Computerized face recognition is seen by many analysts as the optimal means to prevent unauthorized access to computer systems. Facial recognition also has other applications, like improving social networks and the curating of photographs for news media. To be efficient systems need to enable a computer to estimate with precision a person's age based on the analysis of their face. The new advancement comes from the Department of Electronics and Telecommunication Engineering, at the Shri Guru Gobind Singhji Institute of Engineering and Technology, in Vishnupuri, Nanded, India. The researchers suggest that age classification will add a tighter aspect to security systems and surveillance.

Term artificial intelligence appeared in 1956 on one of the seminars at Stanford University. It was dedicated to developing logic, not the computing tasks. In the middle of 80-x in Japan within the framework of the project developing computer of the fifth generation, was made a computer of the sixth generation or it was called neurocomputer. While modeling human's brains they connecting a computer with each other via fast cable and it made neuro-network. These days computer's intellect is really very powerful, but it is not even enough to copy the behavior of primitive animals.

Circa 2009: The world economy had not started showing signs of recovery after the Great Recession. Even as most global businesses languished in distress, making capital expenditure a dream, there was a silver lining that nobody noticed. The world was gradually waking up to the power of automation across industries. Even in such a bleak economic scenario, the global sales of robotic automation solutions showed an upward trajectory in 2009, an International Federation of Robotics study suggests, and has grown a whopping 400% since. Robotics has redefined industries such as manufacturing, e-commerce, logistics, retail, healthcare, hospitality, etc, to grow in scale, optimise costs, increase accuracy and enhance the overall experience for the end user.

Alphabet Inc.'s GOOGL 0.86% Nest Labs in September announced a doorbell equipped with a Qualcomm Inc. QCOM 0.27% chip, a video camera and facial-recognition software that can send an alert to a Nest mobile app if it sees a familiar face. The market for computer-vision systems is nascent, poised to expand from roughly $1 billion last year to $2.6 billion in 2021, according to International Data Corp. Emerging products such as autonomous vehicles and personal robots portend continuing growth, and Intel Corp. INTC 0.25%, Qualcomm and other chip makers are jockeying to supply the brains to new machines. "These [applications] are edging into viability," said IDC analyst Michael Palma. "Maybe not mass viability, but very, very close."

History doesn't exactly repeat itself, but it does run in cycles. One of the most robust theories of such cycles was articulated by economic historian Carlota Perez, in her influential book Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages (Edward Elgar, 2002). It suggests that humanity can get through the current period of upheaval and economic malaise and enter a new "golden age" of broad economic growth, if the world's key decision makers act in concert to help foster one. This may seem far-fetched, but it's happened four times before. We are in the midst of the fifth great surge (as Perez calls them) of technological and economic change since the Industrial Revolution. The last one, the age of oil, automobiles, and mass production, lasted most of the 20th century and still shapes many people's attitudes. Our current surge started around 1970 and has rolled out information and communications technology around the world: It is the age of the computer and the Internet (see Exhibit 1). Each of these surges follows the same broad pattern. First, there is a wave of major new technologies, leading to dramatic changes in industrial production and daily life. For about 20 to 30 years, in a period that Perez calls installation, these technologies are funded largely by speculative investment chasing rapid returns. This age of widening wealth disparity leads to a bubble, which bursts in spectacular fashion, and is followed by a crisis period that Perez calls the turning point. This phase of economic and social turbulence has varied in length from two years to 17. Many efforts to get back to normal are made, usually involving the regulation of financial excesses or the stimulation of production and employment.

Anthony Levandowski has set up a religious nonprofit organization called Way of the Future and devoted to the worship of artificial intelligence (AI). "Our mission is to develop and promote the realization of a Godhead based on artificial intelligence and through understanding and worship of the Godhead contribute to the betterment of society," the organization's founding documents say. The new god is seen by US media as an almighty bot that will cater to all of its adherents' wishes. What will make it different from biblical God, however, is that it will be genuinely kind and will not punish mortals. The Transhumanist Christians, who established their religion in 2013, espouse the idea that people can and should use science and technology to make the world better. They believe that this fully conforms to the Bible where Jesus says, "Be ye perfect as your Father in heaven is perfect," so this is probably why they so happily jumped on Levandowski's AI bandwagon.

This is the first in a series of posts on applying Tim Ferriss' accelerated learning framework to Data Science. My goal is to become a world-class (top 5%) Data Scientist in 6 months, while open-sourcing everything I find and learn along the way. Here's the story behind the journey and an invitation to follow along: There I was, ten yards out, staring my dinner in the face. The only problem was, the wild boar was still alive. For the past 4 weeks I had been backpacking solo throughout Southeast Asia, and just yesterday had decided to spend the final 2 days of my trip doing jungle survival training in Bario, Malaysia.

Anuj Agrawal, 35, has dabbled in the recruitment industry since 2005. With 100 employees and two offices in Noida and Bengaluru, his firm Zyoin offers recruitment and consultancy services to over 300 companies, including Amazon, Goibibo, Play Games and PayU. But there were some constant niggles. With no universal template around which resumes are written and structured, mining and matching thousands with job positions was a huge task. Available parsing technologies were basic and didn't sort and match well. Also, the resumes in their database would often get dated. Last year, Agrawal got a cold email from Anand Kumar, founder of Bengaluru-based Skillate, an artificial intelligence (AI)-based recruitment solution platform that helps companies read and match resumes.

Development of black-box modeling techniques, like support vector machine (SVM), neural networks, etc., has shown rather rapid in the past decades (Yuan et al., 2016; Zhao et al., 2015; Wu et al., 2013). This sort of techniques, compared to white-box modeling methods (also called mechanism-based modeling or first-principles modeling), works without any need of knowing the internal structure or details on variables interaction in systems considered, so they are suited to describe extremely complex objectives, such as human brain (Khosrowabadi et al., 2014), black hole (Grumiller et al., 2012), integrated industrial processes (Gao et al., 2012) and so on. Essentially, blackbox modeling is an input-output data-based approach, and the model precision mainly depends on data quality, model structure and parameters identification algorithm. In order to develop high-precision black-box models, it always needs reliable and representative data, smart mathematical treatment and efficient identification algorithms. All of these are challenging the development of the black-box modeling techniques.

Oracle OpenWorld -- Oracle today announced new artificial intelligence-based apps for finance, human resources, supply chain, manufacturing, commerce, customer service, marketing, and sales professionals. The new Oracle Adaptive Intelligent Apps are built into the existing Oracle Cloud Applications to deliver the industry's most powerful AI-based modern business applications. "The new Adaptive Intelligent Apps enable business users from across the organizations to quickly and easily take advantage of the latest advancements in artificial intelligence," said Steve Miranda, executive vice president of applications development, Oracle. "To make this possible we have eliminated the need for more integrations and embedded AI capabilities across Oracle Cloud Applications. The new AI capabilities combine first- and third-party data with advanced machine learning and sophisticated decision science to deliver the industry's most powerful AI-based modern business applications." The new Oracle Adaptive Intelligent Apps deliver immediate impact within Oracle Enterprise Resource Planning Cloud, Oracle Human Capital Management Cloud, Oracle Supply Chain Management Cloud, and the Oracle Customer Experience Cloud by providing smart and timely insights to end users.