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Showing posts with label Artificial Intelligence. Show all posts
Showing posts with label Artificial Intelligence. Show all posts

Amazing features of Vivo V9 AI camera

Unique features of Vivo V9 AI(Artificial Intelligence) enabled camera can enhance your selfies like never before

features of Viivo v9 AI camera
Vivo V9 AI Camera
Chinese phone makers Vivo launched their latest smartphone Vivo V9 with a almost bezel-free 6.3-inch display, 24MP front camera and 16MP+5MP dual camera setup on the rear. Vivo V9 primarily focuses on high quality selfies.

Vivo claims thats "The V9’s world-leading 24MP front camera turns your every selfie into a work of art. Experience greater brightness, color vibrancy, clarity and dynamic range, even in the dimmest light. Whenever you need it, your V9 is right there with you, capturing every moment with crystal-clear precision. Turn your selfies into masterpieces and shine in every photo."

Features of Vivo V9 AI camera

-Bokeh effect: The Bokeh feature lets you blur the background and highlight the foreground before or after taking the shot.The V9 uses two rear cameras – a 16MP main camera and a 5MP secondary camera* – and is supported by an AI Bokeh algorithm. This algorithm has been optimized based on machine learning of large amounts of data, so it can achieve amazing bokeh shots that rival DSLR camera results.
Bokeh effect
-Shoot and focus later:You can even shoot first and focus later, transforming every photo into an artistic masterpiece.

-AI Face Beauty:Vivo claims - with the help of its AI (artificial intelligence) it will be able to work out your age, sex, skin tone and texture by referring to a database of almost one million facial images from all over the world, it detects your gender, age, skin tone and texture, as well as the lighting environment, and uses this information to deliver astonishingly clear, beautiful selfies.
It can also learn your customized setting preferences and apply these settings automatically each time you take a photo. Think of it as your personal make-up artist, ready to make you look naturally perfect in every shot.

-AI Face Access:With the all-new AI Face Access technology, the V9 scans your facial features and unlocks instantaneously upon activation. It also identifies unauthorized access attempts by detecting light-reflected surfaces and subtle facial movements, helping prevent phone unlocking through use of photos or video.
Vivo V9 AI camera face access

-AR Stickers:Vivo V9 comes with pre installed Augmented Reality(AR) stickers.
AR Sticker features numerous stickers that you can use to decorate your selfies and show off your cute side. Set your style to super-sweet, funny or punk, and transform your look with just a single click. Get ready to be playful and enjoy some serious photo fun.
Vivo v9 AI camera VR stickers
-Time-Lapse Photography:Time-lapse photography is a cinematography technique whereby the frequency at which film frames are captured (aka the frame rate) is much lower that that which will be used to play the sequence back. When you replay this sequence at normal speed, time appears to be moving faster and lapsing

-AI Selfie Lighting:With every photograph, the V9’s algorithm transforms the original 2D photo into a 3D model in order to process the light authentically and artistically. It can allow you to choose from a range of light effects to create model-style pictures. Get ready to transform any environment into your own professional photo studio.

-Group Panorama Selfie:The Vivo V9 also comes with the group selfies that allows you to shoot panoramic pictures from the front-facing camera. The feature requires you to move around the phone to get in as many as people in the frame as you’d like.

Other scene mode that are available with Vivo V9 AI camera:Ultra HD,PPT,Professional,Slow,,Camera Filter,Live,Bokeh,HDR,AI Face Beauty,Panorama,4K video, Palm capture,Gender detection,LED Flash,
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Google Artificial Intelligence can predict flight delays and cheapest fare

Google will now predict if your flight will be delayed using it's Artificial Intelligence algorithm with 80% accuracy

Flight delays are always frustrating for travelers. Google has launched a new feature on its site, which it says can predict flight delays with over 80 percent accuracy, long before the airlines let anyone know.
google flights and artificial intelligence
The tool is available on the website Google Flights, as well as on Google's Flights app.Google Flights is an online flight booking search service which facilitates the purchase of airline tickets through third party suppliers.Google can help you choose right flight with cheapest rate for your journey. So far Google flight has been a flight search service just like any other service provider, but with the introduction of Artificial Intelligence ,Google flights is going to stand out from others.
google flights updated version
Google Flights Updated Version
Google AI has already a made remarkable achievements in various fields.Recently NASA revealed they use Google AI to detect exoplanets.

How Google AI predict flight delays

An Artificial Intelligence system usually learns by examples. Using historic flight status data, Google's machine learning algorithms can predict some delays even when this information is not available from airlines yet.

"We’re at least 80 per cent confident in the prediction. We still recommend getting to the airport with enough time to spare, but hope this information can manage expectations and prevent surprises," says Google in a blog post.

How to check flight delay
Go to google search bar and type in your flight number. That's all.It will give estimated time of departure and arrival along with other information like terminal,gate etc.. 

Here is the search result for Lufthansa LH 401 New York City to Frankfurt flight on 4th February 2018. You can see that Google predicts Flight may be delayed by DELAYED 1 HR, 50 MINS. Reason for the delay is also included. However google suggests 'confirm flight status on an airport monitor'.
Google flight status using Artificial Intelligence
Google Flight status search result 
However, these features are currently limited to American, Delta, and United Airlines.Soon google may include other airlines data too to predict  in flight status using Artificial Intelligence algorithm.
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Scientists develop artificial synapse: A new type of artificial intelligence

Superconducting artificial synapse developed by NIST has been a crucial element missing in neuromorphic AI super computers

Researchers at The National Institute of Standards and Technology (NIST) in the US have developed a artificial superconducting synapse that learns like a biological system and could connect processors and store memories in future computers operating like the human brain. It has been a missing element in neuromorphic computers.

In the nervous system, a synapse is a structure that permits a neuron (or nerve cell) to pass an electrical or chemical signal to another neuron or to the target cell.).It is a connection or switch between two brain cells The synapse is widely believed to be integral for both learning and memory. With approximately 10^15 synapses in the human brain, it is a critical component of neural circuitry.In human brain learning happens by altering behavior of the junction between neurons(synapse).

Neuromorphic computing, is a concept developed by Carver Mead, in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.

This superconducting synapse switch can be envisioned as a new type of artificial intelligence, such computers could boost perception and decision-making for applications such as self-driving cars and cancer diagnosis.

Structure of superconducting artificial synapse

The NIST synapse is a Josephson junction.These junctions are a sandwich of superconducting materials with an insulator as a filling.

Josephson junction (JJ) is a device which allows a current that flows indefinitely long without any voltage applied.Such type of devices consists of two superconductors coupled by a weak link. The weak link can consist of a thin insulating barrier (known as a superconductor–insulator–superconductor junction, or S-I-S), a short section of non-superconducting metal (S-N-S), or a physical constriction that weakens the superconductivity at the point of contact (S-s-S).

The synapse uses standard niobium electrodes and a unique filling made of nanoscale clusters of manganese in a silicon matrix.The nanoclusters—about 20,000 per square micrometer—act like tiny bar magnets.

How does NIST’s artificial synapse works

Our brain performs computation by sending electrochemical signals between neurons. The transmission of these signals is controlled by the synapse, tiny junction between two neurons.
The receptivity of the synapse determines if the post-synaptic neuron 'spikes' in response to a signal.

If the signal is not strong enough the post-synaptic neuron won't fire.
As more signals are sent, the synapse becomes more receptive. This strengthens the neural connectivity enabling learning.
To build computers that mimic the brain, researchers want to build artificial synapses. This requires two things.Spiking behavior and ability to learn.
To provide the spiking behavior the NIST design uses a Josephson junction (JJ), a device made of two superconductors separated by an insulating layer.
When a current of sufficient strength is run through the junction it produces low voltage spikes.
When the current is lower than the 'critical current' there is no response.
To manipulate the critical current , researchers embedded magnetic nano clusters into the insulating layers.These nanoclusters behave like bar magnets, each polarized along a magnetic axis.
When magnets are disordered critical current remains high. By applying current pulses in an applied magnetic field the order of the cluster can be increased.
This reduces the critical current meaning the junction will exhibit spiking behavior for lower currents.
Strengthening the connection between input and output enables the synapse to 'learn' just like a neural synapse.

“The NIST synapse has lower energy needs than the human synapse, and we don’t know of any other artificial synapse that uses less energy,” NIST physicist Mike Schneider said.

The new synapse would be used in neuromorphic computers made of superconducting components, which can transmit electricity without resistance, and therefore, would be more efficient than other designs based on semiconductors or software. Data would be transmitted, processed and stored in units of magnetic flux. Superconducting devices mimicking brain cells and transmission lines have been developed, but until now, efficient synapses—a crucial piece—have been missing.
Watch video..
Video&Image credit NIST
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Artificial Intelligence enabled products and technolgy launched at CES 2018

Artificial Intelligence at CES 2018 Las Vegas

CES 2018 has witnessed lot of applications of Artificial Intelligence. We have picked few worth noticing.

Sony's Aibo robot dog

Sony presented it's latest version of Aibo robot dog - and it's cuter than ever. Sony says it relies on sophisticated sensors and AI programming. The Aibo senses its surroundings, and not just to avoid objects. The little puppy tries to mimic the movement and activities of a real dog.

Aibo responds to touch on three specific areas: on its head, back and under its chin.It's cuteness can't be explained. Watch it below

Self-driving travel bag

CES 2018 witnessed another funny, same time innovative product self driving travel bag CX-1 luanched by China’s ForwardX Robotics. It is first of kind for sure. Four-wheeled travel bag can automatically follows its user around the airports and other places.
cx 1 self driving suitcase
CX-1 self driving travel bag mad by ForwardX Robotics
The smart bag uses cameras and AI(Artificial Intelligence) algorithm to avoid crashes.170-degree wide angle lens and built-in facial recognition software, which allow the device to follow you at up to 7 miles per hour throughout the terminal .Smart wrist band will notify if it gets too far away or when the battery power gets low.

Intel-Ferrari AI partnership for sports broadcasts

At CES 2018, Intel's artificial intelligence and Ferrari, announced partnership in creation and distribution of sports broadcast content , that involve aerial footage captured via drones, which will then be mixed and curated to create personalised feeds.

Announced by Intel CEO Brian Krzanich during his CES 2018 keynote, the three-year partnership with Ferrari North America will see Intel AI tech including the Intel Xeon scalable platform and the Neon Framework used, along with aerial footage captured by Intel drones.

Infrastructure cost inherent in deploying fixed cameras around each track throughout the race calendar is a huge amount.This can be made lot cheaper by using drones and then using AI on top of it.Multiple camera feeds from drones will stream this to a central source, with the AI then mixing and cutting the feeds together.

Amazon Alexa and Google Assistant

AI enabled Google assistant and amazon alexa
Google Assistant& Amazon Alexa Smart Speakers
Amazon and Google presented their latest version of AI enabled voice assistance at CES 2018. Lot of smart speakers were presented at CES 2018. Currently Amazon Alexa and Google Assistant is open to other companies also. These enables them to come out with more innovative products.

Byton Autonomous Electric Vehicle

byton autonomous smart electric car
Byton autonomous smart car 'Concept'
Chinese firm Byton presented their smart electric car at CES 2018 Las Vegas. Manufacturer has focused mainly on smart power rather than horsepower. It features a large touch screen covering entire dashboard area. Byton smart car comes with gesture control system.At launch, Byton says it will include Level 3 self-driving capability, which means the car can drive itself in many situations, but a human driver will have to take control for some. The car will be technically capable of Level 4 self-driving, which means it can handle all driving tasks, a feature that Byton can make available once government regulations permit.Artificial Intelligence is one of the main contributor in achieving autonomous drive.

Hyundai's 'Intelligent Personal Agent'

South Korean Automobile giant Hyundai introduced their 'Intelligent Personal Agent', a voice-enabled virtual assistant system at CES 2018 Las Vegas.Intelligent Personal Agent, co-developed with SoundHound, to be deployed in new models in 2019.System serves as a proactive assistant, predicting driver needs and facilitating vehicle functions.AI enabled Advanced technology can be optimized for future connected car data demands.
hyandai intelligent personal agent for connected cars
Hyundai car concept with Intelligent Personal Agent
A lot more AI supported products, especially wide range of robots were launched during CES 2018.
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NASA uses Artificial intelligence to finds solar system with earth like planet

NASA with the help of Google's Artificial finds a solar system like ours by analyzing data provided by Kepler Space Telescope
NASA uses google AI to spot exoplanets in kepler 90 solar system
Google's Artificial Intelligence and Neural Network algorithm helped NASA to spot exoplanets in Kepler-90 solar system which is similar to our solar system with 8 planets revolving a star.Credits: NASA
NASA's Kepler Space Telescope launched with a mission to spot earth-like planets outside solar system (exoplanets).So far Researchers have used the NASA Kepler Telescope to discover more than 3,000 different exoplanets.

Recently researchers from NASA announced they have achieved some thing new. Using Google's Artificial Intelligence(AI) and data collected by Kepler Telescope scientists have identified a solar system like ours far far away, containing a star and 8 planets revolving it.

The newly-discovered Kepler-90i – a sizzling hot, rocky planet that orbits its star once every 14.4 days.About 30 percent larger than Earth, Kepler-90i is so close to its star that its average surface temperature is believed to exceed 800 degrees Fahrenheit, on par with Mercury. Its outermost planet, Kepler-90h, orbits at a similar distance to its star as Earth does to the Sun.“The Kepler-90 star system is like a mini version of our solar system. You have small planets inside and big planets outside, but everything is scrunched in much closer,” said Vanderburg, a NASA Sagan Postdoctoral Fellow and astronomer at the University of Texas at Austin.

Why Artificial Intelligence?

NASA's Kepler Telescope has been searching alien worlds since 2009.Kepler’s dataset consists of more than 35,000 possible planetary signals. Automated tests, and sometimes human eyes, are used to verify the most promising signals in the data. However, the weakest signals often are missed using these methods.NASA was waiting for the right tool or technology to unearth them.Here comes the tech giant Google's Artificial Intelligence and Neural Network.
Read More about Artificial Intelligence and Neural Networks

How researchers used Gooogle's Artificial Intelligent Neural Network to search earth like planets and solar systems like our's?

Shallue, a senior software engineer with Google’s research team Google AI, came up with the idea to apply a neural network to Kepler data. He became interested in exoplanet discovery after learning that astronomy, like other branches of science, is rapidly being inundated with data as the technology for data collection from space advances.

“In my spare time, I started googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” said Shallue. "Machine learning really shines in situations where there is so much data that humans can't search it for themselves.”

NASA's Website says,'The discovery came about after Christopher Shallue along with another reasearcher Andrew Vanderburg trained a computer to learn how to identify exoplanets in the light readings recorded by Kepler – the minuscule change in brightness captured when a planet passed in front of, or transited, a star. Inspired by the way neurons connect in the human brain, this artificial “neural network” sifted through Kepler data and found weak transit signals from a previously-missed eighth planet orbiting Kepler-90, in the constellation Draco.'

How Neural Network is trained to identify exoplanets?

A neural network is like our brain. If it is taught with enough examples , it can take decisions thereafter.So, researchers trained the neural network to identify transiting exoplanets using a set of 15,000 previously identified exoplanet data.The network learned it and identified similar data patterns in the huge collection of Kepler data.

In the test set, the neural network identified correct set of exoplanet data with 96% accuracy.After number of testings the researchers directed their model to search for weaker signals in 670 star systems that already had multiple known planets. Their assumption was that multiple-planet systems would be the best places to look for more exoplanets.

Neural network will become more precise as it is fed with more and more data patterns. Shallue and Vanderburg plan to apply their neural network to Kepler’s full set of more than 150,000 stars so that more exoplanets can be sifted out.

Read More on NASA Website
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What is artificial neural network and how it works?

What is a neural network?How it works and how it is can be the key element of Artificial Intelligence and Machine learning?

Artificial neural network is inspired by biological nervous system.
An Artificial Neural Network (ANN) is an information processing system that is inspired by the biological nervous systems. Artificial Neural network is an attempt to mimics animal brain. Neural network consist of large number of highly interconnected processing elements (neurones) working in unison to solve complex problems. Whether it is biological or artificial neural network neurons are the building blocks. The concept of Artificial Intelligence and neural networks established before the advent of computers, but many important advances have been boosted by the use of computer emulations.
Artificial intelligence and neural networks already applied in information processing systems, pattern recognition, medical diagnosis etc. Still there is a long way to go. This field offers infinite opportunity in coming decades.

Biological neurons V/S artificial neurons

Human brain is composed of billions of neurons and other specialised cells. Brain is the least explored and most complex organ of our body. We know only less than 1% of our brain. But we have a vague idea about learning mechanism of our brain.


Structure of biological neuron


A neuron receives signals from other neurons through a host of fine structures called dendrites. The neuron sends out spikes of electrical signals through a long, thin stand known as an axon, which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity in the connected neurons. When a neuron receives excitatory input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes. In other words information is stored in these junctions between each neurons.

Biological to artificial neurons

Now let us see how an artificial neuron model is designed to mimic the basic functions of a neuron. An artificial neuron is a device with many inputs and one output. Dendrites will be replaced by input, processing unit will function as cell body and axon will be replaced by output line. We can program/ teach cellbody/processing unit to get desired output.

A simple neuron

The neuron has two modes of operation; the training mode and the using mode. In the training mode, the neuron can be trained to produce a particular output, for a set of inputs. In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output. If the input pattern does not belong in the taught list of input patterns, the firing(excitation) rule is used to determine the output state.

Artificial neural network example

For example, a 3-input neuron is taught to output 1 when the input (X1,X2 and X3) is 111 or 101 and to output 0 when the input is 000 or 001. Then, the truth table is;

X1:
0
0
0
0
1
1
1
1
X2:
0
0
1
1
0
0
1
1
X3:
0
1
0
1
0
1
0
1
OUT:
0
0
?
?
?
1
?
1

Output for the inputs 010,011,100 and 110 is not known to neuron. Here comes the firing rule Take the pattern 010. It differs from 000 in 1 element, from 001 in 2 elements, from 101 in 3 elements and from 111 in 2 elements. Therefore, the 'nearest' pattern is 000 which belongs in the 0-taught set. Thus the firing rule requires that the neuron should not fire when the input is 001. On the other hand, 011 is equally distant from two taught patterns that have different outputs and thus the output stays undefined (0/1).

By applying the firing in every column the following truth table is obtained;

X1:
0
0
0
0
1
1
1
1
X2:
0
0
1
1
0
0
1
1
X3:
0
1
0
1
0
1
0
1
OUT:
0
0
0
?
?
1
1
1

Therefore the firing rule enables neuron to respond 'sensibly' to patterns not seen during training.
More complicated neuron architecture

Above neuron doesn't do anything that conventional computers don't do already.In a neural network output of one neuron is given as input to another neuron. So, more sophisticated and meaningful neuron can be designed obtained by giving weight to each input. The effect that each input has at decision making is dependent on the weight of the particular input. The weight of an input is a number which when multiplied with the input gives the weighted input. These weighted inputs are then added together and if they exceed a pre-set threshold value, the neuron fires. In any other case the neuron does not fire.

Output=f(X1W1+X2W2+………..+XnWn)
This neuron has the ability to adapt to a particular situation(inputs) by changing its weights and/or threshold.

We have seen in biological neurons ‘Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes’

In artificial neurons learning occurs by changing its weights on particular input, thus changing the effect of one neuron on another. Various algorithms exist that cause the neuron to 'adapt'.

Neural network



A neural network is an interconnection of large number of neurons. Output of one neuron will be fed to input of one or more neurons. Apart from the input and output layers , hidden layers are include so that output will be fine tuned and error free. As the number of neurons in a network increases, intelligence of that network will also improve(as in biological neural system).

Neuron:An example

In above figure neuron is trained to recognise the patterns X and Y. The associated patterns are all black and all white respectively as shown below.Let us assume input is captured by 3 X 3 sensor array
.
If we represent black squares with 1 and white squares with 0 then the truth tables for the neuron is.
A1
A2
A3
A4
A5
A6
A7
A8
A9
o/p
1
0
1
0
1
0
1
0
1
X
The truth table for above pattern is as follows.
A1
A2
A3
A4
A5
A6
A7
A8
A9
o/p
1
0
1
1
1
1
0
1
0
Y
Above two input sets are the taught input sets. Now, let us see what will be the output of the neuron if another set of input is introduced.
Truth table for the above pattern is given below. Initially output of the above pattern unknown to the neuron, but, on application of firing rule neuron will conclude above pattern is similar to taught input corresponds to ‘X’.
A1
A2
A3
A4
A5
A6
A7
A8
A9
o/p
1
1
1
0
1
0
1
1
1
X
Neuron will look for the similarity of this input with the taught inputs. This truth table is similar to first truth table in 7 ways(7 inputs are similar) and to the second table in 4 ways only. So, neuron will give the output as ‘X’.
By applying firing rule neuron will conclude above pattern is ‘Y’ since it’s 5 input are similar to taught inputs of ‘Y’ and only 3 inputs are similar to taught inputs of ‘X’.
A1
A2
A3
A4
A5
A6
A7
A8
A9
o/p
1
0
1
0
1
0
0
1
0
Y
Similarly observing other patterns given below you can see that , with only two taught input sets , system can identify up to five set of inputs. If a single neuron can do this much, what would be the capability of a network of tens, thousands or millions of neurons.? Today this algorithm can be implemented using simple computer programs.
A1
A2
A3
A4
A5
A6
A7
A8
A9
o/p
1
0
1
0
1
0
1
1
1
X
A1
A2
A3
A4
A5
A6
A7
A8
A9
o/p
1
1
1
0
1
0
1
0
1
X

A1
A2
A3
A4
A5
A6
A7
A8
A9
o/p
1
0
1
1
1
1
0
0
1
Y
Artificial neural network application

  • Neural networks are best in pattern identification, identifying particular trends in a set of data, they are well suited for prediction or forecasting needs in sales, marketing, customer research, planning etc..
  • Medicine: Artificial neural nets are capable of disease diagnosis and treatment suggestions. Neural networks learn by example so the details of how to recognise the disease are not needed. What is needed is a set of examples that are representative of all the variations of the disease. Their problem solving ability and precision improves after each task(like humans).
  • Humanoid Robots: Artificial intelligence can be incorporated into robotic brains using neural networks.
  • Software :Pattern Recognition in facial recognition, optical character recognition, etc.
  • Self driving vehicles and autopiloting
Many applications of Neural Networks are yet to be explored. Their ability to learn by example makes them very flexible and powerful. With the advancement of high power computing we have a lot to gain from neural networks.Scientists believe that some day a 'consious' networks might be produced.For that neural network will be the biggest contributor along with other technologies.

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