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Artificial Intelligence (AI)

Do Human Translators Have a Reason to Be Afraid of Advancing Technologies

Human Translators are rather than machine translators, human intelligence to convert over one method for directing things toward another.

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Be Afraid of Advancing Technologies

Concerns about how automation will change the world are well justified, but it’s a conversation we sometimes overly simplify.

There’s no doubt that machine learning and artificial intelligence is rapidly becoming more sophisticated, and it will reach a point where many of the jobs we take for granted today won’t exist in the years or decades to come.

We sometimes have a tendency to paint issues in broad strokes. Some jobs artificial intelligence is well equipped to handle, but some jobs are better left to humans.

As a species, we have long seen formal language as a unique component of humanity. The possibility of machines making translators obsolete isn’t just a vocational issue. It’s an existential one. The situation is fairly complex, but also one worth investigating more closely.

1. How Automation is Already Changing the WorkForce

The conversation around automation is dominating the business world, but it can impede recognizing how much the labor market has already changed and why the way we work has to change too.

It’s easy to forget how dramatically the internet has changed our world. The rise of cell phones alone would seem an impossibility to most people just a few decades ago.

Today, our lives are more interconnected than they’ve ever been, and we rely on automation and technology in almost every aspect of our daily life, not just work.

The spread of the personal computer and high-speed internet has made remote work the rule rather than the exception. We’re now able to reach practically anyone in the world from our bedroom or home office, and the notion of the traditional office is rapidly becoming obsolete.

But while these technological advances have made our lives significantly easier, they can also be scary. If technology can so easily disrupt long-standing notions of the office, surely it can fulfill our fundamental jobs.

2. Artificial Intelligence in Language Translation

There are many tasks where the role of automation is rather obvious. Vehicle assembly line jobs can benefit from having robots fulfilling the tasks. We have relegated these simple and monotonous jobs to specialized machinery.

There’s plenty of evidence that automating these processes result in safer and more effectively manufactured products.

Things get significantly more complicated when we talk about language. Human language is often as much an art as it is a science.

Idioms don’t always translate directly from one language to another, and a delicate touch is necessary to ensure that intent carries from one language to another.

3. How Machines Translate Language

That’s where Machine Translation comes in. MT is exactly what it sounds like: a means by which a computer system tries to translate from one language to another.

The most exciting developments are happening in the realm of Neural Machine Translation. NMT uses a neural network that learns from experiences – it replicates the same process a human might experience when trying to pick up a new language.

Machines Translate Language

This is a situation where repetition is important. Machines aren’t capable of contextualizing language in the same way we are, but they’re phenomenal at recognizing patterns of connectivity.

The more data you can feed to a neural network, the more sophisticated the understanding of the language it will develop.

A good neural network has plenty of data to draw from. A neural network will run millions of different translations, guessing the right answer based off of its prior experiences, comparing it against the right answer, and applying that knowledge to its existing data bank.

In reality, neural networks are applying mathematical formulas to language, codifying them in terms of raw numbers that the computer can understand.

4. The Tricky Nature of Tacit Knowledge

When we program computers for machine learning, we’re teaching them to crudely simulate the functions we perform when we think. Scientists are often finding themselves befuddled by how little we know about what we know.

How do you teach a computer the mix of balance, instinct, and learned experience that comes with riding a bike? These things are regarded by experts as “tacit knowledge”.

Consider all the cognitive functions you take for granted. Most humans can read a lot about the emotions and attitudes of their peers through body language alone. These are tasks we didn’t explicitly learn but picked up through years of human experience.

Similar is our ability to infer the meaning of phrases we might not know through context or pick up implied meaning through tone.

Tricky Nature of Tacit Knowledge

One of the greatest ironies of human existence is typified in what’s become known as Polyani’s Paradox. “We can know more than we can tell”. Even given how sophisticated machine learning has become, the ability of a computer to learn is still limited by what we can convey to the system.

And we’re discovering more and more that factors like emotional intelligence, social intelligence, and instinct still give humans an edge in terms of translation.

5. What Machines Can’t Do

If you’re involved in the translation field, you need not be concerned about machines taking your job away from you, at least not right away.

While machines are coming closer to something approaching human intelligence, our attempts at setting up NMT has helped us realize how complex our grasp of language really is.

A recent study from computational linguists has revealed that while machine learning has made some serious leaps and bounds to translate short fragments, it still stumbles with longer-form documents. It’s largely an issue of how these neural networks process information.

Typically, they treat each sentence in isolation, but that means that larger, contextualized meaning to a document is lost. Machines are great at providing us with a literal translation for a fragment of the text, but they’re much worse at applying meaning to that translation. It comes down to that argument of art vs. science.

For a neural network to learn, it needs a teacher. There’s a common phrase in data science: “Garbage in, garbage out”. A mind is only as good as the information it’s fed.

And that’s why we need and will continue to need linguists who can properly analyze language and feed it to machines so they can become more sophisticated with their translations.

What Machines Can’t Do

The complexity of neural networks means that while we can evaluate the quality of the results produced, we can’t know for sure the process that achieved those results. It gains the learning process after sorting through millions of data sets.

Artificial Intelligence often comes to incorrect conclusions, and we can never be sure why it reached those conclusions. That means that even as it becomes more advanced, we’ll need people available to double-check those results.

6. The Role of A.I. Looking Forward

Artificial Intelligence and machine learning can be incredibly powerful, and it will undoubtedly play an important role in language translation. But that role will be focused more on assisting human translators rather than replacing them.

NMT is still in its infancy, and we’re far from a point where we can rely on it to produce reliable results for anything longer than short sentences.

That will change with time, but machine learning is revealing to us the impressive complexity of human language. Translators shouldn’t have to worry about losing their jobs to machines soon, but they can expect to have much of the busy work they deal with to become automated in the years to come.

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Ashley Wilson is a digital nomad writing about business and tech. She has been known to reference Harry Potter quotes in casual conversation and enjoys baking homemade treats for her husband and their two felines, Lady and Gaga. You can get in touch with Ashley via Twitter.

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Artificial Intelligence (AI)

How to Apply Artificial Intelligence in Chat Filters

In this article, we shall discuss how to apply Artificial Intelligence (AI) in chat filters. If you are building a chat room, you need to create a chat filter to remove offensive words from the conversation.

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Top 10 Startups In India That Use Ai To Solve Daily Problems

In this article, we shall discuss how to apply Artificial Intelligence (AI) in chat filters.

If you are building a chat room, you need to create a chat filter to remove offensive words from the conversation. A chat filter is a script commonly used in chat rooms for automatically scanning the users’ comments. This process starts immediately after posting the comments, and filters remove or censor inappropriate words. These filters also decide the flow of the chat in a conversation.

Suppose you want to make a Career in Artificial Intelligence. In that case, you should have a strong foundation of statistics and mathematics, one programming language, preferably Python, and the fundamentals of machine learning and its various algorithms. It would help you to start a career in AI.

The people who are already in the AI and related industries can pursue the AI Program Leader. It will help them upgrade themselves to the latest trends and technologies related to AI and machine learning.

Career in Artificial Intelligence Programming

Image Credit: geeksforgeeks

As technology is advancing with time, the application of AI is increasing in every domain. Human beings capabilities like understanding the complexities of various languages, computer vision, speech, and building new intelligent ideas are now possible using AI technology. For that, we have to update with the latest developments in AI and its advanced areas.

Here, we will discuss the application of Artificial Intelligence in chatbot filters.

There are two categories of chat filters used in chat rooms or internet forums – the basic and advanced chatbot filters. The basic bot filters scan only for particular strings of letters and censor them. It doesn’t take care of the meaning of those letters in the context of the sentence.

Advanced chatbot filters examine the letters or words in the sentence context, and hence, their filtering is more sophisticated. Some more advanced chatting filters use a regular expression to find and replace terms in a sentence.

1. Types of Chat Filters

There are five different types of chatbot filter:

Attribute: In this type of filter, you have to create your quality to create a rule.

Lifespan: In this type, the bot acts based on the lifespan value.

Score: Here, we use the confidence score value to choose the response that should allow the bot trigger.

Resolve Query: In this filter, the bot responds depending on the user’s input.

Trigger: Determine trigger to activate bot responses and actions.

These are the different filters that may apply to a chatbot. We can use multiple filters for a single response. A user can see the reactions only if they meet the criteria in the filter.

2. Need for Artificial Intelligence in Chat Filters

Artificial Intelligence changed the way we think about data. It changed the people’s paradigm about how we integrate information and analyze data, and based upon the data, how to improve the decision-making ability of machines. AI is already interfering with our day-to-day life. From Google search results recommendations to Apple’s virtual assistant Siri, we use AI in every aspect of life.

Typically most filters use a binary allow/disallow list, but we know that languages are not binary. They are complex and modulated.

In many older internet forums, some common swear words will be allowed based on context. One can build a regular expression or RegEx tool, and it can filter the string out of terms, but it cannot distinguish between some critical phrases. For that, we need to apply artificial intelligence and natural language processing in creating chat rooms.

3. Application of Natural Language Processing in Chat Filters

In the case of chatbot filters, we use natural language processing. NLP is a sub-domain of AI that deals with the interaction between computers and human language. It helps the filters process and analyses the vast amount of natural language data that results in a machine capable of understanding the available content more clearly.

One can program our chatbots to reply according to the context of the conversation and the data about the user. For example, one may ask the visitor whether he/she is a vegetarian or non-vegetarian and display the menu based upon the visitor’s reply using chat filters.

Application of Natural Language Processing in Chat Filters

Image Credit: chatbot.com

In another example, consider a situation where you want your bot to forward registered users to your website and the new visitors to a registration form. Then, we have to create a flow to check if the user is registered or not.

Application of Natural Language Processing in Chat Filters

Image Credit: chatbot.com

If a user clicks on yes, it shows him one kind of bot response and if he chooses no, it would lead to a different action. We can implement all this filtering in a chatbot by using NLP.

It is not easy for computers to understand the rules that dictate information passing using natural language processing. Sometimes these rules may be highly complex; for example, when we use a sarcastic remark to convey the message. On the other hand, sometimes there may be situations where these rules may be low-levelled; for example, one can use the character “s” for the plural form of the word.

To comprehensively understand the human language, one needs to know the language and how the terms are connected to the sentence to deliver the desired message.

NLP necessitates the algorithms to identify and extract the natural language rules for converting unstructured language data into structured language data. This is how AI and NLP are applied to chat filters.

Overall, we can say that artificial intelligence can make chatbot filters very easy and efficient. However, the techniques deployed in a particular scenario would vary case by case.

4. Frequently Asked Questions (FAQs)

a. What are the chatbots and chat filters?

Ans: Chatbots are software application for conducting an online conversation between humans and machines. It can be a text-based or text-to-speech-based system and can respond according to the user’s query.

A chat filter is used in a chatbot to censor the inappropriate words or sentences in a chat. Chat filters decide the flow of the conversation based on the user’s input.

b. What are Artificial Intelligence (AI) and Natural Language Processing (NLP)?

Ans: It can learn a specific task by a machine without explicitly programming for that task. AI systems are designed to make decisions by analyzing real-time data.

NLP is a subdomain of AI, specially programmed for interaction between humans and computers. Using NLP, a machine can read, decipher, understand, and make sense of a human language in such a practical manner.

c. How to apply AI into a chat filter?

Ans: Using a particular NLP algorithm, we can apply artificial intelligence into chat filters to smooth the undesirable content in a chat. There are different ways in which we can control the flow of the conversation in a chat room.

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