#Sunspring Movie | Explore Tumblr Posts and Blogs

iamaarushisharma · 5 years

Text

AI Automation in Filmmaking: Roll camera – AI – Action!

Remember Jarvis, Iron Man’s AI-backed companion with British accent and an obedient “at-your-service sir” attitude? How astounding it is to witness a tech-world where imaginary characters can become a reality with the help of AI. Facebook founder, Mark Zuckerberg has already started to work on the subject by inventing his personal home AI assistant called ‘Jarvis’ to create virtual reality visualizations.

It all began with Expressionism

Experts are working persistently to experience pivotal Artificial Intelligence (AI) age not from a decade but from far earlier. Film industry is one such arena utilizing AI in big-small ways to add special effects and gather eyeballs. Surprisingly, the use of AI in movies dates back to the time where before the videos even had audio. Shocking, right? In 1927, German Expressionism film ‘Metropolis’ introduced a robotic character which became a benchmark and allowed experts to forecast the success of AI in film making and advertising in the years to come. Today, the technology providers have upped the ante and companies are training their AI systems and bots to make a complete video. Use of sophisticated technology has allowed the film makers to evade time-consuming activities while saving significant production costs. This is evidenced by many movies which have become benign examples of this smart technology.

Movie Trailer for ‘Morgan’

20th Century Fox collaborated with IBM and its AI system called ‘Watson’ to develop the trailer of the horror movie ‘Morgan’. The 6 minutes trailer was created by Watson in only 24 hours that could have taken weeks if produced by humans. The AI system was trained by the experts by segmenting audio, visuals and other essential elements of 100 horror movies to teach it moments for creating automated trailer. Each shot was labelled with an emotion from 24 various emotions like frightening, eerie, and joy to make the trailer functional. The movie was the first attempt at using the AI for trailers and it not only reduced production cost but also the time from weeks to hours.

‘Zone Out’, the automated short film

The fact is, the movie is fully bizarre, has incoherent and hilarious dialogues, a completely muddled plotline, distorted characters, and eerie soundtrack. However, despite being a mismatch for entertainment, the video demonstrated a positive indication of AI in the entertainment sector. This 5-minute film was written, directed and edited entirely by the AI algorithm called ‘Benjamin’ in just 48 hours. The creation is a collective effort of Los Angeles based director, Oscar Sharp and Ross Goodwin, who is also a creative technologist at Google, to learn dialogue using voice-generation and face-swapping technology. The partners have worked before to work on the same subject by creating another short film- ‘Sunspring’. The movie was less of a failure than ‘Zone out’ as it used real actors and dialogues.

The Endgame’s AI Paradigm

Shout out to all the Marvel fans! James Alexander Hendler, an AI researcher with his expert team used AI algorithms and machine learning programming to convert the very dashing Josh Brolin into the anti-hero- ‘Thanos’. The team worked extensively to train the AI-backed system to scan and record actor’s expressions and face structure to automatically map on the animated character body. This helped the actor to perform with other co-stars instead of alone in front of a blue or green screen. Meanwhile, Digital Domain’s machine learning algorithm saved lots of time by capturing it in real time to perform face mapping and swapping.

AI and robots are rapidly automating the filmmaking process to bring out impressive results and error-free edits but there is still humongous amount of work to be done. Technology is improving and there is a long way to go and develop smart AI algorithms to speed up the entertainment sector but experts like Hendler believe that soon AI and bots will become the norm to film industry. Audiences and fans of expert CGI and animation just have to sit tight and cheer for more that’s coming their way.

Source: https://techpatio.com/2019/articles/ai-automation-filmmaking-roll-camera-ai-action

#ai #Ai Software #ai developers #ai development company #artificil intelligence #AI Solutions #Sphinx Worldbiz

0 notes

cracked · 7 years

Video

youtube

As much as engineers would like to try, it's impossible to replace all liberal arts majors with a bunch of machines. Take writers, for example. Surely they must be immune to the rise of the machine worker, right? Right? Well, while a robot may never write the next Moby Dick, it wouldn't take more than a toaster strapped to a typewriter to come up with garbage like Dumb And Dumber To. The machine writer is coming, so you better get your ass in gear and finish that Goonies 2 spec script before it does.

The movie Sunspring is a short film experiment made for the 48-Hour Film Festival in London, which was written by an AI program called Benjamin. The producers fed the data of dozens of popular movies into this neural network, and it spat out a script, complete with dialogue, based on the prompts given to it. The producers then made a nine-minute film based on Benjamin's screenplay.

The movie is amusing, in an uncanny valley sort of way. Most of the dialogue is what could be called "coherent gibberish" -- the sentences are grammatically correct (mostly), but they are otherwise incomprehensible.

Ironically for a sci-fi movie written by a robot, there's not a lot of science going on in the plot. The dialogue is mostly about misunderstandings, love triangles, and disappointing sex. The movie ends with a nonsensical Gone Girl-esque monologue about the regrets of lost virginity. Despite being utter nonsense, the movie is still kind of engrossing, even if it's in a cloning-experiment-gone-wrong sort of way.

5 WTF Ways The Future Is About To Change Movies

#Movies #Tech #Film #AI #Writing

238 notes · View notes

vesperlord · 6 years

Text

So after going through and reading stories written by AI (and in particular, watching this short film: https://www.youtube.com/watch?v=LY7x2Ihqjmc) I vote that we have AI write all of our movies now,

#ai #sunspring #movies

0 notes

tagsradblog · 7 years

Video

youtube

#sunspring #computer #play #movie #scene #science #fiction #sci-fi #sci #fi

0 notes

theoptioncalledsciart · 7 years

Text

Dream job

youtube

Today’s Sci-Art gem on the Internet is the short film “Sunspring” by Oscar Sharp.

The predictive text system of the phone was hacked by Ross Goodwin and was fed different screenplays from some of the highest grossing films in Hollywood history. It has some insightful and deep dialogues, such as: “I don’t know” “I don’t care.” and my personal favourite: “I am the one who got on this rock with a child and then I left the other two.” I just love the fact that the filmmakers gave the most important part of a film to an A.I. . Also, it is a fantastic way to generalise the screenplays from Hollywood movies

Here is a screenshot of the screenplay the A.I. wrote:

https://soundcloud.com/tigerandman/home-on-the-land References: Sharp, Oscar and Ross Goodwin. Sunspring. 2016. Web. 1 Feb. 2017. Sharp, Oscar and Ross Goodwin. Screenshot of the script of Sunspring. 2016. Image. 1 Feb. 2017.

Sharp, Oscar and Ross Goodwin. Screenshot of the themesong of Sunspring. 2016. Image. 1 Feb. 2017.

1 note · View note

fastforwardlabs · 7 years

Text

Five 2016 Trends We Expect to Come to Fruition in 2017

The start of a new year is an excellent occasion for audacious extrapolation. Based on 2016 developments, what do we expect for 2017?

This blog post covers five prominent trends: Deep Learning Beyond Cats, Chat Bots - Take Two, All the News In The World - Turning Text Into Action, The Proliferation of Data Roles, and What Are You Doing to My Data?

(1) Deep Learning Beyond Cats

In 2012, Google found cats on the internet using deep neural networks. With a strange sense of nostalgia, the post reminds us how far we have come in only four years, with more nuanced reporting as well as technical progress. The 2012 paper predicted the findings could be useful in the development of speech and image recognition software, including translation services. In 2016, Google’s WaveNet can generate human speech, General Adversarial Networks (GANs), Plug & Play Generative Networks, and PixelCNN can generate images of (almost) naturalistic scenes including animals and objects, and machine translation has improved significantly. Welcome to the future!

In 2016, we saw neural networks combined with reinforcement learning (i.e., deep reinforcement learning) beat the reigning champion Lee Sedol in Go (the battle continues online) and solve a real problem; deep reinforcement learning significantly reduces Google’s energy consumption. The combination of neural networks with probabilistic programming (i.e., Bayesian Deep Learning) and symbolic reasoning proved (almost) equally powerful. We saw significant advances in neural network architecture, for example, the addition of long-term memory (Neural Turing Machines) which adds a capacity resembling “common sense” to neural networks and may help us build (more) sophisticated dialogue agents.

In 2017, enabled by open-sourced software like Google’s TensorFlow released in late 2015, Theano, and Keras, neural networks will find (more) applications in industry (e.g., recommender systems), but widespread adoption won’t come easily. Algorithms are good at playing games like Go because games easily allow to generate the amount of data needed to train these advanced, data-hungry algorithms. The availability of data, or lack thereof, is a real bottleneck. Efforts to use pre-trained models for novel tasks using transfer learning (i.e., using what you have learned on one task to solve another, novel task) will mature and unlock a bigger class of use cases.

Parallel work on deep neural network architecture will enhance said architecture, deepen our understanding, and hopefully help us develop principled approaches for choosing the right architecture (there are many) for tasks beyond “CNNs are good for translation invariance and RNNs for sequences”.

In 2017, neural networks will go beyond game playing and deliver on their promise to industry.

(2) Chat Bots - Take Two

2016 had been declared by many the year of the bots, and it wasn’t. The narrative was loud but the results, more often than not, disappointing. Why?

Amongst the many reasons; lack of avenues for distribution, lack of enabling technologies, and the tendency to treat bots as a purely technical not product or design challenge. Through hard work and often failure, the best driver of future success, the bot community learned some valuable lessons in 2016. Bots can be brand ambassadors (e.g., Casper’s Insomnobot-3000) or marketing tools (e.g., Call of Duty’s Lt Reyes). Bots are good for tasks with clear objectives (e.g., scheduling a meeting) while exploration, especially if the content can be visualized, is better left to apps (you can, of course, squeeze it into a chatbot solution). Facebook’s messenger platform added an avenue for distribution; Google (Home, Allo) may follow while Apple (Siri) will probably stay closed. Facebook’s Wit.ai adds technology to enable developers to build bots, at re:Invent 2016, Amazon unveiled Lex.

After excitement and inflated expectations in 2016, we will see useful, goal-oriented, narrow-domain chatbots with use case appropriate personality supported by human agents when the bot’s intent recognition fails or when it wrangles a conversation. We will see more sophisticated intent recognition, graceful error handling, and more variety in the largely human-written template responses while ongoing research into end-to-end dialogue systems promises more sophisticated chatbots in the years to come. After the hype, a small, committed core remains and they will deliver useful chatbots in 2017.

Who wins our “The Weirdest Bot Of 2016” award? The Invisible Boyfriend.

(3) All the News In the World - Turning Text into Action

At the beginning there was the number; algorithms work on numerical data. Traditionally, natural language was difficult to turn into numbers that capture the meaning of words. Conventional bag-of-word approaches, useful in practice, fail to use syntactic information and fail to understand that “great” and “awesome” or Cassius Clay and Muhammad Ali are related concepts.

In 2013, Tomas Mikolov proposed a fast and efficient way to train word embeddings. A word embedding is a numerical representation of a word, say “great”, that is learned by studying the context in which the “great” tends to appear. The word embedding captures the meaning of “great” in the sense that “great” and “awesome” will be close to one another in the multi-dimensional word embedding space, the algorithm learned they are related. Alternatives like GloVe, word2vec for documents (i.e., doc2vec), and underlying methods like skip-gram and skip-thought further improved our ability to turn text into numbers and opened up natural language to machine learning and artificial intelligence.

In 2016, fastText allowed us to deal with out-of-vocab words (words the language model was not originally trained on) and SyntaxNet enhances our ability to not only encode the meaning of words but to parse the syntactic structure of sentences. Powerful, open-source natural language processing tool kits like spaCy allow data scientists and machine learning engineers without deep expertise in natural language processing to get started. FastText? Just pip install! Fuelled by this progress in the field, we saw a quiet but strong trend in industry towards utilizing these new powerful natural language processing tools to build large-scale industry applications that turn 6.6M news articles into a numerical indicator for banking distress or use 3M news articles to assess systemic risk in the European banking system. Algorithms will help us not only to make sense of the information in the world, they will help write content, too, and of course they will help bring our chit chatty chat bots to live.

In 2017, we will expect more data products built on top of vast amounts of news data especially data products that condense information into small, meaningful, actionable insights. Our world has become overwhelming, there is too much content. Algorithms can help! Somewhat ironically, we will also be using machines to create more content. A battle of machines.

In a world shaken by “fake news”, of course, one may regard these innovations with suspicion. As new technology enters the mainstream there is always hesitancy, but the critics are right. How do we know the compression is not biased? How do we train people to evaluate the trustworthiness of the information they consume especially when it has been condensed and computer generated? How do we fix the incentive problem of the news industry, distribution platforms like Facebook do not incentivise for deep, thoughtful writing; they monetize a few seconds of attention and are likely to feed existing biases, not all challenges are technical but should concern technically minded people.

The best AI writer of the year goes to? Benjamin, a recurrent neural network that wrote the movie script Sunspring.

(4) The Proliferation Of Data Roles

Remember when data scientist was branded the sexiest job of the 21st century? How about machine learning engineer? AI, deep learning, or NLP specialist? As a discipline, data science is maturing. Organizations have increasingly recognized the value of data science to their business, entire companies are based on AI products leveraging the power of deep learning for image recognition (e.g., Clarifai) or offering natural language generation solutions (e.g., Narrative Science). With success comes a greater recognition, appreciation of differences, and specialization. What’s more, the sheer complexity of new, emerging algorithms requires deep expertise. 2017 will see a proliferation of data roles.

The opportunity to specialize allows data people to focus on what they are good at and enjoy, great. But there will be growing pains. It takes time to understand the meaning of new job titles; companies will be advertising data science roles when they want machine learning engineers and vice versa. Hype combined with a fierce battle over talent will lead to an overabundance of “trendy” roles blurring useful differences. As a community, we will have to clarify what the new roles mean (and we’ll have to hold ourselves accountable when hiring hits a rough patch).

We will have to figure out processes for data scientists, machine learning engineers, and deep learning/AI/NLP specialists to work together productively which will affect adjacent roles. Andrew Ng, Chief Scientist at Baidu, argues for the new role of AI Product Manager who sets expectations by providing data folks with the test set (i.e., the data an already trained algorithm should perform well on). We may need transitional roles like the Chief AI Officer to guide companies in recognizing and leveraging the power of emerging algorithms.

2017 will be an exciting year for teams to experiment, but there will be battle scars.

(5) What Are You Doing To My Data?

By developing models to guide law enforcement, models to predict recidivism, models to predict job performance based on Facebook profiles, data scientists are playing high stakes games with other people’s lives. Models make mistakes; a perfectly qualified and capable candidates may not get her dream job. Data is biased; word embeddings (mentioned above) encode the meaning of words through the context in which they are used, allow simple analogies, and, trained on Google News articles, reveal gender stereotypes—”man is to programmer as woman is to homemaker”. Faulty reward functions can cause agents to go haywire. Models are powerful tools. Caution.

In 2016, Cathy O’Neill published Weapons of Math Destruction on the potential harm of algorithms which got significant attention (e.g., Scientific American, NPR). FATML, a conference on Fairness, Accountability, and Transparency in Machine Learning had record attendance. The EU issued new regulation including “the right to be forgotten”, giving individuals control over their data, and restricts the use of automated, individual decision-making especially if decisions the algorithm makes cannot be explained to the individual. Problematically, automated, individual decision-making is what neural networks do and their inner workings are hard to explain.

2017 will see companies grappling with the consequences of this “right to an explanation” which Oxford researchers have started to explore. In 2017, we may come to a refined understanding of what we mean when we say: “a model is interpretable”. Human decisions are interpretable in some sense, we can provide explanations for our decisions, but not others, we do not (yet) understand the brain dynamics underlying (complex) decisions. We will make progress on algorithms that help us understand model behavior and exercise the much needed caution when we build predictive models areas like healthcare, education, and law enforcement.

In 2017, let’s commit to responsible data science and machine learning.

– Friederike

Many thanks to Jeremy Karnowski for helpful comments.

#deep learning #language processing #language generation

5 notes · View notes

tastydregs · 7 years

Text

An AI wrote all of David Hasselhoff’s lines in this bizarre short film

reader comments 0

Behold: It's No Game, written by an AI and starring the great David Hasselhoff.

Last year, director Oscar Sharp and AI researcher Ross Goodwin released the stunningly weird short film Sunspring. It was a sci-fi tale written entirely by an algorithm that eventually named itself Benjamin. Now the two humans have teamed up with Benjamin again to create a follow-up movie, It's No Game, about what happens when AI gets mixed up in an impending Hollywood writers' strike. Ars is excited to debut the movie here, so go ahead and watch. We also talked to the film cast and creators about what it's like to work with an AI.

The scenario in It's No Game is sort of like Robocop, with about 20 hits of acid layered on top. Two screenwriters (Tim Guinee and Walking Dead's Thomas Payne) are meeting with a producer (Flesh and Bone's Sarah Hay), who informs them that it doesn't matter if they go on strike because the future is AI writing movies for other AI. As evidence, she shows them Sunspring, gushing about how it "got a million hits." The fact that Sunspring did in fact get a million hits in real life, and that there really is a writer's strike threatening Hollywood, make this movie even more of a reality distortion field.

Things go completely off the rails when the producer brings in "the Hoffbot" (David Hasselhoff, in a tour-de-force performance that's surprisingly moving). The actor has been reprogrammed by nanobots to channel the AI writer Benjamin, and that's just what it sounds like when the Hoffbot springs to life. Checking his K.I.T.T. watch in full Knight Rider style, the Hoffbot spouts odd shreds of dialogue torn from his 80s hits Baywatch and Knight Rider. Not content to torment only Hasselhoff, the nanobots proceed to take over everyone else in the room, forcing them to act out lines cobbled together by more algorithms trained on Shakespeare, Aaron Sorkin films, Golden Age Hollywood scripts, and other surprises. Finally Hay injects herself with nanobots and dances a menacing, disjointed, and beautiful piece of choreography based on French words for ballet moves strung together by yet another algorithm.

Before we move on, let's try to separate truth from science fiction for a moment. There are no nanobots that can take over your body, but there is actually an AI who wrote all those lines (and dance moves). AI is not going to replace writers during the strike, but it might help humans write screenplays more and more often. Hoffbot is a bot, but he really is played by Hasselhoff. Hay really was a ballerina before she acted in a fictional TV series called Flesh and Bone about ballerinas. Also, the shorts that Hasselhoff is wearing in the final, brutal scene? Yep, those are actually shorts he wore on Baywatch.

How to be an AI-powered actor

When I spoke to some of the actors involved with It's No Game, they all seemed unfazed by having to inject emotion into lines that bordered on nonsensical. Partly that was because Sharp and Goodwin played a lot with genre in this short, creating AIs who spouted dialogue that came from recognizable sources. Payne, who played one of the writers, said he enjoyed the challenge of moving from one style to another. At one point he and Guinee jump from a Golden Age romantic kiss right into a tense dialogue from an algorithm that Goodwin dubbed the Sorkinator. "To a certain extent you have to play a bit of mood, so you’re putting across the style of the dialogue," Payne said. "As long as you say it with conviction it will work."

For audiences, the results might not be that different from trying to follow a complicated movie, Payne mused. "People will watch a Sorkin movie and not take in what’s being said, [but] understand the thrust of the scene and know what’s going on."

For Hay, the key to getting into her role was thinking about why her character might want to become a ballerina bot. Unlike the other characters, the producer isn't forced to become an AI puppet. She chooses it. Director Sharp talked to Hay about the idea that most people secretly want to give up control and how the nanobots represent that fantasy. "Wouldn't it be easier if our computers and phones could take over and control us?" Hay asked rhetorically. "Those things freak me out in reality, but I think for her this is joyous. I’m free. When she stabs herself with this [nanobot-activating] pen, it’s like an orgasm for her. Finally I have everything I need right now because I don’t have to make any choices."

After Hay's orgasmic AI takeover, however, we return to the fate of the Hoffbot. Now shivering on the floor, he wears an incongruous outfit of smoking jacket and Baywatch shorts, and stares into the camera with tearful intensity. "I don't know who the hell I am. I wanna be a man," he sobs. "I wanna go to the movies!" The absurdist lines were written by AI, but Hasselhoff said they feel like they came straight from his heart. "This AI really had a handle on what's going on in my life and it was strangely emotional," he explained. Hasselhoff continued:

There was this one line: "I don’t want to see you anymore." I thought, "That’s a great line. I don’t want to see you, I don't want to date you, I wanna talk to you." It’s like I’ve had this conversation with my ex-wife so many times. I want to talk to you and for you to see me and understand me...When you’re acting out these strange lines they become part of your soul and you can actually give meaning to [them].

David Hasselhoff talks about being in

It's No Game

. Right after this, he left for the premiere of

Guardians of the Galaxy Vol. 2

, where he's

featured on the soundtrack

rapping about Starlord.

To act that final scene, Hasselhoff imagined that he had taken over his own body again, fighting off the nanobots to say something that wasn't just a Hoffbot construction. "When he was taken over he had no choice but to say those lines, even when they were wrong. I would never have said those lines," he said. But then, "he wanted to humanize himself. He just wanted to be a man. He wanted to go to the movies. That's my favorite line. I just wanna go to the movies."

Acting the Hoffbot part, though, was just like old times. Hasselhoff talked a lot to Sharp about the divide between himself as an actor and "the Hoff," a caricature whom he described as "created by secretaries 14 years ago in Australia. It’s not who I am." His emotional outburst in that final scene dramatizes the sometimes painful divide between self and commercial creation.

For Sharp, who grew up hooked on Knight Rider, watching Hasselhoff transform into the Hoffbot was sort of like a fan's dream come true. At the same time, it was a fascinating look at how acting works. "Watching David perform those lines, there was this moment where his whole stature changed. His shoulders went back and he took an 80s power stance. The watch goes up to his mouth and it was like muscle memory. An actor’s imagination exists in their body, and his muscle memory kicked in." Then he mused that this electrifying moment was actually crafted by an AI: "Benjamin helps you take these calcified cultural objects that we all recognize too easily and it automatically caricatures them. If Hoff is a caricature of a caricature, Ben takes it a step further."

Enlarge /

Behind the scenes of

It's No Game

(Hoff

may

be wearing

Baywatch

shorts that he happened to have in his car.

Oscar Sharp, Ross Goodwin

Writing in an algorithmic world

Like its predecessor Sunspring, It's No Game was made as part of the 48 Hour Film Challenge at the Sci-Fi London Film Festival. Also like Sunspring, It's No Game has bent the rules of writing. Louis Savy, who organizes Sci-Fi London, called Sharp and Goodwin's latest film with Benjamin "counter-culture hacking...it's exciting if not a little scary."

Perhaps the scariest part of It's No Game, at least for a writer, is the idea that the writer's strike might be resolved by hiring AIs. Sharp is currently writing a screenplay about the de-extinction of wooly mammoths and said that "the possibility of the writer's strike is looming large in my life." But he isn't afraid that Benjamin will become a scab like Robocop did during the Detroit cop strike in the eponymous movie. "In a way we're being a bit satirical about people who are afraid of what Ross [Goodwin] and I are doing."

In the year since Sunspring came out, the two have spent a lot of time answering anxious questions from people who think robots are about to take their jobs. Goodwin said: "I've thought a lot about the invention of photography—before that, creating photorealistic images required talent and training. Then the camera came along. It didn’t make painting irrelevant. The camera set painting free. One of the things we’re doing is setting writing free." Plus, added Sharp, "If we ever get to the point that machines are good enough to replace us, then maybe that means we’ve invented a new form of intelligent life whose stories we want to hear."

Benjamin the AI hasn't become more sophisticated since his first writing job on Sunspring, but he has multiplied. Goodwin trained six different models to write the dialogue in It's No Game, mostly using the same long short-term-memory recursive machine-learning algorithm that generated the screenplay for Sunspring. Put simply, the algorithm learns to create long sentences based on learning rules from a corpus of writing. In this case, the corpuses were comprised of dialogue taken from several collections of films and television series. Only the ballet sequence was written by a different algorithm, called context-free grammar, which uses basic rules to generate short phrases from words. One of the models, called the Soliloquizer, was also used in Sunspring to generate the final, ultra-strange speech; it was trained on the Cornell Movie Dialogues Corpus.

The other models were based on David Hasselhoff shows Knight Rider and Baywatch ("Hoffbot"), Shakespeare ("Robobard"), Golden Age Hollywood ("GoldenAge-O-Matic"), and Aaron Sorkin ("Sorkinator").

This time, Goodwin chose to train his algorithms on subtitle files rather than screenplays. This gave him and Sharp lines of dialogue without the truly odd stage directions and character names from Sunspring. But the biggest change was that Benjamin wasn't as much of a unified voice. Instead he'd become many versions of himself, much the way the AI character played by Scarlett Johansson in Her does. "Having all the different models definitely made Ben feel more like a tool, or more disbursed anyway," Goodwin said. "As I was generating the material, I was thinking: will this soon be a common way to make a screenplay? Writing in multiple voices is challenging for any writer, and it just seemed so useful to be able to summon various voices on demand."

Pondering other people's fears about AI, Sharp returned to the topic of his beloved 80s Hasselhoff shows. "The irony is that story of our tech helping us, of AI helping us, is one that kids found inspiring on Knight Rider," he said with a grin. "Now I have my own thinking car that helps me with my mysterious things."

Listing image by It's No Game

0 notes