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.

SoftBank Robotics Corp. ("SoftBank Robotics") and SoftBank Corp. ("SoftBank") today announced that its humanoid robot'Pepper' will support Google's Android, and that presales of models for developers will begin from July 2016. Ahead of the presales launch, starting today SoftBank Robotics will offer a beta version of'Pepper SDK for Android Studio', a software development kit that enables the development of RoboApps on the Android platform. By making Pepper compatible with Android, Android application developers will be able to utilize their existing knowledge and technologies to develop RoboApps for Pepper. With the high number of Android developers around the world, the possibilities for Pepper RoboApp developers will greatly increase with Android support. SoftBank Robotics will continue to provide its SDK, 'Choregraphe'.

Google this week hosted its annual I/O developers conference, announcing new products that highlight the tech giant's focus on artificial intelligence. Google Translate Product Lead Barak Turovsky lent some content in a post to the company's corporate blog: "The assistant is conversational – an ongoing two-way dialogue between you and Google that understands your world and helps you get things done. It makes it easy to buy movie tickets while on the go, to find that perfect restaurant for your family to grab a quick bite before the movie starts, and then help you navigate to the theater. Turovsky said the company views the current technological landscape as at a "seminal moment. Many of these advances have been thanks to machine learning and artificial intelligence – specifically, areas like natural language processing, voice recognition and translation – and they have helped us build an increasingly useful and assistive experience for users.

Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google IO developer conference highlighted progress in key areas such as Android and especially Artificial Intelligence (AI). AI is going main stream, becoming an essential part of platform operating systems and cloud services. As the creator of AlphaGo, the first AI to defeat a human Go player, Google is arguably out in front in AI services. Leveraging AI to confer competitive advantage for its various platforms is the centerpiece of Google's business strategy. My title for this article was meant to be more tongue-in-cheek than alarmist.