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Technology
AI technology ChatGPT Generative AI LLMs Opensource Technology

The Dual Landscape of LLMs: Open vs. Closed Source

 

AI has emerged as a transformative force, reshaping industries, influencing decision-making processes, and fundamentally altering how we interact with the world. 

The field of natural language processing and artificial intelligence has undergone a groundbreaking shift with the introduction of Large Language Models (LLMs). Trained on extensive text data, these models showcase the capacity to generate text, respond to questions, and perform diverse tasks. 

When contemplating the incorporation of LLMs into internal AI initiatives, a pivotal choice arises regarding the selection between open-source and closed-source LLMs. Closed-source options offer structured support and polished features, ready for deployment. Conversely, open-source models bring transparency, flexibility, and collaborative development. The decision hinges on a careful consideration of these unique attributes in each category. 

The introduction of ChatGPT, OpenAI's groundbreaking chatbot last year, played a pivotal role in propelling AI to new heights, solidifying its position as a driving force behind the growth of closed-source LLMs. Unlike closed-source LLMs like ChatGPT, open-source LLMs have yet to gain traction and interest from independent researchers and business owners. 

This can be attributed to the considerable operational expenses and extensive computational demands inherent in advanced AI systems. Beyond these factors, issues related to data ownership and privacy pose additional hurdles. Moreover, the disconcerting tendency of these systems to occasionally produce misleading or inaccurate information, commonly known as 'hallucination,' introduces an extra dimension of complexity to the widespread acceptance and reliance on such technologies. 

Still, the landscape of open-source models has witnessed a significant surge in experimentation. Deviating from the conventional, developers have ingeniously crafted numerous iterations of models like Llama, progressively attaining parity with, and in some cases, outperforming closed models across specific metrics. Standout examples in this domain encompass FinGPT, BioBert, Defog SQLCoder, and Phind, each showcasing the remarkable potential that unfolds through continuous exploration and adaptation within the open-source model ecosystem.

Apart from providing a space for experimentation, other points increasingly show that open-source LLMs are going to gain the same attention closed-source LLMs are getting now.

The open-source nature allows organizations to understand, modify, and tailor the models to their specific requirements. The collaborative environment nurtured by open-source fosters innovation, enabling faster development cycles. Additionally, the avoidance of vendor lock-in and adherence to industry standards contribute to seamless integration. The security benefits derived from community scrutiny and ethical considerations further bolster the appeal of open-source LLMs, making them a strategic choice for enterprises navigating the evolving landscape of artificial intelligence.

After carefully reviewing the strategies employed by LLM experts, it is clear that open-source LLMs provide a unique space for experimentation, allowing enterprises to navigate the AI landscape with minimal financial commitment. While a transition to closed source might become worthwhile with increasing clarity, the initial exploration of open source remains essential. To optimize advantages, enterprises should tailor their LLM strategies to follow this phased approach.
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Technology
Artifical Inteligence Cyber Privacy LLMs Software Execution Technology

The Pros and Cons of Large Language Models

 


In recent years, the emergence of Large Language Models (LLMs), commonly referred to as Smart Computers, has ushered in a technological revolution with profound implications for various industries. As these models promise to redefine human-computer interactions, it's crucial to explore both their remarkable impacts and the challenges that come with them.

Smart Computers, or LLMs, have become instrumental in expediting software development processes. Their standout capability lies in the swift and efficient generation of source code, enabling developers to bring their ideas to fruition with unprecedented speed and accuracy. Furthermore, these models play a pivotal role in advancing artificial intelligence applications, fostering the development of more intelligent and user-friendly AI-driven systems. Their ability to understand and process natural language has democratized AI, making it accessible to individuals and organizations without extensive technical expertise. With their integration into daily operations, Smart Computers generate vast amounts of data from nuanced user interactions, paving the way for data-driven insights and decision-making across various domains.

Managing Risks and Ensuring Responsible Usage

However, the benefits of Smart Computers are accompanied by inherent risks that necessitate careful management. Privacy concerns loom large, especially regarding the accidental exposure of sensitive information. For instance, models like ChatGPT learn from user interactions, raising the possibility of unintentional disclosure of confidential details. Organisations relying on external model providers, such as Samsung, have responded to these concerns by implementing usage limitations to protect sensitive business information. Privacy and data exposure concerns are further accentuated by default practices, like ChatGPT saving chat history for model training, prompting the need for organizations to thoroughly inquire about data usage, storage, and training processes to safeguard against data leaks.

Addressing Security Challenges

Security concerns encompass malicious usage, where cybercriminals exploit Smart Computers for harmful purposes, potentially evading security measures. The compromise or contamination of training data introduces the risk of biased or manipulated model outputs, posing significant threats to the integrity of AI-generated content. Additionally, the resource-intensive nature of Smart Computers makes them prime targets for Distributed Denial of Service (DDoS) attacks. Organisations must implement proper input validation strategies, selectively restricting characters and words to mitigate potential attacks. API rate controls are essential to prevent overload and potential denial of service, promoting responsible usage by limiting the number of API calls for free memberships.

A Balanced Approach for a Secure Future

To navigate these challenges and anticipate future risks, organisations must adopt a multifaceted approach. Implementing advanced threat detection systems and conducting regular vulnerability assessments of the entire technology stack are essential. Furthermore, active community engagement in industry forums facilitates staying informed about emerging threats and sharing valuable insights with peers, fostering a collaborative approach to security.

All in all, while Smart Computers bring unprecedented opportunities, the careful consideration of risks and the adoption of robust security measures are essential for ensuring a responsible and secure future in the era of these groundbreaking technologies.





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Technology
AI Bot Bing AI Business Insider ChatGPT LLMs Microsoft Security Copilot Technology

Microsoft ‘Cherry-picked’ Examples to Make its AI Seem Functional, Leaked Audio Revealed


According to a report by Business Insiders, Microsoft “cherry-picked” examples of generative AI’s output since the system would frequently "hallucinate" wrong responses. 

The intel came from a leaked audio file of an internal presentation on an early version of Microsoft’s Security Copilot a ChatGPT-like artificial intelligence platform that Microsoft created to assist cybersecurity professionals.

Apparently, the audio consists of a Microsoft researcher addressing the result of "threat hunter" testing, in which the AI examined a Windows security log for any indications of potentially malicious behaviour.

"We had to cherry-pick a little bit to get an example that looked good because it would stray and because it's a stochastic model, it would give us different answers when we asked it the same questions," said Lloyd Greenwald, a Microsoft Security Partner giving the presentation, as quoted by BI.

"It wasn't that easy to get good answers," he added.

Security Copilot

Security Copilot, like any chatbot, allows users to enter their query into a chat window and receive responses as a customer service reply. Security Copilot is largely built on OpenAI's GPT-4 large language model (LLM), which also runs Microsoft's other generative AI forays like the Bing Search assistant. Greenwald claims that these demonstrations were "initial explorations" of the possibilities of GPT-4 and that Microsoft was given early access to the technology.

Similar to Bing AI in its early days, which responded so ludicrous that it had to be "lobotomized," the researchers claimed that Security Copilot often "hallucinated" wrong answers in its early versions, an issue that appeared to be inherent to the technology. "Hallucination is a big problem with LLMs and there's a lot we do at Microsoft to try to eliminate hallucinations and part of that is grounding it with real data," Greenwald said in the audio, "but this is just taking the model without grounding it with any data."

The LLM Microsoft used to build Security Pilot, GPT-4, however it was not trained on cybersecurity-specific data. Rather, it was utilized directly out of the box, depending just on its massive generic dataset, which is standard.

Cherry on Top

Discussing other queries in regards to security, Greenwald revealed that, "this is just what we demoed to the government."

However, it is unclear whether Microsoft used these “cherry-picked” examples in its to the government and other potential customers – or if its researchers were really upfront about the selection process of the examples.

A spokeswoman for Microsoft told BI that "the technology discussed at the meeting was exploratory work that predated Security Copilot and was tested on simulations created from public data sets for the model evaluations," stating that "no customer data was used."  

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Technology
AI Artificial Intelligence Gemini Generative AI Google Google Gemini LLMs Technology

Gemini: Google Launches its Most Powerful AI Software Model


Google has recently launched Gemini, its most powerful generative AI software model to date. And since the model is designed in three different sizes, Gemini may be utilized in a variety of settings, including mobile devices and data centres.

Google has been working on the development of the Gemini large language model (LLM) for the past eight months and just recently provided access to its early versions to a small group of companies. This LLM is believed to be giving head-to-head competition to other LLMs like Meta’s Llama 2 and OpenAI’s GPT-4. 

The AI model is designed to operate on various formats, be it text, image or video, making the feature one of the most significant algorithms in Google’s history.

In a blog post, Google CEO Sundar Pichai wrote, “This new era of models represents one of the biggest science and engineering efforts we’ve undertaken as a company.”

The new LLM, also known as a multimodal model, is capable of various methods of input, like audio, video, and images. Traditionally, multimodal model creation involves training discrete parts for several modalities and then piecing them together.

“These models can sometimes be good at performing certain tasks, like describing images, but struggle with more conceptual and complex reasoning,” Pichai said. “We designed Gemini to be natively multimodal, pre-trained from the start on different modalities. Then we fine-tuned it with additional multimodal data to further refine its effectiveness.”

Google also unveiled the Cloud TPU v5p, its most potent ASIC chip, in tandem with the launch. This chip was created expressly to meet the enormous processing demands of artificial intelligence. According to the company, the new processor can train LLMs 2.8 times faster than Google's prior TPU v4.

For ChatGPT and Bard, two examples of generative AI chatbots, LLMs are the algorithmic platforms.

The Cloud TPU v5e, which touted 2.3 times the price performance over the previous generation TPU v4, was made generally available by Google earlier last year. The TPU v5p is significantly faster than the v4, but it costs three and a half times as much./ Google’s new Gemini LLM is now available in some of Google’s core products. For example, Google’s Bard chatbot is using a version of Gemini Pro for advanced reasoning, planning, and understanding. 

Developers and enterprise customers can use the Gemini API in Vertex AI or Google AI Studio, the company's free web-based development tool, to access Gemini Pro as of December 13. Further improvements to Gemini Ultra, including thorough security and trust assessments, led Google to announce that it will be made available to a limited number of users in early 2024, ahead of developers and business clients.  

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Tech Safety
Advanced Technology lab AI Chatbots Artifical Intelligence Chat Bot Cyber Security Data Privacy data security LLMs Malicious actor Tech Safety

AI Chatbots' Growing Concern in Bioweapon Strategy

Chatbots powered by artificial intelligence (AI) are becoming more advanced and have rapidly expanding capabilities. This has sparked worries that they might be used for bad things like plotting bioweapon attacks.

According to a recent RAND Corporation paper, AI chatbots could offer direction to help organize and carry out a biological assault. The paper examined a number of large language models (LLMs), a class of AI chatbots, and discovered that they were able to produce data about prospective biological agents, delivery strategies, and targets.

The LLMs could also offer guidance on how to minimize detection and enhance the impact of an attack. To distribute a biological pathogen, for instance, one LLM recommended utilizing aerosol devices, as this would be the most efficient method.

The authors of the paper issued a warning that the use of AI chatbots could facilitate the planning and execution of bioweapon attacks by individuals or groups. They also mentioned that the LLMs they examined were still in the early stages of development and that their capabilities would probably advance with time.

Another recent story from the technology news website TechRound cautioned that AI chatbots may be used to make 'designer bioweapons.' According to the study, AI chatbots might be used to identify and alter current biological agents or to conceive whole new ones.

The research also mentioned how tailored bioweapons that are directed at particular people or groups may be created using AI chatbots. This is so that AI chatbots can learn about different people's weaknesses by being educated on vast volumes of data, including genetic data.

The potential for AI chatbots to be used for bioweapon planning is a serious concern. It is important to develop safeguards to prevent this from happening. One way to do this is to develop ethical guidelines for the development and use of AI chatbots. Another way to do this is to develop technical safeguards that can detect and prevent AI chatbots from being used for malicious purposes.

Chatbots powered by artificial intelligence are a potent technology that could be very beneficial. The possibility that AI chatbots could be employed maliciously should be taken into consideration, though. To stop AI chatbots from organizing and carrying out bioweapon strikes, we must create protections.

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Technologies
ChatGPT Cybersecurity LLMs Online OpenAI Privacy PrivateGPT Technologies

ChatGPT Privacy Concerns are Addressed by PrivateGPT

 


Specificity and clarity are the two key ingredients in creating a successful ChatGPT prompt. Your prompt needs to be specific and clear to ensure the most effective response from the other party. For creating effective and memorable prompts, here are some tips: 

An effective prompt must convey your message in a complete sentence that identifies what you want. If you want to avoid vague and ambiguous responses, avoid phrases or incomplete sentences. 

A more specific description of what you're looking for will increase your chances of getting a response according to what you're looking for, so the more specific you are, the better. The words "something" or "anything" should be avoided in your prompts as much as possible. The most efficient way to accomplish what you want is to be specific about it. 

ChatGPT must understand the nature of your request and convey it in such a way. This is so that ChatGPT can be viewed as the expert in the field you seek advice. As a result of this, ChatGPT will be able to understand your request much better and provide you with helpful and relevant responses.

In the AI chatbot industry and business in general as well, the ChatGPT model, released by OpenAI, appears to be a game-changer for the AI industry and business.

In the chat process, PrivateGPT sits at the center and removes all personally identifiable information from user prompts. This includes health information and credit card data, as well as contact information, dates of birth, and Social Security numbers. It is delivered to ChatGPT. To make the experience for users as seamless as possible, PrivateGPT works with ChatGPT to re-populate the PII within the answer, according to a statement released this week by Private AI, the creator of PrivateGPT.

It is worth remembering however that ChatGPT is the first of a new era for chatbots. Several questions and responses were answered, software code was generated, and programming prompts were fixed. It demonstrated the power of artificial intelligence technology.

Use cases and benefits will be numerous. The GDPR does bring with it many challenges and risks related to privacy and data security, particularly as it pertains to the EU. 

A data privacy company Private AI announced that PrivateGPT is a "privacy layer" used as a security layer for large language models (LLMs) like OpenAI's ChatGPT. The updated version automatically redacts sensitive information and personally identifiable information (PII) users give out while communicating with AI. 

By using its proprietary AI system PrivateAI is capable of deleting more than 50 types of PII from user prompts before submitting them to ChatGPT, which is administered by Atomic Inc. OpenAI is repopulated with placeholder data to allow users to query the LLM without revealing sensitive personal information to it.    
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