Before you get back to constructing your underground chamber to protect humanity from the hordes of death-dealing AI robots, we have a more optimistic view of the future for you. Artificial intelligence won't lead to the demise of the human race but may in fact help us deal with the massive scaling up of communication that the internet has made possible and – gasp – actually help humans understand one another a little better. Speaking at the Intel Capital conference in Palm Desert last week, three AI experts took an altogether more pragmatic and positive view of where we are going. "An artificial intelligence machine won't want to become a person," argued Reza Zadeh, the CEO of Matroid. "It will be content to serve customers."

Machine learning is the secret sauce that allows us to use computers to automate tasks in powerful new ways. However, there are a lot of steps that must go right for the ML to work: ideas must be mined from huge amounts of data, clean sample data must be provided to train models, and models must be managed and maintained over time. Here are three startups looking to simplify some of these aspects of the ML lifecycle. Before a data scientist can build a machine learning model, she must first identify patterns in the real world. There are various ways to identify patterns.

Before you get back to constructing your underground chamber to protect humanity from the hordes of death-dealing AI robots, we have a more optimistic view of the future for you. Artificial intelligence won't lead to the demise of the human race but may in fact help us deal with the massive scaling up of communication that the internet has made possible and – gasp – actually help humans understand one another a little better. Speaking at the Intel Capital conference in Palm Desert last week, three AI experts took an altogether more pragmatic and positive view of where we are going. "An artificial intelligence machine won't want to become a person," argued Reza Zadeh, the CEO of Matroid. "It will be content to serve customers."

AI start-up Gamalon raised $20 million with lead investor Intel Capital joined by .406 This brings the total investment in US-based AI firm to more than $32 million including the largest single investment by the US Government in next-generation machine learning over the past five years. Existing investors Boston Seed Capital, Felicis Ventures and Rivas Capital also participated in the Series A round. According to Gamalon, it's the first artificial intelligence system that can process natural language and then explain what ideas are present and how they are organized. The first application of technology will be for enterprises to read, understand, and interact with billions of customer messages and provide individual responses.

An approach to artificial intelligence that embraces uncertainty and ambiguity could paradoxically help make future virtual assistants less confused. Gamalon, an AI startup based in Cambridge, Massachusetts, developed the new technique for teaching machines to handle language, and several businesses are now testing a chatbot platform that uses it. The approach lets a computer hold a more meaningful and coherent conversation by providing a way to deal with the multiple meanings that an utterance might convey. If a person says or types something ambiguous, the system makes a judgment about what was most likely meant. Today's virtual assistants and chatbots typically follow simple rules in order to respond to questions.

Machine learning is becoming extremely powerful, but it requires extreme amounts of data. You can, for instance, train a deep-learning algorithm to recognize a cat with a cat-fancier's level of expertise, but you'll need to feed it tens or even hundreds of thousands of images of felines, capturing a huge amount of variation in size, shape, texture, lighting, and orientation. It would be lot more efficient if, a bit like a person, an algorithm could develop an idea about what makes a cat a cat from fewer examples. A Boston-based startup called Gamalon has developed technology that lets computers do this in some situations, and it is releasing two products Tuesday based on the approach. If the underlying technique can be applied to many other tasks, then it could have a big impact.

Gamalon uses a technique that it calls Bayesian program synthesis to build algorithms capable of learning from fewer examples. "Probabilistic programming will make machine learning much easier for researchers and practitioners," Lake says. In theory, Gamalon's approach could make it a lot easier for someone to build and refine a machine-learning model, too. The company's first products use Bayesian program synthesis to recognize concepts in text.

Machine learning is becoming extremely powerful, but it requires extreme amounts of data. You can, for instance, train a deep-learning algorithm to recognize a cat with a cat-fancier's level of expertise, but you'll need to feed it tens or even hundreds of thousands of images of felines, capturing a huge amount of variation in size, shape, texture, lighting, and orientation. It would be lot more efficient if, a bit like a person, an algorithm could develop an idea about what makes a cat a cat from fewer examples. A Boston-based startup called Gamalon has developed technology that lets computers do this in some situations, and it is releasing two products Tuesday based on the approach. If the underlying technique can be applied to many other tasks, then it could have a big impact.

The old adage that practice makes perfect applies to machines as well, as many of today's artificially intelligent devices rely on repetition to learn. Deep-learning algorithms are designed to allow AI devices to glean knowledge from datasets and then apply what they've learned to concrete situations. For example, an AI system is fed data about how the sky is usually blue, which allows it to later recognize the sky in a series of images. Complex work can be accomplished using this method, but it certainly leaves something to be desired. For instance, could the same results be obtained by exposing deep-learning AI to fewer examples?

Gamalon Inc, emerged from stealth mode this week, announced two machine learning products, based on an in-house technology known as Bayesian Program Synthesis (BPS). The company claims BPS can perform machine learning tasks 100 times faster than conventional deep learning techniques, while providing more accurate results. "We call our way of doing this Bayesian program learning," said Gamalon founder and CEO, Ben Vigoda at a recent TED talk. He believes using Bayesian probabilistic modeling is a much more efficient way, that is, a much less computationally intensive way, to infuse intelligence into machines. Unlike deep learning, which often needs millions of data examples to train a neural network, a Bayesian model can be built with much fewer examples.