Source: Medical Xpress

While the world ravels at the wonders of Artificial intelligence (AI) being the cornerstone of prognosis breakthroughts, a multi-institutional team of researchers at the UNC School of Medicine, Duke University, Ally Bank, Oxford University, Colombia University, and University of Miami have been on a mission to build public trust and evaluate how exactly AI and algorithmic technologies are being approved for use in patient care.

Together, Sammy Chouffani El Fassi, a MD candidate at the UNC School of Medicine and research scholar at Duke Heart Center, and Gail E. Henderson, Ph.D., professor at the UNC Department of Social Medicine, led a thorough analysis of clinical validation data for 500+ medical AI devices, revealing that approximately half of the tools authorized by the U.S. Food and Drug Administration (FDA) lacked reported clinical validation data.

AI has practically limitless applications in health care, ranging from auto-drafting patient messages in MyChart to optimizing organ transplantation and improving tumour removal accuracy. Despite their potential benefit to doctors and patients alike, these tools have been met with skepticism because of patient privacy concerns, the possibility of bias, and device accuracy.

“Although AI device manufacturers boast of the credibility of their technology with FDA authorization, clearance does not mean that the devices have been properly evaluated for clinical effectiveness using real patient data,” said Chouffani El Fassi, who was first author on the paper.

“With these findings, we hope to encourage the FDA and industry to boost the credibility of device authorization by conducting clinical validation studies on these technologies and making the results of such studies publicly available.”

Since 2016, the average number of medical AI device authorisations by the FDA per year has increased from two to 69, indicating tremendous growth in commercialisation of AI medical technologies. The majority of approved AI medical technologies are being used to assist physicians with diagnosing abnormalities in radiological imagining, pathologic slide analysis, dosing medicine, and predicting disease progression.

AI is able to learn and perform such human-like functions by using combinations of algorithms. The technology is then given a plethora of data and sets of rules to follow, so that it can “learn” how to detect patterns and relationships with ease.

From there, the device manufacturers need to ensure that the technology does not simply memorise the data previously used to train the AI, and that it can accurately produce results using never-before-seen solutions.

Regulation During a Rapid Proliferation Of AI Medical Devices

Following the rapid proliferation of these devices and applications to the FDA, Chouffani El Fassi and Henderson et al. were curious about how clinically effective and safe the authorised devices are. Their team analysed all submissions available on the FDA’s official database, titled “Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices.”

“A lot of the devices that came out after 2016 were created new, or maybe they were similar to a product that already was on the market,” said Henderson. “Using these hundreds of devices in this database, we wanted to determine what it really means for an AI medical device to be FDA-authorised.”

Of the 521 device authorisations, 144 were labeled as “retrospectively validated,” 148 were “prospectively validated,” and 22 were validated using randomized controlled trials. Most notably, 226 of 521 FDA-approved medical devices, or approximately 43%, lacked published clinical validation data.

A few of the devices used “phantom images” or computer-generated images that were not from a real patient, which did not technically meet the requirements for clinical validation.

Furthermore, the researchers found that the latest draft guidance, published by the FDA in September 2023, does not clearly distinguish between different types of clinical validation studies in its recommendations to manufacturers.

Types Of Clinical Validation And A New Standard

In the realm of clinical validation, there are three different methods by which researchers and device manufacturers validate the accuracy of their technologies: retrospective validation, prospective validation, and a subset of prospective validation called randomized controlled trials.

Retrospective validation involves feeding the AI model image data from the past, such as patient chest X-rays prior to the COVID-19 pandemic. Prospective validation, however, typically produces stronger scientific evidence because the AI device is being validated based on real-time data from patients.

This is more realistic, according to the researchers, because it allows the AI to account for data variables that were not in existence when it was being trained, such as patient chest X-rays that were impacted by viruses during the COVID pandemic. Randomised controlled trials are considered the gold standard for clinical validation. This type of prospective study utilises random assignment controls for confounding variables that would differentiate the experimental and control groups, thus isolating the therapeutic effect of the device.

For example, researchers could evaluate device performance by randomly assigning patients to have their CT scans read by a radiologist (control group) versus AI (experimental group).

Because retrospective studies, prospective studies, and randomised controlled trials produce various levels of scientific evidence, the researchers involved in the study recommend that the FDA and device manufactures should clearly distinguish between different types of clinical validation studies in its recommendations to manufacturers.

In their Nature Medicine publication, Chouffani El Fassi, Henderson and others lay out definitions for the clinical validation methods which can be used as a standard in the field of medical AI.

“We shared our findings with directors at the FDA who oversee medical device regulation, and we expect our work will inform their regulatory decision making,” said Chouffani El Fassi.

“We also hope that our publication will inspire researchers and universities globally to conduct clinical validation studies on medical AI to improve the safety and effectiveness of these technologies. We’re looking forward to the positive impact this project will have on patient care at a large scale.”

Algorithms Can Save Lives

Chouffani El Fassi is currently working with UNC cardiothoracic surgeons Aurelie Merlo and Benjamin Haithcock as well as the executive leadership team at UNC Health to implement an algorithm in their electronic health record system that automates the organ donor evaluation and referral process.

In contrast to the field’s rapid production of AI devices, medicine is lacking basic algorithms, such as computer software that diagnoses patients using simple lab values in electronic health records. Chouffani El Fassi says this is because implementation is often expensive and requires interdisciplinary teams that have expertise in both medicine and computer science.

Despite the challenge, UNC Health is on a mission to improve the organ transplant space.

“Finding a potential organ donor, evaluating their organs, and then having the organ procurement organization come in and coordinate an organ transplant is a lengthy and complicated process,” said Chouffani El Fassi.

“If this very basic computer algorithm works, we could optimize the organ donation process. A single additional donor means several lives saved. With such a low threshold for success, we look forward to giving more people a second chance at life.”

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Source: Universities of Dundee

To build an AI software for predicting dementia, the team of 20, from the Universities of Edinburgh and Dundee, has approval to use a unique, large data set made up of CT and MRI brain scans from patients in Scotland from 2008 to 2018, representing 1.6 million images.

Approval comes from the Public Benefit and Privacy Panel for Health and Social Care, a part of NHS Scotland. The team will then use artificial intelligence (AI) and machine learning (ML) to analyse the image data alongside linked health records such as demographics and treatment history, without patients being identifiable, to find patterns that could indicate a person’s risk of developing dementia.

The ultimate aim is to build a digital healthcare tool that radiologists can use when scanning for other conditions to determine a person’s dementia risk, and to diagnose early stages of related diseases, such as Alzheimer’s. Isolating a patient group with a high risk of dementia will enable the development of more precise treatments for various types of dementia, mostly Alzheimer’s and vascular dementia. 

The data will be held safely in the Scottish National Safe Haven, commissioned by Public Health Scotland, which provides a secure platform for the research use of NHS electronic data. 

Willy Gilder was given an Alzheimer’s diagnosis in 2021. Image credit – Edinburgh Innovations

Former journalist Willy Gilder, 71, from Edinburgh, was diagnosed with Alzheimer’s three years ago. He said,

“We know that 45% of dementia cases are preventable, and The Lancet has published a list of risk factors including smoking, obesity and air pollution. If you know you’re at risk, you can make changes that are going to improve your brain health. Because I was diagnosed early, I know that keeping very mentally active, for example, is going to help me.

“Possible new treatments in development for Alzheimer’s are likely to work in the early stages of the disease, which is why early diagnosis is important. With long waiting lists for diagnosis, as well as relatively low funding for dementia research in general compared to cancer, a project like this to predict a person’s risk is extremely important.”

The project, called Scottish AI in Neuroimaging to predict Dementia and Neurodegenerative Disease (SCAN-DAN), is one of three ‘pathfinders’ from global research collaboration NEURii, which launched a year ago. 

Comprising global pharmaceutical company Eisai, Gates Ventures, the University of Edinburgh, national health data science organisation Health Data Research UK and medical research charity LifeArc, NEURii aims to translate world-class data, neurology and digital sciences into projects that can enhance quality of life for people living with dementia.

The NEURii collaboration provides funding and expertise to the pathfinder projects to remove barriers to getting digital health tools to market. The research teams are also supported by Edinburgh Innovations, the University of Edinburgh’s commercialisation service.

Professor Will Whiteley, of the University of Edinburgh’s Centre for Clinical Brain Sciences, is co-leading the SCAN-DAN project. He said,

“Better use of simple brain scans to predict dementia will lead to better understanding of dementia and potentially earlier diagnosis of its causes, which in turn will make development of new treatments easier. Currently treatments for dementia are expensive, scarce and of uncertain value. If we can collect data from a large group of people at high risk, who then give their consent to take part in trials, we can really start to develop new treatments. 

“Working with NEURii really gives us the potential for patient impact – to build something that actually makes it into practice. Often modelling doesn’t go anywhere, but motivation from the NEURii team to develop a useful product really concentrates the mind!”

Professor Emanuele Trucco, an expert in AI and medical imaging at the University of Dundee and SCAN-DAN co-lead, said,

“Scotland and the UK are at the forefront of clinical data research, building on the unique National Health Service patient number – called the CHI number in Scotland, as well as the structure, security and good governance of the Scottish National Safe Haven, amongst other data organisations such as the UK Biobank.

“This new data set will be of great use to neurological researchers. And, should we establish a successful proof of concept, we will have a suite of software tools that are smoothly and unobtrusively integrated with routine radiology operations, that assist clinical decision-making and flag the risk of dementia as early as possible.”

The project is backed by the NEURii partner organisations. Dr Ricardo Sáinz Fuertes, Global Director of Digital Health Solutions at Eisai and Programme Director for NEURii, said,

“The spirit of NEURii is to fulfil the promise of data science for healthcare. Within a year, we plan to support SCAN-DAN through to proof of concept by removing obstacles to commercialisation and providing whatever’s needed, be it funding, collaborations or legal or regulatory input. 

“We are joining forces globally, in a way that hasn’t been done before, to provide innovative digital projects with the chance of becoming solutions to complex neurodegenerative disorders like dementia.”

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The 1st Annual ARC Industry Leadership Forum in Singapore brought together a diverse group of industry leaders to explore the rapidly evolving landscape of industrial automation. Industry leaders and experts explored the challenges and opportunities of digital transformation, emphasizing the critical role of collaboration and innovation for industrial automation.

Under the theme of “Accelerating Transformation in the Age of AI, Open Automation, and Sustainability,” the forum provided a comprehensive look at the cutting-edge developments in Open Process Automation (OPA) and Artificial Intelligence (AI) and their impact on industrial operations.

The Rise of Open Process Automation

The Open Process Automation Forum session was chaired by Bob Gill of ARC Advisory Group, who provided a brief historical overview of the OPA initiative and OPAF organization. From its inception in 2016, the OPA Forum has grown into a global initiative with over 100 member companies.

He referenced the ARC Forum in Orlando in 2016, where Don Bartusiak of ExxonMobil spoke about the need for a new type of DCS and one that should be embraced by the industry as a whole.

“We do not want to create a system that is only used by ExxonMobil.”

Gill highlighted the forum’s commitment to developing open, interoperable systems that serve a wide range of industries. This sentiment set the tone for the day’s discussions, which focused on the importance of collaboration and openness in driving industrial innovation.

Petronas: Pioneering OPA in the Petrochemical Industry

Sharul Rashid of PETRONAS provided an in-depth look at how the company has integrated OPA into its operations. Rashid’s presentation emphasized the importance of certification and standards in ensuring the reliability and functionality of OPA systems. Reflecting on the meticulous approach needed to implement OPA in complex industrial environments, he said:

“Certification is very important… it is continuing to gain the competence of all parties so that the system is actually working when you deploy information.”

Rashid also discussed the practical benefits of OPA, such as increased operational flexibility and reduced downtime. By adopting open systems, PETRONAS has been able to accelerate the deployment of new technologies, a critical advantage in an industry where efficiency is paramount.

Challenges in OPA Adoption: The Asia Pacific Perspective

John Kirkman of Enterprise Transformation Partners addressed the challenges of adopting OPA in the Asia Pacific region. Drawing from his extensive experience in process automation standards, Kirkman highlighted the initial excitement among industry players, tempered by the practical difficulties of implementation.

“One of the key things we got back from industry… was the excitement about the Process Automation standards, but there was often hesitation due to a lack of trained engineers,” he explained.

Kirkman’s insights underscored the need for robust educational programs to build a skilled workforce capable of leveraging OPA technologies. He called for greater collaboration between industry and academia to address this gap, a theme that resonated throughout the forum.

ExxonMobil’s Systematic Approach to OPA

Murli Balsubramanian of ExxonMobil Asia Pacific brought a pragmatic perspective to the discussions, detailing the company’s systematic approach to implementing OPA. He described ExxonMobil’s journey from proof-of-concept to full-scale deployment, emphasizing the importance of reducing the total cost of ownership and enhancing operational efficiency through open systems.

“We’ve been very systematic, diligent in our approach… to how we solve the problem of proprietary systems,” he noted.

He also highlighted the significant benefits of OPA, such as decoupling IO from control and leveraging data for more effective decision-making. These capabilities, he argued, are essential for unlocking the full potential of modern industrial processes.

The Role of AI in Industrial Automation

A recurring theme throughout the forum was the integration of AI into automation systems. The discussion on mechanistic AI, led by experts from Emerson and the OPC Foundation, explored how AI can be used to predict equipment failures and optimize operations without the intensive computational demands of traditional AI models.

This approach, known as mechanistic AI, relies on cause-and-effect relationships rather than data-driven algorithms, offering a more sustainable solution for industrial applications. The OPC Foundation’s AI initiative, driven by major industry players like Microsoft and Siemens, was also highlighted. This initiative aims to automate the creation of OPC components and enable assets to interact in natural language, streamlining the integration of AI into existing systems.

Practical Implementations And The Future Of OPA

Bejoy Jose of Yokogawa Engineering Asia provided a comprehensive overview of how data from diagnostics and calibrations can be used to optimize maintenance schedules and improve the stability of industrial systems. His insights into the use of AI for predictive maintenance demonstrated the tangible benefits of these technologies in reducing downtime and enhancing system reliability.

The forum also addressed the challenges of implementing these technologies at scale. Participants noted the importance of standardization and interoperability in ensuring that different systems and devices can communicate effectively. The OPC UA protocol, for example, was highlighted as a critical enabler of this interoperability, allowing data to be transmitted securely and efficiently across various platforms.

Sustainability And The Urgency Of Digitalization

The forum did not shy away from the pressing issue of sustainability in industrial operations. Arne Modersohn of Evonik emphasized the importance of adopting Ethernet-based technologies to advance digitalization in the field. He argued that failing to embrace these technologies could result in missed opportunities to improve efficiency and reduce environmental impact.

The role of AI in promoting sustainability was also discussed, with experts highlighting how AI can be used to monitor energy consumption and reduce carbon footprints. These discussions underscored the critical need for the industrial sector to balance technological advancement with environmental responsibility.

Innovations in Industrial Connectivity

Stefan Hoppe of the OPC Foundation provided a compelling look at the future of industrial connectivity, focusing on the OPC UA protocol. He discussed the importance of maintaining data integrity and context as information moves from field devices to the cloud.

“We believe that OPC UA is the solution for the industrial process industry because only OPC DA is able to describe the data as near as possible in the data source and then move it up by keeping the context of the information through all the layers in the automation pyramid up to the cloud,” Hoppe explained.

Hoppe reiterated the importance of standardization in ensuring that different systems can communicate effectively, regardless of the platform or device. This emphasis on interoperability and flexibility is critical as industries continue to adopt more complex automation and AI systems.

A Call to Action: The Future Of Industrial Automation

As the forum drew to a close, it became evident that OPA and AI are not just technological advancements but strategic imperatives for the future of industrial automation. The forum’s discussions highlighted the significant progress made in developing open, interoperable systems, while also acknowledging the challenges that lie ahead.

The key takeaway from the forum was the importance of collaboration across the industry to overcome these challenges. By working together, industry leaders, technology providers, and educational institutions can ensure that the benefits of OPA and AI are realized across the sector.

The 1st Annual ARC Industry Leadership Forum provided valuable insights into the future of industrial automation. As industries continue to navigate the complexities of digital transformation, the lessons learned from this forum will be instrumental in guiding the next steps in this journey. The integration of OPA and AI into industrial processes represents not just a technological advancement, but a strategic imperative for the industry’s future.

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Source: CNA

If you are using your mobile phone or computer to read this article, you may not know that a tiny chip powers them, along with millions of other electronic devices. Many of these chips are made in Singapore – and now, the country is looking to expand its chip-making capacity.

Government industrial planner JTC told CNA that it is preparing 11% more land in Singapore’s wafer fabrication parks, in a bid to attract more top semiconductor giants and ride the artificial intelligence wave. The semiconductor sector employs about 35,000 people and accounts for almost 20 per cent of the country’s manufacturing output.

Manufacturing is Singapore’s largest industry by far, representing a significant component of its gross domestic product – more than 20%. Currently, nine out of the 15 top semiconductor firms have set up shop in Singapore. 

They include American chipmaker Micron and German wafer manufacturer Siltronic, which opened a S$2.9 billion (US$2.16 billion) advanced manufacturing facility in Tampines last month. These semiconductor firms are currently clustered in four wafer fabrication parks that span 374 ha, the size of more than 500 football fields.

These parks are in Pasir Ris, Tampines, Woodlands and the North Coast, with JTC being the master planner and developer of the land. JTC said the new plot, part of which is in the eastern region of Singapore, will be ready by the end of the year. 

Companies looking to set up there will be able to get customised roads and even new water piping, which helps to sweeten the deal. As chipmaking is a delicate affair, semiconductor fabrication plants need to be close to stable power and water supply for constant cooling. They cannot be near MRT stations or other heavy industries due to mini vibrations that could affect production.

Expansion Of Facility To Boost Capacity

GlobalFoundries, the world’s third-largest contract chipmaker, has had a presence in Singapore since 2010. It runs one of the largest wafer plants here and can produce about 1.5 million 300mm wafers annually. Last year, the firm added 23,000 sqm – equivalent to about four football fields – of space to boost capacity amid a boom in demand.

The company manufactures integrated circuits on wafers designed for smart mobile devices and markets like automotive, aerospace and defence. Some of its biggest customers include semiconductor companies such as Qualcomm. JTC played a “very key role” in building GlobalFoundries’ US$4 billion expanded fabrication plant, said its Senior Vice President Tan Yew Kong. He told CNA that Singapore, which has 55 years of semiconductor history, was able to provide a network to supply materials and infrastructure.

“We are definitely here to stay, and looking at the market sentiment of regionalisation, friendshoring, all these approaches – definitely, having a location like Singapore to support the global footprint is a very necessary thing,” he added.

Friendshoring refers to the act of manufacturing and sourcing from countries with similar geopolitical stances. Mr Tan pointed out that the industry is set to almost double from US$600 billion to US$1 trillion, which means building a factory in advance is “very important”.

“We are pacing ourselves so that when the market is there, our toolsets are all turned on, ready for it. The good news is that actually, our tools are here. We just need to fire it up and get it ready,” he added.

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Workers who fear of being replaced by AI can heave a sigh of relief, at the expense of AI-powered fiascos. At the very least, it is now centered in the service industry. On the other hand, companies are cued to re-evaluate if adopting AI proves to improve things or a recipe for reputational disasters.

Recently, the BBC reported its experimental “AI Overviews” tool has told some users taught to use non-toxic glue to make cheese stick to pizza better. The search engine’s AI-generated responses have also said geologists recommend humans eat one rock per day. 

In a bid to save reputational damage (whatever is left), Google spokesperson reportedly claimed these blunders were “isolated examples”. Some data were created by trolls on Reddit, a platform notorious for its unpopular content, or pieces from The Onion. 

With the vast amount of data inputted into the software known as training, one could not help but wonder if the world is training the AI chatbot to be stupid instead. Despite the bad press, Google insisted the feature was generally working well.

First It Swore

Early this year, UK-based Dynamic Parcel Distribution (DPD)’s AI chatbot wrote a snarky poem at the order of a frustrated customer trying to check on his parcel’s whereabouts. Ashley Beauchamp, a pianist and conductor was annoyed at getting nowhere finding out about his parcel’s whereabouts. Unsuccessful at getting the contact number of DPD’s customer service, he turned to the firm’s chatbot and cajoled it to smear the company’s customer service.

It wrote: “There was once a chatbot named DPD, Who was useless at providing help. DPD was a waste of time, And a customer’s worst nightmare,” the bot continued before concluding: “One day, DPD was finally shut down and everyone rejoiced. Finally they could get the help they needed, From a real person who knew what they were doing.”

Beauchamp’s juicy tweet clocked 2 million views. He said he initially asked the bot to tell him a joke after failing to get information about the status of a parcel and when it did he asked it to write the poem about automated customer service failings.

He also encouraged the bot to swear, which it did at him. Facing the music, Beauchamp told ITV television he had still not received the parcel. “I think they might hold it hostage now. I wouldn’t blame them. That’s totally on me,” he added.

DPD UK said it had used an AI element within its chat system successfully for a number of years alongside its human customer service but an error had occurred after a system update. “The AI element was immediately disabled and is currently being updated,” the company said in a statement reported by ITV. No further word was released on if the chatbot is back.

Followed By (Hilarious) Messed Up Orders

Next to suffer embarrassing fiascos is Macdonald’s in the United States. Its artificial intelligence (AI) powered ordering technology from its drive-through restaurants in the US, after customers shared its comical mishaps online. A trial of the system, developed by IBM and uses voice recognition software to process orders, was announced in 2019.

Unfortunately, viral videos of bizarre misinterpreted orders ranging from bacon-topped ice cream to hundreds of dollars’ worth of chicken nuggets were circulated online. The fast food chain told franchisees it would remove the tech from the more than 100 restaurants it has been testing it in by the end of July, as first reported by trade publication Restaurant Business.

“After thoughtful review, McDonald’s has decided to end our current global partnership with IBM on AOT [Automated Order Taking] beyond this year,” the restaurant chain said in a statement.

It added the chain will continue to evaluate long-term, scalable solutions that will help make informed decisions on a future voice ordering solution by the end of the year

In one video, which has 30,000 views on TikTok, a customer gets increasingly exasperated as she attempts to convince the AI that she wants a caramel ice cream, only for it to add multiple stacks of butter to her order. In another, which has 360,000 views, a person claims that her order got confused with one being made by someone else, resulting in nine orders of tea being added to her bill.

Another popular video includes two people laughing while hundreds of dollars worth of chicken nuggets are added to their order, while the New York Post reported another person had bacon added to their ice cream in error.

These incidents highlight the potential pitfalls of relying too heavily on AI in customer service and order processing. While technology promises efficiency and innovation, reality can sometimes be a far cry from the ideal, leading to humourous yet frustrating user experiences.

As companies like DPD and McDonald’s navigate the complexities of AI implementation, it becomes clear that human oversight remains crucial to ensure customer satisfaction. Both stories serve as a reminder that while AI has the potential to revolutionise industries, its integration must be handled with care, balancing innovation with the irreplaceable value of human touch.

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The BBC reported its experimental “AI Overviews” tool has told some users searching for how to make cheese stick to pizza better that they could use “non-toxic glue”. The search engine’s AI-generated responses have also said geologists recommend humans eat one rock per day.

In a bid to save reputational damage (whatever is left), Google spokesperson reportedly claimed these blunders were “isolated examples”. Some data were created by trolls on Reddit, a platform notorious for its unpopular content, or pieces from The Onion. 

With the vast amount of data inputted into the software known as training, one could not help but wonder if the world is training the AI chatbot to be stupid instead. Despite the bad press, Google insisted the feature was generally working well.

“The examples we’ve seen are generally very uncommon queries, and aren’t representative of most people’s experiences. The vast majority of AI overviews provide high quality information, with links to dig deeper on the web.” it said in a statement.

In one shocking example reported by BBC, a reporter Googled if they could use gasoline to cook spaghetti faster was told “no… but you can use gasoline to make a spicy spaghetti dish” and given a recipe. Google said it had taken action where “policy violations” were identified and was using them to refine its systems.

Despite these incidents which questioned the credibility of AI chatbot’s answers, a lawmaker actually deemed ChatGPT an expert in law. An Arizona state representative behind a new law that regulates deepfakes in elections used an artificial intelligence chatbot, ChatGPT, to write part of the law – specifically, the part that defines what a deepfake is.

Republican Alexander Kolodin’s bill, which passed unanimously in both chambers and was signed by the Democratic governor recently, will allow candidates in Arizona or residents to ask a judge to declare whether a supposed deepfake is real or not, giving candidates a way to debunk AI-generated misinformation.

Kolodin said he used the chatbot ChatGPT to help define what “digital impersonation” is for the bill in part because it was a fun way to demonstrate the technology. He provided a screenshot of ChatGPT’s response to the question of what a deepfake is, which is similar to language that is included in the bill’s definition.

Back to making pizza, would you trust a chatbot’s recommendation “non-toxic glue” to make cheese stick to the pizza dough?

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Source: The Guardian

An Arizona state representative behind a new law that regulates deepfakes in elections used an artificial intelligence chatbot, ChatGPT, to write part of the law – specifically, the part that defines what a deepfake is.

Republican Alexander Kolodin’s bill, which passed unanimously in both chambers and was signed by the Democratic governor recently, will allow candidates in Arizona or residents to ask a judge to declare whether a supposed deepfake is real or not, giving candidates a way to debunk AI-generated misinformation.

Kolodin said he used the chatbot ChatGPT to help define what “digital impersonation” is for the bill in part because it was a fun way to demonstrate the technology. He provided a screenshot of ChatGPT’s response to the question of what a deepfake is, which is similar to language that is included in the bill’s definition.

“I am by no means a computer scientist,” Kolodin said. “And so when I was trying to write the technical portion of it, in terms of what sort of technological processing makes something a deepfake, I was kind of struggling with the terminology. So I thought to myself, well, let me just ask the subject matter expert. And so I asked ChatGPT to write a definition of what was a deepfake.”

That portion of the bill “probably got fiddled with the least – people seemed to be pretty cool with that” throughout the legislative process. ChatGPT provided the “baseline definition” and then “I, the human, added in the protections for human rights, things like that it excludes comedy, satire, criticism, artistic expression, that kind of stuff,” Kolodin said.

Kolodin has used ChatGPT in other lawmaking a couple times, he said, to help write the first drafts of amendments and save time. “​​Why work harder when you can work smarter,” Kolodin replied on Twitter when an Arizona reporter tweeted about his use of ChatGPT in the bill.

The federal government has not yet regulated the use of AI in elections, though groups have been pressuring the Federal Election Commission to do so because the technology has moved much faster than the law, creating concerns it could disrupt elections this year. The agency has said it expects to share more on the issue this summer.

The Federal Communications Commission, meanwhile, will consider whether to require disclaimers on AI-generated content on political ads running on radio and TV, the Associated Press reported Wednesday. The FCC previously made clear that AI-generated voices in robocalls, like an instance in which President Joe Biden’s voice was spoofed to New Hampshire voters, are illegal.

In the absence of federal regulations, many states have advanced bills to regulate deepfakes. It’s typically an area of rare bipartisan agreement.

Some bills have outlawed the use of deepfakes in political contexts in some instances, while others require disclosures that note whether the content is AI-generated. Kolodin’s bill takes a different approach to concern over deepfakes in elections than that of many other states considering how to regulate the technology.

Rather than outlaw or curb usage, Kolodin wanted to give people a mechanism to have the courts weigh in on the truthfulness of a deepfake. Having it taken down would be both futile and a first amendment issue, he said.

“Now at least their campaign has as a declaration from a court saying, this doesn’t look like it’s you, and they could use that for counternarrative messaging,” he said.

The bill does allow for a deepfake to be ordered removed, and the person could seek damages, if it depicts someone in a sexual act or nude, if the person in the deepfake is not a public figure and if the publisher knew it was false and refused to remove it. The Arizona bill also takes a different approach on disclaimers.

Rather than outright requiring them, as some state laws have, it says that a person bringing a potential court action wouldn’t have a case if the publisher of the digital impersonation had conveyed that the image or video was a deepfake or that its authenticity was in dispute, or whether it would be obvious to a reasonable person that it was a deepfake.

Kolodin said disclaimers carry speech concerns for him, too, because they cut into airtime or, in some cases, ruin the joke or the point of a message. He cited a recent instance where the Arizona Agenda, a local publication covering state politics, created a deepfake of the US Senate candidate Kari Lake, where it was obvious to a viewer that the video wasn’t real based on what Lake was saying. (Full disclosure: the reporter of this story was the co-founder of the Arizona Agenda, but is no longer involved.)

“Any reasonable person would have realised that [it was fake], but if you had a label on it, it would have ruined the joke, right?” Kolodin said. “It would have ruined the journalistic impact. And so I think a prescribed label is further than I wanted to go.”

In one instance in Georgia, a state representative trying to convince fellow lawmakers to approve a bill outlawing deepfakes in elections used an AI-generated image and audio of two people who opposed the bill, faking their voices to say they endorsed it.

Kolodin hopes his bill will become a model for other states because he has worried that well-intentioned efforts to regulate AI in elections could trample on speech rights.

“I think deepfakes have a legitimate role to play in our political discourse,” he said. “And when you have politicians regulating speech, you kind of have the fox guarding the hen house, so they’re gonna say, oh, anything that makes me look silly is a crime. I absolutely hope that other state legislators pick this up.”

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Source: Reuters

Taiwanese major chip supplier, TSMC forecasted an annual revenue growth of 10% in the global semiconductor industry, excluding memory chips.

“This is a new golden age of opportunity with AI,” said senior vice-president Cliff Hou, who was speaking at an event in Hsinchu, where the company is headquartered.

In April, TSMC lowered its outlook for the global semiconductor industry excluding memory to a growth rate of around 10% from a previous forecast of more than 10%. World Semiconductor Trade Statistics has forecast growth of 13.1% for the global semiconductor market in 2024.

TSMC has estimated second-quarter sales may rise as much as 30% as it rides a wave of demand for semiconductors used in artificial intelligence applications (AI). Nvidia forecasted its quarterly revenue above estimates and announced a stock split, lifting shares to a record-high territory and impressing investors who have tripled the chipmaker’s market value in the past year on AI optimism.

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Semiconductor competition continues after Malaysia announced her mission to be Southeast Asia’s largest integrated circuit design park last April. Now, Vietnam joins the competition for the same crown, with the support from an American semiconductor titan – Marvell Technology Inc.

Marvell Technology, Inc., a titan in data infrastructure semiconductor solutions, accelerated the growth of its workforce and presence in Vietnam in the past year since the company announced plans to expand R&D, engineering and design activities in the country. Marvell committed to 50% growth of its workforce in Vietnam in three years, a target shared by the company during last year’s U.S.-Vietnam Innovation and Investment Summit attended by Marvell Chairman and CEO Matt Murphy. Today, Marvell is ahead of its plans, achieving more than 30% growth in just eight months.

Marvell has also expanded its physical footprint in Vietnam with a new location in Da Nang, adding to its offices in Ho Chi Minh City. The growth of its footprint demonstrates the company’s commitment to creating a world-class semiconductor design hub in the country.

“We are excited with the momentum we’ve gained since we announced our design center plans last year, including our progress in adding top engineering talent to our team and expanding our number of sites,” said Murphy. “Vietnam is rapidly becoming a center of semiconductor innovation and we are committed to being part of that.”

A new Marvell design center facility in Ho Chi Minh City, which is expected to open within the next year, and other Marvell engineering facilities in Vietnam are focused on high-speed data center optical connectivity, storage, and analogue and mixed-signal semiconductor technologies. These are critical to the build-out of accelerated infrastructure to meet the rising performance and power requirements of artificial intelligence (AI) and cloud data centers.

“Marvell has been part of the technology ecosystem in Vietnam for over ten years. The company is committed to attracting the best and brightest engineering talent to its semiconductor design center in Vietnam, and to contributing to the growth of the country’s semiconductor community,” said Dr. Loi Nguyen, Executive Vice President of Cloud Optics at Marvell and a native of Ho Chi Minh City.

Malaysia’s Economy Minister Rafizi Ramli said the government will offer incentives including subsidised office spaces, exemptions on employment passes, relocation services and lower corporate tax rates for foreign venture capital firms, tech entrepreneurs and unicorns – startups reaching a $1 billion valuation – looking to invest in Malaysia.

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Artificial Intelligence (AI) is probably the world’s most overrated concept now. From medical devices to metalworking equipment, AI has a stake in every pie.

The world has been on tenterhooks on which jobs would be taken over by AI. BBC quoted a report by investment bank Goldman Sachs suggested that AI could replace the equivalent of 300 million full-time jobs across the globe. It concluded many administrative, legal, architecture, and management roles could be affected.

The emergence of AI is hitting the global labour force “like a tsunami” according to International Monetary Fund (IMF) Managing Director Kristalina Georgieva. AI is likely to impact 60% of jobs in advanced economies and 40% of jobs around the world, Georgieva remarked recently at an event in Zurich organised by the Swiss Institute of International Studies. 

“We have very little time to get people ready for it, businesses ready for it, it could bring tremendous increase in productivity if we manage it well, but it can also lead to more misinformation and, of course, more inequality in our society,” Georgieva reiterated.

Ongoing Challenges

While we accept Machine Learning (ML) and AI has become part of our lives, we need to recognise it is still undergoing training. Asking a software to make smart decisions in any situation requires astronomical graphic processors and capital — which may take years before we get there. It is very hard to recreate a program to be human-like will be a big challenge even for the most sophisticated AI engineers.

General Motors’ Cruise, best known for its autonomous driving technology is back on the roads. It earlier suspended operations after hitting a pedestrian in San Francisco.

This time, when Cruise resumes operating, there will be a “safety driver” at the wheel — a safety precaution in case of emergency. Cruise reportedly said it updated its software to better respond to such incidents in the future. Cruise’s Chief Executive and other executives also resigned and the company laid off nearly a quarter of its workforce in the weeks after the accident. 

BBC added Cruise’s own investigation, commissioned through the law firm of Quinn Emanuel Urquhart & Sullivan, cited a number of faults with Cruise including an “us versus them” attitude toward regulators, and a “myopic focus” on avoiding blame in the incident.

“We acknowledge that we have failed to live up to the justifiable expectations of regulators and the communities we serve,” Cruise wrote in its blog post on the report at the time. “In doing so, we also fell woefully short of our own expectations.”

Growing Pains

AI’s entry into automotive, aviation, and the latest in dating apps. Bumble’s founder, Whitney Wolfe Herd — who recently stepped down as CEO — recently addressed how the dating app will utilise artificial intelligence while appearing at The Bloomberg Technology Summit, the New York Post reported. Herd added the app will use the advanced technology to help swiping singles narrow down their matches and not just improve people’s flirting, but also do it for them.

“There is a world where your dating concierge could go and date for you, with another dating concierge… You don’t have to talk to 600 people. It will go scan all of San Francisco for you and say, ‘These are the three people you really ought to meet.’ That’s the power of AI when harnessed the right way,” Wolfe Herd said according to The Independent. 

This new feature could help the wave of singletons reporting dating fatigue who have become exhausted by the disposable dating culture of seemingly endless matches. As AI continues to integrate into diverse sectors, from transportation to personal relationships, its potential to transform our daily lives becomes increasingly evident.

While the journey towards seamless and sophisticated AI applications is ongoing, the advancements showcased by leaders like Whitney Wolfe Herd illustrate the promising future ahead. We are on the cusp of a new era where technology not only augments our abilities but also reshapes our interactions and connections. However, we cannot afford to forget being vigilant in the event of the unexpected.

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