Over the last couple of years, Artificial Intelligence (AI) has changed from a technology with potential to an instrument of national importance across the world. The first demonstration of applications of AI has happened in the consumer space, and significant economic value has been created, mainly through targeted advertising by internet giants in the US and China. According to a report by PwC, AI could contribute a whopping $15.7 trillion to global GDP by 20301. However, for India, AI is much more than just a piece of this pie. For India, the real power of AI lies in its transformative potential to address massive societal challenges that were traditionally considered to be beyond the purview of computing.

In June 2018, India's national think-tank, the NITI Aayog, released a discussion paper on the transformative potential of Artificial Intelligence (AI) in India. This paper said the country could add US$1 trillion to its economy through integrating AI. Since then, some of the biggest moves made by the government to act on this prediction is the formation of a task force on Artificial Intelligence for India's Economic Transformation by the Commerce and Industry Department of the Government of India in 2017, and the Union Cabinet in December 2018. These bodies approved an INR3,660 crore national mission on cyber-physical system technologies that involves extensive use of AI, machine learning, deep learning, big data analytics, quantum computing, quantum communication, quantum encryption, data science and predictive analytics. But, what has been the progress in the nation since these ambitious missions were undertaken by the government?

Joint AI Center (JAIC) acting director Nand Mulchandani said one of JAIC's first lethal AI projects is proceeding into a testing phase now. The JAIC was founded in 2018 to act as the Pentagon's leader in all things AI, and initially focused on non-lethal forms. Mulchandani shared few specifics, but called the project "tactical edge AI" that will involve full human control and likened it to JAIC's "flagship product" for joint warfighting operations. "It is true that many of the products we work on will go into weapons systems. None of them right now are going to be autonomous weapon systems, we're still governed by 3000.09," he said.

A cross-disciplinary team of machine learning, security, policy, and law experts say inconsistent court interpretations of an anti-hacking law have a chilling effect on adversarial machine learning security research and cybersecurity. At question is a portion of the Computer Fraud and Abuse Act (CFAA). A ruling to decide how part of the law is interpreted could shape the future of cybersecurity and adversarial machine learning. If the U.S. Supreme Court takes up an appeal case based on CFAA next year, researchers predict that the court will ultimately choose a narrow definition of the clause related to "exceed authorized access" instead of siding with circuit courts who have taken a broad definition of the law. One circuit court ruling on the subject concluded that a broad view would turn millions of people into unsuspecting criminals.

When you ask Siri a question or request a song through Alexa, you're using automated speech recognition software. Companies use AI services to screen job applicants. Court reporters use speech recognition tools to produce records of depositions and trial proceedings. Physicians use software by Nuance and Suki to dictate clinical notes. If you have a physical impairment, you might use speech recognition software to navigate a web browser. YouTube uses it to create automatic captions, whose malaprops inspired a parody series called Caption Fail.

Facebook in January agreed to a historic $550 million settlement over its face-identifying technology. But now, the federal judge overseeing the case is refusing the accept the deal. Facebook in January agreed to a historic $550 million settlement over its face-identifying technology. But now, the federal judge overseeing the case is refusing the accept the deal. Next week, lawyers for Facebook will be back in court, trying to convince a judge they should be allowed to settle a class action suit that accuses the company of violating users' privacy.

A British chip startup has launched what it claims is the world's most complex AI chip, the Colossus MK2 or GC200 IPU (intelligence processing unit). The MK2 and its predecessor MK1 are designed specifically to handle very large machine-learning models. The MK2 processor has 1,472 independent processor cores and 8,832 separate parallel threads, all supported by 900MB of in-processor RAM. Graphcore says the MK2 offers a 9.3-fold improvement in BERT-Large training performance over the MK1, a 8.5-fold improvement in BERT-3Layer inference performance, and a 7.4-fold improvement in EfficientNet-B3 training performance. BERT, or Bidirectional Encoder Representations from Transformers, is a technique for natural language processing pre-training developed by Google for natural language-based searches.

Advances in hardware technology have facilitated more integration of sophisticated software toward augmenting the development of Unmanned Vehicles (UVs) and mitigating constraints for onboard intelligence. As a result, UVs can operate in complex missions where continuous trans-formation in environmental condition calls for a higher level of situational responsiveness and autonomous decision making. This book is a research monograph that aims to provide a comprehensive survey of UVs autonomy and its related properties in internal and external situation awareness to-ward robust mission planning in severe conditions. An advance level of intelligence is essential to minimize the reliance on the human supervisor, which is a main concept of autonomy. A self-controlled system needs a robust mission management strategy to push the boundaries towards autonomous structures, and the UV should be aware of its internal state and capabilities to assess whether current mission goal is achievable or find an alternative solution. In this book, the AUVs will become the major case study thread but other cases/types of vehicle will also be considered. In-deed the research monograph, the review chapters and the new approaches we have developed would be appropriate for use as a reference in upper years or postgraduate degrees for its coverage of literature and algorithms relating to Robot/Vehicle planning, tasking, routing, and trust.

The unprecedented progress in Artificial Intelligence (AI) [1-6], over the last decade, came alongside of multiple AI failures [7, 8] and cases of dual use [9] causing a realization [10] that it is not sufficient to create highly capable machines, but that it is even more important to make sure that intelligent machines are beneficial [11] for the humanity. This lead to the birth of the new subfield of research commonly known as AI Safety and Security [12] with hundreds of papers and books published annually on different aspects of the problem [13-31]. All such research is done under the assumption that the problem of controlling highly capable intelligent machines is solvable, which has not been established by any rigorous means. However, it is a standard practice in computer science to first show that a problem doesn't belong to a class of unsolvable problems [32, 33] before investing resources into trying to solve it or deciding what approaches to try. Unfortunately, to the best of our knowledge no mathematical proof or even rigorous argumentation has been published demonstrating that the AI control problem may be solvable, even in principle, much less in practice. Or as Gans puts it citing Bostrom: "Thusfar, AI researchers and philosophers have not been able to come up with methods of control that would ensure [bad] outcomes did not take place …" [34].

Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general. Spatial relations between objects can either be explicit -- expressed as spatial prepositions, or implicit -- expressed by spatial verbs such as moving, walking, shifting, etc. Both these, but implicit relations in particular, require significant common sense understanding. In this paper, we introduce the task of inferring implicit and explicit spatial relations between two entities in an image. We design a model that uses both textual and visual information to predict the spatial relations, making use of both positional and size information of objects and image embeddings. We contrast our spatial model with powerful language models and show how our modeling complements the power of these, improving prediction accuracy and coverage and facilitates dealing with unseen subjects, objects and relations.