Daniel Miessler's Blog, page 123

Leveraging the Useless Masses as a Competitive Advantage

Imagine a time, say 10-30 years from now, where 80% of people are not needed in society.

They can’t produce more value, doing traditional tasks, than computers, AI, and robots can. So they are all either fired or can’t get a job in the first place.

It’s a whole separate topic to ask what they’re going to be doing, but let’s assume they’re there and not rioting (yes, it’s a major assumption on its own). They’re likely to be receiving a check and public housing to play video games and watch TV all day.

Anyway, 80% of the world, just doing that. We’ll call them the Betas. At first it’ll be low numbers like we have today, then it’ll be more than half, then 70%, 80%, 90%, etc. I’m not sure where that’ll top out, but I’m guessing around 95% or so. Hard to say since it also depends where you draw the line.

But let’s go with 80%.

We’re still going to have countries at this point. And those countries will still have economies and will still be competing with each other. The global economy will be far more linked, of course, but it’ll still be made up of distinct countries and distinct groups of people.

What I just realized earlier tonight is that the Alphas (the lucky 20% at the top) will have different ways of viewing the Betas (those who aren’t so lucky). Many of them will look down on them, of course, as they already do.

But the smart play, from a competitive standpoint, as a country and as an economy, is to try to harness the power of this group. Let’s say the U.S. is 500M. 80% of that is 400M people. That’s 300 million people who can’t provide traditional work that is cheaper and better than machines or automation. This isn’t actually too far off.

But the key here is the word traditional.

The challenge and opportunity for the Alphas will be to find a way to harness the creative power of the Betas, to find the Alphas within the masses, and/or to use their masses to produce value in some way.

In other words, short-sighted countries will see their Beta population as failures who should be placated and distracted to avoid revolt, but little further thought will be given to them. They’ll be the untouchables. The forgotten people. The Alphas will live in plush Green Zones with spectacular food, the newest tech, and brilliantly curated experiences 24/7, and the Betas will wallow in cookie-cutter manufactured homes that are little more than shelters for media and gaming rigs.

The smart countries, however, will see the Beta population as a fountain of potential. They will build platforms to find the best artists, the best performers, the best thinkers, the best storytellers, the best…everything. Once they’re identified they’ll be extracted, trained, and magnified, and will thus become part of the value creation engine for that country.

In other words the game changes from traditional value (which humans won’t really compete well with computers on) into one of personality, uniqueness, art, music, and other human-advantaged traits. And whoever can extract and nurture the individuals in their populations with these skills most efficiently will have a tremendous advantage economically.

In fact, these competitions will be their own games, and the reward will be entering the Alpha class. You move to the Green Zone, you get the nice house, you’re surrounded by upgraded people, you get upgrades yourself, etc. And once you do, you start producing value for the country and economy in a way that you couldn’t before.

Summary


The world will soon be separated into Alphas and Betas.
Alphas are the shrinking number of people who can provide value in a workforce being cleaned out by computers, AI, and robots. Betas are everyone else.
Dumb countries will treat the Betas like failures, and do their best to ignore them.
Smart countries will treat Betas like a sea of potential Alphas with yet-undiscovered talents, and they’ll run tech platforms designed to discover, train, and make use of the talented within the masses.


Notes


Also included in the Betas will be many millions of people in the service industry who barely make any money for the work they do. But they will have some advantages within the gaming and media consumption world, otherwise there would be no incentive to work vs. not work. They’ll probably have more channels and special abilities within their favorite games.
The truth is that AI is coming for the creative traits in humans as well, and they’ll eventually win there too. But for the next ten or twenty years humans should still have the advantage there.

Recommended for you…

Green Zone, Red Zone

On the Rise of Pokemon GO and AR/VR Gaming

The Bifurcation of America: The Forced Class Separation into Alphas and Betas

Bug Bounty Ethics and the Ubering of Pentesting

IQ and Creativity Bias in a Post-work World

Machine Learning Will Revolutionize Content Discovery

__

I do a weekly show called Unsupervised Learning, where I curate the most interesting stories in infosec, technology, and humans, and talk about why they matter. You can subscribe here.

The Best WordPress Plugin for Related Posts

I’ve been hunting for a solid related posts plugin on WordPress for years.

My go-to has always been Contextual Related Posts, which has performed well for me up until recently. For some reason, when I moved to Ubuntu 16.10 and PHP 7.x and…well, I’m not sure what the variable was—it stopped yielding good results.

My bounce rate went from 75% to over 85% as a result of it breaking.

So every couple of months I search unsuccessfully for a replacement—until now.

I just enabled Similar Posts, and wow—it’s remarkable.

I have around 3,000 posts in my database, and what I look for in a related posts plugin is the ability to harvest the other true nuggets on the site related to whatever’s being displayed.

Contextual Related Posts used to be pretty good at this, but this new plugin is 10x as good at it. Check out the example in the image above, and if you have a site I recommend you try it as well.

I increased the word depth to 500 words, but that was pretty much all I did. I’ll be working on displaying images at the bottom of posts instead of text, but other than that I’m already supremely pleased with it.

Hopefully my bounce rate will drop down to 75% or lower as a result of it being enabled.

Notes


The reason my bounce rate is so high is because so much of my traffic is tutorial-based. They need to know how to do something, they Google it, my site comes up, the click the link, they find the solution, and they leave. It’s not very often that someone stays around for a while after they’re done because they’re usually in the middle of working. But hopefully this plugin will make that more common due to the quality of the matches.

Recommended for you…

WordPress Plugin Drama Continues

Facebook’s Big Play

Are America’s Incarceration and Crime Rates Are Related?

Google

Monitoring SSH Bruteforce Attempts Using Splunk

How To Search Your Site Using Google From Firefox’s Address Bar (with code)

__

I do a weekly show called Unsupervised Learning, where I curate the most interesting stories in infosec, technology, and humans, and talk about why they matter. You can subscribe here.

Criticism from Both Sides as an Indicator of Neutrality

I just put out issue No. 78 of my newsletter, and I got a couple of critical emails about my mention of the Russians hacking the Macron campaign.

Here’s a piece of the first.

And here’s a piece of the second.

So the first is blasting me for being too right wing, and the second is blasting me for being an extreme liberal—but both comments are based on the exact same position. I suppose I take that as a compliment for being neutral?

First, just to state the obvious, I’m neither an extreme liberal nor a conservative. Being generally anti-religion, pro-choice, and believing in a role for government in basic services takes me out of the conservative camp. And my support of logical gun policy, immigration reform, and a growing distain of the extreme left has all but ejected me from the liberal ranks.

In truth, there’s no place for me in any party right now, and I don’t feel the need to force myself into one. I have positions on issues, and that’s it.

So the irony here is that it’s actually both of these commenters who are showing their allegiances. They have rejected a narrative because they believe it to belong to the opposing party, when facts actually don’t have a party.

FACT: Russia has been engaging in information warfare campaigns around the world, focused around key elections, in an attempt to further is political goals.

That is a statement without a political affiliation. If you are in information security or the intelligence community, and you lack some sort of raging bias that blinds you to evidence and reason, you will have reached this conclusion many, many months ago. It’s so obviously true that it’s ceased being interesting.

But somehow the crazy right wingers think it’s a liberal conspiracy once the New York Times writes about it, and the crazy liberals think it’s a right-wing, colonialist campaign if the intelligence community confirms it.

Grow the fuck up. Read more. Find more inputs to formulate your opinions.

If you cannot look at many sources, and many pieces of evidence, and then use that information to determine the most likely truth, then you’re just lost. You’re left to close your eyes and swing wildly at anything that’s been marked as false by your betters.

As I said here before, if you believe nothing you can be convinced of anything.

That Russia is engaging in information warfare around the world is not a liberal or conservative fact, because those don’t exist. It’s just a fact.

Does this mean everything is Russia? Automatically? In all cases? No. Of course not.

But if you read a lot, from a lot of different sources, have a basic understanding of history and the relevant fields of infosec and Intelligence, and you aren’t horribly biased by some political bent yourself, then you should be able to tell the difference.

Try harder.

__

I do a weekly show called Unsupervised Learning, where I curate the most interesting stories in infosec, technology, and humans, and talk about why they matter. You can subscribe here.

Unsupervised Learning: No. 78

This week’s topics: The WannaCry ransomware worm, the president’s EO, Macron hacking, HP backdoors, laptop bans, Amazon releases, Chinese online commerce, CRISPR, Germany and renewable energy, beetles, dental health as social indicator, Reading superpowers, Net Neutrality, serverless, deep learning black box, The Three Body Problem, you can now support the site, The Mechanical Universe, TrueCaller, and more…

This is Episode No. 78 of Unsupervised Learning—a weekly show where I curate 3-5 hours of reading in infosec, technology, and humans into a 15 to 30 minute summary.

The goal is to catch you up on current events, tell you about the best content from the week, and hopefully give you something to think about as well.

The show is released as a Podcast on iTunes, Overcast, Android, or RSS—and as a Newsletter which you can view and subscribe to here.

Newsletter

Every Sunday I put out a curated list of the most interesting stories in infosec, technology, and humans.

I do the research, you get the benefits. Over 5K subscribers.

Recent Newsletters

05/14/2017 – Daniel’s Unsupervised Learning Newsletter: No. 78

05/07/2017 – Daniel’s Unsupervised Learning Newsletter: No. 77

04/23/2017 – Daniel’s Unsupervised Learning Newsletter: No. 76


04/23/2017 – Daniel’s Unsupervised Learning Newsletter: No. 75


04/16/2017 – Daniel’s Unsupervised Learning Newsletter: No. 74


04/09/2017 – Daniel’s Unsupervised Learning Newsletter: No. 73


04/02/2017 – Daniel’s Unsupervised Learning Newsletter: No. 72


03/26/2017 – Daniel’s Unsupervised Learning Newsletter: No. 71


03/19/2017 – Daniel’s Unsupervised Learning Newsletter: No. 70


03/13/2017 – Daniel’s Unsupervised Learning Newsletter: No. 69


03/06/2017 – Daniel’s Unsupervised Learning Newsletter: No. 68


02/27/2017 – Daniel’s Unsupervised Learning Newsletter: No. 67

The podcast and newsletter usually go out on Sundays, so you can catch up on everything early Monday morning.

I hope you enjoy it.

__

I do a weekly show called Unsupervised Learning, where I curate the most interesting stories in infosec, technology, and humans, and talk about why they matter. You can subscribe here.

For a Life Upgrade, Swap TV Time for Reading Time

I have a lot of really smart friends, but I have few friends that read.

Most of my friends watch TV. And when I say “TV”, I mean all its modern forms—actual broadcast television, DVR’d shows, Netflix, whatever. They watch lots of it. Probably 10-30 hours a week, if I had to guess, which ends up being hundreds or thousands of hours per year.

But they don’t read.

And I’ve started to notice a yawning gap between their understanding of the world and my own. When we talk they offer me little clips of wisdom that were interesting five years ago when they first came out, but they have no awareness of the actual research behind it. The only reason they know the story is because it went viral on Facebook, or hit CNN.

So when we hang out, I hear about the inane TV shows they’re watching, and I tell them about the remarkably interesting concepts I’m learning about on a weekly basis through reading.

It makes me feel we’re sparring while I’m in a futuristic, 28-foot battle mech, and they’re naked after being malnourished for a week.

It’s not a fair fight, and that’s what’s frustrating me. They’re choosing to fight—not against me—but against the world, without upgrading themselves.

It’s not me, it’s the upgrades

Then I hear from others how smart I am. Or how productive I am. Or how I’m always doing interesting projects, and writing, and creating things. And I get questions about how I do it.

Well, that’s the thing: I didn’t do anything. I just happen to be in a 28-foot battle mech called reading.

Reading is a genetic upgrade. Every book I read enhances me, like a battle mech, like a smart drug, like an IQ implant, or like a CRISPR upgrade of my creativity. I am so vastly superior to my 10-years-ago-self that I can barely remember being that limited and ignorant.

And it’s all because of reading.

Reading makes you creative, just like exercise gives you energy. When I stop reading, I stop having ideas. It’s a very simple causal relationship described by numerous other people (which you would know if you read more). In short, I’m not smart. Reading makes me smart.

And because I know this, I make choices to keep this IQ & Creativity engine working consistently. I haven’t watched a TV show in months. I spend all that time reading instead. I read on planes. I read in the car (Audible). I read in bed. I go to coffee shops on nights and weekends and read. I read everywhere, constantly.

The Concentration of Wisdom

It’s true that there is good TV out there. John Oliver’s show is quality. Bill Maher has some solid discussion on a frequent basis. VICE is spectacular. And I’m sure there are many others.

But I don’t think they compare to reading books because reading has a naturally higher Quality Density, or Wisdom Concentration.

I honestly believe that reading a good book is ten to twenty times as useful as watching a quality show. I think it’s something about the purity of the thought that’s coming into the brain, and how much work your brain does to consume, model, and structure that input.

It’s almost as if reading is working out in a gym with weights, and watching TV (even good TV) is like watching someone else work out.

I think the “weight” in this metaphor is the creation of the world that’s being described, the placement of characters and concepts within it, and the maintaining of that world in your mind as the story progresses. With reading, your mind is doing all this work itself. It’s engaging in terraforming and world-building on its own, every moment of every book. With TV, all that work is done for you, and you’re just watching from the outside.

Making the switch from TV to reading

So what I recommend for everyone—and especially my friends—is that you consciously make the choice to exchange some of your TV time for reading time.

You might think you don’t have any time to read, but that’s because you’re probably spending close to 100 hours a month watching 11 different shows. And let me just say again: every hour of reading you swap will likely give you the value of 5, 10, 20, or even 100 times as much TV.

So it’s not a matter of not having the time; it’s a matter of making the time.

A side note about video games, by the way. They should be treated much like TV, and not the good kind of TV either. I like video games, and play a few myself, just like I watch a few TV shows. But I never treat either of those as my primary inputs, as they both equate to entertainment as opposed to enrichment.

So bottom line there is this: add up all your TV and video game time that you spend per week. That’s your enrichment / upgrade time budget. That’s the time you have to spend on improving yourself.

You can literally go to the gym. You can engage in some sort of physical activity like running, or walking, or whatever your sport of choice is. You can read. Or you can watch TV and play video games.

My recommendation is that you spend 80% of your time reading, 10% of your time exercising, and 10% of your time on video games or watching TV. Maybe that’s too extreme. Maybe you want to get to 70/15/15. Or 60/20/20. I don’t know what the best mix is, but what I’m arguing is that the more reading you add the better off you’ll be.

And just to be clear, I know this will be harder. Watching TV is extremely easy. And so is playing video games. They provide all the hooks and incentives themselves, so you basically just eat fistfuls of M&Ms until you can’t feel your face anymore.

Reading is harder to start because of the cognitive load I talked about earlier. For those who don’t read, or who aren’t currently reading, it’s harder to pick up the book and get started than it is to sit on a couch and stare at something.

I get it, and I’m asking you to push through it, for your benefit.

My Plea to You

So rather than a summary I’m going to make a plea.

Start reading.

This is not a matter of changing entertainment types—it’s a matter of upgrading yourself, your creativity, your imagination, and your intelligence.

It will make you more prolific in your own personal projects, it’ll make you more effective at work, and more interesting to talk to with strangers and at social events.

But let’s not be ephemeral about it. Let’s make it practical. I recommend you do the following starting next week.

Pick a book from my Unsupervised Learning book list.
Spend an hour a day reading it.

That’s it. An hour a day.

No matter what, you keep reading. It’ll be hard to get into the schedule at first, because your mind will rebel. It’ll say:

Um, this is hard. Why are you making me concentrate? Can’t we do something fun? Let’s watch TV instead. Let’s play a video game.

No. We’re reading this book right now. That’s what we’re doing.

After a week of doing this, and possibly much sooner, it’s no longer going to feel like work to make yourself read. It’ll com naturally, and it’ll become just as enjoyable (if not more) as watching TV or playing a video game.

And I’ll take it a step further. If you want to read a fantasy book, go and download The Name of the Wind and read/listen to that. For sci-fi, get The Three Body Problem. And if you’d prefer non-fiction, start with Homo Deus.

An hour a day.

Make the change.

You’ll love it.

Notes


I have another group of friends, much smaller in number, that read maybe 1-5 books a year, mostly fiction, which almost counts as being a non-reader.
“Reading” also includes other types of high-quality content, such as the the Waking Up, Intelligence Squared, and a16z podcasts, for example.
I’m purposely being a bit of a dick in this post because I think it’s worth it to get you to read. It’s a trick called “being an asshole”. I hope it’s working.
I make T.V. exceptions for Game of Thrones, Black Mirror, and a couple other shows, but in an average month out of a year I watch virtually no media. It’s all binging of a few top-quality shows or nothing at all.
The Wisdom Concentration, or Quality Density of non-fiction books is also extraordinarily high because content is often dense and concise in good non-fiction books.
It’s also worth mentioning that this isn’t about people who are already readers and just have lots of other positive activities going on, and who don’t watch much TV. This is for people who have lots of free time but spend it watching TV or playing video games instead of reading.

__

I do a weekly show called Unsupervised Learning, where I curate the most interesting stories in infosec, technology, and humans, and talk about why they matter. You can subscribe here.

Support

 Make a Donation

Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.

USD

Donate Now 

__

I do a weekly show called Unsupervised Learning, where I curate the most interesting stories in infosec, technology, and humans, and talk about why they matter. You can subscribe here.

Some Quick Takeaways from the 2017 Verizon DBIR

For those who lack the time to read the entire report, here are some of the key findings along with .

Attackers


75% of breaches done by outsiders.
25% involved internal actors.
18% state actors.
51% involved organized crime actors.

I see 25% involving internal actors as quite high, but that depends on the definition of “involved”.

Targets


24% of breaches affected financial organizations
15% of breaches affected healthcare
Public sector were third at 12%
Retail and hospitality combined for another 15% of breaches.


Tactics


62% of breaches used “hacking”
51% of breaches used malware
81% leveraged stolen/weak passwords
43% were social engineering based

What does “hacking” mean? And how much hacking did or did not involve malware?

Other findings


66% of malware got in via email
73% of breaches were financially motivated
21% of breaches were espionage related
27% were discovered by third parties


Analysis

I find the 1/4 insider involvement to be high. Not saying it’s wrong. Just saying it seems high.

I think they could use a better term than “hacking” to describe their most common type of tactic. Perhaps “manual intervention”?

I’d love to see some sort of analysis of controls in this report, or a similar report. So basically what controls from say the CIS set are most recommended this year based on the DBIR findings?

That’s not a bullseye because every company is different, but maybe they could do a recommended controls list for each industry or something.

Anyway, solid stuff as usual from he team. And I enjoyed the summary as well.

Notes


I imagine a lot of these questions were answered in the full version of the report. This is analysis of the executive summary.

__

I do a weekly show called Unsupervised Learning, where I curate the most interesting stories in infosec, technology, and humans, and talk about why they matter. You can subscribe here.

Robert Graham is Wrong About John Oliver Being Wrong About Net Neutrality

For example, he says that without Net Neutrality, Comcast can prefer original shows it produces, and slow down competing original shows by Netflix. This is silly: Comcast already does that, even with NetNeutrality rules.Comcast owns NBC, which produces a lot of original shows. During prime time (8pm to 11pm), Comcast delivers those shows at 6-mbps to its customers, while Netflix is throttled to around 3-mbps. Because of this, Comcast original shows are seen at higher quality than Netflix shows.

Comcast can do this, even with NetNeutrality rules, because it separates its cables into “channels”. One channel carries public Internet traffic, like Netflix. The other channels carry private Internet traffic, for broadcast TV shows and pay-per-view.

Source: Errata Security: John Oliver is wrong about Net Neutrality

Rob has come down on the wrong side of another issue—this time Net Neutrality.

He’s arguing here that Net Neutrality is a wide open issue, with smart people on both sides, and that John Oliver’s treatment was liberal unfairness. That wouldn’t be unprecedented, of course, and I think it’s something to remain vigilant against, but this isn’t a case in point.

Here are two different ways Rob got this violently wrong.

Net Neutrality isn’t about cable channels, it’s about the Internet. It’s about ISPs providing an internet connection and then throttling, tweaking, adjusting, blocking, and otherwise tampering with that connection based on their varied and constantly evolving business associations. In short, it’s saying they’re giving you one thing, which is unfettered access to the Internet, and then giving you something else entirely because it suits them financially.

He claims nothing negative has happened that would have required a Net Neutrality rule to be in place. But this is just false. There have already been numerous abuses that directly show the need for such legislation. We’ve had traffic throttled and outright blocked by ISPs because that traffic competed with services provided by the ISPs parent company. It was blatant and indisputable.

Rob is smart as hell, and he’s right about so many things.

Net Neutrality isn’t one of them.

__

I do a weekly show called Unsupervised Learning, where I curate the most interesting stories in infosec, technology, and humans, and talk about why they matter. You can subscribe here.

My Current Predictions for Thinking Machines

I’ve been thinking a lot about what I called “Getting Better at Getting Better” in my book. It’s the idea of accelerating machine intelligence, where computers aren’t just getting better at solving problems, but the pace at which they get better increases drastically. I think this comes in two forms:

Improved machine learning that improves as we provide higher quantities of quality data.
Evolutionary algorithms that use evolution to innovate.

I’m also reading a book called What to Think About Machines That Think, which is a collection of short thoughts by dozens of experts in various fields on whether computers will soon be able to think like—or better than—humans.

This spawned a few ideas of my own on the topic, but not being an expert in the area I was at first reluctant to capture them. But then I remembered that it’s ok to have raw thoughts as long as you have an appropriate respect for your limitations. So here are my current ideas on the topic of Thinking Machines.

First, I don’t think human intelligence is all that special. I think it’s a matter of complexity, connection counts, etc., and this seems to be what we’re observing with our massive breakthroughs in neural nets and Deep Learning. So it’s mostly a matter of complexity, which is now becoming technologically approachable.
Second, consciousness, as many experts have alluded to in neuroscience, philosophy, etc., is not a single special thing that sits on top of a mountain, but rather an emergent property of multiple, segmented components in the human brain that reach a certain level of complexity. Or as Daniel Dennett says, it’s simply a bag of tricks. Further, it’s my belief that this strange emergent property provided advantage by allowing one to experience and assign blame and praise, which provided tremendous advantage to early adopters who were creating communities. It’s also quite distinct from intelligence.
Third, the core game to be considered when looking at whether AI will become human-like is not intelligence or consciousness, but rather goals. Humans are unique in that our goals come from evolution. At their center they are survival and reproduction, and every other aspiration or ambition sits on top of and secondary to those drives. So in order to make something like a human, it seems to me that you’d have to create something where every component of its being is steeped in a similar sauce.

In other words, we were made over millions of years, step by step, with the goals of survival and reproduction guiding all successful iterations. So if we don’t want to end up with something extremely foreign to ourselves, we’ll need to somehow replicate that same process in machines. Failing to somehow emulate this process will likely result in a painted-on vs. baked-in feel to their goals and ambitions.

So when we talk about the mystery of human intelligence, or thinking machines (which usually means something that reminds us of ourselves), we’re really talking about three things:

Something smart.
Something conscious.
Something with a recognizable goal structure.

The key is realizing how distinct these three things are, and that our “humanness” seems to emanate from the combination of these things, not from one of them in particular.

Summary

So, human intelligence is just a matter of sufficient complexity, which we’re quickly approaching and will soon exceed. Consciousness is separate from intelligence, and will turn out to be a rather unremarkable hack caused by different parts of the brain working independently from each other. And the most difficult component of this entire “replicate humans” equation—instead of super-intelligence or consciousness—will actually end up being the creation of human-like (and human-aligned) goals.

OSS: Intelligence is easy, consciousness is a red-herring, and the hard problem is actually goal creation.

This is my current, non-expert prediction for how the “Thinking Machines” story will play out in coming years and decades.

Notes


Some of these ideas were inspired by Waking Up, by Sam Harris, multiple essays by Daniel Dennett on the nature of consciousness, and dozens of other books I’ve read on various orthogonal topics.
OSS = One Sentence Summary. I think we should be able to take anything interesting and make it a 1,000 page book, a 100 page book, a 1,000 word essay, or a one-sentence summary. I strive to keep this flexibility of explanation in anything I’m learning or trying to understand.
Because this is a prediction, and I love tracking and learning from being wrong, I’ll be updating the text in update sections below the original content and not changing the prediction itself.

__

I do a weekly show called Unsupervised Learning, where I curate the most interesting stories in infosec, technology, and humans, and talk about why they matter. You can subscribe here.

Some Thoughts on Thinking Machines

I’ve been thinking a lot about what I called “Getting Better at Getting Better” in my book. It’s the idea of accelerating machine intelligence, where computers aren’t just getting better at solving problems, but the pace at which they get better increases drastically. I think this comes in two forms: 1) improved machine learning that improves as we provide higher quantities of quality data, and 2) evolutionary algorithms that use evolution to innovate.

I’m also reading a book called What to Think About Machines That Think, which is a collection of short thoughts by dozens of experts in various fields on whether computers will soon be able to think like—or better than—humans.

This spawned a few ideas of my own on the topic, but not being an expert in the area I was at first reluctant to capture them. But then I remembered that I do that all the time, and that I just need to have an appropriate respect for my limitations. So here are some random ideas about the nature of human intelligence, whether machines will be able to achieve it, and similar topics.

First, I don’t think human intelligence is all that special. I think it’s absolutely a matter of the number of connections, and this seems to be what we’re seeing by improving the complexity of our neural nets, which have yielded extraordinary results in Deep Learning.
Second, consciousness, as many experts have alluded to in neuroscience, philosophy, etc., is not a single special thing that sits on top of a mountain, but rather an emergent property of multiple, segmented components in the human brain that reach a certain level of complexity. Or as Daniel Dennett says, it’s simply a bag of tricks. Further, it’s my belief that this strange emergent property provided advantage by allowing one to experience and assign blame and praise, which provided tremendous advantage to early adopters who were creating communities.
Third, the core game to be considered when looking at whether AI will become human-like is not intelligence or consciousness, but rather goals. Humans are unique in that our goals come from evolution. At their center they are survival and reproduction, and every other aspiration or ambition sits on top of and secondary to those drives. So in order to make something like a human, it seems to me that you’d have to create something where every component of its being is steeped in a similar sauce. In other words, we were made over millions of years, step by step, with the goals of survival and reproduction guiding all successful iterations. So if we don’t want to get something extremely foreign to ourselves, we’ll need to somehow replicate that same process in machines. The alternative would be a painted-on vs. baked-in feeling to their goals and ambitions, which I’m not sure would feel as authentic.

__

I do a weekly show called Unsupervised Learning, where I curate the most interesting stories in infosec, technology, and humans, and talk about why they matter. You can subscribe here.