Category Archives: Artificial Intelligence
AI Sparks a Creative Revolution in Business, With an Unexpected Twist – PYMNTS.com
In the race to harness artificial intelligence (AI), businesses are discovering an unexpected wrinkle: AI that sparks individual brilliance may be flattening the creative landscape. As companies from tech startups to Madison Avenue ad agencies embrace these digital muses, theyre grappling with a paradox that could reshape innovation and their bottom lines.
A recent U.K. study on AI-assisted short story writing has thrown a wrench into the notion that machines will simply replace human ingenuity. The research, conducted by a team at the University of Cambridge, found that while AI can serve as a powerful muse for individual creators, its widespread adoption may paradoxically lead to a decline in overall creative output. This surprising finding has executives and creatives alike questioning whether the rush to embrace AI could inadvertently be programming businesses into a creative corner.
What distinguishes todays AI, particularly generative AI, is its dual role in not only boosting efficiency but also fostering creativity, Sarah Hoffman, AI evangelist at AlphaSense, told PYMNTS. This duality is at the heart of the creative conundrum facing industries from advertising to product design.
Experts say AIs role as a creativity catalyst is reshaping workflows and profit margins across industries. From advertising firms churning out campaigns at breakneck speeds to product designers iterating prototypes in days instead of months, the technology is compressing timelines and expanding possibilities. This AI-powered efficiency is allowing businesses to respond more nimbly to market trends, potentially translating into faster time-to-market and increased revenues.
The study of 300 aspiring authors reveals AIs double-edged impact on creativity. When tasked with crafting micro-stories for young adults, AI assistance significantly boosted the less creative writers output making their work up to 26.6% better written and 15.2% less boring. The digital muse, however, left the more naturally creative wordsmiths talents largely untouched.
But heres the plot twist: AI might enhance personal creativity but could dull the collective creative edge. Researchers found AI-assisted stories shared more similarities, potentially leading to a sea of sameness in the creative landscape. As businesses embrace this digital inspiration, they face a new challenge: harnessing AIs power to elevate individual performance without sacrificing the diverse, innovative thinking that drives industries forward.
The paradox is evident in the world of visual art. AI allows you to iterate very quickly and test many ideas in a short period of time, which should potentially expand our creative horizons, Sergei Belousov, lead AI/ML research engineer at ARTA, an AI image generator, told PYMNTS. Yet he cautions, If everyone uses the same AI tools, you can ultimately experience a decline in creativity and individuality because creative pieces will depend on the characteristics of AI you utilize.
This homogenization effect is already being observed. AI is already impacting creative industries, and while it is saving time and money for brands, the output tends to be homogeneous, Sabrina H. Williams, data and communication program director at the University of South Carolina, told PYMNTS. She points to the advertising industry, where AI-generated campaigns risk blending into a sea of algorithmic sameness.
To navigate this new terrain, experts suggest a human-first approach. Williams recommends brainstorming away from digital tools, then using AI as a secondary step. This strategy aligns with Hoffmans view that AI can be an effective brainstorming partner that complements human creativity, especially given that current AI tools still hallucinate and cant be completely trusted.
A more tailored approach to AI implementation could also be key. Invest in tailoring the AI tools to your business specifics and objectives, advised Belousov. A companys internal data is its competitive advantage. It should fuel the training of your in-house AI in order to adapt it to the specifics of your business and optimize the outcomes.
As the creative landscape evolves, a balanced skill set becomes crucial. Businesses need to ensure their employees have hard skills, of course, but also offer training in creative thinking and problem-solving, Williams said. This approach may be vital in industries like product design, where the human touch can differentiate a product in an increasingly AI-influenced market.
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AI Sparks a Creative Revolution in Business, With an Unexpected Twist - PYMNTS.com
The Data That Powers A.I. Is Disappearing Fast – The New York Times
For years, the people building powerful artificial intelligence systems have used enormous troves of text, images and videos pulled from the internet to train their models.
Now, that data is drying up.
Over the past year, many of the most important web sources used for training A.I. models have restricted the use of their data, according to a study published this week by the Data Provenance Initiative, an M.I.T.-led research group.
The study, which looked at 14,000 web domains that are included in three commonly used A.I. training data sets, discovered an emerging crisis in consent, as publishers and online platforms have taken steps to prevent their data from being harvested.
The researchers estimate that in the three data sets called C4, RefinedWeb and Dolma 5 percent of all data, and 25 percent of data from the highest-quality sources, has been restricted. Those restrictions are set up through the Robots Exclusion Protocol, a decades-old method for website owners to prevent automated bots from crawling their pages using a file called robots.txt.
The study also found that as much as 45 percent of the data in one set, C4, had been restricted by websites terms of service.
Were seeing a rapid decline in consent to use data across the web that will have ramifications not just for A.I. companies, but for researchers, academics and noncommercial entities, said Shayne Longpre, the studys lead author, in an interview.
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The Data That Powers A.I. Is Disappearing Fast - The New York Times
States strike out on their own on AI, privacy regulation – Maine Morning Star
As congressional sessions have passed without any new federal artificial intelligence laws, state legislators are striking out on their own to regulate the technologies in the meantime.
Colorado just signed into effect one of the most sweeping regulatory laws in the country, which sets guardrails for companies that develop and use AI. Its focus is mitigating consumer harm and discrimination by AI systems, and Gov. Jared Polis, a Democrat, said he hopes the conversations will continue on the state and federal level.
Other states, like New Mexico, have focused on regulating how computer generated images can appear in media and political campaigns. Some, like Iowa, have criminalized sexually charged computer-generated images, especially when they portray children.
We cant just sit and wait, Delaware state Rep. Krista Griffith, D-Wilmington, who has sponsored AI regulation, told States Newsroom. These are issues that our constituents are demanding protections on, rightfully so.
Griffith is the sponsor of the Delaware Personal Data Privacy Act, which was signed last year, and will take effect on Jan. 1, 2025. The law will give residents the right to know what information is being collected by companies, correct any inaccuracies in data or request to have that data deleted. The bill is similar to other state laws around the country that address how personal data can be used.
Theres been no shortage of tech regulation bills in congress, but none have passed. The 118th congress saw bills relating to imposing restrictions on artificial intelligence models that are deemed high risk, creating regulatory authorities to oversee AI development, imposing transparency requirements on evolving technologies and protecting consumers through liability measures.
In April, a new draft of the American Privacy Rights act of 2024 was introduced, and in May, the Bipartisan Senate Artificial Intelligence Working Group released a roadmap for AI policy which aims to support federal investment in AI while safeguarding the risks of the technology.
Griffith also introduced a bill this year to create the Delaware Artificial Intelligence Commission, and said that if the state stands idly by, theyll fall behind on these already quickly evolving technologies.
The longer we wait, the more behind we are in understanding how its being utilized, stopping or preventing potential damage from happening, or even not being able to harness some of the efficiency that comes with it that might help government services and might help individuals live better lives, Griffith said.
States have been legislating about AI since at least 2019, but bills relating to AI have increased significantly in the last two years. From January through June of this year, there have been more than 300 introduced, said Heather Morton, who tracks state legislation as an analyst for the nonpartisan National Conference of State Legislatures.
Also so far this year, 11 new states have enacted laws about how to use, regulate or place checks and balances on AI, bringing the total to 28 states with AI legislation.
Technologists have been experimenting with decision-making algorithms for decades early frameworks date back to the 1950s. But generative AI, which can generate images, language, and responses to prompts in seconds, is whats driven the industry in the last few years.
Many Americans have been interacting with artificial intelligence their whole lives, and industries like banking, marketing and entertainment have built much of their modern business practices upon AI systems. These technologies have become the backbone of huge developments like power grids and space exploration.
Most people are more aware of their smaller uses, like a companys online customer service chatbot or asking their Alexa or Google Assistant devices for information about the weather.
Rachel Wright, a policy analyst for the Council of State Governments, pinpointed a potential turning point in the public consciousness of AI, which may have added urgency for legislators to act.
I think 2022 is a big year because of ChatGPT, Wright said. It was kind of the first point in which members of the public were really interacting with an AI system or a generative AI system, like ChatGPT, for the first time.
Andrew Gamino-Cheong cofounded AI governance management platform Trustible early last year as the states began to pump out legislation. The platform helps organizations identify risky uses of AI and comply with regulations that have already been put in place.
Both state and federal legislators understand the risk in passing new AI laws: too many regulations on AI can be seen as stifling innovation, while unchecked AI could raise privacy problems or perpetuate discrimination.
Colorados law is an example of this it applies to developers on high-risk systems which make consequential decisions relating to hiring, banking and housing. It says these developers have a responsibility to avoid creating algorithms that could have biases against certain groups or traits. The law dictates that instances of this algorithmic discrimination need to be reported to the attorney generals office.
At the time, Logan Cerkovnik, the founder and CEO of Denver-based Thumper.ai, called the bill wide-reaching but well-intentioned, saying his developers will have to think about how the major social changes in the bill are supposed to work.
Legislature rejects paths to a comprehensive data privacy law in Maine
Are we shifting from actual discrimination to the risk of discrimination before it happens? he added.
But Delawares Rep. Griffith said that these life-changing decisions, like getting approved for a mortgage, should be transparent and traceable. If shes denied a mortgage due to a mistake in an algorithm, how could she appeal?
I think that also helps us understand where the technology is going wrong, she said. We need to know where its going right, but we also have to understand where its going wrong.
Some who work in the development of big tech see federal or state regulations of AI as potentially stifling to innovation. But Gamino-Cheong said he actually thinks some of this patchwork legislation by states could create pressure for some clear federal action from lawmakers who see AI as a huge growth area for the U.S.
I think thats one area where the privacy and AI discussions could diverge a little bit, that theres a competitive, even national security angle, to investing in AI, he said.
Wright published research late last year on AIs role in the states, categorizing the approaches states were using to create protections around the technology. Many of the 29 laws enacted at that point focused on creating avenues for stakeholder groups to meet and collaborate on how to use and regulate AI. Others recognize possible innovations enabled by AI, but regulate data privacy.
Transparency, protection from discrimination and accountability are other major themes in the states legislation. Since the start of 2024, laws that touch on the use of AI in political campaigns, schooling, crime data, sexual offenses and deepfakes convincing computer-generated likenesses have been passed, broadening the scope in how a law can regulate AI. Now, 28 states have passed nearly 60 laws.
Heres a look at where legislation stands in July 2024, in broad categorization:
Many states have enacted laws that bring together lawmakers, tech industry professionals, academics and business owners to oversee and consult on the design, development and use of AI. Sometimes in the form of councils or working groups, they are often on the lookout for unintended, yet foreseeable, impacts of unsafe or ineffective AI systems. This includes Alabama (SB 78), Illinois (HB 3563), Indiana (S 150), New York (AB A4969, SB S3971B and A 8808), Texas (HB 2060, 2023), Vermont (HB 378 and HB 410), California (AB 302), Louisiana (SCR 49), Oregon (H 4153), Colorado (SB 24-205), Louisiana (SCR 49), Maryland (S 818), Tennessee (H 2325), Texas (HB 2060), Virginia (S 487), Wisconsin (S 5838) and West Virginia (H 5690).
Second most common are laws that look at data privacy and protect individuals from misuse of consumer data. Commonly, these laws create regulations about how AI systems can collect data and what it can do with it. These states include California (AB 375), Colorado (SB 21-190), Connecticut (SB 6 and SB 1103), Delaware (HB 154), Indiana (SB 5), Iowa (SF 262), Montana (SB 384), Oregon (SB 619), Tennessee (HB 1181), Texas (HB 4), Utah (S 149) and Virginia (SB 1392).
The Maine Legislaturerejected two competing proposalsfor a comprehensive data privacy law this year, one that would have made the states regulations on companies that collect consumer information online among the strictest in the country and another backed by businesses and technology companies that followed a template increasingly adopted by other states in recent years.
Some states have enacted laws that inform people that AI is being used. This is most commonly done by requiring businesses to disclose when and how its in use. For example, an employer may have to get permission from employees to use an AI system that collects data about them. These states have transparency laws: California (SB 1001), Florida (S 1680), Illinois (HB 2557), and Maryland (HB 1202).
These laws often require that AI systems are designed with equity in mind, and avoid algorithmic discrimination, where an AI system can contribute to different treatment of people based on race, ethnicity, sex, religion or disability, among other things. Often these laws play out in the criminal justice system, in hiring, in banking or other positions where a computer algorithm is making life-changing decisions. This includes California (SB 36), Colorado (SB 21-169), Illinois (HB 0053), and Utah (H 366).
Laws focusing on AI in elections have been passed in the last two years, and primarily either ban messaging and images created by AI or at least require specific disclaimers about the use of AI in campaign materials. This includes Alabama (HB 172), Arizona (HB 2394), Idaho (HB 664), Florida (HB 919), New Mexico (HB 182), Oregon (SB 1571), Utah (SB 131), and Wisconsin (SB 664).
States that have passed laws relating to AI in education mainly provide requirements for the use of AI tools. Florida (HB 1361) outlines how tools may be used to customize and accelerate learning, and Tennessee (S 1711) instructs schools to create an AI policy for the 2024-25 school year which describes how the board will enforce its policy.
The states which have passed laws about computer-generated explicit images criminalize the creation of sexually explicit images of children with the use of AI. These include Iowa (HF 2240) and South Dakota (S 79).
While most of the AI laws enacted have focused on protecting users from the harms of AI, many legislators are also excited by its potential.
A recent study by the World Economic Forum has found that artificial intelligence technologies could lead to the creation of about 97 million new jobs worldwide by 2025, outpacing the approximately 85 million jobs displaced to technology or machines.
Rep. Griffith is looking forward to digging more into the technologies capabilities in a working group, saying its challenging to legislate about technology that changes so rapidly, but its also fun.
Sometimes the tendency when somethings complicated or challenging or difficult to understand is like, you just want to run and stick your head under the blanket, she said. But its like, everybody stop. Lets look at it, lets understand it, lets read about it. Lets have an honest discussion about how its being utilized and how its helping.
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States strike out on their own on AI, privacy regulation - Maine Morning Star
Big Techs AI Ambitions Face Reality Check, Report Shows – PYMNTS.com
Despite big budgets and bold plans, a new survey conducted by PYMNTS Intelligence reveals most large companies are struggling to implement AI in meaningful ways, lagging behind in the race to leverage artificial intelligence for transformative business impact.
The findings detailed in The Impact Of GenAI on a COOs Priorities, the third edition of PYMNTS Intelligences 2024 CAIO Project, offer a sobering reality check for the AI revolution. Surveying chief operating officers from companies with at least $1 billion in annual revenue, the report uncovers a significant gap between the perceived potential of generative AI and its current applications in the corporate world.
Seventy percent of COOs from firms surveyed all with at least $1 billion in revenue agree that GenAI is a critical part of strategic planning, the report stated. Nonetheless, there is a gulf between aspiration and reality.
This disconnect between vision and execution is particularly striking, given AIs high-profile nature in todays business landscape. With tech giants and startups alike touting its transformative power, many had expected to see more rapid and widespread adoption of advanced AI applications in large enterprises.
Instead of leveraging AI for high-level decision-making or innovative product development, many companies deploy the technology for more routine tasks. The survey found that nearly 6 in 10 COOs (58%) say their firms use GenAI for accessing information, while half of the executives say they use it with chatbots for customer service.
This focus on less complex applications extends to other areas as well. The report noted that 53% of COOs use AI technology to create data visualizations. However, the effectiveness of these applications varies, with 22% of respondents indicating that GenAI was not highly effective for this purpose.
The tendency to prioritize mundane tasks over more strategic applications is particularly evident in certain key business areas. COOs are less likely to credit GenAI as necessary for production purposes, such as managing inventory or running logistics, the report states. Just 35% of COOs say GenAI is highly important for HR management and logistics.
This cautious approach to AI implementation may stem from a need for greater familiarity with the technologys full capabilities. The survey revealed that 38% of COOs consider familiarizing themselves with the complete range of AI possibilities a drawback to implementation.
While many firms are playing it safe with their AI deployments, the report suggests that this conservative approach may limit their potential returns on investment. The report finds a clear correlation between strategic AI use and positive financial outcomes.
The report showed that 29% of the firms using the technology in highly impactful and strategic ways report very positive ROI. However, in contrast, just 8.8% of firms using GenAI for more routine and less impactful tasks reported positive ROI.
This disparity in outcomes highlights the potential benefits of more ambitious AI strategies. Companies willing to trust AI with more complex and consequential tasks reap greater rewards.
One example of this disconnect between potential and actual use is in code generation. The report classifies this as a medium impact strategic use of AI, noting, Although using the technology for code generation was highly effective according to all those who used it, just 18% of COOs reported generating code with GenAI.
Beyond its impact on business processes and financial outcomes, the adoption of AI also significantly affects workforce composition and skills requirements. Contrary to fears of widespread job losses due to automation, the survey suggests that AI is driving a shift in labor needs rather than simply eliminating positions.
The report found that 88% reported that their organizations need for analytically skilled workers has increased. This surge in demand for analytical talent comes even as 42% of COOs agree that using GenAI has decreased the companys need for lower-skilled workers.
This shift in workforce requirements presents challenges and opportunities for companies and employees. Firms may need to invest heavily in retraining and upskilling programs to ensure their workforce can effectively leverage AI technologies. Meanwhile, workers with strong analytical skills may be in increasingly high demand.
The focus on analytical skills aligns with the broader trend of data-driven decision-making in modern business. As AI systems generate more insights and predictions, companies need employees to interpret this information and translate it into actionable strategies.
Despite the challenges in implementation, COOs remain optimistic about AIs potential to drive efficiencies and reduce costs. The report showed that executives primarily focus on efficiency-related metrics when assessing their AI investments.
Nearly all COOs surveyed, 92%, report using at least one measure of investment return that focuses on cost reduction, such as reduced operational costs, capital expenditures or headcount, the report stated. This emphasis on cost-cutting metrics outweighs increased profits or market expansion measures, with only 70% of COOs citing profit-related measures of AI success.
This focus on efficiency gains may explain the current preference for using AI in more routine tasks, where the impact on costs is more immediately apparent and easier to quantify.
Looking Ahead
As companies continue to navigate the AI landscape, those who can effectively leverage the technology for strategic purposes may gain a significant competitive advantage. However, realizing this potential will require overcoming implementation hurdles, rethinking traditional approaches to workforce management, and taking calculated risks with more ambitious AI deployments.
The report concluded, The opportunity is ripe for larger firms to focus their AI use in highly impactful ways and employ more analytically skilled workers to fill the gaps they are currently experiencing.
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Big Techs AI Ambitions Face Reality Check, Report Shows - PYMNTS.com
University of Alabama focuses on artificial intelligence – WVTM13 Birmingham
University of Alabama focuses on artificial intelligence
Center at University of Alabama concentrates on incorporating artificial intelligences in workplaces
Updated: 9:57 AM CDT Jul 1, 2024
The University of Alabama is opening a new center to focus on artificial intelligence.According to the university, the Alabama Center for the Advancement of Artificial Intelligence will be housed in the College of Engineering. It will bring together all the studies on artificial intelligence currently underway across the campus under one roof. The center will focus on advancing AI science, promoting human use of the technology, building a workforce proficient in AI and looking for ways to bring AI to industry.The ALA-AL Center, as it is called, is supported by a $2 million donation. There is no word on when the center will be completed and opened.
The University of Alabama is opening a new center to focus on artificial intelligence.
According to the university, the Alabama Center for the Advancement of Artificial Intelligence will be housed in the College of Engineering. It will bring together all the studies on artificial intelligence currently underway across the campus under one roof. The center will focus on advancing AI science, promoting human use of the technology, building a workforce proficient in AI and looking for ways to bring AI to industry.
The ALA-AL Center, as it is called, is supported by a $2 million donation. There is no word on when the center will be completed and opened.
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University of Alabama focuses on artificial intelligence - WVTM13 Birmingham
AllianceBernstein Names Its First Director of Artificial Intelligence – Funds Society
AllianceBernstein (AB) announced Andrew Chin as the firms first Director of Artificial Intelligence.
As a member of ABs Operating Committee with a 27-year career at the firm, Chin previously served as Head of Investment Sciences and Solutions at AB. Throughout his career, he has held various positions, including Head of Quantitative Research and Data Scientist, and served as the firms Chief Risk Officer for over a decade.
The appointment of Andrew to this new position recognizes our companys progress with AI and its future potential, said ABs Chief Operating Officer, Karl Sprules.
In his previous role, he was Head of Investment Sciences and Solutions and a member of the firms Operating Committee. Additionally, he has held several leadership positions in quantitative research, risk management, and portfolio management in the firms New York and London offices since joining AB in 1997.
Chin holds a Bachelors degree in Mathematics and Computer Science, and an MBA in Finance from Cornell University.
As AI continues to play a fundamental and transformative role in enhancing ABs operational, business, and investment research procedures, and improving efficiency across all corporate functions, we look forward to having an industry veteran like Andrew lead our company into the future in this newly created role, added Sprules.
Chin also appreciated the firms commitment to the new role.
This new role signifies the evolution not only of my professional trajectory at AB but also of the increasingly significant role that data science and artificial intelligence are playing across the financial services industry, said Chin.
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AllianceBernstein Names Its First Director of Artificial Intelligence - Funds Society
The Role of Artificial Intelligence in Professional Cleaning – CMM
When one considers technology commonly used in the cleaning industry, autonomous floor scrubbers and smart, Internet of Things-connected dispensers and systems most likely come to mind. Although you may not think about it, these technologies use artificial intelligence (AI).
Machines, integrated circuits, and software used in the cleaning industry tap into AI when they purposely use information to manage and perform cleaning tasks and related operations. When we supply information and purpose to this equipment, we largely control the decisions and outcomes.
AI has advanced rapidly in recent years due to more computing power, large language models in systems such as ChatGPT, and better algorithms, prompting the question: Whos in control, and what does it all mean?
Interestingly, ChatGPT calls itself a language model, and not a reasoning machine. Humans have supplied the information and, to a large extent, its purpose, and hence, have some degree of control over outcomes.
Language models encode what is reflected in human text rather than offering a deep understanding of it, although they may sometimes project the appearance of such deep understanding, notes the book The Age of AI: And Our Human Future, authored by Henry A.Kissinger, Eric Schmidt, and Daniel Huttenlocher.
So, in many ways, humans still control AI, but with advancing technology, AI has more ability to think, at least within certain limits.
Currently, AI is not good at nonrepetitive tasks. However, it is potentially good at repetitive tasks in professional cleaningsuch as emptying trash, dusting, and floor carewith limits that relate mainly to financial considerations. For example, building the perfect dusting robot would be an expensive undertaking, one most useful where the size of an operation justifies the cost of development.
Employee training is an area where AI is already helpful. Just as airline pilots train on simulators, cleaning workers can receive training using augmented reality (AR) and virtual reality (VR).
In the current environment, with the relatively low cost of entry-level custodians and the modest needs of most jobs, technology solutions will not be top-of-mind in most operations, at least as it relates to the labor pertaining to commercial cleaning endeavors.
However, as helper technologies such as AR and VR become less costly to accessdue to supply and demand market pressures or the ability to rent or lease these serviceshelper or service tech will gradually become a part of the daily lives of many workers.
In addition to training workers, we can apply AI to professional cleaning in various ways, such as:
One definition of intelligence is that it is the purposeful ability to capture, adapt, and use information.
As concerns arise regarding the ability of AI to take over society, causing mischief or worse, its wise to remember that AI arose from human intelligence, not vice versa.
In principle, improving human potential through the practical application of knowledge should precede improving AI, and expanding our workers ability to capture, retain, and build on human knowledge and expand their skill set is a top priority.
Workers imbued with a growth mindset through expanded knowledge can, in turn, help inform, develop, and maintain related AI for better cleaning that is grassroots-driven, customer-centric, and financially attractive.
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The Role of Artificial Intelligence in Professional Cleaning - CMM
Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review | npj Imaging – Nature.com
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This Is My Top Artificial Intelligence (AI) ETF to Buy Right Now – 24/7 Wall St.
Investing
Published: July 1, 2024 2:20 pm
Picking the individual winners of the burgeoning artificial intelligence (AI) race is no simple task. But one exchange-traded fund (ETF) that provides investors with exposure to a basket of AI stocks could be the solution.
While many investors struck gold by purchasing shares of NVDIA (NASDAQ: NVDA) before the chipmakers stock took off last year, the AI-adjacent company is more of the exception than the rule.
Pure play AI companies, on the other hand, have had less predictable successes, with some like C3.ai (NYSE: AI) seeing precipitous rises and falls. C3.ai, which produces AI applications for other enterprises, saw its stock surge to $161 per share by late 2020. At the time of writing, shares of the company are now trading for $28.96.
Forecasts suggest that the global AI market could increase exponentially by the early part of the next decade. By some analysts estimates, that growth could be as much as 300 times its 2022 valuation of $39 billion, which would translate to an astounding $1.3 trillion by 2032.
But how do investors identify the likely winners? Rather than picking one or two companies operating in the AI space and simply wishing for the best, ETFs with holdings spread across all facets of the AI industry allow investors to gain exposure to the trend without overexposing themselves to any individual holding.
In this way, not only are these ETFs providing broad exposure to AI with companies offering varying levels of involvement to the technology, but in doing so, these funds are simultaneously reducing overall risk exposure.
And just as ETFs go, the options for ones leveraged to the AI industry are bountiful. However, just like the stocks they hold, not all ETFs are created equally.
There are no fewer than 38 AI-themed ETFs currently trading on the major exchanges in the U.S. Some offer equal weighting, some prefer heavier allocations to the Magnificent Seven stocks. Some are actively managed with portfolio positions constantly shuffled.
They vary considerably by size, too, with some having assets under management (AUM) as low as $532,360 and others reaching as high as $2.72 billion.
But when it comes to finding a fund with the best combination of high growth potential, Big Tech names, diverse AI industry exposure, significant AUM coupled with a modest expense ratio, one ETF in particular takes the cake.
Enter the Global X Artificial Intelligence & Technology ETF (NASDAQ: AIQ), which has posted an eye-catching 138% gain since its inception in May 2018 and has gained over 17% so far in 2024. According to Global Xs website, the ETF has net assets of $2.08 billion and a total expense ratio of 0.68%.
And while its size and per share appreciation have been impressive so far, it is the funds holdings that should garner a lot of attention. By industry, AIQ spans packaged software, semiconductors, internet software and services, information technology services, telecommunications equipment, internet retail, and industrial conglomerates.
That breadth is expansive, but looking at the names among its top weighted holdings provides more insight into why this ETF is an AI powerhouse:
Of course, those are not all of AIQs holdings, but they are the big names with some of the heaviest weightings. And looking at that list, you can see why the AI ETF was capable of producing such enormous gains for shareholders since it debuted in 2018.
As AI expands out of its earliest phase, when it was constricted to pure play stocks, cloud services, and data centers, the technology is now finding its way into streaming services (Netflix), e-commerce (Alibaba), customer relationship management (Salesforce), and numerous other facets of the economy.
Rather than hoping any one of the aforementioned companies emerges as the biggest winner of the next phase of AI implementation, investing in a fund like the Global X Artificial Intelligence & Technology ETF can provide investors with the best of broad exposure and reduced risk.
If you want your portfolio to pay you cash like clockwork, its time to stop blindly following conventional wisdom like relying on Dividend Aristocrats.
Theres a better option, and we want to show you. Were offering a brand-new report on 2 stocks we believe offer the rare combination of a high dividend yield and significant stock appreciation upside.
If youre tired of feeling one step behind in this market, this free report is a must-read for you.
Thank you for reading! Have some feedback for us? Contact the 24/7 Wall St. editorial team.
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This Is My Top Artificial Intelligence (AI) ETF to Buy Right Now - 24/7 Wall St.
Artificial intelligence (AI) in mammographic screening in Norway – Open Access Government
Breast cancer is a significant global health concern, with more than 2 million new cases diagnosed and over half a million women dying from the disease annually.(1) Many countries, including Norway, have implemented mammographic screening to detect breast cancer in an early stage of disease development, as an early intervention has clear benefits on the disease outcome. In Norway, all women aged 50 to 69 years are invited to biennial mammographic screening through the national screening program, BreastScreen Norway.(2)
The screening process involves two-view digital mammography independently interpreted by two breast radiologists. Cases of potential malignancy are flagged and discussed in a consensus meeting to decide whether further assessment (recall) is needed. However, the time-consuming task of screening interpretation only returns a rate of examinations positive for breast cancer of about 0.6%, at the same time as there is an increasing shortage of breast radiologists in Norway and globally.
Artificial intelligence (AI) and deep learning have been introduced in various healthcare domains, including radiology, to enhance efficiency and improve patient care. Promising results for the use of AI-assisted interpretation in mammographic screening have quickly emerged and several vendors offer solutions for AI-assisted breast cancer detection. Most of these AI algorithms will, based on mammography image analysis, provide a risk of malignancy score a probability of cancer being present in the image or examination. By utilising AI in BreastScreen Norway, we may be able to reduce the interpretation volume for the radiologists without compromising the quality of the screening program. AI can analyse mammograms with high accuracy, reducing the burden of incorrect diagnoses and optimising treatment decisions. AI can also assist in assessing breast density, predicting individual risk levels, and evaluating image quality, providing valuable insights for personalised screening approaches.
In 2018, BreastScreen Norway and the Norwegian Computing Centre set forth to develop an AI algorithm for mammography image interpretation. Through two projects, funded by the Research Council of Norway, mammograms from more than 750,000 screening examinations performed in BreastScreen Norway have been included in the development of an advanced AI algorithm, designed to select mammograms with low suspicion of malignancy.
Mammograms from another 650,000 examinations will be used to further develop the algorithm to become even more robust and reliable. Preliminary results assessing the performance of the current version of the AI algorithm have shown the in-house algorithm to be comparable to commercially available algorithms.(3) To be able to use the in-house algorithm in BreastScreen Norway, its clinical value must be evaluated. This will include long and potentially costly processes to secure that the algorithm complies with EU regulations to achieve CE-marking (Conformit Europenne).
BreastScreen Norways now comprehensive database of mammograms from more than one million screening examinations, enables retrospective studies using commercially available and CE-marked breast AI products for different purposes.
In mammographic screening, AI-assisted screening can be included in different modes:
Retrospective analyses from BreastScreen Norway show that screening mammograms were assigned to the highest risk score by AI in 86-89% of screen-detected cancer cases.(4,5)
Furthermore, the highest risk score was assigned in 45% of the screenings where an interval cancer was later diagnosed (cancer detected in the period between to screenings, based on patient-experienced symptoms). In a triage scenario defining 50% of the examinations with the highest AI scores as positive and the remaining 50% as negative, 99.3% of the screen-detected and 85.2% of the interval cancer cases were classified as positive, leaving us to assume that only 0.7% of the screen-detected cancers were classified as false negative for cancer by the AI system and 15% of the interval cancers are potentially true interval cancers, i.e. not missed by the previous screen but indeed have become detectable in the period between two mammographic screenings.(6)
Before breast AI can be implemented into clinical practice, available algorithms must be thoroughly tested in clinical studies exploring algorithm performance, safety, and reliability, as well as patient outcomes, and ethical and legal aspects, including discrepancies between AI and radiologists. Several challenges and questions remain, including how to integrate AI in breast cancer screening, what is considered an acceptable threshold at which AI can be trusted as an independent reader alongside a radiologist, and what is the impact of AI on readers consensus, recall, and breast cancer detection rates, as well as whether implementation of AI indeed alleviates the workload of radiologists? A natural next step on the road to implementing AI in BreastScreen Norway is to test AI-assisted image interpretation in a real-life screening environment.
Therefore, BreastScreen Norway is starting a randomised controlled trial, comparing AI-assisted mammographic screening with the current standard of care (independent double reading). The trial aims to test different modes of AI-assisted image interpretation and breast AI products, using AI risk score to stratify examinations for single or double reading.
The use of AI will also open avenues for including additional risk factors in the stratification process during screening, including environmental and behavioral factors and research-based factors such as genetics in an overall genetic risk for breast cancer based on inherited genetic variants (polygenic risk scores, PRS).
An academic collaboration between members of the Oslo Cancer Cluster (OCC), Norwegian hospitals, the University of Oslo and the company Antegenes in Estonia has shown that stratification of women based on genetic risk can identify women at several-fold higher risk for breast cancer before the current screening age.(7) Such information can be leveraged to invite women at high risk into the screening earlier, whilst offering women at very low baseline genetic risk a later age of screening startup. Such scenarios may benefit the overall detection rate and alleviate the burden on the breast radiographers, but such additional approaches will need thorough exploration in comprehensive studies before entering the road towards clinical implementation.
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Artificial intelligence (AI) in mammographic screening in Norway - Open Access Government