Lifeboat Foundation

Actors and Actresses will never have to worry about reading through pages of scripts to decide whether or not the role is worth their time; AI will do the work for you.

A version of this story first appeared in the Feb. 26 issue of The Hollywood Reporter magazine. To receive the magazine, click here to subscribe.

During his 12 years in UTA’s story department, Scott Foster estimates he read about 5,500 screenplays. “Even if it was the worst script ever, I had to read it cover to cover,” he says. So when Foster left the agency in 2013, he teamed with Portland, Ore.-based techie Brian Austin to create ScriptHop, an artificial intelligence system that manages the volume of screenplays that every agency and studio houses. “When I took over [at UTA], we were managing hundreds of thousands of scripts on a Word document,” says Foster, who also worked at Endeavor and Handprint before UTA. “The program began to eat itself and become corrupt because there was too much information to handle.” ScriptHop can read a script and do a complete character breakdown in four seconds, versus the roughly four man hours required of a reader. The tool, which launches Feb. 16 is free, and is a sample of the overall platform coming later in 2016 that will recommend screenplays as well as store and manage a company’s library for a subscription fee of $29.99 a month per user.

Continue reading “This New Artificial Intelligence Script-Reading Program Could Find Your Next Oscar Role (Exclusive)” »

Neural networks have become enormously successful – but we often don’t know how or why they work. Now, computer scientists are starting to peer inside their artificial minds.

A PENNY for ’em? Knowing what someone is thinking is crucial for understanding their behaviour. It’s the same with artificial intelligences. A new technique for taking snapshots of neural networks as they crunch through a problem will help us fathom how they work, leading to AIs that work better – and are more trustworthy.

In the last few years, deep-learning algorithms built on neural networks – multiple layers of interconnected artificial neurons – have driven breakthroughs in many areas of artificial intelligence, including natural language processing, image recognition, medical diagnoses and beating a professional human player at the game Go.

Continue reading “Brain scan for artificial intelligence shows how software thinks” »

I must admit that this will be hard to do. Sure; I can code anything to come across as responding & interacting to questions, topics, etc. Granted logical/ pragmatic decision making is based on facts/ information that people have at a given point of time; being human isn’t only based on algorithms and prescript data it includes being spontaneous, and sometimes emotional thinking. Robots without the ability to be spontaneous, and have emotional thinking capabilities; will not be human and will lack the connection that humans need.

Some people worry that someday a robot – or a collective of robots – will turn on humans and physically hurt or plot against us.

The question, they say, is how can robots be taught morality?

Continue reading “Bedtime stories for robots could teach them to be human” »

This is one that truly depends on the targeted audience. I still believe that the 1st solely owned & operated female robotics company will make billions.

Beyond correct pronunciation, there is the even larger challenge of correctly placing human qualities like inflection and emotion into speech. Linguists call this “prosody,” the ability to add correct stress, intonation or sentiment to spoken language.

Today, even with all the progress, it is not possible to completely represent rich emotions in human speech via artificial intelligence. The first experimental-research results — gained from employing machinelearning algorithms and huge databases of human emotions embedded in speech — are just becoming available to speech scientists.

Continue reading “How designers are grappling with the challenges of creating a computer ‘personality’” »

GPS is an utterly pervasive and wonderful technology, but it’s increasingly not accurate enough for modern demands. Now a team of researchers can make it accurate right down to an inch.

Regular GPS registers your location and velocity by measuring the time it takes to receive signals from four or more satellites, that were sent into space by the military. Alone, it can tell you where you are to within 30 feet. More recently a technique called Differential GPS (DGPS) improved on that resolution by adding ground-based reference stations—increasing accuracy to within 3 feet.

Now, a team from the University of California, Riverside, has developed a technique that augments the regular GPS data with on-board inertial measurements from a sensor. Actually, that’s been tried before, but in the past it’s required large computers to combine the two data streams, rendering it ineffective for use in cars or mobile devices. Instead what the University of California team has done is create a set of new algorithms which, it claims, reduce the complexity of the calculation by several order of magnitude.

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What you’re looking at is the first direct observation of an atom’s electron orbital — an atom’s actual wave function! To capture the image, researchers utilized a new quantum microscope — an incredible new device that literally allows scientists to gaze into the quantum realm.

An orbital structure is the space in an atom that’s occupied by an electron. But when describing these super-microscopic properties of matter, scientists have had to rely on wave functions — a mathematical way of describing the fuzzy quantum states of particles, namely how they behave in both space and time. Typically, quantum physicists use formulas like the Schrödinger equation to describe these states, often coming up with complex numbers and fancy graphs.

Up until this point, scientists have never been able to actually observe the wave function. Trying to catch a glimpse of an atom’s exact position or the momentum of its lone electron has been like trying to catch a swarm of flies with one hand; direct observations have this nasty way of disrupting quantum coherence. What’s been required to capture a full quantum state is a tool that can statistically average many measurements over time.

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Here is a concept to think about when we’re 20 or 30 years into the future — imagine a world where humans and all living things in it are truly Singular, and the new AI & Humanoid robots are alive and well. Will AI (including Robots) ever need therapy, will AI ever get stressed out or have panic attacks, will any humans know what AI is thinking once we give AI more independence?

I ask these questions because as we enhance and evolve AI to be like humans and interpret and process emotions, feelings, and interact like humans; will AI expeience fully the struggles of everyday life like some humans do? And, when needs counseling or therapy will they go to another AI or will they see a human therapist?

As we evolve AI; we must look at the full longer picture around AI including how human do we really wish to make AI.

Continue reading “What does artificial intelligence see when it watches political ads?” »

What if computers could recognize objects as well as the human brain could? Electrical engineers at the University of California, San Diego have taken an important step toward that goal by developing a pedestrian detection system that performs in near real-time (2−4 frames per second) and with higher accuracy (close to half the error) compared to existing systems. The technology, which incorporates deep learning models, could be used in “smart” vehicles, robotics and image and video search systems.

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Around the world, cities are choking on smog. But a new AI system plans to analyze just how bad the situation is by aggregating data from smartphone pictures captured far and wide across cities.

The project, called AirTick, has been developed by researchers from Nanyang Technological University in Singapore, reports New Scientist. The reasoning is pretty simple: Deploying air sensors isn’t cheap and takes a long time, so why not make use of the sensors that everyone has in their pocket?

The result is an app which allows people to report smog levels by uploading an image tagged with time and location. Then, a machine learning algorithm chews through the data and compares it against official air-quality measurements where it can. Over time, the team hopes the software will slowly be able to predict air quality from smartphone images alone.

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