SwissCognitive Guest Blogger: Zachary Amos – “How AI Will Impact 2024 Elections”

Generative artificial intelligence — models that can create images, videos, audio or text — have become incredibly popular because they’re widely available, easy to use and fast. Unfortunately, their greatest features are also threats. Will this technology permanently improve elections or unfairly sway the polls in one candidate’s favor?

AI’s Impact on Elections Is Global

2024 is a pivotal election year — not just for the United States but the world. Residents of over 50 countries will visit the polls this year alone, including Mexico, South Korea, the United Kingdom, India, South Africa, Taiwan and the European Union.

While most voters have come to expect — and know how to spot — attack ads, online trolling and misinformation around election time, AI has brought the world into uncharted waters. Generative models can create convincing images and videos with only one minute of audio or a few lines of text.

An AI-generated deepfake — real content that has been digitally manipulated with AI — is another massive concern. This technology replaces one person’s likeness or voice with a synthetic alternative.

According to one recent survey, about 78% of people believe bad actors will use AI to influence the U.S. presidential election outcome, with 70% thinking they’ll generate fake information and 62% assuming they’ll convince people not to vote.

AI’s Negative Impacts on Elections

There’s no downplaying AI’s negative impacts on elections.

Disinformation

Most people learn about candidates and current events from social media and internet headlines. In the United States, 82% of adults get their daily news from a digital device. This is an issue in an age where bad actors can create AI-generated disinformation almost instantly.

The Center for Countering Digital Hate, a British nonprofit, recently tested six of the leading AI voice cloning tools. Each produced fake audio snippets of high-profile politicians, with 80% of the tests generating a convincing clip.

Algorithmic Bias

AI systems can learn to make biased decisions if their training data contains skewed or inaccurate information or variables like gender, age, race or sexuality. Algorithms could act with prejudice if governments use this technology to accelerate vote counting or check voter eligibility.

Deepfakes

Divyendra Singh Jadoun is known as the “Indian Deepfaker” for his work on Bollywood clips and TV commercials. Recently, he claimed hundreds of Indian politicians sought his services ahead of the country’s elections, with 50% making unethical requests like defamation or deception. He says he denied them but doesn’t doubt others would accept their offers.

A deepfake can place a politician’s likeness over any body, face and voice to make it seem like they said or did something they never have. Politicians can — and have — used fake videos to make their opponents less likable. They even use AI on themselves to cast doubt on any real wrongdoings that might surface, giving them plausible deniability.

Fake Accounts

AI-powered social media bots spread misinformation and subconsciously influence voters by posting comments, sharing articles, and liking posts about certain politicians or upcoming elections.

Examples of AI Impacting Elections

AI’s effect on elections isn’t just hypothetical — it’s already happening. Of the 112 national elections in the United Kingdom between 2023 and 2024, 19 show signs of AI interference so far. When considering the evidence of AI-generated disinformation, that figure increases.

In Slovakia, days before the election — which was to determine who would lead the country — an audio clip of one of the leading candidates spread online. In it, he bragged about rigging the election. His opponent ended up defeating him.

In the United States, a former political consultant robocalled New Hampshire voters with an AI-generated voice meant to mimic President Biden, directing them not to vote. It reached thousands of people just ahead of the presidential primary. The man faces criminal and felony charges, along with a steep $6 million fine issued by the Federal Communications Commission.

Although the number of voters influenced in these situations remains unclear, one thing is certain — they were affected by AI interference. Going forward, cases like these aren’t going to be outliers. Instead, they may become as routine as attack ads and fake news posts.

AI’s Positive Impacts on Elections

It turns out AI might not be all bad — it still stands to positively impact the election.

Heightened Awareness

People aware of AI’s capabilities may be more likely to approach social media posts, news articles and viral clips with greater skepticism. Their newfound tendency to fact-check content can protect them from disinformation.

Election Administration

AI-powered systems could help administer elections, accelerating the time it takes to count votes, register voters or remind the general public of upcoming election dates. Considering these processes are typically so time-consuming, streamlining and automating them could be substantially beneficial.

Voter Education

Governments can offer AI tools to help voters stay informed. A machine learning model can pull up the latest news, fact-check social media posts, summarize news articles or identify AI-generated content.

AI-Generated Content Will Influence Elections

While rampant disinformation around election time isn’t new, it was obvious to those who could spot the telltale signs of Photoshop or traditional digital manipulation tactics. Now, generative models have muddied the waters. How can people tell what’s real anymore? What happens when politicians shrug off real scandals as some AI-generated hoax?

The question isn’t whether the AI’s positive impacts outweigh its negatives — it’s how to combat bad actors using this technology. Generative and machine-learning models are here to stay, so voters, governments and politicians should work together to figure out how to handle them. Swift, collaborative action may soon be the only thing ensuring fair elections.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

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Copyright: chemeurope.com – “Machine Learning Takes Materials Modeling Into New Era”

The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research such as drug design and energy storage. However, the lack of a simulation technique that offers both high fidelity and scalability across different time and length scales has long been a roadblock for the progress of these technologies. Researchers from the Center for Advanced Systems Understanding (CASUS) at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) in Görlitz, Germany, and Sandia National Laboratories in Albuquerque, New Mexico, USA, have now pioneered a machine learning-based simulation method (npj Computational Materials) that supersedes traditional electronic structure simulation techniques. Their Materials Learning Algorithms (MALA) software stack enables access to previously unattainable length scales.

Electrons are elementary particles of fundamental importance. Their quantum mechanical interactions with one another and with atomic nuclei give rise to a multitude of phenomena observed in chemistry and materials science. Understanding and controlling the electronic structure of matter provides insights into the reactivity of molecules, the structure and energy transport within planets, and the mechanisms of material failure.

Scientific challenges are increasingly being addressed through computational modeling and simulation, leveraging the capabilities of high-performance computing. However, a significant obstacle to achieving realistic simulations with quantum precision is the lack of a predictive modeling technique that combines high accuracy with scalability across different length and time scales. Classical atomistic simulation methods can handle large and complex systems, but their omission of quantum electronic structure restricts their applicability. Conversely, simulation methods which do not rely on assumptions such as empirical modeling and parameter fitting (first principles methods) provide high fidelity but are computationally demanding. For instance, density functional theory (DFT), a widely used first principles method, exhibits cubic scaling with system size, thus restricting its predictive capabilities to small scales.

Hybrid approach based on deep learning

The team of researchers now presented a novel simulation method called the Materials Learning Algorithms (MALA) software stack. In computer science, a software stack is a collection of algorithms and software components that are combined to create a software application for solving a particular problem. Lenz Fiedler, a Ph.D. student and key developer of MALA at CASUS, explains, “MALA integrates machine learning with physics-based approaches to predict the electronic structure of materials. It employs a hybrid approach, utilizing an established machine learning method called deep learning to accurately predict local quantities, complemented by physics algorithms for computing global quantities of interest.”[…]

Read more: www.chemeurope.com

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Copyright: Economist Impact – “Seizing the Opportunity: the Future of AI in Latin America”

This report examines the developments in AI in Latin America, with a focus on Argentina, Brazil, Chile, Colombia and Mexico. It looks at AI investment, trends within the region’s industries, as well as policies and challenges. It further explores how AI is impacting the privacy landscape in the region and identifies potential opportunities for businesses, governments and individuals to better understand their roles in safeguarding data.

The proliferation of national strategies highlights the importance of AI to LATAM’s socioeconomic transformation, while policy continuity has been seen to benefit some countries more than others. The debate surrounding the ethical implications of artificial intelligence is still ongoing, and it is important that adequate measures are in place to ensure user privacy when using AI technology. It has become clear that policymakers must strike a balance between driving economic growth through innovation, while also safeguarding citizens’ information from malicious activity.

Investment into tech startups has increased significantly due to its underinvestment historically prior to Covid-19’s shift online, prompting nations “to catch up”.

Major sectors such as Healthtech & Agtech are forecast for growth but remain underinvested compared with Fintech businesses which have proved most attractive due to strong consumer demand.

Key policy issues include the digital infrastructure divide & cultivating local talent; multinational companies have sought regional hubs, but telecom services are still needed along w/ addressing skilled individual drain elsewhere.

Download the report here.

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Speakers

 

Carolina Pinart
AI Strategy Program Pillar Lead | Use Cases & Roadmap
Nestlé

Daniel Nyfeler
Managing Director
Gübelin Gem Lab

Craig Ganssle
Founder & CEO
FARMWAVE

Alan Boehme
Chief Technology Officer
H&M Group

Eva Schoenleitner
CEO
Crate.io

John (JT) Clark
Partner & Managing Associate
BCG GAMMA

Christian Guttmann
Vice President, Global Head of Artificial Intelligence and Data
TietoEVRY

Mohammad Tanweer
Artificial Intelligence Specialist
Rolls-Royce

Hedvig Kjellström
Professor, KTH Royal INstitute of Technology
Lead AI Scientist, Silo AI

Thomas Degen
Solutions Engineer
Kx Systems

Romaric Redon
Head Advisor on Artificial Intelligence Technologies
Airbus

Martin Rugfelt
CEO
sentian.ai

Tom Allen
Founder
The AI Journal

Barry R. Hix
CEO
Viral Sign

Jaana Heikkilä
Vice President Marketing
Loop AI Group Cognitive Computing

Chuck Brooks

Adjunct Faculty Graduate in Applied Intelligence
Georgetown University

President
Brooks Consulting International
 

Ivan Jursic
Development Engineer
Jumo

Bill Dornbach
Chief Operating Officer
Viral Sign

Stewart Skomra

Chief Executive Officer
OmniQuest

Adrian Munguia
Founder & Director
AI MEXICO

Dovev Goldstein
Founder & Co-CEO
myAir Smart-food

Event Hosts & Facilitators

Dalith Steiger

Co-Founder, Global AI Thought Leader
SwissCognitive – The Global AI Hub

 

Andy Fitze

Co-Founder, Digital Transformation Strategist
SwissCognitive – The Global AI Hub

 

Event Team

Livia Spiesz

Global Events & Communications
SwissCognitive – The Global AI Hub

Valeska Hoenen

Content and Social Media Manager
SwissCognitive – The Global AI Hub

CognitiveVirtual

CognitiveVirtuals are regular worldwide-reaching online events bringing dozens of global AI leaders and experts together to share their views, experiences and expertise in the development of AI to the benefit of business and society. These 3 hour-long events are transparently addressing the development of cognitive technologies – including successes and challenges – while reaching and connecting a global online community of over ½ million followers.

All the sessions and formats are strictly content-driven with a non-sales approach, allowing focused and open discussions with no BS just content. These events provide not only a platform to brainstorm and network but also to position experts, leaders, organisation, research developments, the current status and future outlook of AI. 

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A 12-month series of stories explores the social and economic questions arising from the fast-spreading uses of artificial intelligence.

copyright by www.seattletimes.com

QALANDIA CHECKPOINT, West Bank – For tens of thousands of Palestinians in the West Bank, the daily voyage into Israel for work, family visits and other business begins at this checkpoint near Jerusalem, reminiscent of a passage through a prison portal. Concrete walls surround the grey building at the Qalandia crossing that looks part transit terminal, part military bunker. Inside, families squeeze large suitcases through a labyrinthine hallway to reach a set of high-tech turnstiles armed with something as controversial as the checkpoint itself: facial recognition scanners.

On a recent late morning, a scanner’s green light washed over Khaled Habyeab’s face as he placed his magnetic ID card on a reader. The gate’s clear doors swished open. Like other Palestinians traveling to Israel for work, doctor’s visits or tourism, the Central West Bank resident must carry an ID card and a permit that lists his reason for visiting. Habyeab’s clean record, devoid of arrests and political activity, allowed him to receive a biometrics card from Israel’s Civil Administration office the same day he applied, the 30-year-old restaurant manager said. He planned to spend his day off touring Tel Aviv with his wife.

While the facial recognition systems installed at Qalandia and 26 other checkpoints last summer have drawn the ire of human rights advocates, Habyeab remarked on its effectiveness in comparison to the long waits in previous years when security guards manually checked IDs. The checkpoint was once packed with people waiting in a chaotic scene that resembled “chickens going through the chicken [coop],” he said.

Still, the facial recognition scanners serve as a reminder of the ever-present Israeli occupation of the area, Habyeab said through a translator: “There is no freedom.”

The facial recognition scanners were developed by Israeli artificial intelligence (AI) security startup AnyVision, which has ties with Redmond-based Microsoft. Microsoft’s venture capital fund, M12, came under fire for participating in a $74 million investment in the AI security company last June. AnyVision has not responded to repeated requests for comment.

Seattle and Israel’s AI ecosystem have long had close ties, from Microsoft’s and Amazon’s acquisitions of Israeli startups to ongoing academic collaborations. The global expansion of AI surveillance technology has some roots in Israel, with its fingerprints stretching to the U.S. border with Mexico. A 2016 Privacy International report found that Israel boasts the most surveillance firms per capita in the world. In recent weeks, Israeli Prime Minister Benjamin Netanyahu announced the nation would use counterterrorism measures such as digital surveillance technology to track people with possible exposure to COVID-19. […]

Read more: www.seattletimes.com

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copyright by www.techradar.com

Today, a fresh generation of technologies, fueled by advances in artificial intelligence based on machine learning, is opening up new opportunities to reassess the upper bounds of operational excellence across these sectors.

To stay one step ahead of the pack, businesses not only need to understand the complexities of machine learning but also be prepared to act on it and take advantage.

After all, the latest machine learning solutions can determine weeks in advance if and when assets are likely to degrade or fail, distinguishing between normal and abnormal equipment and process behavior by recognizing complex data patterns and uncovering the precise signatures of degradation and failure.

Anomaly detection

“They can alert operators and even prescribe solutions to avoid the impending failure, or at least mitigate the consequences. The software constructs are autonomous and self-learning. They demonstrate a capability known as unsupervised machine learning, a specific method of learning patterns of performance or behavior using clustering techniques,” said Mike Brooks, Senior Director at Massachusetts-based asset optimization software company AspenTech.

Moreover, he said that it can be used to understand ‘normal’ operational behavior, based on signals from sensors on and around machines, and once the behavioral patterns are learned, analysis of new data can help detect deviations from the norm, called anomalies, highlighting mechanical issues and process changes that affect specific pieces of equipment.

However, he said the downside is that anomaly detection based on unsupervised learning may be fraught with errors and always requires human intervention.

“It is good at detecting correlations but less effective at working out causation. Unaided machine learning may find correlations that can be complete nonsense, such as the meaningless but the true correlation between reduced highway deaths in the US and the number of tonnes of lemons the country imports from Mexico,” he said.

Correlation is not the same as causation

When unsupervised machine learning detects an anomaly, Brooks said the change in behavior patterns could be just a new operating mode, or it could be an impending failure.

A human must take a look at the machine and decide which of the options is correct, he said, but such manual intervention can then help machine learning learn and adapt, effectively ensuring that moving forward it always provides analysis the business can trust.

After all, he added that correlation is not the same as causation, so machine learning needs human guidance to learn properly.

For example, he said that voice recognition technologies use machine learning, but cannot learn without help. The technology assessments need to be highly-stewarded by humans, who intercept unresolved phrases and apply translations to assist learning techniques.[…]

read more – copyright by www.techradar.com

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read more – copyright by reportsherald.com

Machine learning is an area of artificial intelligence and computer science that includes the development of software and algorithms that can make predictions based on data:

Scope of the Report:

This report studies the Machine Learning Courses market status and outlook of Global and major regions, from angles of players, countries, product types and end industries; this report analyzes the top players in global market, and splits the Machine Learning Courses market by product type and applications/end industries.

According to the report, one driver in the market is new learning patterns.

The global Machine Learning Courses market is valued at xx million USD in 2017 and is expected to reach xx million USD by the end of 2023, growing at a CAGR of xx% between 2017 and 2023.

The Asia-Pacific will occupy for more market share in following years, especially in China, also fast growing India and Southeast Asia regions.

North America, especially The United States, will still play an important role which cannot be ignored. Any changes from United States might affect the development trend of Machine Learning Courses.

Europe also play important roles in global market, with market size of xx million USD in 2017 and will be xx million USD in 2023, with a CAGR of xx%.

Market Segment by Companies, this report covers
EdX
Ivy Professional School
NobleProg
Udacity
Edvancer
Udemy
Simplilearn
Jigsaw Academy
BitBootCamp
Metis
DataCamp

Market Segment by Regions, regional analysis covers
North America (United States, Canada and Mexico)
Europe (Germany, France, UK, Russia and Italy)
Asia-Pacific (China, Japan, Korea, India and Southeast Asia)
South America (Brazil, Argentina, Colombia)
Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Market Segment by Applications, can be divided into
Data Mining
Computer Vision
Natural Language Processing
Biometrics Recognition
Search Engines
Medical Diagnostics
Detection Of Credit Card Fraud
Securities Market Analysis
DNA Sequencing[…]

read more – copyright by reportsherald.com

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copyright by info.microsoft.com

About this report Artificial intelligence (AI) will profoundly affect the ways in which businesses and governments engage with consumers and citizens alike. From advances in genetic diagnostics to industrial automation, these widespread changes will have significant economic, social and civic implications. As such, Intelligent Economies explores the transformative potential of AI on markets and societies across the developed and developing worlds.

This report, developed by The Economist Intelligence Unit and sponsored by Microsoft, draws on a survey of more than 400 senior executives working in various industries, including financial services, healthcare and life sciences, manufacturing, retail and the public sector. Survey respondents operate in eight markets: France, Germany, Mexico, Poland, South Africa, Thailand, the UK and the US.

Additionally, we conducted in-depth interviews with business leaders and experts in AI. We would like to thank the following for their insights and contributions to the research:

• Jeff Chen, lecturer, Gordon Institute of Business Science, University of Pretoria • Alex Konnaris, chief information officer, RMA Group • Juergen Maier, chief executive officer, Siemens UK • JP Rangaswami, chief data officer, Deutsche Bank • Daniel Ray, director of data science, NHS Digital • Sandra Wachter, research fellow, Oxford Internet Institute and Alan Turing Institute

Jessica Twentyman was the author of the report; Michael Hoffmann was the editor.

INTELLIGENT ECONOMIES: AIâ€s transformation of industries and society

Executive summary

Artificial intelligence (AI) is moving from the realm of science fiction to real- world adoption among private- and public-sector organisations globally. Today AI is used by financial services companies to serve customers better and detect fraud; by healthcare providers to more accurately diagnose illness and identify more effective treatments; by manufacturers to keep machines up and running on the plant floor and to streamline supply chains; and by city authorities to track and mitigate urban challenges such as traffic, pollution and crime.

As AI becomes increasingly embedded in society, it will not only change the businesses that adopt it but also have significant economic, social and civic effects on citizens and consumers. In short, national and regional economies will become more intelligent in the ways they produce and distribute goods and services. But such transformations will also introduce new challenges. Indeed, policymakers, economists and technology stakeholders are observing these changes closely, often with commitments to protecting the rights of workers whose jobs may be replaced by automation.[…]

read more – copyright by info.microsoft.com

Sponsored by:
INTELLIGENT ECONOMIES: AIâ€s transformation of industries and society
A report from The Economist Intelligence Unit
2© The Economist Intelligence Unit Limited 2018

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copyright by analyticmarket.com

Some key factors such as population aging, personalized service trends and robotic mobility will lead to growth in this industry sector. In addition, due to the development of AI (artificial intelligence) and cognitive computing, these technologies can be incorporated into almost any type of robot, including general-purpose robots that operate on their behalf. Industrial robotics is a decades-old market and will certainly transform with the Internet of Things (IoT) and in particular Industrial IoT (IIoT). However, there is an emerging personal robotics industry that has very different market dynamics than manufacturing or IIoT. Personal robotics includes both physical robots as well as logical (e.g. software) bots that act on behalf of their owners, managers, and/or controllers.

Researcher sees substantial overall industry growth across a wide range of robot types that engage in diverse tasks such as home cleaning, personalized healthcare service, home security, autonomous cars, robotic entertainment and toys, care bots services, managing daily schedule, and many more assistive tasks. A few key factors such as the ageing population, personalization services trends, and robot mobility will drive growth in this industry segment. In addition, developments in Artificial Intelligence (AI) and Cognitive Computing support inclusion of these technologies with virtually every type of robot including general purpose bots that act on behalf of their owner.

This research examines personalized robots, bot software, and systems. The report assesses the impact of AI and evaluates the market for AI enhanced robots and robotic systems for the consumer market. The report includes forecasts for 2019 to 2025 that cover the following: Market coverage globally and by region including APAC, Europe, North America, and Rest of the World.

Leading country coverage includes United States, China, Japan, South Korea, India, Indonesia, Australia, Germany, France, UK, Italy, Spain, Canada, Brazil, and Mexico […]

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Have you ever wanted to see all major swimming pools in Europe?

Want to know where all the wind and solar power supplies in the US are for some brilliant renewable-energy project? Or plot a round-the-world trip hitting every major soccer stadium along the way? It should be possible with a new tool that lets anyone scan the globe through AI “eyes” to instantly find satellite images of matching objects. Descartes Labs, a New Mexico startup that provides AI-driven analysis of satellite images to governments, academics and industry, on Tuesday released a public demo of its GeoVisual Search, a new type of search engine that combines satellite images of Earth with machine learning on a massive scale.

GeoVisual and its potential

The idea behind GeoVisual is pretty simple. Pick an object anywhere on Earth that can be seen from space, and the system returns a list of similar-looking objects and their locations on the planet. It’s cool to play with, which you can do at the Descartes site here. A short search for wind turbines had me dreaming of a family road trip where every pit stop was sure to include kite-flying for the kids. Perhaps this sounds just like Google Earth to you, but keep in mind that tool just allows you to find countless geotagged locations around the world. GeoVisual Search actually compares all the pixels making up huge photos of the world to find matching objects as best it can, an ability that hasn’t been available to the public before on a global scale.

Just fun or is there more to it?

Fun as it is, the tool also gives the public a taste of Descartes’ broader work, which so far has focused largely on agricultural datasets that can do things like analyze crop yields. “The goal of this launch is to show people what’s possible with machine learning. Our aim is to use this data to model complex planetary systems, and this is just the first step,” CEO and co-founder Mark Johnson said via email. “We want businesses to think about how new kinds of data will help to improve their work. And I’d like everyone to think about how we can improve our life on this planet if we better understood it. […]

read more – copyright by www.cnet.com

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