最近DeepSeek的推出引起了很多轰动，也引来了很多头条新闻。虽然生成式人工智能的最新版本背后的技术无疑令人印象深刻，但它的出现在许多方面都体现了当今人工智能的现状。也就是说，它很有趣、很有前途，也许有点被夸大了。
There has been a lot of excitement and many headlines generated by the recent launch of DeepSeek. And, while the technology behind this latest iteration of Generative AI is undoubtedly impressive, in many ways its arrival encapsulates the state of AI today. That is to say, it’s interesting, promising and maybe a little overhyped.
我想知道这是否部分是代沟问题。婴儿潮一代是第一批广泛从事 IT 工作的一代，他们从惨痛的经历中吸取了商业教训。项目必须符合成本合理性，因为技术昂贵，而且需要与稳健的投资回报率案例挂钩。项目进展缓慢，因为它们很复杂，必须与特定的业务需求保持一致，并得到相关利益相关者的认可。人们担心“项目蔓延”，IT 和“业务”之间的关系往往紧张而复杂，相互猜疑。
I wonder whether that may be partly a generational thing. The baby boomer generation was the first to be widely employed in IT and that cohort learned the lessons of business the hard way. Projects had to be cost-justified because technology was expensive and needed to be attached to a robust ROI case. Projects were rolled out slowly because they were complex and had to be aligned to a specific business need, endorsed by the right stakeholders. ‘Project creep’ was feared and the relationship between IT and ‘the business’ was often fraught and complex, characterised by mutual suspicion.
如今，情况有所不同。IT 行业规模庞大，财富 50 强中充斥着大型科技品牌，其他行业也惊叹于软件行业的利润率。对于硅谷和 Sand Hill Road 那些急于寻找下一个大事件的风险投资家来说，这一切可能都很好。但回到企业 IT 的现实世界，我们应该更加谨慎地看待问题，采取适当的务实态度，甚至保持一两次警惕。
Today, the situation is somewhat different. The IT industry is enormous, the Fortune 50 is replete with major tech brands and other sectors marvel at the profit margins of the software sector. That may all be very well for Silicon Valley and the venture capitalists of Sand Hill Road desperate to find The Next Big Thing. But back in the real world of corporate IT, matters should be seen with more caution, an appropriate level of pragmatism and even a raised eyebrow or two.
这又把我们带回到人工智能。人工智能并非新鲜事物，其根源可以追溯到上个世纪中叶。到目前为止，尽管人工智能引起了人们的兴奋，但它在商业世界中只发挥了中等作用。Chat-GPT 等工具的成功使其引起了主流关注，但它仍然受到熟悉问题的困扰。认真部署人工智能成本高昂，开发它需要（至少在 DeepSeek 之前）巨大的计算能力，并且它提供的响应往往值得怀疑。法律责任和版权问题也值得深思。
Which brings us back to AI. AI is far from new and has its roots all the way back in the middle of the previous century. So far, despite all the excitement, it has played only a moderate role in the business world. The success of tools like Chat-GPT has catapulted it to mainstream attention but it is still beset by familiar issues. It is costly to deploy in earnest, it requires (at least until DeepSeek) enormous compute power to develop and it delivers responses that are often questionable. There are also serious questions to be asked about legal liability and copyright.
平衡之举 A balancing act
我们需要在当今人工智能固有的狂热和实验与健康的实用主义之间取得平衡。我们应该从商业案例开始，问问人工智能如何帮助我们。我们的使命是什么？我们的战略机遇和风险在哪里？好吧，现在人工智能如何帮助我们？今天，有太多的“人工智能很棒，让我们看看我们能用它做什么”。
We need to strike a happy balance between the boosterism and experimentation inherent in AI today and a healthy sense of pragmatism. We should begin with the business case and ask how AI helps us. What is our mission? Where are our strategic opportunities and risks? OK, now how can AI help us? Today, there is too much “AI is great, let’s see what we can do with it”.
如今，我认为人工智能是一个巨大的机遇，但其应用案例还有待开发。人工智能擅长处理人类不擅长的大规模计算任务。它能够比我们脆弱的人类大脑更快地研究模式并发现趋势。它不会在早上起床时情绪失控，不会轻易疲劳，也不需要每年在地中海度假两周。它在制作图像、音乐、诗歌和视频等有限的创造性任务上表现得非常出色。但它不善于看清大局。它缺乏人类那种让我们远离危险的谨慎意识，也没有现实世界中由大量变量组成的经验，其中最重要的变量就是人类的情绪和感知。
Today, I see AI as a massive opportunity but use cases need to be worked out. AI is great at massive computation tasks that human beings are bad at. It can study patterns and detect trends faster than our feeble human brains can. It doesn’t get out of the bed on the wrong side in the morning, tire easily or require two weeks holiday in the Mediterranean each year. It is surprisingly excellent at a limited number of creative tasks such as making images, music, poems and videos. But it is bad at seeing the big picture. It lacks the human sense of caution that keeps us from danger, and it has no experience of the real world of work that is composed of an enormous range of variables, not the least of which is human mood and perception.
如今，人工智能在边缘领域表现得十分出色：它能够为回答可预测问题的机器人提供动力，或者为帮助我们以更快速度完成机械任务的代理提供动力。机器人流程自动化是一种有用的辅助手段，它改变了人类与计算机交互的方式：我们现在可以放弃处理信用卡申请或费用报销等枯燥的工作，专注于成为创造性思考者。
AI today is great at the edge: in powering bots that answer predictable questions or agents that help us achieve rote tasks faster than would otherwise be the case. Robotic process automation has been a useful aid and has changed the dynamic of how the human being interacts with computers: we can now hand off dull jobs like processing credit card applications or expense claims and focus on being creative thinkers.
也存在灰色地带。对话式人工智能尚在发展中，但我们可以期待基于二进制朋友的迭代持续学习而快速改进。很快，我们可能会对人工智能猜测我们下一步行动并建议更智能的方式来完成工作的能力印象深刻。同样，人工智能也有机会更多地了解我们的垂直业务，并了解人类在只见树木不见森林时可能错过的趋势。
There are grey areas too. Conversational AI is a work in progress, but we can expect rapid improvements based on iterative continuous learning by our binary friends. Soon we may be impressed by AI’s ability to guess our next steps and to suggest smarter ways to accomplish our work. Similarly, there is scope for AI to learn more about our vertical businesses and to understand trends that humans may miss when we fail to see the forest for the trees.
但我们距离机器人 CEO 还有些距离，我们需要确保人工智能的“决策”受到具有常识、检查、测试和恢复能力的人类老板的制约。未来是人工智能和人类协同工作的未来，但目前我们明智的做法是谨慎部署，并制定合理的预算和适当的承诺水平。
But we are some way off robot CEOs, and we need to ensure that AI ‘decisions’ are tempered by human bosses that have common sense, the ability to check, test and revert. The future is one where AI and humanity work in concert but for now we are wise to deploy with care and with sensible budgets and the appropriate level of commitment.
我们需要密切关注 DeepSeek 的下一个热门项目，并对其进行查询，并且始终从适用性、成本和风险等老套问题开始。我注意到 DeepSeek 的网站标语是“探索未知”。这差不多是对的：我们需要保持冒险和乐观的精神，但要避免迷失在新的技术荒野中。
We need to watch carefully for the next DeepSeek hit, query it and always begin with old-fashioned questions as to applicability, costs and risk. I note that DeepSeek’s website bears the tagline “Into the Unknown”. That’s about right: we need to maintain a spirit of adventure and optimism but avoid getting lost in a new technological wilderness.