AI News, Artificial Intelligence Imagines How Scenes Will Play Out artificial intelligence

A computer program that learns to “imagine” the world shows how AI can think more like us

Machines will need to get a lot better at making sense of the world on their own if they are ever going to become truly intelligent.

Ali Eslami, a research scientist at DeepMind, and his colleagues tested the approach on three virtual settings: a block-like tabletop, a virtual robot arm, and a simple maze.

The work is something of a new direction for DeepMind, which has made its name by developing programs capable of performing remarkable feats, including learning how to play the complex and abstract board game Go.

Tenenbaum says the ability to deal with complex scenes in a modular way is impressive but adds that the approach shows the same limitations as other machine-learning methods, including a need for a huge amount of training data: “The jury is still out on how much of the problem this solves.” Sam Gershman, who heads the Computational Cognitive Neuroscience Lab at Harvard, says the DeepMind work combines some important ideas about how human visual perception works.

10 Ways Artificial Intelligence Could Make Me a Better Doctor

Artificial intelligence (AI) will redesign complete healthcare systems in the near future, but it will also impact the life of the “average doctor”

AI has certainly more revolutionary potential than simply optimizing processes: it can mine medical records or medical images in order to come up with previously unknown implications or signals;

design treatment plans for cancer patients or create drugs from existing pills or re-use old drugs for new purposes.

Imagine how much time you as a GP could spare if healthcare chatbots and instant messaging health apps would give answers to simple patient questions, which do not necessarily need the intervention of a medical professional!

If I could devote the time it takes now to deal with technology (inputting information, looking for papers, etc.) to patients, that would be a huge step towards becoming better.

I try to teach Gmail how to mark an email important or categorize them automatically into social media messages, newsletters, and personal emails, it’s still a challenge.

think I have mastered the skill of searching for information online using dozens of Google search operators and different kinds of search engines for different tasks, but it still takes time.

More and more intelligent personal assistants, such as Siri on iOS or Alexa for Amazon lead us into the future, and there will be soon highly capable, specialized AI-powered chatbots also in the field of healthcare. Bots like HealthTap or Your.Md already aim to help patients find a solution to the most common symptoms through AI. Safedrugbot embodies a chat messaging service that offers assistant-like support to health professionals, doctors who need appropriate information about the use of drugs during breastfeeding.

If I could read 3-4 studies of my field of interest per week, I could not finish it in a lifetime and meanwhile millions of new studies would come out.

With the help of an AI, you could respond to your boss without the need to touch any devices – a toned down version of Joaquin Phoenix’s AI, that arranged the whole publishing process of his book without the need for him to lift a finger.

For example, IBM Watson launched its special program for oncologists – and I interviewed one of the professors working with it – which is able to provide clinicians evidence-based treatment options.

Watson for Oncology has an advanced ability to analyze the meaning and context of structured and unstructured data in clinical notes and reports that may be critical to selecting a treatment pathway.

For example, in the Hungarian county of Kaposvár, the average time from the discovery of a cancerous disease until the actual medical consultation about the treatment plan was 54 days.

Imagine, though, what earthquake-like changes AI could bring into patient management if the usage of a simpler process management tool and follow-up system could halve the waiting time!

They can tell if a doctor, clinic or hospital makes mistakes repetitively in treating a certain type of condition in order to help them improve and avoid unnecessary hospitalizations of patients.

If more patients have a chance to participate in trials, they might become more engaged with potential treatments or even be able to access new treatments before they become FDA approved and freely available.

It is an ambitious long-term exploratory project to build a next generation “cognitive assistant” with analytical, reasoning capabilities and a wide range of clinical knowledge.

Can QA Testing Prevent The (Inevitable?) Robot Apocalypse?

It’s fairly clear that we’ll be able to build a super-intelligent computer someday.

But it’s up for debate how soon this will happen, and exactly how dangerous it will be when we do. Movies are full of plotlines that express our fears around building an artificial intelligence that’s smarter than we are—and that may not have our best interests at heart.

If the robot apocalypse does occur, the last thing we want is for the survivors to look back and say that it could all have been averted if software developers and testers in our time had considered more progressive QA testing and development methodologies.

Let’s look at a few examples of how attack scenarios play out in the movies: In this example, the artificial intelligence takes its directive to protect humanity to an extreme, making harmful decisions in an attempt to protect the species from itself.

Operating under a directive to protect humanity, the AI incites a robot uprising to protect the human race from its own self-destructive tendencies—by taking free will away from individuals.

When its operators see the danger and attempt to shut it down, Skynet decides the entire human race is a threat—and launches nuclear missiles, prompting a worldwide war that wipes out three billion people.

Another example is AUTO, the artificial intelligence controlling the spaceship Axiom in Wall-E. The ship, carrying human refugees from a polluted Earth, is supposed to return immediately if evidence is found that the planet can once again support life.

When programmer Caleb Smith visits the home of software magnate Nathan Bateman, he meets Bateman’s creation, Ava—a robot with the face of a human woman. Ava tells Caleb she is being held captive in Bateman’s house, and confesses her desire—both for Caleb and a chance to see the outside world. She persuades him to help her escape, eliminating her creator and trapping Caleb inside her former prison in the process (watch the scene).

At the root of many attack and social manipulation scenarios, there’s a deeper fear—that any artificial intelligence we create will become far smarter than we can imagine, much faster than we can control. Under these scenarios, the artificial intelligence could become very good at programming itself—much better than its original programmers.

Once it’s that intelligent, it’s easy to imagine the AI exploiting vulnerabilities in our own networks and making incredible gains in technology research and economic performance—easily outstripping our own.

A few examples include: Skynet from Terminator was originally built to eliminate human error when responding to military threats, and given control of all military systems and software—including the entire nuclear weapons arsenal and a fleet of Stealth bombers. But from the moment of its activation, Skynet begins to learn on its own.

He suggests a terrifying scenario in which a computer built to solve the Riemann hypothesis, an as-yet-unsolvable mathematical problem, could transform the entire planet into a computing device in order to speed up its calculations.

The software autonomously hacks any drones within the vicinity of the user’s drone or laptop, creating what the inventor calls an “army of zombie drones.” And anyone can download and use it.

In day-to-day software testing, test-driven development methods involve testing in conjunction with the early development process—rather than as a separate function and after development has been completed.  In this model, the expectations and boundaries for the working software are set up front, minimizing scope creep.

But with automated tests running through the unit level code constantly, we,would be able to expose these threats early on and force the software to automatically shut down.

While we may not be able to restrict the evolution of AI, by using test-first methodologies we can create a good safety net to make sure that dangerous robots are detected and eliminated before real harm is caused.

On an individual level—say, with an intelligent robot used in a law enforcement environment—risk-based testing could reduce the chances of the robot responding to a threat or obstacle with unnecessary lethal force, even if it’s armed.

Testers might find that there is little or no value in robots using lethal force at all or in any but the most extreme circumstances. Robots may be most useful when sent into dangerous situations where a human officer’s life would be at risk, but where they would not have to make the decision to use lethal force.

For instance, a delivery robot’s software is only evaluated to determine whether it successfully delivers a package within a certain timeframe—not the risks inherent in delivery, such as running someone over in the rush to deliver on time.

At the Georgia Institute of Technology, a team of researchers have been attempting to teach human values to robots using Quixote, a teaching method relying on children’s fairy tales. Each crowd-sourced, interactive story is broken down into a flow-chart, with punishments and rewards assigned to various paths the robot can choose.

Artificial Intelligence Makes Inroads in Broadcasting

When you walk around with more computing power in your pocket than it took to launch a Saturn V rocket to the moon, you get the hint that computers are increasingly doing work that we either don’t like doing or never could do before.

“By enabling broadcasters to track how operations are being used across their organization, AI-based solutions can create more efficient operations and bring costs down by identifying trends.

“In a machine learning/artificial intelligence solution, the system could learn enough about the content types [by watching content] and could experiment with various combinations in an offline environment, until you have sufficient confidence that it is providing better management of the supply chain in real time than manual methods, optimizing for cost and quality at the level of each individual piece of content,”

“We’re already seeing artificial intelligence being used as a tool to create content like highlight clips, with Aspera being used for the ingest of video content and the automated delivery of the produced assets,”

“IBM’s AI technology quickly identified key highlights based on cheering, high fives, commentary and TV graphics such as banners within specific video frames,”

“For example, the TVU Transcriber service is available today and ensures FCC compliance of any video content a station puts on air, on social media or any digital media platform.

Preferences] “With the TVU MediaMind Platform, all digital and broadcast production groups can truly collaborate to cover the same story, while allowing each group to customize and deliver the completed program based on viewer demographics,”

“As a result, a station can cost-effectively create targeted content and allow it to better serve digital and broadcast viewers using the same raw videos.

“[Clear] helps automatically recognize elements within audio and video, and generate associated metadata, making it easier to sort, locate and use content across all MAM workflows,”

“All these functionalities require human review and quality control right now, but one of the key characteristics of AI and machine learning is the ability to learn and improve over time,”

Over the past five years, artificial intelligence has moved out of the laboratory and into real products—you only have to go as far as Apple’s Siri and Google’s Alexa to find examples in the real world.

It’s clear that the most efficient use of bandwidth and the ability to quickly create targeted programming are of great interest to broadcasters, and artificial intelligence is helping to make that possible.

The Last Invention of Man

Whereas the rest of the enterprise brought in the money to keep things going, by various commercial applications of narrow AI, the Omega Team pushed ahead in their quest for what had always been the CEO’s dream: building general artificial intelligence.

They’d deliberately chosen this strategy because they had bought the intelligence explosion argument made by the British mathematician Irving Good back in 1965: “Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man, however clever.

Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.” They figured that if they could get this recursive self-improvement going, the machine would soon get smart enough that it could also teach itself all other human skills that would be useful.

For security reasons, it was completely disconnected from the Internet, but it contained a local copy of much of the web (Wikipedia, the Library of Congress, Twitter, a selection from YouTube, much of Facebook, etc.) to use as its training data to learn from.* They’d picked this start time to work undisturbed: Their families and friends thought they were on a weekend corporate retreat.

After its launch in 2005 as a crowdsourcing Internet marketplace, it had grown rapidly, with tens of thousands of people around the world anonymously competing around the clock to perform highly structured chores called HITs, “Human Intelligence Tasks.” These tasks ranged from transcribing audio recordings to classifying images and writing descriptions of web pages, and all had one thing in common: If you did them well, nobody would know that you were an AI.

The MTurk customers typically paid after about eight hours, at which point the Omegas reinvested the money in more cloud-computing time, using still better task modules made by the latest version of the ever-improving Prometheus.

Because they were able to double their money every eight hours, they soon started saturating MTurk’s task supply, and found that they couldn’t earn more than about $1 million per day without drawing unwanted attention to themselves.

Essentially the whole digital economy was up for grabs, but was it better to start by making computer games, music, movies, or software, to write books or articles, to trade on the stock market, or to make inventions and sell them?

It simply boiled down to maximizing their rate of return on investment, but normal investment strategies were a slow-motion parody of what they could do: Whereas a normal investor might be pleased with a 9 percent return per year, their MTurk investments had yielded 9 percent per hour, generating eight times more money each day.

Prometheus could rapidly become extremely skilled at designing appealing games, easily handling the coding, graphic design, ray tracing of images, and all other tasks needed to produce a final ready-to-ship product.

Moreover, after digesting all the web’s data on people’s preferences, it would know exactly what each category of gamer liked, and could develop a superhuman ability to optimize a game for sales revenue.

The Elder Scrolls V: Skyrim, a game on which many of the Omegas had wasted more hours than they cared to admit, had grossed over $400 million during its first week back in 2011, and they were confident that Prometheus could make something at least this addictive in 24 hours using $1 million of cloud-computing resources.

If this brought in $250 million in a week, they would have doubled their investment eight times in eight days, giving a return of 3 percent per hour—slightly worse than their MTurk start, but much more sustainable.

On an Internet-connected computer, on the other hand, running any complicated program created by Prometheus was a risky proposition: Since the Omegas had no way of fully understanding what it would do, they had no way of knowing that it wouldn’t, say, start virally spreading itself online.

These laws of the box were to the software inside like the laws of physics are to us inside our universe: The software couldn’t travel out of the box any more than we can travel faster than the speed of light, no matter how smart we are.

The First Billions The Omegas had narrowed their search to products that were highly valuable, purely digital (avoiding slow manufacturing) and easily understandable (for example, text or movies they knew wouldn’t pose a breakout risk).

Although Prometheus was astonishingly capable by Sunday morning, steadily raking in money from MTurk, its intellectual abilities were still rather narrow: Prometheus had been deliberately optimized to design AI systems and write software that performed rather mind-numbing MTurk tasks.

Turning the screenplay into a final video file required massive amounts of ray tracing of simulated actors and the complex scenes they moved through, simulated voices, the production of compelling musical soundtracks and so on.

The Omegas scheduled their website launch for Friday, giving Prometheus time to produce more content and themselves time to do the things they didn’t trust Prometheus with: buying ads and starting to recruit employees for the shell companies they’d set up during the past months.

To cover their tracks, the official cover story would be that their media company (which had no public association with the Omegas) bought most of its content from independent film producers, typically high-tech startups in low-income regions.

To match their cover story, they chose the corporate slogan “Channeling the world’s creative talent,” and branded their company as being disruptively different by using cutting-edge technology to empower creative people, especially in the developing world.

During the first two weeks of Prometheus, its moviemaking skills improved rapidly, in terms not only of film quality but also of better algorithms for character simulation and ray tracing, which greatly reduced the cloud-computing cost to make each new episode.

As a result, the Omegas were able to roll out dozens of new series during the first month, targeting demographics from toddlers to adults, as well as to expand to all major world language markets, making their site remarkably international compared with all competitors.

Some commentators were impressed by the fact that it wasn’t merely the soundtracks that were multilingual, but the videos themselves: For example, when a character spoke Italian, the mouth motions matched the Italian words, as did the characteristically Italian hand gestures.

Buoyed by aggressive advertising (which the Omegas could afford because of their near-zero production costs), excellent media coverage, and rave word-of-mouth reviews, their global revenue had mushroomed to $10 million a day within a month of launch.

To make themselves less vulnerable and avoid raising eyebrows with excessive cloud computing, they also hired engineers to start building a series of massive computer facilities around the world, owned by seemingly unaffiliated shell companies.

By carefully analyzing the world’s data, it had already during its first week presented the Omegas with a detailed step-by-step growth plan, and it kept improving and refining this plan as its data and computer resources grew.

Since Prometheus could accurately predict how long it would take humans to understand and build things given various tools, it developed the quickest possible path forward, giving priority to new tools that could be quickly understood and built and that were useful for developing more advanced tools.

In regions with high levels of government service, this often focused on community building, culture, and caregiving, while in poorer regions it also included launching and maintaining schools, healthcare, day care, elder care, affordable housing, parks, and basic infrastructure.

In countries where censorship and political interference threatened these efforts, they would initially acquiesce in whatever the government required of them to stay in business, with the secret internal slogan “The truth, nothing but the truth, but maybe not the whole truth.” Prometheus usually provided excellent advice in such situations, clarifying which politicians needed to be presented in a good light and which (usually corrupt local ones) could be exposed.

Their plethora of channels catering to different groups still reflected animosity between the United States and Russia, India and Pakistan, different religions, political factions and so on, but the criticism was slightly toned down, usually focusing on concrete issues involving money and power rather than on ad hominem attacks, scare-mongering and poorly substantiated rumors.

Once phase 2 started in earnest, this push to defuse old conflicts became more apparent, with frequent touching stories about the plight of traditional adversaries mixed with investigative reporting about how many vocal conflict-mongers were driven by personal profit motives.

Several blockbuster movies featured scenarios where global nuclear war started by accident or on purpose and dramatized the dystopian aftermath with nuclear winter, infrastructure collapse, and mass starvation.

Scientists and politicians advocating nuclear de-escalation were given ample airtime, not least to discuss the results of several new studies on what helpful measures could be taken—studies funded by scientific organizations that had received large donations from new tech companies.

Renewed media attention was also paid to global climate change, often highlighting the recent Prometheus-enabled technological breakthroughs that were slashing the cost of renewable energy and encouraging governments to invest in such new energy infrastructure.

Given any person’s knowledge and abilities, Prometheus could determine the fastest way for them to learn any new subject in a manner that kept them highly engaged and motivated to continue, and produce the corresponding optimized videos, reading materials, exercises, and other learning tools.

These offerings bore little resemblance to most present-day online courses: By leveraging Prometheus’ movie-making talents, the video segments would truly engage, providing powerful metaphors that you would relate to, leaving you craving to learn more.

These educational superpowers proved potent tools for political purposes, creating online “persuasion sequences” of videos where insights from each one would both update someone’s views and motivate them to watch another video about a related topic where they were likely to be further convinced.

At the same time, likable characters from the other nation would start appearing in popular shows on the entertainment channels, just as sympathetically portrayed minority characters had bolstered the civil and gay rights movements in the past.

Although there was a palpable new sense of optimism in most countries as education, social services, and infrastructure improved, conflicts subsided and local companies released breakthrough technologies that swept the world, not everybody was happy.

As if the shrinking profits of publicly traded companies weren’t bad enough, investment firms around the world had noticed a disturbing trend: All their previously successful trading algorithms seemed to have stopped working, underperforming even simple index funds.

Virtually every traditional industry was now clamoring for a bailout, but limited government funds pitted them in a hopeless battle against one another while the media portrayed them as dinosaurs seeking state subsidies simply because they couldn’t compete.

This had a simple mathematical explanation: Before Prometheus, the poorest 50 percent of Earth’s population had earned only about 4 percent of the global income, enabling the Omega-controlled companies to win their hearts (and votes) by sharing only a modest fraction of their profits with them.

In carefully optimized campaigns, they portrayed themselves at the center of the political spectrum, denouncing the right as greedy bailout-seeking conflict-mongers and lambasting the left as big-government tax-and-spend innovation stiflers.

Before long, virtually the entire Omega empire supported it, and it launched global projects on an unprecedented scale, even in countries that had largely missed out on the tech boom, improving education, health, prosperity, and governance.

Media had defused international conflict to the point that military spending was largely unnecessary, and growing prosperity had eliminated most roots of old conflicts, which traced back to competition over scarce resources.

For the first time ever, our planet was run by a single power, amplified by an intelligence so vast that it could potentially enable life to flourish for billions of years on Earth and throughout our cosmos—but what specifically was their plan?

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