Category Archives: Ai

Organizations are increasingly turning to AI and ML to enhance their cybersecurity operations. Having algorithms to do some of the most tedious but necessary tasks has taken a lot of stress off of overworked security teams.

But as AI/ML become more ubiquitous within organizations in many other areas, the technologies themselves are at risk of attack. The Harvard Kennedy Centers Belfer Center for Science and International Affairs recently released a report warning of a new type of cybersecurity attack called an artificial intelligence attack.

These attacks, the report said, are different from other types of cyberattacks. AI attacks are enabled by inherent limitations in the underlying AI algorithms that currently cannot be fixed. They present a bigger attack surface by weaponizing data and physical objects that rely on AI and ML.

Businesses rely on AI/ML because it is capable of extremely fast processing, though it also has been proven to create massive blind spots, according to Ben Pick, senior application security consultant at nVisium.

The decision tree algorithms used by AI/ML are based on assumptions and are frequently shown to have severe obstructions and oversights, Pick said in an email interview.

There is also the complexity of additional use cases and capabilities for AI/ML that dont necessarily overlap with security. AI/ML developers who are experts in that field are contributing to projects in addition to your normal team, which makes adding the security component to AI/ML more challenging.

These struggles to incorporate security into devices AI/ML functions falls perfectly in line with threat actors desire to find vulnerabilities through which to make their attacks. Because as much as AI/ML can assist in securing applications, it certainly cannot remove all risks.

A hackers main goal would be to corrupt the inputs to confuse the decision-making algorithms, said Pick. This could lead to a piece of duct tape over a speed limit signcausing an automated vehicle to speed up to an unsafe velocityor facial recognition incorrectly identifying a person.

AI/ML is often used to augment security by acting as a first line of defense against threats, but what about when AI/ML itself is on the front line and is first to be attacked?

Securing AI/ML at the edge could be the solution to mitigate risks to the technology and to the devices using it. Adding security at the edge increases confidence in the results/inferences derived from the models, explained Larry OConnell, VP of marketing for Sequitur Labs.

Also, having high security at the edge allows OEMs to use more sensitive/proprietary models by mitigating the risk of theft, OConnell said in an email interview.

With a strongly defined good baseline, any anomalies or threats can be more easily identified, building and adapting the security and algorithms used to protect devices.

The sheer volume of devices and inputs at and from the edge requires a vast system of inventory and maintenanceon top of securing each device, Pick pointed out. That means fine-tuning AI/ML will require a full understanding of the environments, as well as a large number of human analysts to adapt the AI training inputs.

Also, securing an embedded device requires specialized expertise, much the same way using AI properly does, McConnell added. Organizations should start projects with security included from the beginning.

Planning for security means defining your requirements, such as threat models, and quantifying risk, McConnell said. Security extends to the manufacturing, provisioning, deployment and update phases of the product life cycle. For AI specifically, the process to securely update models should be included as part of the update process.

Organizations will need to take on a greater responsibility to keep AI/ML secure on the edge, as it will be a large attack surface to monitor. This control offers a faster response to potential attacks and, as AI becomes a bigger target, faster response and mitigation will be key.

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Securing AI and ML at the Edge - Security Boulevard

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AI innovation is occurring at a fast clip, with a number of technologies on the hype cycle reaching mainstream adoption within two to five years. Thats according to Gartner, which today released a report identifying four trends driving near-term AI innovation in the enterprise. It finds that while the AI industry remains in an evolutionary state, technologies including edge AI, computer vision, decision intelligence, and machine learning are poised to have a transformational impact on markets in coming years.

Gartner sees evidence of a trend of companies seeking capabilities beyond what current AI tools can often accomplish. Organizations are focusing on implementation, risk management, and ethics as they look to scale AI initiatives. But data leaders run the risk of failing to realize value from these initiatives if they dont prioritize and accelerate investments in AI technologies at various stages of maturity, Gartner warns.

Increased trust, transparency, fairness, and auditability of AI technologies continues to be of growing importance to a range of stakeholders, according to Gartner. Responsible AI can help to achieve a semblance of fairness, trust, and regulatory compliance even if biases are baked into the data and explainability methods fall short. For this reason, Gartner expects that all experts hired for AI development and training work will have to demonstrate competence in responsible AI by 2023.

At the same time, Gartner predicts that emerging small and wide data approaches will enable more robust analytics and AI, reducing organizations dependency on big data. Wide data allows analysts to examine and combine a variety of small and large, unstructured and structured data, while small data is focused on applying analytical techniques that look for useful information within small, individual sets of data.

According to Gartner, by 2025, 70% of organizations will be compelled to shift their focus from big to small and wide data, providing more context for analytics and making AI less data-hungry.

The need for enterprise digital transformation during thepandemichas bolstered investments inAI. AI startups raised a collective $73.4 billion in Q4 2020, a $15 billion year-over-year increase. And according to a recent report from ManageEngine, 80% of companies in the U.S. accelerated their AI adoption over the past two years.

The report finds that the urgency of leveraging AI for business transformation is driving the need to operationalize AI platforms. This means moving AI projects from concept to production, so that AI solutions can be leveraged to solve enterprise-wide problems like customer service automation. Given the complexity and scale of the data and compute resources involved in AI deployments, AI innovation will require these resources to be used at maximum efficiency, Gartner notes.

[Our] research has found that only half of AI projects make it from pilot into production, and those that do take an average of nine months to do so, Svetlana Sicular, research VP at Gartner, said in a statement. Innovations such as AI orchestration and automation platforms and model operationalization are enabling reusability, scalability, and governance, accelerating AI adoption and growth.

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AI focus shifts to small and wide data - VentureBeat

MCLEAN, Va., Sept. 8, 2021 /PRNewswire/ -- Dihuni, a leader in artificial intelligence (AI), data center and Internet of Things (IoT) solutions, today announcedthe addition of two industry veterans to its leadership team: Laura Bacon joins as Chief Operating Officer (COO) and Chief Finance Officer (CFO), and Steve Fey joins as Consultant, Business Strategy for Smart Buildings.

Dihuni CEO Pranay Prakash said, "Our customers have trusted us with their Digital Transformation infrastructure solutions and enabled us to grow continuously. With Laura and Steve's leadership we will continue delivering value in IT, IoT and AI by improving our operations and growth in new market segments.These high-caliber appointments come at an important time when our team is doubling down on our 'Hybrid Commerce' strategy of transactional e-commerce and consultative solutions both delivered with high efficiency and top customer service. I am thrilled to have Laura and Steve join this team."

Laura brings over 30 years of senior level and executive experience to Dihuni. As CFO and COO, Laura is responsible for leading all finance, legal, HR and business operation functions for Dihuni. Laura has held various leadership positions in business management and finance including Honeywell/Tridium and Georgia-Pacific.

Steve is responsible for the strategy and growth of Dihuni's Smart and Connected Buildings business. Steve brings decades of experience in building controls industry from Andover Controls,TycoFire and Security, and Tridium. Steve's more recent experience has focused on IT managed services where he served as President for Proxios, a managed service provider in Richmond, Virginia and most recently as CEO of Totem Buildings, a cybersecurity software and services startup for the building controls industry.

"I am excited to be part of the Dihuni team. I am impressed with its high growth, global customer base and e-commerce platform," said Laura Bacon. "Dihuni has emerged as a leader in end-to-end Digital Transformation solutions and I'm looking forward to working with Pranay and the team to take the company to the next level."

"Dihuni's focus on converging IoT with IT from a single source is exactly what the Smart and Connected Buildings market needs at this time. I am delighted to help them achieve their growth objectives," said Steve Fey.

When compared to 2020, Dihuni has experienced over 400% growth YTD in 2021 despite global component shortages and COVID-19 headwinds. "We have seen increased demand in research, work from home (WFH) solutions, industrial IoT and high-performance server and storage systems. We will continue to add top talent as we grow," Prakash added.

For company and leadership info, visit here.

About Dihuni

Dihuni is a leading provider of Digital Transformation solutions including Deep Learning and Artificial Intelligence (AI), data centers and Internet of Things (IoT).With its e-commerce platform, OptiReady products, solutions design and delivery expertise and access to over 500,000 products from hundreds of partners, Dihuni helps customers achieve their desired digital outcomes by ensuring they have the right hardware, software and services to make that happen. Visit Dihuni athttps://www.dihuni.com

Media Contact : digital@dihuni.comor 703-570-7300

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Dihuni Expands Leadership Team to Drive and Support Aggressive Growth in AI, IoT and IT - Inside NoVA

Hong Kongs long awaited M+ Museum has announced an opening date. When the institution dedicated to modern and contemporary visual culture finally opens its doors on November 12, it will be against a very different political backdrop than its leaders anticipated when M+ was first conceived more than a decade ago.

Evidence of this shift is already evident on the museums newly launched website, where an image of Ai Weiweis photograph of Tiananmen Square has been removed while it awaits review by the authorities,Artnet News has learned.

Earlier this spring, pro-Beijing politicians had accused Ais Study of Perspective: Tiananmen (1997)which depicts the Chinese dissident artist raising a middle finger at Beijings Tiananmen Squareof spreading hatred against China under the countrys national security law, which went into effect in Hong Kong last June.

Another work by Ai, Map of China (2003), has also been censored online. That sculpture, a 3D map of the country made of wood salvaged from demolished Qing Dynasty temples, aims to celebrate Chinas cultural and ethnic diversity. The sculpture and photograph are part of the M+ Sigg Collection, a major Chinese art trove donated to the museum by Swiss entrepreneur Uli Sigg.

Both images could be seen on the beta version of the M+ collection website, but were no longer available when the final site went live on August 10.

M+ is reviewing the treatment of certain images of works having regard to the advice obtained from relevant authorities including the Office for Film, Newspaper and Article Administration, a spokesperson for the museum told Artnet News. The images concerned are not uploaded pending completion of the review.

A screenshot of M+s website, with images of some Ai Weiwei works missing.

Many images of works by Ai are accessible on the website, including Still Life, an installation comprising thousands of axes from the Stone Age that was exhibited when the M+ Sigg Collection was first unveiled in Hong Kong in 2016, as well as other pieces from the Study of Perspective series, including Bundeshaus Bern (1999) and White House (1995).

Ai questioned the inconsistent treatment of the series. Why is M+ not showing Tiananmen but keeping White House? the artisttold Artnet News. (Ai recently wrote an op ed for Artnet News about M+s decision not to show the work in its opening show.)

Hong Kongs Office for Film, Newspaper and Article Administration is responsible for enforcing the film classification system under the Film Censorship Ordinance, controlling the publication of obscene and indecent articles, and the registration of local newspapers. The government proposed in August to amend the Film Censorship Ordinance, giving the chief secretary, the citys number two executive, power to revoke any approval given to a film should its exhibition be contrary to the interests of national security.

Ai Weiwei, Study of Perspective: Tiananmen (1997). M+ Sigg Collection, Hong Kong. By donation, Ai Weiwei.

In addition to the two works by Ai, a number of other objects in the M+ collection are not shown on the website, including some of those by Kacey Wong, who is known for his political art and recently left Hong Kong for Taiwan in self-imposed exile. However, some works that might be considered politically sensitive, such as Liu Heung-Shings photographic series China After Mao and images depicting the summer of 1989 in Beijing following the Tiananmen crackdown, are accessible.

The soon-to-open museum stated that digitization of its 8,000-object-strong collection is an ongoing effort and that the collection will be updated periodically as new works, information and intellectual property rights become available.

The museums inaugural exhibitions will likely be closely watched by Chinese authorities for evidence of its compliance with the rulesand by the international art community to gauge its willingness to confront sensitive subjects. The museum announced on Wednesday that its debut presentations willfeature around 1,500 works spanning fine art, film, design, architecture, and archival items.

The six thematic exhibitions will includea section dedicated to the evolution of Hong Kongs visual culture from the 1960s to the present; a special presentation of the M+ Sigg Collection titled From Revolution to Globalization; and a chronological survey of Chinese art from the 1970s to the 2000s.

M+ will also presentAsian Fields, a monumental installation that comprises tens of thousands of clay figurines by the British artist Antony Gormley, who created the work with more than 300 villagers from Guangdong in the span of five days in 2003.

Compared to leading modern art museums in the West, M+ will look very different, because our vantage point on this side of the world is distinct,Doryun Chong, deputy director of the museum, said in a statement announcing the inaugural program. This is a multidisciplinary contemporary collection, grounded in Asia and like no other in the world.

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Hong Kongs M+ Museum Has Removed Ai Weiweis Famous Tiananmen Square Photo From Its Website While It Awaits Government Review - artnet News

You may well have seen sci-fi movies or television shows where the protagonist asks to zoom in on an image and enhance the results revealing a face, or a number plate, or any other key detail and Google's newest artificial intelligence engines, based on what's known as diffusion models, are able to pull off this very trick.

It's a difficult process to master, because essentially what's happening is that picture details are being added that the camera didn't originally capture, using some super-smart guesswork based on other, similar-looking images.

The technique is called natural image synthesis by Google, and in this particular scenario, image super-resolution. You start off with a small, blocky, pixelated photo, and you end up with something sharp, clear, and natural-looking. It may not match the original exactly, but it's close enough to look real to a pair of human eyes.

(Google Research)

Google has actually unveiled two new AI tools for the job. The first is called SR3, or Super-Resolution via Repeated Refinement, and it works by adding noise or unpredictability to an image and then reversing the process and taking it away much as an image editor might try to sharpen up your vacation snaps.

"Diffusion models work by corrupting the training data by progressively adding Gaussian noise, slowly wiping out details in the data until it becomes pure noise, and then training a neural network to reverse this corruption process," explain research scientist Jonathan Ho and software engineer Chitwan Saharia from Google Research.

Through a series of probability calculations based on a vast database of images and some machine learning magic, SR3 is able to envisage what a full-resolution version of a blocky low-resolution image looks like. You can read more about it in the paper Google has posted on arXiv.

The second tool is CDM, or Cascaded Diffusion Models. Google describes these as "pipelines" through which diffusion models including SR3 can be directed for high-quality image resolution upgrades. It takes the enhancement models and makes larger images out of it, and Google has published a paper on this too.

CDM in action. (Google Research)

By using different enhancement models at different resolutions, the CDM approach is able to beat alternative methods for upsizing images, Google says. The new AI engine was tested on ImageNet, a gigantic database of training images commonly used for visual object recognition research.

The end results of SR3 and CDM are impressive. In a standard test with 50 human volunteers, SR3-generated images of human faces were mistaken for real photos around 50 percent of the time and considering a perfect algorithm would be expected to hit a 50 percent score, that's impressive.

It's worth reiterating that these enhanced images aren't exact matches for the originals, but they're carefully calculated simulations based on some advanced probability maths.

Google says the diffusion approach produces better results than alternative options, including generative adversarial networks (GANs) that pit two neural networks against each other to refine results.

(Google Research)

Google is promising much more from its new AI engines and associated technologies not just in terms of upscaling images of faces and other natural objects, but in other areas of probability modeling as well.

"We are excited to further test the limits of diffusion models for a wide variety of generative modeling problems," the team explains.

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Google's Incredible New Photo AI Makes 'Zoom And Enhance' a Real Thing - ScienceAlert

CalypsoAI and ECS announced a partnership to support the advancement of safe and secure artificial intelligence (AI) infrastructure across the U.S. federal government.

The partnership will see CalypsoAI leverage its AI safety and security software to expand ECS testing capabilities for government customers. CalypsoAI is providing ECS with proprietary capabilities for trustworthy model development, and novel testing and evaluation (T&E). This collaboration will accelerate the deployment of trusted and responsible AI across federal agencies, such as the U.S. Department of Defense, the U.S. Department of Homeland Security, the U.S. Department of Health and Human Services.

As demand for AI-enabled capabilities increases across the federal government, ensuring trust and transparency of these systems will be essential to ensuring the widespread adoption of game-changing AI technology by civil servants, intelligence professionals, and warfighters, said Neil Serebryany, CEO and Founder of CalypsoAI. We look forward to deploying our industry-leading AI security technology across ECS AI platforms to ensure federal agency missions are supported responsibly, from technology development through deployment.

AI safety and security is a priority issue for senior government leaders, such as Deputy Secretary of Defense, Dr. Kathleen Hicks. In May, she released a memo articulating the imperative for the U.S. Department of Defense to create a responsible AI framework to support safe and secure AI development and deployment. CalypsoAIs partnership with ECS supports this framework by further expanding access to AI T&E capabilities to federal customers, which will give them the tools they need to develop and field safe AI capabilities.

We have built the critical open ecosystem for assured AI for the speed, security, and scale of government needs, said Aaron Burciaga, vice president of data & artificial intelligence at ECS. From real-time edge analytics for the warfighter to massive data operations for commerce, we have engineered the future with CalypsoAI. Together we ensure adaptive solutions, responsible data operations, explainable algorithms, and secure environments for the ongoing digital revolution centered on information technology and intelligence technology.

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CalypsoAI and ECS support the advancement of secure AI infrastructure for government customers - Help Net Security

DUBLIN--(BUSINESS WIRE)--The "Asset Tracking and AI in Supply Chain Management Market 2021 - 2026" report has been added to ResearchAndMarkets.com's offering.

This research evaluates asset tracking technologies, solutions, and the overall asset management and logistics ecosystem including major players, strategies and market positioning. The research evaluates the impacts of use case-specific considerations in terms of asset tracking technology and solution selection.

This research also provides an analysis of both fleet-related asset tracking and non-fleet asset tracking markets. Fleet tracking market analysis includes segmentation by IoT-enabled fleet tracking. In terms of non-fleet asset tracking, the research evaluates the market for both living and nonliving things, which have completely different characteristics.

This research also provides detailed analysis and forecasts for AI in SCM by solution (Platforms, Software, and AI as a Service), solution components (Hardware, Software, Services), management function (Automation, Planning and Logistics, Inventory Management, Fleet Management, Freight Brokerage, Risk Management, and Dispute Resolution), AI technologies (Cognitive Computing, Computer Vision, Context-aware Computing, Natural Language Processing, and Machine Learning), and industry verticals (Aerospace, Automotive, Consumer Goods, Healthcare, Manufacturing, and others).

This is the broadest and detailed research of its type, providing analysis across a wide range of go-to-operational process considerations, such as the need for identity management and real-time location tracking, and market deployment considerations, such as AI type, technologies, platforms, connectivity, IoT integration, and deployment model including AI-as-a-Service (AIaaS). Each aspect evaluated includes forecasts from 2021 to 2026 such as AIaaS by revenue in China. It provides an analysis of AI in SCM globally, regionally, and by country including the top ten countries per region by market share.

The research also provides an analysis of leading companies and solutions that are leveraging AI in their supply chains and those they manage on behalf of others, with an evaluation of key strengths and weaknesses of these solutions. It assesses AI in SCM by industry vertical and application such as material movement tracking and drug supply management in manufacturing and healthcare respectively. The research also provides a view into the future of AI in SCM including analysis of performance improvements such as optimization of revenues, supply chain satisfaction, and cost reduction.

Companies Mentioned

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Key Topics Covered:

Asset Tracking Market by Infrastructure, Connection Type, Mobility, Location Method, Solution Type, Supporting Tech and Industry Verticals

1. Executive Summary

2. Asset Tracking Market Segmentation

3. Introduction

4. Asset Tracking Solutions

5. Asset Tracking in Industry Verticals

6. Company Analysis

7. Overall Asset Tracking Market Analysis and Forecasts 2021 - 2026

8. Fleet Tracking Market Analysis and Forecasts 2021 - 2026

9. Non-Fleet Asset Tracking Market Analysis and Forecasts 2021 - 2026

10. IoT enabled Fleet Tracking Market Segment 2021 - 2026

11. IoT Enabled Non-Fleet Asset Tracking 2021 - 2026

12. Video Safety in Fleet Tracking Market 2021 - 2026

13. Emerging Technologies in Fleet Tracking Market 2021 - 2026

14. Slap-and-Track Asset Tracking Solutions Market 2021 - 2026

15. Living Creature Tracking Market 2021 - 2026

16. Conclusions and Recommendations

AI in Supply Chain Management Market by Technology, Processes, Solutions, Management Function, Deployment Model, Business Type and Industry Verticals

1.0 Executive Summary

2.0 Introduction

3.0 AI in SCM Challenges and Opportunities

4.0 Supply Chain Ecosystem Company Analysis

5.0 AI in SCM Market Case Studies

6.0 AI in SCM Market Analysis and Forecasts 2021 - 2026

7.0 Summary and Recommendations

For more information about this report visit https://www.researchandmarkets.com/r/z5g9uh

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Global Asset Tracking and AI in Supply Chain Management Market (2021 - 2026) - Featuring 3M, Adidas and ARI Fleet Among Others -...

Photo: Martin Meissner (AP)

Dont worry, humansartificial intelligence systems arent taking over the world yet. They cant even appear as inventors on U.S. patents.

U.S. federal judge Leonie Brikema ruled this week that an AI cant be listed as an inventor on a U.S. patent under current law. The case was brought forward by Stephen Thaler, who is part of the Artificial Inventor Project, an international initiative that argues that an AI should be allowed to be listed as an inventor in a patent (the owner of the AI would legally own the patent).

Thaler sued the U.S. Patent and Trademark Office after it denied his patent applications because he had listed the AI named DABUS as the inventor of a new type of flashing light and a beverage container. In various responses spanning several months, the Patent Office explained to Thaler that a machine does not qualify as an inventor because it is not a person. In fact, the machine is a tool used by people to create inventions, the agency maintained.

Brikema determined that the Patent Office correctly enforced the nations patent laws and pointed out that it basically all boils down to the everyday use of language. In the latest revision of the nations patent law in 2011, Congress explicitly defined an inventor as an individual. The Patent Act also references an inventor using words such as himself and herself.

By using personal pronouns such as himself or herself and the verb believes in adjacent terms modifying individual, Congress was clearly referencing a natural person, Brikema said in her ruling, which you can read in full at the Verge. Because there is a presumption that a given term is used to mean the same thing throughout a statute, the term individual is presumed to have a persistent meaning throughout the Patent Act.

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The judge also rejected Thalers claim that the Patent Office had to provide evidence that Congress did not want to exclude AI systems from being inventors.

Furthermore, Brikema stated that the nature of an inventor has already been examined in federal courts, which have ruled that neither companies nor states can claim to be inventors on a patent.

For his part, Thaler also tried to argue that the court should respect Congress intent to create a system that would encourage innovation.

Allowing patents for AI-Generated Inventions will result in more innovation. It will incentivize the development of AI capable of producing patentable output by making that output more valuable Thaler said. By contrast, denying patent protection for AI-Generated Inventions threatens to undermine the patent system by failing to encourage the production of socially valuable inventions.

Nonetheless, Thaler didnt have luck with that argument, either. Brikema said that these were policy considerations and thus must be dealt with by Congress, not the courts.

And its not like the Patent Office is refusing to consider what role, if any, AI should have in patents. It has requested comments artificial intelligence in patent policy and reported that the majority of responses reflected the belief that current AI could neither invent nor author without human intervention.

Ryan Abbott, a law professor who oversees the Artificial Inventor Project, told Bloomberg the group would appeal. Although Brikema squashed all of the projects arguments, she didnt say an AI could never be listed as an inventor.

As technology evolves, there may come a time when artificial intelligence reaches a level of sophistication such that might satisfy accepted meanings of inventorship. But that time has not yet arrived, and, if it does, it will be up to Congress to decide how, if it at all, it wants to expand the scope of patent law, Brikema said.

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Judge Says an AI Cant Be an Inventor on a Patent Because Its Not a Person - Gizmodo

AI holds great promise to increase the quality and reduce the cost of health care in developed and developing countries. But one obstacle to using it is patients dont trust it. One key reason is they perceive medical AI to be a black box and they think they know more about physicians decision-making process than they actually do, the authors research found. A remedy: Provide patients with an explanation of how both types of care providers make decisions.

Artificial intelligence-enabled health applications for diagnostic care are becoming widely available to consumers; some can even be accessed via smartphones. Google, for instance, recently announced its entry into this market with an AI-based tool that helps people identify skin, hair, and nail conditions.A major barrier to the adoption of these technologies, however, is that consumers tend to trust medical AI less than human health care providers. They believe that medical AI fails to cater to their unique needs and performs worse than comparable human providers, and they feel that they cannot hold AI accountable for mistakes in the same way they could a human.

This resistance to AI in the medical domain poses a challenge to policymakers who wish to improve health care and to companies selling innovative health services. Our research provides insights that could be used to overcome this resistance.

In a paper recently published in Nature Human Behaviour, we show that consumer adoption of medical AI has as much to do with their negative perceptions of AI care providers as with their unrealistically positive views of human care providers. Consumers are reluctant to rely on AI care providers because they do not believe they understand or objectively understand how AI makes medical decisions; they view its decision-making as a black box. Consumers are also reluctant to utilize medical AI because they erroneously believe they better understand how humans make medical decisions.

Our research consisting of five online experiments with nationally representative and convenience samples of 2,699 people and an online field study on Google Ads shows how little consumers understand about how medical AI arrives at its conclusions. For instance, we tested how much nationally representative samples of Americans knew about how AI care providers make medical decisions such as whether a skin mole is malignant or benign. Participants performed no better than they would have if they had guessed; they would have done just as well if they picked answers at random. But participants recognized their ignorance: They rated their understanding of how AI care providers make medical decisions as low.

By contrast, participants overestimated how well they understood how human doctors make medical decisions. Even though participants in our experiments possessed similarly little factual understanding of decisions made by AI and human care providers, they claimed to better understand how human decision-making worked.

In one experiment, we asked a nationally representative online sample of 297 U.S. residents to report how much they understood about how a doctor or an algorithm would examine images of their skin to identify cancerous skin lesions. Then we asked them to explain the human or the algorithmic providers decision-making processes. (This type of intervention that has been used before to shatter illusory beliefs about how well one understands causal processes. Most people, for instance, believe they understand how a helicopter works. Only when you ask them to explain how it works, do they realize they have no idea.)

After participants tried to provide an explanation, they rated their understanding of the human or algorithmic medical decision-making process again. We found that forcing people to explain the human or algorithmic providers decision-making processes reduced the extent to which participants felt that they understood decisions made by human providers but not decisions made by algorithmic providers. Thats becausetheir subjective understanding of how doctors made decisions had been inflated and their subjective understanding of how AI providers made decisions was unaffected by having to provide an explanation possibly because the had already felt the latter was a black box.

In another experiment, with a nationally representative sample of 803 Americans, we measured both how well people subjectively felt that they understood human or algorithmic decision-making processes for diagnosing skin cancerand then tested them to see how well they actually did understand them. To do this, we created a quiz with the aid of medical experts: a team of dermatologists at a medical school in the Netherlands and a team of developers of a popular skin-cancer-detection application in Europe. We found that although participants reported a poorer subjective understanding of medical decisions made by algorithms than decisions made by human providers, they possessed a similarly limited real understanding of decisions made by human and algorithmic providers.

What can policymakers and firms do to encourage consumer uptake of medical AI?

We found two successful, slightly different interventions that involved explaining how providers both algorithmic and human make medical decisions. In one experiment, we explained how both types of providers use the ABCD framework (asymmetry, border, color, and diameter) to examine features of a mole to make a malignancy-risk assessment. In another experiment, we explained how both types of providers examine the visual similarity between a target mole and other moles known to be malignant.

These interventions successfully reduced the difference in perceived understanding of algorithmic and human decision-making by increasing the perceived understanding of the former. In turn, the interventions increased participants intentions to utilize algorithmic care providers without reducing their intentions to utilize human providers.

The efficacy of these interventions is not confined to the laboratory. In a field study on Google Ads, we had users see one of two different ads for a skin-cancer-screening application in their search results. One ad offered no explanation and the other briefly explained how the algorithm works. After a five-day campaign, the ad explaining how the algorithm works produced more clicks and a higher click-through rate.

AI-based health care services are instrumental to the mission of providing high-quality and affordable services to consumers in developed and developing nations. Our findings show how greater transparency opening the AI black box can help achieve this critical mission.

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For Patients to Trust Medical AI, They Need to Understand It - Harvard Business Review

The detectors have already made an appearance at Lower.com Field.

COLUMBUS, Ohio Several Ohio stadiums are implementing new security systems designed to detect weapons while simultaneously easing the burden on sports fans.

Evolv makes touchless screening systems for weapons detection. The company uses a combination of advanced sensors and artificial intelligence to specifically detect weapons and not just any metal item going through a detector.

What weve done is weve rethought the entire approach, said Anil Chitkara, co-founder and head of corporate development for Evolv.

The detectors have already made an appearance at Lower.com Field. Josh Glessing, vice president of strategy for Haslam Sports, says the addition was a win-win for everyone, especially the fans who no longer have to slow down or stop unless an alert is set off.

One, we alleviate the stress points of people bunching together and moving slowly through, and two, its a frictionless experience, Glessing said. A lot of people dont realize theyre going through security.

10TV spoke with several fans at a recent Columbus Crew game to gauge their thoughts on the new equipment.

It doesnt bother me because it keeps us all safe and together, Cindy Nauer said. All we can do is try and stay one step ahead.

In regard to walking through metal detectors, Terrell Brett said, Its annoying, but at the same time, Id rather have it depending on if something crazy, with everything going on.

Like its name specifies, Chitkara said the Evolv system will evolve with the changing technology of how guns are made; particularly 3D printed firearms.

As the material and makeup of those weapons change, we bring them into our lab, we test them and we run them through our system and then we improve the algorithm to detect them, said Chitkara. Theres a wide range of weapons that it will detect.

Other central Ohio venues are also implementing new metal detectors at the gates. Fans walking through the gates at Ohio Stadium will see a similar metal detector system when the Buckeyes open their season home opener this coming Saturday.

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New security system using AI to find weapons at several Ohio stadiums - 10TV