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In the evolving relationship between technology and society, humans have shown themselves to be incredibly adaptable. What once left us breathless, soon becomes integrated into our everyday lives.<\/p>\n
The astonishing functionalities of large language models (LLM) like\u00a0ChatGPT<\/a>\u00a0were, just a few months ago, the epitome of cutting-edge AI. They are now on course to be mere add-ons and plugins to our text editors and search engines.<\/p>\n We\u2019ll soon find ourselves relying on their capabilities, and seamlessly incorporating them into our routines.<\/p>\n Yet, this rapid acclimatisation leaves us with a lingering question: what\u2019s next? As our expectations shift, we are left wondering about the next innovation that will capture our imagination.<\/p>\n People will try to achieve all kinds of\u00a0smart<\/a>\u00a0\u2013 and\u00a0not-so-smart<\/a>\u00a0\u2013 things with AI. Many ideas will fail, others will have a lasting impact.<\/p>\n Our crystal ball is not much better than yours, but we can try to think about what\u2019s coming next in a structured way. For AI to have a lasting impact, it needs to be not only technologically feasible, but also economically viable, and normatively acceptable \u2013 in other words, it complies with the values that society demands we conform to.<\/p>\n There are some AI technologies waiting on the sidelines right now that hold promise. The four we think are waiting in the wings are next-level GPT, humanoid robots, AI lawyers, and AI-driven science. Our choices appear ready from a technological point of view, but whether they satisfy all three of the criteria we\u2019ve mentioned is another matter. We chose these four because they were the ones that kept coming up in our investigations into progress in AI technologies.<\/p>\n The startup company DoNotPay claims to have\u00a0built a legal chatbot<\/a>\u00a0\u2013 built on LLM technology \u2013 that can advise defendants in court.<\/p>\n The company recently said it would let its AI system help\u00a0two defendants fight speeding tickets<\/a>\u00a0in real-time. Connected via an earpiece, the AI can listen to proceedings and whisper legal arguments into the ear of the defendant, who then repeats them out loud to the judge.<\/p>\n After criticism and a lawsuit for\u00a0practising law without a license<\/a>, the startup postponed the AI\u2019s courtroom debut. The potential for the technology will thus not be decided by technological or economic constraints, but by the authority of the legal system.<\/p>\n Lawyers are well-paid professionals and the costs of litigation are high, so the economic potential for automation is huge. However, the\u00a0US legal system<\/a>\u00a0currently seems to oppose robots representing humans in court.<\/p>\n Scientists are increasingly turning to AI for insights. Machine learning, where an AI system improves at what it does over time, is being employed to identify patterns in data. This enables the systems to propose novel scientific hypotheses \u2013 proposed explanations for phenomena in nature. These may even be capable of surpassing human assumptions and biases.<\/p>\n For example,\u00a0researchers at the University of Liverpool<\/a>\u00a0used a machine learning system called a neural network to rank chemical combinations for battery materials, guiding their experiments and saving time.<\/p>\n The complexity of neural networks means that there are gaps in our understanding of how they actually make decisions \u2013 the so-called\u00a0black box problem<\/a>. Nevertheless, there are techniques that can shed light on the logic behind their answers and this can lead to unexpected discoveries.<\/p>\n While AI cannot currently formulate hypotheses independently, it can inspire scientists to approach problems from new perspectives.<\/p>\n We will soon see more new versions of AI chatbots based on the latest LLM technology, known as GPT-4. We\u2019ll see AI that can handle different types of data, such as images and speech, as well as text. These are called\u00a0multimodal systems<\/a>.<\/p>\n But let\u2019s gaze a little further into the future.\u00a0Auto-GPT<\/a>, an advanced AI tool released by Significant Gravitas, is already\u00a0making waves in the tech industry<\/a>.<\/p>\n Auto-GPT is given a general goal, such as planning a birthday party, and splits it into sub-tasks which it then completes by itself, without human input. This sets it apart from ChatGPT.<\/p>\n Auto-GPT incorporates AI agents, or systems, that make decisions based on predetermined rules and goals. Despite installation limitations, such an functionality problems when used with Windows, Auto-GPT shows great potential in various applications.<\/p>\n Humanoid robots \u2013 those that look and move like us \u2013 have significantly advanced since the first Darpa Robotics Challenge in 2015, a contest where teams built robots to perform a series of complex tasks set by the organisers. These included getting out of a car, opening a door and drilling a hole in a wall. Many struggled to achieve the objectives.<\/p>\n However, startups are now developing \u201chumanoids\u201d capable of doing tasks like these and being used in warehouses and factories.<\/p>\n1. AI legal help<\/strong><\/h4>\n
2. AI scientific support<\/strong><\/h4>\n
3. AutoGPT<\/strong><\/h4>\n
4. Humanoid Robots<\/strong><\/h4>\n