NEW DELHI: There has been a significant increase in the number of searches by job seekers for artificial intelligence (AI) related sectors in India with data scientist profile leading the top slot, says a report. According to data from global job site Indeed, there has been an increase of 179 per cent in the number of searches by job seekers for AI related jobs in India between June 2016 and June 2018. AI-related jobs have seen an increase as companies are increasingly working towards integrating new technology into their core functions, creating new openings for skilled professionals. "While firms across industries are adopting a technology-forward approach, it is crucial that we also prepare the workforce by reskilling and upskilling talent in the requisite capabilities," said Venkata Machavarapu, Head of Engineering, India and Site Director at Indeed India. "Our focus needs to be not only on finding talent with the right skills, but also on equipping existing employees with the required skills to work with AI-powered solutions," Machavarapu added.

Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. By contrast, single-task learning (STL) learns each individual task independently. MTL often leads to better trained models because they can leverage the commonalities among related tasks. However, because MTL algorithms will "transmit" information on different models across different tasks, MTL poses a potential security risk. Specifically, an adversary may participate in the MTL process through a participating task, thereby acquiring the model information for another task. Previously proposed privacy-preserving MTL methods protect data instances rather than models, and some of them may underperform in comparison with STL methods. In this paper, we propose a privacy-preserving MTL framework to prevent the information on each model from leaking to other models based on a perturbation of the covariance matrix of the model matrix, and we study two popular MTL approaches for instantiation, namely, MTL approaches for learning the low-rank and group-sparse patterns of the model matrix. Our methods are built upon tools for differential privacy. Privacy guarantees and utility bounds are provided. Heterogeneous privacy budgets are considered. Our algorithms can be guaranteed not to underperform comparing with STL methods. Experiments demonstrate that our algorithms outperform existing privacy-preserving MTL methods on the proposed model-protection problem.

This paper summarizes the work of building the autonomous system including detection system and path tracking controller for a formula student autonomous racecar. A LIDAR-vision cooperating method of detecting traffic cone which is used as track mark is proposed. Detection algorithm of the racecar also implements a precise and high rate localization method which combines the GPS-INS data and LIDAR odometry. Besides, a track map including the location and color information of the cones is built simultaneously. Finally, the system and vehicle performance on a closed loop track is tested. This paper also briefly introduces the Formula Student Autonomous Competition (FSAC) in 2017.

In the real world, the environment is constantly changing with the input variables under the effect of noise. However, few algorithms were shown to be able to work under those circumstances. Here, Novelty-Organizing Team of Classifiers (NOTC) is applied to the continuous action mountain car as well as two variations of it: a noisy mountain car and an unstable weather mountain car. These problems take respectively noise and change of problem dynamics into account. Moreover, NOTC is compared with NeuroEvolution of Augmenting Topologies (NEAT) in these problems, revealing a trade-off between the approaches. While NOTC achieves the best performance in all of the problems, NEAT needs less trials to converge. It is demonstrated that NOTC achieves better performance because of its division of the input space (creating easier problems). Unfortunately, this division of input space also requires a bit of time to bootstrap.

We present an effective technique for training deep learning agents capable of negotiating on a set of clauses in a contract agreement using a simple communication protocol. We use Multi Agent Reinforcement Learning to train both agents simultaneously as they negotiate with each other in the training environment. We also model selfish and prosocial behavior to varying degrees in these agents. Empirical evidence is provided showing consistency in agent behaviors. We further train a meta agent with a mixture of behaviors by learning an ensemble of different models using reinforcement learning. Finally, to ascertain the deployability of the negotiating agents, we conducted experiments pitting the trained agents against human players. Results demonstrate that the agents are able to hold their own against human players, often emerging as winners in the negotiation. Our experiments demonstrate that the meta agent is able to reasonably emulate human behavior.

The semantic information regulates the expressiveness of a web service. State-of-the-art approaches in web services research have used the semantics of a web service for different purposes, mainly for service discovery, composition, execution etc. In this paper, our main focus is on semantic driven Quality of Service (QoS) aware service composition. Most of the contemporary approaches on service composition have used the semantic information to combine the services appropriately to generate the composition solution. However, in this paper, our intention is to use the semantic information to expedite the service composition algorithm. Here, we present a service composition framework that uses semantic information of a web service to generate different clusters, where the services are semantically related within a cluster. Our final aim is to construct a composition solution using these clusters that can efficiently scale to large service spaces, while ensuring solution quality. Experimental results show the efficiency of our proposed method.

While deep reinforcement learning (DRL) has led to numerous successes in recent years, reproducing these successes can be extremely challenging. One reproducibility challenge particularly relevant to DRL is nondeterminism in the training process, which can substantially affect the results. Motivated by this challenge, we study the positive impacts of deterministic implementations in eliminating nondeterminism in training. To do so, we consider the particular case of the deep Q-learning algorithm, for which we produce a deterministic implementation by identifying and controlling all sources of nondeterminism in the training process. One by one, we then allow individual sources of nondeterminism to affect our otherwise deterministic implementation, and measure the impact of each source on the variance in performance. We find that individual sources of nondeterminism can substantially impact the performance of agent, illustrating the benefits of deterministic implementations. In addition, we also discuss the important role of deterministic implementations in achieving exact replicability of results.

A SPECTRE is haunting workers--the rise of artificial intelligence (AI). The fear is that smart computer programs will eliminate millions of jobs, condemning a generation to minimum-wage drudgery or enforced idleness. There is no need to be so gloomy, say Ken Goldberg of the University of California, Berkeley, and Vinod Kumar, the chief executive of Tata Communications, a unit of India's biggest business house (which stands to profit from the spread of AI). They have produced a report* that is much more optimistic about the outlook for ordinary employees. In many cases, it says, job satisfaction will be enhanced by the elimination of mundane tasks, giving people time to be more creative.

If North Korea's dear leader wakes up tomorrow, takes a crazy pill and decides to lob a nuclear missile at the US mainland, there's a good chance the military will be relying on artificial intelligence to protect us. Reuters reported earlier this summer on the existence of a secretive military effort -- actually, of multiple classified programs in various stages that are all focused on the development of AI-reliant systems to help us anticipate the launch of a missile, as well as to track launchers. Fears about runaway AI notwithstanding, the Pentagon is now apparently taking that kind of an effort and planning to crank it up to 11. The Pentagon's research agency DARPA announced Monday it will be spending $2 billion on AI, the focus of which, according to CNN, will include "creating systems with common sense, contextual awareness and better energy efficiency. Advances could help the government automate security clearances, accredit software systems and make AI systems that explain themselves."

KOLKATA: A US-based startup has started testing the stability of the Kolkata Gate's ring in New Town using artificial intelligence (AI) and internet-of things (IOT) technology that monitors natural vibrations and deflections. Last year, a spherical structure with the Biswa Bangla logo fixed to the Kolkata Gate fell from a height of 40 metres. Since then, the authorities have been looking into all safety aspects of the entire structure, which has a hanging restaurant at the top. Officials said that there is no plan right now to re-attach the globe to the Kolkata Gate. Last week, the startup representatives met senior officials of Hidco.