Major HBR cases concerns on a whole industry, a whole organization or some part of organization; profitable or non-profitable organizations. To make a detailed case analysis, student should follow these steps: Case study method guide is provided to students which determine the aspects of problem needed to be considered while analyzing a case study. It is very important to have a thorough reading and understanding of guidelines provided.
A network of simple neuron-like processing units that collectively per- form complex computations. Neural networks are often organized into layers, including an input layer that presents the data e.
Recurrent connections are also popular when processing sequential data. A neural network with at least one hidden layer some networks have dozens. Most state-of-the-art deep networks are trained using the backpropagation algo- rithm to gradually adjust their connection strengths.
Gradient descent applied to training a deep neural network. The gradient of the objective function e. Model-free and model-based reinforcement learning: Model-free algorithms di- rectly learn a control policy without explicitly building a model of the environment re- ward and state transition distributions.
Model-based algorithms learn a model of the environment and use it to select actions by planning. A model-free reinforcement learning algorithm used to train deep neural networks on control tasks such as playing Atari games. A network is trained to approximate the optimal action-value function Q s, awhich is the expected long-term cumulative reward of taking action ain state sand then optimally selecting future actions.
We will generally be concerned with directed generative models such as Bayesian networks or probabilistic programs which can be given a causal interpretation, although undirected non-causal generative models such as Boltzmann machines are also possible.
While we are critical of neural networks in this article, our goal is to build on their successes rather than dwell on their shortcomings. We see a role for neural networks in developing more human-like learning machines: They have been applied in compelling ways to many types of machine learning problems, demonstrating the power of gradient-based learning and deep hierarchies of latent variables.
Neural networks also have a rich history as computational models of cognition D. They may be endowed with intuitive physics, theory of mind, causal reasoning, and other capacities we describe in the sections that follow.
More structure and inductive biases could be built into the networks or learned from previous experience with related tasks, leading to more human-like patterns of learning and development. It is also important to draw a distinction between AI that purports to emulate or draw inspiration from aspects of human cognition, and AI that does not.
This article focuses on the former. The latter is a perfectly reasonable and useful approach to developing AI algorithms — avoiding cogni- tive or neural inspiration as well as claims of cognitive or neural plausibility. Indeed, this is how many researchers have proceeded, and this article has little pertinence to work conducted under this research strategy.
Across broad swaths of cognition, people are still far better learners and thinkers than machines.
Finally, while we focus on neural network approaches to AI, we do not wish to give the impression that these are the only contributors to recent advances in AI. On the contrary, some of the most exciting recent progress has been in new forms of probabilistic machine learning Ghahramani, dependent on the successful completion of a series of developmental tasks at various stages of life • Erikson suggested eight stages of psychosocial development, successful completion of each stage lead to the development of a building block; these building blocks laid the foundation of psychosocial health and emotional health • The building .
This table lists the developmental Life Stages, and the corresponding Developmental Tasks associated with each life stage.
Developmental Tasks; Life Stage Developmental Task; Infancy (birth to 2 years) Social attachment. Maturation of sensory,perceptual, and . • Lack of people management: During the transition, the outgoing provider, the incoming provider and the company as a whole need to work together, but their end goals are different.
Besides technical skill sets, project management, people management and prior transition experience play an important role. Developmental tasks during midlife relate to, for example, achieving adult responsibilities, maintaining a standard of living, assisting children with the transition into adulthood, and adjusting to the physiological changes of middle age (e.g., menopause).
to make a successful transition into adulthood. Late adolescence The developmental tasks of adolescence that Erikson outlined include the development of a sense of mastery, identity, and inti- Transition to college At completion of high school, about half of America’s youth enroll.
In order to help parents influence healthy adolescent growth, the Raising Teens Project identified 10 critical developmental tasks that teenagers need to undertake to make a successful transition to adulthood.