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Will AI Replace The Intelligentsia? Google’s AI writes mournful poetry

Will AI Replace The Intelligentsia? Google’s AI writes mournful poetry

It is no new news that Artificial Intelligence can now control self driving cars; they can beat the best humans at highly challenging board games like chess, and even fight cancer. But still one thing it cannot do perfectly is communicate.

So, to help solve this problem Google has been feeding its Artificial Intelligence with more than 11,000 unpublished books, which include more than 3000 steamy romantic titles. And in response the AI has penned down its own version of mournful poems.

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The poems read something like this:

I went o the store to buy some groceries.

I store to buy some groceries.

I were to buy any groceries.

Horses are to buy any groceries.

Horses are to buy any animal.

Horses the favourite any animal.

Horses the favourite favourite animal.

Horses are my favourite animal.

And here is another one from Google’s AI:

he said.

“no,” he said.

“no,” i said.

“i know,” she said.

“thank you,” she said.

“come with me,” she said.

“talk to me,” she said.

“don’t worry about it,” she said.

 

The way this happened was, Google’s team fed their AI with unpublished works into a neural network and gave the system two sentences from the book; it was then up to this ingenious artificial intelligence to build its own poetry based on available information.

In example above, the team of researchers gave their AI two sentences one about buying some groceries and the other one about horses being a favourite animal (these are the first and the last lines of the above mentioned passages). The team then directed the artificial intelligence to morph between the two sentences.

In the research paper the team further went on to explain the AI system was able to “create coherent and diverse sentences through purely continuous sampling”.

With the use of an autoanecdoter, which is a type of AI network that makes use of data sets to reproduce a result, in this case that was writing sentences, using much fewer steps the team was able to produce these sentences.

The main principle behind this research is to create an Artificial Intelligence which will be proficient in communicating via “natural language sentences”.

This research holds the possibilities of developing a system that is capable of communicating in a more human-like manner. Such a breakthrough is essential in the creation of more useful and responsive chat bots and Artificial Intelligence powered personal assistants like that of Siri and Google Now.

In a similar project, the researchers at Google have been teaching an AI how to understand language by replicating and predicting the work of bygone authors and poets under their project Gutenberg.

This standalone team at Google fed the AI with an input sentence and then asked it to predict what should come next. And by analysing the text, the AI was capable of identifying what author was likely to have written the sentence and was able to emulate his style.

In another incident, on June, 2015 another team of talented researchers at Google were able to create a chatbot that even threatened its creators. The AI learned the art of conversation by analysis of a million movie scripts thereby allowing it to realize and muse on the meaning of life, the colour of blood, and even on deeper subjects like mortality; Ss, much so that the bot could even get angry on its human inquisitor. When the bot was asked with a puzzling philosophical question about what is the meaning of life, it replied by saying – “to live forever”.

In other such similar works, Facebook has also been teaching its artificial intelligence with the use of children’s books. As per the New Scientist which is a social network, it has been using novels such as The Jungle Book, Alice in Wonderland and Peter Pan.

If you are yearning for some more of AI’s written word, then here are the rest of Google AI’s poems.

You’re right.

“All right.

You’re right.

Okay, fine.

“Okay, fine.

Yes, right here.

No, not right now.

“No, not right now.

“Talk to me right now.

Please talk to me right now.

I’ll talk to you right now.

“I’ll talk to you right now.

“You need to talk to me now. —

 

Amazing, isn’t it?

So, what is it?

It hurts, isn’t it?

Why would you do that?

“You can do it.

“I can do it.

I can’t do it.

“I can do it.

“Don’t do it.

“I can do it.

I couldn’t do it. —

 

There is no one else in the world.

There is no one else in sight.

They were the only ones who mattered.

They were the only ones left.

He had to be with me.

She had to be with him.

I had to do this.

I wanted to kill him.

I started to cry.

I turned to him. —

 

I don’t like it, he said.

I waited for what had happened.

It was almost thirty years ago.

It was over thirty years ago.

That was six years ago.

He had died two years ago.

Ten, thirty years ago. — “it’s all right here.

“Everything is all right here.

“It’s all right here.

It’s all right here.

We are all right here.

Come here in five minutes.

“But you need to talk to me now.

 

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Can Creative AI Predict The Future?

Can Creative AI Predict The Future?

Artificial Intelligence is reaching new heights, as the researchers at Massachusetts Institute of Technology (MIT) have come up with a program that can estimate the future. The machines can predict the possible events that may occur in a given scenario. The scientists have programmed the machines in such a manner that they can transform a still image into a video. However, the experiment is in its initial stage and researchers wish that it would just get better with time.

Predicting the future

According to the researchers at MIT, this computer can view an image and figure out what may happen next. To be able to do so, the data scientists have fed the computers with humungous amounts of images and videos. All the videos and images were similar in terms of category. For example, videos of sea waves and beaches of previous years were input into the machines. So, the next time, when the computer is shown an image of a sea beach, it automatically generated a video from the still, which replicated how waves are hitting the shore and people are playing in the water. Similar experiments were conducted using images of newborn babies, golf players, and train stations. And in each case, the computer produced videos resembling the expression of these babies, movement of the golf clubs and trains approaching towards the platforms, respectively.

Predicting the future

But how does this machine do it?

As soon as enormous amounts of data are fed into the machine, it starts learning just as humans can. In this experiment by MIT, computers became familiar with the happenings at a sea beach. Therefore, the next time it is shown the picture of a sea beach, the machine analysed the image and eventually, showed what happens there. However, the scientists say that these videos have certain limitations.

According to Carl Vondrick, a Ph.D. student at the MIT, “AI can be trained to produce output just like human beings. They can recall an event and more importantly, AI can predict the possible outcomes of the event based on past records.” Thus, the deep learning programs are able to spot the similarity in several events and make predictions according to the past results, which may not be accurate in many times. From another perspective, these AI generated videos are too short, as their duration does not exceed 1 second. Moreover, the videos seem like some animated movements created during the 90’s.

Despite such limitations, scientists are hopeful about the future of AI because this experiment was just the beginning and the results were better than what was estimated. Vondrick expressed his views on how AI can help us stop any negative incident from happening. He said, “A machine can study the movements of an old man, which may enable it to forecast whether the person has a chance of falling. In that case, adequate measures can be taken in order to prevent the accident.”

Progress of the AI

Progress of the AI

Apart from MIT, there are several companies including the search engine giant Google that are working on AI. At the Google Cultural Institute (GCI) in Paris, computers are programmed to create new images and art forms. The GCI has developed an application that helps users to search artworks from the dataset of several museums across the world. What is fascinating is that algorithms solely administer the entire app. It can search the dataset of almost 7 million images and artworks and provide search results that match the search criteria. The most important feature of the program is that the application can figure out the difference between the emotions embedded in different pictures.  It can differentiate a peaceful picture from the rest by analysing its content. In addition, this program, also known as the ‘Deep Dream Project’ can create artworks on its own, which adds to the creativity of AI. Google is also working on the ‘Magenta Project’, which has recently created a piano melody on its own. The duration of the melody is 90-seconds and it is the first tangible music sample ever produced by AI.

Therefore, we can find that AI is enabling the computers to make judgements based on their intuition and at the same time, they are developing a sense of creativity. Days are not far when human beings will depend on AI to make their next move.

To get into the depth of the prowess of AI, opt for Machine Learning course online. DexLab Analytics is a leading Machine Learning training institute in Gurgaon. Go through their course itinerary.

 

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3 Exceptional Free E-Books On Machine Learning

books on e-learning

According to the experts at Wikipedia Machine Learning happens to be computer science sub-field that has its origins in the detailed examination of recognition of patterns as well as the “computational learning theory” as put into practice in the world of A.I. or artificial intelligence.The subject investigates the study as well as the construction of algorithms which have the ability to pick up skills from and make predictions on the basis of the data that is available.

In this blog post we list some of the key texts that help out students and researchers in this particular field of study.

The Math Behind Machine Learning: How it Works – @Dexlabanalytics.

1. Machine Learning, Neural and Statistical Classification

Edited By: D.J. Spiegelhalter, D. Michie and C.C. Taylor

This book has for its base the ESPRIT or EC project Statlog which compared and made evaluations about a broad range of techniques on classification while at the same time assessing their merits and demerits in addition to applications across the range. The volume listed here is the integrated one which conducts a brief examination of a particular method along with their commercial application to real world scenarios. It encourages cross-disciplinarystudy of the fields of machine learning, neural networks as well as statistics.

Uber: Pioneering Machine Learning into Everything it Does – @Dexlabanalytics.

2. Bayesian Reasoning and Machine Learning

Written By: David Barber

The methods of machine learning have the ability to mine out the values out of data sets that are nothing short of being vast without taxing the computational abilities of the computer. They have established themselves as essential tools in industrial applications of a wide range like analysis of stock markets, search engines as well as sequencing of DNA and locomotion of robots. The field is a promising one and this book helps the students of computer science grasp the tough subject even if their mathematical backgrounds are decent at best.

Pandora: Blending Music with Machine Learning – @Dexlabanalytics.

3. Gaussian Processes for Machine Learning

Authors: Christopher Williams and Carl Rasmussen

Gaussian Processes or more known simply as GPs serve as a practical, principled and probabilistic approach to the learning as conducted in kernel machines. The Machine Learning community has been providing increased attention towards GPs throughout the better part of the last decade and the book serves the important function of sufficing as a unified and systematic treatment of the role of practical as well as theoretical aspect of GPs as present in machine learning. There was a long felt need for such a book and it does not disappoint with its self-contained and comprehensive treatment. This book is highly useful for students as well as researchers in the fields of applied statistics and machine learning.

If your appetite for knowledge on machine learning is far from being satiated, contact DexLab Analytics. It is a pioneering Data Science training institute catering for hundreds of aspiring students. Their analytics courses in Delhi are widely popular.

 

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