For a Self-reliant India, to fulfil demand of highly skilled Artificial Intelligence and Cyber Security professionals in the country, Defence Institute of Advanced Technology, DIAT, Pune is conducting the nationwide Online Training and Certification Courses (OTCC) in Cyber Security, Artificial Intelligence & Machine Learning(AI & ML). The School of Computer Engineering and Mathematical Sciences of DIAT has completed two batches of these courses and more than 1600 candidates are successfully trained and certified. The 3rd batch of AI & ML course is on-going. Now DIAT is launching next batches of 16-weeks Online Course on Cyber Security, and 12-weeks Online Course on Artificial Intelligence & Machine Learning (AI & ML)in December 2022. The Graduating students, professionals, or any graduate person can apply for these courses.

At the end of each week, you'll reflect on your learning and plot. Next steps to apply what you've learned in your professional practice. This is an important part of the course that we hope you'll use as a roadmap to better manage your team's data science projects. Now let's talk about what this course is all about. The aim of the course is to equip executives with the knowledge that will enable them to work productively with data scientists.

Minerva CQ, a startup based in the San Francisco Bay Area, is making customer service calls quicker and more efficient for both agents and customers, with a focus on those in the energy sector. The NVIDIA Inception member's name is a mashup of the Roman goddess of wisdom and knowledge -- and collaborative intelligence (CQ), or the combination of human and artificial intelligence. The Minerva CQ platform coaches contact-center agents to drive customer conversations -- whether in voice or web-based chat -- toward the most effective resolutions by offering real-time dialogue suggestions, sentiment analysis and optimal journey flows based on the customer's intent. It also surfaces relevant context, articles, forms and more. Powered by the NVIDIA Riva software development kit, Minerva CQ has best-in-class automatic speech recognition (ASR) capabilities in English, Spanish and Italian.

Google is working on a secretive project that uses machine learning to train code to write, fix, and update itself. This project is part of a broader push by Google into so-called generative artificial intelligence, which uses algorithms to create images, videos, code, and more. It could have profound implications for the company's future and developers who write code. The project, which began life inside Alphabet's X research unit and was codenamed Pitchfork, moved into Google's Labs group this summer, according to people familiar with the matter. By moving into Google, it signaled its increased importance to leaders.

Another advantage of unsupervised learning is that the candidates can complete the entire course on their own in India. There is no teacher supervision involved. However, the downside is that no assistance is provided by the instructor in case of an accident during training. If the candidate has any query regarding anything then they should directly ask their instructor. For those who are interested in doing unsupervised machine learning courses in India, they need to do a little research in this field.

Banana Pi BPI-M6 is the next generation single board computer from Banana Pi in 2022,It is powered by Senary(Synaptics) VS680 quad-core Cortex-A73 (2.1GHz) and One Cortex-M3 processor,Imagination GE9920 GPU.and NPU Up to 6 .75Tops. Onboard 4GB LPDDR4 memory and 16GB EMMC storage, and supports 4 USB 3.0 interface, a gigabit network port.onboard 1 HDMI-rx port and 1 Hdmi-tx port. VideoSmart VS680 solution, an industry-first edge computing SoC that combines a CPU, NPU, and GPU. This new multimodal platform with integrated neural network accelerator is purpose built with perceptive intelligence for applications including smart displays, smart cameras, set-top-boxes and media streamers.The Synaptics VideoSmart VS680 is a multimedia powerhouse that combines a Qdeo 4K-video engine, an audio processor capable of far-field keyword detection and voice recognition, and a proprietary SyNap deep-learning accelerator (DLA). Another new feature is an ISP with HDR capabilities that can handle two 4K cameras. Previous VideoSmart products target the streaming-video set-top-box (STB) market, but the VS680 aims for a broader range of smart-home devices.

We introduce a physiological model-based agent as proof-of-principle that it is possible to define a flexible self-preserving system that does not use a reward signal or reward-maximization as an objective. We achieve this by introducing the Self-Preserving Agent (SPA) with a physiological structure where the system can get trapped in an absorbing state if the agent does not solve and execute goal-directed polices. Our agent is defined using new class of Bellman equations called Operator Bellman Equations (OBEs), for encoding jointly non-stationary non-Markovian tasks formalized as a Temporal Goal Markov Decision Process (TGMDP). OBEs produce optimal goal-conditioned spatiotemporal transition operators that map an initial state-time to the final state-times of a policy used to complete a goal, and can also be used to forecast future states in multiple dynamic physiological state-spaces. SPA is equipped with an intrinsic motivation function called the valence function, which quantifies the changes in empowerment (the channel capacity of a transition operator) after following a policy. Because empowerment is a function of a transition operator, there is a natural synergism between empowerment and OBEs: the OBEs create hierarchical transition operators, and the valence function can evaluate hierarchical empowerment change defined on these operators. The valence function can then be used for goal selection, wherein the agent chooses a policy sequence that realizes goal states which produce maximum empowerment gain. In doing so, the agent will seek freedom and avoid internal death-states that undermine its ability to control both external and internal states in the future, thereby exhibiting the capacity of predictive and anticipatory self-preservation. We also compare SPA to Multi-objective RL, and discuss its capacity for symbolic reasoning and life-long learning.

Curriculum Learning (CL) is a technique of training models via ranking examples in a typically increasing difficulty trend with the aim of accelerating convergence and improving generalisability. Current approaches for Natural Language Understanding (NLU) tasks use CL to improve in-distribution data performance often via heuristic-oriented or task-agnostic difficulties. In this work, instead, we employ CL for NLU by taking advantage of training dynamics as difficulty metrics, i.e., statistics that measure the behavior of the model at hand on specific task-data instances during training and propose modifications of existing CL schedulers based on these statistics. Differently from existing works, we focus on evaluating models on in-distribution (ID), out-of-distribution (OOD) as well as zero-shot (ZS) cross-lingual transfer datasets. We show across several NLU tasks that CL with training dynamics can result in better performance mostly on zero-shot cross-lingual transfer and OOD settings with improvements up by 8.5% in certain cases. Overall, experiments indicate that training dynamics can lead to better performing models with smoother training compared to other difficulty metrics while being 20% faster on average. In addition, through analysis we shed light on the correlations of task-specific versus task-agnostic metrics.

Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems. To address the catastrophic forgetting issue of LL, generative replay methods are widely employed to consolidate past knowledge with generated pseudo samples. However, most existing generative replay methods use only a single task-specific token to control their models. This scheme is usually not strong enough to constrain the generative model due to insufficient information involved. In this paper, we propose a novel method, prompt conditioned VAE for lifelong learning (PCLL), to enhance generative replay by incorporating tasks' statistics. PCLL captures task-specific distributions with a conditional variational autoencoder, conditioned on natural language prompts to guide the pseudo-sample generation. Moreover, it leverages a distillation process to further consolidate past knowledge by alleviating the noise in pseudo samples. Experiments on natural language understanding tasks of ToD systems demonstrate that PCLL significantly outperforms competitive baselines in building LL models.

With its large-scale growth, the ways to quickly find existing research on relevant issues have become an urgent demand for researchers. Previous studies, such as SciBERT, have shown that pre-training using domain-specific texts can improve the performance of natural language processing tasks. However, the pre-trained language model for social sciences is not available so far. In light of this, the present research proposes a pre-trained model based on the abstracts published in the Social Science Citation Index (SSCI) journals.