Understanding AI Pin Technical Limitations

From Vineet Jain: Historically, forecasting, budgeting and variance analysis activity heavily rely on manual efforts and reliance on historical data. With the changes in markets and complexity, the need for more agile and data-driven approaches has become paramount. AI can process information from diverse sources that identify hidden trends and generate predictions above human capabilities, but only if you solve... COMMON CHALLENGES AND LIMITATIONS OF AI-DRIVEN FINANCIAL ANALYSIS ➡ Data Quality and Quantity: In financial analysis, data quality and quantity are critical, and AI models heavily rely on data for accurate predictions. Inaccurate or incomplete data can lead to flawed outcomes, insights and predictions. Additionally, AI models require a significant amount of historical data to train effectively, and this large dataset could be a limitation for several businesses. ➡ Model Overfitting: Overfitting occurs when an AI model performs exceptionally well on the training data but, due to exceptional transactions, fails to generalize this new, unseen data. This can happen when the model captures noise or anomalies in the training data, and the new data is widely skewed. Financial data often contains noise due to extraordinary and time-specific transactions, and without careful regularization and validation, AI models can provide misleading results. ➡ Volatility and Uncertainty: Financial markets are inherently volatile and subject to sudden shifts due to black swan conditions, economic events or geopolitical factors. AI models might struggle to accurately predict extreme events or abrupt changes that fall outside the pattern of historical data. ➡ Bias and Interpretability: Biases in historical data can lead to biased predictions and calculation of financial forecasts. Many AI models, particularly deep learning algorithms, operate as “black boxes,” meaning their decision-making process is complex and challenging to understand. Understanding why a model made a particular prediction is crucial for risk assessment and compliance with regulatory standards, and the biased nature impacts the confidence in the forecast. ➡ Human Expertise and Judgment: While AI can process vast amounts of data, human expertise and judgment remain invaluable. AI may not provide the same level of analytical capability that humans have in particular situations. These financial decisions and situational nuances might be a struggle for AI models. ...for the rest of Vineet's list, check out the full article here: https://lnkd.in/gUJVSmf3

From InsideBigData (12/12/2023) AI is Still Too Limited to Replace People. Commentary by Mica Endsley, a Fellow of the Human Factors and Ergonomics Society (HFES) “NVIDA’s CEO Jensen Huang declared that AI will be “fairly competitive” with people within five years, echoing the rolling “it’s just around the corner” claim we have been hearing for decades. But this view neglects the very real challenges AI is up against. AI has made impressive gains due to improvements in machine learning as well as access to large data sets. Extending these gains to many real-world applications in the natural world remains challenging, however. Tesla and Cruise’s automated vehicle accidents point to the difficulties of implementing AI in high-consequence domains such as military, aviation, healthcare, and power operations. Most importantly, AI struggles to deal with novel situations that it is not trained on. The National Academy of Sciences recently released a report on “Human-AI Teaming” documenting AI technical limitations that stem from brittleness, perceptual limitations, hidden biases, and lack of a model of causation that is crucial for understanding and predicting future events. To be successful, AI systems must become more human-centered. AI rarely fully replaces people; Instead, it must successfully interact with people to provide its potential benefits. But when the AI is not perfect, people struggle to compensate for its shortcomings. They tend to lose situation awareness, their decisions can become biased by inaccurate AI recommendations, and they struggle with knowing when to trust it and when not to. The Human Factors and Ergonomics Society (HFES) developed a set of AI guardrails to make it safe and effective, including the need for AI to be both explainable and transparent in real-time regarding its ability to handle current and upcoming situations and predictability of its actions. For example, ChatGPT provides excellent language capabilities but very low transparency regarding the accuracy of its statements. Misinformation is mixed in with accurate information with no clues as to which is which. Most AI systems still fail to provide users with the insights they need; a problem that is compounded when capabilities change over time with learning. While it may be some time before AI can truly act alone, it can become a highly useful tool when developed to support human interaction.” https://lnkd.in/gvVN2XD4

As engineers and AI developers, it’s crucial to peel back the layers of these technological marvels, understanding the significance of parameters, tokens, and training data size in shaping the capabilities and performance of LLMs. The Foundation of LLMs: Parameters and Tokens Parameters are the backbone of an LLM. Think of them as the inner workings of a complex machine, with each parameter representing a lever or dial that the model can tweak to refine its understanding and output of language. A higher number of parameters indicates a more complex and potentially capable model, but not without the trade-offs of increased computational demands and the risk of overfitting. Tokens, on the other hand, represent the chunks of text - from words to characters to symbols - that the model processes. The scale and variety of tokens a model is trained with are directly proportional to its understanding of language and its ability to generate coherent, diverse, and relevant outputs across different contexts. The Interplay of Parameters and Model Capabilities The relationship between a model's number of parameters and its capabilities isn't linear. While it's generally true that more parameters enable a model to capture more complex patterns in the data, there’s a point beyond which additional parameters yield diminishing returns in performance improvements. Moreover, models with vast parameters require extensive computational resources for training and operation, raising questions about efficiency and environmental impact. Training Data and Vocabulary Size: The efficiency of an LLM also heavily depends on its training data. Models trained on diverse and expansive datasets can understand and generate a wider range of content, displaying a richer vocabulary and more nuanced comprehension of different subjects. This aspect underlines the importance of not just the quantity but the quality of the training data, highlighting ethical considerations such as data bias and the representation of diverse perspectives. Real-world Applications and Limitations The practical applications of LLMs are vast and transformative, spanning from personalized chatbots and content generation to complex problem-solving in fields like medicine and climate science. However, the limitations of these models, particularly concerning ethical use, data biases, and the potential for misuse, necessitate ongoing vigilance and ethical frameworks for their development and deployment. In conclusion, the intricacies of LLMs, from the role of parameters and tokens to the importance of training data, offer a fascinating glimpse into the capabilities and potential of these AI behemoths. As engineers and AI practitioners, we are at the forefront of this technological frontier, tasked with navigating the complexities of model capabilities while steering the future of AI towards a more efficient, responsible, and inclusive horizon.

It's time to talk about the limitations of AI - It's just not as smart as the hype suggests. I know, I know - this might sound strange coming from someone who's built their career around AI. But after years in the trenches, I've noticed something that's rarely discussed. Here's what I've observed across hundreds of AI implementations: AI excels at pattern recognition and following instructions. AI can synthesize existing knowledge brilliantly. AI can accelerate your workflow. But - It can't replace your judgment. And so the best implementations augment human capabilities rather than attempt to replace them. And the more complex and nuanced the task, the more human guidance it needs. This is why we're seeing products like Lovable excel at generating simple websites but struggle at building, let’s say a new Airtable from scratch. Don't get me wrong - AI is transformative. So if you give it clear parameters, it'll execute and save you days of manual work you used to do by yourself. But true innovation? Creative problem-solving? Not even close. So when building your next AI product or feature, remember: work with these limitations, not against them. There are tools (like Traceloop) that can help you take those capabilities to the maximum, as long as you play within the boundaries.