在邮费Inspire last week, we attended the ‘AI – Beyond the Marketing Fluff’ session with Paddy Lawton, Coupa’s General Manager of Spend Analytics. AI has been discussed a lot this year at all of the conferences we’ve attended, it’s clearly a hot topic, and it’s full of buzzwords. That’s why we were interested in this presentation, because it promised to go beyond that and the evolution of artificial intelligence in its long journey towards procurement benefits, and instead give a practical, as opposed to a theoretical, look at machine learning, and how it is realistically used today in spend management. We weren’t disappointed – Paddy can get quite ‘techie’ but he doesn’t forget to apply his knowledge to what the audience really wants to know – how can this software actually help me?
他开始通过解释我们应该如何设想人工智能：将其分类为机器学习和深度学习。为了减少一个长话短程学习，他描述了基于规则，一旦机器知道规则，它就可以对它们执行：查看关键字（在线项目，发票等）并应用规则以了解如何分类，沿途正常化公司名称变化。当系统培训以了解来自上下文的整个句子并具有长期和短期记忆时，深入学习。因此，它还知道这些公司的补贴，并可以将它们放在正确的位置。它正在通过雾来看，并使用发达的智能来思考人类大脑。它通过摄取数据和更多数据以及更多数据来实现这一目标。它吸收的数据越多 - 它获得的智能议题 - 无论是来自发票，合同，POS，T＆E等。
But how can something that understands numbers, read words. That was what was really interesting. By vector representation, he says, all words are represented by vectors. Using Word2vec from Google, even if the machine hasn’t seen something before – has no rules for it – so long as the words are similar, or have a short distance between them, it can infer the meaning. So to get to the word Queen it might apply - King minus Woman equals Queen. It can understand a sentence and even predict the next word, because of the context.
因此，从无论您投入的任何格式，从无论是何种人们都花钱的格式，都是从任何人花钱的情况下都是结构化的，并清理为融资和采购实际使用的格式。这Spend360acquisition has meant Coupa software will be able to help users make good decisions by augmenting their taxonomy, however different each is, by classifying correctly. And not just Coupa data, all your systems’ data, both direct and indirect, cracking masses of classification problems. The value is, if you have loads of portfolio firms, it allows you to see what they’re all buying, across disparate amounts of data – something that traditional classification methods, or manual ones, would be too slow to reap any value. You would never get to see the long tail.
Imagine having the power accurately to categorise a million invoices in about a week, normalise millions of rows of data, and find every bit of spend that has ever gone through the general ledger accounts, no matter the payment source. With that visibility you can take advantage of economies of scale, reduce supplier numbers, control costs and more effectively manage the supply chain because you can see what purchases are made, from whom, where, when and why, globally and from any language or currency. It can make a massive difference getting close to 100% spend visibility – the more you can see, the more you can optimise.(A bit sales-pitchy we concede – but nevertheless, impressive!)
讨论这一点：