IBM的人工智能的优势

IBM很早就开始了他们的人工智能发展，他们的沃森产品主导了企业级的人工智能。

最初的目标是医疗保健部门，专注于做出困难的诊断，我仍然记得他们的第一个公共验证测试。他们解决了一个困扰医生多年的问题，那就是一个有奇怪症状和未被诊断的痛苦疾病的女人。在15分钟内，系统确定了她的问题的原因和可能的治疗方法。

我曾看到这台机器在《危险边缘》(Jeopardy)上成功竞争，最近，它在直播辩论上的表现也令人印象深刻。虽然它输了那场比赛，但我认为裁判对人类有偏见;它比人类更有趣，更有趣。具有讽刺意味的是，可能正是这种幽默让华生陷入了麻烦。但它确实给了我一种未来数字助理能够完成什么的感觉。

诚然，我曾一度认为IBM与苹果的合作可能会为Siri带来令人印象深刻的沃森后端。然而，苹果仍然是领先的“非这里发明”的公司之一，不幸的是，这种情况从未发生。我们仍在与个人人工智能打交道，其能力远不及我们现在在个人数字助理背后的沃森机器所具备的能力。

IBM在世界各地有五个实验室致力于人工智能的发展，他们正在开发独特的处理器和存储系统，以提高速度和智能。他们报告说，目前他们的平均性能每瓦特增长约2.5倍每年。这一性能改进率远远超过了行业标准Morre定律，该定律的性能每两年翻一番。IBM则是这一数字的两倍多。

这种持续的巨大改进也迫使他们重新考虑组件互连，并创建一种新的内存类型。这种内存类型的元素与英特尔的Optane产品惊人地相似，但似乎具有更高的密度和性能，更符合IBM的人工智能努力。这意味着这项技术有可能，如果它还没有进入IBM的主流服务器和存储产品，就像英特尔的Optane一样。

IBM最令人印象深刻的努力之一是开发算法，这些算法允许他们降低精确度，但对精确度几乎没有影响。他们从32位进程开始，并能够将精度降低到2位进程。这种减少大大减少了训练和推理系统的大小和功率需求，而且这种努力还会对其他服务器负载产生影响。

原文：IBM began their AI push early on, and they dominate enterprise-class AI with their Watson offering.

Initially targeted at the healthcare segment and focused on making a difficult diagnosis, I still recall their first public validation test. They took a problem that had stumped doctors for years regarding a woman who had strange symptoms and an undiagnosed painful disease. In 15 minutes, the system identified both the cause and the likely cure for her problem.

I've seen the machine compete successfully on Jeopardy and, more recently, do a pretty impressive job with a live debate. While it lost that competition, I think the judge was biased toward the human; it was funnier and more entertaining than the human was. Ironically, it might have been the humor that got Watson into trouble. But it did give me a sense of what a future digital assistant might be able to accomplish.

Granted, at one time I thought IBM's alliance with Apple might lead to an impressive Watson back end for Siri. However, with Apple still being one of the leading "not invented here" companies, unfortunately that never happened. We are still dealing with personal AIs with capabilities well below what we might have were there a Watson machine behind our personal digital assistants now.

IBM has five labs all over the world working on AI advancement, and they are developing unique processors and memory systems to increase the speed and intelligence of their efforts. They reported that their average performance per watt increase is currently around 2.5x per year. This performance improvement rate is well ahead of the industry standard Morre's Law, which has performance doubling every two years. IBM is more than doubling that.

This sustained vast improvement has also forced them to rethink component interconnect as well as create a new memory type. This memory type has elements that are surprisingly similar to Intel's Optane product, but with what appears to be far higher density and performance more in line with IBM's AI efforts. This suggests that the technology could, if it hasn't already, make it into IBM's mainstream servers and storage products, much like Intel's Optane has.

One of IBM's most impressive efforts is the development of algorithms that allowed them to reduce precision while having little effect on accuracy. They started with a 32-bit process and were able to reduce the precision to a 2-bit process. This reduction significantly reduces the size and power requirements of both the training and inference systems that result, and this effort could have an impact on other server loads as well.