There is a widespread belief among most of the practitioners that Machine Learning (ML) solutions always lead to business improvement. Although ML-based approaches have brought unique capabilities to the businesses, there are some circumstances under which relying on ML solutions might have a negative impact, or even it might not be possible at all. The main objective of this article is to discuss different use cases in which employing ML does not fully address the targeted business problem. This article presents five scenarios and later introduces possible solutions to consider better solutions for each scenario. The most straightforward reason not to use ML solutions is the inadequate quantity of data which hinders training accurate models.

By 2021, digital transformation will add an estimated USD 154 billion to India's GDP, and increase the growth rate by 1 percent annually, according to an IDC study commissioned by Microsoft. The study also predicts that approximately 60 percent of India's GDP will be derived from digital products or services by 2021. With the government's vision of becoming a USD 5 trillion economy by 2024, Amitabh Kant, CEO, NITI Aayog believes technologies like Artificial Intelligence (AI) will propel India to achieve that target and even go beyond. "Our ambition should not just be to become a USD 5 trillion economy. Instead, we should aim to become a USD 10 trillion economy in the long run, growing at 9-10 percent year after year for three decades or more, to be able to lift our young population above the poverty line. All of this is not possible without using a large amount of data, AI and Machine Learning (ML) and bringing disruption in a vast range of areas," Kant said during a fireside chat with Anant Maheshwari, President Microsoft India at the Digital Governance Tech Summit 2019 in New Delhi.

San Francisco is known as a hub of tech innovation, making USF an ideal place to study computer and data science. The location gives students the opportunity to connect professionally with companies everyone knows: Google, Twitter, Facebook – the list goes on. But what opportunities does USF offer students to participate in peer reviewed scholarship, a place where current students and faculty can connect over tech R&D on campus? As of Fall 2018, the answer comes in the form of the weekly MAGICS Lab meetings, a way to gain valuable mentorship and learn about emerging technologies, a place where undergraduate, graduate students, and faculty all have the opportunity to learn, research, and publish together. This group welcomes all skill-levels, from novice to seasoned researchers alike.

With all the excitement and hype about AI that's "just around the corner"--self-driving cars, instant machine translation, etc.--it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you're already using--right now? In the process of navigating to these words on your screen, you almost certainly used AI. You've also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases. We distinguish between AI and machine learning (ML) throughout this article when appropriate. At Emerj, we've developed concrete definitions of both artificial intelligence and machine learning based on a panel of expert feedback. To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI.

Link: The Complete Python 3 Course: Beginner to Advanced his course is designed to fully immerse you in the Python language, so it is great for both beginners and veteran programmers! This diploma in C and Python programming course is a great way to get started in programming. It covers the study of the C and Python group of languages used to build most of the world's object oriented systems. The course is for interested students with a good level of computer literacy who wish to acquire programming skills. It is also ideal for those who wish to move to a developer role or areas such as software engineering.

Education is the premise of progress, in every society, in every family"- Kofi Annan Education is the backbone of every society and education must be made available to everyone. To ensure this we have to modify the traditional method of learning and adopt new technologies in this field. Digitization is turning as an aid in accomplishing the same, it had made learning electronic and widen its availability to 24 7. Anybody with internet access can learn anything at any time, the boundaries to education have been destructed. Technologies in the education sector are changing the methodology for both educators and modern-day learners. People are adopting new methods other than just bookish learning and prefer a more realistic learning experience. Digitization has brought drastic changes in the education sector nationally and globally. Let us see what various personalities have viewpoints on the same. "Yes to some extent digitization can help today's industrial age education industry in reaching the unreachable population of the country, that said, reform is only possible when the education system is rebooted to focus on the needs of the 21st century and beyond.

Inspur has announced the open-source release of TF2, an FPGA-based efficient AI computing framework. The inference engine of this framework employs the world's first DNN shift computing technology, combined with a number of the latest optimization techniques, to achieve FPGA-based high-performance low-latency deployment of universal deep learning models. This is also the world's first open-sourced FPGA-based AI framework that contains comprehensive solutions ranging from model pruning, compression, quantization, and a general DNN inference computing architecture based on FPGA. The open source project can be found at https://github.com/TF2-Engine/TF2. Many companies and research institutions, such as Kuaishou, Shanghai University, and MGI, are said to have joined the TF2 open source community, which will jointly promote open-source cooperation and the development of AI technology based on customizable FPGAs, reducing the barriers to high-performance AI computing technology, and shortening development cycles for AI users and developers.

This paper introduces the modulated Hebbian plus Q network architecture (MOHQA) for solving challenging partially observable Markov decision processes (POMDPs) deep reinforcement learning problems with sparse rewards and confounding observations. The proposed architecture combines a deep Q-network (DQN), and a modulated Hebbian network with neural eligibility traces (MOHN). Bio-inspired neural traces are used to bridge temporal delays between actions and rewards. The purpose is to discover distal cause-effect relationships where confounding observations and sparse rewards cause standard RL algorithms to fail. Each of the two modules of the network (DQN and MOHN) is responsible for different aspects of learning. DQN learns low level features and control, while MOHN contributes to the high-level decisions by bridging rewards with past actions. The strength of the approach is to support a DQN standard framework when temporal difference errors are difficult to compute due to non-observable states. The system is tested on a set of generalized decision making problems encoded as decision tree graphs that deliver delayed rewards after key decision points and confounding observations. The simulations show that the proposed approach helps solve problems that are currently challenging for state-of-the-art deep reinforcement learning algorithms.

Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment. Human knowledge of how to solve these tasks can be incorporated using imitation learning, where the agent learns to imitate human demonstrated decisions. However, human guidance is not limited to the demonstrations. Other types of guidance could be more suitable for certain tasks and require less human effort. This survey provides a high-level overview of five recent learning frameworks that primarily rely on human guidance other than conventional, step-by-step action demonstrations. We review the motivation, assumption, and implementation of each framework. We then discuss possible future research directions.

This paper extends the idea of Universum learning [1, 2] to single-class learning problems. We propose Single Class Universum-SVM setting that incorporates a priori knowledge (in the form of additional data samples) into the single class estimation problem. These additional data samples or Universum belong to the same application domain as (positive) data samples from a single class (of interest), but they follow a different distribution. Proposed methodology for single class U-SVM is based on the known connection between binary classification and single class learning formulations [3]. Several empirical comparisons are presented to illustrate the utility of the proposed approach.