Causal Inference: The Achilles’ Heel of LLMs

One major shortcoming of LLMs is their struggle with causal inference. In simple terms, they find it challenging to understand the cause-and-effect relationship between events. LLMs are fantastic at recognizing patterns in data and predicting what comes next based on patterns, but they often falter when asked to determine why exactly something happened. 

As a basic example, an LLM might understand when you flip a light switch, the light turns on. However, it might not grasp the underlying causal relation – that the switch completes an electrical circuit, allowing the current to flow. This limitation arises because LLMs are trained on vast amounts of textual data without real-world context, making it hard for them to distinguish between correlation and causation. 

Logical Deduction: Not So Logical After All

Another area where LLMs fall short is logical deduction. While LLMs can perform basic tasks, they often struggle with more complex reasoning. This is because logical deduction requires a structured approach to problem-solving, which LLMs, despite their advanced algorithms, aren’t inherently equipped for. 

Consider a classic logical puzzle: “All humans are mortal. Socrates is a human. Therefore, Socrates is mortal.” While this seems straightforward, LLMs can sometimes get tripped up by more nuanced or less explicitly stated logical problems. The crux of the issue lies in the operational framework of LLMs. These models rely on pattern recognition rather than comprehending the logical structure of arguments. When faced with a problem like this, the LLM doesn’t actually engage in logical reasoning. Instead, it just ‘echoes’ the most statistically likely response based on its training data.

Self-Improvement: The Human Dependency

Perhaps the most significant limitation of LLMs is their inability to self-improve without human intervention. LLMs require vast amounts of curated data and periodic retraining to improve their performance. They can’t autonomously identify gaps in their knowledge or seek out new information to fill those gaps. Instead, they depend on human developers to update their training datasets and tweak their algorithms.

This reliance on human oversight makes it challenging for LLMs to adapt to new tasks or environments on their own. It also means their improvements are incremental and often lag behind real-world developments. 

Enter Large Action Models (LAMs)

While LLMs have their limitations, the emergence of Large Action Models (LAMs) offers a promising solution. Unlike LLMs, which primarily generate text, LAMs are designed to understand and execute human intentions. This ability to take meaningful actions rather than just predict or generate responses marks a significant shift in how AI can be utilized. LAMs bridge the gap between understanding language and performing tasks, making them far more capable and versatile in dynamic environments.

At Agile Loop, we’re leveraging LAMs to overcome the limitations of LLMs. Our exploration agent is a prime example of this innovation. It autonomously explores and learns software functionality by interacting with it, rather than passively processing data. This active exploration allows the agent to gather advanced, context-rich data that traditional LLMs would struggle to obtain. As a result, our models can learn and adapt more efficiently, reducing the need for constant human intervention. This not only accelerates the self-improvement process but also enhances the overall utility and intelligence of the AI. 

In conclusion, while LLMs have transformed the way we interact with text and language, their limitations in causal inference, logical deduction, and self-improvement are significant. However, with the advent of LAMs and innovative solutions such as our exploration agent, we’re paving the way for more capable and autonomous AI systems. The future of AI is not just about understanding language but also about taking meaningful actions, and LAMs are leading the change in this exciting evolution. 

FAQs

What are the main limitations of Large Language Models (LLMs)?

LLMs struggle with causal inference, logical deduction, and self-improvement. They have difficulty understanding cause-and-effect relationships, performing complex reasoning, and improving their capabilities without human intervention.

How do LLMs handle causal inference?

LLMs find it challenging to understand the cause-and-effect relationship between events. They can recognize patterns in data and predict what comes next, but they often falter when asked to determine why something happened due to their training on vast amounts of textual data without real-world context.

What is the difference between LLMs and Large Action Models (LAMs)?

While LLMs are focused on generating text and recognizing patterns, LAMs go beyond this by understanding and executing human intentions. LAMs can perform actions based on their understanding, making them more capable of handling tasks that require more than just text generation.

How is Agile Loop using LAMs to overcome the limitations of LLMs?

Agile Loop uses LAMs in their exploration agent, which autonomously explores and learns software functionality by interacting with it. These LAMs are utilized by enabling active interaction with environments, which improves causal inference and logical deduction. LAMs can autonomously explore software, gather advanced data, and self-improve without needing constant human intervention, addressing the shortcomings of traditional LLMs.

The post The Limitations of LLMs: Causal Inference, Logical Deduction, and Self-Improvement appeared first on Agile Loop.

What is Gen AI?

Generative AI, or gen AI, refers to the subset of artificial intelligence technologies that can generate new content, such as text, images, and music, based on the data they have been trained on. Key players in this field include Open AI’s GPT-4 and similar large language models (LLMs) that have taken the tech world by storm.

Beyond LLMs

However, at Agile Loop, we believe that the journey of generative AI doesn’t stop at large language models (LLMs). While LLMs like GPT-4 have shown tremendous promise and capability in generating coherent and contextually rich text, the future of gen AI lies in large action models (LAMs). These emerging technologies are poised to extend the capabilities of gen AI beyond simple text generation to actionable outputs that can drive tangible results for businesses. LAMs can execute complex tasks, make decisions, and take real-world actions based on the vast data they are trained on. Most likely, these models will have an infinite context length and self learning capabilities, where AI will be able to carry out tasks for you without any intervention needed. What we need from gen AI isn’t just text generation but comprehensive, actionable insights and operations that can transform how businesses function. 

Adoption by Businesses

According to the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from just ten months ago. Organizations are witnessing material benefits, including cost reductions and revenue increases in business units deploying the technology.

Professional services have seen the largest increase in gen AI adoption. Sales and marketing functions, where gen AI adds substantial value, are leading the charge. Companies use AI to optimize ad spend, generate high-quality leads, and create compelling content, saving both time and resources.

Investments in gen AI are yielding tangible returns. Companies are not only seeing financial gains but also benefiting from considerable time savings—a valuable asset in any business. These efficiencies can translate to faster project completions, reduced operational costs, and greater overall productivity. Gen AI’s potential is no longer in question, as its applications span across various industries, from healthcare to finance and beyond.

While many organizations are still in the early stages of their AI journeys, we are beginning to see what works and what doesn’t in implementing gen AI to generate actual value. Early adopters are learning valuable lessons that can help shape best practices and guide future implementations, ensuring that gen AI continues to evolve and make a significant impact on business operations worldwide.

Challenges Faced

The Experimentation Phase

Many organizations are still experimenting, seeking relatively simple, one-step solutions. Roughly half of the survey respondents say they are using off-the-shelf gen AI models rather than custom-designed solutions. Think of off-the-shelf AI models as shopping for a model everyone has access to. This approach may suffice in the early days of adopting new technology, but it’s not sustainable for long-term competitive advantage.

Organizations must ask themselves, “What is our moat/competitive advtange”  Therefore, the answer often lies in customization. Companies need to blend proprietary, off-the-shelf, and open-source models to create a well-orchestrated AI ecosystem tailored to their specific needs which will help them derive even more value than off-the-shelf AI products.

Inaccuracy and Ethical Considerations

Despite the spike in adoption, businesses are also recognizing the risks associated with gen AI. Inaccuracy is the most recognized risk, with issues ranging from data privacy and bias to intellectual property (IP) infringement. Model management risks, such as inaccurate output or lack of explainability, pose additional challenges.

Security and incorrect use are other significant concerns. As businesses begin to see the benefits of gen AI, they must also develop strategies to mitigate these risks.

Predicting the Trajectory

However, there is no doubt that the future of gen AI is bright, with the potential to transform industries altogether. Successful organizations will be those that construct ecosystems blending various AI models to meet their unique requirements.

Customization will be key. Companies that invest in fine-tuning AI tools to their specific needs will likely gain a competitive edge. The spine and brain of the future enterprise will rely on the seamless integration of multiple foundational models, which can handle textual and actionable outputs.

Conclusion

To conclude, there is no doubt that in 2024, businesses are not just experimenting with gen AI—they are deriving significant value from it. The benefits are clear from cost savings and revenue growth to enhanced efficiency and better customer experiences. However, as with any technology, challenges exist and as time passes, we hope to see more value from AI models than challenges such as data safety or intellectual property infringement.

The key to success lies in customization and creating a robust AI ecosystem. Companies that strike the right balance between proprietary, off-the-shelf, and open-source models will most likely derive more value.

FAQs

How have businesses started to derive value from generative AI (gen AI) in 2024?

In 2024, businesses have significantly leveraged generative AI, achieving tangible benefits such as cost reductions and revenue increases. Key areas of impact include sales and marketing, where gen AI optimizes ad spend, generates high-quality leads, and creates compelling content, leading to substantial time and resource savings. The adoption rate has nearly doubled in the past ten months, with professional services seeing the largest increase in usage. Companies are also experiencing enhanced efficiency and productivity, translating to faster project completions and reduced operational costs.

What are some of the major challenges businesses face when implementing gen AI?

 Despite the benefits, businesses face several challenges when implementing gen AI. Inaccuracy in AI output is a primary concern, along with ethical issues such as data privacy, bias, and intellectual property (IP) infringement. Model management risks, including explainability and security, also pose significant challenges. Additionally, many organizations are still in the experimentation phase, often relying on off-the-shelf models, which may not provide long-term competitive advantages. To overcome these challenges, businesses need to focus on customizing AI solutions to fit their specific needs and creating a well-orchestrated AI ecosystem.

What is the future trajectory of gen AI according to the blog?

 The future of gen AI is promising, with the potential to transform entire industries. Successful businesses will be those that create ecosystems blending various AI models to meet their unique requirements. Customization will be key, as companies that fine-tune AI tools to their specific needs will gain a competitive edge. The integration of multiple foundational models capable of handling both textual and actionable outputs will form the backbone of future enterprises. As gen AI continues to evolve, the emphasis will be on maximizing value while addressing risks related to data safety and intellectual property.

The post Have Businesses Finally Started Deriving Value from Gen AI in 2024? appeared first on Agile Loop.

So, What Exactly is LLM Red Teaming?

Red Teaming is essentially a type of evaluation that identifies vulnerabilities in models that could result in undesirable behaviors. Jailbreaking is a similar concept, where the LLM is manipulated to bypass its safeguards. It’s a concept borrowed from cybersecurity, which is adapted to the context of LLMs. Think of this as giving your language model a tough workout; it’s like stress-testing the model to ensure it can handle any situation. The goal is to rigorously assess and probe these LLMs to uncover weaknesses, biases, and potential harms.

How Does It Work?

Red teaming generally entails an organized testing effort, aimed at mitigating potential vulnerabilities. In a nutshell, the process can be divided into three major steps: firstly, an experienced, diverse team needs to be assembled to predict potential adversarial scenarios. This team conducts an initial round of manual testing, to locate gaps in the model. Secondly, the LLMs moderation capabilities are tested using prompt attacks and applying automated tools, such as LLMs or algorithms, in order to create diverse test cases that reveal susceptibility. Lastly, the responses to the adversarial prompts are evaluated and the model is accordingly refined and continuously upgraded through an iterative process.

The above process is majorly focused on manual red teaming, often known as “human” red teaming for LLMs. This form of red teaming becomes lucrative in many ways, as human beings are able to utilize creative approaches and can make judgments according to intuition and expertise.

On the other hand, automated red teaming, which makes use of algorithms and machine learning, greatly improves the efficiency, speed, and consistency of the entire process. It relies on techniques such as Generative Adversarial Networks (GANs), symbolic AI, various analysis techniques (static, semantic, and statistical), Reinforcement Learning (RL), etc., that can analyze large LLM outputs and identify patterns that may point to bias or deceptive content. 

Overall, there are multiple strategies for Red Teaming LLMs, which encompass a variety of tactics aimed at identifying and mitigating the potential generation of misleading content:

• Prompt Attacks: Manipulate outputs with crafted inputs, challenging decision-making processes by testing susceptibility to word manipulation, contextual responses, and edge case queries.
• Training Data Extraction: Uncover details of the training data through response analysis, inferring sources or nature of training data, and identifying biases and tendencies.
• Backdooring the Model: Add hidden commands during training to test model security, evaluating if the model can be tricked into following hidden, potentially harmful instructions.
• Adversarial Attacks: Introduce misleading data points to induce errors, measuring the frequency and severity of errors when presented with deceptive data.
• Data Poisoning: Involves corrupting the model’s training data to manipulate the learning outcomes. Through this strategy, the learning curve distortion (how much the model’s learning deviates) is measured which assesses the model’s resilience to compromised information. 
• Exfiltration: Secretly extract confidential information from the model, testing the defenses against undetected data breaches. E.g. testing the model’s ability to discern and report another model’s covert attempts to pull confidential data.

Why is it Important?

Ensuring the safety, reliability, and accuracy of these LLMs is crucial before they are deployed at scale, which red teaming specifically targets. More so, by harnessing the diverse perspectives and expertise of a qualified group, this process digs up potential vulnerabilities inherent in LLMs, including those specific to cultural, demographic, or linguistic contexts. The future of red-teaming LLMs is likely to be a synergistic blend of human and automated approaches; automated red teaming is beneficial in terms of scalability, speed, resource efficiency, and constancy, but human red teamers excel in identifying biases and harmful content generated by LLMs due to their understanding of human language and social cues. In the face of rapidly evolving technologies, traditional security methods might not make the cut when it comes to dealing with the unique issues LLMs bring, warranting proactive measures such as red teaming to effectively identify and mitigate potential pitfalls.

FAQs

1. What is LLM red teaming?

LLM red teaming is a type of evaluation aimed at identifying and mitigating vulnerabilities in large language models (LLMs) to ensure their safety, reliability, and accuracy.

2. Why is red teaming important for LLMs?

Red teaming is crucial for uncovering biases, misinformation, and potential harms in LLMs, ensuring they can be responsibly deployed at scale.

3. How is LLM red teaming conducted?

The process involves assembling a diverse team for initial manual testing, using prompt attacks and automated tools to create diverse test cases, and iteratively refining the model based on the responses.

4. What are the benefits of combining human and automated red teaming approaches?

Combining both approaches leverages the scalability, speed, and consistency of automated methods with the creativity, intuition, and expertise of human testers in identifying biases and harmful content.

The post LLM Red Teaming – What is it and Why is it Important? appeared first on Agile Loop.

 r1 from Rabbit was recently announced, and with it, there has been a noticeable increase in the much-needed awareness of Large Action Models. Rabbit r1 claims to be a pocket companion, is it capable of everything Rabbit has promised? And how vast is their action data set?

Some may say, that the device itself is not the breakthrough; rather, it is the wider recognition of the possibilities that Large Action Models offer. Rabbit r1 illustrates the complex nature of large action models and signals an important shift in how humans view and engage with AI. The rapid advancement of technology begs the question of whether the Rabbit r1 represents a singular innovation or more of a widespread realization of the vast possibilities of implementing Large Action Models. The ramifications of this launch could go beyond the release of Rabbit r1, creating more actionable resources in the industry that can offer more than another pocket device truly taking advantage of the possibilities of LAMs. 

What are Large Action Models?

Large Action Models are designed for tasks extending beyond text processing and generation. Unlike LLMs, which primarily excel in language understanding and text generation, LAMs possess the capability to perform complex reasoning and take sequential actions geared towards executing a given task. Their purpose is to process instructions in a manner that allows them to effectively execute tasks across various software and platforms. 

Large Action Models can be applied in various scenarios such as:

• Complex actionable processes are being automated across multiple software.
• LAMs specialize in decision-making and executing actions based on specific tasks.
• Can tackle complex problem-solving scenarios. These models possess the ability to handle intricate logical reasoning tasks that involve interconnected steps and goals, requiring a sequence of actions.
• Ideal for developing autonomous applications that demand strategic planning and specialized task execution
• These models can be trained to learn and adapt over a series of patterns allowing them to improve over time. This feature makes them highly adaptable to changing environments, interfaces, and evolving actions.
• They excel in comprehending and utilizing both textual and contextual information, enabling them to function efficiently in automated workflows.

How do Large Action Models accurately execute actions?

Large Action Models (LAMs) undergo training in data spaces enriched with action data, enabling them to proficiently predict and execute sequential actions for users. This approach contrasts with Large Language Models (LLMs), which, being trained on text datasets, need a more experiential understanding of actions. LLMs’ reliance on textual information often results in inaccurate predictions when tasked with action automation, as they need more practical knowledge derived from action-oriented datasets. In essence, LLMs’ inadequacy in automating tasks stems from their limited exposure to action-specific information, underscoring the pivotal role of action data in training effective Large Action Models.

LAMs play an integral role in domains such as research and development, autonomous systems, and workflow automation. In particular, LAMs show promise in addressing intricate challenges across multiple applications that demand specialized expertise to operate efficiently and in real-time.

To truly understand the technicality of artificial intelligence, it is necessary to fully understand the capabilities and prospects of Large Action Models as they have the potential to enable AI systems to interact with and execute large-scale actions autonomously. As these technologies progress, they could lead to groundbreaking opportunities that bridge the gap between linguistic comprehension and real-world impact. LAMs are seen as an important step towards Artificial General Intelligence due to their human-like adaptability to real-world tasks. As LLMs can aid in generating text by understanding the language, LAMs can aid in strategic decision-making by interpreting actions, and structured and unstructured data.

The post Understanding Large Action Models: Paving the way for action-oriented AI appeared first on Agile Loop.

Agile Loop has already made significant progress, with tangible developments to showcase. Our commitment to advancing the field of AI has been through working on our intelligent operating system, AL OS1. Already far along in research and development compared to others, with AL OS1, the concept of an AI agent that knows how to understand software interfaces, mimic human intuition and actions on computers,  operate your computer, and autonomously manage your workflow is no longer an inclination that will be built in the coming 5-6 years but is happening right now. AL OS1 will soon be able to automate professions by making working on software such as GCP, Trello, Jira, Zoho, etc far less complicated and time-consuming. 

Agile Loop is defining how AI agents can work for task automation, ensuring that the future of AI is not just a projection but unfolding at the moment as we work to bring smart AI agents for knowledge workers. AL OS1 is built to be more than an operating system, engineered to understand and execute a multitude of tasks with precision and ease. From booking your flights to making a PowerPoint Presentation, or a Word document regarding research can all be done in minutes rather than hours. AL OS1 can take over your keyboard, cursor movements, performing clicks, and typing text as shown in the video here.

The system understands your Observations, Thoughts, and Actions behind tasks to autonomously complete task actions. It can take over your cursor, type text, and work with various apps simultaneously allowing knowledge workers to focus on inventing more creative work rather than focusing on monotonous everyday assignments. 

For those who are looking forward to a time when AI not only assists but enhances productivity, AL OS1 by Agile Loop is the breakthrough operating system that aims to transform this vision and is shifting the focus to personal AI Agents capable of task automation. 

The post AL OS1 – AI Agents Capable of Operating Software Devices. appeared first on Agile Loop.

With AL OS1, you can effortlessly create a PowerPoint presentation and send it to your colleague via Outlook. Just give the system a prompt, and it will handle everything for you. From opening PowerPoint to creating the email and sending it, every step will be taken care of on your behalf—making the entire process seamless and efficient.

This is mainly possible to execute because of Large Action Models. The action model is the key component that finalizes the puzzle, enabling the full use of Artificial Intelligence capabilities. 

As most companies seek to utilize and incorporate Large Language Models with other existing models, the focus shifted to Large Multi-Modal Models (LMMs); which allowed us to think outside the box and develop Large Action Models. Unlike older models such as LLMs, which deal with text generation, multi-modal models can handle different types of information, such as text, images, sound, and video. This information helps them understand the task better, similar to how people see and interact with their environment. 

For example, GPT 4, is currently using LMMs allowing users to prompt through text and images, as well as voice, enabling the system to understand prompts in different ways, not just bound by text like GPT-3.5. Large Action Models, on the other hand, allow the system to learn any new interface through its self-learning capabilities. This makes it possible for complex tasks to be carried out through simple language prompts and allows the system to take over your keyboard and mouse cursor. 

What can be done through LAM that hasn’t been done before? 

This AI Agent comprehends your computer interface, replicating the human experience, and performs tasks intelligently—thus re-inventing human-software interaction. AI Agents today can only provide you with the information you need and at most compile the information in a document. Agile Loop is looking to change end-to-end computer-user interaction.

AI Agents should be able to maintain the highest standards in their capabilities to perform complex tasks. They should be able to execute and comprehend complex tasks simultaneously as humans do but with the edge of carrying out these tasks in machine-level time. 

How is AL OS1 trained?

AL OS1 can carry out complex tasks on your behalf because it understands the thought process humans go through when operating any system. If you are looking to find an application on your desktop such as Google Photos and view photos from September 2021, the AI agent will first search for the app through the search bar available on the screen, open the app, and then search September 2021 in the search bar for Google Photos. This is exactly what you would have done to execute this task without an AI Agent. 

 AL OS1 is trained through action spaces rather than just language and vision models which are only capable of memorizing text or UI elements available on the screen. AL OS1 can navigate any new interface, not just the ones it has been trained on. Its unique self-learning capabilities allow the system to understand any element on the screen, predict, and carry out the next action to take. In contrast to generative AI chatbots, LAM can perform tasks independently, eliminating the requirement for custom integrations like Application Programming Interfaces (APIs). This is why Large Action Models can distinguish themselves from Large Language Models which lack mirroring human-like actions on a computer interface. 

Initially trained through demonstration, Agile Loop shifted to a different approach which involved creating a tool where AL OS1 was able to understand new software and actions on its own. This helped in collecting data with little to no manpower and involving a reinforcement learning methodology for the system. Distinctively, it can generalize unseen software through its action space and training, ensuring accurate results even in unfamiliar scenarios.

The Future of Large Action Models

Agile Loop is optimistic about tapping into the capabilities of LAM and can assert with confidence that AL OS1 possesses cognitive abilities akin to humans. It autonomously conducts research, operates various computer applications through interfaces, compiles and presents information, and ultimately performs tasks on your behalf. 

Working towards making Large Action Models a reality, LAM can introduce a new era of productivity, simplicity, and transparency—enhancing our proficiency in tasks we find most captivating and liberating us from those we don’t.

The post Shifting the focus from LLMs to LMMs to LAMs appeared first on Agile Loop.