Humans and animals readily utilize active sensing, or the use of self-motion, to focus sensory and cognitive resources on the behaviorally most relevant stimuli and events in the environment. Understanding the computational basis of natural active sensing is important both for advancing brain sciences and for developing more powerful artificial systems. Recently, a goal-directed, context-sensitive, Bayesian control strategy for active sensing, termed C-DAC (Context-Dependent Active Controller), was proposed (Ahmad & Yu, 2013). In contrast to previously proposed algorithms for human active vision, which tend to optimize abstract statistical objectives and therefore cannot adapt to changing behavioral context or task goals, C-DAC directly minimizes behavioral costs and thus, automatically adapts itself to different task conditions.

Humans and animals readily utilize active sensing, or the use of self-motion, to focus sensory and cognitive resources on the behaviorally most relevant stimuli and events in the environment. Understanding the computational basis of natural active sensing is important both for advancing brain sciences and for developing more powerful artificial systems. Recently, we proposed a goal-directed, context-sensitive, Bayesian control strategy for active sensing, C-DAC (Context-Dependent Active Controller) (Ahmad & Yu, 2013). In contrast to previously proposed algorithms for human active vision, which tend to optimize abstract statistical objectives and therefore cannot adapt to changing behavioral context or task goals, C-DAC directly minimizes behavioral costs and thus, automatically adapts itself to different task conditions.

The semiconductor chip manufacturing and design work is in full swing with large players working on building process nodes as low as 3nm. But, there is a limit to how many transistors can be infused on a single chip. Even with the introduction of multi-core processors, in which multiple single-core processors could be attached to increase power, concerns over whether this is enough to sustain in the long run looms. At this point, it seems as though we have reached saturation levels, and chants of Moore's law--which states that every 12-18 months, the processing power doubles--slowing down or nearing an end have been restored. However, a new ray of light--the cloud--has been powering Moore's law and will continue to do so at least for the next decade or two, propelling the most cutting-edge innovation.

Humans and animals readily utilize active sensing, or the use of self-motion, to focus sensory and cognitive resources on the behaviorally most relevant stimuli and events in the environment. Understanding the computational basis of natural active sensing is important both for advancing brain sciences and for developing more powerful artificial systems. Recently, a goal-directed, context-sensitive, Bayesian control strategy for active sensing, termed C-DAC (Context-Dependent Active Controller), was proposed (Ahmad & Yu, 2013). In contrast to previously proposed algorithms for human active vision, which tend to optimize abstract statistical objectives and therefore cannot adapt to changing behavioral context or task goals, C-DAC directly minimizes behavioral costs and thus, automatically adapts itself to different task conditions. Here, we propose a myopic approximation to C-DAC, which also takes behavioral costs into account, but achieves a significant reduction in complexity by looking only one step ahead.

C-DAC and Atos have signed up for a partnership on the technology advancement in the areas of Artificial Intelligence, Exascale Computing, and Quantum Computing. Atos is a global leader in the digital transformation with around 12000 employees in more than 70 countries. The company offers end-to-end Orchestrated Hybrid Cloud, Big Data, Business Applications, and Digital Workplace solutions. C-DAC which developed India first supercomputer which is known by the name as PARAM will work together with the Atos in a long-term program on exascale computing. According to the announcement, Atos and the said collaboration has delivered the world's highest-performing commercially available quantum simulator called Atos' Quantum Learning Machine.

C-DAC (Centre for Development of Advanced Computing), a national premier R&D organization under Ministry of Electronics and Information Technology, Government of India, and Atos1, a global leader in digital transformation, today announce that they have signed a Cooperation Agreement for technology advancement in the areas of Quantum Computing, Artificial Intelligence and Exascale Computing. Dr Hemant Darbari, Founder Member and Director General, C-DAC, India is spearheading the C-DAC Mission Mode Programs on Exascale Computing, Microprocessor and Quantum Computing, Artificial Intelligence and Natural Language Computing of national importance. In addition to delivering an Atos' Quantum Learning Machine, the world's highest-performing commercially available quantum simulator, this partnership encompasses the creation of a'Quantum Computing Experience Center' at C-DAC's headquarters in Pune. It aims to bring together users from academic, scientific, research and industry to rapidly acquire skills and develop further expertise in the field of quantum computing with the support from the Government of India. This center will enable advance study of applications of quantum theory, thereby creating new technologies and platforms for information security, connectivity and computing.

C-DAC (Centre for Development of Advanced Computing), a national premier R&D organization under Ministry of Electronics and Information Technology, Government of India, and Atos1, a global leader in digital transformation, today announce that they have signed a Cooperation Agreement for technology advancement in the areas of Quantum Computing, Artificial Intelligence and Exascale Computing. Dr Hemant Darbari, Founder Member and Director General, C-DAC, India is spearheading the C-DAC Mission Mode Programs on Exascale Computing, Microprocessor and Quantum Computing, Artificial Intelligence and Natural Language Computing of national importance. In addition to delivering an Atos' Quantum Learning Machine, the world's highest-performing commercially available quantum simulator, this partnership encompasses the creation of a'Quantum Computing Experience Center' at C-DAC's headquarters in Pune. It aims to bring together users from academic, scientific, research and industry to rapidly acquire skills and develop further expertise in the field of quantum computing with the support from the Government of India. This center will enable advance study of applications of quantum theory, thereby creating new technologies and platforms for information security, connectivity and computing.

Central government of India is planning to launch a National Centre for Artificial Intelligence (AI) and make it operational by July as the work has begun for a launch, said a report by Business Standard. Called as the National Artificial Intelligence Centre, the unit is likely to cost around Rs 400-450 crore and will be part of the Ministry of Electronics and Information Technology (MeitY), and will work in collaboration with other entities of the department such as the National Informatics Centre (NIC) and the Centre for Development of Advanced Computing (C-DAC), said the report citing senior government officials. The report said the AI unit will be for citizens and will help in setting up data platform, skilling, reskilling and research platforms, thereby helping to solve legal, regulatory and cybersecurity challenges. "The panels gave us a lot of fodder to think on, and suggested setting up of a national AI centre," a senior government official told Business Standard. "The centre will try to innovate in AI for government applications. It will look at how AI can be used in health care, education, and agriculture from a public systems delivery perspective."

Humans and animals readily utilize active sensing, or the use of self-motion, to focus sensory and cognitive resources on the behaviorally most relevant stimuli and events in the environment. Understanding the computational basis of natural active sensing is important both for advancing brain sciences and for developing more powerful artificial systems. Recently, a goal-directed, context-sensitive, Bayesian control strategy for active sensing, termed C-DAC (Context-Dependent Active Controller), was proposed (Ahmad & Yu, 2013). In contrast to previously proposed algorithms for human active vision, which tend to optimize abstract statistical objectives and therefore cannot adapt to changing behavioral context or task goals, C-DAC directly minimizes behavioral costs and thus, automatically adapts itself to different task conditions. However, C-DAC is limited as a model of human active sensing, given its computational/representational requirements, especially for more complex, real-world situations. Here, we propose a myopic approximation to C-DAC, which also takes behavioral costs into account, but achieves a significant reduction in complexity by looking only one step ahead. We also present data from a human active visual search experiment, and compare the performance of the various models against human behavior. We find that C-DAC and its myopic variant both achieve better fit to human data than Infomax (Butko & Movellan, 2010), which maximizes expected cumulative future information gain. In summary, this work provides novel experimental results that differentiate theoretical models for human active sensing, as well as a novel active sensing algorithm that retains the context-sensitivity of the optimal controller while achieving significant computational savings.