SoftBank will still use its Naoqi operating system to control Pepper's hardware -- Android will only run on its chest-strapped tablet. The company isn't saying what sort of business deal it's struck, but Google typically takes a 30 percent cut from Android app revenues. We also don't know how much the robot will be able to take advantage of Android's features. The recently announced Google Assistant AI could actually be useful in a humanoid robot that can move on its own. SoftBank is still trying to figure out a purpose for Pepper, hence the need for more developer interest.

Flapping two tiny wings, the small, thin robot wobbles its way towards the underside of a leaf, bumps into the surface and latches on, perching motionless above the ground. Moments later, its wings begin to flap once more and it jiggles off on its way. The little flying machine, dubbed a "RoboBee", has been designed to perch on a host of different surfaces, opening up new possibilities for the use of drones in providing a bird's-eye view of the world, scientists say. Know as micro aerial vehicles, such robots could be invaluable in reconnaissance of disaster zones or to form impromptu communication networks. But there is a hitch: flying takes energy, so the time these robots can spend in the air is limited by the size of the battery pack they can carry.

This repo contains a curated list of R tutorials and packages for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks.

During today's Google I/O 2016 keynote, the company focuses on its efforts in machine learning and AI, to help improve efforts in robotics, medicine and more The machine learning segment was the last part of the keynote, which was presented by Google CEO Sundar Pichai. He stated that the company has open-sourced part of its machine learning platform, TensorFlow, as well as parts of its Cloud Platform APIs. This will allow for developers to access its computer vision APIs and specialized hardware. Google has also started building customized machine learning hardware, called Tensor Processing Units (TPUs). That hardware was used to power AlphaGo, a version of the classic game Go.

The Exponential Organization Executive (ExO) of the future will not just be the CIO, CISO, or VP of IT. ExOs will be business leaders who understand how to take advantage of information in their businesses. They will understand Data (big data) both within and with their cloud providers. They will understand that IP intellectual property of this data can be exposed, developed, segmented, etc. by using APIs, Algorithms, Machine Learning, Deep Learning, and Artificial Intelligence. ExO Executives who "grock" this will apply significant advantage personally and to their businesses. In 2011 Google began a project known simply as Google Brain.

There have been plenty of announcements coming out of Google I/O this week, and yesterday, Google said that one of its projects that was created years ago is helping it accomplish its own custom accelerators for machine learning applications. The result of that project is called a Tensor Processing Unit (TPU), which is a custom ASIC that was built by Google just for machine learning. TPUs run inside Google data centers and have been for more than a year, according to a Google blog post by Norm Jouppi, a hardware engineer at Google. The company found that TPUs deliver better-optimized performance per watt for machine learning. The chip requires fewer transistors per operation, meaning Google can squeeze more operations per second into the silicon, according to Jouppi.

Hi, I am trying to use machine learning techniques for medical image data. The data comes in a special format (Dicom or Nifti). I want to use 3D patches as my training samples. I would like to know what would be the best option if I want for example read every patch in the data set in advance and store all of them in such a way that I can use easily with TensorFlow for example?

There's lots of talk about the effect that FinTech (or, at least the artificial intelligence (AI) and smart machines it's producing) will have on jobs in the financial services industry. Here are a couple of examples. In a recent CFA Institute survey, respondents indicated that automated financial advice tools will have the largest impact on the asset management sector, followed by banking, securities, and insurance. At the recent Milken Institute Global Conference, Daniel Nadler, the chief executive of finance at the analytics firm Kensho, said traditional financial services firms, like J.P. Morgan, will still be here in the long run -- "they'll just be more efficient." He goes on to say "Analysts, young associates, vice presidents -- anyone whose job is moving a column of data from one spreadsheet to another is going to get automated."

A new service called Bark will act as an online watchdog to protect your child from online predators and cyberbullying, as well as looking out for mental health concerns like depression or suicidal thoughts. Using deep learning and GPUs hosted in the Amazon Web Services cloud, Bark scans through social media messages and alerts parents of potential problems by flagging keywords and even analyzing the context around a conversation – it knows when someone is jokingly saying "ugh, I hate you" (for example, because the child just got a new, cool game) versus when someone actually means it. "So much socialization happens through connected devices. As a parent, that raises the question of how you keep them safe in that environment, but still let them explore and harness the power of technology," CEO Brian Bason explains. Since its soft launch a few months ago, Bark has analyzed more than a million messages, and found that 52 percent of its user base had at least one problem identified via its software.

Many years ago, my wife and I went on a trip to Spain. On the way to the airport she realized she had forgotten her Palm Pilot (Remember them?), and she was in despair. Half of my brain is in there! She kept everything in that device: Reservations, addresses, phone numbers. Fast forward to today, where everyone's "half brain" is on their smartphones.