AI News, Amazon is launching a new conference dedicated to AI and robots ... artificial intelligence

Artificial Intelligence

[14] 'Facebook is using AI in private messages to suggest an Uber or remind you to pay a friend,' Kurt Wagner, Recode, April 6, 2017, available from  [15] 'Facebook's AI image search can 'see' what's in photos,' Billy Steele, Engadget, February 2, 2017, available from  [16] Please see any of the below as examples: “Movie written by algorithm turns out to be hilarious and intense,” Annalee Newitz, ArsTechnica, June 9, 2016, available from  “What an ‘infinite’ AI-generated podcast can tell us about the future of entertainment,” James Vincent, The Verge, March 11, 2018, available from  “AI can write surprisingly scary and creative horror stories,” Swapna Krishna, Engadget, October 31, 2017, available from  [17] “Disney Research taught AI how to judge short stories,” Rob Lefebvre, Engadget, October 21, 2017, available from  [18] Please see any of the below as examples: “At This Year’s U.S. Open, IBM Wants To Give You All The Insta-Commentary You Need,” Steven Melendez, Fast Company, September 2, 2016, available from

[36] “Google AI experiment has you talking to books,” Mariella Moon, Engadget, April 14, 2018, available from  [37] “Allen Institute for AI Eyes the Future of Scientific Search,” Cade Metz, Wired, November 11, 2016, available from  [38] “Artificial intelligence is going to make it easier than ever to fake images and video,” James Vincent, The Verge, December 20, 2016, available from  [39] “Facebook: Don't freak out about artificial intelligence,” Richard Nieva, CNET, December 1, 2016, available from  [40] “AI will rob companies of the best training tool they have: grunt work,” Sarah Kessler, Quartz, May 11, 2017, available from  [41] “In the AI Age, “Being Smart” Will Mean Something Completely Different,” Ed Hess, Harvard Business Review, June 19, 2017, available from  [42] “Artificial Intelligence’s White Guy Problem,” Kate Crawford, The New York Times, June 25, 2016, available from  and “AI facial analysis demonstrates both racial and gender bias,” Swapna Krishna, Engadget, February 12, 2018, available from  [43] “AI can be sexist and racist — it’s time to make it fair,” James Zhou and Laura Schiebinger, Nature, July 18, 2018, available from  [44] “Google’s parent company is using AI to make the internet safer for LGBT people,” Maria LaMagna, MarketWatch, March 14, 2018, available from  [45] “The Future of AI Depends on High-School Girls,” Lauren Smiley, The Atlantic, May 23, 2018, available from  [46] “Google’s DeepMind Launches Ethics Group to Steer AI,” George Dvorsky, Gizmodo, October 4, 2017, available from  [47] “Why artificial intelligence researchers should be more paranoid,” Tom Simonite, Wired, February 20, 2018, available from  [48] “Tech’s Ethical ‘Dark Side’: Harvard, Stanford and Others Want to Address It,” Natasha Singer, The New York Times, February 12, 2018, available from  [49] “Artificial intelligence doesn’t have to be evil.

AirAsia deploys AVA chatbot in artificial intelligence push

MANILA -- AirAsia said it launched a chatbot named AVA, tapping artificial intelligence as it unveiled a redesigned mobile app and website.

Svelte and dressed in the flaming red uniform of the low-cost carrier's flight attendants, AVA or AirAsia Virtual Allstar can answer frequently asked questions as well as provide flight information.

Notes on Artificial Intelligence, Machine Learning and Deep Learning for curious people

Deep Learning McKinsey claims that deep learning techniques have the potential to create between $3.5 trillion and $5.8 trillion in value annually in 19 industries!

ANN is modeled using layers of artificial neurons to receive input and apply an activation function along with a human set threshold.

Deep learning has already achieved near or better than human level image classification, speech/hand writing recognition and of course the autonomous driving.

Depth is the number of node layers where there are more than one hidden layers thus need for more computation power for forward/backward optimization while training, testing and eventually running these ANNs.

The most popular applied corporate cases are probably optical character recognition (OCR) to digitize text to automate data entry.

Of course initially these filters don’t know where to look for image features like edges or curves and the previously mentioned weights are random numbers (like a baby with fresh mind).

Because this is a training set we already know the outcome labels thus depending on the success of the prediction, a loss function is calculated and the network makes a back pass while updating its weights.

Now the model performs a backward pass through the network, which is determining which weights contributed most to the loss and finding ways to fine tune these weights so that the loss decreases thru consecutive passes.

RNN can remember the former inputs, which gives them a big edge over other artificial neural networks when it comes to sequential and context-sensitive tasks such as speech recognition .

RNNs are also used for language translations, composing music, writing novels, Wikipedia articles or Shakespearean poems, write AI tweets… You can train it to write machine generated Obama speeches or compose non-existent “Beatles” songs.

Yann LeCun, the director of Facebook AI said: “Generative Adversarial Networks is the most interesting idea in the last ten years in Machine Learning.” GAN makes the neural nets more human by allowing it to CREATE rather than just training it with data sets.

In the starting phase, a Generator model takes random noise signals as input and generates a random noisy (fake) image as the output.

The Discriminator which is the advisory of Generator is fed with both the generated images as well as a certain class of images at the same time, allowing it to tell the generator how the real image looks like.

After reaching a certain point, the Discriminator will be unable to tell if the generate image is a real or a fake image, and that is when we can see images of a certain class (class that the discriminator is trained with) being generated by out Generator that never actually existed before!

Experts sometimes describe this as the generative network trying to “fool” the discriminative network, which has to be trained to recognize particular sets of patterns and models.

GANs could be used for increasing the resolution of an image, recreating popular images or paintings or generating an image from text, producing photo realistic depictions of product prototypes, generate realistic speech audio of real people (OMG!) as well as producing fashion/merchandise shots.

There is already a large choice of NLP engines that are readily available to embed into everyday uses whether it is call centers, chat-bots, translators, auto-predictors, spam filters or the new vast domain of digital assistants.

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