Vodka is a neutral grain spirit that is typically flavorless and odorless. The marketing of vodka is mostly about the bottle and the brand. The contents of the bottle are usually the same from one vodka to another. With the explosion of open source AI generation tools, the contents of the bottle are usually one or more open source models. The difference between AI generation tools is usually the user interface, ease of use, customer support, and marketing.

Data, technology, and people are at hand to make artificial intelligence and machine learning available to all commerce companies. To be certain, artificial intelligence and its sub-field, machine learning, have gone through cycles of inflated expectations followed by disappointments. For example, in the 1950s and 1960s, the United States government funded research for the machine translation of languages. The hope was that Russian-language documents could be instantly translated to English. But by 1966, a report from the Automatic Language Processing Advisory Committee, a government team of seven scientists, essentially killed machine translation research in the U.S. for about a decade.

What better way to get killer robots to do what you want them to do -- get them addicted to vodka. The AI killer robots used by the Russian army are heavy duty alcoholics who feast on copious amounts of vodka. Russian army technician, Colonel Vladimir Dimitrov, revealed how the AI killer robots have to quench their thirst. "We first get them used to the vodka. For a few months they are pumped with the good stuff, then we suddenly withdraw all vodka. The robots naturally go crazy, their addiction is so so great they will do anything for their next fix. This is when we ask them to kill everything in sight, and afterwards they get a big tanker of vodka for a reward."

It has become standard practice in the Natural Language Processing (NLP) community. Release a well-optimized English corpus model, and then procedurally apply it to dozens (or even hundreds) of additional foreign languages. These secondary language models are usually trained in a fully unsupervised manner. They're published a few months after the initial English version on ArXiv, and it all makes a big splash in the tech press. In August of 2016, for example, Facebook released fastText (1), a speedy tool for word-vector embedding calculations.

I remember sitting in a meeting at my old marketing agency in 2013, thinking about how exhausted I was by our company's mundane routine. Rather than channeling our creativity into new marketing programs, my colleagues and I were spending countless hours on administrative tasks, including everything from sending emails to creating spreadsheets to organizing reports. Although we had a sterling work reputation -- our agency was featured on AMC's series The Pitch, won countless awards, and had a robust client roster -- I knew there was untapped potential. I knew we could deliver our services better and faster than competitors while eliminating and automating trivial tasks. Two years later, I launched Five Tier, an AI-driven company that capitalizes on how the future of marketing is automation.

We'll also discuss a case study which describes the step by step process of implementing kNN in building models. This algorithm is a supervised learning algorithm, where the destination is known, but the path to the destination is not. Understanding nearest neighbors forms the quintessence of machine learning. Just like Regression, this algorithm is also easy to learn and apply. Let's assume we have several groups of labeled samples.