How does Artificial Neural Network (ANN) algorithm work?

In the largest survey of its kind so far, researchers have looked at the "DNA" of over 340,000 stars in our home galaxy, to identify the stars with the same chemical composition as the sun. Stars with the same chemistry originated in the same star clusters, and the survey would help astronomers find siblings of the sun before the Milky Way tore apart the stellar nursery where they all formed. The research is led by astronomers from Australia, supported by colleagues from Europe, and is called the Galactic Archaeology survey, or GALAH. It "is a large-scale stellar spectroscopic survey of the Milky Way and designed to deliver chemical information" about a large number of stars. The announcement Tuesday about 342,682 stars was the first major public release of data by GALAH, which will eventually look at over one million stars.

Which statement do you believe? Robots will wipe out our jobs. AI and robotics will make everything free. These extreme viewpoints are both vying for our attention. Singularity University, which aims to solve our global grand challenges through exponential technologies, widely reports that AI is the world's cure.

The Bharat Bandh on April 2 called by Dalit groups was a result of the successful use of artificial intelligence (AI), the geographic information system (GIS) and big data, according to a core group of US-based Dalit activists. The protest, which surprised many because of its sudden eruption, was organised against the alleged dilution of the SC/ST (Prevention of Atrocities) Act. New Jersey-based Deelip Mhaske, who heads a secretive network of some 100 overseas Dalit activists based in the US, the UK, Australia and the Middle East said the protest shows that Dalits no longer need to be affiliated to any political establishment. "A new era for Dalit politics has begun by adopting artificial intelligence, the GIS and social media," Mhaske told PTI in an interview as he gave an insight into how Dalit groups were mobilised during the protest. Most of these Dalit activists in the secretive group are from the IT sector and work in some of the top info-tech companies.

A good number of us shout at our laptops when they misbehave, often to no avail. Perhaps soon they will listen. Could we one day teach them--much like we do children or pets--how to behave? For the majority of human history, we have survived and flourished based on our ability to learn. Today's machines learn--Siri perks up at the sound of your voice, traffic lights react to the flux of cars--but only in limited ways.

Very recently proximal policy optimization (PPO) algorithms have been proposed as first-order optimization methods for effective reinforcement learning. While PPO is inspired by the same learning theory that justifies trust region policy optimization (TRPO), PPO substantially simplifies algorithm design and improves data efficiency by performing multiple epochs of \emph{clipped policy optimization} from sampled data. Although clipping in PPO stands for an important new mechanism for efficient and reliable policy update, it may fail to adaptively improve learning performance in accordance with the importance of each sampled state. To address this issue, a new surrogate learning objective featuring an adaptive clipping mechanism is proposed in this paper, enabling us to develop a new algorithm, known as PPO-$\lambda$. PPO-$\lambda$ optimizes policies repeatedly based on a theoretical target for adaptive policy improvement. Meanwhile, destructively large policy update can be effectively prevented through both clipping and adaptive control of a hyperparameter $\lambda$ in PPO-$\lambda$, ensuring high learning reliability. PPO-$\lambda$ enjoys the same simple and efficient design as PPO. Empirically on several Atari game playing tasks and benchmark control tasks, PPO-$\lambda$ also achieved clearly better performance than PPO.

Traditional programs take data as input and produces data as output. However, a machine learning algorithm takes data as input but produces a program as an output. This machine generated program can now take new data, process it and produce output data. In simple words, it gives computers the capability to extract knowledge from data and store it for future judgment. Machine learning is the subfield of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed".

AI will augment people's capabilities but won't take all jobs. However, there will be socioeconomic upheaval, warns PwC's authority on AI, Anand Rao. Anand Rao is PwC global leader for artificial intelligence (AI) and is the consulting giant's innovation lead for the US analytics practice. Rao has 24 years of industry and consulting experience, helping senior executives to structure, solve and manage critical issues facing their organisations. He has worked extensively on business, technology and analytics issues across a wide range of industry sectors including financial services, healthcare, telecommunications, aerospace and defence, across US, Europe, Asia and Australia.

The swarm consists of SwarmDivers, small rocket-shaped vehicles, that can work cooperatively to carry out impossible feats for single robot submarines, as shown in a new video. The SwarmDiver drones were unveiled last week by Aquabotix, a firm based in Australia. While many autonomous underwater vehicles are already in use, Aquabotix says their SwarmDivers can work as a group, with one operator controlling the entire swarm via a simple interface.

We are surrounded by machine learning, it is "under the hood" of systems we use everyday, says Olivier Klein, head of emerging technologies for Asia Pacific at Amazon Web Services. These systems can range in the spam filter for emails, to the facial recognition systems used at airports. "Cloud is normal, machine learning is the new normal," says Klein. Klein says machine learning helps organisations create new and seamless experiences for customers, or citizens, in the case of government agencies. This can be through the use of a natural interface such as voice or facial recognition to create a frictionless experience for users.