New Delhi: Sanskrit is not restricted to spiritualism, philosophy, or literature, President Ram Nath Kovind on Saturday said, stressing that experts believe that the language is most appropriate for writing algorithms besides use in machine learning and artificial intelligence. The president made the remarks during his address at the 17th convocation of the Shri Lal Bahadur Shastri Rashtriya Sanskrit Vidyapeetha in New Delhi. "The tradition of Sanskrit language, literature and science has been the most effective chapter in the glorious journey of our intellectual growth. "It is said that India's soul is reflected in Sanskrit language, which is the mother of several languages," he said, according to a press release. Kovind said the most important thing is that proliferation of the knowledge available in Sanskrit is very relevant for the welfare of the world. "It is not that the works in Sanskrit are limited to spiritualism, philosophy, devotion, ritualism or literature.

The course combines elements of teaching, coaching and community. For this reason, the batch sizes are small and selective. I will be working with a small/selective group of people to actively transfer their career to AI through education and my network towards specific outcomes/goals. "Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful. As for myself, it allowed me to go into topics of interests that help me in reshaping my career."

Deep learning is the next big thing. Its favorable results in applications with huge and complex data is remarkable. R programming language is very popular among data miners and statisticians. Deep learning refers to artificial neural networks that are composed of many layers. Deep learning is a powerful set of techniques for finding accurate information from raw data.

The Chinese e-commerce giant will join a raft of other tech firms in designing its own processors tailored to in-house machine-learnings tasks. It's another sign of China's increasing desire to use its own chips. The news: Alibaba announced that it's building a chip called Ali-NPU--for "neural processing unit"--designed to handle AI tasks like image and video analysis. The firm says its performance will be 10 times that of a CPU or GPU performing the same task. The firm also announced that it has acquired a Hangzhou-based CPU designer called C-SKY.

Three of the fastest-growing applications of artificial intelligence (AI) today are a manifestation of patriarchal stereotypes -- the booming sexbots industry, the proliferation of autonomous weapon systems, and the increasing popularity of mostly female-voiced virtual assistants and carers. The machines of tomorrow are likely to be either misogynistic, violent or servile. Sophia, the first robot to be granted citizenship, has called for women's rights in Saudi Arabia and declared her desire to have a child all in the span of one month. Other robots are mere receptacles for abuse. The Guardian in 2017 reported that the sex tech industry, including smart sex toys and virtual-reality porn, is estimated to be worth a whopping $30 billion.

After years of failed attempts, a research team in Singapore has successfully taught a pair of robots to do something that many humans still can't: build an IKEA chair. The wooden Stefan chair is not the world's first piece of AI-assembled flatpack furniture: Robots at MIT built a simple Lack table in 2013. A chair is more complicated. And while a robot can be programmed to do a single assembly-line task efficiently, mastering all of the small tasks that IKEA assembly requires is a bigger challenge. Some of the same things humans struggle with, like fiddling with bags of screws, dowels, and doodads while trying to distinguish the slight variations in shape, are also difficult for robots.

Robots and AI have been the talk of town in recent years. Many organizations, such as Foxconn, Amazon, and Siemens, have taken to deploying industrial robots in the workforce for very specific tasks, such as product packaging, assembly, supply chain operations, and so on, automating the manual work traditionally done by the human workers. A survey conducted by the International Federation of Robotics forecasted that by 2018, up to 1.3 million robots will be in service worldwide, with China accounting for more than one-third of all installed. In addition to industrial robots, many other technology giants also made headway building personal robot assistants that we will likely welcome into our homes in the near future. However, with more robots working and living among people and taking more responsibilities in the business environment, we ought to ask ourselves – are these robots adequately secured?

By Mrinal Sinha, Sapient Consulting The phenomenal growth of cloud-based offerings such as Platform as a service (PaaS), Infrastructure as a service (IaaS) and Software as a service (SaaS) has resulted into bigger competition in the market, with the new addition being Machine Learning as a Service (MLaaS). Machine Learning has emerged as one of the fastest evolving technologies today. One of the most critical factors for Machine Learning implementation is to have huge sets of data and to have machine learning (ML) experts or Data scientists who can identify a pattern in data, hiring whom can be difficult and expensive. Moreover, selecting a machine-learning algorithm is a process of trial and error. It is also a trade-off between specific characteristics of the algorithms, such as speed of training, memory usage, predictive accuracy on new data etc.

By Mrinal Sinha, Sapient Consulting The phenomenal growth of cloud-based offerings such as Platform as a service (PaaS), Infrastructure as a service (IaaS) and Software as a service (SaaS) has resulted into bigger competition in the market, with the new addition being Machine Learning as a Service (MLaaS). Machine Learning has emerged as one of the fastest evolving technologies today. One of the most critical factors for Machine Learning implementation is to have huge sets of data and to have machine learning (ML) experts or Data scientists who can identify a pattern in data, hiring whom can be difficult and expensive. Moreover, selecting a machine-learning algorithm is a process of trial and error. It is also a trade-off between specific characteristics of the algorithms, such as speed of training, memory usage, predictive accuracy on new data etc.

While China's two aircraft carriers, the Liaoning and the nearly completed CV-17, have ski ramps that would likely limit them to vertical take-off and landing (VTOL) drones, the next Chinese carrier, CV-18, will likely have electromagnetic catapults. Those catapults would enable CV-18 and its nuclear-powered successors to launch heavier and faster drones propelled by turbofan engines. It's likely the drones Shi mentions will be sophisticated, heavier versions of today's systems. The Lijian, for example, uses a flying wing body (just like the B-2 bomber and X-47B drone) and has two bomb bays that could accommodate 2 tons of artillery. A carrier variant would have reinforced landing gears and structures to handle the forces involved in catapult launch and assisted recovery. They may also have larger fuel tanks for extended range.