"Alexa" was just another female name. Uber hadn't taken anyone for a ride yet. And the buzz around Facebook had more to do with the fact that seemingly everyone you once knew was turning up on "The Social Network," and less about the numerous data and privacy scandals that would tarnish the company's reputation later on. The year was 2010, the dawn of a new decade. And while 10 years is a long time for most every industry, in consumer tech it might as well be a lifetime.

EPISODE SUMMARY Sherrie Wang, a fourth year PhD student at Stanford's Institute for Computational and Mathematical Engineering (ICME), explains how she applies machine learning methods to help solve global food security challenges. EPISODE NOTES Sherrie brings an interdisciplinary and entrepreneurial perspective to her research that she has developed through her work in the fields of computational finance, biomedical engineering, and computer vision. Sherrie explains that about 1 in 9 people do not have access to adequate food. She is using satellite imagery and machine learning to identify and map crops around the world, see where people are most vulnerable, and what interventions or policies have the greatest effect. "There are a lot of problems that technology alone can't solve and we still need to understand the roots of a lot of the problems. That involves talking to people in the earth sciences and agriculture and learning from them. In those conversations, data science just becomes a tool, a very useful tool but it's a tool in the context of some much larger problem," she explained to Stanford's Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast.

Machine learning algorithms, especially deep learning neural networks often produce models that improve the accuracy of prediction. But the accuracy comes at the expense of higher computation and memory consumption. A deep learning algorithm, also known as a model, consists of layers of computations where thousands of parameters are computed in each layer and passed to the next, iteratively. The higher the dimensionality of the input data (e.g., a high-resolution image), the higher the computational need. GPU farms in the cloud are often used to meet these computational requirements.

TensorFlow, an end-to-end open source platform for machine learning, has selected Quantiphi, an award-winning Applied AI and Big Data software and service company, as a Trusted Partner to deliver cutting-edge Machine Learning and Artificial Intelligence solutions to solve complex business problems for enterprises. Being TensorFlow's trusted partner allows Quantiphi to implement cutting-edge AI/ML solutions across industry enterprises, leveraging TensorFlow's latest innovations. As part of the TensorFlow Trusted Partners program, Quantiphi's clients can stay ahead of the innovation curve in AI and ML as we are focused on

Python and R are the de facto standard languages for data science, due to their ease of use and huge array of third party libraries for machine learning and analytics. This video provides an introduction to the ML Toolkit and demonstrates using InterSystems IRIS as both a standalone development platform and an orchestration tool for predictive modeling. Takeaway: The ML Toolkit enables machine learning and other complex application development in the R and Python languages.

Rest assured, Elon--we don't need to fear AI killer robots, at least for the time being. At this juncture, some experts believe artificial intelligence (AI) is the panacea for all of society's woes. Meanwhile, we all know how fearful Tesla CEO Elon Musk is of AI. To paraphrase, Musk has pretty much said that artificial intelligence could possibly result in the end of humanity as we know it. And that would be pretty bad.

In this post, we continue revealing BigML customer success stories that we kicked off with our last post detailing how a number of startups are basing their smart applications and services on the BigML platform. Those companies have profited from adopting BigML rather than taking the costly and risky approach of trying to build their own Machine Learning infrastructure that could divert their attention away from their core predictive use cases. Today, we get into a potpourri of business problems tackled with the help of the BigML platform by large multi-national businesses. We see multiple scenarios play out as businesses with global footprints go about consuming Machine Learning. This also holds true for the sample of predictive use cases outlined in this post as we give you a glimpse of the motivation behind solving each reference application.

The need for a common platform for engineers to build on top of and to integrate into their existing software development and security practices. Broadly, we wanted the taxonomy to be more than an educational tool – we want it to effectuate tangible engineering outcomes. Results: Using this taxonomy as a lens, Microsoft modified its Security Development Lifecycle process for its entire organization. Specifically, data scientists and security engineers at Microsoft now share the common language of this taxonomy, allowing them to more effectively threat model their ML systems before deploying to production; Security Incident Responders also have a bug bar to triage these net-new threats specific to ML, the standard process for vulnerabilities triage and response used by the Microsoft Security Response Center and all Microsoft product teams.

How are computational tools changing filmmaking, and how will it change the video content of the future? To explore these topics we welcome Genevieve Patterson, Chief Scientist and Co-Founder of TRASH, to the show. Tools like those offered by TRASH, Genevieve Patterson's software that uses AI to make and share video, are beginning to edit video automagically for people. While these are currently limited to short, simple, social media-style videos the underlying machine learning technologies are building toward something far more.

So you've been optimizing your email programme for a while now, you've segmented your database, developed customer personas, and you've implemented a range of triggered campaigns. However, your competitors have also raised their game, and the'business as usual' option just won't cut it anymore. You're looking for something that gives you the edge and helps take email campaign performance to the next level. Artificial Intelligence (AI) is already helping marketers by serving personalized content at scale, reducing campaign production times, and enabling them to boost revenues and engagement. Forrester Research has predicted that businesses who use AI to drive marketing will gain $1.2 trillion per annum from those who don't.