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#437477 If a Robot Is Conscious, Is It OK to ...
In the Star Trek: The Next Generation episode “The Measure of a Man,” Data, an android crew member of the Enterprise, is to be dismantled for research purposes unless Captain Picard can argue that Data deserves the same rights as a human being. Naturally the question arises: What is the basis upon which something has rights? What gives an entity moral standing?
The philosopher Peter Singer argues that creatures that can feel pain or suffer have a claim to moral standing. He argues that nonhuman animals have moral standing, since they can feel pain and suffer. Limiting it to people would be a form of speciesism, something akin to racism and sexism.
Without endorsing Singer’s line of reasoning, we might wonder if it can be extended further to an android robot like Data. It would require that Data can either feel pain or suffer. And how you answer that depends on how you understand consciousness and intelligence.
As real artificial intelligence technology advances toward Hollywood’s imagined versions, the question of moral standing grows more important. If AIs have moral standing, philosophers like me reason, it could follow that they have a right to life. That means you cannot simply dismantle them, and might also mean that people shouldn’t interfere with their pursuing their goals.
Two Flavors of Intelligence and a Test
IBM’s Deep Blue chess machine was successfully trained to beat grandmaster Gary Kasparov. But it could not do anything else. This computer had what’s called domain-specific intelligence.
On the other hand, there’s the kind of intelligence that allows for the ability to do a variety of things well. It is called domain-general intelligence. It’s what lets people cook, ski, and raise children—tasks that are related, but also very different.
Artificial general intelligence, AGI, is the term for machines that have domain-general intelligence. Arguably no machine has yet demonstrated that kind of intelligence. This summer, a startup called OpenAI released a new version of its Generative Pre-Training language model. GPT-3 is a natural language processing system, trained to read and write so that it can be easily understood by people.
It drew immediate notice, not just because of its impressive ability to mimic stylistic flourishes and put together plausible content, but also because of how far it had come from a previous version. Despite this impressive performance, GPT-3 doesn’t actually know anything beyond how to string words together in various ways. AGI remains quite far off.
Named after pioneering AI researcher Alan Turing, the Turing test helps determine when an AI is intelligent. Can a person conversing with a hidden AI tell whether it’s an AI or a human being? If he can’t, then for all practical purposes, the AI is intelligent. But this test says nothing about whether the AI might be conscious.
Two Kinds of Consciousness
There are two parts to consciousness. First, there’s the what-it’s-like-for-me aspect of an experience, the sensory part of consciousness. Philosophers call this phenomenal consciousness. It’s about how you experience a phenomenon, like smelling a rose or feeling pain.
In contrast, there’s also access consciousness. That’s the ability to report, reason, behave, and act in a coordinated and responsive manner to stimuli based on goals. For example, when I pass the soccer ball to my friend making a play on the goal, I am responding to visual stimuli, acting from prior training, and pursuing a goal determined by the rules of the game. I make the pass automatically, without conscious deliberation, in the flow of the game.
Blindsight nicely illustrates the difference between the two types of consciousness. Someone with this neurological condition might report, for example, that they cannot see anything in the left side of their visual field. But if asked to pick up a pen from an array of objects in the left side of their visual field, they can reliably do so. They cannot see the pen, yet they can pick it up when prompted—an example of access consciousness without phenomenal consciousness.
Data is an android. How do these distinctions play out with respect to him?
The Data Dilemma
The android Data demonstrates that he is self-aware in that he can monitor whether or not, for example, he is optimally charged or there is internal damage to his robotic arm.
Data is also intelligent in the general sense. He does a lot of distinct things at a high level of mastery. He can fly the Enterprise, take orders from Captain Picard and reason with him about the best path to take.
He can also play poker with his shipmates, cook, discuss topical issues with close friends, fight with enemies on alien planets, and engage in various forms of physical labor. Data has access consciousness. He would clearly pass the Turing test.
However, Data most likely lacks phenomenal consciousness—he does not, for example, delight in the scent of roses or experience pain. He embodies a supersized version of blindsight. He’s self-aware and has access consciousness—can grab the pen—but across all his senses he lacks phenomenal consciousness.
Now, if Data doesn’t feel pain, at least one of the reasons Singer offers for giving a creature moral standing is not fulfilled. But Data might fulfill the other condition of being able to suffer, even without feeling pain. Suffering might not require phenomenal consciousness the way pain essentially does.
For example, what if suffering were also defined as the idea of being thwarted from pursuing a just cause without causing harm to others? Suppose Data’s goal is to save his crewmate, but he can’t reach her because of damage to one of his limbs. Data’s reduction in functioning that keeps him from saving his crewmate is a kind of nonphenomenal suffering. He would have preferred to save the crewmate, and would be better off if he did.
In the episode, the question ends up resting not on whether Data is self-aware—that is not in doubt. Nor is it in question whether he is intelligent—he easily demonstrates that he is in the general sense. What is unclear is whether he is phenomenally conscious. Data is not dismantled because, in the end, his human judges cannot agree on the significance of consciousness for moral standing.
Should an AI Get Moral Standing?
Data is kind; he acts to support the well-being of his crewmates and those he encounters on alien planets. He obeys orders from people and appears unlikely to harm them, and he seems to protect his own existence. For these reasons he appears peaceful and easier to accept into the realm of things that have moral standing.
But what about Skynet in the Terminator movies? Or the worries recently expressed by Elon Musk about AI being more dangerous than nukes, and by Stephen Hawking on AI ending humankind?
Human beings don’t lose their claim to moral standing just because they act against the interests of another person. In the same way, you can’t automatically say that just because an AI acts against the interests of humanity or another AI it doesn’t have moral standing. You might be justified in fighting back against an AI like Skynet, but that does not take away its moral standing. If moral standing is given in virtue of the capacity to nonphenomenally suffer, then Skynet and Data both get it even if only Data wants to help human beings.
There are no artificial general intelligence machines yet. But now is the time to consider what it would take to grant them moral standing. How humanity chooses to answer the question of moral standing for nonbiological creatures will have big implications for how we deal with future AIs—whether kind and helpful like Data, or set on destruction, like Skynet.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Posted in Human Robots
#435614 3 Easy Ways to Evaluate AI Claims
When every other tech startup claims to use artificial intelligence, it can be tough to figure out if an AI service or product works as advertised. In the midst of the AI “gold rush,” how can you separate the nuggets from the fool’s gold?
There’s no shortage of cautionary tales involving overhyped AI claims. And applying AI technologies to health care, education, and law enforcement mean that getting it wrong can have real consequences for society—not just for investors who bet on the wrong unicorn.
So IEEE Spectrum asked experts to share their tips for how to identify AI hype in press releases, news articles, research papers, and IPO filings.
“It can be tricky, because I think the people who are out there selling the AI hype—selling this AI snake oil—are getting more sophisticated over time,” says Tim Hwang, director of the Harvard-MIT Ethics and Governance of AI Initiative.
The term “AI” is perhaps most frequently used to describe machine learning algorithms (and deep learning algorithms, which require even less human guidance) that analyze huge amounts of data and make predictions based on patterns that humans might miss. These popular forms of AI are mostly suited to specialized tasks, such as automatically recognizing certain objects within photos. For that reason, they are sometimes described as “weak” or “narrow” AI.
Some researchers and thought leaders like to talk about the idea of “artificial general intelligence” or “strong AI” that has human-level capacity and flexibility to handle many diverse intellectual tasks. But for now, this type of AI remains firmly in the realm of science fiction and is far from being realized in the real world.
“AI has no well-defined meaning and many so-called AI companies are simply trying to take advantage of the buzz around that term,” says Arvind Narayanan, a computer scientist at Princeton University. “Companies have even been caught claiming to use AI when, in fact, the task is done by human workers.”
Here are three ways to recognize AI hype.
Look for Buzzwords
One red flag is what Hwang calls the “hype salad.” This means stringing together the term “AI” with many other tech buzzwords such as “blockchain” or “Internet of Things.” That doesn’t automatically disqualify the technology, but spotting a high volume of buzzwords in a post, pitch, or presentation should raise questions about what exactly the company or individual has developed.
Other experts agree that strings of buzzwords can be a red flag. That’s especially true if the buzzwords are never really explained in technical detail, and are simply tossed around as vague, poorly-defined terms, says Marzyeh Ghassemi, a computer scientist and biomedical engineer at the University of Toronto in Canada.
“I think that if it looks like a Google search—picture ‘interpretable blockchain AI deep learning medicine’—it's probably not high-quality work,” Ghassemi says.
Hwang also suggests mentally replacing all mentions of “AI” in an article with the term “magical fairy dust.” It’s a way of seeing whether an individual or organization is treating the technology like magic. If so—that’s another good reason to ask more questions about what exactly the AI technology involves.
And even the visual imagery used to illustrate AI claims can indicate that an individual or organization is overselling the technology.
“I think that a lot of the people who work on machine learning on a day-to-day basis are pretty humble about the technology, because they’re largely confronted with how frequently it just breaks and doesn't work,” Hwang says. “And so I think that if you see a company or someone representing AI as a Terminator head, or a big glowing HAL eye or something like that, I think it’s also worth asking some questions.”
Interrogate the Data
It can be hard to evaluate AI claims without any relevant expertise, says Ghassemi at the University of Toronto. Even experts need to know the technical details of the AI algorithm in question and have some access to the training data that shaped the AI model’s predictions. Still, savvy readers with some basic knowledge of applied statistics can search for red flags.
To start, readers can look for possible bias in training data based on small sample sizes or a skewed population that fails to reflect the broader population, Ghassemi says. After all, an AI model trained only on health data from white men would not necessarily achieve similar results for other populations of patients.
“For me, a red flag is not demonstrating deep knowledge of how your labels are defined.”
—Marzyeh Ghassemi, University of Toronto
How machine learning and deep learning models perform also depends on how well humans labeled the sample datasets use to train these programs. This task can be straightforward when labeling photos of cats versus dogs, but gets more complicated when assigning disease diagnoses to certain patient cases.
Medical experts frequently disagree with each other on diagnoses—which is why many patients seek a second opinion. Not surprisingly, this ambiguity can also affect the diagnostic labels that experts assign in training datasets. “For me, a red flag is not demonstrating deep knowledge of how your labels are defined,” Ghassemi says.
Such training data can also reflect the cultural stereotypes and biases of the humans who labeled the data, says Narayanan at Princeton University. Like Ghassemi, he recommends taking a hard look at exactly what the AI has learned: “A good way to start critically evaluating AI claims is by asking questions about the training data.”
Another red flag is presenting an AI system’s performance through a single accuracy figure without much explanation, Narayanan says. Claiming that an AI model achieves “99 percent” accuracy doesn’t mean much without knowing the baseline for comparison—such as whether other systems have already achieved 99 percent accuracy—or how well that accuracy holds up in situations beyond the training dataset.
Narayanan also emphasized the need to ask questions about an AI model’s false positive rate—the rate of making wrong predictions about the presence of a given condition. Even if the false positive rate of a hypothetical AI service is just one percent, that could have major consequences if that service ends up screening millions of people for cancer.
Readers can also consider whether using AI in a given situation offers any meaningful improvement compared to traditional statistical methods, says Clayton Aldern, a data scientist and journalist who serves as managing director for Caldern LLC. He gave the hypothetical example of a “super-duper-fancy deep learning model” that achieves a prediction accuracy of 89 percent, compared to a “little polynomial regression model” that achieves 86 percent on the same dataset.
“We're talking about a three-percentage-point increase on something that you learned about in Algebra 1,” Aldern says. “So is it worth the hype?”
Don’t Ignore the Drawbacks
The hype surrounding AI isn’t just about the technical merits of services and products driven by machine learning. Overblown claims about the beneficial impacts of AI technology—or vague promises to address ethical issues related to deploying it—should also raise red flags.
“If a company promises to use its tech ethically, it is important to question if its business model aligns with that promise,” Narayanan says. “Even if employees have noble intentions, it is unrealistic to expect the company as a whole to resist financial imperatives.”
One example might be a company with a business model that depends on leveraging customers’ personal data. Such companies “tend to make empty promises when it comes to privacy,” Narayanan says. And, if companies hire workers to produce training data, it’s also worth asking whether the companies treat those workers ethically.
The transparency—or lack thereof—about any AI claim can also be telling. A company or research group can minimize concerns by publishing technical claims in peer-reviewed journals or allowing credible third parties to evaluate their AI without giving away big intellectual property secrets, Narayanan says. Excessive secrecy is a big red flag.
With these strategies, you don’t need to be a computer engineer or data scientist to start thinking critically about AI claims. And, Narayanan says, the world needs many people from different backgrounds for societies to fully consider the real-world implications of AI.
Editor’s Note: The original version of this story misspelled Clayton Aldern’s last name as Alderton. Continue reading
Posted in Human Robots
#435370 The Rise of the Robots. Soon!
Are we the masters of our own eventual demise at the…hand of our robot creations? From where I’m standing, it sure looks like it!
Posted in Human Robots
#433513 Get your geek on! Top Robot movies
This guy has a few (very debatable) choices for “Top 5 Cyborg / Android / Robot movies” …what do reckon? No offense, but I can think of many better humanoid, cyborg, etc. movies off the top of my head (in … Continue reading
Posted in Human Robots