Human Evolution through Non-Biological Augmentation
The next phase of human evolution is here, and it’s through non-biological augmentation.
In recent times, the introduction of ChatGPT has sparked renewed interest and discussions about Language Models (LLMs), Artificial Intelligence (AI), and the ultimate goal of Artificial General Intelligence (AGI) into the broader audience. People outside the AI/ML/NLP space are now witnessing the immense potential that these technologies hold, and this has set off a race among top tech companies and open-source pioneers like HuggingFace to pave the way for the next phase of the industry. As we fast forward to the not-so-distant future, LLMs, AIs, and AGIs will become an integral part of our daily interactions, reshaping our digital society and becoming the norm.
The rise of Generative AI Startups has caught our attention, and they are the torchbearers of the digital revolution. While the services and solutions offered by these cutting-edge systems might seem simple, they are slowly but surely transforming the digital landscape. However, it’s important to clarify that these technologies are not yet ready to replace us or lead to a doomsday scenario akin to Skynet from the Terminator franchise. On the contrary, they are here to assist, augment, and elevate our abilities to unprecedented heights.
In this emerging era of AI-augmented reality, two plus one crucial factors will play a pivotal role in shaping the future of this evolution:
The Importance of Quality Data
Data is fuel, and fuel is data. Keep your tank full to enjoy the journey
The performance of AI systems relies heavily on the data they are trained on. Garbage in, garbage out — a common adage in the AI world — holds true. To achieve desirable results, high-quality and diverse data is essential. By feeding these models with extensive and accurate information, we empower them to make well-informed decisions and generate more relevant and coherent outputs. Feeding data models in this space happens in two areas. The first one is the more commonly known area of model training, where a curated set of data is used to train an algorithm, and the output of this training becomes a model (weights) to be used for a variety of tasks. In the era of LLMs, the concept of Data Augmented Generation and Retrieval Augmented Generation is another area where data is fed to the models to be synthesized/morphed into an output based on the instructions passed to the model alongside the data. Ex. Passing a large blob of text extracted from a random research paper on quantum physics along with the instructions to summarise the text blob to be able to explain it to 5yr old. I hope you’re getting the drift by now… for us to get further into this promised AI land, “quality data” becomes “quality fuel,” giving us better mileage.
Usability and Integration
The actual value of AI lies not just in its capabilities but also in its usability. It needs to seamlessly integrate into existing user workflows, making it accessible and intuitive for anyone to benefit from. The more straightforward and user-friendly the interface, the more effectively it will be embraced and adopted by individuals and organizations alike.
Imagine a future where AI becomes a personal digital assistant (Microsoft co-pilot is just the beginning), readily available to help with a wide range of tasks. It could compose emails, write code, design graphics, or even provide personalized healthcare recommendations. The possibilities are endless, and as AI becomes more integrated into our lives, we must ensure that it enhances and empowers us rather than diminishing our role in society.
Ensuring Responsible Growth (GenAI cowboys 🤠 can skip this section)
It starts where it ends and ends where it starts…
While discussions about Language Models (LLMs) and the potential of Generative AI have been gaining momentum, there is one aspect that often goes unnoticed but holds immense significance — the governance of these technologies. As we dive deeper into the world of AI-driven advancements, it becomes increasingly clear that progress in this field may face significant hurdles without proper governance. The current stage of Generative AI, mainly revolving around LLMs, is characterized by its non-deterministic nature. This characteristic poses a considerable challenge when determining the boundaries of governance. Deciding where to start and where to end the scope of regulations, guidelines, and ethical considerations is challenging.
The non-deterministic nature of AI refers to the fact that its outputs can vary depending on many factors, including the input data, model architecture, and even minor tweaks during training. This inherent variability raises questions about accountability and transparency. How can we ensure that AI systems make unbiased decisions? How do we hold AI accountable for errors or biased outputs? These are complex ethical and practical challenges that demand careful attention.
The journey towards AGI and a typeless future will undoubtedly present challenges, including ethical considerations, privacy concerns, and the need for robust safeguards. As we forge ahead, we must strike a balance between innovation and responsibility, harnessing the immense potential of AI while being mindful of its implications.
In conclusion, the rise of AI technologies, powered by LLMs and other remarkable innovations, is set to revolutionize the way we interact with the digital world. By embracing these advancements, we can leverage AI’s power to elevate humanity, creating a future where humans and machines coexist harmoniously, pushing the boundaries of what is possible, and making way for a truly transformative era in human history. AI is not going to replace the workforce as we know it. The workforce who tap into AI will replace the workforce who don’t.
