A few days ago, Elon Musk’s latest venture was finally revealed after months of dropped hints and speculation. The new company, Neuralink, will presumably pursue the futuristic sounding goal of intelligence amplification (IA), joining Kernel, a slightly older effort with analogous goals launched by Bryan Johnson. Practically, these companies will work on developing new techniques to link the human brain to computers. Although many such brain-machine interfaces (BMIs or BCIs) exist today, none of the current systems are capable of precise, large scale recording of the entire brain over long periods of time, while at the same time minimising the risks associated with the initial insertion procedure (ideally forgoing the need to crack the skull open). Clearly, in order to take any meaningful steps towards intelligence amplification, both companies will initially have to focus their efforts on creating a new neural interface, one allowing for a high-bandwidth communication channel between brains and computers.
Fittingly, these efforts arrive at a time where the field of brain-computer interfaces is ripe for momentous achievements. In recent years, following the clinical success of implantable BCI systems in humans (great examples can be found here and here), a multitude of new and promising designs for neural interfaces have been proposed. These novel designs aim to overcome the technical challenges of current generation systems, such as deleterious immune responses, tissue damage and chronic stability issues. Kernel and Neuralink are likely to be pursuing ideas which are extensions of the current state-of-the-art, possibly working on several promising approaches in parallel. Some recent and intriguing developments include: stentrodes, small electrodes inserted through the veins, making for a minimally invasive insertion procedure, brain dust, tiny wireless electrodes designed to be sprinkled throughout the brain and powered externally via ultrasound, and injectable mesh electrodes, nano-scale polymer meshes which are injected with a syringe and then unfolded in place. Considering Elon Musk’s fondness for the sci-fi term neural lace, this last approach may be what Neuralink will focus on at first.
Of course, advances in such fundamental areas as brain-computer interfaces will have far reaching implications, extending well beyond the somewhat distant goal of intelligence amplification pursued by both ventures. Indeed, if they succeed in obtaining even modest gains in the pursuit of improved neural interfaces, they will find willing collaboration partners in areas such as brain disorders treatment, fundamental brain research, control of robotic devices for paralysed patients, and many more. Although their intelligence amplification vision may be lofty and unattainable in the short term (Elon Musk mentioned a timeframe of 5 years, which is somewhere between optimistic and insane, depending what one defines as "meaningful intelligence amplification”), the immediate clinical benefits will be significant. In fact, these efforts are likely to play out similarly to the way SpaceX did—first delivering short term benefits which also happen to be financially viable (e.g. commercial rockets to deliver satellites), before eventually attaining the real goal (in the case of SpaceX, trips to Mars and beyond). Despite having directly applicable benefits, focusing communication efforts around the long term vision is smart and necessary. The hype fuels massive non-technical followings on the internet and beyond, while at the same time, articulating a well reasoned long term mission helps recruit the top minds in the field who share the same vision.
Speaking of long term vision, as Elon Musk dramatically puts it, in this case the stakes are extremely high (existential-threat-level high). With the advent of general-purpose AI around the corner (again, “around the corner” could mean anything between 5 to 100 years depending on whom you ask), the risks associated with super intelligent computers are looming ever more menacingly in the background. Beyond making entire job segments obsolete, what many are afraid of is the immeasurable harm a powerful AI could do in the wrong hands, along with the not-so-unlikely prospect that somebody may trigger an apocalypse scale disaster by mistake (Elon Musk thinks Google is a good candidate for the biggest “oops” moment in humankind). To mitigate the dangerous aspects of AI, the approach both companies are working on (and which I have proposed in the past) is to ensure that by the time we have the technology to build super intelligent AIs, we will also have the technology to seamlessly interface our brains with computers. That way, any advance in AI technology can presumably be used to benefit human intelligence directly, making it hard for AI to become independently more advanced than human intelligence. Problem solved.
As some have been quick to point out, however, it may not be as simple as that. Indeed, one could argue that the same risks associated with super intelligent AIs could be equally problematic in the case of super intelligent humans. There is no guarantee that a super intelligent person, whose motivations, thoughts and desires we cannot begin to imagine (by definition), will not decide to act in a way that is dangerous or harmful to “lesser" humans. In fact, one could articulate plenty of scenarios where intelligence amplification leads to non-desirable futures, such as hyper-segmented societies where the rich can afford to become super intelligent while the poor are stuck with stupid old human brains, leading to a widening economic divide and increasing social tensions.
Still, all this is speculation. The only certainty is that if Neuralink and Kernel succeed, the world will benefit immensely in the short-term, especially when it comes to understanding and treating our ageing brains, a challenge we will increasingly face as humans grows older. Will these companies save us from an AI apocalypse which might never come to pass in the first place? Probably yes, in fact, but only time will tell.
November 24, 2016
In the wake of the US presidential election, an important issue has been brought up: social media is full of fake news. Some go so far as to suggest this may have significantly swayed the final result. Regardless of the answer to that question, the issue of false claims on the internet is a very profound one, and reaches much, much further than the realm of politics. There should be no sugar-coating it: misinformation is dangerous, and a potential threat to society.
As the debate rages on, some of the large internet companies (notably Google and Facebook) have chimed in, offering solutions with varying amounts of commitment. The specifics remain unclear, but there is a general notion that stories should be fact-checked. Based on this information, one supposes, fake news would be labelled, or perhaps even removed. I believe this is the wrong way to address the issue. Here’s why:
1. False facts are dangerous, but partial information is worse
The problem with fake news is unfortunately more complex than presenting wrong facts. A much more effective (and more common) approach is to try and steer people to a certain conclusion by giving them only some of the facts. Let’s take the following example: “Human exposure to X-rays can lead to severe burns, cancer and even death. X-rays are classified as a carcinogen by the World Health Organisation. Yet, every day, thousands of people are exposed to X-rays in hospitals across the world, including children and elderly people, two groups known to have weaker immune systems.”
Should a fact-checking system flag the above statement as false? Certainly not. None of that is factually incorrect. However, it’s clearly a misleading paragraph. This is achieved by leaving out the following piece of information: the doses of X-rays used in hospitals are very low, so low that any of those scary side-effects are extremely unlikely to occur. Humans are exposed to X-rays from natural sources every day at doses not much lower than this. This makes it easy to see why removing false facts is simply not enough.
I specifically avoided using an example from this year’s election (by fear of sparking an irrelevant debate), but one needs only to open a newspaper and pay attention to see examples of this strategy being used every single day (wether purposefully or not).
2. In the real world, information is often more nuanced than right or wrong
A smart engineer from Facebook might object to the first point by saying that a sophisticated enough system could handle the example above too. It might prompt the reader with a warning: “this information is misleading because it leaves out this and that crucial information.” But this would still be wrong, because it would be pushing the reader towards a specific conclusion, therefore introducing bias. The problem here, is that the paragraph about X-rays and the conclusion it leads to cannot be labelled right or wrong. Wether or not you think the exposure of children to X-rays is acceptable considering the minimal risks, is purely subjective. Any system steering you towards one or the other conclusion is a biased system.
Certainly, you may say, some facts are not nuanced. Some facts are simply right or wrong. And to some extent that is true. For instance, Steve Jobs died on October 5th, 2011. Any article claiming otherwise would most certainly be making a false claim. But as I hope to have shown above, the danger of misinformation doesn’t lie in such openly wrong statements (although that certainly constitutes one aspect of it). The real danger of bad news lies in other forms of manipulation which are much more subtle (such as using partial information). A program which identifies simple false claims will most likely miss these, ironically giving them more credibility by not discrediting them.
3. Facts are not dogma; they should be easy to challenge
Nothing is absolute. Every single fact can be challenged, questioned and potentially changed. That is the foundation of our modern world (and science), based on the enlightenment movement of the 18th century. The very idea of developing a central authority with the power to label facts as true or false, regardless of the nobility of its intentions, is a dangerous step backwards. Of course the aim will be good. But the results may be unintended. Facts are challenged all the time. Will having a system that labels every challenge to an established fact as “fake” result in an increased barrier to healthy debate? Who gets to decide which facts are right and which are wrong? And how do we decide when there is enough evidence to retire an outdated fact?
What we should do instead
The notion that we must remove or hide information is fundamentally flawed. People sharing fake news is a symptom, not the issue. Treating the symptom does little to eradicate the underlying problem. The real problem here is that the average reader doesn’t approach news critically (see my article on 7 ways to think more critically for more on this). Perhaps this is caused by a failure of our educational systems to prepare students for the age of information. Regardless of the cause, the issue is present and must be fought.
Certainly we cannot solve the issue by taking facts away. Instead, we should give more. We should provide tools to help develop critical thinking and approach facts with a more scientific mind. Imagine this: a box after each link (on Facebook, or Google, or anywhere else), which shows two columns. One columns says “here are other articles supporting this view", and the other says "here are articles that view this issue differently". Under both columns, a list of articles, their source and the total number of items in each category would be displayed. No special treatment, no stars next to reliable sources (what is a reliable source anyway?). Equal footing. No bias. We give as many tools as possible, make it as easy as we can for readers to come to their own critical conclusions. Nothing more.
Of course this "critical thinking" box could have many other features, such as an information tab which explains what the source of a given fact is, and the methodology (for instance if a fact was obtained through a census, etc). There is one caveat to this magical box sitting below links. It would be very hard to build. Even harder to build well. But the general idea, which is to help users reach their own informed opinion, is a powerful one, and one that big companies can start pursuing today.
It may be embarrassingly cliche, but the age-old adage fits the situation perfectly: give a man a fish and he will eat for a day, but teach a man to fish and he will never go hungry again. We must provide social media users with the tools for critical thinking, not feed them what we believe is right. The benefits of solving the underlying issue, as opposed to just the symptom, is that people will learn to spot dubious claims well beyond Facebook and Google. They will spot them on the radio, at dinner parties and during political speeches. That makes for a better world.
My plea to social media giants is simple: remember that the internet has always been about access to information, not the opposite. Let's solve the problem with more, not less. If not from me, then take it from Benjamin Franklin: "Printers are educated in the Belief that when Men differ in Opinion, both Sides ought equally to have the Advantage of being heard by the Public; and that when Truth and Error have fair Play, the former is always an overmatch for the latter."
October 30, 2016
In today’s uber-connected world of hand-held internet gizmos, not a minute goes by without our brains overdosing on new information. Often throughout the day, we are presented with novel ideas, which we must instantly process and store—salt isn’t what’s killing us, sugar is; we’ll get to mars in fifteen years; we’ll never get to mars in fifteen years; GMOs are bad; GMOs are good. And the list goes on and on. Faced with such a constant stream of (often contradictory) data, our minds must necessarily answer the question: what should I believe?
That simple questions hides layers and layers of far-reaching complexity. That simple question controls nations and the fates of entire species, even entire ecosystems—by driving popular votes and consumer habits. If a majority of people think that global warming is fake, they will elect leaders who think likewise and will refrain from halting its progress.
Clearly then, there is tremendous power in our collective decisions to trust one source rather than another, to believe one fact above its alternative. And the question inevitably becomes: how can each of us guide this process to make sure we arrive at the right conclusions?
By thinking more like scientists. And these are 7 ways to do precisely that.
1. Question everything, accept nothing
If you remember only one point from this article, let it be this one. One of the most important traits which allows a scientist to keep things straight is an almost pathological skepticism, a distrust for new facts.
People will say all kinds of things for all sorts of reasons. Even scientists. Some lie, some are nuts and many are simply genuinely mistaken. In other words, everyone is potentially unreliable. That is why no single source should be trusted.
Science and scientific thinking are extremely robust precisely because they do not depend on a central authority. They are scalable and personal—anyone can apply them. A scientist will not take things for granted, regardless of the source. Instead, he will use the steps outlined below to arrive at his own conclusions.
So the next time someone tells you “but, scientists say…”, just walk away—you know better.
2. No really, question everything
There is no emphasising this enough. Regardless of who said it, regardless where you read it: question it. Articles published in the most prestigious scientific journals get disproven or retracted all the time. Even Nobel prizes have occasionally been attributed to discoveries later shown to be highly questionable.
And be careful, your own ideas aren’t any different. They’re not special. In fact, before you start questioning everything around you, make sure you took the time to stop for a while and question everything you know and believe.
3. Check sources
If you feel like this whole critical thinking things will take a lot of effort, your are right. But there are ways to alleviate the burden. The very first thing you should do when reading an article about a scientific discovery, big or small, is to check the sources.
In science, every new piece of research is published in so called peer-reviewed journals. These journals, unlike traditional ones, have an extra verification step before they publish anything. When you submit your work, these types of journals will send your article to two or more experts from the field, who will scrutinise it, looking for inconsistencies and mistakes. Eventually, they may green-light the article for publication. Although far from an infallible system, it serves as a filter—a first line of defence against bullshit.
Of course, blindly accepting things you read in peer-reviewed journals would be breaking the first rule. Instead, you should use these journals as a guide. If a scientist does not publish his results in a peer-reviewed journal, be extra careful, something fishy is going on. A scientist who avoids a peer-reviewed journal is like a salesman trying to auction goods from the back of a shady van—not a good sign.
4. Seek out confirmation
If you can’t trust any single source, then how can you ever accept new science?
The answer is replication. If it’s sound, then others can (and will) arrive at the same conclusion. That’s how science is built. If someone publishes something new and unexpected, other experts will first raise an eyebrow. Then, they will stroll back to the lab, the new article in their hand, sit down and attempt to replicate the results.
Of course, individuals are not expected to replicate results themselves. I’m not saying you should build a particle accelerator in your basement and start searching for signs of a Higgs Boson. Instead, you should wait for confirmation from third parties
In practical terms, unless you are an active scientist in a given field, your safest bet is to rely on review articles, which are also peer-reviewed and usually offer balanced summaries of recent research (with extensive references, for the brave). And if you’re not into reading scientific articles (an understandable objection), make sure the journals you get your news from put in the effort to cite relevant findings, and that those references make sense.
5. And seek out counter-arguments
There is more than on side to any issue. When you read about a position (GMOs are harmful, global warming is a hoax, coffee is good for your health), make sure you look up what the other side is saying. This is an extension of the question everything mantra. By opening your mind to opposing views, you increase your chances of not missing something important. And if you have the tools to critically and objectively evaluate what both sides are saying, you will reach the right conclusion.
6. Learn to spot borderline statistical claims
These last two tips are more practical and less general. They should help you spot some of the most common symptoms of bad science.
Statistics is a vast and challenging field, which many scientists, who are trained in a different topic, grapple with. Without delving into the intricacies and maths, there is one major principle, which once understood, could prevent a large number of misleading claims: correlation does not imply causation.
That simply means that if two things seem to be correlated—if they seem to change together—it does not mean that one caused the other. Let’s take an example. If data shows that areas with high homelessness have high crime—correlation—you may jump to the conclusion that the homeless commit crimes—causation. This would be a misleading statement, because the data does not show that. This type of wrongful conclusion is particularly misleading when the causation is something you might somehow expect (often because of some form of prior bias). In this example, perhaps a third factor, for instance unemployment, causes both homelessness and high crime rates.
This is the single most pervasive error out there. You can open any major publication, on any given day, and are almost guaranteed to find an article which makes that mistake. If you learn to spot it, you will avoid countless misguided conclusions.
7. Don’t be fooled by bad starting postulates
In science, we often build a line of argumentation starting with a few basic postulates, or hypotheses, from which we logically draw other conclusion. For instance in math, some basic rules (or axioms) are enough to deduct countless other rules by pure logic.
However, there is a trick that is often used to arrive at flawed conclusions (knowingly or not), which is to start with a false postulate. Since everything flows logically from the starting hypothesis, this particular error is hard to spot. In fact, you can build an entire fallacy logically starting from a false statement.
I recently saw a video highlighting this precise problem. In the video, a professor argued as follows: humans shouldn’t eat meat because they are not carnivores. He then went on demonstrating in countless ways how humans are not carnivores (they have long digestive tubes, blunt teeth, etc). Although his arguments were correct, the starting premise was false, and thus the conclusion too. Of course it’s easy to find example of why humans are not carnivores, because they are not: humans are omnivores, eating both plants and meat. The initial statement that: “humans shouldn’t eat meat because they are not carnivores” is flawed because carnivores are not the only types of animals that eat meat.
Arguing against this professor’s position would have been very difficult, because he would have asked you to prove why humans are carnivores, an impossible feat. Instead, the trick was that the very premise of his entire reasoning, that only carnivores eat meat, was wrong.
This type of logical construction—starting from a false premise—is very common, and can be very misleading because all the arguments presented after then initial claim are correct and logical. We thus find ourselves agreeing with everything being said, and may ultimately mistakenly accept the conclusion. Remember to always search for the starting premise and see if it makes sense.
I hope these 7 tips will help guide your critical thinking in the future. And remember: science is ever-changing. No matter what conclusions you may draw today, be prepared to change them tomorrow. As someone once said: “science doesn’t know everything? Of course it doesn't, otherwise it would stop!”
Near the end of his book Sapiens: A Brief History of Humankind (an interesting read, if you haven’t had the chance), Yuval Noah Harari briefly explores some options for the future of human evolution. He narrows it down to three scenarios, which do indeed encompass the major foreseeable alternatives. Specifically, he mentions the bionic, genetic and non-organic options. More than mere buzzwords, these three concepts are used to describe entire fields of human technological progress. To put it briefly, a bionic future would be one where humans merge with machines (think of robotic arms and chips in the brain), improving our bodies beyond biological limitations. A genetic future is one were humans are enhanced through the use of genetic engineering, endowing themselves with features worthy of gods (longer lives, sharper senses, and more). And the non-organic future, well, that one isn’t quite as nice for us. In fact, it has humans completely out of the picture—instead, new life forms (robots, software) learn to replicate and spread independently of their creators.
These three options could be seen as mutually exclusive—forces acting against each other, with the winner determining what the future will look like. In reality, I would argue that all three are components of a larger, cohesive narrative, one that has humans soaring high above all other life, breaking free from the shackles of natural evolution, colonising the solar system and why not the universe too, while we’re at it. So let’s take a look at why genetics, bionics and artificial intelligence are the three indispensable wheels in the great tricycle of human progress. That doesn’t sound very impressive, I know—but a tricycle is all we get.
All three areas are currently being pursued actively by researcher all over the world. Genetic engineering is undergoing a public image boom as advances such as CRISPR grab headlines across the world (you can read more about CRISPR here). Bionics and neuroprosthetics are similarly stroking the public's imagination with robotic hands that feel, and other futuristic inventions. And of course, artificial intelligence, and evolutionary algorithms (algorithms that can change over time, inspired by Darwinian evolution) are widely discussed and have far reaching implications, way beyond asking Siri to set a timer (read this for a take on the intelligence behind artificial intelligence).
Is any of these fields going to mature way before the others? Wouldn’t the premature success of one of these scenarios hinder the development of the others? These are extremely interesting questions. In fact, if scientist and engineers actually spoke about such things at lunch, I’m sure these enquiries would take center stage (center table?). But since they do not, the task befalls to us.
Artificial intelligence is among the chief components of the non-organic future. Because it does not involve humans directly in a medical sense and is not technically impacted by the laws of physics, this approach is likely to evolve faster than the other two. But that does not mean it's game over—the future will not automatically be non-organic. All of these technologies should not be seen as binary—artificial intelligence is not an all-or-nothing proposition, nor are bionics and genetics. Each of these futures will start happening progressively and in parallel. In fact all three have already begun. The advent of pervasive artificial intelligence does not preclude advances in genetics or bionics, if anything it encourages them. Each will provide only modest benefits at first, in the image of Siri for artificial intelligence, current (limited) robotic limbs for bionics and the first simple gene therapies for genetics. Then, as these technologies progress, their usefulness will likewise increase. Self driving cars, new cures for cancer and impressively life-like prosthetics—these are all around the corner of progress, along with much, much more.
I have argued that the only way we can avoid being overtaken and possibly marginalised by future super intelligent machines is by merging with them (you can read the article here). Thankfully, the power of bionics will enable us to interface our minds with computers. Genetics will initially play a supportive role, allowing our organic shells (aka bodies) to withstand the test of time longer. However, further into the future, genetics (with the help of synthetic biology) will allow us to develop organic bionics: seamless interfaces between the living and the artificial—where the very distinction between the two starts breaking down. Eventually, organic computer circuits and humans engineered to better interface themselves with machines will blur all lines.
Clearly then, these three driving forces for the future do not act against each other. In fact, they slowly converge to bring us towards a single goal: the human of the future. By taking advantage of the strengths of machines, and combining them with our own genetically improved bodies, we will create a new form of super humans, or Homo Deus, as Yuval Noah Harari likes to playfully call them. These future beings will be so vastly superior to us in every way, that we could not even begin to imagine what their dreams, thoughts and actions will be like.
Genetics, bionics and artificial intelligence—that is precisely how we will make the future.
September 25, 2016
The world of artificial intelligence (AI) went into a frenzy when AlphaGo, a computer algorithm developed by Google, beat the world’s top Go player. This was remarkable for two reasons. Not only is Go a much harder game for computers to play than chess (which computers mastered almost 20 years ago), but experts in the field were expecting this impressive step to happen as much as ten years later than it did.
Deservedly, the world’s media outlets took this opportunity to re-ignite the debate about artificial intelligence and how it may affect our socio-economical order in the coming years. One particularly important topic in this debate is the notion that improved artificial intelligence could eventually take over many people's jobs. Which is either a terrifying prospect, or an immense opportunity for humankind, depending on whom you ask.
When we imagine computer algorithms taking over jobs and tasks usually performed by humans, most of us get a queasy feeling. People tend to fall into two diametrically opposed categories. The first are those who think computer algorithms could never replace humans (except perhaps in some very simple and repetitive jobs), the second are the tech enthusiasts and futurists who believe that the inevitable progress of science will deliver increasingly complex machines which will not only replace us, but eventually surpass us too (this is called the singularity, which I explore in more detail in this article).
The first group are the AI skeptics. Usually this camp argues that machines cannot make the nuanced, adaptive responses humans can—because they must follow a strict set of rules, and these rules could never account for every possible scenario. Unfortunately this line of reasoning is flawed, because it misunderstands the way modern machine learning algorithms work—relying instead on a more traditional view of computer software, where everything happens according to a set of logical decisions and relationships defined by the programmer (if this happens, then do that). But this is wrong. The whole field of artificial intelligence is based on a very different approach. Indeed, machine learning algorithms are designed to learn how to generalise. The basic idea is as follows: a machine learning algorithm is shown some examples of data (e.g. a photo recognition software may be shown 10 different images of cars, 10 images of cows, etc). From this data (called a training set), the software learns to extrapolate and generalise. Once trained, the algorithm will be able to understand new data (in our example, it will be able to say if a photo contains a car or a cow, even if it has never seen the photo before). This is important, because it means the algorithm was able to learn rules by itself. No programmer had to say: "if it has four wheels, then it is a car” or other arbitrary conditions. Instead, the software extracted some characteristics from the initial data it was shown, and created its own internal representation of a car. In fact, it is often very hard to peer back inside the algorithm and understand how it takes decisions.
Clearly, this is not the kind of simple rule-following-machine we are used to dealing with. Once we realise how these programs work, we start to see why they are called artificially intelligent. In many ways, they mimic the learning process of a child, who accumulates experiences and uses them to extrapolate and approach novel situations with ease. And because AI learns how do to things on itself, we won’t need to understand how our brain solves some complex tasks in order to teach them to machines—we’ll simply let them figure it out.
The second group are the AI enthusiasts. This camp tends to exaggerate in the other direction—placing an overly optimistic amount of hope in artificial intelligence’s ability to overcome all challenges. Although in the long term this group will eventually be right, the reality is that for the moment artificial intelligence is still very limited. Despite AI’s ability to generalise and adapt to novel situations, for now this is confined to a single subject at any given time. The image recognition software which can recognise cars and cows in photographs would be completely unable to play Go for instance. Training a single algorithm to excel at several heterogeneous tasks is the real challenge, and one which may not be easy to solve in the short term.
Although artificial intelligence programs are certainly smarter than what the AI skeptics would think, they are still a lot more stupid than the AI enthusiast like to believe. We may call them intelligent, but in reality they are based on simple statistics and basic networks of interconnected processing nodes (neural networks). Once we understand how these algorithms work, they lose some of their mystique. It’s the same feeling one gets when an impressive card trick is revealed—the magic is simply gone.
But here is something to think about: perhaps the fact that relatively simple computer algorithms can replicate some complex human behaviours is an indication that our brains may be simpler than we think. Deep down, the reason the notion of AI replacing people is so hard to swallow is the human fallacy that our intelligence is somehow the consequence of some deeper, perhaps mystical force, and that a “simple" machine could not possibly replicate the wide range of behaviours and flexible decision making humans demonstrate. Well, we’re in for a humbling surprise—not only will AI surpass humans in almost every possible occupation, it will also demonstrate that intelligence isn’t as unique and special as we thought.
September 13, 2016
Do you constantly hear CRISPR mentioned in the news and around the web, but never truly took the time to really understand what it is and how it works? If so, this (relatively) brief primer is just the thing for you. More than just an unpronounceable acronym, CRISPR is a revolution in the making. Considering the impact this technology will have on our future, it’s well worth understanding what everyone is talking about.
CRISPR (a short-hand used to refer to the CRISPR/Cas9 system) is a novel tool for genetic engineering, the manipulation of DNA in living organisms. Although CRISPR was only mastered in 2013, progress has been swift, and some ethical questions that seemed like distant thought experiments are becoming pressing issue we need to address. Unfortunately, as with most great science, a lot of misinformation and sensational headlines often accompany the scientific breakthroughs. That is why the best way to approach this debate—like any debate—is informed.
How does CRISPR actually work?
First, you should know that CRISPR, originally discovered as part of a naturally occurring immune system found in bacteria, is a cellular mechanism which can cut DNA strands (DNA can be visualised like a long string composed of 4 types of sub-units, which together form a language, not unlike the 0s and 1s used in computers). The way this works is based on two components, a pair of scissors and a template. The template is a short segment of RNA (which can be imagined as a photocopy of a short piece of DNA), which is kept floating around in the organism. The scissors are a protein (called Cas9), which can use these templates to identify a specific part of a string of DNA. Whenever the template matches a segment of DNA, it tells the Cas9 protein to cut the strand at the location it specified.
The real breakthrough was the realisation that this system can easily be hacked by using some Cas9 proteins (the scissors) and a custom designed template (it turns our RNA is something biologists already know how to make). Now, whenever the custom sequence defined in the template is encountered, the Cas9 protein will cut the DNA at the specified location. By defining the right template sequence, it is possible to cut anywhere, at will.
Cas 9: not quite the scissors you had in school.
Of course, there are many subtleties, such as inserting a new piece of DNA where the cut was made. To do that, scientists rely on a naturally occurring system, which detects breaks in DNA and repairs them. By taking advantage of this process, it is possible to “trick” the cell into incorporating an additional piece of DNA while it repairs the cut. However, this is harder to achieve than simple cuts, and is currently posing some challenges. This is why the first set of applications will orbit around removing segments of DNA rather than adding new ones.
Although there are many variations, the underlying mechanisms is always the same—diverting the CRISPR/Cas system to achieve one’s goal.
Okay, but back up a little, why should we even want to change someone’s DNA?
DNA is the blueprint that carries the instructions for the development and normal functioning of living organisms. Each cell in our body has the ability to assemble proteins based on the information contained in DNA. These proteins, in turn, carry out every imaginable function, from responding to stimuli to transporting cargo (i.e. molecules) across long distances and much more. Because DNA literally contains the information on how to assemble the building blocks of an organism, the ability to modify the code can have almost unlimited potential. This is called genetic engineering.
For instance, one of the most commonly discussed (and also controversial) uses of genetic engineering is in designing new plant species (commonly known as GMOs). This concept has been used to create plants which produce added vitamins (which may be missing from some people’s diets—see golden rice), plants which have longer shelf lives, better yields and much more.
Although modifying plants is one of the best-known applications, genetic engineering is also crucial in the manufacturing of certain types of medicine. Insulin for instance, used in the management of type 1 diabetes, is produced by genetically modified organisms. Indeed, bacteria (and recently even plants) have been modified to synthesise insulin in large quantities—so that it can be harvested from bioreactors. The same goes for many other products, such as human growth hormones and some vaccines.
And it doesn’t end there. Genetic engineering is also an invaluable tool for scientific research. Having the ability to modify DNA directly is one of the most important aspects of modern biology. Those white mice in cages? You guessed it, they’re genetically modified. In fact, there are entire libraries of mice that researches can choose from, which have been engineered to have any number of useful characteristics, such as organisms which can act as models of human diseases (i.e. mice that develop Parkinson’s disease). It can easily be argued that many advances in modern biology would not have been possible without the amazing toolbox that genetic engineering provides.
These colourful looking fish are the result of genetic modifications.
All of these things, however, already exist. We already live in a world where genetic engineering is a well established method used in countless industries (I mean, look at those genetically engineered glow-in-the dark fish you can buy, isn’t that straight out of a sci-fi movie?). Some worry about a world were genetic modification are pervasive, enabled by advances such as CRISPR. The truth is we already live in that world. CRISPR is simply the next step.
So why are we talking about this now, what does CRISPR change?
Although the CRISPR/Cas9 system could simply be dismissed as the latest tool in a long line of techniques for increasingly precise and efficient genetic manipulations, it is still a groundbreaking step because of its ease of use and low cost.
One of the biggest reasons everyone is getting excited is gene therapy, or the use of genetic engineering directly on humans for therapeutic reasons. With gene therapy, the stakes become much higher—potentially saving countless lives and relegating some of the worst pathologies to the history books. Being able to efficiently and precisely edit the genomes of living humans would unlock entire new approaches to curing illnesses. Just to give you an example, recent clinical trials have demonstrated that taking someone’s immune cells, genetically engineering them to recognise cancer cells and re-injecting them into the patient's body could slow down or even stop some forms of cancer. Although CRISPR is not strictly necessary to implement these new therapies, having a tool that is easier to use, faster to deploy and more precise could mean these treatments will arrive sooner and be accessible to a larger population.
And gene therapy doesn’t stop at re-engineering immune cells either. In fact, there are so many options that it would make little sense to list them all. For instance, some rare genetic diseases are caused by the lack of a specific gene. With CRISPR, one could simply add the missing sequence, essentially resolving the symptoms. This approach was used at the beginning of 2015 by a group of researcher, in a study that offers a striking illustration of the potential benefits of gene therapy. These scientists took monkeys infected with HIV and gave them a gene which encoded for a specific type of antibody. After the genetic treatment, the apes were cleared of HIV. These types of promising results are what turned genetic engineering (and CRISPR) into the buzzword it is today.
Enough with the scientific stuff, how this will shape the future?
The current applications of genetic engineering include some exciting prospects, such as improved crops, widely available therapies (insulin, etc) and gene therapy’s ability to rid us of countless pathologies. But what comes next?
With a sufficiently precise and reliable tool for genetic modifications in humans, like CRISPR appears to be, there is an almost infinite number of things one could imagine. Very quickly, we could move from eradicating medical conditions to enhancing humans. Once that door is open, almost anything is possible: faster growth, sharper senses, smarter minds—our genes determine everything we are, so changing them can affect anything we want.
At this point, an important distinction has to be made. There are two types of genetic modifications: those affecting somatic cells, which are cells cannot be transmitted further, and those affecting germline cells—which are passed on to future generations. Everything we have discussed in humans so far concerns somatic cells. Such modifications only affect the person receiving them, which means the risk is much more contained. In fact, many countries currently expressly forbid germline modifications. Still, the possibilities offered by modifying somatic cells are vast and promising—even if we do not veer into the dangerous territory of hereditable genetic modification, we will make great strides towards improving the human conditions and ushering in a better future for many.
But germline modification would offer even more potential. With designer babies, for instance, desirable traits could be inserted directly in embryos prior to implantation. This is something many worry about, because the consequences of introducing engineered genes in our collective human DNA are hard to predict, and therefore potentially dangerous. As is often the case, the high risk is also coupled with high reward. Modifying embryos, and therefore the human gene pool, could lead to the complete eradication of any trait which may be considered undesirable. Despite the undeniable potential, many scientists believe we should not attempt this until we develop a more precise understanding of the multifaceted interactions between modified genes and the organism.
Designer babies: could we soon be designing babies, choosing their characteristic from a catalog?
In an attempt to drive a sensible debate, some futurists have argued that such tools may end up only benefiting a small elite of wealthy individuals, leading to an ever increasing gap between the wealthy and genetically improved, and the rest. Clearly this is hard to predict, but if there is one undeniable fact, it is that gene therapy will lead to profound changes in our society. If we are not careful, it might easily take us down a dark and difficult path.
That’s both scary and amazing…but how much of this is just hype?
There has recently been widespread hype around CRISPR and genetic engineering in general. Usually, this kind of intense interest from the general public is not a very welcome thing for a scientific field. The unfortunate result of so much scrutiny is often loud alarmism and hasty conclusions—a misinformed exercise in wild speculation. Of course scientists should strive to disseminate their work and awaken the public’s interest, but when the subject is as polarising as it is with CRISPR, there is bound to be a lot of nonsense thrown around. This is reflected in a generalised exaggeration of both the benefits and the risks. As we have seen, CRISPR, and genetic engineering as a whole, will have a staggering impact on the future of almost all human activities, from the medical to the industrial. However, it is good to part with a slightly more realistic take—bringing us back from the clouds and onto solid ground. In a recent article, Jocelyn Kaiser of Science, one of the world’s leading scientific publications, argued that there are many substantial hurdles and potential setbacks that still need to be addressed before CRISPR can deliver on all the promises scientists have made. Although some may describe it as the holy grail of genetic engineering, others are more wary of hasty conclusions, especially when patients’ hopes are involved. As often in science, for now the consensus should be: cautious optimism.
With the recent controversy surrounding Tesla's autopilot crash, autonomous cars have been at the centre of attention. Of course, in that particular example, most of the controversy stems from a misunderstanding of what autonomous cars are, and what Tesla’s system is. Indeed, the Californian automaker’s approach is to provide the driver with support (think of it like a fancier version of cruise control). An autonomous car on the other hand, is one designed to get its passengers from point A to point B without any intervention from them (except for choosing the destination of course, otherwise you would get the premise for a Steven King novel, not a driverless car).
Truly autonomous transportation is already here. Some startups (BestMile for instance) have placed driverless vehicles on public roads. These are still in the early stages, and it may yet be a couple of years before these futuristic machine become a mundane sight. In all likelihood, we will start seeing public transportation transition to autonomous systems first (Mercedes just tested their driverless bus), followed by the release of fully autonomous private vehicles. The reason for this release order, is that public transportation is easier to solve from an engineering standpoint (fixed itineraries, repeatable scenarios, urban only, etc). In fact, one could even imagine "cheating" and using additional sensors placed along common itineraries to help guide buses around cities.
There is very little doubt that we will soon see the world transition to autonomous vehicles (and hopefully electric too, while we’re at it). These are three ways in which driverless cars will radically transform our lives.
They will give us our cities back
Cities all over the world are designed around transportation (and rightly so, considering we need to, you know, get there somehow)—roads and rails intertwined ad infinitum, mixed with a healthy dose of parking lots. These urbanistic constraints are a consequence of our modes of transportation, which are far from optimised. In fact, the general consensus is that the average car is parked 95% of the time, or roughly 8 hours of use per week—widely inefficient, considering there are many more cars then we would theoretically need. This is why cities are full of parking lots. We are so used to the sight, that we may not even pay attention to it, but our cities are literally filled with cars.
Can you count the parked cars in this picture? In the future doing so might be a lot easier…
This is set to change with the advent of autonomous cars. Fleets of driverless vehicles will carry passengers to and from their destinations, and will not need to wait idly in a parking lot until the same passenger needs a lift again. Instead, these cars will pick up anyone who might need it, at the tap of a finger (much like Uber, click here for Tesla’s plan to do exactly this).
Arguably, these autonomous cars will still need to park somewhere (to charge, and to avoid wasting energy when there is little need for them, such as in the middle of the night). However, these charging centres needn’t be scattered around city centres and residential neighbourhood, but could be placed in less conspicuous locations.
The impact this will have on the way our cities are organised will be profound. A lot of space will be repurposed and given back to the people. The cities of the future are likely to be a lot more human friendly and human-centric. Not only will parking spaces be dramatically reduced, but streets could also theoretically become narrower. This is because a fleet of autonomous cars will take up much less space than an equivalent number of human-driven cars. Since "smart" cars can communicate with each other and are not limited by slow human reflexes, they can move in highly optimised streams, with reduced distances between cars and matched speeds.
Rejoice: switching to autonomous cars will make our cities more beautiful and enjoyable to live in. Gone will be the endless parking lots.
They will give us a lot more time
Driving to work is something most people do not enjoy and understandably so, considering it’s essentially a big waste of time. While driving a car, you can’t do anything else (except perhaps listen to an audio book). It requires focus, and if we are being perfectly honest, is utterly boring. Yet many people spend huge amounts of time doing just that. The average American for instance, spends around 2 hours per week commuting, or roughly 100 hours per year (this includes all types of commutes, not just cars).
Consequently, one can compute that Americans collectively spend 3.4 million years commuting, every year. That’s a lot of “wasted” time, a lot of untapped potential. Of course, computing it this way is purposefully misleading, because multiplying a small amount of time with the population of a large country predictably gives a big number (like the fact that Americans collectively spend 200 thousand years brushing their teeth every year). Still, the point is that people spend a lot of time in their cars, unable to do any work (or to relax) because they have to focus on the task at hand.
Clearly, autonomous cars will radically change this, since any time spent in a car will essentially be free time (much more so than in a crowded metro or train). The interior of autonomous cars will look very different from the cars we are used to today. They will be designed like small living rooms, where the occupants can enjoy their travels, rather than a cockpit built around the controls. Some will use this time to catch up on some much needed sleep, others for leisure, and some for getting a head start to a long day’s work—the net result being a workforce which is more productive, more rested and generally happier.
Ironically, this will most likely lead to commutes getting longer on average, as they become less and less taxing.
They will save our lives
Cars are dangerous. A staggering 1.3 million people are killed worldwide in car crashes every year, and an additional 20-50 million are injured. Those numbers will seem absurd to future generations, for whom autonomous cars will be an extremely safe mode of transportation.
Most road accidents are caused by human error—switching to driverless cars will make them orders of magnitude safer. Regardless of the ethical and practical hurdles that we will need to overcome in the coming decade (questions like: who is responsible when a software bug causes a tragic bus crash?) the end result will be a dramatic reduction in mortality on the roads.
The good thing about the safety benefits, is that we won’t need to wait for fully autonomous vehicles to start reaping the rewards of smarter technology. Already today, Tesla’s cars appear to be a few times safer than regular cars when autopilot is enabled (although there is arguably not enough data yet to really draw any conclusions). With every software update and every new car model, they will only become safer. This trend will be similar to what happened with commercial aircrafts (where amongst other things, the introduction of autopilot made flights safer). To put things into perspective, if the rate of airplane accidents was still as high today as it was in 1973, there would be a fatal crash every two days (because the number of flights is much higher today, see here).
To conclude, driverless cars will have a profound effect on most aspects of our society. Not only will they save tens of thousands of lives, but they will also make our cities more liveable and give us back some much needed time. I don’t know about you—but me? I can’t wait!
Did you think this article was overly optimistic? Do you see any negative trends that could accompany the release of driverless cars or some other positive ones that weren’t mentioned? Share your thoughts in the comments below!
a brief tour of the world’s oldest question
July 13, 2016
The existence of free will is a classical question in philosophy, and one which has been around for a while (unless you consider Greek antiquity the recent past). Unlike some of the more far-fetched thought experiments philosophers like to indulge themselves in, free will is something everyone can relate to, and about which everyone seems to have a strong opinion. This arguably makes it hard to approach the question rationally, considering everyone starts with a deeply rooted prior position.
Before we dive into the thick of the subject, we need to define a few things. Let’s start with epiphenomenalism, which is the belief that conscious experiences are a by-product of our brains’ activity. Adherents of this view state that the brain's activity is the sole cause of any observable behaviour, and that conscious emotions cannot influence the output of the system. To borrow Thomas Huxley’s words: “consciousness is completely without power…as the steam-whistle which accompanies the work of a locomotive engine is without influence upon it’s machinery.” In this framework, we might think we cry because we are sad, but in reality, there is a common cause to both events. Something made us cry, and that same thing gave rise to the conscious experience of sadness, thus creating the impression that we cry because we are sad, when in reality we cry for the same reason we feel sadness, whatever that reason may be. Although epiphenomenalism is the fancy name for it, this is identical to saying humans are automatons, responding to external stimuli in deterministic ways.
Another concept which needs defining is qualia. Simply put, a quale is "the way it feels” to experience something. For instance, the subjective experience of pain is a quale. If one were to describe the feeling of pain to another person, no matter how precise the description, the listener could never experience the quale associated with it, and would certainly learn something new about pain once he experienced it for the first time.
Okay, now that the defining is out of the way, let’s get to it.
The following reasoning, which is anchored in our current understanding of the brain and the nervous system (limited as it may be), is, in my opinion, the strongest argument for epiphenomenalism yet. It goes like this: the only way humans can interact with the outside world is through the motor system—the nerves and muscles controlling our movements, speech and everything else. If we follow the chain of events leading to any motor action, we will find that the muscle contraction responsible for the movement was the result of the firing of a motor-neuron (a type of nerve cell). If we trace that neuron backwards, we will find that it originates somewhere (perhaps in the spinal cord, or in the brain). At this point, we may ask ourselves why that neuron fired. We know from neurophysiology that a neuron fires whenever all the other neurons which are connected to it manage to affect its voltage sufficiently (depolarise it and trigger an action potential). So in order for that specific motor neuron to be active, some of the ones connected directly to it had to be active before. In turn, those neurons themselves could only be active if their own partners were—you can start to see where this is going. If we continue following the chain of events, we can explain all of the activity a person produces as a logical (and inevitable) consequence of the past state of all the cells in his or her brain, spinal cord and nerves.
A neuron’s dendritic (input) tree. This “tree” allows the neuron to create contacts (synapses) with a large number of other cells.
In fact, there is a growing body of empirically evidence supporting this view. The readiness potential (first reported in 1964) is a stereotypical activity which can be measured in specific brain areas (such as the motor cortex). What makes this particular type of activity interesting, is that it has repeatedly been shown to precede the conscious decision to move. Some scientists have even designed an experiment in which a computer predicts whether or not a subject will press a button, before they do so (and more importantly, even before they consciously decide to do so). Of course such studies still have some weaknesses (for instance, how does one precisely determine the point in time where a conscious decision is taken?) Still, these types of results are certainly enough to give pause.
This seems to lead to the inevitable conclusion that free will cannot exist. Any quale, any conscious experience, must either be a side product of the brain’s activity (like the whistle of the locomotive), or perhaps, an intrinsic property associated with the state of the network of cells in the brain (this view is called materialism, or physicalism). However, these subjective experiences cannot shape the outcome in any way.
In order for any characteristic to survive the process of natural selection, it should confer an advantage in terms of survival.
Of course, this raises the question: why do we have consciousness at all, if it is useless? In other words, we might as well be philosophical zombies (a philosophical zombie is a being which acts exactly like we would expect a human to, but does not experience qualia, or any type of conscious experience). This is of course a very difficult question to answer. Indeed, it would seem odd that consciousness exists at all, if it provides no evolutionary advantage. One possible answer to this conundrum, which has recently become popular again (owing in part to the work of Giulio Tononi and Christoph Koch), is panpsychism, or the idea that consciousness may actually be an intrinsic property of the universe (like mass). In this view, every object has some degree of “consciousness”. Of course, a rock may have little (or perhaps none), while a structured group of cells (like a brain) may have a lot. The reason this theory is interesting, is that it helps solve the evolutionary argument: if it is impossible to have a system which processes information, without having an associated subjective experience, then it would follow that as evolution selects for increasingly complex organisms, these organisms must automatically have a growing amount of consciousness and subjective experiences. This is exactly what Giulio Tononi tries to formalise in his integrated information theory of consciousness (I encourage you to read more about this fascinating science here).
In Giulio Tononi’s theory, how tightly connected a set of nodes are determines how high their consciousness (or phi) is.
In practical terms however, the brain is a chaotic system—meaning that although it is most likely purely deterministic in nature, predicting the outcome is not feasible on any relevant time scale. Like the weather, it would be impossible to predict a person’s actions based on the current state of his brain, for more than the immediate future. This leads to the following comforting conclusion: we do not have free will, yet nobody can predict what we will do next (outside of some specific “press the button” types of situations).
One of the most popular arguments against epiphenomenalism (or any theory which argues for the non-existence of free will) is the “obvious absurdity” argument—it is plain for everyone to see that we have free will, since we constantly take decisions and execute actions according to our own desires. This argument, although very emotionally convincing (like when the old professor suddenly lifts his arm in the air and exclaims: “no free will my ass!”), entirely side-steps the problem. If epiphenomenalism is true, and we are under the illusion that our conscious experiences precede and cause our physical actions, then we would by definition be under the illusion of having free will. Any individual’s conviction of possessing free will is therefore not evidence for, nor against, either position. To paraphrase B. de Spinoza's pithy remarks, we are under the illusion of freedom simply because we are conscious of our actions, and unconscious of the causes whereby those actions are determined.
There may yet be one way to escape the inevitable conclusion that free will does not exist: quantum physics. If we look at the behaviour of very small systems (atoms, molecules, etc.), the classical laws of physics we are accustomed to at our macroscopic scale start breaking down. In the world of the tiny, matter behaves in a fundamentally different way. It behaves according to statistical laws, which means some questions cannot be answered precisely, but only in terms of probabilities. For instance, it is impossible to predict when a radioactive atom will decay. This means that at such small scales, determinism has some wiggle room. This wiggle room is where the theories known collectively as quantum consciousness come into play. Indeed, some scientists argue that the processes underlying the brain’s function are subject to the laws of quantum mechanics (because neurons operate at a scale where such phenomena are relevant). That the brain is subject to quantum effects does not, per se, tell us anything about free will. However, some have argued that free will may be introduced into the system by playing with the probabilistic nature of quantum mechanics. In this far-fetched view, the outcome is not altered on average, but free will intervenes by changing the outcome at the level of single quantum events. To give a simple analogy, this is like saying that free will may change the number we get when throwing a dice, but on average, each number must still appear in equal proportions. So if we force a specific number to appear now, we must compensate for it later. This very limited wiggle room may still be enough to give us a small nudge in the direction of free will.
The great physicists of the past century may ultimately hold the key to free will.
So far, none of the quantum brain theories have gained widespread adoption (mainly because there are no convincing ones yet). Still, quantum mechanics may be regarded as the last hope for those trying to prove the existence of free will.
I hope this article has awakened your curiosity about the subject of free will. If it has, I encourage you to read up on it to get a more balanced view of the question. If you find something interesting, do come back and share it in the comments!
But it’s nothing to worry about
June 11, 2016
Bringing up the singularity—the point of no return after which our feeble human minds are unable to even fathom what the future may bring—is a sure-fire way to ensure the fun engineering party you’re attending (what? a fun engineering party? invite us next time!) ends in a passionate feud. This seemingly all pervasive topic is a favourite amongst the technogeek elite—the sic-fi writers, futurists, engineers and electric-car-building-billionaires of this world. In more practical terms, the singularity is often defined as the point where artificial intelligence (AI) algorithms become more capable than their human creators.
There are two prevailing opinions regarding what would happen after the singularity (hence the passionate feud). The first view, which is very optimistic, goes a little like this: once sentient machines become smarter than us, we must trust that they will generously continue helping us to achieve our goals (cure illnesses, improve our quality of life, etc). Of course, the defenders of this approach will argue that we can make sure machines will always have our best interest in mind by including some basic rules deep within their design (such as Asimov’s three rules of robotics). The second view is—how shall we put this lightly—slightly more pessimistic. According to this version of events, sentient machines will realise we are not helpful to them (or worse, harmful to them) and destroy us, enslave us or otherwise harm us. This is the Terminator style apocalypse, and it sells well (to be fair, the movie where the robots and humans live happily ever after would never make a big splash at the box office). Hopefully, by the time you reach the end of this article, you will disagree with both views.
But wait, you may ask, before I start preparing my house for the machine apocalypse (though if you are, remember to pack electromagnets), are we even sure this will happen? As often when the matter at hand involves predicting the future, there are conflicting reports. Ray Kurzweil, author of The singularity Is Near, believes that, well, the singularity is near. More seriously, he narrows it down to the year 2045, give or take. Others, like the scientist and author Steven Pinker, dismiss the idea altogether. Some of the criticism about the concept of a technological singularity is very reasonable and embodies the kind of scientific skepticism which should be revered. For instance, some argue that technological progress will not indefinitely follow an exponential growth, but may on the contrary slow down as each further improvement becomes harder because of increasing levels of complexity and knowledge (this is known as the complexity brake).
Regardless of how we look at the problem, I think it is only fair to assume that mass extinction aside, we will inevitably develop artificial intelligence on par, or superior, to our own. It may take longer than expected, much longer, but that does not prevent it from happening. To argue that it is impossible to develop an algorithm smarter than ourselves is likely a common, although misguided, type of anthropocentric wishful thinking. There is no escaping it—the singularity, as in machines which are smarter than humans, will happen sooner or later.
Should we prepare? Can we prepare? That would probably depend on which side of the argument you stand, the optimistic side, or the apocalyptic one. I believe the singularity won’t matter, because by then, we will be so intimately merged with machines, that it won’t be us vs them. Instead, the singularity will directly benefit us. With our current technology, it makes a lot of sense to distinguish between humans, and the tools they use—the machines. But in the future, as we develop increasingly complex and efficient brain to machine interfaces, the distinction will start to blur. We will have a tight symbiotic relationship with our technology, allowing us to take full advantage of it. By that time, asking about the singularity won’t make much sense anymore. The evolution of the human mind and its capabilities will be a progressive process, and the day we become incrementally smarter, thanks to our merger with computers, will not be a significant day at all. I once read this “mind-blowing fact” somewhere: there is a specific day in your life, when your mother carried you in her arms for what was to be the last time, yet that day bore no particular importance, and nobody noticed it. The singularity will be similar. We may see it thinking back, decades later, maybe pin down the year it happened, but it will carry no special significance as it unfolds.
Of course, there is still the possibility that we will develop super-intelligent AI before we have the technology to seamlessly interface ourselves with computers. If that were to happen, then we may find ourselves faced with the common dilemma we discussed earlier. Both areas (AI and neuroprosthetics) are almost unimaginably complex. Both have captured the imagination of thousands of scientists and engineers around the world. Both have spawned enormous and very active fields of research. And in many instances, there is a fair amount of overlap (AI is used on a daily basis in neuroprosthetics, and the study of the brain is often key to designing novel AIs). It thus seems reasonable to assume that they will continue to develop in parallel, and that extreme advances in one will not happen without similar advances in the other.
Although the singularity is inevitable, it won’t be something to worry about. In fact, we won’t even notice it.
Why Steve Jobs was right, but half a century too early
May 23, 2016
In 1981, Steve Jobs famously compared computers to “bicycles for the mind”—although in all likelihood he meant only Apple computers. The metaphor was born when he chanced upon an interesting chart, showing the efficiency of locomotion for various animals (the energy consumed to cover a certain distance). Although Steve later claimed humans were mediocre in terms of efficiency, the truth is that humans are amongst the best walkers in the animal kingdom (a direct consequence of bipedal locomotion, allowing humans to save copious amounts of energy from step to step—this is called inverted pendulum walking). Still, humans are amongst the worst sprinters, and fairly lousy long distance runners (charts). Regardless of where we stand in the charts, human ingenuity has allowed us to transcend our physical limitations, and soar high above all animals with one simple invention: the bicycle.
This is a very striking illustration of the power of human minds—allowing us to invent solutions to overcome most of our physical shortcomings. We thrive in environments we are not suited for, and repeatedly outsmart animals who would otherwise overpower us. The reason Steve’s metaphor is so compelling, is the suggestion that computers might increase the efficiency of our thinking like bicycles improved the efficiency of our walking. Being able to enhance the way our minds function would be a key turning point, leading to a staggering snowball effect, where smarter minds are able to enhance themselves further, in a dizzying spiral of increasing intelligence.
Sadly, I believe we can all agree: the personal computer revolution Steve helped pioneer did not produce the bicycle for the mind. Sure, computers may be the most important tool we have ever built—together with the internet—allowing us to connect, share and create knowledge in new ways, much like the invention of print did centuries ago. In science, computing has allowed the creation of entire fields of research and unprecedented mathematical prowess. Still, that is not enough to qualify as a bicycle for the mind. Despite making us more efficient during certain tasks, such as searching for information, or tackling specific problems, computers do not enhance our minds in a pervasive, generalised way. Perhaps computers are more akin to the invention of the wheel—a necessary stepping stone towards the creation of the bicycle, and a useful tool in itself—but carrying a wheel in your backpack does not make you walk any more efficiently (not that I have tried, mind you).
In my opinion, the main reason computers fall short of Steve's vision is our extremely limited ability to interact with them. Our minds operate at the blistering speed of thoughts. Yet, when using a computer, we cannot send instructions at our usual speed—we have to rely on a much slower approach, dragging a pointer around an interface and clicking on things, or other similarly clumsy artifices. As many frustrated users will attest, operating computers this way can be painfully slow at times, and many tasks which our minds can envision and plan out in a matter of milliseconds, may take dozens of seconds to physically execute on a machine. Technically, this means the communication between our minds and computers is low bandwidth (particularly in the brain to computer direction), preventing us from truly developing a symbiosis with them.
In order for a wheel (or two) to become a bicycle, someone had to think of a way of interfacing human bodies with this useful invention. In that case, the answer was to place a person directly above a set of wheels, where the pedals act as an optimised physical interface, drawing from the natural motion of the legs to thrust the bicycle forward. The same holds true for computers. In order for our minds to truly take advantage of them, we need to invent an interface between the mind and the machine allowing for seamless exchange of information—in other words, the pedals.
For our minds to take full advantage of computers, they need to be able to interact at their natural speed, and in their natural language—thoughts. Imagine being able to control your phone, or your laptop, by simply thinking about it. For instance, opening a photo, changing the brightness and renaming it would be a breeze—no fiddling with menus and settings. Similarly, performing an internet search would no longer require opening a browser, then a new page, and typing the query before hitting enter. Instead, a simple thought would immediately bring up the corresponding answer on screen. This way, computers would become an extension of the mind, rather than a mere tool.
We could even take this one step further—albeit by unquestionably entering the realm of science fiction: what if, instead of bringing up the answer on a screen, it was sent back to our brain directly in the form of a thought. Imagine wondering about a question, and instead of seeing symbols appear on a screen, simply suddenly becoming aware of the answer. Now that, is what I would call a bicycle for the mind.
Knowing what the bicycle is, the more pressing question becomes: when will I have one? The simple answer: sooner then you might imagine.
The missing puzzle piece to achieve this vision is an interface providing a seamless link between the mind and the machine. Such technologies already exist—we have the ability to record the brain’s activity with relative precision, and extract a bunch of information from it—but they still present many issues which need to be addressed. To illustrate how far we have already come, one simply needs to look at the recent clinical trials where such interfaces allowed paralysed patients to control robotic arms, type on a keyboard or play games directly with their minds (video). Still, solving these complex issues may well take us another decade or two, but the cold and inexorable progress of science ensures that we will get there eventually. Once we have the interface, it will only be a matter of putting the pedals and the wheels together.
When Steve jobs envisioned the bicycle for the mind, he wasn’t wrong—he was simply half a century too early.
Why we might already be living in it
January 21, 2016
In the 1999 classic the matrix, the Wachowski Brothers depicted an alternative reality in which all humans are connected to a central machine (the matrix) feeding them a form of hyper-realistic virtual reality, which only exists in their collective minds. By keeping them in this state, sentient machines are able to harvest the energy produced by each person to feed their own power needs.
The thought is disturbing. How would someone know whether what he sees is an illusion, instead of reality? The answer: it’s impossible. As Descartes famously said, the only thing one can be sure about is that one is indeed a thinking entity, everything else may very well be a trick. And this fact alone, in my opinion, makes the plot so compelling. Because as the movie reveals its secret, we are brought to reflect on our own condition, and we cannot help but conclude that we too, may be part of a matrix.
In fact, I believe most of us are already living in a version of the matrix today. Let me explain why.
The world has changed radically in the last decades. The invention and subsequent mass adoption of the internet has transformed almost every aspect of our lives in a profound way. It is the most significant revolution in the way humanity shares, seeks and generates knowledge since Gutenberg invented the printing press over half a millennia ago. The internet has become our main source of data. For most millennials it might seem odd to use other means to find information on a day to day basis. We use it to read the news. We use it to search for snippets of knowledge. In fact, almost all of the new information we acquire throughout the day comes from the internet (from our social networks, wikipedia, search engines and other places). Ironically, when we interact with each other in person, we usually share information we obtained online (did you see what Chris posted on Facebook? Did you hear the news about Italy?).
This is not, per se, a bad thing. Though this is a matter of opinion. It is simply the way of our modern world. Some may cling to the past, regretting better days where one could be blissfully ignorant of facts, without someone grabbing his hand-held computer and finding the relevant piece of information more quickly than it takes to change the subject. But there is no going back, and the internet will inevitably play an increasingly central role in our lives.
So far so good you might say, why all the fuss about the matrix? Well, your patience is about to pay off. The problem here is subtle. The problem is that there is no such thing as the internet (as in a single unified entity). We interact with the internet in very stereotyped manners: we either load up a search engine (most likely Google) and type in our query, or we navigate directly to our website of interest (most likely Facebook) and browse the content available there. However, in an effort to improve our experience in the online world, these companies are resorting to a very smart trick: they are tailoring the results and the content to better match our tastes and expectations. This is not new, and this is not secret. It is however, something many people are unaware of. The underlying motives are good: users are provided with a more enjoyable experience, and the search engines (or social networks and others) receive more traffic from happy users. It’s what one may call a win-win. Except it isn't.
How could this type of customisation be harmful? Let me give you a simple example. When I search for the keyword python, the first five pages do not mention anything about snakes or english comedians. Because Google has learnt that I am interested in programming, it helpfully brings up lots of pages about coding, hiding away what I am probably not looking for (Monthy Python or python snakes). On the other hand, when I do the same search anonymously, a menacing snake appears on the first page.
This becomes slightly disturbing when we consider the implications. If I had never heard of the British comedians Monthy Python, or about the snake, I might never chance upon them. I would remain unaware of their existence. This creates a sort of vicious cycle, where we are fed what we look at most, and automatically look at more of what we are fed. It becomes harder to chance upon new things, new ideas. By feeding us the content we are looking for, these services are also hiding away the content we aren’t looking for.
This was a rather harmless example, but one can easily see how it might be more pernicious when talking about politics, history or conspiracy theories.
Of course, humans have an innate tendency to do this on their own. People will often seek out others with similar opinions, or interacts with groups who share their ideals. Reinforcing this tendency by customising our access to the internet capitalises on this natural drive.
At the end of the day, these services contribute to the creation of what we may call a personality inertia, where people with a certain personality are encouraged to move forward in that direction, and where it becomes increasingly arduous for someone to diversify and expand.
Not all is dark however. Google for instance, is aware of this and tries to prevent it to some extent (by mixing in results they think you will like with some “untainted” results). But they might be the most benevolent in the bunch (do no evil, right?), what about news aggregators? What about Facebook, or Youtube? Can we truly believe all of these services are sacrificing some traffic for the sake of diversity?
Our digital world is a prison of sorts. Like the humans in the matrix, we are shown an illusion of the real world, a version of the real world designed to keep us happy, to reinforce us in our beliefs and sentiments.
This brings us to the following spooky conclusion: what you see when you look at your monitor, is not this beautiful, interconnected mesh of minds so many call the internet. Rather, what you see is a version of the internet tailored specifically for you, which in many ways is simply a reflection of your own personality.
In an interesting twist of events, if we dig deeper, we discovers that the way this customised internet is built and tailored for each one of us is through the use of machine learning algorithms. It turns out that what is keeping us in this virtual world, drawing one final and uncanny parallel to the movie, is precisely the same thing that was keeping the people from the movie trapped in the matrix: artificial intelligence. Luckily not the energy harvesting kind – for now.