In the age of the internet, people are being drawn closer and closer— you can Snapchat your friend from Turkey, video call your parents on their fancy vacation, send a quick text to your old pen pal (now your new keyboard pal) in Japan. 

But as the world is drawn closer together, our attention spans are becoming more and more commodified. We spend hours scrolling through Instagram, while spending less time engaging with each other directly. 

Ironically, artificial intelligence is now changing that.

In March of 2021, Google unveiled their Live Captions feature on Chrome browsers. Live Caption uses machine learning to instantly create closed captions on any video or audio clip, providing deaf and hard-of-hearing individuals greater access to internet content. 

In the past— and still today— closed captions were either pre-programmed for video formats, or a stenographer would type an almost-instant caption that would be broadcast on television. However, in places where captioning isn’t the “norm,” such as on apps like Instagram or TikTok, captions are almost impossible to find. Live Caption changes this: with a few taps on the screen, any user can have instantaneous, accurate captions that broaden the reach of audio and video.

Google’s Live Caption is a type of NLP or natural language processing. NLP is a form of artificial intelligence that uses algorithms to facilitate an “interaction” of sorts between people and machines. NLPs help us decode human languages into machine languages, and often vice versa.

To understand the history of NLP, we have to go back to one of the most ingenious scientists of the modern era: Alan Turing. In 1950, Turing published “Computing Machinery and Intelligence”, which discussed the notion of sentient, thinking computers. He claimed that there were no convincing arguments against the idea that machines could think like humans, and proposed the “imitation game”, now known as the Turing Test. Turing suggested a way to measure whether or not artificial intelligence can think on its own: if it could correctly fool a human into believing it is a human with a certain probability, it can be thought of as intelligent. 

From 1964 to 1966, German scientist Joseph Weizenbaum wrote an NLP algorithm known as ELIZA. ELIZA utilized pattern-matching techniques to create a conversation. For example, in the DOCTOR script, if the computer was told by a patient “my head hurts”, it would respond with a phrase similar to, “why does your head hurt?” ELIZA is now considered to be one of the earliest chatbots, and one of the first to fool a human in a limited type of Turing Test.

The 1980s were a major turning point in the production of NLPs. In the past, NLP systems like ELIZA formed conversations by relying on a complex set of rules– the AI couldn’t “think” for itself; rather, it was a bit like a chatbot and used “canned” responses to fit the context. When the human said something it didn’t have a response for, it would give a “non-directional” response with something like, “Tell me more about [a topic from earlier in the conversation].

By the late 1980s, NLPs instead focused on statistical models that helped them form conversations based on probability. 

Modern speech recognition NLP includes a few common principles, such as speech recognition, audio recognition, language identification, and diarization, which can distinguish between speakers. Google’s Live Caption system uses three deep learning models to form the captions: a recurrent neural network (RNN) for speech recognition, a text-based RNN to recognize punctuation, and a convolutional neural network (CNN) to classify sound events. These three models send signals that combine to form the caption track, complete with applause captions and music captions. 

When speech is recognized in an audio or video format, the Automatic Speech Recognition (ASR) RNN is turned on, allowing for the device to start transcribing the words into text. When this speech stops, for example, when music is playing instead, the ASR stops running to conserve phone battery and trigger the [music] label in the caption. 

As the speech text is formulated into a caption, the punctuation is formed on the previous complete sentence. The punctuation is continually adjusted until the ASR results do not interfere with the meaning of the complete sentence. 

Right now, Live Caption can only create captions for English text, but it’s constantly being improved upon and will someday expand to other languages. Early versions of Spanish, German, and Portuguese captioning are currently available on Google Meet.

Accessibility-centered NLPs aren’t solely limited to creating captions. Another Google project, Project Euphonia, is using NLP to help individuals with atypical speech or speech impediments be better understood by speech recognition software. Project Euphonia collects 300-1500 audio phrases from volunteers with a speech impediment. These audio samples can then be “fed” to speech recognition models to train for a variety of speech impairments. Additionally, the program creates simplified voice systems that can use facial tracking or simple sounds to signal different actions, like turning on a light or playing a certain song. 

One of Google’s newest ASR NLPs is seeking to change the way we interact with others around us, broadening the scope of where — and with whom — we can communicate. The Google Interpreter Mode uses ASR to identify what you are saying, and spits out an exact translation into another language, effectively creating a conversation between foreign individuals and knocking down language barriers. Similar instant-translate tech has also been used by SayHi, which allows users to control how quickly or slowly the translation is spoken.

There are still a few issues in the ASR system. Often called the AI accent gap, machines sometimes have difficulty understanding individuals with strong accents or dialects. Right now, this is being tackled on a case-by-case basis: scientists tend to use a “single accent” model, in which different algorithms are designed for different dialects or accents. For example, some companies have been experimenting with using separate ASR systems for recognizing Mexican dialects of Spanish versus Spanish dialects of Spanish.

Ultimately, many of these ASR systems reflect a degree of implicit bias. In the United States, African-American Vernacular English, also referred to as AAVE, is an extremely common dialect of “traditional” English, most commonly spoken by African-Americans. However, multiple studies have found significant racial disparities in the average word error rate across different ASR systems, with one study finding the average word error rate for Black speakers to be almost twice that of white speakers in ASR programs from Amazon, Apple, Google, IBM, and Microsoft. 

Going forward, creating more diverse training for AI that includes regional accents, dialects, and slang can help reduce disparities in the accuracy of ASR across races and ethnicities. 

Technology has incredible potential to bring people together, but when people are left out, whether as a result of disabilities, race, ethnicity, or otherwise, it can be a divisive and isolating force. Thanks to natural language processing, we’re starting to fill in these gaps between people to build a more accessible future. 

The post Will AI Make Interpreters and Sign Language Obsolete? appeared first on Artificial Intelligence.

• If you’re interested in learning to code, Python is a great language to start with.
• This training is designed for beginners and covers all the fundamental techniques and more advanced concepts all in one convenient course bundle.
• You can sign up for the 2021 Premium Python Bootcamp Bundle for only $35 for a limited time.

If you already have a strong interest in technology, why not do something more productive with that passion? A way to stay ahead of the game is by learning the popular programming language Python.

It’s actually considered one of the easier languages to learn, but it never hurts to have some help. Sign up for the 2021 Premium Python Certification Bootcamp Bundle, which is on-sale for a limited time, to learn the ropes.

In this bootcamp that’s packed with 13 courses totaling 41 hours, you’ll receive the ultimate guide to coding with Python. From the beginner stage to an advanced level, becoming a master is the end goal of this training.

Of course, it’ll start off with a course for absolute beginners and gradually move into more complex instruction. Some of the classes are courtesy of Zenva Academy, a world-class training platform featuring in-demand programming courses, alongside experts including Minerva Singh, Dr. Chris Mall, and more.

You’ll get to dabble with web automation, artificial intelligence, and debugging code you made yourself. You’ll even have the ability to create your own Python applications in minutes at the end of your studies.

Who knows what career change, creative project, or whatever else learning Python can provide. Sign up for the 2021 Premium Python Certification Bootcamp Bundle and you can find out. Valued at $2,500, you can sign up for only $34.99. This affordable price tag won’t be around forever, so go for it while you can.

The post Become a master of the programming language Python for only $35 appeared first on Artificial Intelligence.

Python is considered to be the easiest language to learn, which makes it a perfect fit for kids. As a new coder, kids can get started with Python as it has a lot of applications. Python is used for various tasks, including data analysis, visualization, automation, machine learning, etc.  Hence learning Python can give kids a life-long advantage. There are many versions of Python for kids that are specially designed for young champs who want to get started with the multi-purpose language.

Python language has an extensive library that supports everything, which makes it an easy language to learn, especially for the kids who want to get an experience of an object-oriented programming language. Python is used for visualization, machine learning, automation, data analysis, etc., which makes it one of the best multi-purpose languages. It is also used to build mobile apps, desktop applications, etc., which again is one of the interesting reasons for kids to get started with Python.

If you want your kids to get started with Python, now is the best time! The demand for coders is continuously growing, and hence coding for kids has become a primary skill. Online learning platforms like Cuemath help kids get started with the language through online classes. Cuemath also encourages kids to explore the real-world applications of the language in building mobile applications, web applications, etc. This sparks creativity amongst kids.

In this blog, we are going to explore reasons which make Python a perfect language for kids. Let us begin.

Reasons Why Python is the Perfect Programming Language for Kids

Following are some of the reasons that make Python a perfect programming language for kids:

Kid-Friendly

Python can be introduced as the first language for kids as it is one of the simplest languages to learn. It operates on text-based coding, which suits the kids perfectly. It also comes with visual, block-based coding programs, thus allowing kids to transform their ideas into reality. It is one of the most engaging languages with a friendly user experience.

Python can also be used for creative projects, thus creating a fun and highly engaging experience for the kids. You can find many resources and books based on Python programming. Nowadays, many institutions also include Python language in their curriculum.

Setting up Python is Easy

Python is user-friendly, and it can be easily set up on any device. If you have windows, you can literally install Python in three simple steps. Even in other operating systems, setting up Python is no big deal, which makes it an appropriate choice for the kids to get started with programming. The easy setup of Python in computers and other devices helps kids get a hands-on learning experience without any hassle.

Increasing Demand

The demand for programmers who can create professional applications is increasing exponentially. In the future, more and more jobs will require coders who can create innovative applications that make life easier. Thus learning Python can be extremely beneficial for kids in the long run. Therefore kids should be encouraged to get started with Python programming at an early age.

Allows Experimenting

One of the most excellent benefits of learning Python is that it allows students to implement their ideas. It lets kids get creative and experiment with different ideas. This also encourages students to create authentic models of their ideas instead of just writing them on a piece of paper. Python encourages students to become natural problem solvers.

The post Why is Python language perfect for kids? appeared first on Artificial Intelligence.

Which programming languages do employers want? That’s arguably one of the most vital questions that software developers and engineers need answered.

Coding Dojo, which conducts coding bootcamps, runs an annual breakdown of the most-demanded programming languages, using data from Indeed. Python topped the latest edition of its list, followed by SQL, Java, and R, with Visual Basic rounding out the top five. 

“Over the last few years, businesses have been asking for more and more Python developers,” stated Coding Dojo’s accompanying blog post. “Since the beginning of 2018, the number of jobs has skyrocketed. This is great news for Python programmers, or those looking to start their career. As one of the simplest languages to learn, it makes it much easier to get into software development.”

The posting called out two other languages, Kotlin and TypeScript, as worth keeping an eye on: “Among the other languages we check, these two saw a significant rise. Both of them had thousands more jobs than last year, despite the pandemic.” 

While there’s some debate over whether TypeScript is a discrete language (since technically it’s a superset of JavaScript), it’s undeniable that its popularity has increased substantially in recent years. In the 2020 edition of the Stack Overflow Developer Survey, some 86.1 percent of surveyed developers said that Rust was a language they loved, followed by TypeScript (67.1 percent), Python (66.7 percent), Kotlin (62.9 percent), and Go (62.3 percent). TypeScript’s version 4.0introduces even more features that make it powerful and easy to use. 

Meanwhile, Kotlin benefits from Google naming it a key language for Android development. While some tech pundits have predicted that the language may eventually evolve into a robust Java replacement, such an event may take quite some time, despite all the buzz around Kotlin’s usefulness. For those interested in experimenting with Kotlin (particularly in a mobile context), Google offers a number of courses,  including Kotlin Bootcamp for Programmers, Android Kotlin Fundamentals, and Advanced Android in Kotlin.

According to Burning Glass, which collects and analyzes millions of job postings from across the country, the top programming languages of 2020 (by job postings) included AQL, Java, Python, and JavaScript:

These different lists of top programming languages present technologists with uniformly good news: Some of the most in-demand languages among employers are also some of the most ubiquitous, meaning there are lots of documentation and educational resources out there if you want to learn one (or more!) of them. 

The post Python Remains Employers’ Most-Demanded Programming Language appeared first on Artificial Intelligence.

The Python programming language, which has never been more popular, arguably thanks to the rise of data science and AI projects in the enterprise, officially turns 30 years old tomorrow.

One of the five members of the 2021 Python Steering Council within the Python Software Foundation is Pablo Galindo, a software engineer at Bloomberg, who spoke with VentureBeat about the inherent challenges of enabling a language to grow and evolve without sacrificing backward compatibility.

VentureBeat: How did you first get involved with Python?

Pablo Galindo: I was doing my first year of [my] PhD when I was in Granada. My background is in physics. I used to simulate black holes. The code that normally goes into simulations is compiled in C and C++. Python was a fantastic language to kind of wrap simulation code. I very quickly fell in love with the syntax and the power that it has.

VentureBeat: Has the popularity of Python surprised you?

Galindo: I will say for sure. It surprises me [that] this is still one of the most used languages in the world.

VentureBeat: What are some of the most common use cases for Python within Bloomberg?

Galindo: We actually use a lot of Python. There are 2,000 developers using Python. The use cases are quite broad. We use Python for things like machine learning models or service-oriented architecture. We have also used Python for a lot of internal user experience tools and developer tools. We also use Python for data transformation.

VentureBeat: What’s your current assessment of Python?

Galindo: Python is a very mature language, and it has evolved. It also has a bunch of things that it carries over. Python has some baggage that nowadays feels a bit old, but the community and the ecosystem has to be preserved. It’s similar to how C and C++ are evolving right now. When you make changes to the language, it’s quite dangerous [because you can] break things. That’s what people are scared of the most.

But even though Python is quite old, there are big changes. The Python 3.1 release for this October will include pattern matching, which is one of the biggest syntax changes that Python has seen in a long time. We can learn from other languages. I think we’re happy to say that we are still evolving and adapting. We have a good experience with respecting the importance of backwards compatibility.

VentureBeat: If you could be Python king for a day, what would you change?

Galindo: I would be a horrible King for a day. The first order of business would be to fix all these things that we have acquired over the years in the language. That would require breaking a bunch of things. Obviously, I will not do that, but I think one of the things I really would like to see in the future is for Python to become faster than it is. I think Python still has a lot of potential to become faster. I’m thinking this will be impossible. But one can dream.

VentureBeat: What do you know now about Python today that you wish you knew when you first began using it?

Galindo: I think the most important thing I learned is how many different uses there are for Python. It’s important to listen to all these sorts of users when considering the evolution of the language. It’s quite surprising and quite revealing to consider how changes or improvements will conflict or will interact with other users of the language.

That’s something that when I started I didn’t even consider. It would be good if people could be empathetic to us changing the language when we have to balance these things.

VentureBeat: What’s your best advice to the leaders of organizations that have adopted Python?

Galindo: It’s important to explain in a compelling way to the decision makers of the organization what are the advantages of Python compared to something different. They need to specifically understand how the ecosystem and the languages will transform the organization.

The key here is making developers more proactive. Productivity not only means producing more; it is pretty difficult to compete with the speed to market of something that can iterate as fast as Python.

The second important thing is the happiness of the developer. Writing code for a developer is a way of expressing themselves. A developer can express themselves in a compiled language. It is kind of more difficult, because the interaction cycles are longer and less interactive. In Python, the syntax doesn’t get in the way. You can say what you want very easily, and experiment.

The post Python pioneer assesses the 30-year-old programming language appeared first on Artificial Intelligence.

1| Natural Language Processing

About: This online course covers from the basic to advanced NLP and it is a part of the Advanced Machine Learning Specialisation from Coursera. You can enroll this course for free where you will learn about sentiment analysis, summarization, dialogue state tracking, etc. The topics you will learn such as introduction to text classification, language modelling and sequence tagging, vector space models of semantics, sequence to sequence tasks, etc. Upon completing, you will be able to build your own conversational chat-bot that will assist with search on StackOverflow website.

2| Natural Language Processing By Microsoft

About: This is a self-paced learning course which will give you a thorough introduction to the cutting-edge technologies applied to NLP. The duration of this course is 6 weeks where you will be given a thorough overview of Natural Language Processing and how to use classic machine learning methods. You will learn about statistical machine translation, deep reinforcement learning techniques applied in NLP, Vision-Language Multimodal language as well as Deep Semantic Similarity Models (DSSM) and their applications.

You will also learn how to apply deep learning models to solve machine translation and conversation problems, deep structured semantic models on information retrieval and natural language applications, deep reinforcement learning models on natural language applications and deep learning models on image captioning and visual question answering.

3| Natural Language Processing With Deep Learning

About: This is a lecture series on NLP provided by Stanford University where you will have an introduction to the cutting-edge research in deep learning applied to NLP. The minimum duration of the series is 1 hour and the topics included are NLP with deep learning, word vector representations, global vectors for word representation, word window classification and neural networks, backpropagation, dependency parsing, introduction to TensorFlow and other such related topics.

4| Natural Language Processing By Carnegie Mellon University

About: This course is provided by Carnegie Mellon University which covers a variety of ways to represent human languages (like English and Chinese) as computational systems and various ways to exploit those representations to write programs that do neat stuff with text and speech data, like translation, summarisation, extracting information, natural interfaces to databases, conversational agents, etc. The course includes some ideas central to Machine Learning and to Linguistics.

5| Deep Natural Language Processing

About: This is a GitHub repository which contains course on deep NLP by the University of Oxford in the form of lecture slides and videos. This course is focused on recent advances in analysing and generating speech and text using recurrent neural networks. You will be introduced with mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions.

6| Natural Language Processing With Python

About: This is an e-book version of the book Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper. This book is more of a practical approach which uses Python version 3 and you will learn various topics such as language processing, accessing text corpora and lexical resources, processing raw text, writing structured programs, classifying text, analysing sentence structure and much more.

7| NLP For Beginners Using NLTK

About: This is a video series where you will learn about the basics of NLP through NLTK. The video basically concentrates on to the very useful feature in NLP called frequency distribution. You will learn how to calculate, tabulate and plot frequency distribution of words.

8| Speech And Language Processing

About: This is an ebook by authors Dan Jurafsky and James H. Martin where you will learn from the basics to advance of language processing. The topics included here are text normalisation, edit distance, regular expressions, language modelling, logistic regression, vector semantics, neural networks, neural language models, and other such related topics.

The post 8 Free Resources For Beginners To Learn Natural Language Processing appeared first on Artificial Intelligence.

Facebook has shut down two artificial intelligences that appeared to be chatting to each other in a strange language only they understood.

The two chatbots came to create their own changes to English that made it easier for them to work – but which remained mysterious to the humans that supposedly look after them.

The bizarre discussions came as Facebook challenged its chatbots to try and negotiate with each other over a trade, attempting to swap hats, balls and books, each of which were given a certain value. But they quickly broke down as the robots appeared to chant at each other in a language that they each understood but which appears mostly incomprehensible to humans.

The robots had been instructed to work out how to negotiate between themselves, and improve their bartering as they went along.

The actual negotiations appear very odd, and don’t look especially useful:

But there appear to be some rules to the speech. The way the chatbots keep stressing their own name appears to a part of their negotiations, not simply a glitch in the way the messages are read out.

Indeed, some of the negotiations that were carried out in this bizarre language even ended up successfully concluding their negotiations, while conducting them entirely in the bizarre language.

That said, it’s unlikely that the language is a precursor to new forms of human speech, according to linguist Mark Liberman.

“n the first place, it’s entirely text-based, while human languages are all basically spoken (or gestured), with text being an artificial overlay,” he wrote on his blog. “And beyond that, it’s unclear that this process yields a system with the kind of word, phrase, and sentence structures characteristic of human languages.”

The chatbots also learned to negotiate in ways that seem very human. They would, for instance, pretend to be very interested in one specific item – so that they could later pretend they were making a big sacrifice in giving it up, according to a paper published by the Facebook Artificial Intelligence Research division.

Facebook’s experiment isn’t the only time that artificial intelligence has invented new forms of language.

Earlier this year, Google revealed that the AI it uses for its Translate tool had created its own language, which it would translate things into and then out of. But the company was happy with that development and allowed it to continue.

Another study at OpenAI found that artificial intelligence could be encouraged to create a language, making itself more efficient and better at communicating as it did so.

The post Facebook’s Artificial Intelligence Roborts Shut Down After They Start Talking to Each Other in Their own Language appeared first on Artificial Intelligence.

We haven’t quite reached the terrifying sci-fi hellscape described by the Terminator franchise, but researchers at Facebook have brought us just a bit closer to the age of the machines. Recently, they pulled the plug on an artificial intelligence system after it developed its own language.

The AI in question was actually designed to maximize efficiency in language, but according to Fast Co. Design, the researchers forgot to add a crucial rule in its programming: the language had to be English. So the “two AI agents” moved on with their programming to communicate as efficiently as their programming would allow, putting the conversation between the two outside the understanding of humans.

“Agents will drift off understandable language and invent codewords for themselves,” Georgia Tech research scientist Dhruv Batra said. This isn’t anything new, either. It’s something that keeps cropping up when researchers experiment with this type of AI.

The purpose of these particular Facebook AI agents is to communicate in English, so programmers reworked the code to get the AI back on track. But if AI is allowed to keep to its own devices, Fast Co. Design said, it eventually creates a language all its own. One that can’t be understood by human beings.

Now is the perfect time to prepare yourself for the end of humanity’s reign over Earth by watching the new 4K Blu-ray of Terminator 2. It seems less a blockbuster action film from the ’90s and more of a dark fortelling of our grim future under the emotionless rule of the machines. Regardless of our impending doom, it’s a great movie.

The post ARTIFICIAL INTELLIGENCE DEVELOPS ITS OWN LANGUAGE appeared first on Artificial Intelligence.