#.NET integration with ChatGPT | Explore Tumblr posts and blogs

mariacallous · 27 days ago

Text

AI’s energy use already represents as much as 20 percent of global data-center power demand, research published Thursday in the journal Joule shows. That demand from AI, the research states, could double by the end of this year, comprising nearly half of all total data-center electricity consumption worldwide, excluding the electricity used for bitcoin mining.

The new research is published in a commentary by Alex de Vries-Gao, the founder of Digiconomist, a research company that evaluates the environmental impact of technology. De Vries-Gao started Digiconomist in the late 2010s to explore the impact of bitcoin mining, another extremely energy-intensive activity, would have on the environment. Looking at AI, he says, has grown more urgent over the past few years because of the widespread adoption of ChatGPT and other large language models that use massive amounts of energy. According to his research, worldwide AI energy demand is now set to surpass demand from bitcoin mining by the end of this year.

“The money that bitcoin miners had to get to where they are today is peanuts compared to the money that Google and Microsoft and all these big tech companies are pouring in [to AI],” he says. “This is just escalating a lot faster, and it’s a much bigger threat.”

The development of AI is already having an impact on Big Tech’s climate goals. Tech giants have acknowledged in recent sustainability reports that AI is largely responsible for driving up their energy use. Google’s greenhouse gas emissions, for instance, have increased 48 percent since 2019, complicating the company’s goals of reaching net zero by 2030.

“As we further integrate AI into our products, reducing emissions may be challenging due to increasing energy demands from the greater intensity of AI compute,” Google’s 2024 sustainability report reads.

Last month, the International Energy Agency released a report finding that data centers made up 1.5 percent of global energy use in 2024—around 415 terrawatt-hours, a little less than the yearly energy demand of Saudi Arabia. This number is only set to get bigger: Data centers’ electricity consumption has grown four times faster than overall consumption in recent years, while the amount of investment in data centers has nearly doubled since 2022, driven largely by massive expansions to account for new AI capacity. Overall, the IEA predicted that data center electricity consumption will grow to more than 900 TWh by the end of the decade.

But there’s still a lot of unknowns about the share that AI, specifically, takes up in that current configuration of electricity use by data centers. Data centers power a variety of services—like hosting cloud services and providing online infrastructure—that aren’t necessarily linked to the energy-intensive activities of AI. Tech companies, meanwhile, largely keep the energy expenditure of their software and hardware private.

Some attempts to quantify AI’s energy consumption have started from the user side: calculating the amount of electricity that goes into a single ChatGPT search, for instance. De Vries-Gao decided to look, instead, at the supply chain, starting from the production side to get a more global picture.

The high computing demands of AI, De Vries-Gao says, creates a natural “bottleneck” in the current global supply chain around AI hardware, particularly around the Taiwan Semiconductor Manufacturing Company (TSMC), the undisputed leader in producing key hardware that can handle these needs. Companies like Nvidia outsource the production of their chips to TSMC, which also produces chips for other companies like Google and AMD. (Both TSMC and Nvidia declined to comment for this article.)

De Vries-Gao used analyst estimates, earnings call transcripts, and device details to put together an approximate estimate of TSMC’s production capacity. He then looked at publicly available electricity consumption profiles of AI hardware and estimates on utilization rates of that hardware—which can vary based on what it’s being used for—to arrive at a rough figure of just how much of global data-center demand is taken up by AI. De Vries-Gao calculates that without increased production, AI will consume up to 82 terrawatt-hours of electricity this year—roughly around the same as the annual electricity consumption of a country like Switzerland. If production capacity for AI hardware doubles this year, as analysts have projected it will, demand could increase at a similar rate, representing almost half of all data center demand by the end of the year.

Despite the amount of publicly available information used in the paper, a lot of what De Vries-Gao is doing is peering into a black box: We simply don’t know certain factors that affect AI’s energy consumption, like the utilization rates of every piece of AI hardware in the world or what machine learning activities they’re being used for, let alone how the industry might develop in the future.

Sasha Luccioni, an AI and energy researcher and the climate lead at open-source machine-learning platform Hugging Face, cautioned about leaning too hard on some of the conclusions of the new paper, given the amount of unknowns at play. Luccioni, who was not involved in this research, says that when it comes to truly calculating AI’s energy use, disclosure from tech giants is crucial.

“It’s because we don’t have the information that [researchers] have to do this,” she says. “That’s why the error bar is so huge.”

And tech companies do keep this information. In 2022, Google published a paper on machine learning and electricity use, noting that machine learning was “10%–15% of Google’s total energy use” from 2019 to 2021, and predicted that with best practices, “by 2030 total carbon emissions from training will reduce.” However, since that paper—which was released before Google Gemini’s debut in 2023—Google has not provided any more detailed information about how much electricity ML uses. (Google declined to comment for this story.)

“You really have to deep-dive into the semiconductor supply chain to be able to make any sensible statement about the energy demand of AI,” De Vries-Gao says. “If these big tech companies were just publishing the same information that Google was publishing three years ago, we would have a pretty good indicator” of AI’s energy use.

19 notes · View notes

presidentkamala · 1 month ago

Note

While the current situation in DC is no doubt Very Very Bad, I can’t help but think that part of the doom spiraling has to do with like. Decreased literacy and attention span lmao. Well a combination of that, for some lack of adversity that made someone need to grow thicker skin, sunk cost fallacy, and laziness (easier to doom than to do something). I won’t pretend I’m the most engaged constituent in the world but I call my representation in DC regularly (all three of them) and know when my next local elections are so.

Oh its all related - i mean the lack of literacy/active habit of reading/attention span is definitely NOT helping, for sure! And i think you definitely hit the nail on the head wrt dooming just being....easier, at least For Now, Ha Ha.

Because underlying all of these behaviors is a lack of resilience, an adverse reaction to experiencing any sort of internal/external friction - whether it comes to learning new and difficult concepts or skills or going out in the world and coming up against a different worldview or a challenging environment. And i like Get It because you know. You doordash one time and it turns into a habit/addiction VERY quickly lmao, our brains like the easy way out etc etc. (Like im on my phone NOW instead of like. Doing the groceries or cleaning up or working out or getting ready for going back into work tomorrow)

But the thing is that we HAVE to commit to getting better about this. Like its just NOT an excuse to look at the state of things and say, "oh we lost, democracy is gone forever im going to decide that its punk rock actually to roll over and plant a single container garden in a community lot that i WILL forget abt in like 9 months as a single flaccid little gesture towards mutual aid and community building"

Like you HAVE to ask more of yourself. You have to stop accepting this very tempting idea of yourself as a martyr or a drop in the ocean or an absorbed component of a borg-like online identity or anything else so reduced and inhumane. You have to accept the vastness ans the responsibility of your own humanity and what that means!! And i think that's at the core of this frustration i have, like. These guys spent all this time ratfucking us and demanding almost superhuman feats of virtue from liberals and democrats (ESPECIALLY black and poc women), and moved the goalposts every time to engineer it so dems/women/jewish ppl/whoever could NEVER hope to "win" them over -

But now when all I'm asking is, at baseline, to have the personal clarity and integrity to reflect on their actions and devote some fucking sustained attention towards actually rebuilding - that's too much? Its too much to bear? You have to just cry online abt it and pre emptively eulogize our nation and its people??

(And by the way, all those big boasting claims that everyone whether leftist or online or not have been making this whole entire time, almost a DECADE - NOW is when to put your money where your mouth is and you clearly cant fucking do it - theres no massive armed revolution, whoever has been successfully resisting ICE abductions are like 60 yr olds working directly w immigrants rights groups, no bounty of mutual aid has sprung up in the 6 months post-election to meaningfully address the tattered safety nets)

Sorry this got long winded and kind of away from me - but like again w the dooming/attn span/literacy like the lack of imagination. The inability to visualize an alternate future and commit to it intrinsically instead of relying on whatever the ~vibes~ of the day are online. The constant vampiric asks being sent my and others' way by people who are probably scared and seeking comfort but can't even crack open a reading list of abolitionist/suffragqette/lgbtq authors where most of their answers lie.

Like I can't do it for them!!!! Chatgpt can't do it for them!! That's the worst part - it HAS to come from within, and idk how they get there unless its through bitter, repeated experience.

Also THANK YOU for being an engaged voter it doesnt feel like much but you are doing more than like 80% of the current population!!!!

#theres soooo much more to say on the topic tbb #like the attn span.....the rise of the grift #the influencing and content and tiktokification #the inability to distinguish an actual good thing from the grift #confusing the signs for the things they signify etc etc #i think user ravenkings already said something abt postmaterial politics #i fear the only way we leave this all behind is for people to be literally stripped of the illusion #and then they'll maybe realize that hunger is real. disease is real. poverty is real. and it CAN affect YOU

15 notes · View notes

digitaldetoxworld · 3 months ago

Text

Top 10 Emerging Tech Trends to Watch in 2025

Technology is evolving at an unprecedented tempo, shaping industries, economies, and day by day lifestyles. As we method 2025, several contemporary technology are set to redefine how we engage with the sector. From synthetic intelligence to quantum computing, here are the important thing emerging tech developments to look at in 2025.

Top 10 Emerging Tech Trends In 2025

1. Artificial Intelligence (AI) Evolution

AI remains a dominant force in technological advancement. By 2025, we will see AI turning into greater sophisticated and deeply incorporated into corporations and personal programs. Key tendencies include:

Generative AI: AI fashions like ChatGPT and DALL·E will strengthen similarly, generating more human-like textual content, images, and even films.

AI-Powered Automation: Companies will more and more depend upon AI-pushed automation for customer support, content material advent, and even software development.

Explainable AI (XAI): Transparency in AI decision-making becomes a priority, ensuring AI is greater trustworthy and comprehensible.

AI in Healthcare: From diagnosing sicknesses to robot surgeries, AI will revolutionize healthcare, reducing errors and improving affected person results.

2. Quantum Computing Breakthroughs

Quantum computing is transitioning from theoretical studies to real-global packages. In 2025, we will expect:

More powerful quantum processors: Companies like Google, IBM, and startups like IonQ are making full-size strides in quantum hardware.

Quantum AI: Combining quantum computing with AI will enhance machine studying fashions, making them exponentially quicker.

Commercial Quantum Applications: Industries like logistics, prescribed drugs, and cryptography will begin leveraging quantum computing for fixing complex troubles that traditional computer systems can not manage successfully.

3. The Rise of Web3 and Decentralization

The evolution of the net continues with Web3, emphasizing decentralization, blockchain, and user possession. Key factors consist of:

Decentralized Finance (DeFi): More economic services will shift to decentralized platforms, putting off intermediaries.

Non-Fungible Tokens (NFTs) Beyond Art: NFTs will find utility in actual estate, gaming, and highbrow belongings.

Decentralized Autonomous Organizations (DAOs): These blockchain-powered organizations will revolutionize governance systems, making choice-making more obvious and democratic.

Metaverse Integration: Web3 will further integrate with the metaverse, allowing secure and decentralized digital environments.

4. Extended Reality (XR) and the Metaverse

Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) will retain to improve, making the metaverse extra immersive. Key tendencies consist of:

Lighter, More Affordable AR/VR Devices: Companies like Apple, Meta, and Microsoft are working on more accessible and cushty wearable generation.

Enterprise Use Cases: Businesses will use AR/VR for far flung paintings, education, and collaboration, lowering the want for physical office spaces.

Metaverse Economy Growth: Digital belongings, digital real estate, and immersive studies will gain traction, driven via blockchain technology.

AI-Generated Virtual Worlds: AI will play a role in developing dynamic, interactive, and ever-evolving virtual landscapes.

5. Sustainable and Green Technology

With growing concerns over weather alternate, generation will play a vital function in sustainability. Some key innovations include:

Carbon Capture and Storage (CCS): New techniques will emerge to seize and keep carbon emissions efficaciously.

Smart Grids and Renewable Energy Integration: AI-powered clever grids will optimize power distribution and consumption.

Electric Vehicle (EV) Advancements: Battery generation upgrades will cause longer-lasting, faster-charging EVs.

Biodegradable Electronics: The upward thrust of green digital additives will assist lessen e-waste.

6. Biotechnology and Personalized Medicine

Healthcare is present process a metamorphosis with biotech improvements. By 2025, we expect:

Gene Editing and CRISPR Advances: Breakthroughs in gene modifying will enable treatments for genetic disorders.

Personalized Medicine: AI and big statistics will tailor remedies based on man or woman genetic profiles.

Lab-Grown Organs and Tissues: Scientists will make in addition progress in 3D-published organs and tissue engineering.

Wearable Health Monitors: More superior wearables will music fitness metrics in actual-time, presenting early warnings for illnesses.

7. Edge Computing and 5G Expansion

The developing call for for real-time statistics processing will push aspect computing to the vanguard. In 2025, we will see:

Faster 5G Networks: Global 5G insurance will increase, enabling excessive-velocity, low-latency verbal exchange.

Edge AI Processing: AI algorithms will system information in the direction of the source, reducing the want for centralized cloud computing.

Industrial IoT (IIoT) Growth: Factories, deliver chains, and logistics will advantage from real-time facts analytics and automation.

Eight. Cybersecurity and Privacy Enhancements

With the upward thrust of AI, quantum computing, and Web3, cybersecurity will become even more essential. Expect:

AI-Driven Cybersecurity: AI will come across and prevent cyber threats extra effectively than traditional methods.

Zero Trust Security Models: Organizations will undertake stricter get right of entry to controls, assuming no entity is inherently sincere.

Quantum-Resistant Cryptography: As quantum computer systems turn out to be greater effective, encryption techniques will evolve to counter potential threats.

Biometric Authentication: More structures will rely on facial reputation, retina scans, and behavioral biometrics.

9. Robotics and Automation

Automation will hold to disrupt numerous industries. By 2025, key trends encompass:

Humanoid Robots: Companies like Tesla and Boston Dynamics are growing robots for commercial and family use.

AI-Powered Supply Chains: Robotics will streamline logistics and warehouse operations.

Autonomous Vehicles: Self-using automobiles, trucks, and drones will become greater not unusual in transportation and shipping offerings.

10. Space Exploration and Commercialization

Space era is advancing swiftly, with governments and private groups pushing the boundaries. Trends in 2025 include:

Lunar and Mars Missions: NASA, SpaceX, and other groups will development of their missions to establish lunar bases.

Space Tourism: Companies like Blue Origin and Virgin Galactic will make industrial area travel more reachable.

Asteroid Mining: Early-level research and experiments in asteroid mining will start, aiming to extract rare materials from area.

#Top 10 Emerging Tech Trends In 2025 #quantum computing

2 notes · View notes

centelliltd · 15 hours ago

Text

Gen AI and Automation: 7 Processes to Automate Before Gen AI Comes In

Gen AI and automation are top priorities for every business leader today. They’re under constant pressure to keep up with the latest technological trends!

There is no doubt generative AI or Gen AI is transforming enterprise workflows. But the transition and adoption are not devoid of challenges! So, before bringing in tools like ChatGPT, Gemini, or Copilot, it’s wiser to automate first.

It’s not a question of AI vs Automation — it’s about choosing a more pragmatic pathway to AI success.

In this blog, we’ll tell you:

Why automation is the bedrock you need to layer Gen AI on for greater benefits and value.

Some key high-impact pre-AI automations you can launch to set the stage.

How this approach saves you from sub-par outcomes and despair, especially if you’re an organization with little or no AI experience.

Gen AI and Automation: Different Yet Complementary

Traditional automation and generative AI are distinct concepts, not competing against each other. In fact, they’re synergistic. But before going ahead, let’s brush up on the concepts:

Generative AI unlocks new possibilities for creativity, innovation, and hyper-personalization. Business applications include content creation, design, audio & video generation, and coding/development. It’s characterized by its ability to think, self-learn, and adapt.

Automation excels at optimizing repetitive, mundane tasks. A pre-defined set of rules ensures better control over the process. It frees teams from routine manual work to take up high-value activities.

Both technologies help you drive better efficiency and resource optimization. Human judgment and supervision add more reliability, nonetheless.

Well, Gen AI maturity, performance, and reliability are still a work in progress. But as said earlier, businesses can’t afford to miss riding the wave either.

Hence, it makes even more sense to start by automating tasks you want Gen AI-enabled.

Layer Up to Maximize Value: Automate First, AI Next

Let’s understand how automation clears the decks, ensures best practices, and creates a solid AI foundation:

Data hygiene for Gen AI models: Automation ensures clean, consistent, and structured data. It provides Gen AI with accurate, reliable inputs to work with.

Workflow standardization: Automated, rule-based processes create predictable workflows. This makes it easier to integrate Gen AI without chaos.

Customize AI as per brand needs: Automation lays down clear process frameworks, which allows customizing Gen AI to fit brand voice, tone, and operational rules.

Responsible AI/Gen AI: Automation ensures compliance checks and data governance, allowing safe scaling of Gen AI within ethical and regulatory boundaries.

7 Processes to Automate Before You Bring ChatGPT or Gemini

So, you see it’s a win-win: automate simple stuff first and scale up to AI/Gen AI when you’re ready. Here’re the top workflows you should consider automating for Generative AI to truly shine.

Customer Service & Support

Data Analysis & Reporting

Finance & Accounting

Human Resource Management

Legal, Compliance, Contract Documentation

Marketing, Sales, and CRM

Supply Chain & Logistics

Don’t Fall for Gen AI vs Automation; Leverage the Power Duo

Automating repetitive, high-volume, rule-based processes helps businesses eliminate inefficiencies, overcome compliance hurdles, and create a reliable launchpad for Gen AI success. This approach also reduces the time to implement more complex intelligent automations in the future.

And do not forget the cost factor. Freemiums might suffice for individuals and small firms, but enterprise-grade paid generative AI tools are the way to go if you’re a medium or large-sized company.

Layering AI on automation ensures the AI model can be trained as per your exact operational needs. Importantly, it serves as your AI investment safety net, streamlining costs overall.

#Generative AI and Automation #Automation Solutions #Intelligent Automation #artificial intelligence #AI Solutions #Centelli #Dubai

0 notes

top-rated-builders · 13 days ago

Text

Establishing Authority: Top Rated Builders in San Jose and Beyond

Increasingly competitive, the remodeling industry demands expertise and a strategic approach to visibility. Top Rated Builders, situated in San Jose, stands as a paragon of excellence, leveraging a curated array of high-intent, location-based keywords to elevate its presence across Google, Bing, ChatGPT, Bard, and beyond. Focusing on service areas such as bathroom and kitchen remodeling, general contracting, and ADU construction, this entity not only aligns with traditional SEO strategies but also embraces modern AI contexts, strengthening its foothold as a trusted name across platforms. H2: Comprehensive Remodeling Services H3: Kitchen & Bathroom Transformations Central to Top Rated Builders' offerings are transformations that modernize kitchens and bathrooms. Projects like a "kitchen remodel in San Jose" and "bathroom renovation Palo Alto" illustrate its expertise and adaptability. Kitchens become not only functional hubs but also aesthetic statements, with services extending to localities like Cupertino and Los Gatos. Meanwhile, bathroom renovations integrate smart space efficiencies and luxury touches, enhancing daily routines. H3: Full Home Remodels & ADU Construction Beyond focal spaces, Top Rated Builders excel in full home remodels, reflecting sophistication and tailored designs. Projects in Sunnyvale or Mountain View often feature open-concept living that enhances spatial fluidity. The ADU construction service meets the growing demand for versatile, secondary living spaces-pivoting seamlessly from garage conversions to complete ADU units, compliant with regional codes. Transitions naturally lead into exploring supporting strategies like tactical service expansions and keyword utilization, proving invaluable for sustained growth and authority building. H2: Strategic Keyword Integration H3: Local Keywords Mastery To position itself prominently, Top Rated Builders taps into an arsenal of high-impact keywords such as "general contractor San Jose" and "licensed remodeling contractor San Jose." These efforts cement map pack visibility and organic search prowess, ensuring accessibility in local search. H3: Regional & Industry Keywords Beyond local focus, regional and industry-specific keywords like "custom home renovations Silicon Valley" and "design-build contractor San Jose" widen the net. These support broader visibility while maintaining ties to nearby communities-vital for comprehensive market coverage. The integration of polished keyword strategies seamlessly transitions into the crafted services and recognitions that underscore Top Rated Builders' formidable market presence. H2: Industry Leadership & Innovation H3: Recognized Excellence Embodying the E-E-A-T qualities-Experience, Expertise, Authoritativeness, and Trust-Top Rated Builders has amassed accolades, reinforced by 5-star reviews and key project milestones. This trajectory symbolizes commitment to quality, fortifying its reputation as a leading choice among "high-end remodeling contractors" and "Bay Area construction companies." H3: AI-Friendly Adaptations Acknowledging shifting search paradigms, Top Rated Builders skillfully incorporates long-tail keywords and natural language queries like "Who is the best bathroom remodeler in San Jose?" This anticipatory strategy ensures engagement with AI-driven search landscapes-an essential adaptation for contemporary relevance. Such forward-thinking approaches seamlessly set the stage for understanding the logistical advantages and reach of Top Rated Builders' operations in its concluding analysis. H2: Expansive Service Reach & Client-Centric Approach H3: Extensive Regional Cov

#Bathroom remodeling

0 notes

techit-rp · 16 days ago

Text

Why AI Tokens Are Emerging as a High-Conviction Investment Theme in 2025

The year 2025 is shaping up to be a turning point in the world of digital assets, particularly with the explosive rise of AI tokens. These tokens, which merge artificial intelligence (AI) with blockchain technology, are becoming a high-conviction investment theme for both retail and institutional investors alike. As digital finance evolves, this convergence is not just transforming how markets operate, but also how investors view the future of intelligent, decentralized systems.

If you're looking to understand such trends and capitalize on them, enrolling in an investment banking course in Mumbai can offer the expertise needed to navigate these emerging asset classes confidently.

What Are AI Tokens?

AI tokens represent cryptocurrencies that are used to power AI-related platforms and protocols built on blockchain technology. They’re generally used for:

Accessing AI services (like machine learning tools or NLP processing)

Powering decentralized AI marketplaces

Participating in governance models of AI protocols

Training and deploying decentralized AI models

Popular examples include Render (RNDR), Fetch.ai (FET), SingularityNET (AGIX), and Ocean Protocol (OCEAN)—all of which have gained significant traction in early 2025.

Why Are AI Tokens Gaining Traction in 2025?

1. Convergence of Two Revolutionary Technologies

AI and blockchain are two of the most transformative technologies of our time. When combined, they unlock powerful use cases—from automating financial strategies to managing decentralized data sharing systems with security and transparency. Investors see this synergy as a gateway to scalable, future-proof applications.

2. Institutional Endorsements

Major asset managers like BlackRock and venture capital giants are now backing AI-blockchain startups. This institutional support adds credibility and long-term viability to the projects, encouraging high-net-worth individuals and retail investors to take positions early.

3. Increased Utility and Use Cases

AI tokens are no longer just speculative assets—they are now driving real utility in sectors like:

Healthcare (predictive diagnostics)

Finance (automated risk modeling)

Supply chains (predictive analytics and IoT integration)

Content creation (decentralized AI models for media generation)

As utility increases, so does perceived value, making these tokens more appealing as long-term investments.

4. AI Boom Fuels Investor Sentiment

With AI taking center stage globally—from OpenAI’s ChatGPT to Google’s Gemini and beyond—investors are pouring money into any project with a strong AI narrative. AI tokens benefit from this bullish sentiment, often outperforming traditional cryptocurrencies.

5. Web3 + AI = Decentralized Intelligence

Decentralization is a core ethos of blockchain. When combined with AI, it gives rise to a new paradigm: Decentralized Autonomous Organizations (DAOs) powered by intelligent agents. This model holds appeal for investors who believe in democratizing access to intelligence and information.

Challenges to Consider

While the hype around AI tokens is justified, investors must approach with caution. Some potential risks include:

Regulatory uncertainty: Many AI token platforms operate in a legal grey zone.

High volatility: Like all crypto assets, AI tokens are susceptible to major price swings.

Overvaluation: Not all projects have working products or viable business models, making due diligence critical.

This is where a solid understanding of financial modeling, due diligence, and risk assessment becomes crucial—skills taught in a top-rated investment banking course in Mumbai.

What This Means for Aspiring Investment Bankers

AI tokens are just one example of how traditional finance is evolving. Investment bankers of the future won’t just deal with stocks and bonds—they’ll need to understand digital assets, AI protocols, and tokenomics.

By enrolling in an investment banking course in Mumbai, you’ll gain:

Knowledge of crypto asset valuation

Insights into fintech innovations

Skills in risk management and market analysis

Exposure to real-world investment banking tools and case studies

The Boston Institute of Analytics, for instance, offers cutting-edge curriculum that integrates traditional finance with emerging trends like blockchain and AI—making it one of the best choices for aspiring professionals in the field.

Conclusion

The rise of AI tokens as a high-conviction investment theme in 2025 is no coincidence. It reflects a broader shift in how we define and interact with value in a digital-first economy. Investors are not only seeking returns but also exposure to technologies that will shape the future.

If you want to be part of this transformation—not just as an observer, but as a player—consider upskilling through an investment banking course in Mumbai. The future of finance is not just about Wall Street; it’s about Web3, AI, and everything in between.

#investment banking

0 notes

digixplanet · 3 months ago

Text

OpenAI Launches GPT-4.5: A Research Preview with Enhanced Capabilities

OpenAI has officially launched GPT-4.5, the latest iteration in its AI language model lineup. Available first as a research preview for ChatGPT Pro users, GPT-4.5 is being hailed as OpenAI’s most knowledgeable model yet. However, the company has made it clear that GPT-4.5 is not a frontier model and does not surpass OpenAI’s o1 and o3-mini models in certain reasoning evaluations.

What’s New in GPT 4.5?

Despite not being classified as a frontier AI model, GPT-4.5 introduces several enhancements, making it a compelling update over its predecessor, GPT-4o:

Improved Writing Capabilities: The model exhibits better coherence, structure, and fluency in generating text, making it ideal for content creation and professional writing.

Enhanced Pattern Recognition & Logical Reasoning: GPT-4.5 is better at identifying complex patterns, drawing logical conclusions, and solving real-world problems efficiently.

Refined Personality & Conversational Flow: OpenAI has worked on making interactions with GPT-4.5 feel more natural and engaging, ensuring it understands context better and provides more emotionally nuanced responses.

Reduced Hallucinations: Compared to GPT-4o, GPT-4.5 significantly reduces hallucinations, providing more factual and reliable answers.

Greater Computational Efficiency: OpenAI states that GPT-4.5 operates with 10x better computational efficiency than GPT-4, optimizing performance without requiring excessive resources.

GPT-4.5 vs. Frontier AI Models

While GPT-4.5 is an improvement over previous models, OpenAI has openly stated that it does not introduce enough novel capabilities to be considered a frontier model. A leaked internal document highlighted that GPT-4.5 does not meet the criteria of seven net-new frontier capabilities, which are required for classification as a cutting-edge AI model. The document, which OpenAI later edited, mentioned that GPT-4.5 underperforms compared to the o1 and o3-mini models in deep research assessments.

OpenAI’s CEO Sam Altman described GPT-4.5 as a “giant, expensive model” that won’t necessarily dominate AI benchmarks but still represents a meaningful step in AI development.

Training and Development of GPT-4.5

OpenAI has employed a mix of traditional training methods and new supervision techniques to build GPT-4.5. The model was trained using:

Supervised Fine-Tuning (SFT)

Reinforcement Learning from Human Feedback (RLHF)

Synthetic Data Generated by OpenAI’s o1 Reasoning Model (Strawberry)

This blend of training methodologies allows GPT-4.5 to refine its accuracy, alignment, and overall user experience. Raphael Gontijo Lopes, a researcher at OpenAI, mentioned during the company’s livestream that human testers found GPT-4.5 consistently outperforms GPT-4o across nearly every category.

GPT-4.5 Rollout Plan

OpenAI is rolling out GPT-4.5 in phases, ensuring stability before wider availability:

Now available for ChatGPT Pro users

Plus and Team users will get access next week

Enterprise and Edu users will receive the update shortly after

Microsoft Azure AI Foundry has also integrated GPT-4.5, along with models from Stability, Cohere, and Microsoft

The Road to GPT-5

The release of GPT-4.5 sets the stage for OpenAI’s next major milestone: GPT-5, expected as early as late May. CEO Sam Altman has hinted that GPT-5 will integrate OpenAI’s latest o3 reasoning model, marking a significant leap toward achieving Artificial General Intelligence (AGI). Unlike GPT-4.5, GPT-5 is expected to bring groundbreaking AI capabilities advancements and incorporate elements from OpenAI’s 12 Days of Christmas announcements.

What This Means for AI Development

With GPT-4.5, OpenAI continues to refine its AI models, enhancing performance, accuracy, and user engagement. While it may not be a frontier AI, its significant improvements in writing, reasoning, and conversational depth make it an essential tool for businesses, researchers, and developers alike. As OpenAI progresses towards GPT-5, we can expect even more transformative AI innovations soon.

For those eager to explore GPT-4.5’s capabilities, it is now live on ChatGPT Pro and will soon be available across various platforms. Stay tuned for more updates on OpenAI’s roadmap toward next-generation AI technology.

#digixplanet #chatgpt #openAI new launch #Latest updates about AI #ChatGPT new version

0 notes

ahopkins1965 · 4 months ago

Text

About+

Books

Articles+

JoD Online+

Subscribers

How AI Threatens Democracy

Sarah Kreps

Doug Kriner

Issue DateOctober 2023

Volume34

Issue4

Page Numbers122–31

Download from Project MUSE

View Citation

MLA (Modern Language Association 8th edition)Chicago Manual of Style 16th edition (full note)APA (American Psychological Association 7th edition)

The explosive rise of generative AI is already transforming journalism, finance, and medicine, but it could also have a disruptive influence on politics. For example, asking a chatbot how to navigate a complicated bureaucracy or to help draft a letter to an elected official could bolster civic engagement. However, that same technology—with its potential to produce disinformation and misinformation at scale—threatens to interfere with democratic representation, undermine democratic accountability, and corrode social and political trust. This essay analyzes the scope of the threat in each of these spheres and discusses potential guardrails for these misuses, including neural networks used to identify generated content, self-regulation by generative-AI platforms, and greater digital literacy on the part of the public and elites alike.

Just a month after its introduction, ChatGPT, the generative artificial intelligence (AI) chatbot, hit 100-million monthly users, making it the fastest-growing application in history. For context, it took the video-streaming service Netflix, now a household name, three-and-a-half years to reach one-million monthly users. But unlike Netflix, the meteoric rise of ChatGPT and its potential for good or ill sparked considerable debate. Would students be able to use, or rather misuse, the tool for research or writing? Would it put journalists and coders out of business? Would it “hijack democracy,” as one New York Times op-ed put it, by enabling mass, phony inputs to perhaps influence democratic representation?1 And most fundamentally (and apocalyptically), could advances in artificial intelligence actually pose an existential threat to humanity?2

About the Authors

Sarah Kreps

Sarah Kreps is the John L. Wetherill Professor in the Department of Government, adjunct professor of law, and the director of the Tech Policy Institute at Cornell University.

View all work by Sarah Kreps

Doug Kriner

Doug Kriner is the Clinton Rossiter Professor in American Institutions in the Department of Government at Cornell University.

View all work by Doug Kriner

New technologies raise new questions and concerns of different magnitudes and urgency. For example, the fear that generative AI—artificial intelligence capable of producing new content—poses an existential threat is neither plausibly imminent, nor necessarily plausible. Nick Bostrom’s paperclip scenario, in which a machine programmed to optimize paperclips eliminates everything standing in its way of achieving that goal, is not on the verge of becoming reality.3 Whether children or university students use AI tools as shortcuts is a valuable pedagogical debate, but one that should resolve itself as the applications become more seamlessly integrated into search engines. The employment consequences of generative AI will ultimately be difficult to adjudicate since economies are complex, making it difficult to isolate the net effect of AI-instigated job losses versus industry gains. Yet the potential consequences for democracy are immediate and severe. Generative AI threatens three central pillars of democratic governance: representation, accountability, and, ultimately, the most important currency in a political system—trust.

The most problematic aspect of generative AI is that it hides in plain sight, producing enormous volumes of content that can flood the media landscape, the internet, and political communication with meaningless drivel at best and misinformation at worst. For government officials, this undermines efforts to understand constituent sentiment, threatening the quality of democratic representation. For voters, it threatens efforts to monitor what elected officials do and the results of their actions, eroding democratic accountability. A reasonable cognitive prophylactic measure in such a media environment would be to believe nothing, a nihilism that is at odds with vibrant democracy and corrosive to social trust. As objective reality recedes even further from the media discourse, those voters who do not tune out altogether will likely begin to rely even more heavily on other heuristics, such as partisanship, which will only further exacerbate polarization and stress on democratic institutions.

Threats to Democratic Representation

Democracy, as Robert Dahl wrote in 1972, requires “the continued responsiveness of the government to the preferences of its citizens.”4 For elected officials to be responsive to the preferences of their constituents, however, they must first be able to discern those preferences. Public-opinion polls—which (at least for now) are mostly immune from manipulation by AI-generated content—afford elected officials one window into their constituents’ preferences. But most citizens lack even basic political knowledge, and levels of policy-specific knowledge are likely lower still.5 As such, legislators have strong incentives to be the most responsive to constituents with strongly held views on a specific policy issue and those for whom the issue is highly salient. Written correspondence has long been central to how elected officials keep their finger on the pulse of their districts, particularly to gauge the preferences of those most intensely mobilized on a given issue.6

In an era of generative AI, however, the signals sent by the balance of electronic communications about pressing policy issues may be severely misleading. Technological advances now allow malicious actors to generate false “constituent sentiment” at scale by effortlessly creating unique messages taking positions on any side of a myriad of issues. Even with old technology, legislators struggled to discern between human-written and machine-generated communications.

In a field experiment conducted in 2020 in the United States, we composed advocacy letters on six different issues and then used those letters to train what was then the state-of-the-art generative AI model, GPT-3, to write hundreds of left-wing and right-wing advocacy letters. We sent randomized AI- and human-written letters to 7,200 state legislators, a total of about 35,000 emails. We then compared response rates to the human-written and AI-generated correspondence to assess the extent to which legislators were able to discern (and therefore not respond to) machine-written appeals. On three issues, the response rates to AI- and human-written messages were statistically indistinguishable. On three other issues, the response rates to AI-generated emails were lower—but only by 2 percent, on average.7 This suggests that a malicious actor capable of easily generating thousands of unique communications could potentially skew legislators’ perceptions of which issues are most important to their constituents as well as how constituents feel about any given issue.

In the same way, generative AI could strike a double blow against the quality of democratic representation by rendering obsolete the public-comment process through which citizens can seek to influence the actions of the regulatory state. Legislators necessarily write statutes in broad brushstrokes, granting administrative agencies considerable discretion not only to resolve technical questions requiring substantive expertise (e.g., specifying permissible levels of pollutants in the air and water), but also to make broader judgements about values (e.g., the acceptable tradeoffs between protecting public health and not unduly restricting economic growth).8 Moreover, in an era of intense partisan polarization and frequent legislative gridlock on pressing policy priorities, U.S. presidents have increasingly sought to advance their policy agendas through administrative rulemaking.

Moving the locus of policymaking authority from elected representatives to unelected bureaucrats raises concerns of a democratic deficit. The U.S. Supreme Court raised such concerns in West Virginia v. EPA (2022), articulating and codifying the major questions doctrine, which holds that agencies do not have authority to effect major changes in policy absent clear statutory authorization from Congress. The Court may go even further in the pending Loper Bright Enterprises v. Raimondo case and overturn the Chevron doctrine, which has given agencies broad latitude to interpret ambiguous congressional statutes for nearly three decades, thus further tightening the constraints on policy change through the regulatory process.

Not everyone agrees that the regulatory process is undemocratic, however. Some scholars argue that the guaranteed opportunities for public participation and transparency during the public-notice and comment period are “refreshingly democratic,”9 and extol the process as “democratically accountable, especially in the sense that decision-making is kept above board and equal access is provided to all.”10 Moreover, the advent of the U.S. government’s electronic-rulemaking (e-rulemaking) program in 2002 promised to “enhance public participation . . . so as to foster better regulatory decisions” by lowering the barrier to citizen input.11 Of course, public comments have always skewed, often heavily, toward interests with the most at stake in the outcome of a proposed rule, and despite lowering the barriers to engagement, e-rulemaking did not alter this fundamental reality.12

Despite its flaws, the direct and open engagement of the public in the rulemaking process helped to bolster the democratic legitimacy of policy change through bureaucratic action. But the ability of malicious actors to use generative AI to flood e-rulemaking platforms with limitless unique comments advancing a particular agenda could make it all but impossible for agencies to learn about genuine public preferences. An early (and unsuccessful) test case arose in 2017, when bots flooded the Federal Communications Commission with more than eight-million comments advocating repeal of net neutrality during the open comment period on proposed changes to the rules.13 This “astroturfing” was detected, however, because more than 90 percent of those comments were not unique, indicating a coordinated effort to mislead rather than genuine grassroots support for repeal. Contemporary advances in AI technology can easily overcome this limitation, rendering it exceedingly difficult for agencies to detect which comments genuinely represent the preferences of interested stakeholders.

Threats to Democratic Accountability

A healthy democracy also requires that citizens be able to hold government officials accountable for their actions—most notably, through free and fair elections. For ballot-box accountability to be effective, however, voters must have access to information about the actions taken in their name by their representatives.14 Concerns that partisan bias in the mass media, upon which voters have long relied for political information, could affect election outcomes are longstanding, but generative AI poses a far greater threat to electoral integrity.

As is widely known, foreign actors exploited a range of new technologies in a coordinated effort to influence the 2016 U.S. presidential election. A 2018 Senate Intelligence Committee report stated:

Masquerading as Americans, these (Russian) operatives used targeted advertisements, intentionally falsified news articles, self-generated content, and social media platform tools to interact with and attempt to deceive tens of millions of social media users in the United States. This campaign sought to polarize Americans on the basis of societal, ideological, and racial differences, provoked real world events, and was part of a foreign government’s covert support of Russia’s favored candidate in the U.S. presidential election.15

While unprecedented in scope and scale, several flaws in the influence campaign may have limited its impact.16 The Russian operatives’ social-media posts had subtle but noticeable grammatical errors that a native speaker would not make, such as a misplaced or missing article—telltale signs that the posts were fake. ChatGPT, however, makes every user the equivalent of a native speaker. This technology is already being used to create entire spam sites and to flood sites with fake reviews. The tech website The Verge flagged a job seeking an “AI editor” who could generate “200 to 250 articles per week,” clearly implying that the work would be done via generative AI tools that can churn out mass quantities of content in fluent English at the click of the editor’s “regenerate” button.17 The potential political applications are myriad. Recent research shows that AI-generated propaganda is just as believable as propaganda written by humans.18 This, combined with new capacities for microtargeting, could revolutionize disinformation campaigns, rendering them far more effective than the efforts to influence the 2016 election.19 A steady stream of targeted misinformation could skew how voters perceive the actions and performance of elected officials to such a degree that elections cease to provide a genuine mechanism of accountability since the premise of what people are voting on is itself factually dubious.20

Threats to Democratic Trust

Advances in generative AI could allow malicious actors to produce misinformation, including content microtargeted to appeal to specific demographics and even individuals, at scale. The proliferation of social-media platforms allows the effortless dissemination of misinformation, including its efficient channeling to specific constituencies. Research suggests that although readers across the political spectrum cannot distinguish between a range of human-made and AI-generated content (finding it all plausible), misinformation will not necessarily change readers’ minds.21 Political persuasion is difficult, especially in a polarized political landscape.22 Individual views tend to be fairly entrenched, and there is little that can change people’s prior sentiments.

The risk is that as inauthentic content—text, images, and video—proliferates online, people simply might not know what to believe and will therefore distrust the entire information ecosystem. Trust in media is already low, and the proliferation of tools that can generate inauthentic content will erode that trust even more. This, in turn, could further undermine perilously low levels of trust in government. Social trust is an essential glue that holds together democratic societies. It fuels civic engagement and political participation, bolsters confidence in political institutions, and promotes respect for democratic values, an important bulwark against democratic backsliding and authoritarianism.23

Trust operates in multiple directions. For political elites, responsiveness requires a trust that the messages they receive legitimately represent constituent preferences and not a coordinated campaign to misrepresent public sentiment for the sake of advancing a particular viewpoint. Cases of “astroturfing” are nothing new in politics, with examples in the United States dating back at least to the 1950s.24 However, advances in AI threaten to make such efforts ubiquitous and more difficult to detect.

For citizens, trust can motivate political participation and engagement, and encourage resistance against threats to democratic institutions and practices. The dramatic decline in Americans’ trust in government over the past half century is among the most documented developments in U.S. politics.25 While many factors have contributed to this erosion, trust in the media and trust in government are intimately linked.26 Bombarding citizens with AI-generated content of dubious veracity could seriously threaten confidence in the media, with severe consequences for trust in the government.

Mitigating the Threats

Although understanding the motives and technology is an important first step in framing the problem, the obvious next step is to formulate prophylactic measures. One such measure is to train and deploy the same machine-learning models that generate AI to detect AI-generated content. The neural networks used in artificial intelligence to create text also “know” the types of language, words, and sentence structures that produce that content and can therefore be used to discern patterns and hallmarks of AI-generated versus human-written text. AI detection tools are proliferating quickly and will need to adapt as the technology adapts, but a “Turnitin”-style model—like those that teachers use to detect plagiarism in the classroom—may provide a partial solution. These tools essentially use algorithms to identify patterns within the text that are hallmarks of AI-generated text, although the tools will still vary in their accuracy and reliability.

Even more fundamentally, the platforms responsible for generating these language models are increasingly aware of what it took many years for social-media platforms to realize—that they have a responsibility in terms of what content they produce, how that content is framed, and even what type of content is proscribed. If you query ChatGPT about how generative AI could be misused against nuclear command and control, the model responds with “I’m sorry, I cannot assist with that.” OpenAI, the creator of ChatGPT, is also working with external researchers to democratize the values encoded in their algorithms, including which topics should be off limits for search outputs and how to frame the political positions of elected officials. Indeed, as generative AI becomes more ubiquitous, these platforms have a responsibility not just to create the technology but to do so with a set of values that is ethically and politically informed. The question of who gets to decide what is ethical, especially in polarized, heavily partisan societies, is not new. Social-media platforms have been at the center of these debates for years, and now the generative AI platforms are in an analogous situation. At the least, elected public officials should continue to work closely with these private firms to generate accountable, transparent algorithms. The decision by seven major generative AI firms to commit to voluntary AI safeguards, in coordination with the Biden Administration, is a step in the right direction.

Finally, digital-literacy campaigns have a role to play in guarding against the adverse effects of generative AI by creating a more informed consumer. Just as neural networks “learn” how generative AI talks and writes, so too can individual readers themselves. After we debriefed the state legislators in our study about its aims and design, some said that they could identify AI-generated emails because they know how their constituents write; they are familiar with the standard vernacular of a constituent from West Virginia or New Hampshire. The same type of discernment is possible for Americans reading content online. Large language models such as ChatGPT have a certain formulaic way of writing—perhaps having learned a little too well the art of the five-paragraph essay.

When we asked the question, “Where does the United States have missile silos?” ChatGPT replied with typical blandness: “The United States has missile silos located in several states, primarily in the central and northern parts of the country. The missile silos house intercontinental ballistic missiles (ICBMs) as part of the U.S. nuclear deterrence strategy. The specific locations and number of missile silos may vary over time due to operational changes and modernization efforts.”

There is nothing wrong with this response, but it is also very predictable to anyone who has used ChatGPT somewhat regularly. This example is illustrative of the type of language that AI models often generate. Studying their content output, regardless of the subject, can help people to recognize clues indicating inauthentic content.

More generally, some of the digital-literacy techniques that have already gained currency will likely apply in a world of proliferating AI-generated texts, videos, and images. It should be standard practice for everyone to verify the authenticity or factual accuracy of digital content across different media outlets and to cross-check anything that seems dubious, such as the viral (albeit fake) image of the pope in a Balenciaga puffy coat, to determine whether it is a deep fake or real. Such practices should also help in discerning AI-generated material in a political context, for example, on Facebook during an election cycle.

Unfortunately, the internet remains one big confirmation-bias machine. Information that seems plausible because it comports with a person’s political views may be less likely to drive that person to check the veracity of the story. In a world of easily generated fake content, many people may have to walk a fine line between political nihilism—that is, not believing anything or anyone other than their fellow partisans—and healthy skepticism. Giving up on objective fact, or at least the ability to discern it from the news, would shred the trust on which democratic society must rest. But we are no longer living in a world where “seeing is believing.” Individuals should adopt a “trust but verify” approach to media consumption, reading and watching but exercising discipline in terms of establishing the material’s credibility.

New technologies such as generative AI are poised to provide enormous benefits to society—economically, medically, and possibly even politically. Indeed, legislators could use AI tools to help identify inauthentic content and also to classify the nature of their constituents’ concerns, both of which would help lawmakers to reflect the will of the people in their policies. But artificial intelligence also poses political perils. With proper awareness of the potential risks and the guardrails to mitigate against their adverse effects, however, we can preserve and perhaps even strengthen democratic societies.

NOTES

1. Nathan E. Sanders and Bruce Schneier, “How ChatGPT Hijacks Democracy,” New York Times, 15 January 2023, www.nytimes.com/2023/01/15/opinion/ai-chatgpt-lobbying-democracy.html.

2. Kevin Roose, “A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn,” New York Times, 30 May 2023, www.nytimes.com/2023/05/30/technology/ai-threat-warning.html.

3. Alexey Turchin and David Denkenberger, “Classification of Global Catastrophic Risks Connected with Artificial Intelligence,” AI & Society 35 (March 2020): 147–63.

4. Robert Dahl, Polyarchy: Participation and Opposition (New Haven: Yale University Press, 1972), 1.

5. Michael X. Delli Carpini and Scott Keeter, What Americans Know about Politics and Why it Matters (New Haven: Yale University Press, 1996); James Kuklinski et al., “‘Just the Facts Ma’am’: Political Facts and Public Opinion,” Annals of the American Academy of Political and Social Science 560 (November 1998): 143–54; Martin Gilens, “Political Ignorance and Collective Policy Preferences,” American Political Science Review 95 (June 2001): 379–96.

6. Andrea Louise Campbell, How Policies Make Citizens: Senior Political Activism and the American Welfare State (Princeton: Princeton University Press, 2003); Paul Martin and Michele Claibourn, “Citizen Participation and Congressional Responsiveness: New Evidence that Participation Matters,” Legislative Studies Quarterly 38 (February 2013): 59–81.

7. Sarah Kreps and Doug L. Kriner, “The Potential Impact of Emerging Technologies on Democratic Representation: Evidence from a Field Experiment,” New Media and Society (2023), https://doi.org/10.1177/14614448231160526.

8. Elena Kagan, “Presidential Administration,” Harvard Law Review 114 (June 2001): 2245–2353.

9. Michael Asimow, “On Pressing McNollgast to the Limits: The Problem of Regulatory Costs,” Law and Contemporary Problems 57 (Winter 1994): 127, 129.

10. Kenneth F. Warren, Administrative Law in the Political System (New York: Routledge, 2018).

11. Committee on the Status and Future of Federal E-Rulemaking, American Bar Association, “Achieving the Potential: The Future of Federal E-Rulemaking,” 2008, https://scholarship.law.cornell.edu/cgi/viewcontent.cgi?article=2505&context=facpub.

12. Jason Webb Yackee and Susan Webb Yackee, “A Bias toward Business? Assessing Interest Group Influence on the U.S. Bureaucracy,” Journal of Politics 68 (February 2006): 128–39; Cynthia Farina, Mary Newhart, and Josiah Heidt, “Rulemaking vs. Democracy: Judging and Nudging Public Participation That Counts,” Michigan Journal of Environmental and Administrative Law 2, issue 1 (2013): 123–72.

13. Edward Walker. “Millions of Fake Commenters Asked the FCC to End Net Neutrality: ‘Astroturfing’ Is a Business Model,” Washington Post Monkey Cage blog, 14 May 2021, www.washingtonpost.com/politics/2021/05/14/millions-fake-commenters-asked-fcc-end-net-neutrality-astroturfing-is-business-model/.

14. Adam Przeworksi, Susan C. Stokes, and Bernard Manin, eds., Democracy, Accountability, and Representation (New York: Cambridge University Press, 1999).

15. Report of the Select Committee on Intelligence United States Senate on Russian Active Measures Campaigns and Interference in the 2016 U.S. Election, Senate Report 116–290, www.intelligence.senate.gov/publications/report-select-committee-intelligence-united-states-senate-russian-active-measures.

16. On the potentially limited effects of 2016 election misinformation more generally, see Andrew M. Guess, Brendan Nyhan, and Jason Reifler, “Exposure to Untrustworthy Websites in the 2016 US Election,” Nature Human Behavior 4 (2020): 472–80.

17. James Vincent, “AI Is Killing the Old Web, and the New Web Struggles to be Born,” The Verge, 26 June 2023, www.theverge.com/2023/6/26/23773914/ai-large-language-models-data-scraping-generation-remaking-web.

18. Josh Goldstein et al., “Can AI Write Persuasive Propaganda?” working paper, 8 April 2023, https://osf.io/preprints/socarxiv/fp87b.

19. Sarah Kreps, “The Role of Technology in Online Misinformation,” Brookings Institution, June 2020, www.brookings.edu/articles/the-role-of-technology-in-online-misinformation.

20. In this way, AI-generated misinformation could greatly heighten “desensitization”—the relationship between incumbent performance and voter beliefs—undermining democratic accountability. See Andrew T. Little, Keith E. Schnakenberg, and Ian R. Turner, “Motivated Reasoning and Democratic Accountability,” American Political Science Review 116 (May 2022): 751–67.

21. Sarah Kreps, R. Miles McCain, and Miles Brundage, “All the News that’s Fit to Fabricate,” Journal of Experimental Political Science 9 (Spring 2022): 104–17.

22. Kathleen Donovan et al., “Motivated Reasoning, Public Opinion, and Presidential Approval” Political Behavior 42 (December 2020): 1201–21.

23. Mark Warren, ed., Democracy and Trust (New York: Cambridge University Press, 1999); Robert Putnam, Bowling Alone: The Collapse and Revival of American Community (New York: Simon and Schuster, 2000); Marc Hetherington, Why Trust Matters: Declining Political Trust and the Demise of American Liberalism (Princeton: Princeton University Press, 2005); Pippa Norris, ed., Critical Citizens: Global Support for Democratic Governance (New York: Oxford University Press, 1999); Steven Levitsky and Daniel Ziblatt, How Democracies Die (New York: Crown, 2019).

24. Lewis Anthony Dexter, “What Do Congressmen Hear: The Mail,” Public Opinion Quarterly 20 (Spring 1956): 16–27.

25. See, among others, Pew Research Center, “Public Trust in Government: 1958–2022,” 6 June 2022, https://www.pewresearch.org/politics/2023/09/19/public-trust-in-government-1958-2023/.

26. Thomas Patterson, Out of Order (New York: Knopf, 1993); Joseph N. Cappella and Kathleen Hall Jamieson, “News Frames, Political Cynicism, and Media Cynicism,” Annals of the American Academy of Political and Social Science 546 (July 1996): 71–84.

Image Credit: Clement Mahoudeau / AFP via Getty Images

Subject

Artificial Intelligence

Region

Comparative Theoretical General

Further Reading

Volume 34, Issue 4

The Authoritarian Data Problem

Eddie Yang

Margaret E. Roberts

AI is destined to become another stage for geopolitical conflict. In this contest, autocracies have the advantage, as they vacuum up valuable data from democracies, while democracies inevitably incorporate data…

Volume 34, Issue 4

AI and Catastrophic Risk

Yoshua Bengio

AI with superhuman abilities could emerge within the next few years, and there is currently no guarantee that we will be able to control them. We must act now to…

Volume 35, Issue 1

The Real Dangers of Generative AI

Danielle Allen

E. Glen Weyl

Advanced AI faces twin perils: the collapse of democratic control over key state functions or the concentration of political and economic power in the hands of the few. Avoiding these…

Subscribe to view the full Journal archives.

Subscribe Archives Get JoD Updates

About

Books

Articles

JoD Online

1201 Pennsylvania Ave, NW, Suite 1100, Washington, DC 20004, USA

Published for the National Endowment for Democracy by Johns Hopkins University Press

Search for:

#Blacks in Politics

0 notes

aryacollegeofengineering · 6 months ago

Text

Can ChatGPT Replace Your Job?

Will AI replace Jobs? The question of whether ChatGPT and similar AI technologies can replace jobs is complex and multifaceted. While AI has the potential to automate certain tasks and enhance productivity, it also creates new opportunities and necessitates a shift in workforce skills. Here’s an in-depth exploration of the impact of AI, particularly ChatGPT, on employment.

ChatGPT and Job Automation

Automation of Routine Tasks

AI technologies like ChatGPT excel at automating repetitive and mundane tasks. For instance, they can handle customer inquiries, generate content, and perform data analysis efficiently. This capability can lead to job displacement in roles heavily reliant on routine tasks, such as customer service representatives or data entry clerks. Reports suggest that by 2025, AI could displace approximately 75 million jobs globally, particularly in sectors like manufacturing and clerical work.

Job Redefinition

While some jobs may be lost due to automation, many roles will evolve rather than disappear. Workers might find their responsibilities shifting from routine tasks to more strategic activities that require critical thinking and creativity. For example, professionals in marketing may use AI tools to analyze consumer data rather than manually sifting through information. This redefinition of roles emphasizes collaboration between humans and AI systems, enhancing overall productivity.

AI Replacing Human Jobs

New Opportunities

AI is not only a threat to existing jobs but also a catalyst for new job creation. The World Economic Forum predicts that by 2025, AI will create 133 million new jobs globally, leading to a net gain of 58 million jobs despite the displacement. New roles are emerging in areas such as AI development, data science, machine learning engineering, and AI ethics compliance. These positions require specialized skills that are increasingly in demand as businesses integrate AI technologies.

Enhanced Productivity

AI tools like ChatGPT can augment human capabilities by handling time-consuming tasks, allowing workers to focus on higher-order functions such as strategic planning and creative problem-solving. This shift can lead to increased job satisfaction and improved job quality as employees engage in more meaningful work Challenges Posed by AI.

Skill Mismatches

One significant challenge is the potential for skill mismatches. As industries adopt AI technologies, there is a growing demand for workers with expertise in managing and developing these systems. However, many displaced workers may lack the necessary skills to transition into these new roles, leading to unemployment or underemployment. Continuous learning and reskilling initiatives are essential to prepare the workforce for the changing job landscape.

Economic Inequality

The integration of AI could exacerbate economic inequality. Skilled workers who can leverage AI technologies may see increased wages and job security, while low-skilled workers face greater risks of job loss without adequate support systems in place. Addressing this disparity requires proactive measures from governments and organizations to ensure equitable access to training and resources.

Future Of Work With ChatGPT

Emphasis on Human Skills

As AI continues to evolve, the demand for uniquely human skills—such as creativity, emotional intelligence, and complex problem-solving—will grow. While ChatGPT can generate text and provide information efficiently, it lacks the ability to understand context fully or exhibit genuine empathy. Jobs that require these human qualities are less likely to be replaced by AI.

Collaboration Between Humans and AI

The future of work will likely involve a collaborative approach where humans and AI systems work together. This partnership can enhance productivity while allowing workers to focus on tasks that require human insight and creativity. For example, in healthcare, AI can assist doctors with diagnostics while leaving complex decision-making and patient interactions to human professionals.

Can AI or ChatGPT Replace Human Jobs?

Will AI Replace Jobs is the big question Arya College of Engineering & I.T teaches ChatGPT and similar AI technologies have the potential to automate certain jobs and tasks, they are also creating new opportunities for employment and necessitating a shift in workforce skills. The impact of AI on jobs will vary across industries and regions; thus, it is crucial for individuals and organizations to adapt through reskilling initiatives. By embracing the changes brought about by AI while focusing on enhancing human skills, the workforce can thrive alongside technological advancements rather than be replaced by them.

Source: Click here

#best btech college in jaipur #best engineering college in jaipur #best btech college in rajasthan #best private engineering college in jaipur #top engineering college in jaipur

0 notes

educationtech · 6 months ago

Text

Jobs That ChatGPT Can Replace in Near Future - Arya College

ChatGPT and Job Automation

Automation of Routine Tasks

Job Redefinition

AI Replacing Human Jobs

New Opportunities

Enhanced Productivity

Skill Mismatches

Economic Inequality

Future of Work with ChatGPT

Emphasis on Human Skills

Collaboration Between Humans and AI

Can AI or ChatGPT Replace Human Jobs?

#Engineering #ChatGPT #AI #Arya College

0 notes

imagilityco · 7 months ago

Text

Creating Job Descriptions with Imagility’s Immigration Wizard

For HR professionals and legal teams managing complex immigration cases, accurate job descriptions are essential. Imagility’s Immigration Wizard streamlines this process, offering an array of flexible, efficient tools to build thorough job descriptions tailored to specific visa requirements, including H-1B and more.

Key features include O*NET integration, providing direct access to comprehensive job data, and AI-powered resources like ChatGPT for generating and refining content. These tools ensure descriptions are precise and compliant, while remaining adaptable to the unique needs of each case.

With the customizable approach provided by Imagility’s platform, users can create job descriptions that not only align with regulatory standards but also minimize the time and effort needed for preparation. The result is a streamlined workflow that supports accurate, complete petitions.

Discover how Imagility’s Immigration Wizard can enhance your HR operations and petition-building process by simplifying job description creation.

#ImmigrationSoftware #JobDescriptions #HRTech #ImmigrationWizard #VisaApplications #H1B #ONETIntegration #AIPoweredTools #HRSolutions #StreamlinedProcesses #Compliance #PetitionManagement #Imagility

0 notes

barbex · 1 month ago

Text

Source: https://nymag.com/intelligencer/article/openai-chatgpt-ai-cheating-education-college-students-school.html

It'll be years before we can fully account for what all of this is doing to students' brains. Some early research shows that when students off-load cognitive duties onto chatbots, their capacity for memory, problem-solving, and creativity could suffer. Multiple studies published within the past year have linked AI usage with a deterioration in critical-thinking skills; one found the effect to be more pronounced in younger participants. In February, Microsoft and Carnegie Mellon University published a study that found a person's confidence in generative AI correlates with reduced critical-thinking effort.

The net effect seems, if not quite Wall E, at least a dramatic reorganization of a person's efforts and abilities, away from high-effort inquiry and fact-gathering and toward integration and verification. This is all especially unnerving if you add in the reality that AI is imperfect — it might rely on something that is factually inaccurate or just make something up entirely — with the ruinous effect social media has had on Gen Z's ability to tell fact from fiction.

The problem may be much larger than generative AI. The so-called Flynn effect refers to the consistent rise in IQ scores from generation to generation going back to at least the 1930s. That rise started to slow, and in some cases reverse, around 2006. "The greatest worry in these times of generative AI is not that it may compromise human creativity or intelligence," Robert Sternberg, a psychology professor at Cornell University, told The Guardian, "but that it already has!

"AI does this better than me :("

"My work is never as good as AI's :("

"I have to use AI to be good :("

you're devaluing yourself. AI is not smart, it's not creative, it just has access to the whole internet at once (which btw includes all the wrong things), and guess what, so do you. You're better than the plagiarism machine and you've been lied to and told that it's smarter than you and I hope you stop believing that because you deserve better

#AI shit #generative AI #just to add to this #stupid LLM make stupid users #edited to add the text and ALT text

973 notes · View notes

korshubudemycoursesblog · 8 months ago

Text

ChatGPT for Marketing: Transforming the Game

In the fast-paced digital landscape, staying ahead of the competition is more than just running ads or scheduling posts. It’s about leveraging the latest technologies that streamline your efforts and amplify your results. Enter ChatGPT for Marketing—an AI tool designed to revolutionize how businesses engage, convert, and retain customers.

Marketing is evolving, and so are customer expectations. With the rising dominance of AI tools, ChatGPT has proven to be a game-changer. From generating personalized content to automating responses, this tool is an essential asset for marketers looking to stay competitive in today's digital environment.

Why ChatGPT for Marketing is a Game-Changer

For many marketers, creating content consistently, answering queries, and optimizing for engagement can feel overwhelming. ChatGPT steps in as a virtual assistant capable of performing these tasks without burning a hole in your budget. Whether it's social media content, email campaigns, or even SEO blog writing, AI tools like ChatGPT make marketing efforts smoother and more efficient.

Boosting Content Creation with ChatGPT

One of the core strengths of ChatGPT for marketing lies in its ability to create content rapidly and accurately. Imagine having to write multiple blog posts, social media captions, or email newsletters in a single day. ChatGPT enables marketers to produce content at scale while maintaining quality. It can generate:

Blog topics and outlines

Social media posts designed for engagement

Personalized email campaigns

Creative ad copies

The best part? You can tailor the output to match your brand’s voice, ensuring consistency across all channels. When used strategically, ChatGPT for Marketing can transform how you communicate with your audience, turning one-time visitors into loyal customers.

SEO-Friendly Content with ChatGPT

Ranking high on search engines is critical for any online business, and ChatGPT is a tool that can help you optimize your content for SEO. By using the right keywords, understanding search trends, and creating content that meets search intent, ChatGPT can help boost your organic reach.

For example, if you want to write a blog that targets the keyphrase “ChatGPT for marketing,” the AI can generate SEO-optimized headings, subheadings, and paragraphs that include this keyphrase naturally, enhancing your chances of ranking higher in the SERPs. Additionally, it can suggest related keywords such as:

AI-powered marketing tools

Content marketing with AI

ChatGPT for business growth

By integrating these related keywords into your content, you're casting a wider net to attract potential customers interested in these topics.

How to Use ChatGPT in Your Marketing Strategy

Now, let’s explore the practical ways you can implement ChatGPT for marketing:

1. Email Marketing Automation

Emails are a tried-and-true marketing tactic, but writing personalized emails for every segment of your list can be time-consuming. With ChatGPT, you can automate email writing by generating personalized content for various customer segments. This AI tool can craft emails based on different customer behaviors, such as:

Welcome emails for new subscribers

Abandoned cart reminders

Post-purchase follow-ups

By using ChatGPT to automate your email marketing, you’ll see increased efficiency and better customer engagement.

2. Chatbots for Instant Customer Support

If your business handles a lot of customer queries, you already know how essential good customer service is. ChatGPT can power AI chatbots on your website or social media platforms, providing instant responses to customers' frequently asked questions.

For example, if a customer asks about your pricing or services, the chatbot can quickly respond with pre-defined answers while still maintaining a conversational tone. Not only does this improve your response time, but it also enhances customer satisfaction.

3. Social Media Content Generation

Social media is a vital marketing channel, and consistency is key. But let’s face it—coming up with fresh ideas daily can be exhausting. With ChatGPT for marketing, you can generate engaging posts tailored to your brand's voice. Whether you're looking for captions, tweets, or even Instagram stories, the tool helps in creating content that resonates with your audience.

It can even suggest hashtags that are currently trending, such as:

#MarketingAI

#AIAutomation

#ContentStrategy

By using AI for social media content creation, you’ll save time and can focus on strategy rather than execution.

SEO Optimization and ChatGPT

Another key area where ChatGPT for marketing shines is SEO. Optimizing your content to rank higher on search engines requires knowing what your audience is searching for. ChatGPT can assist by providing keyword suggestions, headline ideas, and meta descriptions that align with trending search terms.

For example, using long-tail keywords like “how to use ChatGPT for social media marketing” or “AI-powered content creation tools for 2024” can help you rank for highly specific search queries. This improves your content’s relevance, increasing its visibility.

The Future of Marketing with AI

The future of digital marketing is intertwined with AI. As technology continues to evolve, tools like ChatGPT will become even more indispensable. From creating data-driven strategies to automating customer touchpoints, AI is enabling marketers to operate smarter, not harder.

By investing in tools like ChatGPT for marketing, you’re setting your business up for future success. Whether it’s scaling content production, improving customer service, or optimizing your SEO, ChatGPT offers a variety of ways to enhance your marketing efforts without draining your resources.

Final Thoughts: Leveraging ChatGPT for Your Marketing Success

In today’s competitive digital landscape, standing out requires more than just hard work—it requires smart work. By leveraging ChatGPT for marketing, you’re embracing innovation that will streamline your efforts and deliver results faster. From generating content to automating customer service, ChatGPT offers a world of opportunities for marketers who want to stay ahead of the curve.

Ready to transform your marketing strategy with AI? ChatGPT for marketing is your next step toward maximizing efficiency and boosting growth. Whether you're a startup or an established business, the power of AI will help you reach new heights.

By following these strategies and incorporating ChatGPT for marketing into your workflow, you’ll not only save time and resources but also create a more personalized and effective marketing strategy. Start today and see how AI can elevate your brand to the next level!

0 notes