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\nApple once led the pack with its intelligent assistant Siri, but in just a few years,\u00a0Amazon, Microsoft and Google have chipped away at its lead.<\/p>\n
Siri is a critical component of Apple\u2019s\u00a0vision for the future, so integral that it was willing to spend\u00a0$200 million to acquire\u00a0Lattice Data over the weekend.\u00a0<\/a>The\u00a0startup was working to transform the way businesses deal with\u00a0paragraphs\u00a0of text and\u00a0other information that lives outside neatly structured databases.\u00a0These engineers are uniquely prepared to assist Apple with building a next-generation\u00a0internal knowledge graph to\u00a0power Siri and its next generation of intelligent products and services.<\/p>\n Broadly\u00a0speaking, the Lattice Data deal was an acquihire. Apple paid roughly $10 million for each of Lattice\u2019s 20 engineers. This is generally considered to be fair market value. Google paid about $500 million for DeepMind back in 2014.<\/a> At that time, the startup had roughly 75 employees, of which a portion were machine learning developers. Give or take a few million, the math pretty much works out. But beneath the surface, the deal signals that Apple is willing to spend significant capital shoring up the backbone of Siri.<\/p>\n Apple and its peers grapple with the challenge of teaching conversational assistants basic knowledge about the world. Apple relies on a number of partnerships, including a major one with Yahoo, to provide Siri with the facts it needs to answer questions. It competes with\u00a0Google, a company that possesses what is largely considered to be the cr\u00e8me de la cr\u00e8me of knowledge graphs. Apple surely has an interest in improving the size and quality of its knowledge graph while unshackling itself from partners.<\/p>\n Lattice\u2019s experienced engineers\u00a0are particularly important to Apple as it designs future products for an\u00a0AI-first world. Companies like Microsoft, Facebook and Google have already declared their intentions to build up infrastructure\u00a0to support the implementation\u00a0of machine learning in as many products and services as possible. Apple brought on\u00a0Rus Salakhutdinov<\/a>\u00a0in October 2017 to lead research efforts at the company, and it has acquired startups like\u00a0Turi<\/a> and RealFace<\/a>,\u00a0but it still has a lot of work to do if it intends to remain competitive in AI in the long run.<\/p>\n \u201cGoogle is applying machine and deep-learning to about 2,500 different use cases internally now. Apple should be doing the same,\u201d asserted\u00a0Chris Nicholson, CEO of Skymind<\/a>, the creators of the DL4J deep learning library.<\/p>\n