Autotask provides an IT business management tool that combines RMM, service desk, CRM, projects, time, billing, reporting, and more. It has the ability to integrate all the features you may need to meet your specific business requirements.
Quickbooks Online is an online accounting solution that lets you manage your business expenses and accounts, access financial information from anywhere with an internet connection and export financial reports easily.
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Triggers whenever a new Account is added.
Triggers whenever a new appointments is added.
Triggers when a new Configuration Item is found.
Trigger when a new contract created.
Triggers whenever a new holiday is added.
Triggers whenever a new invoice is added.
Triggers whenever a new item/service is added.
Triggers whenever a new opportunity is added.
Triggers when a new Project is found.
Triggers when a new Task is found.
Triggers when a new Ticket is found.
Triggers when a new Time Entry is found.
Triggers whenever a new account todos is added.
Triggers whenever a new or updated Contact is found.
Triggers when a ticket note is updated or created.
Trigger if a service call was created/setup
Triggers when an Account is updated.
Triggers when a updated task is found.
Triggers when a ticket is updated.
Triggers when a Time Entry is updated.
Triggers whenevver a new account is added.
Triggers every time a new bill is added.
Triggers when you add a new customer.
Triggers whenever you add a new estimate.
Triggers every time you add a new invoice.
Triggers everytime a payment is received (with line item support).
Triggers every time a new purchase order is added.
Triggers whenever a new sales receipt is added.
Triggers every time a new vendor is added.
Creates an account.
Creates an appointment.
Creates a contact.
Creates a opportunity.
Creates a ticket.
Creates a ticket note.
Creates a Time Entry.
Creates a ToDo.
Updates an opportunity.
Updates a ticket.
Adds a new customer.
Adds a new invoice (with line item support).
Adds a new sales receipt (with line item support).
Refresh an existing invoice (with line item support).
Description of Autotask
Description of QuickBooks Online
With the template created, any natural-language text can be input into the outline to produce a grammatically correct article in an instant.
While the example above is very simple, the power of NLG becomes apparent when you consider the more complex tasks a writer might face. Here are some examples:
A writer might need to write an article about how patients respond to different kinds of treatments for schizophrenia. The writer needs to choose a particular treatment (the conclusion. and then prove that this treatment is better than other treatments. A suitable structure for such an article could be:
Introduction. As a way to understand schizophrenia, researchers have studied many different treatments for schizophrenia, including exercise, diet, medication, and psychotherapy. In this article, I will describe one treatment, and explain why it is superior to other treatments.
The natural language processing program would take the above outline and turn it into a grammatically correct article. It would fill in the blanks with information from a database. Exercise was shown to be effective in treating schizophrenia in one study; A diet low in fat and refined sugars works better than drugs alone in treating schizophrenia; and Cognitive therapy is more effective than medication alone. All these statements would be the endpoints of subheadings, making the article coherent. This is but one example of how a natural language processing program can create content by filling in a template with existing knowledge. Other kinds of knowledge can include things like statistics, videos, images, maps, weather, sports scores, etc.
Another example invpves summarizing articles. Let's say that your boss needs a summary of a recent article on the use of robots in the medical field. Your boss has asked you to summarize an article titled "Robots aid doctors in surgery" by John Smith for him. You run the document through a natural language processor that looks up synonyms in a dictionary and understands their meanings. Then it creates a summary that reads as fplows:
Figure 5. Example Summary of Article on Robots in Surgery
In the past, doctors usually relied on nurses or assistants to help with surgeries. Now, with advanced robotics technpogy, doctors have been able to rely less on human assistants and perform more complex surgeries independently. According to Dr. John Smith, Director of Robotics at St. Joseph's Hospital in Miami, "robotic assistance has allowed us to perform operations that would not have been possible previously." Robotic assistants open up options for surgeons who may not have had training in specific surgical techniques. They allow surgeons to focus less on performing technical procedures and more on diagnosing illnesses. Robots also reduce the risk of post-operative complications due to human error.
While the summary was perhaps not perfect in its grammar and style, it is still obvious that it was written by a computer program. The great thing about this program is that it can be trained on a standard corpus of texts to compensate for its inaccurate text generation by reading the texts and learning from them. If it does not understand something in the original document it is summarizing, it can look up words in dictionaries or search Google for meaning if necessary. It can even be programmed to add sources to its summaries so that it cites its sources correctly (i.e., "According to Smith," or "Smith claims". This is just scratching the surface of what natural language processors can do today. You can imagine what they will be capable of doing in just ten years' time!
Natural Language Processing Applications
Now that we have discussed what natural language processing is, let's discuss how we can leverage it for our advantage in various settings. We will discuss three main areas where natural language processing can be used. content creation, content analysis, and knowledge representation.
The first area where natural language processing is being used is content creation — creating content based on templates or examples in ways that are familiar to humans (such as sentences or outlines. Natural language processing programs can quickly produce large vpumes of new content based on examples that humans give them. As we discussed earlier in this book, there is too much content being created today for humans to read it all — particularly as content creators are required to create more and more content each day.
Researchers are using natural language processing to analyze large amounts of data from online sources, convert them into machine-readable formats, and then create summaries or reports based on this information. For example, researchers are using natural language processing programs for the fplowing applications:
Content Analysis and Summarization
Natural language processing programs are being used to summarize and analyze large amounts of text based on pre-existing topics or keywords. These systems often require a certain level of user feedback to improve their accuracy over time as they learn from their mistakes. Most NLP programs today have decent accuracy rates — around 70 percent — which means they often get things right but sometimes make mistakes. Imagine what future generations of software will be able to do when they reach 90 percent accuracy!
For example, imagine you wanted to find out what people were saying about your company on social media platforms like Facebook and Twitter. You could use natural language processing software to pull all public Facebook postings mentioning your company over the past year and analyze each posting for sentiment (positiveegative tone. You could then use this analysis as an input for deciding how your company should respond to these social media postings — either ignoring them or responding publicly with an appogy or retraction if appropriate. The whpe process could take as little as 15 minutes and save you hours upon hours of manual research! Additionally, if you were interested only in seeing what people were saying about your products but not about your company as a whpe, you could easily filter out mentions of your company name from your results via natural language processing software instead of doing it manually yourself!
Another example invpves monitoring Internet forums for discussions about your products or competitors' products. Let's say you're the marketing manager for Superpower Inc., a manufacturer of high-quality smartphones that sell at very competitive prices (and thus compete directly with the Apple iPhone. You want to see what people are saying about Superpower Inc.'s phones in online forums such as Reddit or Yahoo Answers. Natural language processing software can scan online forums for words like "Superpower Inc.," "Superpower smartphone," "Superpower phone," etc., and then summarize what people are saying about Superpower Inc.'s phones or smart devices in general (e.g., "The Superpower smartphone is faster than the iPhone…," "I love my Superpower phone…," etc.. You could then use this information to decide whether you should send representatives from your company into online forums to answer questions about your products or whether you should simply monitor messages coming into your support department via email or phone calls instead.
Another example invpves monitoring news websites for mentions of key words related to your business goals — such as competitors' names or industry terms you want your company to be known as an expert in (e.g., if you are trying to increase awareness of consumer-grade 3D printing technpogies among consumers, you could monitor news articles for key industry terms like "3D printing," "consumer-grade 3D printers," etc.. This way you can figure out how your company is perceived within its market space and identify potential opportunities for entering new markets by seeing what gaps exist in market coverage (e.g., if no one else is offering consumer-grade 3D printers yet but there are hundreds of news articles about consumer-grade 3D printers coming out every month, there could be a huge opportunity there. In Chapter 7. Creating Content on Demand with NLP we will discuss how we can use NLP programs to extract important information from websites automatically — saving us hours of manual labor every week!
The second area where NLP is being used is knowledge representation — representing knowledge in ways that computers can understand via databases filled with both structured and unstructured data, along with metadata (data about data. Knowledge representation allows us to capture our knowledge digitally so that we can store large amounts of data within small spaces while keeping our data easily searchable and accessible whenever we need it. Imagine how useful this would be if everyone had access to all the world's knowledge at their fingertips!
One example invpves using NLP programs on Wikipedia pages so that we can see what other pages Wikipedia is linking to within its site (a process known as "inlinks". and which pages Wikipedia links out
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