Anamind helps organizations build business planning and forecasting capability. With simplicity at the core of our approach we offer a world class planning system - PLANAMIND, process consulting services, and training for business planning. This course has been specifically designed by us to help planning professionals as well as aspirants of this function worldwide, to understand and build their skills in business planning. The course contains both quantitative and qualitative aspects of planning. This course is business oriented and not purely academic in nature.

This year's survey aims to define the current market trends around the concept of "work," including the impact of the Gig Economy, the extent to which specific technologies pave the road for the Future of Work, and the performance, strategies, and capabilities within today's contingent workforce management (CWM) programs. For the purposes of this research study, "non-employee" talent includes temporary workers (sourced via staffing suppliers), freelancers, independent contractors, robotics, professional services, and "gig" workers.

From medicines to materials, small organic molecules are indispensable for human well-being. To plan their syntheses, chemists employ a problem solving technique called retrosynthesis. In retrosynthesis, target molecules are recursively transformed into increasingly simpler precursor compounds until a set of readily available starting materials is obtained. Computer-aided retrosynthesis would be a highly valuable tool, however, past approaches were slow and provided results of unsatisfactory quality. Here, we employ Monte Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. MCTS was combined with an expansion policy network that guides the search, and an "in-scope" filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on 12 million reactions, which represents essentially all reactions ever published in organic chemistry. Our system solves almost twice as many molecules and is 30 times faster in comparison to the traditional search method based on extracted rules and hand-coded heuristics. Finally after a 60 year history of computer-aided synthesis planning, chemists can no longer distinguish between routes generated by a computer system and real routes taken from the scientific literature. We anticipate that our method will accelerate drug and materials discovery by assisting chemists to plan better syntheses faster, and by enabling fully automated robot synthesis.

Figure 1 depicts a flow diagram of a process for including parallelism when generating a virtual machine migration plan according to an embodiment. Exemplary embodiments relate to using machine learning for virtual machine (VM) migration plan generation. Embodiments can enforce both a colocation and an anti-colocation policy using colocation and anti-colocation contracts. A VM migration plan can be created by processing a first mapping of VMs to hosts along with a second mapping of VMs to hosts. Pre-processing can be performed followed by machine search techniques with heuristics and pruning mechanisms to generate serialized optimal paths from the first state (i.e., an origin state) to a second state (i.e., a goal state).

In this paper we present UbuntuWorld 1.0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system. Specifically, we propose to use the Bash terminal as a simulator of the Ubuntu environment for a learning-based agent, and demonstrate the usefulness of adopting reinforcement learning (RL) techniques for basic problem solving and troubleshooting in this environment. We provide a plug-and-play interface to the simulator as a python package where different types of agents can be plugged in and evaluated, and provide pathways for integrating data from online support forums like Ask Ubuntu into an automated agent's learning process. Finally, we show that the use of this data significantly improves the agent's learning efficiency. We believe that this platform can be adopted as a real-world test bed for research on automated technical support.

Google has released an update that will allow G Suite users to access coworkers' real-time free/busy information through both Google Calendar's Find a Time feature and Microsoft Outlook's Scheduling Assistant interchangeably. G Suite admins can enable the new Calendar Interop management feature through the Settings for Calendar option in the admin console. Admins will also be able to easily pinpoint issues with the setup via a troubleshooting tool, which will also provide suggestions for resolving those issues, and can track interoperability successes and failures for each user through logs Google has made available. The new feature is available on Android, iOS and web versions of Google Calendar as well as desktop, mobile and web clients for Outlook 2010, for admins who choose to enable it. Google says the full rollout should be completed within three days.

Disney says its parks are "Where Dreams Come True," but that statement was never so literal as it was for two foster kids during a recent visit to the park. Janielle and Elijah Gilmour, ages 12 and 10, got the surprise of a lifetime in April, when foster parents Courtney and Tom Gilmour announced news of their official adoption date during a visit to Walt Disney World. "We planned it as soon as we got the [official] date, which was the Friday before our trip," Courtney tells Fox News. What Courtney didn't plan on, however, was that Disney would catch wind of the duo's plans and offer to lend a mouse-like, white-gloved hand. After arriving at the park from Portland, Penn., Courtney tweeted out a photo of the family's Walt Disney World celebration buttons, and the park got in touch to offer a private meet-and-greet with Mickey Mouse himself.

Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model can be used to evaluate a plan, it does not prescribe how to construct a plan. Here we introduce the "Imagination-based Planner", the first model-based, sequential decision-making agent that can learn to construct, evaluate, and execute plans. Before any action, it can perform a variable number of imagination steps, which involve proposing an imagined action and evaluating it with its model-based imagination. All imagined actions and outcomes are aggregated, iteratively, into a "plan context" which conditions future real and imagined actions. The agent can even decide how to imagine: testing out alternative imagined actions, chaining sequences of actions together, or building a more complex "imagination tree" by navigating flexibly among the previously imagined states using a learned policy. And our agent can learn to plan economically, jointly optimizing for external rewards and computational costs associated with using its imagination. We show that our architecture can learn to solve a challenging continuous control problem, and also learn elaborate planning strategies in a discrete maze-solving task. Our work opens a new direction toward learning the components of a model-based planning system and how to use them.

We consider an orienteering problem (OP) where an agent needs to visit a series (possibly a subset) of depots, from which the maximal accumulated profits are desired within given limited time budget. Different from most existing works where the profits are assumed to be static, in this work we investigate a variant that has arbitrary time-dependent profits. Specifically, the profits to be collected change over time and they follow different (e.g., independent) time-varying functions. The problem is of inherent nonlinearity and difficult to solve by existing methods. To tackle the challenge, we present a simple and effective framework that incorporates time-variations into the fundamental planning process. Specifically, we propose a deterministic spatio-temporal representation where both spatial description and temporal logic are unified into one routing topology. By employing existing basic sorting and searching algorithms, the routing solutions can be computed in an extremely efficient way. The proposed method is easy to implement and extensive numerical results show that our approach is time efficient and generates near-optimal solutions.

The flight plan for the ill-fated 1970 Apollo 13 mission which had to be altered following the an emergency on board has been unearthed. The 352-page document bears the annotations made by all three crew members recording in detail the actions they had to take after an explosion ripped off part of their space ship. Apollo 13 was to be the third mission to land on the moon, but just under 56 hours into flight, an oxygen tank explosion forced the crew to cancel the lunar landing and move into the Aquarius lunar module to return back to Earth. The drama that unfolded during the Apollo 13 mission was re-told in the Hollywood film starring Tom Hanks, Kevin Bacon as Swigert and the late Bill Paxton as Haise. The mission is famous for the line ''Houston, we have had a problem here', which is often misquoted as'Houston, we have a problem'. The flight plan for the ill-fated Apollo 13 mission which had to be drastically altered following the'Houston, we have had a problem' emergency on board has been unearthed The Apollo 13 mission which set off on April 11, 1970 was meant to culminate in a third moon landing, with Lovell and Haise voyaging to the lunar surface while Swigert orbited in the command module Odyssey.