Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. We are witnessing a historic moment for technology advancement. Today we can pull together the best hardware, affordable infrastructure and vast amounts of data to fundamentally transform the way we conduct business.

"Alexa, how are you different than Siri?" "I'm more of a home-body" I'm away from my desk, so I guess I can't ask Alexa. No problem, I've got an iPhone in my pocket. "Hey Siri, what's the status of my Amazon order?" "I wish I could, but Amazon hasn't set that up with me yet." IPAs (intelligent personal assistants*) are in their infancy, but they are a next major step in human-computer interaction. With the expected concurrent growth of IoT and connected devices, IPAs will be everywhere soon.

Microsoft Ventures today announced two steps that point to how the tech giant's VC arm wants to get involved in artificial intelligence in a big way. First, it's now going to pursue investments through a special fund dedicated to AI startups that focus on "inclusive growth and positive impact on society." Second, it is the first announced backer for Element AI, a new incubator out of Montreal co-founded by "the godfather of machine learning" Yoshua Bengio, which is dedicated to the space. As with its news in May first announcing Microsoft Ventures and its initial focus on cloud-based startups, the VC firm is not specifying just how much money it intends to invest in artificial intelligence, or in Element AI specifically. Element AI, in which Microsoft is taking a seed investment, is not the only AI investment that Microsoft Ventures is making public today. The broader area of AI feels like the technology of the moment, with its many branches used across any and every service, platform and feature to -- simply put -- make tech more efficient, smarter -- and possibly more frightening.

In recent years, AI (artificial intelligence) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself -- AI DAOs. All of them are opportunities. This article will explore these applications. Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB. We can think of blockchains as "blue ocean"databases: they escape the "bloody red ocean" of sharks competing in an existing market, opting instead to be in a blue ocean of uncontested market space.

CRISPR gene editing technology became nearly a household name with its potential to affect humanity. And a baby was born with three parents. While some decry the developed world is falling apart due to changing political environments, science and technology innovation is likely to continue thriving. In fact, innovation is occurring so fast, I believe 2017 will be the year governments begin to consider forming new science, technology, and futurist agencies and organizations to better contend with the rapid change. The old ones are mired in bureaucracy, conservative religious ideology, and the past--unable to contend with issues like nanotechnology, artificial intelligence, and virtual reality.

Because all of the low-hanging scientific and technological fruit has supposedly been plucked. You can invent broad technologies like electrification, the light bulb, plumbing and sanitation, the telephone, refrigeration, the internal combustion engine, and the digital computer only once. Therefore most new technologies will consist of slight improvements on the old ones and that will not propel future economic growth. But have all broad technologies really been invented already? Below are three core technologies whose elaborations during the next decade will conjure into existence a world with far less transactional friction, amazing cures, and much smarter machines.

At the start of a new year, what is there to look forward to? According to predictions from think tanks and tech experts, advances in automation and artificial intelligence will threaten the jobs of millions of workers. The CEO of one company, Capgemini, goes further, predicting that AI will be one of the key factors dividing society into the haves and have-nots, with highly skilled engineers at the one end of the spectrum and low-paid unqualified worker drones at the other, with nothing in between. There will be massive redundancies, for sure. Is it time to rethink the welfare system and pay everyone a minimum living wage whether they work or not?

What were the main advances in machine learning/artificial intelligence in 2016? Everyone now seems to be doing machine learning, and if they are not, they are thinking of buying a startup to claim they do. Now, to be fair, there are reasons for much of that "hype". Can you believe that it has been only a year since Google announced they were open sourcing Tensor Flow? TF is already a very active project that is being used for anything ranging from drug discovery to generating music.

Statistics and Machine Learning Toolbox provides functions and apps to describe, analyze, and model data. You can use descriptive statistics and plots for exploratory data analysis, fit probability distributions to data, generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models. For multidimensional data analysis, Statistics and Machine Learning Toolbox provides feature selection, stepwise regression, principal component analysis (PCA), regularization, and other dimensionality reduction methods that let you identify variables or features that impact your model. The toolbox provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models.

This is the first of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist Michael Copeland. Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives. All those statements are true, it just depends on what flavor of AI you are referring to.