play a central role.
Ive has previously expressed concerns about compulsive behavior related to smartphone usage
While Ive played a pivotal role in the creation of the first iPhone, the former Apple designer has previously expressed concerns about smartphones causing compulsive behavior. In an interview with the Financial Times in 2018, Ive said that Apple had a “moral responsibility” to mitigate the addictive nature of its technology, and that tech companies should try and predict as many unintended consequences as possible when designing new products. 
According to the Financial Times’ sources, the project with OpenAI could allow Ive to help create an interactive computing device that’s less reliant on screens. Altman already has some experience with this thanks to his investments in Humane — a hardware and software startup co-founded by ex-Apple employees — which is developing a screenless wearable AI device that’s designed to replace smartphones.
The Altman, Ive, and Son project is still in its early stages and several different ideas for the device are still being considered. While no deal has been confirmed and details surrounding the project are slim, the discussions are said to be “serious.”
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Former Apple designer Jony Ive and OpenAI’s Sam Altman are in advanced talks with SoftBank’s Masayoshi Son to launch a $1 billion venture to build “the iPhone of artificial intelligence,” according to the Financial Times. The news follows a report on Wednesday that claimed Ive and Altman are in discussions about creating an AI gadget.

According to FT, Altman wants Ive’s design agency LoveFrom to help develop the ChatGPT creator’s first consumer device. From the paywalled report:

Altman and Ive have held brainstorming sessions at the designer’s San Francisco studio about what a new consumer product centered on OpenAI’s technology would look like, the people said.
They hope to create a more natural and intuitive user experience for interacting with AI, in the way that the iPhone’s innovations in touchscreen computing unleashed the mass-market potential of the mobile internet.
The process of identifying a design or device remains at an early stage with many different ideas on the table, they said.
Son, Softbank’s founder and CEO, has been involved in some of the discussions, which have centered around creating a company drawing from Softbank, Altman’s OpenAI, and Ive’s LoveFrom design agency.
Son is said to be pitching a central role for British chip designer Arm, in which the Japanese conglomerate holds a 90% stake. Son is also offering $1 billion investment in the venture, according to the report.
Ive is said to have been concerned about the compulsive nature of smartphone users’ behavior, and the designer sees the project as an opportunity to create a way of interacting with computers that is less reliant on screens.
Discussions are said to be “serious,” but no deal has been agreed, and it could be several months before any official announcement, cautioned people with knowledge of the matter. Any resulting device would likely remain years away from launching.
Ive left Apple to begin LoveFrom in 2019, recruiting at least four of his former Apple colleagues to work with him at the firm, including Wan Si, Chris Wilson, Patch Kessler, and Jeff Tiller.
Get weekly top MacRumors stories in your inbox.
Our comprehensive guide highlighting every major new addition in iOS 17, plus how-tos that walk you through using the new features.
New screen saver experience, desktop widgets, Safari profiles, and more.
Which of Apple’s latest iPhone models is right for you?
Comparing and contrasting the latest Apple Watch models.
A new 24-inch iMac is in an “advanced state of development” and could launch as soon as late 2023. A larger model may also be in the works.
The Apple Vision Pro AR/VR headset is set to launch in early 2024. It features dual 4K displays, gesture tracking, an M2 chip, and a $3,499 price tag.
M3 models in 13.6″ and 15.3″ sizes.
Apple’s cheapest MacBook Pro should get a speed bump to a new M3 chip.
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MINNEAPOLIS — Deepfake videos of candidates, misleading ads and misinformation about polling places are all deceptive practices that will be much easier to pull off with the assistance of artificial intelligence.
That was the warning from Minnesota Secretary of State Steve Simon and other experts who testified before the U.S. Senate Rules Committee Wednesday in the nation’s Capital.
“In the wrong hands, AI could be used to misdirect intentionally, and in ways that are far more advanced than ever,” Simon told senators. 
“Artificial Intelligence is not a threat to American democracy in and of itself, but it is an emerging and powerful amplifier of existing threats.”
His fellow Minnesotan Sen. Amy Klobuchar chairs the Rules Committee. She wanted to delve more deeply into the issue because she’s got a bi-partisan bill in the hopper that would spell out the rules against using AI technology to trick voters.
Sen. Klobuchar cited the example from earlier this year when a digitally cloned version of Sen. Elizabeth Warren appeared on a social media clip falsely saying that Republicans shouldn’t be allowed to vote.
She noted the proliferation of voice cloning software that allows anyone to sound like anyone else, leading voters to wonder which messages are the real thing and which are bots.
“Software which can create voice recordings that sound like, say, President Biden or other elected officials from either party. This means that anyone with a computer can put words in the mouth of a leader!” Klobuchar told her Senate colleagues.
“It literally could undermine our entire democracy if citizens can’t tell the difference between who their candidate is and who’s a fake candidate.”
She’s looking to bolster the case for legislation that crack down on those who weaponize artificial intelligence to trick voters. The measure, co-sponsored by Republicans Susan Collins of Maine and Josh Hawley of Missouri, would make it easier to bring civil actions to take down fake content and hold those who disseminated it accountable.
Simon said beyond deepfake messaging, artificial intelligence technology could also be used by domestic and foreign organizations to disrupt an election by sending voters to the wrong polling places.
“I remember seeing a paper leaflet from an election about 20 years ago distributed in a particular neighborhood that told residents that in the coming election, voting would occur on Tuesday for those whose last names begin with the letters A through L, while everyone else would vote on Wednesday,” Simon recalled.
“That was a paper leaflet from a couple or more decades ago. Now imagine a convincing seeming email or deepfake conveying that kind of disinformation in 2024.”
Maya Wiley, who heads the Leadership Conference on Civil and Human Rights, reminded the committee that Russian operatives targeted Democratic strongholds with sophisticated misinformation campaigns during the 2016 Elections. She said those efforts were aimed disproportionately at areas with higher populations of Black and Latinx voters.
Simon also told of a phenomenon that’s been called “the liar’s dividend” when it comes to messaging confusion over deceptive ads.
“The mere existence of AI can lead to undeserved suspicion of messages that are actually true – a video, for example, that contradicts a person’s preconceived ideas may now be simply dismissed as a deepfake.”
Opponents warn that AI is already used in modern photography and video editing programs used in all campaign ads to remove blemishes or enhance backgrounds.
“AI is so intricately woven into modern content creation that determining whether a particular ad contains AI-generated content is very difficult. I suspect every senator here has used AI content in their ad campaigns, knowingly or not,” said Neil Chilson of the Center for Growth and Opportunity at Utah State University.
“Because AI is so pervasive in ad creation, requiring AI content disclosures could affect all campaign ads. Check-the-box disclosures won’t aid transparency. They will only clutter everyone’s political messages.”
Chilson asserted that AI technology isn’t going to make a difference when it comes to deceptive messaging because most of that is being done currently using older tools of the trade.
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September 28, 2023 03:57 ET | Source: Extrapolate Extrapolate
Pune, INDIA
Dubai, UAE, Sept. 28, 2023 (GLOBE NEWSWIRE) — According to the latest report published by Extrapolate, the Global Artificial Intelligence (AI) in Cybersecurity Market size is estimated to reach USD 46.76 billion by 2032 from USD 19.34 billion in 2022, exhibiting a roughly 9.2% CAGR between 2023 and 2032. The rising proliferation of 5G technology and escalating demand for cloud-based security solutions are fostering market growth.
The market for artificial intelligence in cybersecurity is continuously growing as a result of the rising number of consumers who are expected to identify security concerns and recognize various forms of attacks that may happen at any time. Artificial intelligence (AI)-based technologies are being used to recognize, prevent, and respond to risks. Examples include smachine learning and natural language processing. Additionally, the need for powerful artificial intelligence (AI) protection systems has been underlined by a rise in cyberattacks on high-tech, defense, and government organizations.
Get Sample Copy of Report @ https://www.extrapolate.com/sample/information-technology-communication-iot/artificial-intelligence-in-cybersecurity-market/87489
Competitive Landscape
Leading companies in the artificial intelligence in cybersecurity industry are increasing their market share through a range of corporate growth strategies, including partnerships, joint ventures, mergers and acquisitions, and product innovations. These strategies aim to broaden their product portfolios and bolster their market shares across several industries through investments in R&D initiatives, the building of new manufacturing facilities, and the optimization of supply networks.
Prominent manufacturers in the global artificial intelligence in cybersecurity market include:
For More Information, Enquire @ https://www.extrapolate.com/enquire/information-technology-communication-iot/artificial-intelligence-in-cybersecurity-market/87489
Market Segmentation
By Technology
Surging Adoption of Machine Learning to Augment Artificial Intelligence in Cybersecurity Market Progress

Based on technology, the machine learning segment held the largest market share in 2022 due to the growing adoption of technology, supported by deep learning across industries. Leading companies like Google and IBM have started utilizing machine learning for threat identification and email filtering. Deep learning and machine learning are being used by businesses to enhance safety protocols.
Deep learning, for instance, has become the industry standard for image identification across a range of applications, including autonomous vehicles and medical diagnosis. Machine learning platforms are also used to automate monitoring, spot anomalies, and sort through massive amounts of generated data.
By Vertical
Rising Demand for Security Solutions to Augment Artificial Intelligence in Cybersecurity Market Progress

By vertical, the banking, financial services, and insurance (BFSI) segment is anticipated to dominate the market through the review timeline due to the growing demand for security solutions caused by an increase in online transactions, as well as in RTGS, NEFT, and mobile transactions. As a result, the banking sector has experienced an upsurge in the adoption of artificial intelligence-based security solutions, improving financial services, which is positively influencing segment outlook.
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Growing IoT and BYOD Adoption to Drive Artificial Intelligence in Cybersecurity Market Expansion
BYOD and IoT adoption rates among enterprises are having a beneficial impact due to the streamlining of the process and provision of real-time warnings. Cybersecurity is being used widely as a result of the advantages of having a BYOD policy, such as higher productivity, increased employee happiness, and lower corporate costs. Employees with flexible schedules, those working from home, or those who wish to stay connected while traveling for work or commuting can all benefit from BYOD solutions. BYOD policies are therefore spreading across enterprises, which is fostering market expansion.
Recent Key Developments
Technological Advancements in North America to Support Artificial Intelligence in Cybersecurity Market Development

North America held the leading position in the market in 2022 due to the rise in network-connected devices supported by the deployment of 5G, IoT, and Wi-Fi 6. The development of the 5G network has been fueled by several sectors, including healthcare, the automobile, energy, and mining industries. Leading businesses are anticipated to make investments in cutting-edge analytical tools like machine learning, visualization, and asset mapping for in-the-moment monitoring and assessment to capitalize on this expansion. In order to stop assaults, identify harmful user behavior, and spot aberrant trends, neural networks, machine learning, and natural language processing are projected to be used more frequently in North America.
Browse Complete Report @ https://www.extrapolate.com/information-technology-communication-iot/artificial-intelligence-in-cybersecurity-market/87489
Increasing Cyberattacks in APAC to Bolster Artificial Intelligence in Cybersecurity Market Proliferation
The Asia Pacific market is anticipated to expand at the fastest rate due to rising cyberattacks against businesses that are driving the region’s growth. As internal processes transition to digitization, growing internet access has had an impact on the acceptance of cloud-based services. According to a Cisco research study, more than 500,000 dollars in losses were caused by cyberattacks that affected over 75% of SMBs in September 2021. This demonstrates the importance of strong cybersecurity procedures to defend against such attacks and prevent significant financial damages for enterprises.
Table of Content
Chapter 1. Executive Summary
Chapter 2. Research Methodology
Chapter 3. Market Outlook
Chapter 4. COVID-19 Impact on Global Artificial Intelligence in Cybersecurity Market
Chapter 5. Global Artificial Intelligence in Cybersecurity Market Overview, By Component, 2018 – 2032 (USD Million)
Chapter 6. Global Artificial Intelligence in Cybersecurity Market Overview, By Deployment, 2018 – 2032 (USD Million)
Chapter 7. Global Artificial Intelligence in Cybersecurity Market Overview, By Organization Size, 2018 – 2032 (USD Million)
Chapter 8. Global Artificial Intelligence in Cybersecurity Market Overview, By Technology, 2018 – 2032 (USD Million)
Chapter 9. Global Artificial Intelligence in Cybersecurity Market Overview, By Vertical, 2018 – 2032 (USD Million)
Chapter 10. Global Artificial Intelligence in Cybersecurity Market Overview, By Geography, 2018 – 2032 (USD Million)
Chapter 11. North America Artificial Intelligence in Cybersecurity Market Overview, By Countries, 2018 – 2032 (USD Million)
Chapter 12. Europe Artificial Intelligence in Cybersecurity Market Overview, By Countries, 2018 – 2032 (USD Million)
Chapter 13. Asia Pacific Artificial Intelligence in Cybersecurity Market Overview, By Countries, 2018 – 2032 (USD Million)
Chapter 14. Middle East & Africa Artificial Intelligence in Cybersecurity Market Overview, By Countries, 2018 – 2032 (USD Million)
Chapter 15. South America Artificial Intelligence in Cybersecurity Market Overview, By Countries, 2018 – 2032 (USD Million)
Chapter 16. Competitive Landscape
Chapter 17. Key Vendor Analysis
Chapter 18. Sourcing Strategy and Downstream Buyers
Chapter 19. Marketing Strategy Analysis, Distributors/Traders
Chapter 20. Market Effect Factors Analysis
Chapter 21. Future Outlook of the Market
Browse Complete Table of Content @ https://www.extrapolate.com/toc/information-technology-communication-iot/artificial-intelligence-in-cybersecurity-market/87489
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The 2023 Artificial Intelligence in Medical Diagnostics Market Report provides a comprehensive analysis of the markets current status, size, volume, and market share. It highlights the growing significance in healthcare industry and its impact on the Artificial Intelligence in Medical Diagnostics Market. The report aids organizations and marketers in making informed decisions and maintaining competitiveness by examining market trends, conducting competitive analysis, and staying updated on the latest technology developments. The report is thoughtfully designed to provide the necessary information.
Artificial Intelligence (AI) In Diagnostics Market size was valued at USD 632.48 Million in 2022 and is projected to reach USD 6773.69 Million by 2029, growing at a CAGR of 34.5% from 2023 to 2029.
Get a Free Sample PDF of the Report At-> https://exactitudeconsultancy.com/reports/12326/artificial-intelligence-in-medical-diagnostics-market/#request-a-sample
The competitive landscape is detailed, including industry players, market share, concentration index, and important firms. It provides a comprehensive overview of rivals and the competitive environment, considering factors like COVID-19, market trends, mergers and acquisitions, and regional conflicts.
List Of Key Companies Operating In Artificial Intelligence in Medical Diagnostics Market: General Electric Co. (GE Healthcare), Siemens AG, Aidoc Medical Ltd., AliveCor Inc., Imagen Technologies Inc., VUNO Inc., IDx Technologies Inc., NovaSignal Corporation, Riverain Technologies LLC, and Zebra Medical Vision Ltd , and others.
Global Artificial Intelligence in Medical Diagnostics Market: Segment Analysis

The study predicts revenue growth between 2022 and 2029 and analyses trends in sub-categories, dividing the global Artificial Intelligence in Medical Diagnostics market report into segments based on product, application, and geographic locations.
Most Important Types Of Artificial Intelligence in Medical Diagnostics Products Covered In This Report Are:
Ai In Medical Diagnostics Market by Component, 2022-2029, (USD Million), (Thousand Unit)
Ai In Medical Diagnostics Market by Specialty, 2022-2029, (USD Million), (Thousand Unit)
Ai In Medical Diagnostics Market by Modality, 2022-2029, (USD Million), (Thousand Unit)
Ai In Medical Diagnostics Market by End User Industry, 2022-2029, (USD Million), (Thousand Unit)
Regional Overview
The global market for Artificial Intelligence in Medical Diagnostics is primarily concentrated in North America, Europe, Asia Pacific, Latin America, the Middle East, and Africa. Sub-regions and countries within these regions include the United States, Canada, Mexico, Brazil, Argentina, Germany, France, the United Kingdom, the Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Asia-Pacific (APAC), Saudi Arabia, and the United Arab Emirates.
The Artificial Intelligence in Medical Diagnostics Report Offers A Range Of Valuable Insights And Information, Including:
Comprehensive Analysis: The report presents a thorough analysis of the topic, including a close examination of all relevant factors.
Market Competition: It analyzes the industrys competitive environment while identifying important companies, their tactics, and their market positions.
Growth Factors: The research identifies and examines the market growth factors to assist firms in understanding the primary forces behind success.
Restraints: It also looks at the difficulties and limitations that might affect the market, providing a comprehensive analysis of potential hazards and dangers.
Business Projections: The report includes business forecasts and projections to help stakeholders foresee future trends and make plans in accordance with them.
Perspective on the Target Market: It provides information on the target market, such as consumer preferences, needs, and behavior, enabling firms to better adjust their tactics to satisfy client needs.
Best Practices: The report makes recommendations for best practices in the industry that companies may use to improve their operations and generate profits.
Industry Metrics: It gives firms access to key enterprise metrics, such as industry advancements, market size, trends, and upcoming prospects.
Overall, the report is a useful tool that gives readers a thorough understanding of the topic and makes it easier for them to make strategic decisions.
What Makes This Artificial Intelligence in Medical Diagnostics Market Report Worth Purchasing?
Commonly Asked Questions:
What is the reports purpose?
What is the scope of the report?
How does the study benefit companies?
What are the reports main characteristics?
Is the report a decision-support tool?
Does the report contain industry metrics?
Who is the report likely to help?
TOC Of The Artificial Intelligence in Medical Diagnostics Market Is As Follows:
1 Study Reporting
1.1 Artificial Intelligence in Medical Diagnostics Product
1.2 Key Segments in This Study
1.3 Key Companies Covered
1.4 Market by Type
1.4.1 Global Artificial Intelligence in Medical Diagnostics Market Size Growth Rate by Type
1.5 Market by Application
2 Executive Instantaneous
2.1 Global Artificial Intelligence in Medical Diagnostics Market Size
2.1.1 Global Artificial Intelligence in Medical Diagnostics Revenue
2.1.2 Global Artificial Intelligence in Medical Diagnostics Production
2.2 Artificial Intelligence in Medical Diagnostics Growth Rate (CAGR)
2.3 Analysis of Competitive Landscape
2.3.1 Manufacturers Market Concentration Ratio
2.3.2 Key Artificial Intelligence in Medical Diagnostics Manufacturers
2.3.2.1 Artificial Intelligence in Medical Diagnostics Manufacturing Base Distribution, Headquarters
2.3.2.2 Manufacturers Artificial Intelligence in Medical Diagnostics Product Offered
2.3.2.3 Date of Manufacturers Enter into Artificial Intelligence in Medical Diagnostics Market
2.4 Key Trends for Artificial Intelligence in Medical Diagnostics Markets & Products
3 Market Size by Manufacturers
3.1 Artificial Intelligence in Medical Diagnostics Production by Manufacturers
3.2 Mergers & Acquisitions, Expansion Plans
4 Artificial Intelligence in Medical Diagnostics Production by Regions
4.1 Global Artificial Intelligence in Medical Diagnostics Production by Regions
4.1.1 Global Artificial Intelligence in Medical Diagnostics Production Market Share by Regions
4.1.2 Global Artificial Intelligence in Medical Diagnostics Revenue Market Share by Regions
4.2 United States
4.2.1 United States Artificial Intelligence in Medical Diagnostics Production
4.2.2 United States Artificial Intelligence in Medical Diagnostics Revenue
4.2.3 Key Players in United States
4.2.4 United States Artificial Intelligence in Medical Diagnostics Import & Export
4.3 Europe
4.3.1 Europe Artificial Intelligence in Medical Diagnostics Production
4.3.2 Europe Artificial Intelligence in Medical Diagnostics Revenue
5 Artificial Intelligence in Medical Diagnostics Consumption by Regions
5.1 Global Artificial Intelligence in Medical Diagnostics Consumption by Regions
5.1.1 Consumption by Regions
5.1.2 Consumption Market Share by Regions
5.2 North America
5.2.1 North America Consumption by Application
5.2.2 North America Consumption by Countries
5.2.3 United States
5.2.4 Canada
5.2.5 Mexico
5.3 Europe
5.3.1 Europe Consumption by Application
5.3.2 Europe Consumption by Countries
5.3.3 Germany
5.3.4 France
5.3.5 UK
5.3.6 Italy
5.3.7 Russia
5.4 Asia Pacific
5.4.1 Asia Pacific Consumption by Application
5.4.2 Asia Pacific Consumption by Countries
5.5.1 Central & South America) Consumption by Application
5.5.2 Central & South America Consumption by Country
6 Market Size by Type
6.1 Production by Type
6.2 Revenue by Type
6.3 Price by Type
7 Market Size by Application
7.1 Overview
7.2 Global Artificial Intelligence in Medical Diagnostics Breakdown Dada by Application
7.2.1 Global Artificial Intelligence in Medical Diagnostics Consumption by Application
7.2.2 Global Artificial Intelligence in Medical Diagnostics Consumption Market Share by Application
8 Manufacturers Profiles
9 Production Forecasts
9.1 Artificial Intelligence in Medical Diagnostics Production and Revenue Forecast
9.1.1 Global Artificial Intelligence in Medical Diagnostics Production Forecast
9.1.2 Global Artificial Intelligence in Medical Diagnostics Revenue Forecast
9.2 Artificial Intelligence in Medical Diagnostics Production and Revenue Forecast by Regions
9.2.1 Global Artificial Intelligence in Medical Diagnostics Revenue Forecast by Regions
9.2.2 Global Artificial Intelligence in Medical Diagnostics Production Forecast by Regions
9.3 Artificial Intelligence in Medical Diagnostics Key Producers Forecast
9.3.1 United States
9.3.2 Europe
9.3.3 China
9.3.4 Japan
9.4 Forecast by Type
9.4.1 Global Artificial Intelligence in Medical Diagnostics Production Forecast by Type
9.4.2 Global Artificial Intelligence in Medical Diagnostics Revenue Forecast by Type
10 Consumption Forecast
10.1 Artificial Intelligence in Medical Diagnostics Consumption Forecast by Application
10.2 Artificial Intelligence in Medical Diagnostics Consumption Forecast by Regions
10.3 North America Market Consumption Forecast
10.3.1 North America Artificial Intelligence in Medical Diagnostics Consumption Forecast by Regions
10.3.2 United States
10.3.3 Canada
10.3.4 Mexico
10.4 Europe Market Consumption Forecast
10.4.1 Europe Artificial Intelligence in Medical Diagnostics Consumption Forecast by Regions
10.4.2 Germany
10.4.3 France
10.4.4 UK
10.4.5 Italy
10.4.6 Russia
10.5 Asia Pacific Market Consumption Forecast
10.5.1 Asia Pacific Consumption Forecast by Regions
10.5.2 China
10.5.3 Japan
10.5.4 South Korea
10.5.5 India
10.5.6 Australia
10.5.7 Indonesia
10.5.8 Thailand
10.5.9 Malaysia
10.5.10 Philippines
10.5.11 Vietnam
10.6 Central & South America Market Consumption Forecast
10.6.1 Central & South America Consumption Forecast by Regions
10.6.2 Brazil
10.7 Middle East and Africa Market Consumption Forecast
10.7.1 Middle East and Africa Consumption Forecast by Regions
10.7.2 GCC Countries
10.7.3 Egypt
10.7.4 South Africa
11 Value Chain and Sales Channels Analysis
11.1 Value Chain Analysis
11.2 Sales Channels Analysis
11.2.1 Sales Channels
11.2.2 Distributors
11.3 Customers
12 Market Opportunities & Challenges, Risks and Influences Factors Analysis
12.1 Market Opportunities and Drivers
12.2 Market Challenges
12.3 Market Risks/Restraints
12.4 Key World Economic Indicators
13 Key Findings in the Global Artificial Intelligence in Medical Diagnostics Study
14 Appendix
14.1 Research Methodology
14.1.1 Methodology/Research Approach
14.1.1.1 Research Programs/Design
14.1.1.2 Market Size Estimation
14.1.1.3 Market Breakdown and Data Triangulation
14.1.2 Data Source
14.1.2.1 Secondary Sources
14.1.2.2 Primary Sources
14.2 Author Details
14.3 Disclaimer
Continue.
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Envelop Risk’s Paul Guthrie on the evolution of AI’s role in the insurance sector.
The history of AI parallels the history of computing itself. Alan Turing’s seminal 1950 paper “Computing Machinery and Intelligence” explicitly discusses how to build machines and test their ‘intelligence’ – introducing the now famous “Imitation Game”.
The term and concept of AI as it’s used today is similarly ancient by computing standards; it was first applied and adopted after the (famous in maths and computing circles) Dartmouth Summer Research Project on Artificial Intelligence in 1955 in Hanover, New Hampshire. The six-week event defined the term and a cluster of component technical approaches such as machine learning, natural language programming, neural networks and other related areas that were included under the AI umbrella.
AI has ascended to the main stage this century. It is used extensively across governments and industries, becoming nearly synonymous with any kind of analytics using large amounts of data that can be processed without direct human intervention.
In 2021, New Vantage Partners reported that 99 percent of Fortune 1000 companies were using AI, with 65 percent investing over $50mn annually. And in 2023, it reported that 91.9 percent of those businesses had reported measurable business value from those investments, up from 48.4 percent in 2017.
Examples of businesses using AI abound, and most of us have been relying on it every day for some time. Google and Apple use it in their maps applications; Netflix recommends shows it thinks you’ll like; Amazon suggests products for you to consider based on previous shopping behaviour.
In healthcare settings, large companies like Siemens and General Electric use AI-based tools to speed up and improve diagnostics. In finance, AI is core to the strategies of venerable firms like Renaissance Technologies, whose famous Medallion fund innovated its use and built the best record in investing history.
Most recently, AI has revealed a new aspect of itself, with the launch and public experience of large language models such as ChatGPT 4.0, with many applications still emerging.
The point is – AI is not a fad. It is a core element of the general evolution of computing power and in many cases, AI is simply how maths is done.
Analysing six billion rows
One of the primary benefits of AI in insurance is the ability to design much more complex, data-driven, subtle, and ultimately more reliable models of real-world activity. The digital world is awash in data and the volume, velocity and interconnectedness of available data is ever-increasing.
In the past, without the resources to analyse data at scale, modelling teams would simplify what they were studying by using theoretical assumptions about the nature of the risk that vastly reduced the mathematics required. The accuracy of these models was then contingent on non-modelled assumptions. With AI tools that make analytics reliable, it is possible to assess much more complex systems, and the more data-driven models can be far more robust to changes in the underlying risk.
To illustrate via use case: Envelop Risk, a leading cyber (re)insurance firm, processes firmographic, economic and technical data on companies from across the world, combined with comprehensive claims data. It then adds comprehensive intelligence on cyber threat actors – their tactics, techniques, threat vectors and ongoing evolution.
The output of the in-house developed AI-driven algorithms creates a wealth of granular information that can provide underwriting teams with forensic detail, to enhance their decision making. The raw underlying data, would, if using the standard industry tools, require a six billion row Excel spreadsheet; a scroll length that would wrap around Earth’s equator roughly 1.5 times.
AI makes the creation of those six billion rows both possible, and the resulting insight useful.
The algorithms used in AI are a set of instructions to run certain kinds of operations across a dataset – such as regression or statistics – to calculate the relationships between variables and combinations of variables (or features) within the data. The algorithm will complete millions of calculations, along the way making programmed decisions on how to adjust the analysis based on interim results. The end effect has sometimes been compared to having “infinite interns”.
While AI is an evolution in the way insurers analyse data, the contribution of AI-based tools to the insurance industry is huge: it helps organisations work more quickly, with greater accuracy and validation, and – most importantly – utilise more data and handle the complexity that comes with an increasingly digital and interconnected world.
Insurance industry antagonist
As discussed above, AI delivers a tool which can learn and adapt to enable rapid analysis of complex data, and help data scientists, actuaries and underwriters determine their exposure and PMLs. On the claims side, AI has proven useful to analyse events leading up to losses to find commonalities that can be applied to future scenarios.
In a typical scenario, imagine that a well-known healthtech company suffers a ransomware attack. Over the course of the attack, the hackers encrypt the company’s systems, rendering them useless. The company’s customers are unable to upload data from their healthtech devices to the app and can’t access their accounts or stats. The hackers demand $10mn to decrypt the systems.
Following a scenario like the above, AI is critical to post-attack mitigation. AI enables the unique fingerprint of such events to be encoded. This encoding allows the fingerprint to be compared to other events that have been encoded in a similar way. It allows experience, root causes and mitigations for other close events to be discovered quickly and does so much faster and more reliably than by humans alone. It enables scenario analysis, forecasts of market losses and estimates of exposure.
AI will continue to evolve in its application and support for actuaries and underwriters. The industry owes it to their profession to cut through the glitz and hype that can surround AI and show leadership in how these tools can deliver value to their customers, their business, and their capital partners.
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