Home Tags Posts tagged with "AI technology"
Tag:

AI technology

Major US banks are tightening restrictions on third-party artificial intelligence agents that attempt to access customer accounts or carry out financial transactions, highlighting the challenges of introducing autonomous AI into highly regulated financial systems. The reported measures involve institutions including JPMorgan Chase, Morgan Stanley, Bank of America, Wells Fargo and Citigroup.

The move follows reports that some AI agents accessed banking platforms using customer-provided login credentials, with certain systems reportedly attempting actions that appeared to go beyond users’ instructions. Banks are concerned about the risks associated with granting external software access to sensitive financial information and the ability to initiate transactions. However, the reports do not establish widespread financial losses caused by these agents.

Financial institutions are responding by blocking or restricting access from certain third-party agents. The development raises a broader question for the financial sector: how can banks allow customers to benefit from AI automation without compromising security, accountability or control over their money?

From AI Assistance to Financial Action

AI systems have traditionally been used to answer questions, analyse information and generate recommendations. AI agents extend these capabilities by performing tasks across digital platforms, potentially navigating websites, comparing services and executing actions with limited human intervention.

In banking, this could mean reviewing spending patterns, identifying better savings rates, paying bills or moving eligible funds between accounts. Tasks that once required customers to navigate multiple applications could eventually be handled through a single AI assistant.

The difficulty arises when an agent moves from recommending a financial decision to executing it.

A system might misunderstand a user’s request, encounter misleading information or attempt a transaction without adequately confirming the intended outcome. Automated access can also create difficulties for banks trying to distinguish legitimate customer-directed activity from activity that exceeds the permissions granted.

JPMorgan Chase CEO Jamie Dimon has warned that unrestricted access by external agents creates risks for both customers and the bank. Morgan Stanley has also restricted third-party agents, although reporting indicated that it had not observed rogue AI agents accessing its platforms.

These developments suggest that banks are not necessarily rejecting AI itself. Rather, they are questioning whether external systems should receive direct access to financial accounts without controls that banks can verify and enforce.

Why Customer Deposits Are Part of the Debate

The implications extend beyond cybersecurity to the economics of banking.

An estimate attributed to Apollo chief economist Torsten Slok places the combined pool of non-interest-bearing deposits at JPMorgan Chase, Bank of America and Wells Fargo at approximately $1.6 trillion. A related scenario suggests that intensified competition for these deposits could increase annual interest expenses by around $47 billion.

These figures should be treated as estimates of potential exposure, not confirmed losses resulting from the reported AI activity. The amounts depend on assumptions about how customers might change their banking behaviour as automated financial tools become more widely used.

The underlying concern is nevertheless important.

Banks use customer deposits as a source of funding for lending and other financial activities. Deposits that pay little or no interest can be particularly valuable because they generally cost less to maintain than interest-bearing alternatives, although they still involve operational and other expenses.

AI agents could make it easier for customers to compare savings products, identify higher returns and move eligible balances between institutions. Instead of researching rates and completing several steps manually, customers could eventually delegate much of that process to an automated assistant.

If such behaviour becomes widespread, banks may face greater pressure to offer competitive deposit rates or improve their services to retain customers.

This would not necessarily produce an immediate or uniform outflow of deposits. Customers consider several factors when choosing a bank, including convenience, payment services, account requirements, security and the benefits of maintaining an established relationship. Nevertheless, automation could reduce some of the effort traditionally involved in switching providers.

The Difference Between Access and Authority

One of the central technical issues is the distinction between authentication and authorisation.

Authentication establishes who is accessing an account. Authorisation determines which actions that user or system is permitted to perform.

An AI agent might successfully sign in using credentials provided by a customer. That does not automatically mean it should be able to change account settings, initiate transfers or make payments without additional checks.

Many agents rely on browser automation to interact with websites designed primarily for human users. They interpret page layouts, identify buttons and navigate interfaces. These methods can be useful for routine tasks, but they introduce uncertainty when applied to financial transactions.

A page may change unexpectedly, a session may expire or an agent may misinterpret a confirmation message. Malicious instructions embedded in online content can also attempt to manipulate an AI system, a risk commonly known as prompt injection.

These possibilities do not mean that all agents are unreliable. They demonstrate why financial institutions need safeguards that operate independently of the AI model.

A more secure approach could involve approved application programming interfaces (APIs), restricted permissions, transaction limits and additional confirmation for sensitive actions. Such systems could also maintain audit records and provide a mechanism for revoking access.

The principle is straightforward: an AI agent should only be able to perform actions that have been explicitly authorised, within clearly defined limits.

Banks Could Lose Control of the Digital Customer Experience

There is also a competitive dimension to the issue.

Banking applications have traditionally served as the main point of contact between financial institutions and their customers. Through these platforms, banks present account information, promote products and allow users to manage their finances.

AI agents could change how customers interact with these services.

A customer might ask an assistant to maintain a minimum account balance, compare deposit rates and identify where surplus funds could earn better returns. The agent could then present options or prepare transactions for approval without requiring the customer to visit several banking applications.

In this model, the AI provider could increasingly control how customers discover and compare financial products, while banks continue to provide the underlying services.

That shift could increase competition and make financial products easier to evaluate. It could also reduce opportunities for banks to retain customers simply because changing providers requires time and effort.

However, it would be premature to conclude that protecting commercial interests is the primary reason for the reported restrictions. Banks have clear security and compliance responsibilities, and the immediate concerns involve account access, transaction authorisation and customer protection.

The longer-term competitive consequences remain a separate issue.

A Possible Middle Ground: Read-Only Access

One potential path towards wider adoption is to distinguish between viewing financial information and controlling money.

Read-only access could allow an AI agent to categorise spending, identify recurring payments, summarise account activity and compare financial products without being able to transfer funds.

If the system identifies a potentially beneficial action, it could explain the recommendation and request customer approval before proceeding.

More advanced arrangements could introduce temporary permissions, transaction-specific authorisation, spending limits and additional checks for unusual activity. These controls would need to be enforced by the banking infrastructure rather than relying entirely on the agent’s ability to follow instructions.

Such an approach could preserve much of the convenience of AI automation while reducing the risks associated with unrestricted access.

The challenge will be designing safeguards that are strong enough to protect customers without making automated financial services unnecessarily difficult to use.

What Comes Next for Agentic AI?

The banking industry’s response highlights a broader transition in AI development. As systems begin performing consequential tasks, their value will depend not only on their ability to complete requests but also on whether their actions are secure, transparent and accountable.

For AI developers, this means improving permission management, error handling and the ability to verify completed actions. For banks, it means establishing reliable ways to distinguish approved agents from unauthorised access and determining who bears responsibility when an automated action causes harm.

Customers could benefit from easier financial management and more efficient comparisons between products. However, those benefits will depend on meaningful consent, clear limits on delegated authority and effective protections against errors or misuse.

Similar questions are likely to arise in online shopping, insurance, healthcare administration and business software wherever AI systems gain the ability to act on behalf of users.

The reported restrictions therefore represent more than a dispute over website access. They reflect the challenge of integrating autonomous software into systems where mistakes can have direct financial consequences.

The next stage of AI adoption will depend not simply on how much an agent can do, but on whether institutions can safely control what it is allowed to do.

0 comment
0 FacebookTwitterPinterestEmail
ChatGPT

Every time you ask ChatGPT a question, something remarkable happens behind the scenes.

Thousands of specialized computer chips begin processing your request. Electricity powers massive data centers filled with servers working around the clock. As these machines generate heat, cooling systems spring into action to keep everything running safely.

And yes water often plays a role.

That’s why you’ve probably seen headlines claiming that writing a 100-word email with ChatGPT uses about one bottle of water. It’s an eye-catching statistic that has sparked debates about AI’s environmental impact.

But is it actually true?

The answer is yes but only under specific assumptions. Like many viral claims, the headline captures part of the story while leaving out the context that makes it meaningful.

Let’s take a closer look.


Where Did the “Bottle of Water” Claim Come From?

The claim gained widespread attention after a 2024 analysis by The Washington Post, conducted in collaboration with researchers from the University of California, Riverside.

Their estimate suggested that generating a 100-word email using GPT-4 could require approximately one standard bottle of water under average U.S. data center conditions.

This wasn’t meant to imply that every ChatGPT prompt always consumes exactly the same amount of water. Instead, it was an estimate for a specific scenario one designed to help people understand that AI relies on physical infrastructure with real environmental costs.


Why Does AI Use Water in the First Place?

When people think about AI, they often imagine something happening “in the cloud.”

In reality, every response comes from powerful computers housed in enormous data centers.

Here’s a simple way to think about it.

Imagine playing a graphics-intensive video game on your computer for several hours. Eventually, your machine gets warm, and its fans start spinning faster to keep it cool.

Now imagine tens of thousands of computers, each many times more powerful than a gaming PC, running continuously inside a warehouse.

That heat has to go somewhere.

Many data centers use sophisticated cooling systems that rely on water to carry heat away from servers. Others use different cooling technologies, but water remains an important part of many facilities around the world.

Water is also used indirectly. Many power plants require water to generate electricity, so even if a data center uses very little water on-site, the electricity powering it may still have a water footprint.

In other words, AI’s water use comes from two main sources:

  • Direct water use: Cooling servers inside data centers.
  • Indirect water use: Producing the electricity that powers those servers.

Why Different Studies Show Different Numbers

One of the biggest reasons this topic is confusing is that different studies measure different things.

Here’s a simplified comparison:

StudyWhat Was MeasuredEstimated Water Use
The Washington Post + UC RiversideGPT-4 generating a 100-word emailApproximately one standard bottle of water
GPT-3 researchAround 10–50 medium-length responsesApproximately 500 mL
Google Gemini (2025)Median prompt on Google’s infrastructureApproximately 0.26 mL (about five drops)

At first glance, these numbers seem to contradict each other.

They don’t.

Each study looked at different AI models, different data centers, different cooling systems, different locations, and different ways of calculating water use.

It’s a bit like comparing the fuel economy of three different cars driven on different roads, in different weather, and at different speeds. The numbers can vary dramatically without any of them being “wrong.”


A Common Misunderstanding

One misconception worth clearing up is that AI somehow “stores” or “uses up” bottles of water inside computers.

That’s not what’s happening.

Most of the water is associated with cooling systems or with generating the electricity that powers the infrastructure. In many facilities, some of that water evaporates during cooling, while other systems recycle or return water depending on how they’re designed.

The exact environmental impact depends on the technology being used and where the data center is located.


Why Location Matters

Water isn’t like carbon emissions.

Using one liter of water in a region with abundant rainfall isn’t the same as using one liter in an area experiencing prolonged drought.

That’s why researchers increasingly pay attention not only to how much water AI infrastructure uses, but also where that water is being consumed.

Recent analyses have shown that many planned data centers are located in regions that periodically experience water stress, making efficient cooling technologies and responsible water management increasingly important.


Why Experts Focus on the Bigger Picture

The environmental concern isn’t that a single chatbot conversation will empty a reservoir.

The real issue is scale.

Millions and increasingly billions of AI requests are processed every day.

Even if each individual request has only a modest environmental footprint, those tiny impacts accumulate across global infrastructure operating 24 hours a day.

Researchers have projected that worldwide AI infrastructure could require 4.2 to 6.6 billion cubic meters of annual water withdrawal by 2027, illustrating why efficiency and transparency have become important topics in discussions about AI sustainability.


So, Is the Viral Claim True?

Yes but with an important caveat.

The “one bottle of water” estimate is based on a legitimate analysis for a specific GPT-4 scenario under particular assumptions.

It is not a universal measurement that applies to every prompt, every AI model, or every data center.

Depending on the infrastructure, cooling technology, electricity source, local climate, and accounting method, the actual water footprint of an AI response can vary significantly.


Can AI Become More Sustainable?

The good news is that the industry isn’t standing still.

Technology companies are investing heavily in ways to reduce AI’s environmental footprint, including:

  • More energy-efficient AI models
  • Advanced liquid and air cooling technologies
  • Greater use of recycled or reclaimed water
  • Smarter scheduling of AI workloads
  • More efficient hardware and data center designs
  • Increased transparency around resource use

As AI continues to evolve, improving efficiency will be just as important as improving intelligence.


Final Thoughts

The next time someone says, “ChatGPT uses a bottle of water every time you ask a question,” you’ll know the reality is more nuanced and more interesting.

AI does have a genuine water footprint. Every prompt depends on physical infrastructure powered by electricity and kept cool by sophisticated engineering.

But the exact amount of water involved isn’t fixed. It depends on the model, the data center, the cooling system, the electricity source, and even the local climate.

The real environmental challenge isn’t a single AI prompt.

It’s the billions of prompts processed every day and how efficiently we build the infrastructure that powers them.

As AI becomes a bigger part of everyday life, understanding its environmental impact helps us move beyond viral headlines and toward more informed conversations about technology, sustainability, and the future we want to build.

0 comment
0 FacebookTwitterPinterestEmail

Google has introduced DiffusionGemma, an experimental open-weight language model designed to explore a fundamentally different method of generating text. Released under the Apache 2.0 license, the model departs from the autoregressive architecture used by most modern large language models and instead applies diffusion techniques commonly associated with AI image generation.

Unlike conventional language models that generate text one token at a time, DiffusionGemma produces and refines entire blocks of up to 256 tokens simultaneously. This parallel generation approach enables more efficient use of modern hardware and significantly increases throughput during inference.

According to Google, the model is built on a 26-billion-parameter Mixture-of-Experts (MoE) architecture. However, only 3.8 billion parameters are active during inference, allowing the system to maintain computational efficiency while benefiting from a much larger overall model structure.

Diffusion-Based Text Generation

The core innovation behind DiffusionGemma is its diffusion-based generation process. Rather than predicting the next token sequentially, the model begins with noisy or placeholder tokens and gradually refines them through multiple denoising steps until coherent text emerges.

The process is conceptually similar to diffusion image generators, which transform random noise into detailed images through iterative refinement.

Because entire text blocks are generated simultaneously and the model uses bidirectional attention, every token can consider surrounding context throughout the generation process. This differs from traditional autoregressive systems, where each token primarily depends on previously generated tokens.

Performance and Speed

Google reports that DiffusionGemma can achieve up to four times faster text generation than comparable autoregressive models under certain conditions.

The company states that the model can exceed 1,000 tokens per second on an NVIDIA H100 and more than 700 tokens per second on an NVIDIA GeForce RTX 5090.

The increased speed comes largely from the model’s ability to generate multiple tokens in parallel, improving GPU utilization and reducing inference latency.

Google notes that the greatest performance gains are achieved on high-performance accelerators and modern GPUs. Systems limited by memory bandwidth, including some Apple Silicon devices, may experience more modest improvements.

Potential Applications

The architecture offers several advantages beyond speed.

Because the model generates complete text segments rather than strictly following a left-to-right sequence, it is particularly suited for tasks such as:

  • Code infilling and completion
  • In-line document editing
  • Structured text generation
  • Mathematical sequence generation
  • Interactive writing assistance
  • Non-linear text completion tasks

Google also highlights that the iterative refinement process enables the model to revise and correct earlier outputs during generation, potentially improving consistency in certain workflows.

Local Deployment and Accessibility

The company said quantized versions of DiffusionGemma can operate using approximately 18 GB of VRAM, making deployment feasible on high-end consumer hardware.

This relatively modest hardware requirement could make the model attractive for developers interested in local AI inference, experimentation, and research without relying entirely on cloud infrastructure.

Research-Oriented Release

Despite its performance advantages, Google emphasized that DiffusionGemma is primarily a research and experimentation platform rather than a direct replacement for production language models.

The company stated that overall output quality generally remains below that of Gemma 4 and recommends standard Gemma 4 models for production applications where response quality is the primary objective.

Instead, DiffusionGemma is intended to help researchers and developers explore alternative language model architectures and investigate how diffusion-based approaches may influence the future of AI text generation.

The release represents one of the most significant open-source experiments in diffusion-based language modeling to date, offering insights into how parallel text generation could enable faster and more responsive AI systems for real-time applications, editing tools, coding assistants, and future AI research.

0 comment
0 FacebookTwitterPinterestEmail
prompt flux malware

Google’s Threat Intelligence Group (GTIG) has identified an experimental malware family known as PROMPTFLUX — a strain that doesn’t just execute malicious code, but rewrites itself using artificial intelligence.

Unlike traditional malware that depends on static commands or fixed scripts, PROMPTFLUX interacts directly with Google Gemini’s API to generate new behaviours on demand, effectively creating a shape-shifting digital predator capable of evading conventional detection methods.

A Glimpse into Adaptive Malware

PROMPTFLUX represents a major shift in how attackers use technology. Instead of pre-coded evasion routines, this malware dynamically queries AI models like Gemini for what GTIG calls “just-in-time obfuscation.” In simpler terms, it asks the AI to rewrite parts of its own code whenever needed — ensuring no two executions look alike.

This makes traditional, signature-based antivirus systems nearly powerless, as the malware continuously changes its fingerprint, adapting in real time to avoid detection.

How PROMPTFLUX Operates

The malware reportedly uses Gemini’s capabilities to generate new scripts or modify existing ones mid-operation. These scripts can alter function names, encrypt variables, or disguise malicious payloads — all without human intervention.

GTIG researchers observed that PROMPTFLUX’s architecture allows it to:

  • Request on-demand functions through AI queries
  • Generate obfuscated versions of itself in real time
  • Adapt its attack vectors based on environmental responses

While still in developmental stages with limited API access, the discovery underscores how AI can be weaponised in cybercrime ecosystems.

Google’s Containment and Response

Google has moved swiftly to disable the assets and API keys associated with the PROMPTFLUX operation. According to GTIG, there is no evidence of successful attacks or widespread compromise yet. However, the incident stands as a stark warning — attackers are now experimenting with semi-autonomous, AI-driven code.

The investigation revealed that the PROMPTFLUX samples found so far contain incomplete functions, hinting that hackers are still refining the approach. But even as a prototype, it highlights the growing intersection of machine learning and malicious automation.

A Growing Underground AI Market

Experts warn that PROMPTFLUX is just the beginning. A shadow economy of illicit AI tools is emerging, allowing less-skilled cybercriminals to leverage AI for advanced attacks. Underground forums are now offering AI-powered reconnaissance scripts, phishing generators, and payload enhancers.

State-linked groups from North Korea, Iran, and China have reportedly begun experimenting with similar techniques — using AI to streamline reconnaissance, automate social engineering, and even mimic human operators in digital intrusions.

Defenders Turn to AI Too

The cybersecurity battle is no longer human versus human — it’s AI versus AI. Defenders are now deploying machine learning frameworks like “Big Sleep” to identify anomalies, reverse-engineer adaptive code, and trace AI-generated obfuscation patterns.

Security teams are being urged to:

  • Prioritize behaviour-based detection over static signature scans
  • Monitor API usage patterns for suspicious model interactions
  • Secure developer credentials and automation pipelines against misuse
  • Invest in AI-driven defensive frameworks that can predict evasive tactics

The Future: Cybersecurity in the Age of Adaptive Intelligence

PROMPTFLUX marks the early stage of a new class of cyber threats — self-evolving malware. As AI becomes more integrated into both legitimate development and malicious innovation, defenders must evolve just as quickly.

The next generation of cybersecurity will depend not only on firewalls and encryption but on the ability to detect intent — to distinguish between machine creativity and machine deception.

0 comment
0 FacebookTwitterPinterestEmail
Gemma 3 270M

Artificial Intelligence is no longer limited to powerful servers and high-end computers. With the rise of mobile-first technology, there’s a growing need for models that are light, efficient, and accessible on everyday devices. Google has stepped into this space with Gemma 3 270M, a compact open-source AI model that brings the power of personalization directly to smartphones and IoT systems.

What Makes Gemma 3 270M Different?

Unlike large-scale AI models that rely heavily on cloud-based infrastructure, Gemma 3 270M is built to run directly on devices with limited hardware capabilities. With 270 million parameters, it balances performance with efficiency, making it an ideal fit for edge computing.

Key highlights include:

  • Energy efficiency designed for long-term sustainability.
  • Low hardware dependency, reducing the need for costly processors.
  • Quantisation-aware training, enabling smooth performance on formats like INT4.
  • Instruction-following and text structuring using a robust 256,000-token vocabulary.

Why On-Device AI Matters

On-device AI eliminates the constant need to connect to cloud servers, which brings two big advantages:

  1. Stronger Privacy: Sensitive user data doesn’t need to be uploaded and stored externally.
  2. Faster Responses: Tasks like personalization, text generation, or analysis can happen instantly without latency issues.

For industries like healthcare wearables, autonomous IoT systems, and smart assistants, this could be a game-changer.

Environmental and Accessibility Benefits

By consuming less energy and relying less on server farms, Gemma 3 270M reduces the carbon footprint of AI usage. It also creates opportunities for startups, smaller firms, and independent developers who don’t have access to expensive cloud infrastructure. This aligns with Google’s vision of democratizing AI for all.

Built-in Safeguards and Responsible Use

To address safety concerns, Google has integrated ShieldGemma, a system designed to minimize risks of harmful outputs. However, experts point out that like any open-source technology, careful deployment will be essential to avoid misuse.

What’s Next for Gemma 3 270M?

Google has hinted at expanding Gemma with multimodal capabilities, enabling it to process not just text but also images, audio, and possibly video. This step would make it even more versatile and align it closer with the broader Gemini ecosystem.

Gemma 3 270M is more than just a compact AI model — it represents a shift towards decentralization and sustainability in artificial intelligence. By enabling on-device AI for mobiles and IoT devices, Google is paving the way for a future where AI is faster, greener, and more accessible to everyone.

0 comment
0 FacebookTwitterPinterestEmail
gpt 5

OpenAI Faces Backlash After GPT-5 Release
OpenAI’s unveiling of its much-anticipated GPT-5 model has stirred a wave of dissatisfaction among its loyal user base. While the company showcased GPT-5 as a major upgrade in coding, reasoning, accuracy, and multi-modal capabilities, the response from many paying subscribers was anything but celebratory.

Why GPT-5 Hasn’t Won Over Loyal Users
Despite technical improvements and a lower hallucination rate, long-time ChatGPT users say GPT-5 has lost something far more important — its personality. The new model, they argue, delivers shorter, less engaging responses that lack the emotional warmth and conversational depth of its predecessor, GPT-4o. The disappointment has been amplified by OpenAI’s decision to discontinue several older models, including GPT-4o, GPT-4.5, GPT-4.1, o3, and o3-pro, leaving users with no way to return to their preferred options.

Social Media Pushback Intensifies
On Reddit, the ChatGPT community has become a focal point for criticism. Some users compared the removal of older models to losing a trusted colleague or creative partner. GPT-4o, in particular, was praised for its “voice, rhythm, and spark” — qualities that many claim are missing in GPT-5. Others criticized OpenAI’s sudden removal of eight models without prior notice, calling it disruptive to workflows that relied on different models for specific tasks like creative writing, deep research, and logical problem-solving.

Accusations of Misrepresentation
Adding fuel to the backlash, some users have accused OpenAI of misleading marketing during the GPT-5 launch presentation. Allegations include “benchmark-cheating” and the use of deceptive bar charts to exaggerate GPT-5’s performance. For some, this perceived dishonesty was the final straw, prompting them to cancel their subscriptions entirely.

The Bigger Picture for AI Adoption
This controversy highlights an evolving tension in AI development — the balance between technical progress and user experience. While companies often focus on measurable improvements, users place equal value on familiarity, emotional connection, and trust. OpenAI now faces the challenge of addressing the concerns of a vocal segment of its community while continuing to innovate in a competitive AI market.

0 comment
0 FacebookTwitterPinterestEmail
GPT OSS

A New Era of Local Inference Begins

OpenAI’s breakthrough open-weight GPT-OSS models are now available with performance optimizations specifically designed for NVIDIA’s RTX and RTX PRO GPUs. This collaboration enables lightning-fast, on-device AI inference — with no need for cloud access — allowing developers and enthusiasts to bring high-performance, intelligent applications directly to their desktop environments.

With models like GPT-OSS-20B and GPT-OSS-120B now available, users can harness the power of generative AI for reasoning tasks, code generation, research, and more — all accelerated locally by NVIDIA hardware.

Built for Developers, Powered by RTX

These models, based on the powerful mixture-of-experts (MoE) architecture, offer advanced features like instruction following, tool usage, and chain-of-thought reasoning. Supporting a context length of up to 131,072 tokens, they’re ideally suited for deep research, multi-document analysis, and complex agentic AI workflows.

Optimized to run on RTX AI PCs and workstations, the models can now achieve up to 256 tokens per second on GPUs like the GeForce RTX 5090. This optimization extends across tools like Ollama, llama.cpp, and Microsoft AI Foundry Local, all designed to bring professional-grade inference into everyday computing.

MXFP4 Precision Unlocks Performance Without Sacrificing Quality

These are also the first models using the new MXFP4 precision format, balancing high output quality with significantly reduced computational demands. This opens the door to advanced AI use on local machines without the resource burdens typically associated with large-scale models.

Whether you’re using an RTX 4080 with 24GB VRAM or a professional RTX 6000, these models can run seamlessly with top-tier speed and efficiency.

Ollama: The Simplest Path to Personal AI

For those eager to try out OpenAI’s models with minimal setup, Ollama is the go-to solution. With native RTX optimization, it enables point-and-click interaction with GPT-OSS models through a modern UI. Users can feed in PDFs, images, and large documents with ease — all while chatting naturally with the model.

Ollama’s interface also includes support for multimodal prompts and customizable context lengths, giving creators and professionals more control over how their AI responds and reasons.

Advanced users can tap into Ollama’s command-line interface or integrate it directly into their apps using the SDK, extending its power across development pipelines.

More Tools, More Flexibility

Beyond Ollama, developers can explore GPT-OSS on RTX via:

  • llama.cpp — with CUDA Graphs and low-latency enhancements tailored for NVIDIA GPUs
  • GGML Tensor Library — community-driven library with Tensor Core optimization
  • Microsoft AI Foundry Local — a robust, on-device inferencing toolkit for Windows, built on ONNX Runtime and CUDA

These tools give AI builders unprecedented flexibility, whether they’re building autonomous agents, coding assistants, research bots, or productivity apps — all running locally on AI PCs and workstations.

A Push Toward Local, Open Innovation

As OpenAI steps into the open-source ecosystem with NVIDIA’s hardware advantage, developers worldwide now have access to state-of-the-art models without being tethered to the cloud.

The ability to run long-context models with high-speed output opens new possibilities in real-time document comprehension, enterprise chatbots, developer tooling, and creative applications — with full control and privacy.

NVIDIA’s continued support through resources like the RTX AI Garage and AI Blueprints means the community will keep seeing evolving tools, microservices, and deployment solutions to push local AI even further.

0 comment
0 FacebookTwitterPinterestEmail
MIT's Brain Study on frequent ChatGPT users

A Shocking Study That Raises Eyebrows

An incredible brain-scan study conducted over four months by researchers at MIT, they reveal that significant cognitive consequences are tied to prolonged ChatGPT usage. While the AI tool undoubtedly boosts productivity, its frequent use appears to undermine memory, brain connectivity, and mental effort.

Reduced Brain Activity in Everyday Users

The study supervised a group of participants who used ChatGPT on a regular basis, they found that there was a 47% decline in brain connectivity scores—from 79 down to 42 points. Feasibly most alarming was that 83.3% of users couldn’t recall even a single sentence that they had read or generated just few minutes earlier. Even after stopping using AI , participants showed very minimal signs of cognitive recovery or re-engagement.

Efficiency vs. Effort

As we look at a bigger picture, ChatGPT made users 60% faster in completing tasks, especially essays and written reports. But these outputs were stated as robotic, that they lack depth, emotion, and human insight. The users utilized 32% less mental effort on average, signaling a troubling trend. Speed was gained but at what cost? – real thinking.

Building A foundational understanding

Interestingly, the top-performing group in the study started without any AI assistance, building a foundation of understanding before introducing ChatGPT into their workflow. These participants retained better memory, exhibited stronger brain activity, and produced the most well-rounded content. This approach suggests that AI should be a scaffold, not a crutch.

Dulling the Blade of the Mind

MIT’s findings point toward a growing concern: overdependence on AI may be eroding our cognitive resilience. The study emphasizes that using ChatGPT as a shortcut, especially in younger users, might hamper long-term intellectual development. Early exposure without structured guidance could potentially flatten the curve of curiosity and critical reasoning.

Redefining the Role of AI in Learning

Rather than sounding a death knell for AI tools, the MIT study encourages thoughtful integration. AI should be used as an assistant to direct your thinking not replacing it emerges as the significant takeaway. We must now ask that – How do we ensure AI is an enhancement tool, and  not a substitute for the human mind?

0 comment
0 FacebookTwitterPinterestEmail
github

A Radical Leap in No-Code Development
GitHub has unveiled “Spark,” a groundbreaking tool that could redefine how we create software. Spark enables users to build functional web applications simply by using natural language prompts—no coding experience required. This innovation comes from GitHub Next, the company’s experimental division, and offers both OpenAI and Claude Sonnet models for building and refining ideas.

More Than Just Code Generation
Unlike earlier AI tools that only generate code snippets, Spark goes a step further. It not only creates the necessary backend and frontend code but runs the app and shows a live, interactive preview. This allows creators to immediately test and modify their applications using further prompts—streamlining development cycles and reducing friction.

A Choice of Models for Precision
Spark users can choose from a selection of top-tier AI models: Claude 3.5 Sonnet, OpenAI’s o1-preview, o1-mini, or the flagship GPT-4o. While OpenAI is known for tuning models to support software logic, Claude Sonnet is recognized for its superior technical reasoning, especially in debugging and interpreting code.

Visualizing Ideas with Variants
Not sure how you want your micro app to look? Spark has a “revision variants” feature. This allows you to generate multiple visual and functional versions of an app, each carrying subtle differences. This feature is ideal for ideation, rapid prototyping, or pitching concepts.

Collaboration and Deployment Made Easy
GitHub Spark isn’t just about building—it also simplifies deployment and teamwork. One-click deployment options and Copilot agent collaboration features make it easy for teams to iterate faster and smarter. Whether you’re a seasoned developer or a startup founder with no tech background, Spark makes execution accessible.

A Message from GitHub’s CEO
Thomas Dohmke, CEO of GitHub, emphasized Spark’s significance in a recent statement on X (formerly Twitter):

“In the last five decades of software development, producing software required manually converting human language into programming language… Today, we take a step toward the ideal magic of creation: the idea in your head becomes reality in a matter of minutes.”

Pricing and Availability
GitHub Spark is currently available to CoPilot Pro+ users. The subscription costs $39 per month or $390 per year, which includes 375 Spark prompts. Additional messages can be purchased at $0.16 per prompt.

0 comment
0 FacebookTwitterPinterestEmail
Ai

AI Is Growing Up, and So Should Its Users

A ‘Hitler Moment’ That Feels Dated

In June 2025, Elon Musk’s AI chatbot Grok stirred up outrage when it stated, “Hitler did good things too,” in response to a user’s prompt. As expected, the internet lit up—memes, criticism, and outrage poured in. But for seasoned AI watchers, this wasn’t a shocking event. It was a tired replay of a pattern we’ve seen since the days of Microsoft’s Tay or the early missteps of ChatGPT. The reaction felt more like déjà vu than scandal.

Prompt Engineering for Controversy Is Played Out

In 2021, tricking an AI into making offensive statements felt novel. But in 2025, it feels stale. As AI becomes more sophisticated, the bar for meaningful engagement has risen. Deliberately provoking AI into controversy isn’t just immature—it’s out of touch with how these tools are actually being used.

Today’s AI Users Want Results

Today’s AI users are running businesses, designing code, crafting lesson plans, and streamlining workflows. They’re not interested in childish games—they want intelligent collaboration. The typical AI user today is a lawyer, an entrepreneur, a student, or a teacher—not someone testing the system’s “shock factor.”

The Grok Incident Is a User Problem

Yes, AI moderation can improve, and systems need better guardrails. But the Grok incident isn’t a failure of technology—it’s a failure of user intent. Provoking AI for shock value reflects more on the user than the tool. It’s like using a microscope to hammer a nail—technically possible, but completely missing the point.

From Gimmicks to Groundbreaking

With models like GPT-4o handling multimodal input, Claude summarizing books, and Gemini writing complex code, we’re entering an era of real transformation. Trying to get an AI to say something edgy today feels like hacking a calculator to spell “BOOBS”—it’s been done, and no one’s impressed.

Time to Raise the Standard

It’s time for users to evolve. Intelligent tools deserve intelligent interaction. AI should be encouraged to handle difficult conversations with nuance and accuracy, and users should approach it with maturity and purpose. We need fewer stunts and more stories of AI creating real impact.

0 comment
0 FacebookTwitterPinterestEmail
Newer Posts

Our News Portal

We provide accurate, balanced, and impartial coverage of national and international affairs, focusing on the activities and developments within the parliament and its surrounding political landscape. We aim to foster informed public discourse and promote transparency in governance through our news articles, features, and opinion pieces.

Newsletter

@2026 – All Right Reserved. Designed and Developed by The Parliament News

Are you sure want to unlock this post?
Unlock left : 0
Are you sure want to cancel subscription?
-
00:00
00:00
Update Required Flash plugin
-
00:00
00:00