The experimental Smart Tissue Autonomous Robot (STAR) recently sewed a piglet's gut together using a computer program and camera-based guidance, overseen by a team of doctors and computer scientists from the Children's National Health System in Washington DC and Johns Hopkins University. The procedure took 50 minutes, as opposed to 8 minutes when performed by a surgeon, but (unfortunately for doctors) resulted in more evenly spaced sutures and less leakage from the gut. And with iterative improvements, it's likely that the time difference can be shrunk. Meanwhile, FDA-approved robotic surgery on humans is making strides as well, though it requires a surgeon to operate the mechanical arm. The potential treatment paradigm, highlighted by The Economist this month, raises questions about whether patients will trust robots with their lives, and who is liable if something goes wrong. Another question robots pose: Are doctors in line for a string of layoffs?

Of course, if you are an experienced Python programmer you will be able to skip this step. Even if so, I suggest keeping the very readable Python documentation handy. KDnuggets' own Zachary Lipton has pointed out that there is a lot of variation in what people consider a "data scientist." This actually is a reflection of the field of machine learning, since much of what data scientists do involves using machine learning algorithms to varying degrees. Is itnecessary to intimately understand kernel methods in order to efficiently create and gain insight from a support vector machine model?

Regression is still one of the most widely used predictive methods. If you are unfamiliar with Linear Regression, check out my: Linear Regression using Excel lesson. It will explain the more of the math behind what we are doing here. This lesson is focused more on how to code it in Python. What we have is a data set representing years worked at a company and salary.

WIRE)--Cornerstone OnDemand (NASDAQ:CSOD), the global leader in cloud-based talent management software solutions, today announced the launch of its suite of people analytics products, all of which are available today. This includes the introduction of Cornerstone View, an interactive data visualization application that gives business leaders deeper intelligence about their people, as well as Cornerstone Planning, an intuitive workforce planning application that helps organizations easily create, manage and execute accurate hiring plans over multiple time horizons. The company also added two new dashboards to Cornerstone Insights, its predictive and prescriptive analytics solution that equips business leaders with the intelligence to better recruit, train, manage and develop their people. Cornerstone's portfolio of analytics offerings applies sophisticated data science and the most refined machine learning system for talent management to help organizations harness the power of real-time people data. The Cornerstone Analytics suite is built on top of the world's largest network of shared talent data, representing more than 16 years of workforce management activity across 25 million users globally, nearly 2,700 organizations across all industries and seven talent management dimensions –recruiting, onboarding, training and development, performance management, compensation management, succession planning and workplace collaboration.

This week, New Orleans has played host to an enormous international gathering of cancer researchers at the American Association for Cancer Research Annual Meeting. At the meeting, FLG's own Katie Draper caught up with Niven Narain of Boston-Based biopharma company BERG to learn about cancer drug development, and explore how artificial intelligence can contribute to precision medicine. FLG: Tell us a bit about yourself and your role at BERG. NN: Sure, I'm Niven Narain, Founder and CEO of BERG, and as Founder much of my role involves bringing forth the vision of the technologies, and to put forward these products that are now in clinical trials for cancer, namely BPM31510. The vision is really to get these drugs to patients as quickly as possible, but importantly using a precision medicine approach so that we're not falling into a one-size-fits all.

The backpropagation algorithm for calculating gradients has been widely used in computation of weights for deep neural networks (DNNs). This method requires derivatives of objective functions and has some difficulties finding appropriate parameters such as learning rate. In this paper, we propose a novel approach for computing weight matrices of fully-connected DNNs by using two types of semi-nonnegative matrix factorizations (semi-NMFs). In this method, optimization processes are performed by calculating weight matrices alternately, and backpropagation (BP) is not used. We also present a method to calculate stacked autoencoder using a NMF. The output results of the autoencoder are used as pre-training data for DNNs. The experimental results show that our method using three types of NMFs attains similar error rates to the conventional DNNs with BP.

This work focuses on dynamic regret of online convex optimization that compares the performance of online learning to a clairvoyant who knows the sequence of loss functions in advance and hence selects the minimizer of the loss function at each step. By assuming that the clairvoyant moves slowly (i.e., the minimizers change slowly), we present several improved variation-based upper bounds of the dynamic regret under the true and noisy gradient feedback, which are {\it optimal} in light of the presented lower bounds. The key to our analysis is to explore a regularity metric that measures the temporal changes in the clairvoyant's minimizers, to which we refer as {\it path variation}. Firstly, we present a general lower bound in terms of the path variation, and then show that under full information or gradient feedback we are able to achieve an optimal dynamic regret. Secondly, we present a lower bound with noisy gradient feedback and then show that we can achieve optimal dynamic regrets under a stochastic gradient feedback and two-point bandit feedback. Moreover, for a sequence of smooth loss functions that admit a small variation in the gradients, our dynamic regret under the two-point bandit feedback matches what is achieved with full information.

There are some human attributes robots could never replace - or at least that's what you might hope. But one university has brought that into question by replacing one of their teaching assistants with a machine. Student Tyson Bailey began to wonder if Jill was a computer and posted his suspicions on Piazza. 'We were taking an AI course, so I had to imagine that it was possible there might be an AI lurking around,' said Bailey, who lives in Albuquerque, New Mexico. 'Then again, I asked Dr. Goel if he was a computer in one of my first email interactions with him.

This list of hand-picked leaders was compiled by Wojtek Aleksander, from GetResponse.com. Other bigger lists (sometimes created by robots) can be found here and are usually based on your Klout score, which in my opinion is not accurate. The list below is truly original and I would even add, somewhat unexpected, as you won't find Bernard Marr, Kirk Borne and other well known gurus. Just in case you're wondering, @FILWD stands for Fell In Love With Data, which happens to be the name of Enrico Bertini's blog. While the Assistant Professor at NYU doesn't talk much on Twitter himself, he uses the platform very effectively to share news and insights about data visualizations and adds his highly-valued opinions.

SUNNYVALE, CA--(Marketwired - May 13, 2016) - Niara, a provider of security analytics for attack detection and incident response, today announced that it has been named a Cool Vendor in the 2016 Cool Vendors in UEBA, Fraud Detection and User Authentication1 report by Gartner, Inc. Key findings of the report show "technologies that respond to user behavior are increasingly prevalent in security markets, although clients must consider how to integrate products with existing technologies to identity threats and suspicious behaviors both within and outside the organization." Additionally, Chief Information Security Officers (CISOs) and other security leaders should investigate UEBA technology to accelerate risk identification and escalation. "We believe being recognized as a Gartner Cool Vendor further validates that Niara's advanced user and entity behavioral analytics approach is becoming a priority for organizations looking to accelerate attack detection and response," said Sriram Ramachandran, CEO and co-founder of Niara. "Niara's analytics modules detect attacks and risky behaviors inside organizations, and empower security teams with advanced incident investigation and threat hunting capabilities." Niara's security analytics platform leverages data from any data source within the network and security infrastructure, applying unsupervised and supervised machine learning algorithms, statistical modeling and other analytics techniques to unearth security events that get past traditional solutions.