I am a Post-Doctoral Associate in the Computer Science Department at Courant Institute of Mathematical Sciences, New York University. I am working in the Laboratory of prof. Since January 2017 I will be an Assistant Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering. I received my PhD from the Department of Electical Engineering at Columbia University in the City of New York, where I was also the Fu Foundation School of Engineering and Applied Science Presidential Fellowship holder (in years 2009-2012). I was co-advised by prof.

We've been naughty at Basecamp. Since before the launch of version 3, almost a year ago, we've had an internal API for chatbots. This allowed us to pipe in GitHub commits and control our operations infrastructure from Campfire. It was a quick, hackish version, but it was enough to make it work -- well, work for us. Finally it's time to atone and bring chatbots to everyone!

Today a new nonprofit organization called the Partnership on Artificial Intelligence to Benefit People and Society announced its establishment. Participants include Amazon, Google (and Google's DeepMind) Facebook, IBM, and Microsoft. Those companies will put up money and research resources (that could mean talent, open-source code, or data). Company representatives will sit on the organization's board alongside people from academia, the nonprofit world, and policy and ethics experts, according to a statement. Plans call for discussions, studies, reports, best practices, and public outreach in general.

The world's largest technology companies hold the keys to some of the largest databases on our planet. Much like goods and coins before it, data is becoming an important currency for the modern world. The data's value is rooted in its applications to artificial intelligence. Whomever owns the data, effectively owns AI. Right now that means companies like Facebook, Amazon, Alphabet, IBM, and Microsoft have a ton of power.

Researchers have developed machine learning software that can accurately diagnose a patient's breast cancer risk 30 times faster than doctors, based on mammogram results and personal medical history. The system could help doctors give better diagnoses the first time around -- which means fewer mammogram callbacks and false positives. "This software intelligently reviews millions of records in a short amount of time, enabling us to determine breast cancer risk more efficiently using a patient's mammogram," said one of the researchers, Stephen Wong, from Houston Methodist Research Institute. "This has the potential to decrease unnecessary biopsies." A mammogram is a breast X-ray that aims to spot any potentially cancerous cells before symptoms arise.

At Experian DataLabs in Carmel Valley, a team of scientists is thwarting bad guys with math. A top-five U.S. credit card issuer recently dumped about 6 billion transaction records on Experian DataLabs to see if its fancy machine learning mathematical formulas could do a better job of rooting out credit card fraud than the bank's existing system. Experian scientists used neuro-embedding/natural language processing techniques to understand the "syntax" of the credit card data, said Honghao Shan, a Ph.D. computer scientist. "We thought we had figured it out and went back to them," said Eric Haller, head of Experian DataLabs. "They said, how did you do that? You identified fraud that we can't identify ourselves. And it turns out we reduced their false positives by half."

Let's face it: artificial intelligence is scary. After decades of dystopian science fiction novels and movies where sentient machines end up turning on humanity, we can't help but worry as real world AI continues to improve at such a rapid rate. Sure, that danger is probably decades away if it's even a real danger at all. But there are many more immediate concerns. Will automated robots cost us jobs?

Splunk started its life as a log analysis system and has since grown into a general solution for analyzing and acting on machine-generated data. With Splunk Enterprise 6.5, the company's enterprise-level offerings now feature machine learning, an ingredient that's all but obligatory for any big data product. But Splunk's approach is less opaque than most, and it encourages enterprise devs to build with it instead of merely deploying it. Splunk has two offerings for machine learning: a prepackaged set of functionalities for common use cases, and a developer toolkit for building custom machine learning models that can be leveraged against data harvested with Splunk. Enterprises getting their feet wet with either Splunk, machine learning, or a combination of the two can start with the Splunk IT Service Intelligence, Splunk User Behavior Analytics, and Splunk Enterprise Security bundled solution sets.

Operational intelligence software developer Splunk is expanding the machine-learning capabilities of its products, debuting new releases of its flagship Splunk Enterprise platform and several applications that leverage machine data for business intelligence, security and other tasks. "Machine data is absolutely key to digital transformation," said President and CEO Doug Merritt in a keynote speech Tuesday that kicked off the .conf2016 "Machine learning enables organizations to get deeper insights from their machine data and ultimately increases the opportunity our customers can gain from digital transformation." He went on to say that the "machine data fabric" is the most effective way for businesses to "collect, store, analyze, interpret and share" data throughout an enterprise. Splunk's software is used to collect and analyze operational data, including machine data generated by IT systems and networks, security systems and Internet of Things devices, to generate actionable insights.

In Joseph Sirosh's keynote presentation at the Data Science Summit on Monday, Wee Hyong Took demonstrated using R in SQL Server 2016 to detect fraud in real-time credit card transactions at a rate of 1 million transactions per second. The demo (which starts at the 17:00 minute mark) used a gradient-boosted tree model to predict the probability of a credit card transaction being fraudulent, based on attributes like the charge amount and the country of origin. Then, a stored procedure in SQL Server 2016 was used to score transactions streaming into the database at a rate of 3.6 billion per hour. Later in the keynote (starting at 25:00), John Salch, VP of Technology and Platforms at PROS describes using R to determine prices for airline tickets, hotel rooms, and laptops. PROS has been using R for a while in development, but found running R within SQL Server 2016 to be 100 times (not 100%, 100x!) faster for price optimization.