A recent survey conducted by O’Reilly paints this exact picture. It gives AI-creators an in-depth look at how consumers identify and use AI technology, showcasing the heightened misunderstanding that consumers have of AI and its use.

AI takes over popular culture

Television and the big screen have played a large role in introducing AI into our homes, but how does this depiction impact how we develop and implement the technology?

For those working to incorporate AI technology into products and develop new ways to use it, robots and cars are not an everyday focus. The areas of advancement instead look at AI that learns from our actions to more efficiently help us in our day-to-day lives, answering questions for us and completing tasks through speech recognition and language processing at work and at home.

But how do we harness the excitement around the fantasy of AI to increase everyday adoption?

The true potential of AI

One of the best ways to merge the fantasy and reality of AI is to truly understand what consumers think and what they believe is the potential of the technology.

In our survey, when asked what the most useful form of AI is, more than half (58%) of consumers regarded smart home technology as the most vital. This was closely followed by home security systems (54%), travel recommendations (52%), and virtual assistants (50%). This provides insight into how AI creators can expand their ideas of where AI can be useful to encourage consumers to adopt it in their personal lives.

While AI is already present in our homes—thanks to smart speakers from Amazon, Apple and Google—more and more consumer groups appreciate the success of smart home technology and are willing to adopt it in the future.

Answering the questions: What is AI? And why should I care?

Survey respondents were also asked what application of AI excited them the most in the future. Fraud detection (28%) topped the list as the most exciting area for AI development.

It was the most commonly cited use by men. This is despite only 11% of consumers closely associating fraud detection with AI.

While self-driving cars also generated great excitement among 24% of respondents, interestingly, it was the most popular choice among women, younger consumers, and those working in the AI industry by a significant margin (50%). With fraud detection coming out on top, we can start to see the shift from fantasy to practicality, a trend that AI-creators should leverage to reinforce the pragmatic use of AI within the workforce.

It is up to a wide range of individuals including developers, marketers, product managers and sales to ensure that AI is used and understood correctly. For successful consumer AI adoption, developers should focus their efforts on leveraging AI to make consumers’ everyday lives easier, augmenting existing experiences to make them more seamless and exciting. While there might be an indifference with fantasy and reality, more and more consumer groups appreciate the success of smart home technology and are watching the development of autonomous vehicles very closely.

It’s up to these sectors to capitalise on the hype, but the results are also a call for the creators of work-focused AI to make solutions that capture the imagination and generate excitement. Not only this, but developers need to have in mind consumer needs relatively clearly even at the start of the process when an idea might be more amorphous.

What’s next?

What does the future hold for AI? While the notion of AI has been perpetuated in popular culture and science fiction throughout our lives, individuals are yet to understand the meaning that AI has and that it isn’t an ‘out-there’ concept. In fact, it is already all around us. It plays a role in our homes and at work, in ways that we wouldn’t expect.

Ultimately, AI creators need to bear this in mind and continuously learn from consumer attitudes towards AI to ensure individuals continue to stay engaged with technology, making the most out of the fantasy.

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Older shows like Voyager shot their effects during filming rather than adding them digitally after the fact. Remastering these scenes in a higher resolution is incredibly difficult, to say the least.

Star Trek fan Billy Reichard has employed AI to do the painstaking work that upgrades the image quality as much as possible. For some idea of how long it might take a human to perform the same work; it takes the AI around six hours per episode.

There are 172 episodes of Star Trek: Voyager spanning across seven seasons. The AI, in its current form, will take around 1,032 hours to remaster all of the seasons. If a human was able to work at the same pace as the AI then it would still take them 43 days.

Naturally, there are some limitations as to what the AI has been able to achieve. The aspect ratio, for example, remains a nostalgic 4:3 rather than the 16:9 widescreen we’re used to today.

Another problem is with audio syncing due to Voyager’s use of variable frame rates. You can see the issue during the example clip below:

Reichard says the audio syncing problem has since been fixed and newer videos turned out better, such as this one.

The work is frankly stunning for such an old show and another example of the possibilities being opened up by advancements in AI. Reichard plans to continue remastering the rest of the episodes but needs to save up for the software which he’s currently using on a 30-day free trial.

It’s unlikely CBS will ever sanction Reichard’s work but we can hope it may one day undertake a similar endeavour and release an official version. We can’t wait to see what other retro shows look like with a modern makeover thanks to AI.

(Photo by Stefan Cosma on Unsplash)

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Most sports have been established for some time and ‘new’ ones are generally just similar but with some tweaked rules.

Design agency AKQA set out to create a truly new sport with help from an AI – meet, Speedgate.

Speedgate features six-player teams (take that, five-a-side lovers…) which compete on a field with three open-ended gates.

To score, players must first kick a ball through a centre gate which they’re unable to step through. After which, they can score a point by putting a ball through either of the end gates. If a teammate catches the ball and ricochets it back through, an extra point is scored.

To keep things fast-paced, the ball cannot be still for more than three seconds.

Rather than a rewrite of the rules of an existing sport, Speedgate sounds more like a collision of several. There are elements of soccer (I’m a Brit, so don’t tell my mates I called it that but NFL is king…), basketball/netball in terms of not standing still for long, and I’d imagine even some dodgeball.

That’s probably of little surprise when you hear how the AI came to create its new sport.

Data from 400 existing sports was fed into a neural network before it spat out some basic outlines for a sport and its accompanying rules. Some of these rules, while pretty awesome, were unrealistic. Many of us would like to see things like exploding Frisbees – but health and safety people are sticklers.

Playtests were then used to whittle down the sports to one champion. AKQA is now in discussions with the Oregon Sports Authority to get a Speedgate league going this summer.

AI even created the logo and motto, though we think the latter could do with some work (“face the ball to be the ball to be above the ball”).

It’s a cool example of what AI is able to create, but let’s make a pact to keep clips of The Hunger Games locked away. After all, AIs are already beating us at our own creations

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The solution, Credentials Sharing Insight, helps to source missing income for services like Netflix, Amazon Prime, and Hulu.

Media research firm Magid claims 26% of millennials share passwords for video streaming services. Parks Associates, meanwhile, predicts that in 2021, $9.9 billion of pay-tv revenues and $1.2 billion of OTT revenues will be lost to credentials sharing.

Jean Marc Racine, CPO and GM EMEA of Synamedia, says:

“Casual credentials sharing is becoming too expensive to ignore. Our new solution gives operators the ability to take action.

Many casual users will be happy to pay an additional fee for a premium, shared service with a greater number of concurrent users. It’s a great way to keep honest people honest while benefiting from an incremental revenue stream.”

Credentials Sharing Insight works by analysing usage patterns of a user and flagging suspicious activity.

Once a video streaming provider subscribes to Credentials Sharing Insight, when suspicious patterns are found about users it will return a probability score — indicating how certain the system is that it’s found an infringer.

This score puts the power into streaming providers’ hands over how strict they want to be. A solution which ends up accidentally blocking users for legitimate use is unlikely to lead to continued loyalty.

Various measures can also be put in place for the severity of the offence. A person sharing their password to a family member, for example, could be prompted by email to upgrade to a premium account which allows it. Someone profiting by selling access to an account to many people could be hit with a fine.

Synamedia claims that Credentials Sharing Insight is already in trials with a number of pay-TV operators and is available as either a cloud or on-premise offering.

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Creating 3D renders is a painstaking and time-consuming process requiring specific skills and can be incredibly costly. By reducing the barriers, Nvidia could enable more ideas to move from concept into reality.

During the NeurIPS AI conference in Montreal, Nvidia set up a dedicated area showing its technology. The company used its DGX-1 supercomputer for the demonstration so this isn’t achievable on your average household computer, at least for now.

The AI running on the DGX-1 took footage taken via a self-driving car’s dashcam, extracted a high-level semantics map using a neural network, and then used Unreal Engine 4 to generate the virtual world.

In the animation below, the top left image represents the input map. The bottom-right represents Nvidia’s video synthesis (vid2vid), while the others show competing approaches:

Beditor Catanzaro, VP of Applied Deep Learning Research at NVIDIA, said:

“NVIDIA has been inventing new ways to generate interactive graphics for 25 years, and this is the first time we can do so with a neural network.

Neural networks — specifically generative models — will change how graphics are created. This will enable developers to create new scenes at a fraction of the traditional cost.”

The result of the demo is a simple driving game that allows participants to navigate an urban scene.

“The capability to model and recreate the dynamics of our visual world is essential to building intelligent agents,” Nvidia’s researchers wrote in a paper. “Apart from purely scientific interests, learning to synthesize continuous visual experiences has a wide range of applications in computer vision, robotics, and computer graphics.”

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A pair of AIs could help to make your Halloween festivities this year spookier by auto-generating some creepy material.

The first AI, created by Botnik Studios, uses stories from the ‘Give Yourself Goosebumps’ book series to create a brand new interactive story.

Nicky Martin, Managing Editor at Botnik, explained:

“We took ‘Give Yourself Goosebumps’ as our inspiration and threw those books into our predictive text writing software.

Our goal was to create a playable ‘Choose Your Own Adventure’ style book where every word comes from ‘Give Yourself Goosebumps’.”

The book is titled ‘Give yourself Goosebumps: Welcome To Sand Hands’ and claims to have over 20 different endings.

It starts with the protagonist missing the school bus. A possible solution, of course, involves a shortcut by “walking — through Zombie Forest!”

A look at the first couple of pages shows the story is far from ready for publishing but it’s amusing and draws the reader in:

I don’t know about you, but I do want to know what ‘shmare’ is and why ‘Aunt Mom’ is keeping cheese sauce in her purse. I also want to know why the protagonist is friends with someone who loves their cousin’s feet, how the person is hearing yellow, and more about Pancakes Rocky who frankly sounds a bit of a legend that deserves his own spin-off.

There is so much going on and we’re only two pages in. The AI writer is even rather poetic, calling heavy schoolbooks ‘like bricks made of facts’.

From what I’ve read it doesn’t seem likely this story will be giving anyone nightmares, but the scare factor could be ramped up with some creepy music.

An AI experiment from MIT called ‘The Uncanny Musicbox’ aims to replicate the atmospheric soundtracks to any good horror flick.

The researchers achieved this using a large number of midi files in tandem with several horror movie soundtracks as ‘primer melodies’ to give the AI a starting point to make up the rest.

You can hear demo mixes below with the soundtrack they’re based on:

• Psycho
• Halloween
• Dollhouse
• Exorcist

The experiment from MIT is the latest in a series dating back to 2016 where each year a different horror-themed AI project was conducted.

In 2016, an experiment called Nightmare Machine was among the first AI projects that tackles a specific challenge: can AI not only detect, but induce extreme emotions such as fear in humans?

In 2017, Shelley, the world’s first collaborative AI Horror Writer, wrote over 200 horror stories collaboratively with Twitter users to create the first AI-Human horror anthology ever put together.

All of these projects show how AI can help scare us, for us weirdos that enjoy it.

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The current amount of content is staggering. Netflix has distribution rights from studios around the world and reinvests its profits into creating lauded original content such as ‘Stranger Things’ and ‘Orange Is The New Black’ which cannot be found elsewhere.

Discovering content among vast libraries of content is a problem that’s never truly been cracked. YouTube is one such example of a platform with so much content that it’s difficult for a viewer to find the best for them. Subsequently, it’s frustrating for a new creator to find their audience.

Netflix has long used a system of recommending content to users based on viewing habits and ratings. The problem is, the trailers may not pitch the show well for each person and their own tastes.

For example, think of a Editor Bond-esque thriller. A person who clearly likes action could be shown more scenes of the usual car chases and explosions. On the other hand, if a viewer has been flagged as a bit of a romantic, more of those scenes could be used.

Similarly, if a viewer has a preference for particular actors — that happen to appear in the film or show — then more of their scenes could be shown in the trailer.

The whole concept is aimed at ensuring that, if content is being recommended to a viewer, the trailer shows it off to the best it can. Most trailers are notorious for showing off the best bits and leaving the film without many surprises; so it will need to strike a good balance.

IBM researchers showed off a similar idea last year called the “cognitive movie trailer” which used machine learning to generate a horror movie trailer. The exact method Netflix is using has not been revealed; but it’s likely to be similar.

What are your thoughts about trailers being personalised using AI?

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