Google had some big announcements to make at its annual developers conference. Here's a quick rundown of what the firm unveiled onstage. Google Home: The company introduced a wireless speaker and smart appliance hub called Google Home, which will be released later this year. The product will be able to stream audio and video like Google's Chromecast devices, as well as control smart appliances. It will also work with smartphones -- you can tell Google Home to change your dinner reservations, and the device will be able to adjust your schedule accordingly.

Those two words are recognized worldwide as a way for any internet user to find an answer to nearly any question. But until now, it's mostly been about typing into a search bar. Now, Google wants you to speak up and chat with your mobile phone and other devices within your home, all powered by its own technology. The Mountain View, California, company revealed two projects, Google Assistant and Google Home, at its annual developer conference, Google I/O, in San Francisco Wednesday. Google Assistant is the company's effort to make search more personalized and efficient for users through machine learning and artificial intelligence.

Google has launched a so-called smart messaging application named Allo that features its new Google Assistant. With the aid of artificial intelligence, the app will learn how users speak and eventually generate automatic replies for them based on their previous conversations, the Alphabet unit said at its Google I/O 2016 developers conference Wednesday. Pronounced "Aloe," the product is Google's latest attempt to gain a foothold in the huge messaging app market, where it has so far failed to make an impact against market leaders WhatsApp, Snapchat and Facebook's Messenger. Allo will be based on users' phone numbers rather than their Google accounts (although users will be able to connect that way in the event they desire to do so). It includes all the features expected in a messaging app, including customer stickers that Google commissioned specifically for Allo.

SAN FRANCISCO – Google is expected to dive deeper into virtual reality and artificial intelligence Wednesday during an annual conference that serves as a launching pad for its latest products and innovations. Although Google keeps its plans under wraps until the big event, the conference agenda makes it clear that virtual reality and artificial intelligence, or "machine learning," will be among the focal points. That has spurred speculation that Google is getting ready to release a virtual-reality device to compete with Facebook's new Oculus Rift headset, as well as the Samsung's Gear VR and the Vive from HTC and Valve. Reporters and bloggers from around the world will attend, ensuring that whatever the company unveils will also be featured in stories, pictures and video delivered to a vast audience of consumers. The three-day showcase also attracts thousands of computer programmers, giving Google an opportunity to convince them why they should design applications and other services that work with its gadgets and an array of software that includes the Chrome Web browser and Android operating system for mobile devices.

Australian physicists have used an online optimization process based on machine learning to produce effective Bose-Einstein condensates (BECs) in a fraction of the time it would normally take the researchers. A BEC is a state of matter of a dilute gas of atoms trapped in a laser beam and cooled to temperatures just above absolute zero. BECs are extremely sensitive to external disturbances, which makes them ideal for research into quantum phenomena or for making very precise measurements such as tiny changes in the Earth's magnetic field or gravity. The experiment, developed by physicists from ANU, University of Adelaide and UNSW ADFA, demonstrated that "machine-learning online optimization" can discover optimized condensation methods "with less experiments than a competing optimization method and provide insight into which parameters are important in achieving condensation," the physicists explain in an open-access paper in the Nature group journal Scientific Reports. Optical dipole trap used in the experiment, showing the three laser beams and the condensate (red-yellow oval in blue square) (credit: P. B. Wigley et al./Scientific Reports) The team cooled the gas to around 5 microkelvin.

With rapid growth of medical informatics technology, a large number of electronic health records (EHRs) have been available in recent years, including a huge mass of data, such as clinical narratives. They have been being used not only to support computerized clinical systems (e.g., computerized clinical decision support systems [1][2]), but also to help the development of clinical and translational research [3]. One of the challenges to use them is that much information is embedded in clinical notes, but cannot be directly accessible for computerized clinical systems which rely on structured information. Therefore, natural language processing (NLP) technologies, which can extract structured information from narrative text, have received great attention in medical domain [4], and many clinical NLP systems have been developed for different applications [5]. Clinical concept recognition (CCR) as a fundamental task of clinical NLP has also attracted great attention, and a large number of systems have been developed to recognize clinical concepts from various types of clinical notes in last two decades.

Google would like to remind you that you can talk with it. Today Google is announcing a "Google Assistant" that essentially performs the same tasks as other Google interfaces do, but in a conversational mode. It doesn't have a name, it just has the power of Google and its deep mine of data behind it. In the past few years, we've seen every other big tech company launch a personal assistant: Apple's Siri. Facebook's M. All are already iconic assistants with distinct personalities -- or at least with the distinct sense that they have personalities. That's mainly because each has a name and a personified intelligence that you imagine you relate to.

Physicists may soon join the ranks of the unemployed due to artificial intelligence (AI). Australian physicists have created an AI that can run and even improve a complex physics experiment with little oversight. They've automated an experiment creating an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate. Bose-Einstein condensates are some of the coldest objects in the Universe, far colder than outer space--typically less than a billionth of a degree above absolute zero. They are extremely sensitive to external disturbances, which allows them to make very precise measurements such as tiny changes in the Earth's magnetic field or gravity.

Editor's note: This is an excerpt from the upcoming report Data Science for Modern Manufacturing: Global Trends: Big Data Analytics for the Industrial Internet of Things, by Li Ping Chu. Sign up to be notified when the report becomes available. The field of artificial intelligence has been around for decades, and the world has seen massive advances in what is considered deep learning (e.g. IBM's Deep Blue and Google's AlphaGo), but it's only within the past decade that we've seen practical applications of machine learning in an enterprise setting. In the past few years, there has been an explosion in the number of products available that integrate machine learning within a business intelligence platform.

They find that we split the task into a hierarchy of different jobs, with different elements apparently handled in different parts of the brain. Particular parts of the cortex, for example, show greater activity if extra line changes are required; other regions simply become more excited as the overall goal inches closer. It arises from a collaboration between Oxford University, University College London and Google's artificial intelligence firm DeepMind. AI researchers are keen to learn from the brain's ability to plan a complex task by grouping together various actions and outcomes. This type of strategy is much more efficient than rattling through all the possible ramifications of each individual step - such as a simple computer program might do.