Artificial Intelligence- a term that is recited by everyone alike and usually misunderstood. This is way different from the classical case of the general public not understanding the scientists. The idea of AI- something that rivals our intelligence, not only entertains us but also scares us to the same extent, also distracting us, unfortunately.

A Little Story

I remember reading a story somewhere about a pregnant woman. Her fetus was diagnosed with Downs syndrome. The ultrasound machine showed some white spots that indicated the presence of the abnormality. She was suggested to go for amniocentesis to be sure if the fetus has the genetic modification. This was a risky test where the chances of a fetus being killed were 1 in 300.

Cutting the Long story short- later, they realized that the analysis was done almost ten years back in the UK. These white spots indicated the deposition of calcium which acted as the predictor of the Down Syndrome. Apparently, the fact was ignored that the machines used today are far advanced with more pixels per square inch. Those white spots are just white noise and not something to be worried about.

The fact of the matter is that the analysis has stayed as they were as the technology is advancing. The problem lies in the time of analysis and the time it is used in. Before we use a data, we must keep in mind where when data came into existence and how relevant it is in today’s time.

The Real face Of Artificial Intelligence

We are still far away from bringing together Humans and computers in such a way that it enhances Human life. While some think this problem comes along with and is subservient to the creation of Artificial Intelligence, others also consider it as the chance to create a new branch of engineering.

The machine learning of past several decades has become AI today. The phrase AI came into existence in the late 1950s for referring to the aspiration of providing software and hardware human-level intelligence. In the 1980s, when the backpropagation algorithm was rediscovered, it brought the ‘AI revolution’. We sure have come a long way since Machine Language and first ambitious use of AI.

Way To Go

The system still has shortcomings and issues like security and privacy. These are not just the only problem, these are challenges. The success in the field of AI is limited right now. Truth be told, we still have a long way to go before we can realize the AI aspirations that could imitate humans. Unfortunately, even a limited success catches the attention of media and hypes it up.

We already depend on the technology quite a lot. But the revolution in the field of AI is still awaited. It is going to be very complex and challenging.

Neural networks have given automatic language transition a new face. It is an algorithm that is inspired by the human brain. They used huge labeled datasets to be trained to do a variety of complex tasks like the human brain that performs only after gaining the knowledge about something.

Now suppose, you were given Chinese and Arabic books and need to learn to translate Chinese into Arabic. Will you be able to do that? No one among us can. But now, the computer can do that.

The Breakthrough Story

Yes, you heard it right. Two teams of computer scientists have gained success in translating languages with the use of Artificial Intelligence. Both groups, one from Facebook and other from the University of the Basque Country (UPV), independently created techniques through which neural networks can translate languages. Artificial intelligence can do that without using human intervention or a dictionary. It is all about unsupervised machine learning.

However, its bilingual evaluation understudy score came to 15 in both directions. It is lower than that of Google translate which is 40 or Human who can score 50. But, it is way better than word-to-word translation. Isn’t it? And this is just the beginning. No one knows where this road is headed and what surprises it will bring.

The secret is unveiled

When you hear such wonderful and magical things, a question often haunts your brain- how is it done?

The techniques of both the groups first recognize the pattern in each language. They identify commonly paired words like shoe-socks, tree-leaves, table-chair, etc. that are common across the languages.

Once these patterns are recognized, the neural network then links these co-occurrences in both the languages. This develops a bilingual dictionary on the accuracy of the translation, without any human feedback. Then, these dictionaries are used for translating the whole sentences.

It is more like a giant atlas with words for cities. The maps in each language will resemble each other with just different names. All computers do is figure out a way to overlay one map over another and voila, you have a bilingual dictionary ready.

Apart from this technique, neural network uses two more ways- back translation and denoising.

In back translation, a sentence is translated to the required language and then back again to the original language. If there is any discrepancy between the two sentences, neutral networks adjust itself and then tries to make a more accurate translation.

Programs like Google Translate has experienced a dramatic hike in the ability of language interpretation in past few years. Thanks to the new machine learning techniques and of course to the numerous online text data. It is on these data that the algorithms can be trained.

Everyday machines are acquiring the human-like abilities of language. Along with it, they are also inheriting the deeply ingrained biases hidden in the patterns of language. AI has been seen exhibiting a striking gender and racial biases. Many studies have proved that AI is biased towards gender and races. Is it really biased?

Is AI Biased Or We Are?

People say that this shows AI is prejudiced and biased. No, it shows we are biased and prejudiced and AI is learning it. Because we are assisting AI in its learning. It is reinforced in AI because algorithms are unequipped in consciously counteracting the learned biases, unlike humans.

Embedded Biases

Machines learn from Word Embedding. This process has transformed the way computers interpret text and speech. This method has successfully helped computers in making sense of the language in past few years.

Word Embedding builds up a mathematical representation of language. The meaning of a word in it is distilled into a series of numbers, called Word Vector and based on which related words and terms words frequently appear together.

For example, in the mathematical language space, words for flowers and pleasantness are clustered closer while words for insects and unpleasantness appear close together. Thus, to AI flower is connected to pleasantness and insect to unpleasantness.

This purely statistical approach has captured the social and cultural context of words differently than a dictionary. Some of these implicit biases in the experiments of human psychology has been captured by these algorithms. Words like female and women became closely associated with arts, humanities, and home while the words male and the man appeared closer to the professions like Math and engineering.