Interpretation is a personal experience. So is conjecture and imagination. Google’s DeepDream Program has opened new doors for all three. The question is- is this weird and yet refreshingly different way of looking at things the first step towards a future where impossibility is considered unreal?

Simplifying DeepDream Software

You may describe DeepDream as a neural network. Consider it to be similar to the image recognition technology of Google. This machine is taught to recognise patterns, coloured pixels in this case, in data.

Pre-operating on a bank of image data, DeepDream was taught to recognise certain shapes. Then, the creators went ahead and showed it to create images based on its guesses.

Recognising, Enhancing & Iterating

For instance, if DeepDream is asked to search for birds, it will catch anything that looks remotely close to a bird and will enhance it to look more like a bird. Then, it will iterate the whole process of recognition and improve on this modified image.

The result- an image full of morphed bird faces. So, you see, in a way, it is making things appear out of nowhere. The outcomes can be sometimes fascinating, sometimes beautiful and at times nightmarish.

Brilliant and Yet Unnerving

And I am not just talking about the results that come out of Google’s DeepDream generator. I am speaking about the implications of a machine that fakes so convincingly.

Art is an output of passion, interest, logic, contemplation, and other such intense emotions. DeepDream code is a tool. It works on commands and some analytical intelligence. And yet, it produces results that are flawless in being, well, whatever they are.

Why Do DeepDream Images Resemble Hallucinations

When you hallucinate, your brain recognises things, exaggerates them and runs your personal version of the world on a loop in front of your eyes.

That is in a way what DeepDream app does. You can say that they consider all possibilities that could have caused a particular sensory input, and then they exaggerate the result, iteration per iteration.

Now, these options are somewhat limited. This is because this machine is in the learning phase, so it only recognises things it has seen before – data that it has been fed and that it now knows and hence can create.

This is why you will come across a lot of dog faces in many DeepDream images.

Where Can You Create Your Own DeepDream Image?

Google’s DeepDream Generator is the place to go to. It is online and fairly fast in processing and vastly featured. However, there are other options, both free of cost and premium ones that you can find via a simple Google search.

The Ultimate Implications of DeepDream Technology

As long as this machine grows on generic images, creating birds and dinosaurs, and breaks in a kung fu panda image, for instance, that’s acceptable. But consider a possibility where it is made to study Picasso or Monet or Da Vinci- their styles, their strokes, everything.

If it creates something out of that knowledge, the results would be incredible because they would be based on the best.

Only, how comfortable are we with this? Is this, in a way, the beginning of an era where we lose humanity to machines? Or is it just another visual style that people will get bored of in a few years, or use as an aid to developing digital art?

All in All- It Isn’t Bad or Good

To some, it is intriguing. To others, it is just illogical stuff made of nightmares. The reception is mixed.

But to be honest, Google’s DeepDream also is just another trend. That it stays or withers away is something only time will reveal. However, it will be interesting to see the several debates that it will unfold, with this being a piece of technology that solidifies the belief that machines might just take over someday.

Primary Keyword: Google DeepDream 

LSI Keywords: DeepDream software, Google DeepDream generator, DeepDream code, DeepDream Technology, DeepDream app, DeepDream images.

References:

1. https://psmag.com/why-Google-s-deep-dream-is-future-kitsch-5963c28fde12#.hxme2g87n
2. http://www.networkworld.com/article/2974718/software/deep-dream-artificial-intelligence-meets-hallucinations.html
3. https://research.Googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html
4. http://motherboard.vice.com/read/why-Googles-neural-networks-look-like-theyre-on-acid
5. https://www.fastcodesign.com/3048941/why-Googles-deep-dream-ai-hallucinates-in-dog-faces
6. https://www.reddit.com/r/explainlikeimfive/comments/3cbelv/eli5_can_anyone_explain_Googles_deep_dream/

 

 

With advancements we make in the world of technology, humanity has proven to be creative in many ways. Every discovery takes us deeper into the innovative things that can be done with the use of technology. And maybe it was with this dominant thought in mind that Google came up with the DeepDream Algorithm. 

Ever since Google introduced its DeepDream Algorithm to the world, it has made dreams come true. This amazing app developed by Google’s engineers make use of Artificial Neural Networks and shows you exactly what it can see in the image provided. And these resulting images are exactly what result in the dream-like effect. The results certainly make you feel as if they were coming from your thoughts and thus came the unusual name DeepDream. While artificial intelligence isn’t something unheard of anymore, this new development by Google is certainly one of a kind.

Hard Work & Creativity

The DeepDream Algorithm is certainly not a simple piece of code. Engineers at Google worked hard to create such a code that enables a computer to identify an image inside another image, that too with a lot of precision. And of course, it was not done in a single attempt. Constant testing and error detection were required to create this amazing technology. One of the blog posts on the Official Google Research Blog is imperative in understanding how this technology works. This blog post explains how the Artificial Neural Networks are implemented in the DeepDream Algorithm.

Reason for Developing DeepDream

The main reason engineers at Google decided to develop this algorithm is that they wanted to know what would happen if they tried to create a software different from others – one that can identify things that are not part of the picture but still appear to be present. This idea was then implemented, and software was developed that could not only just identify these imaginary images, but also refine them to make the picture clearer to the human eye.  

Confusing isn’t it? Here is a simple example. Suppose we ask the network to look for pictures of cats in the picture. This leads to the creation of a feedback loop. If a part of the image seems a little like a cat, then the software will identify it each time it passes that part thus refining it until a detailed picture of a cat appears.

That’s not all; we can even allow the network to create its results by not giving it any input. This creates a series of new achievements that can be endless.

Harmful for Painters & Artists?

One would think that such a creative and artistic technology could prove harmful to the livelihood of painters and artists who follow the rules of their brush. Well, the answer to this is probably not. Not everyone who tried the DeepDream Algorithm managed to create good pictures. Most of the pictures look like a painting of a mandala you would find in a dorm room.

This DeepDream technology is not just for entertainment; it can also be used as a portal to learn more about the science of dreaming. One of the many things not understood by man is how humans experience some high quality, intense dreams in their sleep. With the DeepDream technology, there may be a chance to learn how some features of such ideas are related to what we view while awake. But no evidence of this has been provided yet.

If you are a Windows user and want to try to create your artistic images using the DeepDream Algorithm, then you can find the steps here.

LSI Keywords: DeepDream Algorithm,DeepDream technology, Artificial Neural Networks, creative and artistic technology

Technology is moving ahead. And with it, so is our power to see the world in enhanced technological terms. The DeepDream Algorithm is like a whole other dimension of vision and changing visual data.

What Is DeepDream Algorithm?

DeepDream Algorithm is a program developed by Google. It works to find and accentuate various patterns in pictures, thus producing psychedelic and hallucinogenic effects.

This project has been designed to detect faces, and other patterns in your pictures to give it an abstract effect.

Why DeepDream?

Initially, this algorithm was developed by Google for an entirely different reason. DeepDream was created to analyse what a complex neural network sees while looking at a picture.

This was a program developed primarily for scientists and engineers. However, now it has taken an abstract art form. Or trippy psychedelic art.

And not to forget, DeepDream has become the pathway to new display pictures.

Prisma & DeepDream

Prisma is a rip-off from the parent program – DeepDream. DeepDream is an open source program. As such other web services and mobile applications are free to use and further develop the DeepDream Algorithm.

Prisma is one such rip-off which has become the favourite tool of modern social networkers. The pictures produce a trippy effect which is loved by the present gen.

Why So Popular?

The DeepDream Algorithm is a popular software because of a few reasons:

• People love psychedelic and abstract art forms. It is almost as if you are hallucinating.
• DeepDream lets you see the picture as a very complex neural network sees.
• The fact that you can perceive what it is like to hallucinate popularises the program even more.

The Dreams of AI And How DeepDream Works

DeepDream recreates images in a way that seems dream-like. Interestingly, the recreated images are supposedly dreamed by the computers. The dreams of AI.

The process which is making artificial intelligence dream is called Inceptionism. Yes, after the movie Inception which dealt with layers of ideas.

In here, it is the AI which dreams in layers. And recreates the images we feed them. However, they cannot dream by themselves. They dream what we tell them to imagine.

We, humans, have the ability to conjure stuff, mix things up and create dream-like experiences. But computers can’t do that. Hence AI dreams of what we tell them to. Or, a preset bundle of instructions.

Changing the Way We See the World

It’s not so much changing the way we see the world as changing the way AI sees the world. Sounds weird? After all, how can AI see the world?

Well, it sees through the codes that humans are developing. But the outcome is so human like that it’s creepy. DeepDream allows the computer to recognise patterns and twist and turn them into hallucinogenic shapes.

Often, we humans find abstract and creepy shapes in clouds and tree leaves. DeepDream is recreating our vision through AI. What we see is being shown. Through lines of codes.

Isn’t it fascinating how technology is evolving so fast?

The Advancing Technology And The DeepDream World

It is clear that technology of the world is on a high, producing psychedelic images and feeding the imagination of the artists, thereby promoting abstract art. It is changing the world and how we see the world.

Efforts and questions are being raised. Could machines be created to dream on their own? Could they be made to be creative? Without human help?

The DeepDream Algorithm has apparently sparked off interesting and curious questions for the future. Visions and dreams are changing their meanings. And our perception too.

Maybe someday soon, the AI might dream on its own.

Primary keyword – DeepDream

LSI keywords – DeepDream Algorithm, Prisma, artificial intelligence, goals of AI, psychedelic images

Reference links:

• http://singularityhub.com/2015/06/19/this-is-what-happens-when-machines-dream/
• https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html?m=1

The engineers knew they were on to something massive when they decided to put a camera on a mobile phone. Fast-forward to 2017: amateur photography and selfie culture are at an all-time high, and the trend is likely only to grow in the future. The latest innovation in this space is the use of artificial intelligence and neural networks to create some of the most psychedelic pictures on the web: Welcome to Project DeepDream.

Overview

Lately, you might have found social media flooded with images that could best be described as ‘trippy,’ ‘psychedelic’ or ‘outright crazy.’ These pictures are courtesy Google’s Project DeepDream, or Prisma or in some cases, both.

Both applications take ordinary pictures and deliberately convert them into over-processed images with many layers of effects.

What Is It?

Google’s DeepDream is primarily a computer vision software which utilises convolutional neural networkto deeply analyse images and find patterns within them, which could be later manipulated to create desired effects.

DeepDream was originally designed for the 2014 ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) and made available to the public in July 2015.

Soon after that, Google published this software and made the DeepDream code open source; enabling anyone to use the system and make their image manipulation software and apps for the web, desktop, and mobile phones. One such app is, the much loved, Prisma.

What Are Neural Networks and AI?

The concepts that lie at the core of these capabilities are Neural Networks and Artificial Intelligence. Let’s elaborate on these.

One of the key elements of Artificial Intelligence is its ability to learn, adapt to the situation and make decisions accordingly, without human interference.

Neural Network is a tool of Artificial Intelligence, which is focused on developing systems and machines which can be ‘trained’ over a period into recognising patterns and predicting models, like a biological brain.

The ‘Brains’ Behind It

Even though Neural Network is designed to train itself and make decisions like a biological brain would, it does so by using mathematical algorithms and not biological neurons.

In that case, why is it still called ‘neural’ network? That is because the system is based on an extensive collection of computational neural units, each with its function, and connected in the form of layers, similar to how biological neurons are connected by axons.

How Does It Work

We know that Neural Networks (and DeepDream) operate in layers. Each layer of the network, starting from the most basic and moving up, analyses the picture and adds its set of effects.

For Example, if a part of the cloud in a picture looks a bit like fish, the basic layers of the network will analyse that and make it look ‘more’ like a fish. As the image is passed on to consecutively more complex layers, that part of the cloud starts closely resembling a fish and eventually looks exactly like one.

What Are Its Applications?

DeepDream is in its early stages, and there’s a lot of work to be done and a lot to be understood about the functioning of the network.

As it continues to improve, Artificial Intelligence will significantly see a boost in its applications as its ability to concentrate on more important features and ignore the unimportant ones will improve.

And as AI improves, so will a robots’ ability to understand the world around them and to better blend in the society. The future looks bright.

Applications like DeepDream and Prisma are classic examples of taking a seemingly mundane piece of technology and doing something extraordinary while also engaging the masses. With an increase in its user-base, feedback from users and overall development of the technology, Artificial Intelligence is a step closer to being an integral part of our everyday lives.

References:

https://en.wikipedia.org/wiki/Artificial_neural_network

https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

http://mastersofmedia.hum.uva.nl/blog/2015/10/30/googles-deepdream-algorithms-on-lsd/

http://www.smh.com.au/digital-life/digital-life-news/googles-surreal-deepdream-image-algorithm-goes-viral-20150709-gi8e1x.html

LSI Keywords:

Google DeepDream Project

Neural Network technology

DeepDream Algorithm

DeepDream working process

Neural Network layers

Supply chains are evolving to become complex entities that are dependent on each other. This has made it inevitable for businesses to branch out and extend beyond the boundaries of the enterprise.

Let us take a look at the technology trends in logistics that drove the industry practices in 2016.

1. Radio Frequency Identification (RFID) Technology

This involves the use of RFID tags and tag readers to keep a continuous tab on objects like individual units of goods, platforms on which goods are stacked and moved, or shipping equipment.

This is particularly relevant in industries producing valuable and perishable goods like semiconductors. This technology has also been successfully used to minimise handheld picking, scanning, and shipping of inbound and outbound goods, hence achieving savings in labour and quick processing times.

2. Multi-enterprise Visibility Systems and Omnichannel Solutions

Shared risk and visibility drive businesses today. To mitigate these issues, multi-enterprise supply chain strategies have been implemented widely. These systems provide a comprehensive view of the metrics and processes across the network. These strategies have been broadly adopted in the high-tech and automotive industries.

Similarly, cloud-based platforms are being increasingly used as part of the shipping strategy for an increase in customer base. The cloud is the primary resource that enables rapid data processing and makes Internet of Things (IoT) possible.

3. People-enabling Software

Companies needed a technology that helps people in resolving problems efficiently. They needed to locate solutions to problems outside the internal business processes. They also wanted to use that information to quickly put out the fire.

Technologies like shared portals, live communication, and search capabilities have helped businesses identify the right people with solutions, in real time, across the globe.

4. Bluetooth Technology

In spite of IoT coming to be increasingly relevant in the supply chain, non-data driven technologies like Bluetooth devices are being used in tandem for quality control and accountability. This can be attributed to the low cost and low energy use of these devices.

Bluetooth enabled devices can be easily mounted on docks and shipments to relay information about the package and its proximity. This real-time data update improves accuracy and efficiency of logistical processes.

Industries like the governing authority of FDA have been seen to implement this technology.

5. Transportation Management System (TMS)

The rise of cloud, Software-as-a-Service (SaaS) applications and web-based systems has made it possible for small shipping companies to take advantage of high-end transportation management systems. The ROI for TMS cloud-based systems has consistently seen a definitive growth.

Trends Predicted To Excel In The Future

Some of the cutting-edge technological solutions that are in their infancy today could morph into popular strategies to upgrade supply chain performance. Some of these are listed below.

• Autonomous Vehicles and Drones

Establishments like Amazon and Intel have successfully used unmanned aerial vehicles or drones to deliver products in minutes. Although this technology is currently not viable for massive roll out, shipping entities are depending on the insurance industry to embrace this technology in the future.

• Robots in Warehouses, Loading, and Unloading

Robotic systems are being created to run the logistics industry more efficiently. 2016 was the year when this technology was increasingly adopted for use in warehouses, distribution centres and probably even at the customer’s doorstep!

Technological innovations dictate how the world does business today. This has also spilt over to the logistics industry function, making it an integral part of the shipping processes today.

LSI Keywords – Supply chains, Radio Frequency Identification, RFID tags and tag readers, Multi-enterprise Visibility Systems, Omni-channel Solutions, multi-enterprise supply chain strategies, cloud-based platforms, Internet of Things, People-enabling Software, Bluetooth Technology, Bluetooth enabled devices, Transportation Management System, Software-as-a-Service, web-based systems, high-end transportation management systems, TMS cloud based systems, Autonomous Vehicles and Drones, Robots in Warehouses, Robotic Systems