Smart glasses

Vision for future – Smart Glasses

by Erik, Franz and Michelle

Structure:

  1. what they are for and how they work
  2. pro’s and con’s
  3. smart glasses today
  4. dystopia or utopia?
  5. conclusion
  1. What they are for and how they work

Smart glasses provide life monitoring services. They have got a reality technology to assist you with your everyday home and business life. Ok that sounds good but how do they work? They work with a combination of display, sensors, accelerometers, software and internet connectivity. Also there is a touchpad or voice control added in the glasses. You can use it with a handset or both, too.

2. Pro’s and con’s

What is true about the dystopian view on smart glasses?

If you wear smart glasses there is like with usual glasses a higher risk to get a headache or eye strain. So it is not really healthy to wear them for a longer time and they can have a really bad impact on your eyes if you have an eye disease or your vision is not full developed yet.

There is also a problem in practice, the distraction, because you cannot see what is in front of you or what happens around you if you are focusing on what the smart glasses shows you. On this way blind spots are created and you might risk your safety for example by failing to see a car or something else. This problem can cause bad accidents.

Another point at risk is the privacy because of the potential of being recorded. People with smart glasses can make recordings (also secret ones) of persons who actually do not want to be recorded and this would be an intervention in their private living.

However, these arguments against smart glasses are not really new. Recordings are also made through other devices. Paying attention on your environment especially in “dangerous” situations would prevent the risk of distraction. Usual glasses and smartphone or computer displays can also be unhealthy. So these arguments are also not really convincingly. Smart glasses instead a futuristic technology with indeed benefits.

Smart glasses – technical masterpiece!

Smart glasses are a true “hands-free-experience”. You can handle them easily because Smart glasses are completely controlled by your voice. You do not need your hands and fingers to use them so you have more flexibility. This almost illusionary invention is kind of a masterpiece because it has also a lot of gadgets. This device does most of the things your smartphone can do. Smart glasses are taking technology on a new level and are making your life easier especially if you are on the move and need a view on the environment. In a world where nearly everything gets faster and faster and where time is money the principle of multi-tasking is more and more important and also for that smart glasses are created. Moreover you can every time communicate with others so you do not need to take an extra device and because smart glasses are always on you stay connected.

3. Smart glasses today

In the last few years there has been an increase in so called smart glasses which has been highly supported by many large companies. Even though there are some changes that still remain the capabilities and the price are evolving for the better.

In the near future smart glasses are going to support factory workers and technicians in their task and work life as well as everybody’s daily life.

All in all this future investment is going to make workforce efficient and life less difficult.

The previously released prototypes have to be designed more simply and more conveniently before they can be sellers for everyone on the market.

First of all the graphic processors have to be small, lightweight and power efficient in order to conquer with the comfortable and convenient glasses nowadays.

Chips like NVIDIA Tegra x1 or intel m7 are required for accelerating real world tracking and fluid rendering.

Unfortunately the eye-tracking function and voice and gesture recognition end in massive battery and heat problems which have to be avoided.

For a full AR (Augmented Reality) experiences there has to be a high-resolution front-camera and cloud-hosted services. In conclusion the visual impression of videos and information will be reformed and overwhelming.

But these innovations require a high security standard to ensure that private information is not misplaced. Therefor regulatory regimes are needed but yet not exist.

For these reasons the market entrance has to be carefully reconsidered and tested, which takes time and leads to a slow progress.

4. Utopia or dystopia?

Smart devices will surely change our future life, although there are many technologies nowadays that have already had a big impact on our present life.

With smart glasses factory employees are able to perform more convincing and to organize their duties and tasks more efficiently.

With the help of a smart device as compact as a smart glass one is going to have the freedom to organization ones daily life and take actions in their own hand.

Living in an utopia means living in a world where life is easy, simple and ideal. Consequently there are no problems, difficulties and issues. Everybody can live their effect life without any harm.

Having access to an aid like a smart glass would make one step forward into pursuing once individual dream lifestyle.

5. Conclusion

Sine there are many problems left to solve it takes time to enforce smart glasses on the market.

Keeping personal information private is a worldwide human right.

cIn consequence these issues have to be dissolved by launching a new and larger security system.

Nevertheless the smart glasses are a huge opportunity in the digital revolution which cannot be missed.

Even though there is a lot of criticism and doughts towards new technologies it is necessary to support and secure the research and developments to combat the human challenges at our time.

Deepfakes


Starting World War III from Home

by Cornelius and Jacob

Imagine watching the news and seeing Trump declare war on Russia, China and North Korea. With the help of artificial intelligence that is not as farfetched as it sounds. You might even believe it with Trump insulting world leaders on a daily basis. Manipulating videos to make politicians say whatever you like them to is easy for everyone with a bit of practice and artificial intelligence. The technology is called DeepFake.

Figure 1: Recognising Facial Features

For creating DeepFakes a Neural Network (Ai) uses images of the targeted person to analyse their mimic and facial features by putting points on special facial marks. With enough video footage or pictures, the Ai knows how that person’s mimic would look being afraid, happy or saying “supercalifragilisticexpialidocious”. Snapchat uses such a program to create funny features like face swap.

Especially for the film industry DeepFake has valuable applications and can potentially save millions. Instead of using Photoshop frame by frame, DeepFake can be used to bring dead actors back to life or to correct mouth movements, while dubbing a movie. In the movie “Rogue One” from 2016, Disney spent millions to recreate Princess Lea, although her actor, Carrie Fisher, had aged by 34 years.[1] A few years later, fans created comparable footage for free with the help of DeepFake programs.

Despite these benefits the DeepFake technology can also be abused. DeepFake has its highest demand in the pornography industry. In Fact, 96% of all DeepFake videos contain adult content. Especially female celebrities are targeted, but also ordinary women or teenage girls find themselves having become famous in Pornography. It is impossible to guard one’s privacy. Any picture can be used to steal your face and violate your dignity.

https://www.youtube.com/watch?v=cQ54GDm1eL0
Figure 2: Using Deepfake on President Obama

With media basically being the fourth pillar of democracy and video as one of its most important tools the application of DeepFake becomes very dangerous. In early 2019 a Fox-News employee used DeepFake to mock President Trump’s appearance during his Oval Office address. Already in 2018, the famous comedian and Obama enactor Jordan Peele used DeepFake, making Obama address the danger of DeepFakes in politics. By having Obama insult Trump as “Dipshit” and pointing out that “our enemies can make it look like anyone can say anything at any point in time”, Jordan Peele tries to raise awareness.

New technologies like “Lyrebird” can even train an Ai to imitate a person’s voice with analyzing just a few minutes of audio. The Ai is able to fake an emotional state making the target sound concerned, elated or hateful. Just few parts of conversation like sounds created by mouth movement or breathing cannot be imitated yet.

With a few pictures and a few minutes of audio one controls the power the targeted person holds. The CEO of a British energy company was called by his boss from the German parent company and ordered to transfer 220.000 Euro to a Hungarian supplier. Because the CEO wrongly recognised his boss’s voice and his typical intonations, he obliged and made the payment.[2]

Then how do we combat the threat of DeepFakes? Pictures, videos and voice messages could be certified via blockchain. Blockchain would ensure the authenticity of media, but would consume vast amounts of energy. The Cryptocurrency Bitcoin, which also uses blockchain as a technology, consumes 66.7-Terawathours per year, as much as the Czech Republic.

Figure 3: Spotting DeepFake Altered Footage

The alternative would be to fight fire with fire. Microsoft and Facebook recently invested 10 million USD in the development of Ai that is trained to spot Ai altered DeepFake videos. Google has released 3,000 DeepFakes that shall help researchers train their Ai.[3] Since especially the government is interested in the control of DeepFake, the Pentagon also has allocated some of its budget to the fight against the misuse of DeepFake. New technologies are on the horizon and are just a few steps away of being implemented to fight DeepFakes.

The Wall Street journal calls the DeepFake fight a “cat and mouse game” with an endless chase. Although it is easy to imagine a dark painted dystopian future, we believe that the cat is faster and stronger than the mouse with its fangs an inch away from the mouse’s neck. The development of DeepFake detection Ai will be successful and return some of the authenticity media once had. Nevertheless, this reasoning is no excuse to not stay vigilant, especially right now with the upcoming presidential election in 2020. Think twice before you believe what you see!


[1] https://economictimes.indiatimes.com/tech/software/facebook-microsoft-join-hands-to-fight-deepfakes/articleshow/71005930.cms

[2] https://www.wsj.com/articles/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402

[3] https://www.marketwatch.com/story/movie-watch-disney-is-up-against-great-expectations-as-rogue-one-hits-theaters-2016-12-15

Artificial Intelligence


Did Trump really just say that?

by Aaaaaaaaaaaaron, Konstantin, Hans and Yannick

Artificial intelligence (AI) can be categorized into normal AI, machine learning and deep learning where machine learning is still a part of AI and deep learning is also a part of machine learning.

Any program is a form of artificial intelligence when it enables the computer to mimic human intelligence by using if-then rules, decision trees but also machine learning and deep learning. Machine learning is a subset of AI that includes abstruse statistical techniques that enable the machines to improve at tasks with experience. Deep learning on the other hand is a subset of machine learning composed of algorithms that permit the computer to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data. You can see deep learning as a rocket engine where the fuel is the huge amount of data fed to the algorithm. Deep learning can get more abstract than machine learning but it also needs more data.

The artificial neural networks (ANN) that the algorithms of deep learning are based on are inspired by actual biological neural networks. Artificial neural networks consist of many layers of artificial neurons with connections like synapses between them to process the data. They earn the ability to be self-learning by adding or deleting new artificial neurons and synapses to manage different tasks. Artificial neurons can also be weighted differently and thresholds for neurons can be added like in real life. Deep learning is used for image processing, pattern and speech recognition, autonomous vehicles, deep learning robots like housekeeping robots, early warning systems, etc. Deep learning is also used to create deepfakes. Deepfakes are videos where the original face of a person has been replaced with someone else. If you give the AI enough pictures of the person you want to make a deepfake of it will be able to calculate how that person would look like while smiling, being angry or while saying certain things. The technology that powers deepfakes, known as Generative Adversarial Networks, was only invented in 2014. GANs are made up of two rival computer networks. A synthesiser creates content, which the detector or discriminator compares to images of the real thing. If the discriminator detects an artefact (distortion in the created image) the synthesiser will start over again. Through hundreds of thousands of cycles of trial and error, the two systems can create immensely lifelike videos. With this you can make celebrities or politicians say anything you want them to say or place their face on anyone else’s body.

Sometimes this can be useful when someone is trying to create a funny harmless video where you can easily determine if it is faked. It can also be used in movies to make them look more realistic and save the editors some time because the AI will edit the movie for them. But this technology does more harm than good. Through deepfakes it has become very easy to create realistic faked videos. Anyone with enough data and enough computing power can, without the consent of the person that appears in the video, create hoaxes that lead to misinformation of the society and humiliation of those in the video. Today 93 Million Selfies are taken everyday so it is not that hard to collect enough data and GPUs (graphic cards) that make the calculations for the AI are becoming faster and cheaper year by year. Apps to create deepfakes like “FakeApp” have also already been released making the technology even more accessible for the private person. Another big problem is that 96% of deepfakes are used for pornography. Not only are celebrities faces edited onto the bodies of “pornstars” but there are is also a lot of fake revenge porn. This is a crime and also a form of identity theft violating the privacy of the affected, their human dignity and their basic human rights while possibly destroying their reputation. Once those fake videos are uploaded on the internet it is almost impossible to remove them again and it is also not easy to find out who the video was made of. When it became easier to edit photos through photoshop etc. people stopped considering that the image they are seeing is real but videos were still regarded as real. This credibility is now lost because of deepfakes. But this can be also seen in a positive way. The first deepfakes were not created by big companies but by individuals. If someone was able to create this technology in their bedroom throwing together a bunch of existing tools, someone with a bigger budget must have pulled it off a long time ago. There is no doubt large organizations with massive resources haven’t explored these techniques. Who knows, maybe we’ve seen some of their work, on the news, without knowing it. So thank deepfakes for making us realize once again that we can’t take everything we see for granted. Right know there is a “war” going on between programmers improving the AI to make deepfakes harder to detect and other companies developing AI to detect and debunk deepfakes. Large corporations such as Facebook and Microsoft have taken initiatives to detect and remove deepfake videos. The two companies announced earlier this year that they will be collaborating with top universities across the U.S. to create a large database of fake videos for research. If enough companies work together the “war” can be won and the credibility of videos will come back to some extend. Synthetic videos produced for purposes such as parody or education would need to be watermarked and the rest would be filtered out in the best possible way. As long as the “war” continues, attention must also be paid to audio deepfakes as those are on the rise at the moment. Nevertheless, you should not demonize AI or deep learning because of deepfakes being a bad thing in general, since AI can still make our lives easier and shape the future in a positive way if we make them reliable enough.

Sources

https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html

https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

https://towardsdatascience.com/notes-on-artificial-intelligence-ai-machine-learning-ml-and-deep-learning-dl-for-56e51a2071c2

https://www.cnbc.com/2019/10/14/what-is-deepfake-and-how-it-might-be-dangerous.html

https://bernardmarr.com/default.asp?contentID=1927

https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/

https://www.geeksforgeeks.org/difference-between-machine-learning-and-artificial-intelligence/

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