Human chemistry feels complicated, but the intricacies of relationships are ripe for machine learning. How do you feel about this person, how do they feel about you? A great deal of human interaction can be patterned and captured in a very large AI system. Laurie predicts we will casually use the data we know about us and the people around us to manipulate our body chemistry. The surprise is that it will be easy.

Voice is a Big Deal in tech this year. Amazon has probably sold 10m Echos, you couldn't move for Alexa partnerships at CES, Google has made its own and, it seems, this is the new platform. There are a couple of different causes for this explosion, and, also, a couple of problems. First, voice is a big deal because voice input now works in a way that it did not until very recently. The advances in machine learning in the past couple of years mean (to simplify hugely) that computers are getting much better at recognizing what people are saying.

At the heart of AI systems are statistical models that have no concept of social inequality, fairness, or hardships. In Cathy O'Neil's book, Weapons of Math Destruction (WMD), she points out that big data is discriminating nearly at every juncture of our society and pummeling the poor at each opportunity. Her book points to many avenues of misuse of data, but most offensive is through the use of proxies. Data statistics that are designed for one purpose but are repurposed to be used for economic or convenience sake. There are a number of examples of this.

Which is the most interesting, latest, and easy Machine Learning project available now? What is the most I/O(more interested in write I/O) bound machine learning algorithm? What are the best sources to pick up projects for beginners who are interested in the field of machine learning and data science? What is the best or most interesting aspect of machine learning to work in? What are some of the most interesting machine learning ideas that you came up with that were unsuccessful?

WASHINGTON/BOSTON One of two current members of the U.S. Securities and Exchange Commission raised questions on Thursday for companies like Snap Inc that offer shareholders unequal voting rights, saying the agency should "focus on how some innovations may prove detrimental to investors."

Editor's note: Chloe (as well as other members of SVDS) will be speaking at TDWI Accelerate in Boston. Find more information, and sign up to receive our slides here. From the outside, data science is often thought to consist wholly of advanced statistical and machine learning techniques. However, there is another key component to any data science endeavor that is often undervalued or forgotten: exploratory data analysis (EDA). At a high level, EDA is the practice of using visual and quantitative methods to understand and summarize a dataset without making any assumptions about its contents.

Anima Anandkumar is giving a talk at Strata Hadoop World San Jose and a tutorial and talk at Strata Hadoop World London. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Anima Anandkumar, a leading machine learning researcher, and currently a principal research scientist at Amazon. I took the opportunity to get an update on the latest developments on the use of tensors in machine learning.

Google has already carved out a niche for itself in machine learning with projects like TensorFlow and Google Brain. Now, it's adding data science provider Kaggle, which runs contests related to machine learning and provides services for data discovery and analysis, to the fold. The company also is moving ahead with other machine learning projects, including an API providing intelligence for video. Google Cloud is gaining access to Kaggle's community of more than 850,000 data scientists and vice versa. Kaggle and Google Cloud will continue to support machine learning training and deployment, while the community gets the capability to store and query large data sets.

As robots continue to play a growing role in our daily lives, white collar jobs in many sectors including accounting and financial operations are quickly becoming a thing of the past. Business are gravitating towards software to automate bookkeeping tasks, saving considerable amounts of both time and money. In fact, since 2004, the number of full-time finance employees at large companies has declined a staggering 40% to roughly 71 employees for every $1 billon of revenue, down from 119 employees, according to a report by top consulting firm The Hackett Group. These numbers show that instead of resisting change, companies are embracing the efficiencies of this new technology and exploring how individual businesses can leverage automation and, more importantly, artificial intelligence – aka robots. As technology becomes more sophisticated and particularly with the use of Artificial Intelligence (AI) we're able to automate multiple steps in a process.

Although China could initially only observe the advent of the Information-Technology Revolution in Military Affairs, the People's Liberation Army might presently have a unique opportunity to take advantage of the military applications of artificial intelligence to transform warfare. When the United States first demonstrated its superiority in network-centric warfare during the first Gulf War, the PLA was forced to confront the full extent of its relative backwardness in information technology. Consequently, the PLA embarked upon an ambitious agenda of "informatization" (信息化). To date, the PLA has advanced considerably in its capability to utilize information to enhance its combat capabilities, from long-range precision strike to operations in space and cyberspace. Currently, PLA thinkers anticipate the advent of an "intelligentization Revolution in Military Affairs" that will result in a transformation from informatized ways of warfare to future "intelligentized" (智能化) warfare.