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It’s about exponential efficiency and improvement
Gartner最近描述道 generative AI as one of the most disruptive technologies ever: “Generative AI will progress rapidly in both scientific discovery and technology commercialization. 生成式人工智能(采用率)很高, because exploration of generative AI methods is growing and proving itself in a wide range of 行业, 包括生命科学, 医疗保健, 制造业, 材料科学, 媒体, 娱乐, 汽车, 航空航天, 国防和能源.”
麦肯锡预测 that generative AI “could add the equivalent of $2.6万亿到4万亿美元.4 trillion annually across 63 use cases we analyzed. By comparison, the United Kingdom’s entire GDP in 2021 was $3.1万亿年. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases.” The applications are almost limitless.
生成式人工智能的影响
这听起来有点像1989年, when Tim Berners-Lee wrote “Information Management: A Proposal,” laying out the structure and theory of the web. 这个发明, 当然, changed our business and personal lives so dramatically, it’s impossible to imagine life without it.
Generative AI is about to do the same. Traditional AI has already transformed businesses with the superpower to ingest vast data sets, 识别显著模式, and make decisions and predictions based on that data.
现在, progress in transformer-driven deep neural networks paved the way for generative AI platforms, 包括ChatGPT, Bing聊天, 吟游诗人, 骆驼, 和DALL-E. These technologies are unique – they also learn patterns from the input training data but have the additional capability to generate new data with similar characteristics as the training set.
效率和改进
This content “generation” is the magic. The optimization loop of generative AI delivers amazing efficiencies when detailed data is supplied. This enables AI to identify complex patterns that are either too large for a person to digest or so unrecognizable that they wouldn't see them. Generative AI’s detailed pattern recognition creates significant efficiencies in processes, with results as detailed as the dataset we provide. And as generative AI continues to gain capabilities, efficiencies grow.
甚至超越效率, the AI optimization loop delivers instructional improvement much, 好多了，好多了, 快得多. The time saved with efficiencies can be leveraged for improvement, as AI becomes a bespoke teacher catering to your personal learning style. This exponential improvement means you could design a safer EV with a longer range or learn to play the piano like Beethoven.
While there are justifiable concerns about the potential misuse of generative AI, including Intellectual property and deepfakes, the possibilities for good are overwhelming. 例如，在a 最近 《沙巴体育安卓版下载》杂志 article, Kingston University re搜索er Oded Ben-Tal, pointed out that generative AI shouldn’t be about stealing a composer’s IP. It’s a tool more like turntables — when artists discovered they could use turntables to scratch records and sample their sounds, 他们创造了全新的体裁. It also has the potential to significantly reduce biases in creating creative assets. By using machine learning algorithms, generative AI can analyze vast amounts of data and generate new content that is free from human biases. This technology can help to eliminate the unconscious biases that often creep into the creative process, 比如性别, 比赛, 文化刻板印象. 另外, generative AI can create content that is inclusive and representative of diverse perspectives, which can help to promote greater equity and enrich life for all.
Like the internet, generative AI will transform our businesses — and our lives. Soon, it will be impossible to imagine life without it.
