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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.



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 Get Started With Artificial Intelligence

Artificial Intelligence, among other emerging technologies, is becoming central to the continuous technical revolution, and it is evolving. Therefore it is important to learn about the fundamentals of Artificial Intelligence to understand how it changes and affects different industries.




Why learning Artificial Intelligence is the best choice

Artificial Intelligence, among other emerging technologies, is becoming central to the continuous technical revolution, and it is evolving. Therefore it is important to learn about the fundamentals of Artificial Intelligence to understand how it changes and affects different industries.

It’s no wonder, then, that the AI industry is full of job openings — so many of them, in reality, that the area now fronts a different challenge: There are too many job roles and few fitted applicants. On the upside, that means it provides a virtually assured and well-paying profession for those who have got the right skills.

In this article, we wish to know all about AI, how to get started with Artificial Intelligence, why learning Artificial Intelligence is the best choice, and how to get an ideal job.

1. What is Artificial Intelligence?

Artificial Intelligence strives to imitate and transcend the ability of the human mind using nothing but devices.

The fundamental purposes of Artificial Intelligence are:

  • Expedite up repetitious methods by using humanoids or machines instead of human beings
  • Out-think human minds through training and memory
  • Identify patterns and make judgments swiftly and efficiently

Artificial Intelligence strives to develop devices that conceive and reason instead of operating in a comparatively bound space with pre-built systems, methods, and results. Smart Artificial Intelligence devices identify patterns and memorize earlier events and learn from them, making each following judgment more intelligent, rational, and original.

How To Get Started With Artificial Intelligence

AI is a significant model change in advanced computing and requires a profoundly systematic and logical way to devise machine systems that conceive and learn. In simple words, develop humanoids that are not just robots.

And though it may seem strange, AI abilities are all over the place. A few examples of Artificial Intelligence systems include speech, face and fingerprint identification (available on all smartphones and home devices), email spam blockers, virtual assistant (Siri, Alexa), customer assistance using catboats, Expert systems, game playing, an intelligent humanoid automaton, etc.

3. How to Get Started with Artificial Intelligence

Step-1: Pick a topic you are interested in

Initially, choose a topic that is interesting for you. It will assist you in staying motivated and committed in the training process. Concentrate on a specific problem and seek a solution, rather than just aimlessly reading about everything you can get on the internet.

Step 2: Get a prompt solution

The aim is to get any basic solution that includes the query. You require an algorithm that will process information into an acceptable structure for machine learning, prepare a simple model, give a decision, and assess its working.

step-3: Enhance your simple explication

Once you have a simple explanation, it is time for some creative skills. Work on improving all the elements and assess the modifications to conclude whether these changes are worth your effort and time. For example, sometimes, developing preprocessing and data refining gives a tremendous gain on investments than advancing a training model itself.

Step-4: Share your Solution

Publish your solution and share it to get assessed. This will help acquire relevant guidance about your study from other intellects and add up the initial experience in your portfolio.

Follow steps one-four for other queries as well.

Pick other problems and follow the same steps for all tasks. If you have commenced with tabular data form, choose a query that includes operating with pictures or unorganized text. It is also vital to study how to form enigmas for machine learning accurately.

Finish a Kaggle competition

This competition enables you to examine your abilities, working on the same problems several other engineers work on. You will be asked to undertake various ways, picking the most efficient explications. This competition can also prepare you for collaboration as you communicate with people on the panel, exchanging your thoughts and learning from others.

Apply machine learning professionally

You have to decide what your career aims are and build your portfolio. If you are not yet ready to apply for machine learning jobs, seek more projects that will make your portfolio effective.

Artificial Intelligence (AI)

4. Why learning Artificial Intelligence is the best choice

The understanding of Artificial Intelligence initiates lots of new openings. It is sufficient to comprehend the basics of this technology to learn how machine tools work. As you study more about AI, you get an opportunity to grow as a developer who will build high-level AI applications. There are infinite opportunities in this domain.

For those who desire to start learning Artificial Intelligence, It has many options available. The internet enables everyone to enroll in online classes. Some of the courses focus on students who already have a specific level of technological expertise and converge on coding. In contrast, other Artificial Intelligence and machine learning online paths will help even those who do not have knowledge and beginners in the programming and engineering field.

5. How to take a job in Artificial Intelligence?

Humanoids may be arriving for some tasks, but they will create new job roles as well. Here are some ways to enhance your application, whether you are operating in the domain required to fill.

1. Check out the best online courses for ai and machine learning

Similar to many tech-based areas, there are numerous online courses for ai and machine learning topics, letting someone learn more about the area as a whole or gain more specialized knowledge. Some options offer ai certification online that can reinforce a resume.

2. Join outside organizations

Acquiring from others in the field can assist you in enhancing your abilities, so check out social meetups. Getting associated with organizations that allow data scientists to work on the new data, exercise, and improve their skills while studying with their companions.

3. Add Quality industry experience.

Many of the in-demand AI jobs positions are technical by nature, but understanding how to interpret those advancements for other businesses or customers is crucial for any business.

4. Put a habit of reading a lot.

Those working in the Artificial Intelligence field should always be studying, and reading is a way to do that. Read a lot. Not just about your domain but all the related sectors.

5. Be prepared to grasp

AI is a swiftly advancing field, so be ready to examine as many experiences and opportunities in AI as feasible.

Keeping up to date on the most advanced research in AI is very important. There is so much progress befalling each day. This may involve seeking opportunities inside the current working place or outside. Make sure that you do not get restricted to one area.

Apart from having the standard skill-set and education, these are additional methods to be a powerful applicant for these profitable jobs.


Since its beginning, Artificial Intelligence has been performing a requisite role in the technology sector, advancing the quality of living across multiple enterprises. And speaking about what tomorrow endures, it is difficult to foretell. However, with the way AI is developing, It appears the innovations in the following times will be fabulous.

These inventions will only be prosperous when there are well-qualified people who serve in Artificial Intelligence. Suppose you have a desire to work with this wondrous technology. In that case, it is time you check out the online classes for Artificial Intelligence which can assist you in growing an expert in the Artificial Intelligence field.

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