AI Blazing Past Other Tech at Warp Speed
In the rapidly evolving world of technology, few areas have seen more explosive growth in recent years than artificial intelligence (AI). The rate of progress and pace of innovation in AI has far outstripped other major tech fields like mobile software development and internet technologies.
While other industry players have certainly seen significant advancements, AI stands out as being on an accelerating trajectory fueled by a potent combination of big data, computing power, and algorithmic breakthroughs.
The extraordinary rate of change in AI can be attributed to what is known as the “AI getting smarter causes AI to become better at getting smarter faster” feedback loop. As AI systems become more capable, they are better able to assist and accelerate their own development through things like enhanced data processing, model optimization, and even generating training data synthetically. This self-amplifying dynamic, combined with the exponentially increasing computational resources available, has allowed AI to rapidly improve at an ever-increasing pace over just the last few years.
In contrast, mobile software advancement has proceeded at a more linear, relatively constrained rate dictated largely by factors like Moore’s Law around chip performance improvements and yearly release cycles of new mobile hardware and operating system versions. The mobile app ecosystem, while certainly dynamic, has matured to the point where innovations tend to be more incremental. Similarly, progress on the internet and web technologies has been impactful but not necessarily exponential, with growth tied to factors like increases in bandwidth, browser capabilities, content creation tools, etc.
The remarkable recent strides in AI have been driven by developments across a number of key areas:
Language Models:
Large language models like GPT-3 trained on internet-scale data have achieved remarkable performance on a wide variety of language understanding and generation tasks, abilities that continue to rapidly improve with techniques like unsupervised pretraining and multi-task finetuning. The introduction of ChatGPT showcased how large language models can engage in open-ended dialogue.
Computer Vision:
Deep learning-powered computer vision models have attained human-level performance on many image recognition benchmarks. They can now generate highly realistic images from text prompts alone through innovations like diffusion models and DALL-E. Video understanding is rapidly catching up.
Robotics:
AI systems are becoming much better at tasks like robotic grasping and manipulation by training on immense amounts of data in simulation. Reinforcement learning algorithms can learn complex behaviors from scratch allowing humanoid robots to develop skills like walking, jumping, and object re-orientation with surprising agility.
Game AI:
AI agents are mastering complex games like chess, go, and DOTA at superhuman levels through self-play reinforcement learning, and can now defeat top human players in genres like real-time strategy games by learning complex strategic decision-making capabilities.
Biomedical AI:
AI systems are achieving expert-level performance on things like medical diagnosis, scientific discovery, and drug/protein design by drawing insights from massive biological datasets. Models like AlphaFold are rapidly advancing our understanding of biochemical structure and function.
While the rate of progress in AI has been astounding, it’s also important to put it in context. Many challenges remain like developing robust common sense reasoning, generalized intelligence that can adapt to a wide range of domains and distributions, alignment of advanced AI systems with human values and intentions, and achieving artificial general intelligence (AGI).
Limitations around data quality, compute and energy requirements, interpretability/explainability of deep learning models, cascading errors, reward hacking, and more will need to be navigated. Concerns exist around the implications of advanced AI systems on areas like privacy, security, and workforce disruption. Ethical AI development frameworks will be vital.
Nonetheless, by most extrapolations from current trends, the pace of AI advancement shows no signs of slowing down in the near future. AI capabilities are expanding in capability and scope at a blistering rate of change. The remarkable recent breakthroughs foreshadow much greater disruption and transformation yet to come across the technological landscape and society as a whole.
While mobile and internet tech progress is valuable, the accelerating progress in AI stands out as a singularly disruptive phenomenon, one where we are still likely in the very early innings of its growth and global impact. As AI systems become increasingly autonomous and capable, their ability to drive their own development and innovation will force the rate of change to further accelerate, propelling us into a future potentially shaped by intelligences extending radically beyond the human mind. Buckle up.
