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“We cannot be conscious of what we are not conscious of.” – Julian Jaynes, The Origin of Consciousness in the Breakdown of the Bicameral Mind
Unlike the director leads you to believe, the protagonist of Ex Machina, Andrew Garland’s 2015 masterpiece, isn’t Caleb, a young programmer tasked with evaluating machine consciousness. Rather, it’s his target Ava, a breathtaking humanoid AI with a seemingly child-like naïveté and an enigmatic mind.
Like most cerebral movies, Ex Machina leaves the conclusion up to the viewer: was Ava actually conscious? In doing so, it also cleverly avoids a thorny question that has challenged most AI-centric movies to date: what is consciousness, and can machines have it?
Hollywood producers aren’t the only people stumped. As machine intelligence barrels forward at breakneck speed—not only exceeding human performance on games such as DOTA and Go, but doing so without the need for human expertise—the question has once more entered the scientific mainstream.
Are machines on the verge of consciousness?
This week, in a review published in the prestigious journal Science, cognitive scientists Drs. Stanislas Dehaene, Hakwan Lau and Sid Kouider of the Collège de France, University of California, Los Angeles and PSL Research University, respectively, argue: not yet, but there is a clear path forward.
The reason? Consciousness is “resolutely computational,” the authors say, in that it results from specific types of information processing, made possible by the hardware of the brain.
There is no magic juice, no extra spark—in fact, an experiential component (“what is it like to be conscious?”) isn’t even necessary to implement consciousness.
If consciousness results purely from the computations within our three-pound organ, then endowing machines with a similar quality is just a matter of translating biology to code.
Much like the way current powerful machine learning techniques heavily borrow from neurobiology, the authors write, we may be able to achieve artificial consciousness by studying the structures in our own brains that generate consciousness and implementing those insights as computer algorithms.
From Brain to Bot
Without doubt, the field of AI has greatly benefited from insights into our own minds, both in form and function.
For example, deep neural networks, the architecture of algorithms that underlie AlphaGo’s breathtaking sweep against its human competitors, are loosely based on the multi-layered biological neural networks that our brain cells self-organize into.
Reinforcement learning, a type of “training” that teaches AIs to learn from millions of examples, has roots in a centuries-old technique familiar to anyone with a dog: if it moves toward the right response (or result), give a reward; otherwise ask it to try again.
In this sense, translating the architecture of human consciousness to machines seems like a no-brainer towards artificial consciousness. There’s just one big problem.
“Nobody in AI is working on building conscious machines because we just have nothing to go on. We just don’t have a clue about what to do,” said Dr. Stuart Russell, the author of Artificial Intelligence: A Modern Approach in a 2015 interview with Science.
The hard part, long before we can consider coding machine consciousness, is figuring out what consciousness actually is.
To Dehaene and colleagues, consciousness is a multilayered construct with two “dimensions:” C1, the information readily in mind, and C2, the ability to obtain and monitor information about oneself. Both are essential to consciousness, but one can exist without the other.
Say you’re driving a car and the low fuel light comes on. Here, the perception of the fuel-tank light is C1—a mental representation that we can play with: we notice it, act upon it (refill the gas tank) and recall and speak about it at a later date (“I ran out of gas in the boonies!”).
“The first meaning we want to separate (from consciousness) is the notion of global availability,” explains Dehaene in an interview with Science. When you’re conscious of a word, your whole brain is aware of it, in a sense that you can use the information across modalities, he adds.
But C1 is not just a “mental sketchpad.” It represents an entire architecture that allows the brain to draw multiple modalities of information from our senses or from memories of related events, for example.
Unlike subconscious processing, which often relies on specific “modules” competent at a defined set of tasks, C1 is a global workspace that allows the brain to integrate information, decide on an action, and follow through until the end.
Like The Hunger Games, what we call “conscious” is whatever representation, at one point in time, wins the competition to access this mental workspace. The winners are shared among different brain computation circuits and are kept in the spotlight for the duration of decision-making to guide behavior.
Because of these features, C1 consciousness is highly stable and global—all related brain circuits are triggered, the authors explain.
For a complex machine such as an intelligent car, C1 is a first step towards addressing an impending problem, such as a low fuel light. In this example, the light itself is a type of subconscious signal: when it flashes, all of the other processes in the machine remain uninformed, and the car—even if equipped with state-of-the-art visual processing networks—passes by gas stations without hesitation.
With C1 in place, the fuel tank would alert the car computer (allowing the light to enter the car’s “conscious mind”), which in turn checks the built-in GPS to search for the next gas station.
“We think in a machine this would translate into a system that takes information out of whatever processing module it’s encapsulated in, and make it available to any of the other processing modules so they can use the information,” says Dehaene. “It’s a first sense of consciousness.”
In a way, C1 reflects the mind’s capacity to access outside information. C2 goes introspective.
The authors define the second facet of consciousness, C2, as “meta-cognition:” reflecting on whether you know or perceive something, or whether you just made an error (“I think I may have filled my tank at the last gas station, but I forgot to keep a receipt to make sure”). This dimension reflects the link between consciousness and sense of self.
C2 is the level of consciousness that allows you to feel more or less confident about a decision when making a choice. In computational terms, it’s an algorithm that spews out the probability that a decision (or computation) is correct, even if it’s often experienced as a “gut feeling.”
C2 also has its claws in memory and curiosity. These self-monitoring algorithms allow us to know what we know or don’t know—so-called “meta-memory,” responsible for that feeling of having something at the tip of your tongue. Monitoring what we know (or don’t know) is particularly important for children, says Dehaene.
“Young children absolutely need to monitor what they know in order to…inquire and become curious and learn more,” he explains.
The two aspects of consciousness synergize to our benefit: C1 pulls relevant information into our mental workspace (while discarding other “probable” ideas or solutions), while C2 helps with long-term reflection on whether the conscious thought led to a helpful response.
Going back to the low fuel light example, C1 allows the car to solve the problem in the moment—these algorithms globalize the information, so that the car becomes aware of the problem.
But to solve the problem, the car would need a “catalog of its cognitive abilities”—a self-awareness of what resources it has readily available, for example, a GPS map of gas stations.
“A car with this sort of self-knowledge is what we call having C2,” says Dehaene. Because the signal is globally available and because it’s being monitored in a way that the machine is looking at itself, the car would care about the low gas light and behave like humans do—lower fuel consumption and find a gas station.
“Most present-day machine learning systems are devoid of any self-monitoring,” the authors note.
But their theory seems to be on the right track. The few examples whereby a self-monitoring system was implemented—either within the structure of the algorithm or as a separate network—the AI has generated “internal models that are meta-cognitive in nature, making it possible for an agent to develop a (limited, implicit, practical) understanding of itself.”
Towards conscious machines
Would a machine endowed with C1 and C2 behave as if it were conscious? Very likely: a smartcar would “know” that it’s seeing something, express confidence in it, report it to others, and find the best solutions for problems. If its self-monitoring mechanisms break down, it may also suffer “hallucinations” or even experience visual illusions similar to humans.
Thanks to C1 it would be able to use the information it has and use it flexibly, and because of C2 it would know the limit of what it knows, says Dehaene. “I think (the machine) would be conscious,” and not just merely appearing so to humans.
If you’re left with a feeling that consciousness is far more than global information sharing and self-monitoring, you’re not alone.
“Such a purely functional definition of consciousness may leave some readers unsatisfied,” the authors acknowledge.
“But we’re trying to take a radical stance, maybe simplifying the problem. Consciousness is a functional property, and when we keep adding functions to machines, at some point these properties will characterize what we mean by consciousness,” Dehaene concludes.
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How many cyborgs did you see during your morning commute today? I would guess at least five. Did they make you nervous? Probably not; you likely didn’t even realize they were there.
In a presentation titled “Biohacking and the Connected Body” at Singularity University Global Summit, Hannes Sjoblad informed the audience that we’re already living in the age of cyborgs. Sjoblad is co-founder of the Sweden-based biohacker network Bionyfiken, a chartered non-profit that unites DIY-biologists, hackers, makers, body modification artists and health and performance devotees to explore human-machine integration.
Sjoblad said the cyborgs we see today don’t look like Hollywood prototypes; they’re regular people who have integrated technology into their bodies to improve or monitor some aspect of their health. Sjoblad defined biohacking as applying hacker ethic to biological systems. Some biohackers experiment with their biology with the goal of taking the human body’s experience beyond what nature intended.
Smart insulin monitoring systems, pacemakers, bionic eyes, and Cochlear implants are all examples of biohacking, according to Sjoblad. He told the audience, “We live in a time where, thanks to technology, we can make the deaf hear, the blind see, and the lame walk.” He is convinced that while biohacking could conceivably end up having Brave New World-like dystopian consequences, it can also be leveraged to improve and enhance our quality of life in multiple ways.
The field where biohacking can make the most positive impact is health. In addition to pacemakers and insulin monitors, several new technologies are being developed with the goal of improving our health and simplifying access to information about our bodies.
Ingestibles are a type of smart pill that use wireless technology to monitor internal reactions to medications, helping doctors determine optimum dosage levels and tailor treatments to different people. Your body doesn’t absorb or process medication exactly as your neighbor’s does, so shouldn’t you each have a treatment that works best with your unique system? Colonoscopies and endoscopies could one day be replaced by miniature pill-shaped video cameras that would collect and transmit images as they travel through the digestive tract.
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Security is another area where biohacking could be beneficial. One example Sjoblad gave was personalization of weapons: an invader in your house couldn’t fire your gun because it will have been matched to your fingerprint or synced with your body so that it only responds to you.
Biohacking can also simplify everyday tasks. In an impressive example of walking the walk rather than just talking the talk, Sjoblad had an NFC chip implanted in his hand. The chip contains data from everything he used to have to carry around in his pockets: credit and bank card information, key cards to enter his office building and gym, business cards, and frequent shopper loyalty cards. When he’s in line for a morning coffee or rushing to get to the office on time, he doesn’t have to root around in his pockets or bag to find the right card or key; he just waves his hand in front of a sensor and he’s good to go.
Evolved from radio frequency identification (RFID)—an old and widely distributed technology—NFC chips are activated by another chip, and small amounts of data can be transferred back and forth. No wireless connection is necessary. Sjoblad sees his NFC implant as a personal key to the Internet of Things, a simple way for him to talk to the smart, connected devices around him.
Sjoblad isn’t the only person who feels a need for connection.
When British science writer Frank Swain realized he was going to go deaf, he decided to hack his hearing to be able to hear Wi-Fi. Swain developed software that tunes into wireless communication fields and uses an inbuilt Wi-Fi sensor to pick up router name, encryption modes and distance from the device. This data is translated into an audio stream where distant signals click or pop, and strong signals sound their network ID in a looped melody. Swain hears it all through an upgraded hearing aid.
Global datastreams can also become sensory experiences. Spanish artist Moon Ribas developed and implanted a chip in her elbow that is connected to the global monitoring system for seismographic sensors; each time there’s an earthquake, she feels it through vibrations in her arm.
You can feel connected to our planet, too: North Sense makes a “standalone artificial sensory organ” that connects to your body and vibrates whenever you’re facing north. It’s a built-in compass; you’ll never get lost again.
Biohacking applications are likely to proliferate in the coming years, some of them more useful than others. But there are serious ethical questions that can’t be ignored during development and use of this technology. To what extent is it wise to tamper with nature, and who gets to decide?
Most of us are probably ok with waiting in line an extra 10 minutes or occasionally having to pull up a maps app on our phone if it means we don’t need to implant computer chips into our forearms. If it’s frightening to think of criminals stealing our wallets, imagine them cutting a chunk of our skin out to have instant access to and control over our personal data. The physical invasiveness and potential for something to go wrong seems to far outweigh the benefits the average person could derive from this technology.
But that may not always be the case. It’s worth noting the miniaturization of technology continues at a quick rate, and the smaller things get, the less invasive (and hopefully more useful) they’ll be. Even today, there are people already sensibly benefiting from biohacking. If you look closely enough, you’ll spot at least a couple cyborgs on your commute tomorrow morning.
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