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Artificial intelligence has received its fair share of hype recently. However, it’s hype that’s well-founded: IDC predicts worldwide spend on AI and cognitive computing will culminate to a whopping $46 billion (with a “b”) by 2020, and all the tech giants are jumping on board faster than you can say “ROI.” But what is AI, exactly?
According to Hilary Mason, AI today is being misused as a sort of catch-all term to basically describe “any system that uses data to do anything.” But it’s so much more than that. A truly artificially intelligent system is one that learns on its own, one that’s capable of crunching copious amounts of data in order to create associations and intelligently mimic actual human behavior.
It’s what powers the technology anticipating our next online purchase (Amazon), or the virtual assistant that deciphers our voice commands with incredible accuracy (Siri), or even the hipster-friendly recommendation engine that helps you discover new music before your friends do (Pandora). But AI is moving past these consumer-pleasing “nice-to-haves” and getting down to serious business: saving our butts.
Much in the same way robotics entered manufacturing, AI is making its mark in healthcare by automating mundane, repetitive tasks. This is especially true in the case of detecting cancer. By leveraging the power of deep learning, algorithms can now be trained to distinguish between sets of pixels in an image that represents cancer versus sets that don’t—not unlike how Facebook’s image recognition software tags pictures of our friends without us having to type in their names first. This software can then go ahead and scour millions of medical images (MRIs, CT scans, etc.) in a single day to detect anomalies on a scope that humans just aren’t capable of. That’s huge.
As if that wasn’t enough, these algorithms are constantly learning and evolving, getting better at making these associations with each new data set that gets fed to them. Radiology, dermatology, and pathology will experience a giant upheaval as tech giants and startups alike jump in to bring these deep learning algorithms to a hospital near you.
In fact, some already are: the FDA recently gave their seal of approval for an AI-powered medical imaging platform that helps doctors analyze and diagnose heart anomalies. This is the first time the FDA has approved a machine learning application for use in a clinical setting.
But how efficient is AI compared to humans, really? Well, aside from the obvious fact that software programs don’t get bored or distracted or have to check Facebook every twenty minutes, AI is exponentially better than us at analyzing data.
Take, for example, IBM’s Watson. Watson analyzed genomic data from both tumor cells and healthy cells and was ultimately able to glean actionable insights in a mere 10 minutes. Compare that to the 160 hours it would have taken a human to analyze that same data. Diagnoses aside, AI is also being leveraged in pharmaceuticals to aid in the very time-consuming grunt work of discovering new drugs, and all the big players are getting involved.
But AI is far from being just a behind-the-scenes player. Gartner recently predicted that by 2025, 50 percent of the population will rely on AI-powered “virtual personal health assistants” for their routine primary care needs. What this means is that consumer-facing voice and chat-operated “assistants” (think Siri or Cortana) would, in effect, serve as a central hub of interaction for all our connected health devices and the algorithms crunching all our real-time biometric data. These assistants would keep us apprised of our current state of well-being, acting as a sort of digital facilitator for our personal health objectives and an always-on health alert system that would notify us when we actually need to see a physician.
Slowly, and thanks to the tsunami of data and advancements in self-learning algorithms, healthcare is transitioning from a reactive model to more of a preventative model—and it’s completely upending the way care is delivered. Whether Elon Musk’s dystopian outlook on AI holds any weight or not is yet to be determined. But one thing’s certain: for the time being, artificial intelligence is saving our lives.
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The best storytellers react to their audience. They look for smiles, signs of awe, or boredom; they simultaneously and skillfully read both the story and their sitters. Kevin Brooks, a seasoned storyteller working for Motorola’s Human Interface Labs, explains, “As the storyteller begins, they must tune in to… the audience’s energy. Based on this energy, the storyteller will adjust their timing, their posture, their characterizations, and sometimes even the events of the story. There is a dialog between audience and storyteller.”
Shortly after I read the script to Melita, the latest virtual reality experience from Madrid-based immersive storytelling company Future Lighthouse, CEO Nicolas Alcalá explained to me that the piece is an example of “reactive content,” a concept he’s been working on since his days at Singularity University.
For the first time in history, we have access to technology that can merge the reactive and affective elements of oral storytelling with the affordances of digital media, weaving stunning visuals, rich soundtracks, and complex meta-narratives in a story arena that has the capability to know you more intimately than any conventional storyteller could.
It’s no understatement to say that the storytelling potential here is phenomenal.
In short, we can refer to content as reactive if it reads and reacts to users based on their body rhythms, emotions, preferences, and data points. Artificial intelligence is used to analyze users’ behavior or preferences to sculpt unique storylines and narratives, essentially allowing for a story that changes in real time based on who you are and how you feel.
The development of reactive content will allow those working in the industry to go one step further than simply translating the essence of oral storytelling into VR. Rather than having a narrative experience with a digital storyteller who can read you, reactive content has the potential to create an experience with a storyteller who knows you.
This means being able to subtly insert minor personal details that have a specific meaning to the viewer. When we talk to our friends we often use experiences we’ve shared in the past or knowledge of our audience to give our story as much resonance as possible. Targeting personal memories and aspects of our lives is a highly effective way to elicit emotions and aid in visualizing narratives. When you can do this with the addition of visuals, music, and characters—all lifted from someone’s past—you have the potential for overwhelmingly engaging and emotionally-charged content.
Future Lighthouse inform me that for now, reactive content will rely primarily on biometric feedback technology such as breathing, heartbeat, and eye tracking sensors. A simple example would be a story in which parts of the environment or soundscape change in sync with the user’s heartbeat and breathing, or characters who call you out for not paying attention.
The next step would be characters and situations that react to the user’s emotions, wherein algorithms analyze biometric information to make inferences about states of emotional arousal (“why are you so nervous?” etc.). Another example would be implementing the use of “arousal parameters,” where the audience can choose what level of “fear” they want from a VR horror story before algorithms modulate the experience using information from biometric feedback devices.
The company’s long-term goal is to gather research on storytelling conventions and produce a catalogue of story “wireframes.” This entails distilling the basic formula to different genres so they can then be fleshed out with visuals, character traits, and soundtracks that are tailored for individual users based on their deep data, preferences, and biometric information.
The development of reactive content will go hand in hand with a renewed exploration of diverging, dynamic storylines, and multi-narratives, a concept that hasn’t had much impact in the movie world thus far. In theory, the idea of having a story that changes and mutates is captivating largely because of our love affair with serendipity and unpredictability, a cultural condition theorist Arthur Kroker refers to as the “hypertextual imagination.” This feeling of stepping into the unknown with the possibility of deviation from the habitual translates as a comforting reminder that our own lives can take exciting and unexpected turns at any moment.
The inception of the concept into mainstream culture dates to the classic Choose Your Own Adventure book series that launched in the late 70s, which in its literary form had great success. However, filmic takes on the theme have made somewhat less of an impression. DVDs like I’m Your Man (1998) and Switching (2003) both use scene selection tools to determine the direction of the storyline.
A more recent example comes from Kino Industries, who claim to have developed the technology to allow filmmakers to produce interactive films in which viewers can use smartphones to quickly vote on which direction the narrative takes at numerous decision points throughout the film.
The main problem with diverging narrative films has been the stop-start nature of the interactive element: when I’m immersed in a story I don’t want to have to pick up a controller or remote to select what’s going to happen next. Every time the audience is given the option to take a new path (“press this button”, “vote on X, Y, Z”) the narrative— and immersion within that narrative—is temporarily halted, and it takes the mind a while to get back into this state of immersion.
Reactive content has the potential to resolve these issues by enabling passive interactivity—that is, input and output without having to pause and actively make decisions or engage with the hardware. This will result in diverging, dynamic narratives that will unfold seamlessly while being dependent on and unique to the specific user and their emotions. Passive interactivity will also remove the game feel that can often be a symptom of interactive experiences and put a viewer somewhere in the middle: still firmly ensconced in an interactive dynamic narrative, but in a much subtler way.
While reading the Melita script I was particularly struck by a scene in which the characters start to engage with the user and there’s a synchronicity between the user’s heartbeat and objects in the virtual world. As the narrative unwinds and the words of Melita’s character get more profound, parts of the landscape, which seemed to be flashing and pulsating at random, come together and start to mimic the user’s heartbeat.
In 2013, Jane Aspell of Anglia Ruskin University (UK) and Lukas Heydrich of the Swiss Federal Institute of Technology proved that a user’s sense of presence and identification with a virtual avatar could be dramatically increased by syncing the on-screen character with the heartbeat of the user. The relationship between bio-digital synchronicity, immersion, and emotional engagement is something that will surely have revolutionary narrative and storytelling potential.
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On a dark night, away from city lights, the stars of the Milky Way can seem uncountable. Yet from any given location no more than 4,500 are visible to the naked eye. Meanwhile, our galaxy has 100–400 billion stars, and there are even more galaxies in the universe.
The numbers of the night sky are humbling. And they give us a deep perspective…on drugs.
Yes, this includes wow-the-stars-are-freaking-amazing-tonight drugs, but also the kinds of drugs that make us well again when we’re sick. The number of possible organic compounds with “drug-like” properties dwarfs the number of stars in the universe by over 30 orders of magnitude.
Next to this multiverse of possibility, the chemical configurations scientists have made into actual medicines are like the smattering of stars you’d glimpse downtown.
But for good reason.
Exploring all that potential drug-space is as humanly impossible as exploring all of physical space, and even if we could, most of what we’d find wouldn’t fit our purposes. Still, the idea that wonder drugs must surely lurk amid the multitudes is too tantalizing to ignore.
Which is why, Alex Zhavoronkov said at Singularity University’s Exponential Medicine in San Diego last week, we should use artificial intelligence to do more of the legwork and speed discovery. This, he said, could be one of the next big medical applications for AI.
Dogs, Diagnosis, and Drugs
Zhavoronkov is CEO of Insilico Medicine and CSO of the Biogerontology Research Foundation. Insilico is one of a number of AI startups aiming to accelerate drug discovery with AI.
In recent years, Zhavoronkov said, the now-famous machine learning technique, deep learning, has made progress on a number of fronts. Algorithms that can teach themselves to play games—like DeepMind’s AlphaGo Zero or Carnegie Mellon’s poker playing AI—are perhaps the most headline-grabbing of the bunch. But pattern recognition was the thing that kicked deep learning into overdrive early on, when machine learning algorithms went from struggling to tell dogs and cats apart to outperforming their peers and then their makers in quick succession.
[Watch this video for an AI update from Neil Jacobstein, chair of Artificial Intelligence and Robotics at Singularity University.]
In medicine, deep learning algorithms trained on databases of medical images can spot life-threatening disease with equal or greater accuracy than human professionals. There’s even speculation that AI, if we learn to trust it, could be invaluable in diagnosing disease. And, as Zhavoronkov noted, with more applications and a longer track record that trust is coming.
“Tesla is already putting cars on the street,” Zhavoronkov said. “Three-year, four-year-old technology is already carrying passengers from point A to point B, at 100 miles an hour, and one mistake and you’re dead. But people are trusting their lives to this technology.”
“So, why don’t we do it in pharma?”
Trial and Error and Try Again
AI wouldn’t drive the car in pharmaceutical research. It’d be an assistant that, when paired with a chemist or two, could fast-track discovery by screening more possibilities for better candidates.
There’s plenty of room to make things more efficient, according to Zhavoronkov.
Drug discovery is arduous and expensive. Chemists sift tens of thousands of candidate compounds for the most promising to synthesize. Of these, a handful will go on to further research, fewer will make it to human clinical trials, and a fraction of those will be approved.
The whole process can take many years and cost hundreds of millions of dollars.
This is a big data problem if ever there was one, and deep learning thrives on big data. Early applications have shown their worth unearthing subtle patterns in huge training databases. Although drug-makers already use software to sift compounds, such software requires explicit rules written by chemists. AI’s allure is its ability to learn and improve on its own.
“There are two strategies for AI-driven innovation in pharma to ensure you get better molecules and much faster approvals,” Zhavoronkov said. “One is looking for the needle in the haystack, and another one is creating a new needle.”
To find the needle in the haystack, algorithms are trained on large databases of molecules. Then they go looking for molecules with attractive properties. But creating a new needle? That’s a possibility enabled by the generative adversarial networks Zhavoronkov specializes in.
Such algorithms pit two neural networks against each other. One generates meaningful output while the other judges whether this output is true or false, Zhavoronkov said. Together, the networks generate new objects like text, images, or in this case, molecular structures.
“We started employing this particular technology to make deep neural networks imagine new molecules, to make it perfect right from the start. So, to come up with really perfect needles,” Zhavoronkov said. “[You] can essentially go to this [generative adversarial network] and ask it to create molecules that inhibit protein X at concentration Y, with the highest viability, specific characteristics, and minimal side effects.”
Zhavoronkov believes AI can find or fabricate more needles from the array of molecular possibilities, freeing human chemists to focus on synthesizing only the most promising. If it works, he hopes we can increase hits, minimize misses, and generally speed the process up.
Proof’s in the Pudding
Insilico isn’t alone on its drug-discovery quest, nor is it a brand new area of interest.
Last year, a Harvard group published a paper on an AI that similarly suggests drug candidates. The software trained on 250,000 drug-like molecules and used its experience to generate new molecules that blended existing drugs and made suggestions based on desired properties.
An MIT Technology Review article on the subject highlighted a few of the challenges such systems may still face. The results returned aren’t always meaningful or easy to synthesize in the lab, and the quality of these results, as always, is only as good as the data dined upon.
Stanford chemistry professor and Andreesen Horowitz partner, Vijay Pande, said that images, speech, and text—three of the areas deep learning’s made quick strides in—have better, cleaner data. Chemical data, on the other hand, is still being optimized for deep learning. Also, while there are public databases, much data still lives behind closed doors at private companies.
To overcome the challenges and prove their worth, Zhavoronkov said, his company is very focused on validating the tech. But this year, skepticism in the pharmaceutical industry seems to be easing into interest and investment.
AI drug discovery startup Exscientia inked a deal with Sanofi for $280 million and GlaxoSmithKline for $42 million. Insilico is also partnering with GlaxoSmithKline, and Numerate is working with Takeda Pharmaceutical. Even Google may jump in. According to an article in Nature outlining the field, the firm’s deep learning project, Google Brain, is growing its biosciences team, and industry watchers wouldn’t be surprised to see them target drug discovery.
With AI and the hardware running it advancing rapidly, the greatest potential may yet be ahead. Perhaps, one day, all 1060 molecules in drug-space will be at our disposal. “You should take all the data you have, build n new models, and search as much of that 1060 as possible” before every decision you make, Brandon Allgood, CTO at Numerate, told Nature.
Today’s projects need to live up to their promises, of course, but Zhavoronkov believes AI will have a big impact in the coming years, and now’s the time to integrate it. “If you are working for a pharma company, and you’re still thinking, ‘Okay, where is the proof?’ Once there is a proof, and once you can see it to believe it—it’s going to be too late,” he said.
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Many people get frustrated with technology when it malfunctions or is counterintuitive. The last thing people might expect is for that same technology to pick up on their emotions and engage with them differently as a result.
All of that is now changing. Computers are increasingly able to figure out what we’re feeling—and it’s big business.
A recent report predicts that the global affective computing market will grow from $12.2 billion in 2016 to $53.98 billion by 2021. The report by research and consultancy firm MarketsandMarkets observed that enabling technologies have already been adopted in a wide range of industries and noted a rising demand for facial feature extraction software.
Affective computing is also referred to as emotion AI or artificial emotional intelligence. Although many people are still unfamiliar with the category, researchers in academia have already discovered a multitude of uses for it.
At the University of Tokyo, Professor Toshihiko Yamasaki decided to develop a machine learning system that evaluates the quality of TED Talk videos. Of course, a TED Talk is only considered to be good if it resonates with a human audience. On the surface, this would seem too qualitatively abstract for computer analysis. But Yamasaki wanted his system to watch videos of presentations and predict user impressions. Could a machine learning system accurately evaluate the emotional persuasiveness of a speaker?
Yamasaki and his colleagues came up with a method that analyzed correlations and “multimodal features including linguistic as well as acoustic features” in a dataset of 1,646 TED Talk videos. The experiment was successful. The method obtained “a statistically significant macro-average accuracy of 93.3 percent, outperforming several competitive baseline methods.”
A machine was able to predict whether or not a person would emotionally connect with other people. In their report, the authors noted that these findings could be used for recommendation purposes and also as feedback to the presenters, in order to improve the quality of their public presentation. However, the usefulness of affective computing goes far beyond the way people present content. It may also transform the way they learn it.
Researchers from North Carolina State University explored the connection between students’ affective states and their ability to learn. Their software was able to accurately predict the effectiveness of online tutoring sessions by analyzing the facial expressions of participating students. The software tracked fine-grained facial movements such as eyebrow raising, eyelid tightening, and mouth dimpling to determine engagement, frustration, and learning. The authors concluded that “analysis of facial expressions has great potential for educational data mining.”
This type of technology is increasingly being used within the private sector. Affectiva is a Boston-based company that makes emotion recognition software. When asked to comment on this emerging technology, Gabi Zijderveld, chief marketing officer at Affectiva, explained in an interview for this article, “Our software measures facial expressions of emotion. So basically all you need is our software running and then access to a camera so you can basically record a face and analyze it. We can do that in real time or we can do this by looking at a video and then analyzing data and sending it back to folks.”
The technology has particular relevance for the advertising industry.
Zijderveld said, “We have products that allow you to measure how consumers or viewers respond to digital content…you could have a number of people looking at an ad, you measure their emotional response so you aggregate the data and it gives you insight into how well your content is performing. And then you can adapt and adjust accordingly.”
Zijderveld explained that this is the first market where the company got traction. However, they have since packaged up their core technology in software development kits or SDKs. This allows other companies to integrate emotion detection into whatever they are building.
By licensing its technology to others, Affectiva is now rapidly expanding into a wide variety of markets, including gaming, education, robotics, and healthcare. The core technology is also used in human resources for the purposes of video recruitment. The software analyzes the emotional responses of interviewees, and that data is factored into hiring decisions.
Richard Yonck is founder and president of Intelligent Future Consulting and the author of a book about our relationship with technology. “One area I discuss in Heart of the Machine is the idea of an emotional economy that will arise as an ecosystem of emotionally aware businesses, systems, and services are developed. This will rapidly expand into a multi-billion-dollar industry, leading to an infrastructure that will be both emotionally responsive and potentially exploitive at personal, commercial, and political levels,” said Yonck, in an interview for this article.
According to Yonck, these emotionally-aware systems will “better anticipate needs, improve efficiency, and reduce stress and misunderstandings.”
Affectiva is uniquely positioned to profit from this “emotional economy.” The company has already created the world’s largest emotion database. “We’ve analyzed a little bit over 4.7 million faces in 75 countries,” said Zijderveld. “This is data first and foremost, it’s data gathered with consent. So everyone has opted in to have their faces analyzed.”
The vastness of that database is essential for deep learning approaches. The software would be inaccurate if the data was inadequate. According to Zijderveld, “If you don’t have massive amounts of data of people of all ages, genders, and ethnicities, then your algorithms are going to be pretty biased.”
This massive database has already revealed cultural insights into how people express emotion. Zijderveld explained, “Obviously everyone knows that women are more expressive than men. But our data confirms that, but not only that, it can also show that women smile longer. They tend to smile more often. There’s also regional differences.”
Yonck believes that affective computing will inspire unimaginable forms of innovation and that change will happen at a fast pace.
He explained, “As businesses, software, systems, and services develop, they’ll support and make possible all sorts of other emotionally aware technologies that couldn’t previously exist. This leads to a spiral of increasingly sophisticated products, just as happened in the early days of computing.”
Those who are curious about affective technology will soon be able to interact with it.
Hubble Connected unveiled the Hubble Hugo at multiple trade shows this year. Hugo is billed as “the world’s first smart camera,” with emotion AI video analytics powered by Affectiva. The product can identify individuals, figure out how they’re feeling, receive voice commands, video monitor your home, and act as a photographer and videographer of events. Media can then be transmitted to the cloud. The company’s website describes Hugo as “a fun pal to have in the house.”
Although he sees the potential for improved efficiencies and expanding markets, Richard Yonck cautions that AI technology is not without its pitfalls.
“It’s critical that we understand we are headed into very unknown territory as we develop these systems, creating problems unlike any we’ve faced before,” said Yonck. “We should put our focus on ensuring AI develops in a way that represents our human values and ideals.”
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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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