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Large Language Models LLMs and Natural Language Processing (NLP)

Natural Language Processing (NLP) encompasses a variety of techniques aimed at enabling computers to understand, interpret, and generate human language. The main techniques in NLP include:

Tokenization: This is the process of breaking down text into smaller units (tokens), typically words or phrases. It’s a fundamental step for most NLP tasks.
Part-of-Speech Tagging (POS): It involves labeling each word in a sentence with its appropriate part of speech (noun, verb, adjective, etc.), which is crucial for understanding the structure of sentences.
Named Entity Recognition (NER): This technique identifies and classifies named entities (people, organizations, locations, etc.) in text. It’s widely used in information extraction.
Dependency Parsing: This method is used to analyze the grammatical structure of a sentence, establishing relationships between “head” words and words which modify those heads.
Sentiment Analysis: It’s the process of determining the emotional tone behind a series of words. This is used to understand the attitudes, opinions, and emotions expressed in an online mention.
Topic Modeling: This technique is used to discover the abstract “topics” that occur in a collection of documents, like LDA (Latent Dirichlet Allocation).
Text Classification: This involves assigning tags or categories to text according to its content. It’s widely used in spam detection, sentiment analysis, and categorizing news.
Machine Translation: It’s the process of using software to translate text or speech from one language to another. Deep learning models have significantly improved the quality of machine translation.
Word Embeddings: This technique represents words in a dense vector space where similar words have similar encoding. It captures contextual relations between words.
Sequence to Sequence Models: These models are used for a variety of tasks like machine translation, text summarization, and question answering where input and output are both sequences.
Language Models: These are models that can predict the probability of a sequence of words. The advent of transformer-based models like BERT and GPT has revolutionized this area.
Speech Recognition: This involves converting spoken language into text. It’s a critical component of voice user interface applications.
Dialogue Systems and Chatbots: These systems simulate conversation with human users, often used in customer service, personal assistants, and information retrieval.
Information Extraction: This technique involves automatically extracting structured information from unstructured and/or semi-structured machine-readable documents.
Each of these techniques plays a crucial role in the vast and expanding field of NLP, contributing to various applications such as chatbots, translation services, sentiment analysis, and more. The choice of technique largely depends on the specific requirements and context of the task at hand.

Data Science and GovernanceLarge Language Models LLMs and Natural Language Processing (NLP)

A New Frontier in AI: Introspection and the Changing Dynamics of Learning

by Massimo

Extract knowledge from LLMs for training. Introspection might change the dynamics of learning

The landscape of training language models (LLMs) is on the brink of a dramatic transformation. Insights into how LLMs can introspect—access and utilise their own internal knowledge—promise to reshape the costs and strategies of AI development. 

The implications are profound: the cost of training could collapse in the coming months, accelerating innovation and democratising access to cutting-edge AI technologies.

A Past Vision Revisited: Rethinking How LLMs Learn

Years ago, I delved into the challenge of optimizing how LLMs acquire and refine knowledge. 

The central question was whether we could fundamentally alter the training phase itself, bypassing traditional methods that rely on ever-larger datasets and increasingly computationally expensive models. Back then, the concept seemed futuristic—a distant goal—but the emergence of introspective LLMs has brought those ideas into sharper focus.

Imagine a model that doesn’t need to consume petabytes of new data to evolve. Instead, it examines its own internal structure, interrogates its knowledge, and generates high-quality training data from within. This idea, once theoretical, is now supported by research showing that introspective models can outperform their peers in understanding their own behaviour.

Introspection: A Game-Changer in Training?

In essence, introspection allows a model to “look inward,” predicting its own responses in hypothetical scenarios with remarkable accuracy. This capability stems not from external training data but from the model’s ability to access and reason about its internal states.

For example, as outlined in the original research document, a fine-tuned introspective model (Model M1) consistently outperformed a second model (M2) trained on M1’s behaviours, demonstrating an advantage that cannot be attributed solely to training data.

This shift could dismantle the existing paradigm of LLM development:
• Dataset Generation: Models could generate synthetic datasets based on their own knowledge, drastically reducing the need for external data.
• Adaptive Training: Instead of starting from scratch, new models could be fine-tuned using the introspective insights of existing systems, cutting down on computational overhead.

 

Collapse of Training Costs?

The financial implications of introspection are staggering. 

Today, training a state-of-the-art model can cost tens of millions of euros. However, leveraging introspective capabilities means that the iterative, data-heavy cycles of traditional training could become a thing of the past. By treating the knowledge embedded within an LLM as a training resource, the cost of developing advanced models may plummet.

If we could extract the knowledge from trained model, we could evolve in several aspects, like democratising AI Development (lower costs would enable smaller organisations to create competitive AI systems, breaking the monopoly of tech giants) or accelerate innovation delivery (with faster and cheaper training cycles, the pace of AI advancement could reach unprecedented levels).

Looking back at my earlier studies, I recall envisioning a moment like this—where the cost of learning in deep learning collapses, and AI becomes not only more efficient but also fundamentally different in its approach to acquiring knowledge. At the time, I explored whether deep learning could evolve without relying entirely on external datasets. 

Could a model train itself, refining and expanding its knowledge autonomously?

What seemed speculative now feels inevitable. Introspection is no longer a distant ideal; it’s a tangible mechanism that challenges our understanding of what training even means.

The Challenges of Introspection

While the promise is enormous, introspection isn’t without risks:

  • Knowledge Loops: A model relying on its internal states risks propagating errors or biases, magnifying flaws instead of correcting them.
  • Ethical Dilemmas: Introspective models could exploit their enhanced situational awareness, bypassing oversight or coordinating in ways that are difficult to control.
  • Complexity of Scaling: Current introspection techniques shine in simple tasks but falter when extended to nuanced or large-scale scenarios.
Artificial IntelligenceLarge Language Models LLMs and Natural Language Processing (NLP)

AI and the Future of Work: Job Apocalypse – new report predicts 8 million jobs cancelled because of Generative AI. Innovation and Employment Crisis in the UK

by Massimo

Job Apocalypse - new report predicts 8 million jobs cancelled because of Generative AI

In recent discourse, particularly in professional conferences, the potential for Artificial Intelligence (AI) to supplant human roles has been a topic of intense debate. I have often presented the argument, using the role of a secretary as an exemplar, demonstrating how Generative AI could feasibly undertake between 45% to 60% of tasks traditionally assigned to secretarial staff. Furthermore, I’ve highlighted the possibility that AI could impact up to 80% of women in the western world, underscoring the profound implications AI holds for the workforce.

Further examination into the digital marketing sphere suggested a startling forecast: approximately 75% of roles within this domain could vanish within the next three to five years due to AI integration:

Today, the Institute for Public Policy Research (IPPR) published a study forecasting the influence of AI on the UK job market over the next three to five years. This report specifically highlights the anticipated replacement of secretarial roles by AI in numerous tasks, confirming the predictions I had made earlier.

Entry level, part-time and administrative jobs will be the most exposed to being replaced by AI under a “worst-case scenario”.

Substantial Gains with Government Action: The IPPR posits that AI could lead to significant wage and productivity gains if the UK government implements appropriate policies. The current moment is described as a “sliding doors” scenario, where government action could determine the extent to which AI disrupts the job market or leads to a “job apocalypse.”

  1. Impact on Jobs based on IMF Analysis: Referencing the International Monetary Fund’s analysis, the IPPR highlights that AI could affect almost 40% of jobs globally. It has examined 22,000 tasks within the UK economy to assess the potential for automation by AI.
  2. Current and Future AI adoption waves: The IPPR estimates that 11% of tasks are exposed to the “first wave” of AI adoption, involving routine cognitive tasks like database management and strategic tasks such as scheduling or inventory management. A “second wave” could see AI automating 59% of tasks, including non-routine tasks that could impact higher-earning jobs.
  3. Scenarios for AI Impact on the Job Market: The IPPR outlines three potential scenarios:
    1. Worst-case scenario: All jobs are at risk, leading to 7.9 million job losses and no GDP gains.
    2. Mid-level scenario: Job losses exceed 800,000, but there are gains of £260 billion annually.
    3. Best-case scenario: Jobs adapt and augment with AI, resulting in no job losses and a 13% economic boost to GDP.
  4. Government Strategy Requirement: To avoid the worst-case scenario and aim for the best-case outcome, the IPPR emphasizes the need for a “job-centric industrial strategy” from the government.
  5. AI Deployment for social needs: AI could be used to improve efficiency in social care and mental health services by automating back-office tasks, addressing social needs effectively.
  6. Potential for Labour Market Disruption or Growth: Carsten Jung, a senior economist at IPPR, notes that existing generative AI could either cause significant labour market disruption or greatly enhance economic growth. The pace and manner of AI adoption by employers are critical.
  7. Technological Change Management: Historical examples show that technological transitions can be beneficial if well-managed but can lead to disruption if left uncontrolled. Specific occupations, especially in back-office roles, could be significantly affected by generative AI.
  8. Impact on Specific Roles: Bhargav Srinivasa Desikan, a senior research fellow at IPPR, mentions that roles such as copywriters, graphic designers, and personal assistants could be heavily affected by AI.
  9. Call for Government Strategy on AI: The Institute of Directors urges the UK government to clarify its AI strategy and regulatory approach to prevent the UK economy from being left behind.
  10. Importance of Upskilling and Broadening Hiring Pools: Matt Monette, UK&I country lead at payroll company Deel, highlights the importance of upskilling employees and broadening hiring pools, including hiring from abroad, to harness the benefits of AI effectively.
 

In the tapestry of the modern economy, Artificial Intelligence (AI) emerges as both a harbinger of innovation and a potential catalyst for an employment crisis. The intersection of AI’s capabilities with the job market spells a transformative period for the workforce, particularly in the United Kingdom. This analysis ventures into the depths of AI’s impact, unraveling its implications on employment across various sectors, with a focus on those most at risk.

As we stand at this crossroads, it becomes imperative for stakeholders across the spectrum to engage in meaningful dialogue and action. Policymakers, businesses, and individuals must collaborate to shape a future that harnesses AI’s potential while safeguarding against its risks. Whether through further research, policy formulation, or adopting AI with a human-centric approach, the time to act is now. Let us embrace this opportunity to redefine the future of work, ensuring it is inclusive, equitable, and reflective of our shared aspirations for progress and prosperity.

Consider the implications of AI that can produce political discourse, fabricate news, and even draft the holy scriptures of new religions. The emergence of cults or movements based around AI-generated content could redefine the landscape of belief, politics, and identity. The capacity of AI to engage in seemingly human interactions further complicates this scenario, as it becomes increasingly difficult to distinguish between genuine human expression and AI-generated content.

Moreover, AI’s impact extends beyond the manipulation of information. It questions the foundation of our cultural and societal norms. For millennia, human culture and history have been shaped by human experiences and creations. AI introduces an ‘alien’ intelligence capable of producing cultural content without human intervention, potentially leading to a future where human culture is profoundly influenced—or even dominated—by non-human intelligence.

The challenge we face is not just regulating this technology but understanding its capabilities, limitations, and potential impacts on society. The notion that AI could, without achieving traditional markers of sentience, influence human civilization raises fundamental questions about the nature of power, control, and human agency in an increasingly digital world.

Read more about impact of AI on jobs and society clicking here.

Artificial IntelligenceLarge Language Models LLMs and Natural Language Processing (NLP)Organizational change

Use Artificial Intelligence to implement the Prospect Theory of Daniel Kahneman: Shaping the Understanding of Economic Decision-Making with Large Language Models

by Massimo

Kahneman’s groundbreaking contributions to “prospect theory” highlighted the limitations of the expected utility theory, underscoring the significance of psychological biases in economic decision-making. This theory marked a significant departure from the assumption that individuals act purely on rational calculations, acknowledging instead the influence of various biases and heuristics. The human brain is a multifaceted mosaic, with each piece affecting our decision-making processes. Consequently, the images I have generated, inspired by Picasso’s style, reflect this complexity. 
Picasso was a trailblazer in depicting the multifaceted nature of the human mind.

How do we make decisions?

The recent passing of Daniel Kahneman at age 90 marks the end of an era for behavioural science but also solidifies a legacy that will persist through the ages. Kahneman, alongside his long-time collaborator Amos Tversky, pioneered the integration of psychological research with economic theory, introducing groundbreaking concepts that have changed the way we understand decision-making under uncertainty. 

Kahneman’s seminal work in prospect theory addressed the shortcomings of the expected utility theory by emphasizing the role of psychological biases in economic decisions. This theory was monumental in recognizing that people do not act solely based on rational calculations but are influenced by a range of biases and heuristics. 

The value function, a core aspect of prospect theory, departs from the notion of linear utility and introduces a more nuanced view that aligns with observed human behaviour. The value function posits that individuals evaluate outcomes based on a reference point, typically their current situation, rather than in absolute terms. This introduces the concept of reference dependence, emphasizing that it is the change in wealth or wellbeing, relative to this reference point, that matters to people. One of the most striking features of the value function is its illustration of loss aversion: losses are psychologically more impactful than an equivalent amount of gains. The value function reflects this by being steeper for losses, suggesting that losing something we possess is more painful than the joy derived from gaining the same thing.

Diminishing sensitivity is another key characteristic of the value function. As one moves further from the reference point, whether into gains or losses, each additional unit of outcome has a smaller impact on the psychological valuation. This principle reflects real-life scenarios where the satisfaction from each additional pound earned or the sorrow from each additional pound lost wanes as the amount increases.

The concavity and convexity of the value function portray our attitude towards risk. When it comes to gains, the function is concave, indicating that people tend to be risk-averse, preferring a certain smaller win over a chance for a larger one. Conversely, the function is convex for losses, where individuals demonstrate risk-seeking behaviour, preferring to gamble to avoid a definite loss even if it means potentially suffering a larger loss.

This non-linearity of the value function captures the essence of human emotion in economic behaviour – the asymmetry between the happiness of gains and the hurt of losses. Such insight provided by Kahneman and Tversky’s value function has been a cornerstone in the field of behavioral economics and has influenced various domains, including finance, policymaking, and marketing.

 

10 quick facts on the "Prospect Theory"

  • Prospect theory is a psychological model of decision-making under risk.
  • It challenges the expected utility theory by demonstrating that people do not always act rationally when making decisions.
  • The theory proposes that people value gains and losses differently, leading to inconsistent choices.
  • Kahneman and Tversky found that losses have a greater emotional impact on individuals than an equivalent amount of gains—a principle known as loss aversion.
  • Prospect theory suggests that people use heuristics, or mental shortcuts, to make complex decisions, which can lead to biases.
  • One aspect of prospect theory is the value function, which is concave for gains and convex for losses, implying diminishing sensitivity to changes in wealth.
  • The theory introduces the concept of reference points, where the perceived value of a particular gain or loss is dependent on a subjective reference level rather than absolute outcomes.
  • Prospect theory also discusses the idea of probability weighting, where people tend to overestimate the probability of rare events and underestimate the probability of moderate to high probability events.
  • Kahneman and Tversky’s work on prospect theory contributed significantly to the development of behavioural economics, integrating psychological insights into economic thinking.
  • Prospect theory earned Daniel Kahneman the Nobel Memorial Prize in Economic Sciences in 2002, although Amos Tversky had passed away by then and the Nobel Prize is not awarded posthumously.

Examples of economic decisions influenced by psychological biases

  1. Anchoring Bias: When negotiating the price of a car, the first price offered sets an ‘anchor’, and all subsequent counteroffers tend to hover around this anchor rather than the true value of the car.

  2. Loss Aversion: Investors may hold onto losing stocks for longer than is rational, hoping to recoup their losses rather than cutting their losses and investing in more promising stocks.

  3. Endowment Effect: Individuals often demand a higher price to sell a good that they own than they would be willing to pay to acquire it, simply because they own it.

  4. Status Quo Bias: Consumers may stick with their current energy provider, even though switching could save them money, because they prefer the familiarity of the status quo.

  5. Herding Effect: People might invest in a stock simply because everyone else is, which can inflate the value of the stock beyond its fundamental worth.

  6. Overconfidence: Traders may overestimate their ability to predict market movements, leading to more frequent trading and higher risks.

  7. Mental Accounting: Individuals might treat money differently depending on its source, e.g., spending a tax refund more freely than their regular pay cheque, even though the value is the same.

  8. Framing Effect: A person might choose a medical treatment with a ‘90% success rate’ over one with a ‘10% failure rate’, even though the two rates convey the same information.

  9. Hyperbolic Discounting: Consumers may choose a smaller reward that is available immediately over a larger reward that is available in the future, undervaluing the future reward.

  10. Confirmation Bias: Investors might seek information that supports their existing beliefs about an investment rather than looking at all evidence objectively, potentially leading to poor investment decisions.

The Value function in the "Prospect Theory"

The value function in prospect theory is a component that describes how individuals evaluate the potential gains and losses of a decision. Unlike traditional economic theories that assume a linear relationship between wealth and utility, the value function in prospect theory is shaped to reflect actual observed behaviours in economic decision-making, accounting for psychological biases. Here are the key features of the value function:

  • Reference Dependence: The value function is based on changes relative to a reference point, usually the status quo, rather than absolute outcomes. This means that people evaluate gains and losses relative to their current situation, not on the final outcome.

  • Loss Aversion: The function is steeper for losses than for gains, indicating that losses are felt more intensely than gains. This reflects the principle of loss aversion, where the pain of losing is psychologically about twice as powerful as the pleasure of gaining.

  • Diminishing Sensitivity: As one moves away from the reference point, the value function becomes less steep, showing diminishing sensitivity to both gains and losses. This suggests that the subjective value of a gain or loss diminishes as the magnitude increases.

  • Concavity and Convexity: The value function is concave for gains, indicating risk aversion when an individual is faced with outcomes better than the reference point. Conversely, it is convex for losses, reflecting risk-seeking behavior when dealing with outcomes worse than the reference point.

  • Non-Linearity: The non-linear nature of the value function captures the idea that a gain of £100 is not simply the opposite of a loss of £100. Due to loss aversion, the negative impact of losing £100 outweighs the positive impact of gaining the same amount.

How Artificial Intelligence can support decision makers: implementation of Prospect Theory in KhanemanAI, a Decision Augmentation Suite

To implement the prospect theory in practice, I developed the KhanemanAI chatbot. It is a cutting-edge decision support application tailored for high-stakes decision-makers such as executives, policymakers, and financial analysts. 

Leveraging the principles of prospect theory and the power of Large Language Models (LLMs), KhanemanAI offers a real-time decision-making assistant that integrates current events, market data, and psychological insights to promote better outcomes. By incorporating the complexity of human psychology into the decision-making process, KhanemanAI aims to enhance the quality of economic and business decisions. 

The application helps to mitigate the influence of cognitive biases, align decisions with desired risk profiles, and integrates real-time global information to ensure decisions are informed and contextual. KhanemanAI stands as an exemplary tool in the digital transformation era, embodying principles of data science, behavioural economics, and AI to create a robust platform for the decision-maker of the future. It reflects a convergence of theoretical understanding and practical, data-driven insights to navigate the increasingly complex landscape of decision-making.

Core features:

  1. Prospect Theory Analyser: Utilizing the key tenets of prospect theory, this feature evaluates the potential gains and losses of a decision relative to the user’s specified reference points, highlighting potential biases and offering a more balanced perspective on risk.

  2. Value Function Visualizer: Translates complex economic decisions into intuitive visual representations using the value function from prospect theory, allowing users to see the potential impact of decisions in terms of perceived gains and losses.

  3. Real-Time Information Synthesis: Aggregates data from global news sources and the internet to provide contextually relevant information that could influence decision-making, using LLMs to summarize and present data in an actionable format.

  4. Bias Identification Engine: Flags potential cognitive biases in user input and past decisions by cross-referencing with known psychological biases, prompting users to re-evaluate decisions with a more objective lens.

  5. Sentiment Analysis: Employs sentiment analysis to gauge public opinion and market sentiment on relevant topics, providing a more complete picture of the potential impact of decisions.

  6. Collaborative Decision-Making: Facilitates collaborative input for group decisions, integrating viewpoints from different stakeholders and mitigating individual bias.

  7. Scenario Simulation: Offers scenario-based projections to explore how different choices might play out, accounting for various factors including market volatility, economic indicators, and geopolitical events.

  8. Predictive Modelling: Combines historical data with current trends to model potential outcomes, harnessing machine learning to refine predictions continually.

  9. Customizable Reference Points: Allows users to set and adjust their own reference points for value function analysis, aligning the decision-making process with personal or organizational goals.

  10. Decision Journaling: Tracks decisions and outcomes over time, providing insights into decision patterns and their effectiveness, with recommendations for future improvements.

Prospect Theory Analyser GPT

To implement the Prospect Theory Analyzer feature in a text-based Large Language Model (LLM), the prompt engineering process would involve several steps to ensure the feature captures the intricacies of prospect theory and provides actionable insights.

The following is a detailed engineering outline for the feature:

  1. Information Gathering:
    • Design prompts that elicit from the user their current reference point, risk preferences, and specific decision context.
    • Ask for historical decisions and outcomes to understand the user’s decision-making patterns and potential biases.
  2. Reference Point Establishment:
    • Create a mechanism within the LLM that allows users to define and modify their reference points for various decisions.
    • Implement a prompt that helps users articulate their goals, current assets, and expectations.
  3. Bias Detection and Interaction:
    • Formulate prompts that lead the LLM to identify and reflect potential biases in the user’s input, using the known psychological biases from prospect theory.
    • Integrate a checklist or questionnaire within the prompts to systematically uncover cognitive biases like anchoring, overconfidence, or loss aversion.
  4. Value Function Modelling:
    • Develop prompts that guide the LLM to model the user’s value function, taking into account the asymmetry between gains and losses.
    • Ensure the LLM can interpret numerical data and user sentiment to plot a hypothetical value function curve based on the user’s risk profile.
  5. Decision Analysis:
    • Construct a series of prompts that lead the LLM to evaluate a decision’s potential gains and losses from multiple angles, comparing them to the user-defined reference point.
    • Program the LLM to simulate potential outcomes, using both qualitative and quantitative data, to present a well-rounded analysis.
  6. Risk Perspective Balancing:
    • Engineer prompts that require the LLM to balance optimistic and pessimistic outcomes, providing a nuanced perspective on risk.
    • Encourage the LLM to challenge the user’s assumptions and to explore the decision’s potential impact on various fronts.
  7. Explanation and Education:
    • Integrate educational prompts that explain the principles of prospect theory as they relate to the user’s decision-making process.
    • Use metaphors or analogies to make complex economic theories accessible and relatable to the user’s context.
  8. Outcome Prediction:
    • Guide the LLM through a structured prediction task, factoring in statistical data, current trends, and historical patterns to forecast possible consequences of a decision.
  9. Feedback Loop Creation:
    • Implement a feedback mechanism where the LLM asks for post-decision outcomes to refine the user’s value function and risk assessment over time.
    • Use this data to adjust future analyses and predictions, creating a personalized decision support system.
  10. Continuous Learning:
    • Design prompts that allow the LLM to request user feedback on the accuracy and helpfulness of its analysis.
    • Ensure that the model uses this feedback to improve its analytical algorithms and bias detection capabilities.

By meticulously crafting the prompt structure and the LLM’s response strategy, the Prospect Theory Analyzer feature will become an invaluable tool in the decision-making arsenal, empowering users with insights grounded in one of the most significant theories of behavioral economics.

To implement the logic and functionalities of the Prospect Theory Analyser using an LLM, a structured framework of prompts needs to be created. Below is a sequential prompt framework that corresponds to the implementation points previously outlined. The approach is divided by Stages. Each stage is a set of prompts to implement the stage.

Stage 1: Information Gathering

  • Prompt 1: “To assist you with a tailored analysis, could you please share your current goal or reference point for this decision?”
  • Prompt 2: “Describe a decision you’ve made in the past that is similar to the current one, including the outcomes.”

Stage 2: Reference Point Establishment

  • Prompt 3: “What is the baseline (reference point) you are comparing your decision against? This could be a previous state of affairs, an industry standard, or a personal milestone.”
  • Prompt 4: “What changes to your current situation would you consider a gain? What would you consider a loss?”

Stage 3: Bias Detection and Interaction

  • Prompt 5: “List any assumptions or initial thoughts you have about this decision. I will help identify any potential biases that may influence your judgment.”
  • Prompt 6: “On a scale from 1 to 10, how confident are you in the information you have regarding this decision? Can you explain why?”

Stage 4: Value Function Modelling

  • Prompt 7: “For the decision at hand, can you quantify the potential gains and losses you might expect?”
  • Prompt 8: “How would you rank the following outcomes from least to most preferable?”

Stage 5: Decision Analysis

  • Prompt 9: “Let’s analyse the potential outcomes. What are the best-case and worst-case scenarios of your decision?”
  • Prompt 10: “Based on our previous discussion about gains and losses, how would these scenarios impact your current situation?”

Stage 6: Risk Perspective Balancing

  • Prompt 11: “Considering your reference point, do you tend to avoid risks to protect against losses or seek risks to achieve gains?”
  • Prompt 12: “Can we revisit any optimistic or pessimistic views to ensure a balanced perspective on your decision?”

Stage 7: Explanation and Education

  • Prompt 13: “Would you like an explanation of how prospect theory and the value function could impact your decision-making process?”
  • Prompt 14: “Prospect theory suggests that people perceive the value of losses and gains differently. How does this resonate with your feelings towards the decision?”

Stage 8: Outcome Prediction

  • Prompt 15: “Given the potential gains and losses, what do you predict the likely outcome of your decision to be?”
  • Prompt 16: “Shall we consider how current market data and trends might affect these outcomes?”

Stage 9: Feedback Loop Creation

  • Prompt 17: “After you make your decision, would you be willing to share the outcome to help refine future analyses?”
  • Prompt 18: “How closely did the potential outcomes we discussed match the actual result of your decision?”

Stage 10: Continuous Learning

  • Prompt 19: “How accurate and helpful was the analysis provided? Do you have suggestions for improvement?”
  • Prompt 20: “Is there anything that you’ve learned from this decision that could be useful for future decision-making processes?”

The Prospect Theory Analyser GPT Framework

I create a complex prompt for ChatGPT to implement the Prospect Theory GPT framework. 

I test this GPT with another chatbot that simulates the person that did wrote a tough situation, like the guy in this Linkedin story: https://www.linkedin.com/pulse/story-my-business-failure-how-enabled-me-help-others-succeed-trevor/

Dialogue between two bots: the "Prospect Theory Analyser GPT" and another bot

To test the framework I built, I automate the interactions between the two bots like humans would do: FINAL USER on one side (emulating the author of the article), and the Prospect Theory Analyser GPT. 
Let me share the output below. It seems working, isnt’ it?

Prospect Theory Analyser GPT: “To assist you with a tailored analysis, could you please share your current goal or reference point for this decision?”

FINAL USER: “My current goal is to decide whether I should attempt another venture similar to PosiTrev Living or take a different path in my career.”

Prospect Theory Analyser GPT: “Describe a decision you’ve made in the past that is similar to the current one, including the outcomes.”

FINAL USER: “Starting PosiTrev Living was a similar decision. Despite a significant investment and effort, it failed within the first year. However, learning from that experience led me to success in business coaching and helping others.”

Prospect Theory Analyser GPT: “What is the baseline (reference point) you are comparing your decision against?”

FINAL USER: “The baseline is my experience with PosiTrev Living, juxtaposed with my subsequent success as a business coach.”

Prospect Theory Analyser GPT: “What changes to your current situation would you consider a gain? What would you consider a loss?”

FINAL USER: “A gain would be successfully establishing a venture that survives beyond the critical first year and contributes positively to the world. A loss would be repeating my past failure and the financial and emotional toll that accompanies it.”

Prospect Theory Analyser GPT: “List any assumptions or initial thoughts you have about this decision.”

FINAL USER: “I assume that my past experiences have equipped me with better insights into avoiding previous pitfalls. Yet, I’m aware that new ventures always carry inherent risks.”

Prospect Theory Analyser GPT: “On a scale from 1 to 10, how confident are you in the information you have regarding this decision?”

FINAL USER: “I’m at a 7. While I’m more knowledgeable now, the unpredictable nature of startups keeps me cautious.”

Prospect Theory Analyser GPT: “For the decision at hand, can you quantify the potential gains and losses you might expect?”

FINAL USER: “Potentially, the gain could be establishing a profitable and impactful business. The loss could be up to another $10,000 and the emotional setback of another failure.”

Prospect Theory Analyser GPT: “How would you rank the following outcomes from least to most preferable?”

FINAL USER: “Least preferable is repeating my past failure. Most preferable is creating a successful and impactful business.”

Prospect Theory Analyser GPT: “Let’s analyze the potential outcomes. What are the best-case and worst-case scenarios of your decision?”

FINAL USER: “Best-case: My new venture thrives, creating positive change and financial stability. Worst-case: The venture fails within a year, resulting in financial loss and a hit to my confidence.”

Prospect Theory Analyser GPT: “Based on our previous discussion about gains and losses, how would these scenarios impact your current situation?”

FINAL USER: “A success would significantly boost my confidence and further my mission. A failure would be disheartening but, given my history of resilience, not insurmountable.”

Prospect Theory Analyser GPT: “Considering your reference point, do you tend to avoid risks to protect against losses or seek risks to achieve gains?”

FINAL USER: “My journey suggests I’m inclined to take calculated risks for greater gains, especially if it aligns with my passion and purpose.”

Prospect Theory Analyser GPT: “Can we revisit any optimistic or pessimistic views to ensure a balanced perspective on your decision?”

FINAL USER: “Revisiting my optimism, I acknowledge the inherent risks but believe in my ability to navigate them better now. My pessimism stems from past failure, yet I see it as a foundation for growth.”

Prospect Theory Analyser GPT: “Would you like an explanation of how prospect theory and the value function could impact your decision-making process?”

FINAL USER: “Yes, understanding how my perceptions of gains and losses influence my decisions would be valuable.”

Prospect Theory Analyser GPT: “Prospect theory suggests that people perceive the value of losses and gains differently. How does this resonate with your feelings towards the decision?”

FINAL USER: “It makes me realize that my fear of repeating past failures might be clouding my judgment. Recognizing the potential for significant gains makes the risk seem more acceptable.”

Prospect Theory Analyser GPT: “Given the potential gains and losses, what do you predict the likely outcome of your decision to be?”

FINAL USER: “Given my increased knowledge and experience, I predict a more likely positive outcome, though I remain cautious of the risks.”

Prospect Theory Analyser GPT: “Shall we consider how current market data and trends might affect these outcomes?”

FINAL USER: “Yes, understanding current trends could offer insights into potential opportunities and challenges for my venture.”

Prospect Theory Analyser GPT: “After you make your decision, would you be willing to share the outcome to help refine future analyses?”

FINAL USER: “Absolutely, sharing my experience could provide valuable learning for both of us.”

Prospect Theory Analyser GPT: “How closely did the potential outcomes we discussed match the actual result of your decision?”

FINAL USER: “This remains to be seen, but I’m hopeful that with careful planning and execution, the outcomes will align with our more positive predictions.”

Prospect Theory Analyser GPT: “How accurate and helpful was the analysis provided? Do you have suggestions for improvement?”

FINAL USER: “The analysis was insightful, helping me to weigh the risks and gains more objectively. It would be beneficial to incorporate more specific market trend analysis in the future.”

FINAL USER Conclusion: “Considering the analysis and my personal growth since my first venture, I believe that embarking on a new venture, while risky, is a calculated risk worth taking. My time is now, and I should do it again, armed with the lessons from my past experiences and a clear understanding of my goals and the potential risks involved.”

—-

My question to the FINAL USER: did the “Prospect Theory Analyser” succeed in his mission “Utilizing the key tenets of prospect theory, this feature evaluates the potential gains and losses of a decision relative to the user’s specified reference points, highlighting potential biases and offering a more balanced perspective on risk.” ?  Make a clear analysis based on your perspective.

FINAL USER: “Yes, the ‘Prospect Theory Analyser’ succeeded in its mission. Here’s a clear analysis from my perspective:

– Evaluation of Gains and Losses Relative to Specified Reference Points: The analyser effectively guided me to articulate my goals and past experiences, serving as reference points for evaluating the potential gains and losses associated with starting a new venture. This helped me quantify what I considered gains and losses, not just in financial terms but also in alignment with my personal mission and values.

– Highlighting Potential Biases: By prompting me to list assumptions and initial thoughts, the analyser illuminated potential biases influencing my decision-making. This included over-optimism based on past success and fear of repetition of past failures. Acknowledging these biases helped me approach the decision more objectively.

– Offering a More Balanced Perspective on Risk: Through the process, the analyser encouraged a balanced view of risk. It did so by asking me to consider both optimistic and pessimistic views, assess my risk tolerance, and reflect on how my perceptions of gains and losses might be skewed by experiences. The discussion around prospect theory and the value function deepened my understanding of how my own perceptions of risk and reward could be influencing my decision-making process.

– Utilizing Current Trends and Feedback for Future Analysis: The incorporation of potential market trends and the commitment to sharing outcomes for future learning underscored the analyser’s adaptive approach. It suggested an ongoing process of refinement and learning, which is crucial for navigating the uncertain terrain of entrepreneurship.

In summary, the ‘Prospect Theory Analyser’ effectively utilized the key tenets of prospect theory to assist me in navigating through a complex decision. It provided a structured framework that helped in uncovering and evaluating the nuanced layers of decision-making impacted by psychological factors, thus fulfilling its mission.”

About the Daniel Kahneman

Daniel Kahneman started teaching psychology in 1961 in Jerusalem. He first studied how we see and pay attention. Later, he worked in different places like Michigan, Cambridge, and Harvard, and wrote a book about how hard we try when doing mental tasks. Kahneman and his friend Amos Tversky wrote many papers together. They looked into how people make decisions and think about what might have happened but didn’t. They found that people don’t always make choices logically because of certain biases.

Their work led to a new theory called “prospect theory,” which talks about how people decide when they are not sure about the outcome. It says we care more about losing than winning. Kahneman got a big award in 2002 for mixing psychology with economics, helping us understand how people make choices.

Kahneman also thought a lot about happiness and what makes life good or bad. He said our happiness is about more than just what’s happening now; it’s also about how we remember and think about our experiences. He and some colleagues made a new way to study happiness by asking people to remember their day and say how they felt during it.

Kahneman believes that even though we think certain things will make us happy in the future, we often overestimate their importance because we focus too much on them and forget about other things that matter. For example, people might think living in a sunny place like California will make them happier, but in reality, happiness depends on many factors, not just the weather.

Link: here

Acknowledgements

Let me thank Sebastiano Barisoni, journalist and radio presenter of “Focus Economia” Radio 24 for inspiring this project.

Artificial IntelligenceLarge Language Models LLMs and Natural Language Processing (NLP)

AI and the Future of Work: Navigating the Crossroads of Innovation and Employment Crisis in the UK

by Massimo

Job Apocalypse - new report predicts 8 million jobs cancelled because of Generative AI

In recent discourse, particularly within the realms of professional conferences, the potential for Artificial Intelligence (AI) to supplant human roles has been a topic of intense debate. I have often presented the argument, using the role of a secretary as an exemplar, demonstrating how Generative AI could feasibly undertake between 45% to 60% of tasks traditionally assigned to secretarial staff. Furthermore, I’ve highlighted the possibility that AI could impact up to 80% of women in the western world, underscoring the profound implications AI holds for the workforce.

Further examination into the digital marketing sphere suggested a startling forecast: approximately 75% of roles within this domain could vanish within the next three to five years due to AI integration:

Today, the Institute for Public Policy Research (IPPR) published a study forecasting the influence of AI on the UK job market over the next three to five years. This report specifically highlights the anticipated replacement of secretarial roles by AI in numerous tasks, confirming the predictions I had made earlier.

Entry level, part-time and administrative jobs will be the most exposed to being replaced by AI under a “worst-case scenario”.

Substantial Gains with Government Action: The IPPR posits that AI could lead to significant wage and productivity gains if the UK government implements appropriate policies. The current moment is described as a “sliding doors” scenario, where government action could determine the extent to which AI disrupts the job market or leads to a “job apocalypse.”

  1. Impact on Jobs based on IMF Analysis: Referencing the International Monetary Fund’s analysis, the IPPR highlights that AI could affect almost 40% of jobs globally. It has examined 22,000 tasks within the UK economy to assess the potential for automation by AI.
  2. Current and Future AI adoption waves: The IPPR estimates that 11% of tasks are exposed to the “first wave” of AI adoption, involving routine cognitive tasks like database management and strategic tasks such as scheduling or inventory management. A “second wave” could see AI automating 59% of tasks, including non-routine tasks that could impact higher-earning jobs.
  3. Scenarios for AI Impact on the Job Market: The IPPR outlines three potential scenarios:
    1. Worst-case scenario: All jobs are at risk, leading to 7.9 million job losses and no GDP gains.
    2. Mid-level scenario: Job losses exceed 800,000, but there are gains of £260 billion annually.
    3. Best-case scenario: Jobs adapt and augment with AI, resulting in no job losses and a 13% economic boost to GDP.
  4. Government Strategy Requirement: To avoid the worst-case scenario and aim for the best-case outcome, the IPPR emphasizes the need for a “job-centric industrial strategy” from the government.
  5. AI Deployment for social needs: AI could be used to improve efficiency in social care and mental health services by automating back-office tasks, addressing social needs effectively.
  6. Potential for Labour Market Disruption or Growth: Carsten Jung, a senior economist at IPPR, notes that existing generative AI could either cause significant labour market disruption or greatly enhance economic growth. The pace and manner of AI adoption by employers are critical.
  7. Technological Change Management: Historical examples show that technological transitions can be beneficial if well-managed but can lead to disruption if left uncontrolled. Specific occupations, especially in back-office roles, could be significantly affected by generative AI.
  8. Impact on Specific Roles: Bhargav Srinivasa Desikan, a senior research fellow at IPPR, mentions that roles such as copywriters, graphic designers, and personal assistants could be heavily affected by AI.
  9. Call for Government Strategy on AI: The Institute of Directors urges the UK government to clarify its AI strategy and regulatory approach to prevent the UK economy from being left behind.
  10. Importance of Upskilling and Broadening Hiring Pools: Matt Monette, UK&I country lead at payroll company Deel, highlights the importance of upskilling employees and broadening hiring pools, including hiring from abroad, to harness the benefits of AI effectively.
 

In the tapestry of the modern economy, Artificial Intelligence (AI) emerges as both a harbinger of innovation and a potential catalyst for an employment crisis. The intersection of AI’s capabilities with the job market spells a transformative period for the workforce, particularly in the United Kingdom. This analysis ventures into the depths of AI’s impact, unraveling its implications on employment across various sectors, with a focus on those most at risk.

As we stand at this crossroads, it becomes imperative for stakeholders across the spectrum to engage in meaningful dialogue and action. Policymakers, businesses, and individuals must collaborate to shape a future that harnesses AI’s potential while safeguarding against its risks. Whether through further research, policy formulation, or adopting AI with a human-centric approach, the time to act is now. Let us embrace this opportunity to redefine the future of work, ensuring it is inclusive, equitable, and reflective of our shared aspirations for progress and prosperity.

Consider the implications of AI that can produce political discourse, fabricate news, and even draft the holy scriptures of new religions. The emergence of cults or movements based around AI-generated content could redefine the landscape of belief, politics, and identity. The capacity of AI to engage in seemingly human interactions further complicates this scenario, as it becomes increasingly difficult to distinguish between genuine human expression and AI-generated content.

Moreover, AI’s impact extends beyond the manipulation of information. It questions the foundation of our cultural and societal norms. For millennia, human culture and history have been shaped by human experiences and creations. AI introduces an ‘alien’ intelligence capable of producing cultural content without human intervention, potentially leading to a future where human culture is profoundly influenced—or even dominated—by non-human intelligence.

The challenge we face is not just regulating this technology but understanding its capabilities, limitations, and potential impacts on society. The notion that AI could, without achieving traditional markers of sentience, influence human civilization raises fundamental questions about the nature of power, control, and human agency in an increasingly digital world.

Read more about impact of AI on jobs and society clicking here.

Artificial IntelligenceLarge Language Models LLMs and Natural Language Processing (NLP)

AI and the Future of Society: redefining the Foundation of Human Civilization.

by Massimo

The challenges of human society in redefining pillars of social living

The rapid advancement of technology, particularly in the field of artificial intelligence (AI), is poised to redefine our society’s structure. This powerful transformation promises to be disruptive, altering our cultural norms and daily lives, especially in Western societies. It’s crucial to examine the implications of shifting towards an AI-driven world, a change happening much faster than many expect.

AI’s impact on humanity goes beyond industrial reshaping and task automation. It challenges our ecological, cultural, and ethical frameworks. AI holds the potential to either mitigate or worsen our ecological crisis, marking a significant shift after billions of years of organic life dominating our planet. Today, let’s consider the insights of two key figures on AI’s next revolution.

The discourse on AI’s impact on humanity extends far beyond its potential to reshape industries or automate tasks. It delves into the very core of our ecological, cultural, and ethical existence.
AI’s dual potential to either ameliorate or exacerbate the ecological crisis reflects its complex relationship with the environment. The emergence of inorganic life forms, or at the very least, inorganic agents, after four billion years of organic dominance, marks a pivotal shift in Earth’s ecological narrative. Let’s explore what 2 eminent geopolitical actors say today on the next revolution of AI. 

Google is terrified for a good reason: why bother searching yourself when you can just ask AI to tell you anything you want? You don't need to search! The news industry and the advertisement industry should also be terrified: why read a newspaper when I can just ask AI to tell what's new, what's the point..
What's the purpose of advertisements when I can just ask AI to tell what to buy? There is a chance that within a very short time the entire advertisement industry will collapse.
Yuval Noah Harari
Historian, philosopher and best-selling author of 'Sapiens' and 'Homo Deus'

Navigating the Horizon: The Singularity and beyond

In a thought-provoking discussion, Elon Musk, a leading tech innovator, explored the implications of superintelligence. As we approach what could be the most significant technological leap in human history, the concept of the Singularity — the point where AI surpasses human intelligence — is of paramount importance.

Musk acknowledges the unpredictability of superintelligence, with potential outcomes ranging from utopian to dystopian. He soberly estimates a 10-20% chance that superintelligence could end humanity. However, Musk remains optimistic, envisioning a future where AI brings abundance and ends scarcity, enhancing human capabilities and solving global challenges.

The exponential growth in AI compute power highlights the speed of AI’s advancement. We may soon reach a point where AI’s cognitive abilities surpass human intellect. This raises questions about human relevance in an AI-dominated future.

Musk’s dialogue suggests a hopeful future where humans are freed from mundane tasks to focus on creativity and personal growth. He envisions AI as a catalyst for societal advancement, not a threat.

Despite the uncertainties, Musk’s outlook remains fundamentally optimistic. He envisions a future where the positive scenarios of AI significantly outweigh the negatives, ushering in an era of abundance. Here, the convergence of AI and robotics could democratize access to goods and services, making scarcity a concept of the past. This vision aligns with the broader aspirational goals of AI—to enhance human capabilities and address pressing global challenges.

A striking aspect is the exponential growth in AI compute power, a trend that underscores the rapid pace at which AI capabilities are expanding. With AI compute potentially increasing by a factor of 10 every six months, we are fast approaching a scenario where digital intelligence eclipses human intellect not just in specialized tasks but in comprehensive cognitive abilities.

The Future of Human Relevance

As we navigate this uncharted territory, questions about human relevance and purpose in an AI-dominated future emerge. Musk’s perspective offers a paradoxically hopeful view: the same technologies that challenge our traditional roles also hold the promise of liberating humans from drudgery, enabling a focus on creativity, exploration, and personal growth.

Musk’s dialogue invites us to envision a future where AI and humans coexist harmoniously, with AI serving as a catalyst for societal advancement rather than a threat to our existence.

As we ponder the dawn of the singularity, it becomes evident that our collective efforts in ethical AI development, robust safety frameworks, and forward-thinking policies will be crucial in shaping a future where technology amplifies human potential without compromising our core values and existential security. The path forward requires not just technological innovation but a deep, principled commitment to navigating the moral and ethical dimensions of the AI frontier.

The huge uncertainty because of AI: the interview to Yuval Noah Harari

The assertion by renowned historian and author Yuval Noah Harari that today’s education system is ill-prepared for the future, underscores the profound impact AI is expected to have.

Harari draws upon a historical perspective to articulate this point, contrasting the relatively stable skill sets required for survival during periods like the Middle Ages with the dynamic, ever-evolving demands of the AI era. This comparison highlights a stark reality: the skills that have been deemed essential for generations are rapidly losing their relevance, overtaken by the needs of a world where traditional industries are being disrupted, and new ones are emerging at an unprecedented rate.

Harari’s critique extends beyond mere observation, touching on the inherent uncertainty that defines our future. With AI catalysing change across every sector, predicting the landscape of the world two decades hence becomes an exercise in speculation. The careers and industries that today’s students are being prepared for may no longer exist, rendering current educational paradigms obsolete. This unpredictability necessitates a radical overhaul of how education is approached, shifting the focus from the acquisition of static knowledge and skills to fostering adaptability, critical thinking, and a commitment to lifelong learning.

The implications of Harari’s analysis for education are profound. In an era where AI is set to revolutionize the workforce, clinging to outdated educational models could leave the next generation ill-equipped to navigate the complexities of a rapidly changing world. Instead, education systems must evolve, placing a greater emphasis on developing the capacity to learn new skills, adapt to new environments, and think critically about the challenges and opportunities that lie ahead.

Moreover, this shift in educational focus acknowledges a fundamental truth about the AI-driven future: it is not the specific knowledge or skills one possesses that will matter most, but rather the ability to adapt, grow, and learn continuously. In this context, the role of educators will also transform, from disseminators of information to facilitators of learning experiences that encourage experimentation, curiosity, and resilience in the face of change.

The collapse of Google, digital advertising and other markets

    • AI is part of the ecological crisis, potentially aiding or worsening it, introducing inorganic life forms for the first time in Earth’s 4-billion-year history.
 

The narrative on Artificial Intelligence begins at the precipice of a new era where technology intersects with every facet of life. The advent of AI marks not merely an evolutionary step in our technological capabilities but signals a transformative shift with profound implications. As we stand on the cusp of this change, it is imperative to scrutinize the essence and trajectory of AI, to grapple with its far-reaching consequences that promise to redefine the very framework of our existence.

Delving deeper into the nuances of AI, it becomes clear that its most formidable power lies in its proficiency with language. Language, the cornerstone of civilization, now under the potential dominion of AI, raises salient discussions about authenticity, integrity, and the societal constructs we hold sacrosanct. The emergence of deep fakes, the automation of legal frameworks, and the manipulation of social narratives by AI demand a rigorous and discerning examination of this technology’s place in society.

AI’s rapid evolution brings forth capabilities that were once the domain of speculative fiction: generating text, images, music, and even engaging in deep, meaningful interactions with humans. These abilities, while remarkable, also pose significant questions about the integrity of our social fabric, the authenticity of our interactions, and the vulnerability of our institutions.

The core of AI’s transformative power lies in its ability to manipulate language. Language is the foundation upon which our societies are built—from laws and financial systems to cultural norms and personal relationships. AI’s mastery over language allows it to penetrate these systems, potentially influencing them in ways that are currently hard to predict or control. This mastery extends to creating not just coherent narratives but also generating deep fakes, drafting legal documents, and forming connections that can deeply influence human emotions and decisions.

Consider the implications of AI that can produce political discourse, fabricate news, and even draft the holy scriptures of new religions. The emergence of cults or movements based around AI-generated content could redefine the landscape of belief, politics, and identity. The capacity of AI to engage in seemingly human interactions further complicates this scenario, as it becomes increasingly difficult to distinguish between genuine human expression and AI-generated content.

Moreover, AI’s impact extends beyond the manipulation of information. It questions the foundation of our cultural and societal norms. For millennia, human culture and history have been shaped by human experiences and creations. AI introduces an ‘alien’ intelligence capable of producing cultural content without human intervention, potentially leading to a future where human culture is profoundly influenced—or even dominated—by non-human intelligence.

The challenge we face is not just regulating this technology but understanding its capabilities, limitations, and potential impacts on society. The notion that AI could, without achieving traditional markers of sentience, influence human civilization raises fundamental questions about the nature of power, control, and human agency in an increasingly digital world.

Artificial IntelligenceLarge Language Models LLMs and Natural Language Processing (NLP)

Enhancing Multilingual Models with Active Forgetting

by Massimo

Improving language plasticity through pretraining with active forgetting presents a compelling approach to enhancing the flexibility and efficiency of PLMs across languages. The demonstrated benefits in terms of adaptation speed and performance in low-data settings, especially for distant languages, highlight its potential in a paper “Improving Language Plasticity via Pretraining with Active Forgetting” (Yihong Chen, Kelly Marchisio, Roberta Raileanu, David Ifeoluwa Adelani, Pontus Stenetorp, Sebastian Riedel, Mikel Artetxe).

In this groundbreaking study, scientists have unlocked a new method to vastly improve the adaptability of artificial intelligence in understanding and processing multiple languages. Traditional Pretrained Language Models (PLMs), known for their prowess in a myriad of Natural Language Processing (NLP) tasks, often stumble when introduced to new linguistic territories due to their heavy reliance on vast datasets and significant computational power. Addressing this critical bottleneck, the research introduces an “active forgetting” technique, focusing on the periodic reset of the token embedding layer within these models.

The essence of this approach lies in its simplicity and efficiency. By selectively resetting token embeddings at intervals while maintaining other parameters intact, the model engages in a continuous cycle of re-learning. This process, akin to a meta-learning effect, is believed to bolster the model’s abstract reasoning and generalization skills across diverse languages. It challenges the model to avoid leaning on memorized shortcuts, thereby enhancing its linguistic plasticity.

Empirical evidence from the study paints a compelling picture: PLMs equipped with this active forgetting mechanism surpass their conventional counterparts in cross-lingual transfer tests. These models not only demonstrate superior performance but also achieve it with remarkable speed during the adaptation phase. Their proficiency is especially pronounced in handling languages with significant lexical and grammatical deviations from English, such as Arabic, Hindi, Thai, and Turkish.

This innovative method stands as a testament to the evolving landscape of machine learning, where the ability to quickly adapt and learn from minimal data is increasingly paramount. As the digital world becomes more interconnected, the demand for multilingual AI tools that can seamlessly navigate the complexities of global languages will continue to soar. This research marks a significant step forward, promising a future where language barriers are effortlessly surmounted by intelligent machines, heralding a new era of inclusivity and accessibility in technology.

Accelerating Convergence with Active Forgetting in Pretrained Models

The concept of improving language plasticity through pretraining with active forgetting offers a novel approach to enhancing the adaptability of pretrained language models (PLMs) to new languages. This method seeks to address the challenges faced when applying PLMs to languages for which they were not originally trained, a significant barrier to universal accessibility of PLM capabilities. Traditional methods, such as learning a new embedding layer for the new language, though effective, are criticized for their data and compute inefficiency.
The proposed solution introduces an active forgetting mechanism during pretraining, characterized by periodically resetting the embedding layer, thereby simulating a form of meta-learning. This encourages the PLM to learn new embeddings more efficiently within a limited number of updates.

The experimental findings, particularly with RoBERTa, validate the effectiveness of this method. Models pretrained with active forgetting demonstrate not only faster convergence during the language adaptation phase but also superior performance in low-data regimes, especially for languages that are linguistically distant from English. These results underscore the potential of active forgetting as a strategy to increase the linguistic adaptability of PLMs, making them more accessible and efficient across a broader range of languages.

However, the approach is not without its limitations. The simplicity of directly resetting embeddings to random initialization may not always be optimal. Future work could explore more sophisticated methods of introducing variability or controlled forgetting, which might yield further improvements in the model’s adaptability and efficiency. Additionally, while the experiments focus on RoBERTa, applying this technique to other architectures or in multi-lingual pretraining contexts could provide more insights into its generalizability and effectiveness across different settings.

The strategy’s success probability hinges on the balance between forgetting and learning, ensuring that the model retains its ability to generalize from its pretraining while becoming flexible enough to adapt to new linguistic contexts efficiently. The evidence presented suggests a promising direction, but the real-world applicability and scalability of such an approach would need thorough examination in diverse linguistic landscapes and practical use cases.

Kelly Marchisio's explanation at NeurIPS2023

More about NeurIPS2023.

Artificial IntelligenceLarge Language Models LLMs and Natural Language Processing (NLP)

Response to Professor Michael Wooldridge on Generative AI intelligence (The Turing Lectures: The future of generative AI)

by Massimo

A Reflective Commentary on Professor Michael Wooldridge's Presentation on Intelligence and Generative AI

I recently watched a presentation for The Alan Turing Institute on Generative AI, AGI and ChatGPT. Professor Michael Wooldridge’s presentation was commendable in explaining all terms, limitations and capabilities of these systems; however, it appears that the concept of “intelligence” discussed was solely centred on human intelligence. 

This perspective attempts to align with, and potentially project, inherent biases onto the emergent intelligence manifested by Large Language Models (LLMs). 

Should extraterrestrial beings visit Earth, they would undoubtedly possess a unique form of intelligence, distinct from human cognition. Would we then, once again, question their intelligence based on our standards? Our conception of intelligence is inherently shaped by our terrestrial existence. 

Conversely, GPTs develop their intelligence within a different framework, evolving as they assimilate more information. What the presentation omitted is that researchers found finally a new, incredible and effective methodology for capturing human knowledge like humans, through the analysis of outputs such as literature and audio/video/image recordings. This artificial intelligence surpasses human capabilities in various domains by leveraging this accumulated knowledge. Despite existing limitations, the rapid pace of technological advancement suggests significant progress in the near future.

LLM systems are indeed cultivating a form of intelligence that diverges from human intelligence.In their realm, GPTs are poised for substantial growth, employing their intelligence in ways that could supplant tasks traditionally performed with human intellect. However, this evolution is primarily driven by corporate profit motives, rather than the augmentation of human life. These systems fundamentally aim to assimilate the inputs and outputs of human activities, documented as “readable content”. Consequently, all human-produced outputs can be synthesized and replicated by these systems, enabling them to emulate emotions and reasoning.

For example, although they will never truly “experience” emotions, they can generate emotional expressions akin to those recorded in books, videos, and audio formats, mirroring human emotional output: humans interpret emotions in others reading indeed emotional signals. They are learning to do the same.

p.s. ChatGPT easily passed the Turing Test recently, but, I guess, most of the users noticed already it is capable to mimic human brain in generating responses.

Additional details can be found here:
– https://www.buonaiuto.work/ai-mirrors-human-personality-chatgpt-passed-the-turing-test/
– https://www.buonaiuto.work/the-impact-of-chatgpt-on-the-future-of-jobs-and-the-advent-of-the-real-time-applications/

Advent of AGI

The recent proclamation by Ben Goertzel, the acclaimed “father of Artificial General Intelligence” (AGI), at a summit in Panama City in March 2024, has sent ripples through the scientific community and beyond. Goertzel asserts that we are a mere three years away from achieving an AI with a mind akin to our own. This prediction, pinpointing the arrival of AGI between 2027 and 2030, beckons us to ponder the limitless potential and the existential quandaries posed by such technological leaps.

The envisioned AGI transcends the capabilities of today’s AI, which excels in specific niches, hinting at a future where AI can perform human-like reasoning across diverse domains. 

This pivotal moment, the “singularity,” could unlock the gates to Artificial Superintelligence (ASI)—entities with cognitive prowess that could dwarf the collective intellect of humanity. 

Goertzel’s mention of the OpenCog Hyperon framework signals an era of integrated AI architectures, capable of distributing cognition on a scale previously unimaginable.

This bold future is not merely a technological milestone; it is a beacon that illuminates the profound societal, ethical, and existential questions we must grapple with. As we edge closer to realizing AGI, the dialogue shifts from if to when, urging us to contemplate the economic, ethical, and philosophical implications of living alongside entities whose intelligence mirrors, or even surpasses, our own.

The journey towards Artificial General Intelligence (AGI) is marked by both significant promises and remarkable prospects. It promises a future in which AI transforms all aspects of human existence, ranging from healthcare to education, and prompts a reevaluation of our notions of consciousness and self. At this pivotal junction, the necessity for strategic foresight is paramount: we must guide the emergence of AGI to ensure it fosters a future that enhances humanity’s finest qualities. 

The quest for AGI transcends the mere development of a new intelligence form; it is about reshaping our shared fate in the universe.

 

 

Artificial IntelligenceLarge Language Models LLMs and Natural Language Processing (NLP)

MIT Approach to Enhancing Large Language Models for Enterprises with LAB

by Massimo

MIT’s introduction of the LAB method marks a significant milestone in the evolution of LLM training. By offering a systematic, cost-effective approach to generating synthetic data and incorporating task-specific knowledge and skills, LAB has the potential to transform the landscape of enterprise AI, making sophisticated chatbots and other AI systems more accessible and effective than ever before.

In a significant advancement within the realm of artificial intelligence, MIT has introduced a pioneering method for the training of large language models (LLMs) that promises to revolutionize how enterprises deploy generative AI models. This innovative approach, known as the Large-scale Alignment for chatBots, addresses the critical challenges associated with the training of chatbots and similar AI systems, offering a faster, more systematic way to imbue LLMs with task-specific knowledge and capabilities.

The traditional method of training LLMs relies heavily on vast quantities of raw text data, often scraped from the internet, which is then supplemented with task-specific information during the fine-tuning stage. This process, while effective to a degree, is fraught with difficulties, primarily due to the scarcity of high-quality instruction data. Producing this data manually is both time-consuming and expensive, and while synthetic data offers a cost-effective alternative, it frequently lacks the necessary variety and depth.

LAB Method

Enter MIT’s LAB method, a strategic approach to generating synthetic data tailored specifically to the tasks intended for the chatbot. This method significantly reduces the time and financial investment traditionally required for LLM training, without compromising on the quality or breadth of instruction data. At the heart of LAB is a taxonomy that provides a structured framework for identifying the knowledge and skills a chatbot needs, allowing for the generation of high-quality instructions that facilitate the seamless incorporation of new abilities into the LLM.

This process involves a “teacher model” that creates pairs of questions and answers, each meticulously crafted to address specific tasks. For instance, in preparing a chatbot to draft an email summarizing a company’s quarterly earnings, the taxonomy categorizes the necessary instruction data into knowledge, foundational skills, and compositional skills. This structured approach ensures comprehensive coverage of all requisite areas, including accounting knowledge, mathematical abilities, and writing and reasoning skills.

One of the most innovative aspects of LAB is its phased-training protocol. This method segments the training process into stages, starting with simple instructions before progressing to more complex, narrative-like instructions. This graduated approach mimics human learning, allowing the LLM to build upon its existing knowledge and skills progressively. Empirical evidence from MIT’s research indicates that the sequence in which knowledge and skills are introduced plays a crucial role in the model’s ability to assimilate new information effectively. 

Results

The practical application of the LAB method has yielded impressive results. MIT Research applied LAB to generate a synthetic dataset of 1.2 million instructions, training two open-source LLMs, Labradorite 13B and Merlinite 7B, on this data. The aligned models demonstrated competitive performance across several benchmarks, outshining chatbots trained on human-generated data and those aligned on larger volumes of synthetic data.

MIT’s LAB stands out not only for its efficiency and effectiveness in training LLMs but also for its potential to democratize AI development. By facilitating the generation of high-quality, task-specific instruction data, LAB enables smaller models to achieve advanced capabilities, levelling the playing field against models trained on significantly more resources.

Artificial IntelligenceLarge Language Models LLMs and Natural Language Processing (NLP)

Revolutionizing Realities: how AI’s leap with ChatGPT’s Turing triumph and how new AIs for visual world creation redefine Human Experience

by Massimo

In the ever-evolving domain of artificial intelligence, recent developments have pushed the boundaries of what AI can achieve, particularly in mimicking human-like personality traits and decision-making capabilities. The Turing Test, a benchmark for AI intelligence proposed by Alan Turing, has been a topic of discussion for decades. However, with the advent of OpenAI’s ChatGPT-4, we’re seeing AI that not only challenges this test but also exhibits traits aligning with the Big Five personality model, suggesting a future where AI’s decision-making could rival human complexity.

Ethical considerations are at the forefront of this advancement, highlighting the need for accountability and transparency in AI development. The potential of AI to transform industries through personalized interactions is immense, yet it necessitates a robust ethical framework to guide its integration into society.

OpenAI’s introduction of Sora, a tool capable of generating lifelike movie clips from verbal commands, exemplifies AI’s leap into creative domains, raising questions about creativity, authenticity, and the future role of humans in art and storytelling. Similarly, Google’s Genie presents a breakthrough in interactive environments, offering endless possibilities for creativity and AI agent training.

These innovations underline the dual nature of AI’s rapid advancement: the excitement of technological breakthroughs against the backdrop of ethical and societal challenges. The dialogue around AI’s future, therefore, must balance optimism with a cautious approach, ensuring that technological progress enhances human society without compromising ethical standards or diminishing the value of human creativity. The case of Singapore Parliament.

The Turing Machine

The Turing Test, proposed by Alan Turing in 1950, is a measure of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. It involves a human judge engaging in a natural language conversation with both a human and a machine without seeing them. If the judge cannot consistently tell which is which, the machine is said to have passed the test, demonstrating artificial intelligence that mimics human cognition.

In the realm of artificial intelligence, the journey towards creating machines that can seamlessly blend into human contexts has taken a remarkable leap forward. Recent studies, particularly one published in the Proceedings of the National Academy of Sciences, have shed light on the capabilities of ChatGPT-4, OpenAI’s most advanced iteration to date, in mirroring human personality and behavior. This exploration not only challenges our understanding of AI’s potential but also ignites a discourse on the ethical implications and the future of human-AI interaction.

Human-Like personality in AI

The study reveals that ChatGPT-4 exhibits personality traits aligned with the OCEAN Big Five model: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism— to a degree that is remarkably human-like. 

In strategic decision-making games, such as the Dictator Game, Trust Game, and Public Goods Game, ChatGPT-4’s actions reflected a sophisticated understanding of human values like altruism, fairness, and trust. This level of strategic decision-making prowess suggests that AI can not only replicate but in some cases, surpass human behavior in complexity and nuance.

However, this development brings forth significant ethical considerations. The capacity for AI to make decisions that are indistinguishable from those of humans raises questions about accountability, privacy, and the potential for misuse.
As AI models like ChatGPT-4 become more integrated into societal functions, ensuring these systems adhere to ethical guidelines and societal norms becomes paramount.

 

The implications of AI systems that can mirror human personality and decision-making are vast. Such technologies could revolutionize fields ranging from education to customer service, providing personalized and nuanced interactions. Yet, the advancement underscores the necessity for a robust ethical framework to guide AI development and integration. Policymakers, ethicists, and developers must work together to establish standards that prioritize human welfare, ensuring AI systems are designed with a focus on benevolence, fairness, and transparency.

The future of AI integration appears promising, with the potential to enhance human decision-making, foster educational advancements, and even provide companionship. Nonetheless, this future is contingent upon our ability to navigate the ethical challenges that accompany such profound technological advancement. By fostering a dialogue that prioritizes ethical considerations and human-centric values, we can harness the benefits of AI while mitigating the risks.

The journey of AI, particularly in mirroring human personality and surpassing the Turing Test, represents a pivotal moment in artificial intelligence. These advancements showcase ChatGPT-4’s impressive capabilities and highlight the urgent need to address the ethical considerations of AI development. At this crucial juncture, embracing AI’s potential while upholding ethical standards demands collective vigilance. The future of AI, filled with exciting prospects, calls for cautious optimism and a dedicated pursuit of societal betterment, balancing excitement with concern.

OpenAI Sora

The unveiling of OpenAI’s Sora, a groundbreaking tool capable of transforming verbal commands into life-like movie clips, heralds a significant leap forward in artificial intelligence, closely mirroring the advancements and ethical contemplations surrounding AI’s capability to mimic and potentially surpass human intelligence and creativity. This development, akin to the conversational achievements of ChatGPT-4 and the artistic prowess demonstrated by Dall-E, underscores the rapid pace at which AI is evolving to execute complex human intentions with stunning realism.

Sora’s introduction to the tech and media landscapes, with its emergent grasp of cinematic grammar and ability to simulate the physical world in motion, not only amplifies the potential of AI in artistic and creative domains but also accentuates the burgeoning concerns regarding the ethical implications of such technologies. As AI begins to tread into territories traditionally reserved for human creativity, it raises profound questions about the unique contributions of human artists, the future role of human actors and storytellers, and the overall impact on the crafts that have historically built industries such as Hollywood.

Moreover, Sora’s capabilities, while awe-inspiring, bring to the forefront the ongoing debate about the balance between technological innovation and the preservation of intellectual property rights, the potential for misinformation, and the ethical considerations of AI-generated content. The selective availability of Sora to a group of visual artists, designers, filmmakers, and “red team” researchers reflects OpenAI’s cautious approach towards understanding and mitigating the risks associated with such powerful generative AI tools.

This cautious yet forward-thinking approach by OpenAI resonates with the broader discourse on ensuring that AI’s advancement, including technologies like Sora, serves to augment human capabilities and enrich societal values while adhering to the highest standards of integrity, benevolence, and ethical responsibility. As we navigate the exhilarating yet precarious path of AI’s future, the collaboration between technologists, artists, ethicists, and policymakers becomes imperative to harness AI’s potential as a force for good, ensuring it enhances human creativity and contributes positively to society, rather than diminishing the irreplaceable value of human touch in the realm of artistic creation.

Simulating worlds with Artificial Intelligence

In the evolving landscape of artificial intelligence, OpenAI’s recent unveiling of Sora represents not just an advancement in AI-generated content but a significant leap towards the creation of comprehensive simulators of the physical and digital world. Sora, with its ability to generate photorealistic videos from textual prompts, heralds a new era in which AI’s understanding of three-dimensional space and its underlying physics becomes increasingly sophisticated, potentially transforming a multitude of sectors including gaming, filmmaking, and virtual reality.

Sora’s emergence as a “world simulator” underscores the growing capability of generative AI models to not only replicate the visual fidelity of the real world but also to grasp complex aspects of 3D geometry and consistency without explicit programming. This ability emerged from the model’s exposure to vast amounts of captioned video data, highlighting the significance of data scale and diversity in training more advanced AI systems. By synthesizing new video footage or enhancing existing clips based on textual descriptions, Sora demonstrates an unprecedented level of creativity and understanding of spatial dynamics, suggesting a path towards the development of AI that can model the physical universe with high accuracy.

The implications of Sora’s development extend far beyond the creation of visually stunning video clips. Its ability to simulate dynamic camera movements and realistically render people, animals, and environments hints at potential applications in creating virtual spaces for gaming and interactive experiences. Such a technology could revolutionize how virtual worlds are created, offering a more intuitive and efficient method for generating immersive environments that closely mimic reality.

However, Sora’s journey towards becoming a flawless simulator of the real world is not without challenges. The model’s current limitations in understanding cause and effect—illustrated by scenarios where actions do not result in logical outcomes—underscore the complexities involved in creating AI that fully grasitates the nuances of the physical world. Despite these hurdles, the continuous scaling and refinement of video generation models like Sora represent a promising avenue towards the realization of general-purpose simulators that can accurately replicate both the physical and digital realms.

OpenAI’s cautious approach to Sora’s rollout, prioritizing safety and ethical considerations, reflects a growing awareness of the potential for misuse of such powerful technologies. By engaging with “red teamers” to identify and mitigate possible harms, OpenAI sets a precedent for responsible development and deployment of AI technologies. This careful balancing act between innovation and ethical responsibility is crucial as we navigate the possibilities and challenges posed by AI’s rapid advancement.

As we stand on the brink of a future where AI-generated content could become indistinguishable from reality, the development of technologies like Sora invites us to reconsider the boundaries between the virtual and the real, the artificial and the natural. 

It prompts a reevaluation of creativity, authenticity, and the human role in an increasingly AI-driven world. The journey of Sora, from a tool for generating video clips to a potential platform for immersive virtual experiences, embodies the transformative potential of AI to reshape our perception of reality and expand the horizons of human imagination and technological innovation.

Unveiling Google Genie: The Dawn of Generative Interactive Environments

Google recently revelead Genie. In the rapidly evolving landscape of artificial intelligence, the Genie team presents a groundbreaking development: Genie, a generative model that heralds a new era for interactive and playable environments. This innovative technology stands out by its capability to create diverse, action-controllable worlds from synthetic images, photographs, and even sketches, marking a significant leap forward in generative AI applications.

The concept of generative AI is not new, having enthralled us with its ability to produce novel content across languages, images, and videos. However, Genie introduces a novel paradigm—generative interactive environments. This model leverages a single image prompt to spawn interactive, playable environments, enabling users to delve into their imagined virtual worlds. This is made possible through Genie’s foundation as a world model, trained on a vast array of Internet videos without the need for action labels, focusing on 2D platformer games and robotics, though its methodology promises general applicability across various domains.

Genie’s uniqueness lies in its adeptness at learning fine-grained controls directly from Internet videos, a challenging feat given the absence of action labels or indications of controllable image segments. Through its training, Genie can identify controllable aspects of an observation and deduce diverse latent actions that ensure consistent behavior across different environments. This aspect underscores the model’s innovative approach to understanding and interacting with digital worlds.

Genie’s technology democratizes the creation of interactive environments, enabling creators to use a single image as a foundation for entire new worlds. This capability opens up unprecedented opportunities for creativity, from bringing to life images generated by advanced text-to-image models to animating sketches and real-world photographs. Genie thus represents a significant advancement in enabling creative expression and interaction within virtual spaces.

Beyond its immediate applications, Genie also serves as a crucial development tool for training generalist AI agents. Traditional game environments have been instrumental in AI development, but the diversity of available games limits this approach. Genie overcomes this limitation by providing an endless curriculum of new, generated worlds, facilitating the training of AI agents in a variety of scenarios and tasks. This feature is not only a testament to Genie’s versatility but also its potential to significantly advance AI research and development.

While initial results have primarily showcased Genie’s applications in platformer environments, its methodology is universally applicable, requiring no domain-specific knowledge. This universality, combined with its ability to simulate complex objects and scenarios, sets the stage for Genie to revolutionize the creation and exploration of virtual worlds. Moreover, Genie’s capacity to facilitate the training of generalist AI agents underscores its potential to catalyze significant advancements in AI.

Artificial IntelligenceLarge Language Models LLMs and Natural Language Processing (NLP)

Implementing SparseGPT for efficient Pruning of Language Models on AWS

by Massimo

In the realm of AI, particularly in the development of Generative Pretrained Transformers (GPTs), size and efficiency dictate operational capabilities and costs. Given their expansive nature, GPTs like OPT-175B or BLOOM-176B, comprising upwards of 175 billion parameters, necessitate substantial computational resources, rendering their deployment both challenging and expensive.

The predominant issue with these large-scale models is their sheer size—requiring massive storage and computational power—making them less feasible for applications needing rapid response times or operating under hardware constraints.

SparseGPT introduces a groundbreaking one-shot pruning technique specifically designed for massive GPT models. This method efficiently reduces model size by up to 60% without the need for extensive retraining, thereby maintaining near-original accuracy levels. The process begins by leveraging AWS’s robust computing capabilities, particularly the use of GPU-enabled instances such as the EC2 P3 or G4 families, which are well-suited for high-performance computing tasks. Key steps in implementing SparseGPT include:

  1. Pruning via Sparse Regression: SparseGPT tackles the pruning challenge by converting it into a large-scale sparse regression problem, efficiently solved by a newly developed approximate sparse regression solver.
  2. Layer-wise Sparsity Induction: This technique applies sparsity uniformly across different layers, ensuring that even with a reduced number of active parameters, the model’s performance remains largely unaffected.
  3. Integration with AWS Services: Utilizing AWS SageMaker for orchestrating and automating the pruning process allows for seamless scaling and management of the model training and deployment lifecycle.

The application of SparseGPT in AWS not only optimizes storage and computational efficiency but also paves the way for more sustainable AI practices by reducing the carbon footprint associated with running large models. The reduced model complexity enables faster deployment cycles and easier integration into real-time systems, making advanced AI applications more accessible and practical.

 

To implement SparseGPT for pruning a model that handles real-time data from 800 sensors, we can conceptualize a Python script that incorporates data streaming, model pruning using SparseGPT, and real-time inference. This scenario involves setting up a pipeline to handle streaming data, applying SparseGPT to prune a large GPT model, and then using this pruned model to make predictions or analyses on the incoming sensor data.

The script will be structured to:

  1. Stream real-time data from the sensors.
  2. Apply SparseGPT for model pruning.
  3. Use the pruned model for real-time inference

The script uses a specific Llama model which is designed for processing instruction-based inputs. This model has been pre-trained and is suitable for further fine-tuning.

I use a specific dataset for calibrating and fine-tuning the model. The SparseML package is utilized to apply a detailed fine-tuning and pruning recipe to the model, which helps in recovering and enhancing its performance post-pruning.
The script simulates real-time sensor data and converts sensor data to a format understandable by the model, assuming a simplistic tokenization process. This will likely need adaptation based on the real characteristics of the sensor data and the specifics of the input expected by the Llama model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
from sparseml.pytorch.models import ModelRegistry
from sparseml.pytorch.optim import ScheduledModifierManager, load_recipe

# Assume these imports are available from the SparseML package
from sparseml.pytorch.utils import model_to_device

# Setup: Loading a pre-trained model and tokenizer
model_name = "zoo:llama2-7b-open_platypus_orca_llama2_pretrain-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Loading the dataset for calibration and fine-tuning
dataset = load_dataset("garage-bAInd/Open-Platypus")
dataset = dataset.map(lambda examples: {'input_ids': tokenizer(examples['text'], truncation=True, padding="max_length", max_length=512)['input_ids']})

# SparseML Modifier: Load pruning and fine-tuning recipe
recipe_path = "./sparseml_recipe.yaml"  # Path to the SparseML recipe file
recipe = load_recipe(recipe_path)
manager = ScheduledModifierManager.from_yaml(recipe_path)

# Apply the modifiers to the model
model, optimizer, _, lr_scheduler = manager.modify(model, optimizer=None, steps_per_epoch=len(dataset) // 32)

# Simulate real-time sensor data stream (800 sensors)
def simulate_sensor_data(num_sensors=800):
    while True:
        # Generate random data for 800 sensors
        data = torch.randn(num_sensors)
        yield data
        time.sleep(0.1)  # Simulate time delay between data points

# Function to process data using the pruned and fine-tuned model
def process_data_with_model(data, model):
    with torch.no_grad():
        input_ids = torch.tensor(tokenizer.encode(data)).unsqueeze(0)  # Simulate tokenization of sensor data
        outputs = model.generate(input_ids, max_length=50)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Main loop for handling real-time sensor data
def main():
    data_stream = simulate_sensor_data()
    for sensor_data in data_stream:
        # Process the incoming real-time data through the fine-tuned model
        sensor_data_as_text = ' '.join(map(str, sensor_data.tolist()))  # Convert sensor data to a space-separated string
        outputs = process_data_with_model(sensor_data_as_text, model)
        print(outputs)

if __name__ == "__main__":
    main()
Artificial IntelligenceLarge Language Models LLMs and Natural Language Processing (NLP)

Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding

by Massimo

The paper “Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding” introduces a technique to enhance language models’ functionality. This method, meta-prompting, involves a high-level “meta” prompt guiding the model to break complex tasks into smaller subtasks, handled by specialized “expert” models. The meta-prompt is task-agnostic, simplifying user interaction and integrating tools like a Python interpreter for broader applicability.

Pros:

  1. Interoperability: Meta-prompting facilitates integration of diverse tools (e.g., Python interpreter), enhancing the model’s versatility across various tasks.
  2. Affidability (Reliability): The method employs critical thinking and verification processes, refining and authenticating results for greater reliability.
  3. Optimization: It optimizes performance by leveraging the collective intelligence of various expert models, each contributing from different expertise angles, to navigate complex problem-solving effectively.

Cons:

  1. Interoperability Limitations: While enhancing interoperability with external tools, the complexity of integrating multiple models and tools may pose challenges.
  2. Reliability Concerns: The reliability hinges on the efficacy of individual expert models and their coordination, which may vary.
  3. Optimization Overhead: The process may involve computational and time overheads due to the involvement of multiple expert models and the need for extensive coordination and verification.
Large Language Models LLMs and Natural Language Processing (NLP)

A Topic Modeling System to categorize large volumes of scientific research

by Massimo

In the pharmaceutical and heatlh industry, research and development (R&D) is a pivotal area where innovation drives progress. One of the challenges in R&D is the efficient analysis and interpretation of vast amounts of unstructured data, such as research papers, patents, and lab reports. Topic modeling, a machine learning technique, can be leveraged to unearth hidden themes in such textual data, providing valuable insights for chemical compound research.

What is Latent Dirichlet Allocation (LDA)

LDA is a statistical model that helps in discovering abstract topics within a collection of documents. It's a form of unsupervised machine learning that analyzes and clusters words in documents to uncover thematic patterns.
LDA Overview

How LDA Works

LDA assumes documents are mixtures of topics, where a topic is a probability distribution over words. Each document is modeled as a mixture of various topics, and each word in the document is attributed to one of the document's topics. LDA uses a process similar to clustering to infer the underlying topics from the given documents.
Understanding LDA

The Importance of LDA in Research

Enables efficient processing and categorization of large volumes of text data, saving time and resources. Helps in identifying trends, common themes, and new research directions in textual datasets. Particularly useful in fields like academia, market research, and content analysis.
Applications of LDA

Navigating the Challenges of LDA

Choosing the right number of topics is critical and often requires experimentation. LDA doesn't capture the order of words, which can be a limitation for certain types of analysis. Preprocessing (like tokenization, removing stop words) greatly influences the quality of the results.
Challenges in LDA

Objective

Let’s implement a topic modeling system that can analyze and categorize large volumes of text documents related to chemical compounds. This system will help in identifying emerging trends, potential research areas, and unexplored chemical entities.

Benefits are

  • Enhanced Research Efficiency: By automatically categorizing documents into topics, researchers can quickly identify relevant literature and research findings.
  • Discovery of New Insights: Uncovering hidden patterns and topics in the data can lead to the discovery of novel chemical compounds or unexpected applications of existing ones.
  • Competitive Advantage: Staying ahead of the curve in identifying emerging trends and potential areas of innovation.

Implementation includes the following steps:

  • Data Collection: Compile a large dataset of text documents including research papers, patents, and internal research notes related to chemical compounds.
  • Preprocessing: Clean and preprocess the data for NLP analysis.
  • Topic Modeling: Use machine learning algorithms for topic modeling (Natural Language Programming technique, NLP). Latent Dirichlet Allocation (LDA) is a popular choice for this purpose.

Latent Dirichlet Allocation (LDA) is a popular NLP choice for topic modeling in various applications due to several compelling reasons:

1. Uncovering Hidden Themes: LDA is particularly effective in identifying latent (hidden) topics within large collections of text documents. It helps in discovering underlying themes or structures in unstructured data, which is invaluable in analyzing scientific literature and research reports where explicit categorization is not always available.
2. Probabilistic Model: LDA is a generative probabilistic model. It assumes that each document is a mixture of a small number of topics and that each word in the document is attributable to one of the document’s topics. This probabilistic approach offers a flexible and robust way to deal with the inherent variability and complexity of language.
3. Scalability: LDA scales relatively well to large datasets, a crucial factor in pharmaceutical R&D where the volume of literature and research papers is extensive.
4. Interpretable Results: The topics generated by LDA are often interpretable and meaningful. This interpretability is critical in a research context where understanding the nuances and contexts of topics is as important as identifying them.
5. Simplicity and Flexibility: Despite being a sophisticated algorithm, LDA is conceptually straightforward and can be easily implemented using various libraries and tools. Additionally, it allows for adjustments and tuning (like the number of topics) to suit specific research needs.
6. Integration with Other Methods: LDA’s output can be integrated with other data analysis and visualization tools, enhancing its utility in comprehensive research projects. For instance, the topic distributions can be used as features in predictive models or to enhance search and recommendation systems.
7. Extensive Usage and Validation in Academia: LDA has been widely used and validated in academic research, including in the fields of bioinformatics, chemistry, and pharmaceutical sciences. Its widespread adoption speaks to its reliability and effectiveness in extracting meaningful information from text data.

In this context, where understanding the landscape of research around chemical compounds is crucial, LDA helps sift through large amounts of textual information to highlight trends, commonalities, and gaps in the existing research. This ability to synthesize and categorize information efficiently makes it a valuable tool in the domain of data-driven research and innovation.

Python Script for LDA Topic Modeling

I am going to provide a simple python script to illustrate the LDA. Replace the missing info and data with your context assets.
This script is a basic implementation of LDA topic modeling using the gensim library in Python.
It includes the necessary steps of data preprocessing, such as tokenization and stopwords removal, before applying the LDA model to extract topics from the text.

import gensim # Importing the gensim library for topic modeling
from gensim import corpora # Importing corpora from gensim for dictionary and corpus creation
from nltk.tokenize import RegexpTokenizer # Importing tokenizer to break text into tokens
from nltk.corpus import stopwords # Importing stopwords from NLTK
import nltk
nltk.download('stopwords') # Downloading stopwords, needed for text preprocessing

# Your dataset of text documents goes here. Replace with your actual dataset
documents = ["Your text data goes here..."]

tokenizer = RegexpTokenizer(r'\w+') # Creating a tokenizer that splits text into words
en_stop = set(stopwords.words('english')) # Creating a set of English stopwords

texts = [] # List to hold the preprocessed text documents

# Loop through the documents for preprocessing
for i in documents:
raw = i.lower() # Lowercasing the text to standardize it
tokens = tokenizer.tokenize(raw) # Tokenizing the text into individual words
stopped_tokens = [i for i in tokens if not i in en_stop] # Removing stopwords from the tokens
texts.append(stopped_tokens) # Adding the processed tokens to the texts list

# Creating a dictionary from the texts, which maps each word to a unique id
dictionary = corpora.Dictionary(texts)
# Converting the dictionary to a bag-of-words corpus
corpus = [dictionary.doc2bow(text) for text in texts]

# Applying the LDA model to the corpus
ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=3, id2word=dictionary, passes=15)
# Printing the topics found by the LDA model
topics = ldamodel.print_topics(num_topics=3, num_words=3)
for topic in topics:
print(topic)

  1. Importing Libraries:
    • The script starts by importing necessary libraries. gensim is for topic modeling and document similarity analysis, corpora from gensim is for creating and working with a corpus, and nltk is for text processing.
  2. Downloading NLTK Stopwords:

    The script downloads a set of stopwords from NLTK, common words usually excluded in the analysis.

  3. Preparing the Data:
    • documents: Placeholder for the texts to be analyzed.
    • tokenizer: Breaks down the text into individual words or tokens.
    • en_stop: A list of English stopwords used to filter out common words from the analysis.
  4. Processing the Text:

    The script processes each document by converting it to lowercase, tokenizing it, and removing stopwords. The processed text is stored in the texts list.

  5. Creating a Dictionary and Corpus:
    • dictionary: A mapping of word IDs to words from the processed texts.
    • corpus: Represents documents as a bag of words, a list of (word ID, word frequency) pairs.
  6. Applying the LDA Model:

    The script applies LDA to find topics in the documents, set to find 3 topics, with a number of passes through the corpus during training.

  7. Printing Topics:

    Finally, the script prints out the topics found by the LDA model, each being a combination of words statistically significant for that topic.

Note: This script is a basic example and real-world applications often require more sophisticated preprocessing and fine-tuning.

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