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Showing posts with label Chatbots. Show all posts

AI-Powered Shopping Is Transforming How Consumers Buy Holiday Gifts

 

Artificial intelligence is emerging with a new dimension in holiday shopping for consumers, going beyond search capabilities into a more proactive role in exploration and decision-making. Rather than endlessly clicking through online shopping sites, consumers are increasingly turning to AI-powered chatbots to suggest gift ideas, compare prices, and recommend specialized products they may not have thought of otherwise. Such a trend is being fueled by the increasing availability of technology such as Microsoft Copilot, ChatGPT from OpenAI, and Gemini from Google. With basic information such as a few elements of a gift receiver’s interest, age, or hobbies, personalized recommendations can be obtained which will direct such a person to specialized retail stores or distinct products. 

Such technology is being viewed increasingly as a means of relieving a busy time of year with thoughtfulness in gift selection despite being rushed. Industry analysts have termed this year a critical milestone in AI-enabled commerce. Although figures quantifying expenditures driven by AI are not available, a report by Salesforce reveals that AI-enabled activities have the potential to impact over one-twentieth of holiday sales globally, amounting to an expenditure in the order of hundreds of billions of dollars. Supportive evidence can be derived from a poll of consumers in countries such as America, Britain, and Ireland, where a majority of them have already adopted AI assistance in shopping, mainly for comparisons and recommendations. 

Although AI adoption continues to gain pace, customer satisfaction with AI-driven retail experiences remains a mixed bag. With most consumers stating they have found AI solutions to be helpful, they have not come across experiences they find truly remarkable. Following this, retailers have endeavored to improve product representation in AI-driven recommendations. Experts have cautioned that inaccurate or old product information can work against them in AI-driven recommendations, especially among smaller brands where larger rivals have an advantage in resources. 

The technology is also developing in other ways beyond recommenders. Some AI firms have already started working on in-chat checkout systems, which will enable consumers to make purchases without leaving the chat interface. OpenAI has started to integrate in-checkout capabilities into conversations using collaborations with leading platforms, which will allow consumers to browse products and make purchases without leaving chat conversations. 

However, this is still in a nascent stage and available on a selective basis to vendors approved by AI firms. The above trend gives a cause for concern with regards to concentration in the market. Experts have indicated that AI firms control gatekeeping, where they get to show which retailers appear on the platform and which do not. Those big brands with organized product information will benefit in this case, but small retailers will need to adjust before being considered. On the other hand, some small businesses feel that AI shopping presents an opportunity rather than a threat. Through their investment in quality content online, small businesses hope to become more accessible to AI shopping systems without necessarily partnering with them. 

As AI shopping continues to gain popularity, it will soon become important for a business to organize information coherently in order to succeed. Although AI-powered shopping assists consumers in being better informed and making better decisions, overdependence on such technology can prove counterproductive. Those consumers who do not cross-check the recommendations they receive will appear less well-informed, bringing into focus the need to balance personal acumen with technology in a newly AI-shaped retail market.

AI Image Attacks: How Hidden Commands Threaten Chatbots and Data Security

 



As artificial intelligence becomes part of daily workflows, attackers are exploring new ways to exploit its weaknesses. Recent research has revealed a method where seemingly harmless images uploaded to AI systems can conceal hidden instructions, tricking chatbots into performing actions without the user’s awareness.


How hidden commands emerge

The risk lies in how AI platforms process images. To reduce computing costs, most systems shrink images before analysis, a step known as downscaling. During this resizing, certain pixel patterns, deliberately placed by an attacker can form shapes or text that the model interprets as user input. While the original image looks ordinary to the human eye, the downscaled version quietly delivers instructions to the system.

This technique is not entirely new. Academic studies as early as 2020 suggested that scaling algorithms such as bicubic or bilinear resampling could be manipulated to reveal invisible content. What is new is the demonstration of this tactic against modern AI interfaces, proving that such attacks are practical rather than theoretical.


Why this matters

Multimodal systems, which handle both text and images, are increasingly connected to calendars, messaging apps, and workplace tools. A hidden prompt inside an uploaded image could, in theory, request access to private information or trigger actions without explicit permission. One test case showed that calendar data could be forwarded externally, illustrating the potential for identity theft or information leaks.

The real concern is scale. As organizations integrate AI assistants into daily operations, even one overlooked vulnerability could compromise sensitive communications or financial data. Because the manipulation happens inside the preprocessing stage, traditional defenses such as firewalls or antivirus tools are unlikely to detect it.


Building safer AI systems

Defending against this form of “prompt injection” requires layered strategies. For users, simple precautions include checking how an image looks after resizing and confirming any unusual system requests. For developers, stronger measures are necessary: restricting image dimensions, sanitizing inputs before models interpret them, requiring explicit confirmation for sensitive actions, and testing models against adversarial image samples.

Researchers stress that piecemeal fixes will not be enough. Only systematic design changes such as enforcing secure defaults and monitoring for hidden instructions can meaningfully reduce the risks.

Images are no longer guaranteed to be safe when processed by AI systems. As attackers learn to hide commands where only machines can read them, users and developers alike must treat every upload with caution. By prioritizing proactive defenses, the industry can limit these threats before they escalate into real-world breaches.



Think Twice Before Uploading Personal Photos to AI Chatbots

 

Artificial intelligence chatbots are increasingly being used for fun, from generating quirky captions to transforming personal photos into cartoon characters. While the appeal of uploading images to see creative outputs is undeniable, the risks tied to sharing private photos with AI platforms are often overlooked. A recent incident at a family gathering highlighted just how easy it is for these photos to be exposed without much thought. What might seem like harmless fun could actually open the door to serious privacy concerns. 

The central issue is unawareness. Most users do not stop to consider where their photos are going once uploaded to a chatbot, whether those images could be stored for AI training, or if they contain personal details such as house numbers, street signs, or other identifying information. Even more concerning is the lack of consent—especially when it comes to children. Uploading photos of kids to chatbots, without their ability to approve or refuse, creates ethical and security challenges that should not be ignored.  

Photos contain far more than just the visible image. Hidden metadata, including timestamps, location details, and device information, can be embedded within every upload. This information, if mishandled, could become a goldmine for malicious actors. Worse still, once a photo is uploaded, users lose control over its journey. It may be stored on servers, used for moderation, or even retained for training AI models without the user’s explicit knowledge. Just because an image disappears from the chat interface does not mean it is gone from the system.  

One of the most troubling risks is the possibility of misuse, including deepfakes. A simple selfie, once in the wrong hands, can be manipulated to create highly convincing fake content, which could lead to reputational damage or exploitation. 

There are steps individuals can take to minimize exposure. Reviewing a platform’s privacy policy is a strong starting point, as it provides clarity on how data is collected, stored, and used. Some platforms, including OpenAI, allow users to disable chat history to limit training data collection. Additionally, photos can be stripped of metadata using tools like ExifTool or by taking a screenshot before uploading. 

Consent should also remain central to responsible AI use. Children cannot give informed permission, making it inappropriate to share their images. Beyond privacy, AI-altered photos can distort self-image, particularly among younger users, leading to long-term effects on confidence and mental health. 

Safer alternatives include experimenting with stock images or synthetic faces generated by tools like This Person Does Not Exist. These provide the creative fun of AI tools without compromising personal data. 

Ultimately, while AI chatbots can be entertaining and useful, users must remain cautious. They are not friends, and their cheerful tone should not distract from the risks. Practicing restraint, verifying privacy settings, and thinking critically before uploading personal photos is essential for protecting both privacy and security in the digital age.

Why Running AI Locally with an NPU Offers Better Privacy, Speed, and Reliability

 

Running AI applications locally offers a compelling alternative to relying on cloud-based chatbots like ChatGPT, Gemini, or Deepseek, especially for those concerned about data privacy, internet dependency, and speed. Though cloud services promise protections through subscription terms, the reality remains uncertain. In contrast, using AI locally means your data never leaves your device, which is particularly advantageous for professionals handling sensitive customer information or individuals wary of sharing personal data with third parties. 

Local AI eliminates the need for a constant, high-speed internet connection. This reliable offline capability means that even in areas with spotty coverage or during network outages, tools for voice control, image recognition, and text generation remain functional. Lower latency also translates to near-instantaneous responses, unlike cloud AI that may lag due to network round-trip times. 

A powerful hardware component is essential here: the Neural Processing Unit (NPU). Typical CPUs and GPUs can struggle with AI workloads like large language models and image processing, leading to slowdowns, heat, noise, and shortened battery life. NPUs are specifically designed for handling matrix-heavy computations—vital for AI—and they allow these models to run efficiently right on your laptop, without burdening the main processor. 

Currently, consumer devices such as Intel Core Ultra, Qualcomm Snapdragon X Elite, and Apple’s M-series chips (M1–M4) come equipped with NPUs built for this purpose. With one of these devices, you can run open-source AI models like DeepSeek‑R1, Qwen 3, or LLaMA 3.3 using tools such as Ollama, which supports Windows, macOS, and Linux. By pairing Ollama with a user-friendly interface like OpenWeb UI, you can replicate the experience of cloud chatbots entirely offline.  

Other local tools like GPT4All and Jan.ai also provide convenient interfaces for running AI models locally. However, be aware that model files can be quite large (often 20 GB or more), and without NPU support, performance may be sluggish and battery life will suffer.  

Using AI locally comes with several key advantages. You gain full control over your data, knowing it’s never sent to external servers. Offline compatibility ensures uninterrupted use, even in remote or unstable network environments. In terms of responsiveness, local AI often outperforms cloud models due to the absence of network latency. Many tools are open source, making experimentation and customization financially accessible. Lastly, NPUs offer energy-efficient performance, enabling richer AI experiences on everyday devices. 

In summary, if you’re looking for a faster, more private, and reliable AI workflow that doesn’t depend on the internet, equipping your laptop with an NPU and installing tools like Ollama, OpenWeb UI, GPT4All, or Jan.ai is a smart move. Not only will your interactions be quick and seamless, but they’ll also remain securely under your control.

PocketPal AI Brings Offline AI Chatbot Experience to Smartphones With Full Data Privacy

 

In a digital world where most AI chatbots rely on cloud computing and constant internet connectivity, PocketPal AI takes a different approach by offering an entirely offline, on-device chatbot experience. This free app brings AI processing power directly onto your smartphone, eliminating the need to send data back and forth across the internet. Conventional AI chatbots typically transmit your interactions to distant servers, where the data is processed before a response is returned. That means even sensitive or routine conversations can be stored remotely, raising concerns about privacy, data usage, and the potential for misuse.

PocketPal AI flips this model by handling all computation on your device, ensuring your data never leaves your phone unless you explicitly choose to save or share it. This local processing model is especially useful in areas with unreliable internet or no access at all. Whether you’re traveling in rural regions, riding the metro, or flying, PocketPal AI works seamlessly without needing a connection. 

Additionally, using an AI offline helps reduce mobile data consumption and improves speed, since there’s no delay waiting for server responses. The app is available on both iOS and Android and offers users the ability to interact with compact but capable language models. While you do need an internet connection during the initial setup to download a language model, once that’s done, PocketPal AI functions completely offline. To begin, users select a model from the app’s library or upload one from their device or from the Hugging Face community. 

Although the app lists models without detailed descriptions, users can consult external resources to understand which model is best for their needs—whether it’s from Meta, Microsoft, or another developer. After downloading a model—most of which are several gigabytes in size—users simply tap “Load” to activate the model, enabling conversations with their new offline assistant. 

For those more technically inclined, PocketPal AI includes advanced settings for switching between models, adjusting inference behavior, and testing performance. While these features offer great flexibility, they’re likely best suited for power users. On high-end devices like the Pixel 9 Pro Fold, PocketPal AI runs smoothly and delivers fast responses. 

However, older or budget devices may face slower load times or stuttering performance due to limited memory and processing power. Because offline models must be optimized for device constraints, they tend to be smaller in size and capabilities compared to cloud-based systems. As a result, while PocketPal AI handles common queries, light content generation, and basic conversations well, it may not match the contextual depth and complexity of large-scale models hosted in the cloud. 

Even with these trade-offs, PocketPal AI offers a powerful solution for users seeking AI assistance without sacrificing privacy or depending on an internet connection. It delivers a rare combination of utility, portability, and data control in today’s cloud-dominated AI ecosystem. 

As privacy awareness and concerns about centralized data storage continue to grow, PocketPal AI represents a compelling alternative—one that puts users back in control of their digital interactions, no matter where they are.

DeepSeek AI: Benefits, Risks, and Security Concerns for Businesses

 

DeepSeek, an AI chatbot developed by China-based High-Flyer, has gained rapid popularity due to its affordability and advanced natural language processing capabilities. Marketed as a cost-effective alternative to OpenAI’s ChatGPT, DeepSeek has been widely adopted by businesses looking for AI-driven insights. 

However, cybersecurity experts have raised serious concerns over its potential security risks, warning that the platform may expose sensitive corporate data to unauthorized surveillance. Reports suggest that DeepSeek’s code contains embedded links to China Mobile’s CMPassport.com, a registry controlled by the Chinese government. This discovery has sparked fears that businesses using DeepSeek may unknowingly be transferring sensitive intellectual property, financial records, and client communications to external entities. 

Investigative findings have drawn parallels between DeepSeek and TikTok, the latter having faced a U.S. federal ban over concerns regarding Chinese government access to user data. Unlike TikTok, however, security analysts claim to have found direct evidence of DeepSeek’s potential backdoor access, raising further alarms among cybersecurity professionals. Cybersecurity expert Ivan Tsarynny warns that DeepSeek’s digital fingerprinting capabilities could allow it to track users’ web activity even after they close the app. 

This means companies may be exposing not just individual employee data but also internal business strategies and confidential documents. While AI-driven tools like DeepSeek offer substantial productivity gains, business leaders must weigh these benefits against potential security vulnerabilities. A complete ban on DeepSeek may not be the most practical solution, as employees often adopt new AI tools before leadership can fully assess their risks. Instead, organizations should take a strategic approach to AI integration by implementing governance policies that define approved AI tools and security measures. 

Restricting DeepSeek’s usage to non-sensitive tasks such as content brainstorming or customer support automation can help mitigate data security concerns. Enterprises should prioritize the use of vetted AI solutions with stronger security frameworks. Platforms like OpenAI’s ChatGPT Enterprise, Microsoft Copilot, and Claude AI offer greater transparency and data protection. IT teams should conduct regular software audits to monitor unauthorized AI use and implement access restrictions where necessary. 

Employee education on AI risks and cybersecurity threats will also be crucial in ensuring compliance with corporate security policies. As AI technology continues to evolve, so do the challenges surrounding data privacy. Business leaders must remain proactive in evaluating emerging AI tools, balancing innovation with security to protect corporate data from potential exploitation.

How AI Agents Are Transforming Cryptocurrency

 



Artificial intelligence (AI) agents are revolutionizing the cryptocurrency sector by automating processes, enhancing security, and improving trading strategies. These smart programs help analyze blockchain data, detect fraud, and optimize financial decisions without human intervention.


What Are AI Agents?

AI agents are autonomous software programs that operate independently, analyzing information and taking actions to achieve specific objectives. These systems interact with their surroundings through data collection, decision-making algorithms, and execution of tasks. They play a critical role in multiple industries, including finance, cybersecurity, and healthcare.


There are different types of AI agents:

1. Simple Reflex Agents: React based on pre-defined instructions.

2. Model-Based Agents: Use internal models to make informed choices.

3. Goal-Oriented Agents: Focus on achieving specific objectives.

4. Utility-Based Agents: Weigh outcomes to determine the best action.

5. Learning Agents: Continuously improve based on new data.


Evolution of AI Agents

AI agents have undergone advancements over the years. Here are some key milestones:

1966: ELIZA, an early chatbot, was developed at MIT to simulate human-like conversations.

1980: MYCIN, an AI-driven medical diagnosis tool, was created at Stanford University.

2011: IBM Watson demonstrated advanced natural language processing by winning on Jeopardy!

2014: AlphaGo, created by DeepMind, outperformed professional players in the complex board game Go.

2020: OpenAI introduced GPT-3, an AI model capable of generating human-like text.

2022: AlphaFold solved long-standing biological puzzles related to protein folding.

2023: AI-powered chatbots like ChatGPT and Claude AI gained widespread use for conversational tasks.

2025: ElizaOS, a blockchain-based AI platform, is set to enhance AI-agent applications.


AI Agents in Cryptocurrency

The crypto industry is leveraging AI agents for automation and security. In late 2024, Virtuals Protocol, an AI-powered Ethereum-based platform, saw its market valuation soar to $1.9 billion. By early 2025, AI-driven crypto tokens collectively reached a $7.02 billion market capitalization.

AI agents are particularly valuable in decentralized finance (DeFi). They assist in managing liquidity pools, adjusting lending and borrowing rates, and securing financial transactions. They also enhance security by identifying fraudulent activities and vulnerabilities in smart contracts, ensuring compliance with regulations like Know Your Customer (KYC) and Anti-Money Laundering (AML).


The Future of AI in Crypto

Tech giants like Amazon and Apple are integrating AI into digital assistants like Alexa and Siri, making them more interactive and capable of handling complex tasks. Similarly, AI agents in cryptocurrency will continue to take new shapes, offering greater efficiency and security for traders, investors, and developers.

As these intelligent systems advance, their role in crypto and blockchain technology will expand, paving the way for more automated, reliable, and secure financial ecosystems.



How is Brave’s ‘Leo’ a Better Generative AI Option?


Brave Browser 

Brave is a Chromium-based browser, running on Brave search engine, that restricted tracking for personal ads. 

Brave’s new product – Leo – is a generative AI assistant, on top of Anthropic's Claude and Meta's Llama 2. Apparently, Leo promotes user-privacy as its main feature. 

Unlike any other generative AI-chatbots, like ChatGPT, Leo offers much better privacy to its users. The AI assistant does not store any of the user’s chat history, neither does it use the user’s data for training purposes. 

Moreover, a user does not need to make an account in order to access Leo. Also, if a user is leveraging its premium experience, Brave will not link their accounts to the data they may have used. / Leo chatbot has been put to test for three months now. However, Brave is now making Leo available to all users of the most recent 1.60 desktop browser version. As soon as Brave rolls it out to you, you ought to see the Leo emblem on the sidebar of the browser. In the upcoming months, Leo support will be added to the Brave apps for Android and iPhone.

Privacy with Leo AI Assistant 

User privacy has remained a major concern when it comes to ChatGPT and Google Bard or any AI product. 

A better option in AI chatbots, along with their innovative features, will ultimately be the one which provides better privacy to its users. Leo, in this case, has a potential to bring a revolution, taking into account that Brave promotes the chatbot’s “unparalleled privacy” feature straight away. 

Since users do not require any account to access Leo, they need not verify their emails or phones numbers as well. This way, the user’s contact information is rather secure. 

Moreover, if the user chooses to use $15/month Leo Premium, they receive tokens that are not linked to their accounts. However, Brave notes that, this way, “ you can never connect your purchase details with your usage of the product, an extra step that ensures your activity is private to you and only you.”

The company says, “the email you used to create your account is unlinkable to your day-to-day use of Leo, making this a uniquely private credentialing experience.”

Brave further notes that all Leo requests will be sent via an anonymous server, meaning that Leo traffic cannot be connected to user’s IP addresses. 

More significantly, Brave will no longer host Leo's conversations. As soon as they are formed, they will be disposed of instantly. Leo will also not learn from those conversations. Moreover, Brave will not gather any personal identifiers, such as your IP address. Leo will not gather user data, nor will any other third-party model suppliers. Considering that Leo is based on two language models, this is significant.