AI News, Artificial intelligence made this robot dog a very good boy

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.


Animatronics refers to the use of cable-pulled devices or motors to emulate a human or an animal, or bring lifelike characteristics to an otherwise inanimate object.

Modern animatronics tend to use robotics and have found widespread applications in movie special effects and theme parks and have, since their inception, been primarily used as a spectacle of amusement.[1][2]

Animatronic figures are often powered by pneumatics, hydraulics, and/or by electrical means, and can be implemented using both computer control and human control, including teleoperation.

Figures are covered with body shells and flexible skins made of hard and soft plastic materials and finished with details like colors, hair and feathers and other components to make the figure more lifelike.

Autonomatronics was also defined by Walt Disney Imagineers, to describe a more advanced audio-animatronic technology featuring cameras and complex sensors to process information around the character's environment and respond to that stimulus.[9]

The 'figure' was described as able to walk, pose and sing, and when dismantled was observed to consist of anatomically accurate organs.[20]

The 5th-century BC Mohist philosopher Mozi and his contemporary Lu Ban are attributed with the invention of artificial wooden birds (ma yuan) that could successfully fly in the Han Fei Zi[21]

and in 1066, the Chinese inventor Su Song built a water clock in the form of a tower which featured mechanical figurines which chimed the hours.

Approximately 1220–1230, Villard de Honnecourt wrote The Portfolio of Villard de Honnecourt which depicts an early escapement mechanism in a drawing titled How to make an angel keep pointing his finger toward the Sun and an automaton of a bird, with jointed wings which led to their design implementation in clocks.

One of the earliest of these large clocks was the Strasbourg Clock, built in the fourteenth century which takes up the entire side of a cathedral wall.

In 1454, Duke Philip created an entertainment show named The extravagant Feast of the Pheasant, which was intended to influence the Duke's peers to participate in a crusade against the Ottomans but ended up being a grand display of automata, giants, and dwarves.[29]

While some of these robots were, in fact, animatronics, at the time they were thought of simply as robots because the term animatronics had yet to become popularized.

Walt Disney is often credited for popularizing animatronics for entertainment after he bought an animatronic bird while he was vacationing, although it is disputed whether it was in New Orleans[32]

In 1951, two years after Walt Disney discovered animatronics, he commissioned machinist Roger Broggie and sculptor Wathel Rogers to lead a team tasked with creating a 9' tall figure that could move and talk simulating dance routines performed by actor Buddy Ebsen.

Two Muppet characters, Dr. Bunsen Honeydew and his assistant, Beaker, pilot the vehicle through the park, interacting with guests and deploying special effects such as foggers, flashing lights, moving signs, confetti cannons and spray jets.

Laffing Sal is one of the several automated characters that were used to attract carnival and amusement park patrons to funhouses and dark rides throughout the United States.[39]

Animatronics are used in situations where a creature does not exist, the action is too risky or costly to use real actors or animals, or the action could never be obtained with a living person or animal.

Its main advantage over CGI and stop motion is that the simulated creature has a physical presence moving in front of the camera in real time.

The 1993 film Jurassic Park used a combination of computer-generated imagery in conjunction with life-sized animatronic dinosaurs built by Stan Winston and his team.

Since the first game's release in August 2014, the franchise has expanded to include five sequels, two spin-off games, three full-length novels, two informational guidebooks, and a theatrical film currently in development.

To provide further strength a piece of fabric is cut to size and embedded in the foam rubber after it is poured into the mould.

Once the mould has fully cured, each piece is separated and attached to the exterior of the figure providing the appearance and texture similar to that of 'skin'.[53]

Foam latex is a lightweight, soft form of latex which is used in masks and facial prosthetics to change a person's outward appearance, and in animatronics to create a realistic 'skin'.[57]

Disney has a research team devoted to improving and developing better methods of creating more lifelike animatronics exteriors with silicone.[59]

RTV silicone (room temperature vulcanization silicone) is used primarily as a molding material as it is very easy to use but is relatively expensive.

To create more realistic movement in large figures, an analog system is generally used to give the figures a full range of fluid motion rather than simple two position movements.[63]

FACS defines that through facial expression, humans can recognize 6 basic emotions: anger, disgust, fear, joy, sadness, and surprise.

Animatronics has been developed as a career which combines the disciplines of mechanical engineering, casting/sculpting, control technologies, electrical/electronic systems, radio control and airbrushing.

Individuals interested in animatronics typically earn a degree in robotics which closely relate to the specializations needed in animatronics engineering.[67]

The fusion of animatronics with artificial intelligence results in androids, as is usually known, robots that imitate human behavior.

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