Tag Archives: fight
#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.
Image Credit: Ico Maker / Shutterstock.com Continue reading
Posted in Human Robots
#437373 Microsoft’s New Deepfake Detector Puts ...
The upcoming US presidential election seems set to be something of a mess—to put it lightly. Covid-19 will likely deter millions from voting in person, and mail-in voting isn’t shaping up to be much more promising. This all comes at a time when political tensions are running higher than they have in decades, issues that shouldn’t be political (like mask-wearing) have become highly politicized, and Americans are dramatically divided along party lines.
So the last thing we need right now is yet another wrench in the spokes of democracy, in the form of disinformation; we all saw how that played out in 2016, and it wasn’t pretty. For the record, disinformation purposely misleads people, while misinformation is simply inaccurate, but without malicious intent. While there’s not a ton tech can do to make people feel safe at crowded polling stations or up the Postal Service’s budget, tech can help with disinformation, and Microsoft is trying to do so.
On Tuesday the company released two new tools designed to combat disinformation, described in a blog post by VP of Customer Security and Trust Tom Burt and Chief Scientific Officer Eric Horvitz.
The first is Microsoft Video Authenticator, which is made to detect deepfakes. In case you’re not familiar with this wicked byproduct of AI progress, “deepfakes” refers to audio or visual files made using artificial intelligence that can manipulate peoples’ voices or likenesses to make it look like they said things they didn’t. Editing a video to string together words and form a sentence someone didn’t say doesn’t count as a deepfake; though there’s manipulation involved, you don’t need a neural network and you’re not generating any original content or footage.
The Authenticator analyzes videos or images and tells users the percentage chance that they’ve been artificially manipulated. For videos, the tool can even analyze individual frames in real time.
Deepfake videos are made by feeding hundreds of hours of video of someone into a neural network, “teaching” the network the minutiae of the person’s voice, pronunciation, mannerisms, gestures, etc. It’s like when you do an imitation of your annoying coworker from accounting, complete with mimicking the way he makes every sentence sound like a question and his eyes widen when he talks about complex spreadsheets. You’ve spent hours—no, months—in his presence and have his personality quirks down pat. An AI algorithm that produces deepfakes needs to learn those same quirks, and more, about whoever the creator’s target is.
Given enough real information and examples, the algorithm can then generate its own fake footage, with deepfake creators using computer graphics and manually tweaking the output to make it as realistic as possible.
The scariest part? To make a deepfake, you don’t need a fancy computer or even a ton of knowledge about software. There are open-source programs people can access for free online, and as far as finding video footage of famous people—well, we’ve got YouTube to thank for how easy that is.
Microsoft’s Video Authenticator can detect the blending boundary of a deepfake and subtle fading or greyscale elements that the human eye may not be able to see.
In the blog post, Burt and Horvitz point out that as time goes by, deepfakes are only going to get better and become harder to detect; after all, they’re generated by neural networks that are continuously learning from and improving themselves.
Microsoft’s counter-tactic is to come in from the opposite angle, that is, being able to confirm beyond doubt that a video, image, or piece of news is real (I mean, can McDonald’s fries cure baldness? Did a seal slap a kayaker in the face with an octopus? Never has it been so imperative that the world know the truth).
A tool built into Microsoft Azure, the company’s cloud computing service, lets content producers add digital hashes and certificates to their content, and a reader (which can be used as a browser extension) checks the certificates and matches the hashes to indicate the content is authentic.
Finally, Microsoft also launched an interactive “Spot the Deepfake” quiz it developed in collaboration with the University of Washington’s Center for an Informed Public, deepfake detection company Sensity, and USA Today. The quiz is intended to help people “learn about synthetic media, develop critical media literacy skills, and gain awareness of the impact of synthetic media on democracy.”
The impact Microsoft’s new tools will have remains to be seen—but hey, we’re glad they’re trying. And they’re not alone; Facebook, Twitter, and YouTube have all taken steps to ban and remove deepfakes from their sites. The AI Foundation’s Reality Defender uses synthetic media detection algorithms to identify fake content. There’s even a coalition of big tech companies teaming up to try to fight election interference.
One thing is for sure: between a global pandemic, widespread protests and riots, mass unemployment, a hobbled economy, and the disinformation that’s remained rife through it all, we’re going to need all the help we can get to make it through not just the election, but the rest of the conga-line-of-catastrophes year that is 2020.
Image Credit: Darius Bashar on Unsplash Continue reading
Posted in Human Robots
#437261 How AI Will Make Drug Discovery ...
If you had to guess how long it takes for a drug to go from an idea to your pharmacy, what would you guess? Three years? Five years? How about the cost? $30 million? $100 million?
Well, here’s the sobering truth: 90 percent of all drug possibilities fail. The few that do succeed take an average of 10 years to reach the market and cost anywhere from $2.5 billion to $12 billion to get there.
But what if we could generate novel molecules to target any disease, overnight, ready for clinical trials? Imagine leveraging machine learning to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.
Welcome to the future of AI and low-cost, ultra-fast, and personalized drug discovery. Let’s dive in.
GANs & Drugs
Around 2012, computer scientist-turned-biophysicist Alex Zhavoronkov started to notice that artificial intelligence was getting increasingly good at image, voice, and text recognition. He knew that all three tasks shared a critical commonality. In each, massive datasets were available, making it easy to train up an AI.
But similar datasets were present in pharmacology. So, back in 2014, Zhavoronkov started wondering if he could use these datasets and AI to significantly speed up the drug discovery process. He’d heard about a new technique in artificial intelligence known as generative adversarial networks (or GANs). By pitting two neural nets against one another (adversarial), the system can start with minimal instructions and produce novel outcomes (generative). At the time, researchers had been using GANs to do things like design new objects or create one-of-a-kind, fake human faces, but Zhavoronkov wanted to apply them to pharmacology.
He figured GANs would allow researchers to verbally describe drug attributes: “The compound should inhibit protein X at concentration Y with minimal side effects in humans,” and then the AI could construct the molecule from scratch. To turn his idea into reality, Zhavoronkov set up Insilico Medicine on the campus of Johns Hopkins University in Baltimore, Maryland, and rolled up his sleeves.
Instead of beginning their process in some exotic locale, Insilico’s “drug discovery engine” sifts millions of data samples to determine the signature biological characteristics of specific diseases. The engine then identifies the most promising treatment targets and—using GANs—generates molecules (that is, baby drugs) perfectly suited for them. “The result is an explosion in potential drug targets and a much more efficient testing process,” says Zhavoronkov. “AI allows us to do with fifty people what a typical drug company does with five thousand.”
The results have turned what was once a decade-long war into a month-long skirmish.
In late 2018, for example, Insilico was generating novel molecules in fewer than 46 days, and this included not just the initial discovery, but also the synthesis of the drug and its experimental validation in computer simulations.
Right now, they’re using the system to hunt down new drugs for cancer, aging, fibrosis, Parkinson’s, Alzheimer’s, ALS, diabetes, and many others. The first drug to result from this work, a treatment for hair loss, is slated to start Phase I trials by the end of 2020.
They’re also in the early stages of using AI to predict the outcomes of clinical trials in advance of the trial. If successful, this technique will enable researchers to strip a bundle of time and money out of the traditional testing process.
Protein Folding
Beyond inventing new drugs, AI is also being used by other scientists to identify new drug targets—that is, the place to which a drug binds in the body and another key part of the drug discovery process.
Between 1980 and 2006, despite an annual investment of $30 billion, researchers only managed to find about five new drug targets a year. The trouble is complexity. Most potential drug targets are proteins, and a protein’s structure—meaning the way a 2D sequence of amino acids folds into a 3D protein—determines its function.
But a protein with merely a hundred amino acids (a rather small protein) can produce a googol-cubed worth of potential shapes—that’s a one followed by three hundred zeroes. This is also why protein-folding has long been considered an intractably hard problem for even the most powerful of supercomputers.
Back in 1994, to monitor supercomputers’ progress in protein-folding, a biannual competition was created. Until 2018, success was fairly rare. But then the creators of DeepMind turned their neural networks loose on the problem. They created an AI that mines enormous datasets to determine the most likely distance between a protein’s base pairs and the angles of their chemical bonds—aka, the basics of protein-folding. They called it AlphaFold.
On its first foray into the competition, contestant AIs were given 43 protein-folding problems to solve. AlphaFold got 25 right. The second-place team managed a meager three. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today’s most intractable diseases.
Drug Delivery
Another theater of war for improved drugs is the realm of drug delivery. Even here, converging exponential technologies are paving the way for massive implications in both human health and industry shifts.
One key contender is CRISPR, the fast-advancing gene-editing technology that stands to revolutionize synthetic biology and treatment of genetically linked diseases. And researchers have now demonstrated how this tool can be applied to create materials that shape-shift on command. Think: materials that dissolve instantaneously when faced with a programmed stimulus, releasing a specified drug at a highly targeted location.
Yet another potential boon for targeted drug delivery is nanotechnology, whereby medical nanorobots have now been used to fight incidences of cancer. In a recent review of medical micro- and nanorobotics, lead authors (from the University of Texas at Austin and University of California, San Diego) found numerous successful tests of in vivo operation of medical micro- and nanorobots.
Drugs From the Future
Covid-19 is uniting the global scientific community with its urgency, prompting scientists to cast aside nation-specific territorialism, research secrecy, and academic publishing politics in favor of expedited therapeutic and vaccine development efforts. And in the wake of rapid acceleration across healthcare technologies, Big Pharma is an area worth watching right now, no matter your industry. Converging technologies will soon enable extraordinary strides in longevity and disease prevention, with companies like Insilico leading the charge.
Riding the convergence of massive datasets, skyrocketing computational power, quantum computing, cognitive surplus capabilities, and remarkable innovations in AI, we are not far from a world in which personalized drugs, delivered directly to specified targets, will graduate from science fiction to the standard of care.
Rejuvenational biotechnology will be commercially available sooner than you think. When I asked Alex for his own projection, he set the timeline at “maybe 20 years—that’s a reasonable horizon for tangible rejuvenational biotechnology.”
How might you use an extra 20 or more healthy years in your life? What impact would you be able to make?
Join Me
(1) A360 Executive Mastermind: If you’re an exponentially and abundance-minded entrepreneur who would like coaching directly from me, consider joining my Abundance 360 Mastermind, a highly selective community of 360 CEOs and entrepreneurs who I coach for 3 days every January in Beverly Hills, Ca. Through A360, I provide my members with context and clarity about how converging exponential technologies will transform every industry. I’m committed to running A360 for the course of an ongoing 25-year journey as a “countdown to the Singularity.”
If you’d like to learn more and consider joining our 2021 membership, apply here.
(2) Abundance-Digital Online Community: I’ve also created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is Singularity University’s ‘onramp’ for exponential entrepreneurs—those who want to get involved and play at a higher level. Click here to learn more.
(Both A360 and Abundance-Digital are part of Singularity University—your participation opens you to a global community.)
This article originally appeared on diamandis.com. Read the original article here.
Image Credit: andreas160578 from Pixabay Continue reading
Posted in Human Robots