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Lego Finds An Inventive Way to Combine AI and Motion Tracking

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Lego Finds An Inventive Way to Combine AI and Motion Tracking

Lego toy systems have been around for generations and have been considered by many as a way to stimulate the imagination. Quite a few users have at some point imagined having a Lego figure in their own image they could use with their sets.

Realizing that fact, Lego has decided to try and make that dream come true. As Gizmodo reports, Lego will try to realize that dream for anybody who visits there theme park that will open in New York in 2020. To do this the company will employ sophisticated motion tracking and neural network facial recognition.

The theme park, named Legoland New York Resort will be located in Goshen, New York, which is about 60 miles northwest of New York City and it will open on July 4, 2020.

According to Mobile ID World, this possibility will be featured in a Lego Factory Adventure Ride “that takes park guests through a tour of a “factory” showing them how the iconic little plastic bricks are made.”

Using Holovis’ Holotrack technology, the Lego Factory Adventure Ride will feature a segment where park guests are turned into one of Lego’s iconic miniature figures. Holotrack leverages the use of the same artificial intelligence and deep learning technologies that have made deepfake videos possible, taking an individual’s image and translating it onto a screen. The guest’s mini-figures will mimic their movements and appearance, copying their hair, glasses, clothing, and facial expressions. The time it takes to render a guest into a Lego figure is reported to be about half a second.”

But this is certainly not the new AI development in which Lego is involved. Back in 2013 Lego Engineering, used artificial intelligence to explore movement, using Lego building blocks. In 2014, researchers and programmers started using Lego Mindstorms EV3 robot with AI by connecting the brain of a worm to the sensors and motors of an EV3 robot using a computer program. AI development enthusiasts have been using Mindstorms EV3 for a while now trying particularly to develop robotic movement.

In  2004 and 2016, two research projects were published which researched how Lego could be used in teaching AI. The first employed Lego’s Mindstorms, while the latter, published by Western Washington University discussed 12-years of teaching experience on AI using Lego systems, including EV3.

But the company’s biggest advancement in the field of AI came this year when in August when it announced that it will “begin trials of a new system to aid those with visual disabilities in following LEGO instructions.”

The system is called Audio & Braille Building Instructions, and uses “AI to pair digital traditional-style visual instructions with verbal or tactile Braille directions, and was developed in collaboration with life-long LEGO fan Matthew Shifrin, who is blind.”

The system is in the early stages of development and currently supports “a handful of sets at present while the development team seeks feedback from users.”  The feedback will be used to implement the feedback which will add to more sets “in the first half of 2020, with an eventual goal of supporting all-new LEGO product launches. “ The official instructions created by the new AI-driven program will be available for free from legoaudioinstructions.com

 

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Former diplomat and translator for the UN, currently freelance journalist/writer/researcher, focusing on modern technology, artificial intelligence, and modern culture.

Deep Learning

Deep Learning Is Re-Shaping The Broadcasting Industry

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Deep Learning Is Re-Shaping The Broadcasting Industry

Deep learning has become a buzz word in many endeavors, and broadcasting organizations are also among those that have to start to explore all the potential it has to offer, from news reporting to feature films and programs, both in the cinemas and on TV.

As TechRadar reported, the number of opportunities deep learning presents in the field of video production, editing and cataloging are already quite high. But as is noted, this technology is not just limited to what is considered repetitive tasks in broadcasting, since it can also “enhance the creative process, improve video delivery and help preserve the massive video archives that many studios keep.”

As far as video generation and editing are concerned, it is mentioned that Warner Bros. recently had to spend $25M on reshoots for ‘Justice League’ and part of that money went to digitally removing a mustache that star Henry Cavill had grown and could not shave due to an overlapping commitment. The use of deep learning in such time-consuming and financially taxing processes in post-production will certainly be put to good use.

Even widely available solutions like Flo make it possible to use deep learning in creating automatically a video just by describing your idea. The software then searches for possible relevant videos that are stored in a certain library and edits them together automatically.

Flo is also able to sort and classify videos, making it easier to find a particular part of the footage. Such technologies also make it possible to easily remove undesirable footage or make a personal recommendation list based on a video somebody has expressed an interest in.

Google has come up with a neural networkthat can automatically separate the foreground and background of a video. What used to require a green screen can now be done with no special equipment.”

The deep fake has already made a name for itself, both good and bad, but its potential use in special effects has already reached quite a high level.

The area where deep learning will certainly make a difference in the restoration of classic films, as the UCLA Film & Television Archive, nearly half of all films produced prior to 1950 have disappeared and 90% of the classic film prints are currently in a very poor condition.

Colorizing black and white footage is still a controversial subject among the filmmakers, but those who decide to go that route can now use Nvidia tools, which will significantly shorten such a lengthy process as it now requires that the artist colors only one frame of a scene and deep learning will do the rest from there. On the other hand, Google has come up with a technology that is able to recreate part of a video-recorded scene based on start and end frames.

Face/Object recognition is already actively used, from classifying a video collection or archive, searching for clips with a given actor or newsperson, or counting the exact time of an actor in a video or film. TechRadar mentions that Sky News recently used facial recognition to identify famous faces at the royal wedding.

This technology is now becoming widely used in sports broadcasting to, say, “track the movements of the ball, or to identify other key elements to the game, such as the goal.” In soccer (football) this technology, given the name VAR is actually used in many official tournaments and national leagues as a referee’s tool during the game.

Streaming is yet another aspect of broadcasting that can benefit from deep learning. Neural networks can recreate high definition frames from low definition input, making it possible for the viewer to benefit from better viewing, even if the original input signal is not fully up to the standard.

 

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Cybersecurity Experts Defend from AI Cyberattacks

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Cybersecurity Experts Defend from AI Cyberattacks

Not everybody with good intentions is set to use the advantages of artificial intelligence. Cybersecurity is certainly one of those fields where both those trying to defend a certain cyber system and those trying to attack it are using the most advanced technologies.

In its analysis of the subject, World Economic Forum (WEF) cites an example when in march 2019, “the CEO of a large energy firm sanctioned the urgent transfer of €220,000 to what he believed to be the account of a new Eastern European supplier after a call he believed to be with the CEO of his parent company. Within hours, the money had passed through a network of accounts in Latin America to suspected criminals who had used artificial intelligence (AI) to convincingly mimic the voice of the CEO.” For their part, Forbes cites an example when “two hospitals in Ohio and West Virginia turned patients away due to a ransomware attack that led to a system failure. The hospitals could not process any emergency patient requests. Hence, they sent incoming patients to nearby hospitals.”

This cybersecurity threat is certainly the reason why Equifax and the World Economic Forum convened the inaugural Future Series: Cybercrime 2025. Global cybersecurity experts from academia, government, law enforcement, and the private sector are set to meet in Atlanta, Georgia to review the capabilities AI can give them in the field of cybersecurity. Also, Capgemini Research Institute came up with a report that concludes that building up cybersecurity defenses with AI is imperative fro practically all organizations.

In their analysis, WEF, indicated four challenges in preventing the use of AI in cybercrime. The first is the increasing sophistication of attackers – the volume of attacks will be on the rise, and “AI-enabled technology may also enhance attackers’ abilities to preserve both their anonymity and distance from their victims in an environment where attributing and investigating crimes is already challenging.”

The second is the asymmetry in the goals – while defenders must have a 100% success rate, the attackers need to be successful only once. “While AI and automation are reducing variability and cost, improving scale and limiting errors, attackers may also use AI to tip the balance.”

The third is the fact that as “organizations continue to grow, so do the size and complexity of their technology and data estates, meaning attackers have more surfaces to explore and exploit. To stay ahead of attackers, organizations can deploy advanced technologies such as AI and automation to help create defensible ‘choke points’ rather than spreading efforts equally across the entire environment.”

The fourth would be to achieve the right balance between the possible risks and actual “operational enablement” of the defenders. WEF is the opinion that “security teams can use a risk-based approach, by establishing governance processes and materiality thresholds, informing operational leaders of their cybersecurity posture, and identifying initiatives to continuously improve it.” Through their Future Series: Cybercrime 2025 program, WEF, and its partners are seeking “to identify the effective actions needed to mitigate and overcome these risks.”

For their part, Forbes has identified four steps of direct use of AI in cybersecurity prepared by their contributor Naveen Joshi and presented in the bellow graphic:

Cybersecurity Experts Defend from AI Cyberattacks

In any case, it is certain that both defenders and attackers in the field of cybersecurity will keep on developing their use of artificial intelligence as the technology itself reaches a new stage of complexity.

 

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Expert Says “Perfectly Real” DeepFakes Will Be Here In 6 Months

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Expert Says "Perfectly Real" DeepFakes Will Be Here In 6 Months

The impressive but controversial DeepFakes, images and video manipulated or generated by deep neural networks, are likely to get both more impressive and more controversial in the near future, according to Hao Li, the Director of the Vision and Graphics Lab at the University of Southern California. Li is a computer vision and DeepFakes expert, and in a recent interview with CNBC he said that “perfectly real” Deepfakes are likely to arrive within half a year.

Li explained that most DeepFakes are still recognizable as fake to the real eye, and even the more convincing DeepFakes still require substantial effort on the part of the creator to make them appear realistic. However, Li is convinced that within six months, DeepFakes that appear perfectly real are likely to appear as the algorithms get more sophisticated.

Li initially thought that it would take between two to three years for extremely convincing DeepFakes to become more commonplace, making that prediction at a recent conference hosted at the Massachusetts Institute of Technology. However, Li revised his timeline after the revelation of the recent Chinese app Zao and other recent developments concerning DeepFakes technology. Li explained to CNBC that the methods needed to create realistic DeepFakes are more or less the method currently being used and that the main ingredient which will create realistic DeepFakes is more training data.

Li and his fellow researchers have been hard at work on DeepFake detection technology, anticipating the arrival of extremely convincing DeepFakes. Li and his colleagues, such as Hany Farid from the University fo California Berkely, experimented with state of the art DeepFake algorithms to understand how the technology that creates them works.

Li explained to CNBC:

“If you want to be able to detect deepfakes, you have to also see what the limits are. If you need to build A.I. frameworks that are capable of detecting things that are extremely real, those have to be trained using these types of technologies, so in some ways, it’s impossible to detect those if you don’t know how they work.”

Li and his colleagues are invested in creating tools to detect DeepFakes in acknowledgment of the potential issues and dangers that the technology poses. Li and colleagues are far from the only group of AI researchers concerned about the possible effects of DeepFakes and interested in creating countermeasures to them.

Recently, Facebook started a joint partnership with MIT, Microsoft and the University of Oxford to create the DeepFake Detection Challenge, which aims to create tools that can be used to detect when images or videos have been altered. These tools will be open source and usable by companies, media organizations, and governments. Meanwhile, researchers from the University of Southern California’s Information Sciences Institute recently created a series of algorithms that could distinguish fakes videos with around 96% accuracy.

However, Li also explained that the issue with DeepFakes is the way they can be misused, and not the technology itself. Li noted several legitimate possible uses for DeepFake technology, including in the entertainment and fashion industries.

DeepFake techniques have also been used to replicate the facial expressions of people with their faces obscured in images. Researchers used Generative Adnversail Networks to create an entirely new face that had the same expression of a subject in an original image. The techniques developed by the Norwegian University of Science and Technology could help render facial expressions during interviews with sensitive people who need privacy, such as whistleblowers. Someone else could let their face be used as a stand-in for the person who needs anonymity, but the person’s facial expressions could still be read.

As the sophistication of Deepfake technology increases, the legitimate use cases for Deepfakes will increase as well. However, the danger will also increase, and for this reason, the work on detecting DeepFakes done by Li and others grows even more important.

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