Upcoming industry trends in AI
Author: Suraj Amonkar | Client Partner | AI@Scale, Machine Vision and Conv AI
Not quite human... yet One of the biggest questions asked regarding where AI is headed is whether its complexity will eventually rival that of the human brain. Our ability to create mind maps to link seemingly unconnected ideas is an intriguing concept that begs the question: can machines develop similar capabili- ties? Although natural language processing and computer vision technologies have advanced significantly, they still lack a fundamental understanding of the interrelatedness of ideas. This is especially evident in scientific knowledge — machines currently can’t understand complex concepts such as gravity and space-time. While some research has explored embedding knowledge graphs into models, the absence of a comprehensive understanding of concepts and their relationships is a significant challenge AI faces. In the immediate and medium-term future, human interven- tion will still be required to ensure that AI produces accurate and appropriate results. The question we should be asking in the longer term is not whether AI will replace humans but how much human intervention will be required for a specific task. As Fractal CEO, Srikanth Velamakanni, puts it: “rather than AI replacing humans, we might see humans using AI replace humans not using AI.” Currently, that seems to be where things are headed, which leaves two distinct challenges: Determining the optimal balance of human-machine interaction Restricting the damage — whether intentional or unintentional — humans can cause through program- ming bias and unethical application of AI technology
Artificial intelligence (AI) has come a long way in the past two decades. However, it wasn’t until Google’s Attention Mechanisms paper was published in 2014 that the wider community began to realize its true potential. Since then, we’ve seen the rise of large language models, and the latest iteration — GPT-4 — is a significant inflection point in AI’s journey. Despite the latest developments, however, there are still several challenges facing AI and its relationship with humans. Addressing these complex issues is likely slower and more difficult than advancing the technology itself. But with the global impact, AI promises to deliver; it’s becoming essential to tackle these challenges with the same vigor we’ve shown thus far in evolving AI. The simple/complex dichotomy The evolution of AI seems to be going in the opposite direction of human evolution. Animals prioritize survival, vision, and speech over more complex tasks, yet AI has already tackled some of the more complex problems and is now moving towards mastering natural language process- ing (NLP) which, in many regards, is less complex. While some primitive capabilities that animals possess may be harder to achieve, we are still progressing toward general- izability, which is the holy grail of AI. With the advent of deep learning, we’ve built more generalized models capable of object detection, sentiment analysis, and topic identification. This generalizability is the breakthrough researchers and developers have been striving for, but there is still a long way to go, as AI can still hallucinate and get simple things wrong (e.g., tests on GPT-3 have proven it responds with both correct and incorrect answers). Current AI needs to be fine-tuned to ensure that the solutions to simple tasks aren’t overwhelmingly complicat- ed. Although the complexity of AI’s world model makes this challenging, with continued effort and advancement, we are slowly inching toward true general AI, with more distinct human-like qualities and abilities.
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