Beyond The AI Frenzy: The Race for Dominance in Large Language Models
- Richard Walker
- May 7, 2024
- 8 min read
Riding the Wave of AI Hype: ChatGPT and the Emergence of AI Titans
Just 18 months ago, ChatGPT, OpenAI’s chatbot, caused a frenzy in the AI world. Much like a pop star making a surprise album drop, the release of ChatGPT was met with a whirlwind of attention and awe. Yet, as is often the case with technology, today its powers are as commonplace as a cup of coffee on a Monday morning.
Several tech behemoths, including Anthropic, Google, and Meta, have since stepped onto the stage, unveiling their own models and transforming ChatGPT from a marvel into an old hat. These new models, dubbed Claude, Gemini, and Llama, have each added their own unique flair to the capabilities of ChatGPT.
Navigating the Nexus of information: Pioneering the AI Frontier
The Unrelenting Thirst for AI Innovation
The appetite for innovation in AI technologies is akin to a tech buffet that never ends. The pace of development is blisteringly fast. Anthropic's Claude 3 and Meta's Llama 3 have quickly risen to the top, while OpenAI is rumoured to be working on GPT-5, a model expected to have capabilities that would make even the most advanced current large language models (LLMs) blush.
Yet, for every naysayer dismissing these developments as mere tech hype, there's a Silicon Valley investor ready to back these next-generation models with billions of dollars. The future, it seems, holds a promise of exponential growth, as long as we continue to feed these models with more data and more powerful computer chips.
The Challenge of Beauty: Data and New AI Technologies
Data, the lifeblood of AI, may soon become a challenge. Researchers predict that the well of high-quality textual data on the public internet could run dry by 2026. Like parched explorers in a desert, AI labs are turning to alternative sources, including the private web, brokers, news websites, and the internet's vast audio and visual data.
But when the natural springs of data run dry, fear not, for we can simply make our own. Companies are building large networks of researchers to generate and annotate data. Others are generating synthetic data, with one LLM creating billions of pages of text to train another.
More Silicon: The Drive for Better Hardware in AI Technologies
Better hardware is another potential game-changer in the world of AI. The graphics-processing units (GPUs) originally designed for video gaming have become the go-to chip for most AI programmers, with their ability to run intensive calculations in parallel. Yet, it seems the fashion in AI technology is shifting towards chips designed specifically for AI models.
The Human Brain: The Old is New Again in AI Technologies
Despite the incredible progress in AI, some scientists are looking back to a long-standing source of inspiration – the human brain. There's a recognition that AI models need to get better at reasoning and planning, just like us humans. This is a clear call for more sophisticated learning algorithms.
The Transformer's Downfall and the Emergence of Mamba
Transformers, a type of neural-network architecture, have been the mainstay of AI models since 2017. However, their reign may soon be over. The introduction of an alternative architecture, Mamba, is a game-changer. This new model reads sequentially, updating its worldview as it progresses, much like how humans process information.
The Future of AI: More Breakthroughs Needed
While transformer-based models might still be in favour, the continued progress in AI technologies will require human expertise and fundamental breakthroughs. As we push these models into more complex applications, the next generation of AI models needs to stun the world, just like ChatGPT did in 2022. It's safe to say, the AI world is gearing up for another frenzy
How the new AI models Claude, Gemini, and Llama differ from ChatGPT in terms of capabilities and performance.
The AI landscape is seeing the emergence of new players offering diverse capabilities and performance metrics. Claude, developed by Anthropic, is designed to provide engaging and expressive responses with significant improvements in output quality and style across its iterations. It is also noted for its flexibility, catering to a variety of user requirements with different performance levels. It is important to note that transitions between different generations of Claude models might result in noticeable improvements in performance.
On the other hand, Google's Gemini, renowned for its high performance, surpasses current state-of-the-art results on most of the widely used academic benchmarks, demonstrating exceptional capabilities. In contrast, Llama 3, an AI model from Meta, is a significant addition to the wide range of open-source AI models. Its design focus is to address complex tasks, setting it apart from other large language AI models.
ChatGPT, developed by OpenAI, is an AI model characterized by its performance in explainability, calibration, and faithfulness. The focus on information extraction capabilities while maintaining these key metrics presents an edge in applications that require high-level accuracy and reliability. As the AI landscape continues to evolve, it's critical for asset managers to keep abreast of these developments to leverage their potential fully.
The challenges and solutions in sourcing high-quality textual data for training AI models.
The sourcing of high-quality textual data for training AI models presents unique challenges and opportunities. One of the prominent challenges is the risk of running out of high-quality text data for AI training due to current trends, as highlighted in a paper published last year.
This looming shortage is primarily due to the rapid pace of AI development and the insatiable demand for high-quality data to train increasingly sophisticated models.
Despite these challenges, several solutions have emerged to ensure a steady supply of quality textual data for AI training. One effective strategy is to focus on the right data at the inception of the model development. Understanding the specific needs of the AI model and sourcing relevant and quality data can significantly improve the performance of the AI model.
Additionally, there are specific techniques that can be employed to augment the volume and diversity of training data. These include data augmentation, text expansion, and paraphrasing. These techniques can increase the diversity and volume of the training data, enabling models to learn more effectively. It becomes crucial for asset managers seeking to leverage AI to remain aware of these challenges and solutions. This awareness will allow for more effective use of AI, potentially leading to better investment decision-making and improved returns.
The shift from GPUs to AI-specific chips in AI technology - what does this mean for future development?
The shift from GPUs to AI-specific chips in AI technology is indicative of the increasing emphasis on AI across tech giants. This trend has been highlighted by OpenAI's CEO Sam Altman's ambitious plans to raise billions for an AI chip venture, Huawei's reorientation of production focus from phones to AI chips, and Nvidia's introduction of a new top-tier AI chip. These developments underscore the intense competition and high demand in the AI tech industry.
Furthermore, this shift holds enormous implications for the future of AI development. AI-specific chips are designed to significantly outperform GPUs in terms of both speed and power efficiency, making them indispensable for advanced AI applications. As such, they are expected to become the new standard, driving the next wave of AI advancements and applications.
For asset managers, this paradigm shift provides a unique investment opportunity. The transition to AI-specific chips is poised to offer semiconductor companies the chance to capture 40 to 50 percent of total value from the technology stack, marking the best opportunity they've had in years. Companies are expected to capture most value in compute, memory, and networking, with high growth also predicted in storage. Therefore, the AI chip market could be a profitable avenue for investment, with a high potential for significant returns.
The transformative impact of the Mamba architecture on AI models and how it compares to traditional Transformer-based models.
The Mamba architecture is redefining the landscape of AI models with its simplified and integrated approach. Unlike conventional Transformer-based models that stack linear attention-like blocks and multi-layer perceptron (MLP) blocks, Mamba merges these two fundamental blocks into a singular Mamba block. This innovative approach is part of a broader class of models called State Space Models (SSMs), positioning Mamba as an alternative to the widespread Transformer architectures in AI.
Analysis has found that Mamba exhibits superior performance in language modelling tasks compared to Transformers. Specifically, a Mamba model of a given size not only outperforms a Transformer model of the same size but also matches the performance of a Transformer model that is twice its size. This observation holds true for both pretraining and downstream evaluation phases, indicating that Mamba offers a more efficient utilization of computational resources.
However, it is worth noting that this does not diminish the value of Transformer-based models, which have contributed to significant breakthroughs in AI over the years. Further studies and real-world applications are required to uncover any potential disadvantages of the Mamba architecture and better understand the trade-offs between Mamba and Transformer models. Asset managers should remain cognizant of these evolving AI architectures, as they could significantly impact the efficiency and capability of AI-driven investment strategies.
OpenAI's rumored GPT-5 .
OpenAI's rumored GPT-5, as speculated, may provide a substantial leap forward in AI language models' capabilities. Particularly, its integration into existing platforms such as ChatGPT could offer a more nuanced and sophisticated level of conversation, far surpassing the capabilities of current models. This development signifies a shift towards AI models that could potentially outcompete humans in tasks requiring creative divergent thinking, a previously exclusive human domain.
As our understanding of AI's capabilities and limitations expands, the implications of these advancements for the financial industry are momentous. The integration of large-language
AI models, such as GPT-5, into the financial services sector could generate opportunities to establish best practices, thereby driving the industry forward. This shift also presents opportunities for asset managers to leverage these AI advancements for predictive and generative tasks, potentially leading to more efficient and precise decision-making processes.
However, it's important to note that these are based on speculative reports, given that OpenAI has not officially confirmed any details about GPT-5. As such, while the potential for a more advanced AI language model is immense, its exact capabilities and impact remain to be seen. Yet, given the rapid pace of AI development, the anticipation of GPT-5 underscores the need for asset managers to stay abreast of these advancements and their potential implications for the financial sector.
References and Links
ChatGPT is over one year old. Here’s how it changed the tech world.
Title: OpenAI's ChatGPT took the AI world by storm a year ago and China is ...
Title: A Year of ChatGPT: 5 Ways the AI Marvel Has Changed the World
Might the well of high-quality textual data on the public internet could run dry by 2026?
Title: Large language models are getting bigger and better - The Economist
Title: Quantitative text analysis | Nature Reviews Methods Primers
Title: How We Do Things With Words: Analyzing Text as Social and Cultural Data
Mamba, a new AI model, reads sequentially and updates its worldview as it progresses, similar to how humans process information?
Title: Mamba Explained
Title: An Introduction to the Mamba LLM Architecture: A New ... - DataCamp
Title: Mamba: Redefining Sequence Modeling and Outforming ... - Unite.AI
Link: https://www.unite.ai/mamba-redefining-sequence-modeling-and-outforming-transformers-architecture/
AI-specific chips: designed to significantly outperform GPUs in terms of both speed and power efficiency?
Title: AI Chips vs. GPUs: Understanding the Key Differences
Title: What is a GPU? An expert explains the chips powering the AI boom, and ...
Title: What is a GPU? An expert explains the chips powering the AI boom, and ...
OpenAI is rumoured to be working on GPT-5, a model expected to have capabilities that would make even the most advanced current large language models blush?
Title: OpenAI is rumored to be dropping GPT-5 soon - Tom's Guide
Title: GPT-5: What to Expect from New OpenAI Model - Codecademy
Title: OpenAI's GPT-5, their next-gen foundation model is coming soon
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