How does one deal with the unexpected? Our world is full of surprises and we humans are often able to correctly identify a problem and respond appropriately. Consider a new driver encountering their first traffic circle; a student experiencing a hard drive failure in the middle of an assignment; an unexpected question being asked during a job interview. In situations where we have a goal (i.e., reach a destination or submit a completed assignment), we may need to alter our original plan when the unexpected occurs. Could we enable autonomous artificial intelligent agents to do the same?
A long standing area of artificial intelligence is the field of automated planning. The traditional planning problem is to generate a sequence of actions given a concrete, specific goal (e.g., I will be home at dinnertime) and a set of specific actions (e.g., drive-car, fill-gas-tank, walk, etc). Generating plans that are hopefully efficient and optimal from start to finish under different circumstances (e.g., delayed effects) is an active area of research. After a plan has been generated, and during the execution of the plan, the environment may change. For example, a robot retrieving packages in a warehouse may discover it has dropped its package. Or perhaps another robot has broken down due to a hardware failure and is blocking the path of this robot. How can a robot (or any A.I. agent) know something unexpected has happened without knowing all possible future failures?
Fundamental research on autonomy aims to find general approaches to solve this problem. One approach is to generate expectations: facts that should be true during different stages of a plan's execution. When an expectation is violated, a discrepancy occurs between the expected and perceived facts. A new trend in autonomy is to include goal reasoning capabilities. In the event of a failure, the original goal may no longer be warranted. Perhaps robust autonomous agents need to generate and change their goals in response to a changing environment.
Autonomous systems still have a long way to go and open research questions on autonomous systems remain. Funding agencies consistently seek new research on autonomy for diverse operations ranging from cybersecurity to military and vehicular autonomy. What will autonomous systems be like in the future? Will we achieve autonomous agents that can handle any situation they encounter?
- Dustin Dannenhauer
Oracle on Tuesday released a series of updates to its Transportation Management and Global Trade Management clouds. The updates aim to help companies streamline and simplify compliance with shifting global trade regulations, as well as speed up customer fulfillment, Oracle said. Key to the new features is the injection of data into shipment routes and automated event handling. For instance, routing decisions will now take into account factors such as historic traffic patterns, hazardous material restrictions and tolls when planning shipments. Changes to transportation planning software are designed to improve outbound order fulfillment.
Researchers often try to capture as much information as they can, either by using existing architectures, creating new ones, going deeper, or employing different training methods. This paper compares different ideas and methods that are used heavily in Machine Learning to determine what works best. These methods are prevalent in various domains of Machine Learning, such as Computer Vision and Natural Language Processing (NLP). Throughout our work, we have tried to bring generalization into context, because that's what matters in the end. Any model should be robust and able to work outside your research environment.
The basic argument is increasingly deployed by frustrated executives and self-promoting private-equity groups: Companies are doing dumb things to meet the market's quarterly expectations, and hurting their long-term prospects as a result. Take the company private and executives no longer have to care about the short term, allowing them to invest for the long run and help the company, their loyal shareholders and wider society. The trouble is that none of this applies to Tesla. It is hard to think of a company that cares less about sucking up to Wall Street than Tesla. Mr. Musk earlier this year rejected "boring bonehead questions" from analysts on his quarterly earnings call; the company offers no guidance on quarterly earnings; and it has frequently and unapologetically reported losses far worse than expected (only twice has it made a quarterly profit, both times a surprise).
The interview is one of the most fundamental aspects of an organization's hiring process, but getting those appointments on everyone's calendars can be a logistical nightmare. And relying on a tedious manual system may leave candidates with a bad first impression. "People don't have their calendars up-to-date, or they cancel and reschedule constantly," says Lin Lin Phan, talent operations manager at MuleSoft, a San Francisco-based technology firm. "Things happen, and we're the ones who have to step in and find a replacement interviewer before it has a negative effect on the candidate's experience." Fortunately, technology can help with automation tools that streamline the scheduling process.
Linear Squared, a Sri Lankan company offering Machine Learning and Advanced Data Analytics solutions, has launched a fully automated planning platform for apparel industry. The company claims the solution, Capacity Squared, to be the world's first AI driven production planning software. The process of capacity planning on a shopfloor has always been manual, which consumes more time and is prone to human errors and biases. In unforeseeable situations like delay of raw materials, missed targets etc., sometimes even the well-planned schedule runs on low efficiency. Thus, the solution lies in the optimisation of the planning schedule without expanding the factory by adding new machinery or hiring labour.
In Reinforcement Learning, the agents take random decisions in their environment and learns on selecting the right one out of many to achieve their goal and play at a super-human level. Policy and Value Networks are used together in algorithms like Monte Carlo Tree Search to perform Reinforcement Learning. Both the networks are an integral part of a method called Exploration in MCTS algorithm. They are also known as policy iteration & value iteration since they are calculated many times making it an iterative process. Let's understand why are they so important in Machine Learning and what's the difference between them?
The government has been urged to speed up the publication of its guidance for a'no deal' Brexit, after a survey of 800 businesses by the Institute of Directors found that fewer than a third of them have carried out any Brexit contingency planning. Recently, the Brexit debate has been dominated by the potential implications of the UK leaving the EU without any kind of deal in place next March. Some of the details have been pretty alarming, but the whole point about contingency planning is that it has to take account of the worst-case scenario. The UK produces roughly 60% of the food it consumes. Of the remaining 40%, about three-quarters is imported directly from the European Union, including a lot of fresh fruit and vegetables like citrus fruits, grapes and lettuces.
Artificial Intelligence (AI) has already started to influence processes and automate decision making in manufacturing, health care, finance and customer service industries. By many measures, HR appears to be next on that list. While the technology is still nascent, the building blocks exist to suggest that machine learning could ease the burden of complex analysis, surface insights, and trigger actions on behalf of managers in the workplace. One way is by comparing real-time data to historical data or benchmarks to identify statistically significant deviations from the norm. For example, if scheduled labor hours as a percent of sales is significantly higher than the norm in a group of stores, the system can detect this and send that information proactively to management.
Welcome to part 4 of my series on the AI of Total War. A game that completely re-built the campaign AI systems to accommodate for an increasingly more complex series of mechanics, resources and consequences. Rome II's adoption of the Monte Carlo Tree Search (MCTS) algorithm is a critical step in bring the campaign AI up to spec for more contemporary entries in the series. In this entry I'm going to look at how the MCTS systems were improved upon, as well as how the diplomacy systems have been scaled up for the modern era as Rome gave way to 2015's Total War: Attila. Attila is the ninth entry in the Total War franchise and transposes the conflict to the late 4th and early 5th century: an phase of history known as the Migration Period.
The government must fully implement its artificial intelligence (AI) sector deal to ensure that the number of jobs created by AI and automation balance the number of jobs lost, PwC has stated in its latest report. IT organisations in the UK and across Europe are starting to accelerate the move to the cloud. Read more about the key areas in which senior IT managers are planning to invest in over the next 12 months. You forgot to provide an Email Address. This email address doesn't appear to be valid.