Online retailer eBay is attempting to extend its machine language capabilities beyond automatic language translation to e-commerce uses designed to make product searches more relevant. As automation improves, the company said one goal eliminating the search box. Meanwhile, development cycles have been reduced as more machine learning libraries are released to the open source community. "As machines get better at decoding natural language, commerce should become increasingly conversational -- eventually rendering the search box redundant," eBay CEO Devin Wenig noted recently. Wenig added that the pace of machine intelligence development has quickened over the last year.

This post was contributed by Chelsea Douglas, a Software Engineer at Plotly. Want to play with the code from this post? I recently had the chance to team up with Domino Data Lab to produce a webinar that demonstrated how to use Plotly to create data visualizations inside of Domino notebooks. In this post, I'll share a few of the benefits that I discovered while using Plotly and Domino together. Plotly is a web-based data visualization platform for data scientists and engineers.

A couple of years ago, machine learning suddenly started appearing on the horizon in enterprise software. Systems of engagement ceased to be the new frontier of innovation as'systems of intelligence' rapidly became all the rage. Nowadays, no self-respecting enterprise software vendor can be seen to be without a strategy for applying artifical intelligence to their systems. These AI-enriched, cloud-based systems promise a higher level of automation and productivity by discovering patterns in past behavior and then acting on them when the same conditions reoccur. Benefits are promised across every kind of enterprise function -- whether it's raising the success rate of salespeople, improving collections, planning projects more efficiently or fixing defective equipment before it fails.

In Part I of this series, we introduced the theory and intuition behind the VAE, an exciting development in machine learning for combined generative modeling and inference--"machines that imagine and reason." To recap: VAEs put a probabilistic spin on the basic autoencoder paradigm--treating their inputs, hidden representations, and reconstructed outputs as probabilistic random variables within a directed graphical model. With this Bayesian perspective, the encoder becomes a variational inference network, mapping observed inputs to (approximate) posterior distributions over latent space, and the decoder becomes a generative network, capable of mapping arbitrary latent coordinates back to distributions over the original data space. The beauty of this setup is that we can take a principled Bayesian approach toward building systems with a rich internal "mental model" of the observed world, all by training a single, cleverly-designed deep neural network. These benefits derive from an enriched understanding of data as merely the tip of the iceberg--the observed result of an underlying causative probabilistic process.

From Blade Runner to I.Robot, to Transformers, Hollywood's robots-take-control genre has long profited from fears surrounding artificial intelligence's future role in society. But it's looking like, at this particular juncture, AI is more likely to determine what life insurance or hiking boots people might buy than whether cyborgs or humans will control the world. No worthwhile data management platform or analytics solution emerges today without touting its machine learning capabilities and powers of predictive analytics. But machine learning's roots are not foreign to business people. Delivering a primer on the topic at a client conference, SAS Manager of Data Science Technologies Wayne Thompson noted that the main difference between statistics and machine learning is that "statistics focuses more on inferential analysis or hypothesis testing to make predictions about a larger population than the sample represents. Machine learning uses massive amounts of observational data and, as a branch of artificial intelligence, focuses on automation."

Now, I think it's a projection of alpha male's psychology onto the very concept of intelligence. If we create intelligence, that's intelligent design. I mean our intelligent design creating something, and unless we program it with a goal of subjugating less intelligent beings, there's no reason to think that it will naturally evolve in that direction, particularly if, like with every gadget that we invent we build in safeguards. As we develop smarter and smarter artificially intelligent systems, if there's some danger that it will, through some oversight, shoot off in some direction that starts to work against our interest then that's a safeguard that we can build in.

It's a given that chatbots will become an intricate part of the future of our online interactions. To a certain extent, that future has already arrived. I know I've personally engaged with about ten different chatbots this week, so far. And we can expect chatbots to take up more and more space in our lives. Some argue that chatbots will revolutionize marketing by creating the "Human 2.0," allowing us to know more about our leads, handle more requests at the same time, and automate many of the tasks we currently have to do manually.

What are the differences between data science, data mining, machine learning, statistics, operations research, and so on? Here I compare several analytic disciplines that overlap, to explain the differences and common denominators. Sometimes differences exist for nothing else other than historical reasons. Sometimes the differences are real and subtle. I also provide typical job titles, types of analyses, and industries traditionally attached to each discipline.

With the use of education chatbots, Prepathon CEO Allwin Agnel explained that the artificial intelligence-driven education technology bots are able to execute precise and detailed tasks that can improve or alter educational experiences by facilitating personalized learning. As the equity gap in American education continues, Microsoft co-founder Bill Gates has been urging educators, investors and tech companies to be more open in investing time and money in artificial intelligence-driven education technology programs. Gates believed that these AI-based EdTech platforms could personalize and revolutionize school learning experience while eliminating the equity gap. With that said, Gates is reportedly excited about the evolving field of personalized learning and artificial intelligence tutor bots. According to Venture Beat, the world's richest man will also like the Mumbai-based company called Prepathon as it opted to create bots with specialized single concentration and purpose.

It's hard to imagine 2016 Presidential election candidates trying to figure out how to use an AI personal assistant, much less lead us into a new era of a digital solutions economy powered by AI. For the last 50 years, technology has been the fundamental driving force for the growth of our economy. My grandparents, who were born in Europe saw the advent of truly life altering technologies at that time such as automobiles, electricity and refrigeration. Over the years being a CTO and venture capitalist, I've recognized that the rate of change and innovation have been accelerating – in particular over the past decade it seems. Facebook, a social networking service was founded by Mark Zuckerberg with his Harvard roommates in 2004.