人工智能正在吞噬数据科学 - KDnuggets

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人工智能正在吞噬数据科学
图片由作者使用 Midjourney 创建

作为21世纪技术革命的基石，数据科学被视为每个行业的未来。但仔细观察就会发现，数据科学作为一门学科只会存在很短一段时间，这是数据匮乏的过去和智能系统主导的未来之间的过渡。

不久前，我们还被数据稀疏和数据存储成本高昂的问题所困扰。今天快进。由于我们新发现的数字支柱，包括互联网、社交媒体、电子商务和物联网设备，我们不断被数据淹没。在这个大数据时代开始时，数据科学已经发展成为一种获取洞察、预测趋势和做出决策的工具，帮助我们理解这些海量数据集。大数据时代已经全面到来，我们已经坚定地融入其中。

However, changes are becoming apparent as the ability to handle big data increases. The focus is no longer the vast amounts of data we generate non-stop; we have turned our attention to the ever-proliferating complex data-fuelled AI systems. The key question is no longer just “What insights can I derive from this data?” We instead ask “What AI system can I run with this data?” The last decade has focused on mastering big data. Next, we promise to move on to designing and implementing more powerful AI systems.

这一新兴趋势标志着数据科学与人工智能职业道路融合的新阶段： other AI-powered singularity. It’s no longer just about the ability to analyze data, it’s also about building, training and maintaining AI systems that can learn, adapt and make autonomous decisions. This consolidation of roles represents an increasingly AI-centric situation.

To see this change in action, just look at OpenAI’s ChatGPT project. Initially, the project focused on collecting and organizing large amounts of data to train models. However, the focus soon shifted to attempt to create and improve large-scale systems capable of generating meaningful, contextual natural language responses. Interactions between data and systems will become more dynamic, and AI will use data in increasingly complex and innovative ways.

And imagine a future where AI-powered smart cities are the norm. The unseemly amounts of data that will be generated from sensors, devices, human interactions, and beyond will be consumed by AIs to control traffic flow, energy consumption, public safety, and more. This goes beyond data analysis. It’s about developing giant AI systems that can understand and manage complex urban ecosystems.

Data science may appear to be evolving into a branch of contemporary AI, and that’s because, well, it is. But fret not, as this is but an evolutionary step to keep pace with the evolving technology landscape, much like the emergence of data science from statistics to handle the once-emerging “big data.” Just as statistics are an integral part of data science, data science itself will continue to play an important role in an AI-driven future.

十多年前开始的与数据相关的转型仍在继续推进，尽管其目的地尚不明显。然而，方向很明确：科技行业的未来职业不仅需要理解孤立的数据，而且需要理解数据作为复杂和多功能人工智能系统的命脉。在此背景下，数据科学最终将被视为通往以人工智能为中心的未来道路上的一个重要里程碑。不过，别搞错了；数据科学作为自己的实体将最终会被回顾。

因此，随着人工智能的最新进展开始在世界各地留下印记，请密切关注其不可避免的数据科学消耗。正如 data 现在已经很大了，我们的也很大了愿望对于它可以培育的系统。

数据万岁！

马修·梅奥（Matthew Mayo） (@马特梅奥13) 是数据科学家和 KDnuggets 的主编，KDnuggets 是开创性的在线数据科学和机器学习资源。他的兴趣在于自然语言处理、算法设计和优化、无监督学习、神经网络和机器学习的自动化方法。 Matthew 拥有计算机科学硕士学位和数据挖掘研究生文凭。可以通过 kdnuggets[dot]com 的 editor1 联系到他。