Google translate is now able to translate even more languages, thanks to AI

The #AI that works like a human brain for Google translate  #technology

  • Google is trying to make Google Translate more accurate by expanding the number of languages that are supported by its neural machine translation software.
  • The Californian search giant announced on Monday that Hindi, Russian, and Vietnamese will be powered by neural machine translation in the next couple of weeks.
  • Eight other languages are already using neural machine translation technology.
  • “Neural translation is a lot better than our previous technology, because we translate whole sentences at a time, instead of pieces of a sentence,” wrote Barak Turovsky, product lead on Google Translate, in the blog post.
  • Turovsky added that Google will be rolling out neural machine translation to other languages in the coming weeks.

Google is trying to make Google Translate more accurate by expanding the number of languages that are supported by its neural machine translation software.
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Time to Accept Artificial Intelligence as Part of the Family?

Time to Accept #ArtificialIntelligence as Part of the Family? #AI #Fintech #Martech #tech

  • By now, many of us have heard about or might even own one of the popular, sleek multi-functional voice-first devices, such as the Amazon Echo, also known as “Alexa”, the name used when waking the device to give a verbal command.
  • This joke is terrible for many reasons, not the least of which is that I ended up anthropomorphized a digital device, which may be one of the biggest issues with this devices.
  • Related: There’s No Doubt That Amazon Alexa Is the Next Big Thing

    First, according to Voice Labs Voice Report for 2017, 6.5 million voice-first devices — defined as an always-on piece of hardware utilizing artificial intelligence (AI) with primarily a voice interface, both for input and output — were shipped in 2016.

  • While Amazon and Google (and Siri on our iPhones) have an early lead in this sector, there are sure to be new entrants.
  • Here are predictions for the strategies of just the big players:

    The crazy thing is that even with the potential for 24 million devices to be in our homes soon, the potential impact still remains remarkably unclear.

Millions of households are welcoming these new voice-first home assistant devices into and as part of their families — even with all the uncertainties and unintended consequences.
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AI Tech Talk: An Overview of AI on the AWS Platform

New on the AWS #AI Blog,

  • AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs.
  • For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
  • For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale.
  • Launch instances of the AMI, pre-installed with open source deep learning frameworks (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances.
  • Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of the managed AI services, the AI Platform offerings, and the AI Frameworks you can run on the AWS Cloud.

AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
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Using Machine Learning to Name Malware

Using #MachineLearning To Name #Malware. #BigData #DataScience #AI

  • All of the post-processing to settle on one common name has already been done and you can find the library that can guess the virus names at this github repo .
  • Using Machine Learning to Name Malware – Artificial Intelligence on Using Machine Learning to Name Malware
  • items() if v} print(“We have to guess the family name in the following result:\n”) print(to_guess) l_of_l = get_list_of_token_lists([to_guess]) m = tfidf.transform(l_of_l) els_to_pos = {e: tfidf.vocabulary_[e] for e in l_of_l[0]} els_to_scores = {k: m[:, v].
  • “We have to guess the family name in the following result:” {‘AVG’: ‘MLoader’, ‘Ad-Aware’: ‘Gen:Application.
  • If we just see the eggs and we know the probabilities ahead of time, we can figure out which egg belongs to which dinosaur using Viterbi algorithm .

Extracting information from malware names using Conditional Random Fields.
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Using Machine Learning to Name Malware

Using #MachineLearning To Name #Malware. #BigData #DataScience #AI #Cybersecurity

  • All of the post-processing to settle on one common name has already been done and you can find the library that can guess the virus names at this github repo .
  • items() if v} print(“We have to guess the family name in the following result:\n”) print(to_guess) l_of_l = get_list_of_token_lists([to_guess]) m = tfidf.transform(l_of_l) els_to_pos = {e: tfidf.vocabulary_[e] for e in l_of_l[0]} els_to_scores = {k: m[:, v].
  • “We have to guess the family name in the following result:” {‘AVG’: ‘MLoader’, ‘Ad-Aware’: ‘Gen:Application.
  • Using Machine Learning to Name Malware
  • Let’s create some training data to label parts of virus names with their corresponding tags.

Extracting information from malware names using Conditional Random Fields.
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The Making of a Cheatsheet: Emoji Edition — Emily Barry

The Making of a Cheatsheet: Emoji Edition  #MachineLearning #DataScience

  • Another thing I love is data science.
  • Linear regression especially is a thing you learn constantly in other contexts and may not realize it’s used in data science.
  • Nor did I set out to make an emoji cheatsheet.
  • There are a few important things in data science that might have just ended up having their own section in a perfect world where a 3-D cheatsheet is a thing.
  • The emoji choices throughout this sheet were very intentional.

Read the full article, click here.


@MikeTamir: “The Making of a Cheatsheet: Emoji Edition #MachineLearning #DataScience”


I’ve mentioned this before, but I really love emoji. I spend so much of my time communicating with friends and family on chat that emoji bring necessary animation to my words that might otherwise look flat on the screen. 💁


The Making of a Cheatsheet: Emoji Edition — Emily Barry

The Making of a Cheatsheet: Emoji Edition — Emily Barry

This is AWESOME!

  • Another thing I love is data science.
  • Linear regression especially is a thing you learn constantly in other contexts and may not realize it’s used in data science.
  • Nor did I set out to make an emoji cheatsheet.
  • There are a few important things in data science that might have just ended up having their own section in a perfect world where a 3-D cheatsheet is a thing.
  • The emoji choices throughout this sheet were very intentional.

Read the full article, click here.


@RasuShrestha: “This is AWESOME! “#MachineLearning: Emoji Edition”

Blog: by @emilyinamillion #DataScience”


I’ve mentioned this before, but I really love emoji. I spend so much of my time communicating with friends and family on chat that emoji bring necessary animation to my words that might otherwise look flat on the screen. 💁


The Making of a Cheatsheet: Emoji Edition — Emily Barry