Daniel Miessler's Blog, page 106
December 30, 2017
The Real Internet of Things: Peer-to-peer Value Exchange
To benefit from the work I put into my typography, read natively at: The Real Internet of Things: Peer-to-peer Value Exchange.
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These are published chapters from my book The real Internet of Things, published on January 1st, 2017.
We’ve talked about how the future of work is largely person-to-person interaction mediated by a daemon-powered tech layer, but the peer-to-peer model goes far beyond employment.
What daemonized peer-to-peer really enables is less reliance on centralized institutions.
If you are in need of medical attention and there are 38,761 people within one square kilometer, it may not make sense to call on a centralized authority to provide that service. What if, upon injuring your leg in an accident, your DA could simply beacon out to nearby people. Less than 90 seconds later someone with the proper training, equipment, credentials, and ratings shows up and provides assistance. A micropayment of currency, appreciation, and a high rating is sent from daemon to daemon and the two people go on their way.
The same will apply to safety. Imagine a woman walking alone in a dangerous area and receiving a notification from her DA:
It’s not safe here. I’m getting you some company.
Within a few seconds she’s joined by three other people on the street (outlined in green within her view) who smile and walk with her to her destination. There is another exchange of appreciation, smiles, and/or currency, which is reflected on both sides.
Now think of how this could apply to building homes, providing fresh and healthy food, and many other core human needs. Institutions will still have a role, of course, but we the people are in fact the ultimate institution.
Daemonization will allow us to provide ourselves with what in the past needed to be abstracted. It’s bottom-up vs. top-down at the ultimate scale.
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Daniel
The Real Internet of Things: Desired Outcome Management
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These are published chapters from my book The real Internet of Things, published on January 1st, 2017.
Now that we’ve talked about the infrastructure for collecting, analyzing, and presenting information, we can move on to a concept I call Desired Outcome Management (DOM).
The assumption underpinning DOM is the simple claim that we want to improve things but we don’t know exactly how to go about it.
DOM provides a model for improving almost anything, and data plays one of the central roles.
DOM is broken into a few main components:
Define your goals. This could be for a business, a city, a family, a department, a country, a team, or an individual. Examples are things like: graduate from a top-10 university, make 100K/year, reach the top 10 ranking in quality of life, attain 150K in passive income, have a happy and fulfilled family, etc.
Define your model. A model in this case is a method or approach for attaining a goal or set of goals. For example, if you want to live a fulfilled life, there might be a Tony Robbins model, or a Dr. Phil model, or a model you make for yourself. It’ll have statements in it like, “You need to be healthy to be happy. You need to exercise. You need to eat plenty of raw foods, etc.”
Capture data. From there, you need to capture data about your entity’s behavior, from the real world, and get it into the system. So if you have a model that talks about diet, you need inputs regarding what you eat, how much you exercise, etc. If your model cares about grades in school, you need to get those grades into the system.
Provide Ratings. Next your system needs to provide clear ratings on how you’re doing in the various areas you’ve chosen to monitor. I prefer A through F, but you can use anything you want as long as it’s both clear and simple. Ratings will also include a composite, overall score for your progress vs. your goals.
Provide Recommendations. Finally, the system tells you exactly what to do to improve your ratings in the various areas and overall. So if you’re tracking health, for example, and you have a C in activity because you’ve been sedentary, the system will tell you what to do to improve it. It’ll give clear and prescriptive advice, such as, “Row 500 meters, do one set of push-ups, and one set of sit-ups every morning.” If you’re working on building a great team, the advice after a bad rating might be, “Have more frequent team meetings, and focus on building trust through reduced competitive focus.”
Adjustment. The last component of the system is the means by which the model can be updated. Updates to the system come in the form of modifications to the model. This can be addition, subtraction, or changes in importance for elements under consideration. For example, if you’re tracking a family’s health and happiness, a new study could come out that says shared laughter is crucial to individual happiness. This will be incorporated into the model and recommendations accordingly, based on the research. Similar adjustments will also be made to the model as new information about the world is made available to us.
The adjustment phase is where algorithms will be so crucial. Using machine learning, evolutionary algorithms, and still-undiscovered AI techniques we will continue to extract increasingly valuable insights from the data we have. And because of our access to realtime data through Universal Daemonization, the data being fed into these models will be continuous and fluid.
DOM is just a methodology—a name for a simple yet powerful concept.
Have goals
Have an approach to achieving them
Bring in data about the world
Rate how you’re doing
Recommend changes based on where you could improve
Adjust the approach based on new data
(optional and/or occasional) Ensure that your goals have not changed
This is a framework for using technology, data, and science to steward humanity’s progress forward.
Summary
It’s one thing to be able to capture data in realtime, move it around, and analyze it.
It’s quite another to be able to use that data to power your models for improving outcomes.
That’s what frameworks similar to DOM, realtime data, and machine learning and evolutionary algorithms will help us accomplish.
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Stay curious,
Daniel
The Real Internet of Things: Getting Better at Getting Better
To benefit from the work I put into my typography, read natively at: The Real Internet of Things: Getting Better at Getting Better.
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These are published chapters from my book The real Internet of Things, published on January 1st, 2017.
Once we are powered by realtime data and the infrastructure that makes use of it, the intelligence of our algorithms will become paramount.
Two areas seem particularly promising: machine learning and evolutionary algorithms.
Machine Learning
Machine Learning is basically the upgrade to our previous-best method of analyzing data—statistics. Where statistics are largely static (the model for extracting truth from data doesn’t improve as you add data), with machine learning the analysis actually improves itself automatically.
Machine Learning, in other words, is the ability for computers to learn without being explicitly programmed. And when you apply that to the algorithms doing realtime data analysis of trillions of objects, we can expect the results to be truly remarkable.
We’re not just learning about the world; we’re improving our ability to learn about the world automatically. And the more data we see the better it gets at improving itself.
Evolutionary Algorithms
As excited as I am about machine learning, I’m even more excited about evolutionary algorithms—especially when they’re eventually combined.
Evolutionary algorithms work by modeling evolution’s method of improving things. It has three basic steps:
Collect lots of different things together
Combine or mate them with each other
Introduce randomness into the output
Test that output against the environment to see what wins
Another way to say that is:
Descent with Modification
Natural Selection
That means lots of varied input, combined, random mutation, and then selection of winners.
It’s important that you have a good, varied pool to start with. It’s also important that you add randomness to the output step so that completely new things are created. And finally, it’s crucial that you have a good environment to test in (one that truly represents success or failure).
In nature this is easy—it’s just the real world the organism is trying to survive and reproduce in. In the digital world it’s a bit more complex.
But the concept is the same, and so is the benefit.
The promise of evolutionary algorithms is that they will allow us to create, very quickly, solutions that human designers couldn’t possibly conceive of (and definitely not in that span of time). They work by taking simple inputs, mating them together, adding some random component, and then automatically testing the output to see how successful that generation is. The winners go on and reproduce, with some randomness, and new outputs are tested again.
This is repeated through a number of generations until the line either dies out or something successful is created.
What’s so spectacular about this is that with constantly improving hardware, combined with better ways of modeling reality, we can go through thousands or millions of generations of evolution looking for solutions to our problems, all in minutes or hours. Using this technique we can potentially outperform the creative capabilities of billions of the smartest humans, doing their best on a problem for hundreds of years, all in the span of a few hours.
Now imagine that mechanism for improvement, i.e. the one that got single-celled organisms all the way to the point of being able to explore our solar system, and combine that with machine learning algorithms trained to improve the quality of the evolutionary algorithms.
It’s difficult to overstate the benefits that can come from being able to accelerate not just our ability to learn, but our ability to learn how to learn. That’s precisely what the combination of machine learning and evolutionary algorithms can do—both on their own and when used together to enhance each other.
Summary
Traditionally the best method we’ve had for learning about the world has been statistics, which are largely static; the analysis model doesn’t improve when you get more data.
With machine learning, the system gets smarter by itself, i.e., without needing to be reprogrammed.
Evolutionary algorithms leverage the power of descent with modification and natural selection to create and test possible solutions to problems that we never could as humans.
Combining these two—with machine learning improving the modeling and testing capabilities of evolutionary algorithms—may be one of the most powerful advances in technology we’ll see for the foreseeable future.
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Stay curious,
Daniel
The Real Internet of Things: The Four Components of Information Architecture
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These are published chapters from my book The real Internet of Things, published on January 1st, 2017.
There are many different information technologies that will be invented and adopted in the coming decades, but I believe there are four (4) primary categories that they will all fall into.
Realtime Data
Data Transfer
Analysis Algorithms
Presentation Interfaces
Realtime Data
As I spoke about in the realtime data chapter, knowledge of the current state of the world is extraordinarily empowering. It allows us to ask questions about the state of the world and adjust behavior as a result. The more realtime the better, and the more standardized and usable the format the better.
Data Transfer
Now that we have the data available, we need to be able to get it to the algorithms that will perform work on it. The protocols will have to be not only standardized, but built to allow trillions of tiny queries and updates, since even one object’s various state attributes could be changing in tens, dozens, hundreds, or thousands of times per second.
Analysis Algorithms
Once we have this data the focus turns to the algorithms that will do the analysis. As we talked about in the ‘Businesses as Daemons’ chapter, companies will largely compete as data analysis algorithms. Companies will largely have access to the same data; the question will be what you can do with that same data that gives you the competitive advantage.
Presentation Interfaces
Finally we have the output step. We’ve captured the realtime data, we’ve moved it to where it’ll be analyzed in a standard and efficient way, some company has done their unique analysis on it, and now we’re going to display it to someone or something. That’s presentation, and it will be another opportunity for companies to differentiate.
Creating the ability to track and present realtime data about objects (and ultimately the world) is hard. That’s an engineering problem. The other engineering problem is creating the protocols that will allow us to constantly poll and update objects for their state changes, which will be trillions per second in any large set of objects (like a company, or a city).
Those are efficiency and scalability problems.
The algorithm and presentation steps are significantly more creativity and innovation based. They are ultimately what will differentiate competitors in a long-term business market.
There will be innovators solving the engineering problems as well, but it’s infrastructure. It’s the connective tissue that enables the competition in the spaces of algorithmic analysis and presentation of results.
There is also the option for the output of one algorithm to be sent to one or many others as well, of course.
Summary
Realtime data is collected from the world.
The data gets evaluated by algorithms.
The output of those algorithms gets presented in some useful way.
The collection and transfer of the realtime data are engineering problems, and the analysis and presentation are creative/innovative problems.
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Stay curious,
Daniel
December 28, 2017
Your Mind’s Software is More Important Than Its Hardware
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Many people see intelligence as a function of brain power. So if you have a bigger brain, or smart people in your family, then you’re likely to be smart yourself.
I’m sure that’s true to some degree, but I think a better model is that of a computer…where you have two components working together: 1) the hardware, which is your actual brain, and 2) the software that runs on it, which is your education and experience.
One of the most descriptive characteristics of computer software and hardware is the frequency that it gets updated. In general, hardware doesn’t get upgraded often (or ever) before it’s replaced, while software is often updated many times during a product’s lifecycle. Importantly, it’s possible to have dramatically different capabilities and performance based on the software you’re running, even if the hardware is the same.
When it comes to success in life your mind’s software is far more important than its hardware.
We each run an operating system on our brain, and that operating system comes from the combination of formal education and life experience we’ve accumulated over our lifetimes.
To me it’s critical to understand that this operating system requires constant updates to be able to best manage life in our modern and constantly evolving society. Being successful in life is about processing information and making decisions, and if the world is changing your software has to change with it. Here are some mappings from education equivalent to operating system.
Elementary School –> Windows 98
High School –> Windows XP
Bachelor’s Degree –> Windows 10
Advanced Degree –> Ubuntu
Avid Reader –> Alpha Zero / IBM Watson
These are obviously tongue in cheek, but I think they reveal metaphorical truth regarding our limitations in processing the world. Here are some numbers around opinions held by Americans, for example.
One third of Americans don’t know any branches of government, and only a quarter know all three.
Only 12% of people running Windows 98 on their brains (High school diploma or less) believe that humans evolved without God being involved in the process. The number is almost three times as high for postgraduates (Ubuntu).
A quarter of Americans don’t know what country we declared our independence from.
For most facts like these, the percentage of people holding a ridiculous belief is directly correlated to their education level. This isn’t bad hardware. It’s bad software. And to fix it you don’t need a better brain—you need a better way of seeing and processing the world.
The metaphor for operating system isn’t perfect, but the general idea is the lack of bugs, limitations, and the ability to constantly update based on new information as you get to higher and higher levels.
What interests me most, however, is not making fun of people without college educations. That’s mean, stupid, and worst of all—unhelpful. What’s interesting and useful is realizing the importance of reading in all of this. Having a college education gets you to Windows 10 level, which is pretty decent, but many of the most famous and smartest people in the world are, first and foremost, voracious readers, and that’s what gets you to the IBM Watson / Machine Learning level of understanding.
You don’t really start getting old until you stop learning. Every book teaches me something new or helps me see things differently. I was lucky to have parents who encouraged me to read. Reading fuels a sense of curiosity about the world, which I think helped drive me forward in my career and in the work that I do now with my foundation. ~ Bill Gates
When Warren Buffet was asked what he owes his success to, he pointed at a stack of books and said:
Read 500 pages every day. That’s how knowledge works. It builds up, like compound interest. All of you can do it, but I guarantee not many of you will do it. ~ Warren Buffet
This is why I use the IBM Watson / Machine Learning metaphor for people who read a lot (let’s say more than 20 good books a year). It’s because they are constantly taking in the best input available and using that content to upgrade their model of how the world works.
College doesn’t give you this. College gives you base knowledge, and hopefully some idea of how to find more information and think about new inputs. But it doesn’t supply an infinite number of new lessons and experiences.
Only reading does that.
Another way to look at that is to think about how many good ideas and good books you consume while getting a bachelors, a masters, or even a PhD in college. 20? 30? 50? The number is probably quite low if you add them up, and if you become a significant reader of good books on similar topics you’re likely to catch up and exceed that knowledge level in a very short amount of time.
Being an aggressive reader is the equivalent of having a major computer upgrade purely through software—and if you’re relying on your old-school college education you’re effectively stuck on an OS that is no longer receiving patches.
Summary
Ok, so what’s the point here? What’s the takeaway?
The human mind can be thought of as a computer, complete with both hardware and software.
The software is more important than the hardware, so running Windows 10 on a 2 CPU computer with 128GB of memory is better than running Windows 98 on 24 CPUs and a terabyte of RAM.
The smartest people in the world aren’t just the people who went to college—they’re the people who read constantly.
If you’re someone without any formal education, try to get one as soon as possible, and ensure that all your loved ones do the same. It’s the equivalent of going from a 20-year-old OS to a modern one.
To get true state of the art information processing capabilities, you must make reading a central part of your life.
Notes
There are obviously different reasons why some people are unwilling and/or unable to initiate this software upgrade of education and/or reading, but those reasons are numerous and nuanced, and are thus worthy of their own post.
Thanks to Mark Cunningham for talking through some of these concepts with me.
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Stay curious,
Daniel
December 27, 2017
We Should Be Cautious When Building Evolutionary Algorithms
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A while back I wrote that Evolutionary Algorithms Could be More Significant Than Machine Learning. The reason for this is that they keep improving towards a specific goal, without additional input, by leveraging the powers of evolution, i.e., reproduction, variation, heritability, and differential success.
Reinforcement Learning is considered a subset of Machine Learning by many, so this isn’t technically a one-vs-other scenario.
This means that evolutionary algorithms, properly configured, can produce extraordinary results in a very short time, and the problem is that we won’t always what they’re going to create—or what impact that creation will have on society—before it happens.
Two recent examples:
Deep Blue took over 10 years, and required many experts and millions of dollars to create before it could beat a human at Chess. Alpha Go beat the best human at Go, but it took dozens of engineers giving their experience and programming, required 140 Google CPUs, and took hundreds of hours to finish. Alpha Go Zero beat Alpha Go in 3 days using only 4 CPUs and didn’t require any initial training whatsoever.
Facebook uses many of the concepts of an evolutionary algorithm to constantly adjust content and UI/UX to make people spend more time in the platform. We thought we were building a useful social media platform, but what we actually built was a giant sinkhole of human attention that seems to be causing significant mental health issues.
Bret Weinstein talked about this point of social media and addiction situation on Sam Harris’ podcast.
The obvious and much discussed case is Artificial General Intelligence (AGI), which will be able to use these same techniques to improve itself very quickly, but there are many more present, near-term, and realistic examples that we need to watch for as well.
The takeaway here is quite simple: we need to be very careful about creating anything that both self-improves and has significant interaction with humanity. Failing to do so can product all manner of harm to us, from an annoyance (really good advertising) to catastrophe (AGI without morality).
Use. Evolutionary Algorithms. Wisely.
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I spend 5-20 hours a week collecting and curating content for the site. If you're the generous type and can afford fancy coffee whenever you want, please consider becoming a member at just $10/month.
Stay curious,
Daniel
December 26, 2017
Analysis of Sam Harris’ Podcast With Bret Weinstein
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One of the few podcasts that I follow…religiously…is Waking Up, by Sam Harris. As a fan of Sam’s from the beginning of his public life, and someone who’s had a number of email exchanges with him over the years, I think his podcast format is just spectacular. He does one guest per show and takes them through where they agree, where they might disagree, he gets them to talk about their main topics they care about, and then gets them to comment on various important issues happening at the time.
Episode #109, with Bret Weinstein, was one of my favorites in a long time—both because of the quality of Bret as a guest (he’s an Evolutionary Biologist) and because of the level of interest I have in the topics they were discussing. The episode was so good that I decided to listen to it again, while taking notes, and this post will be me capturing various parts of it and commenting on them.
Some of this will be direct quotes, other parts will be me paraphrasing, and some of it could be somewhat incorrect since I’m not an expert in Bret’s field.
You don’t have Inuit marathon runners because they’ve been selected to retain heat, whereas Ethiopians and Kenyans have been selected to dissipate heat, and this should not be surprising or controversial.
Culture is units of information transmitted from one member of a species to another.
Humans dominate because we’ve offloaded more of the day to day processing required for success from our computer’s hardware (the brain) to the software (culture).
Humans are evolutionarily odd.
We are the most nurture-based creatures in history.
Our success is determined by nurture far more than any other creature on earth, and this is because it provides a distinct evolutionary advantage.
Humans are basically so amazing because we do so much more in software (which is easier to be upgraded) than in hardware.
Culture can move horizontally, but it normally moves vertically as it’s passed down from parents to offspring.
The cultural piece is every bit as biological and evolutionary as the genetic layer, and it’s a special trick deployed by the genetic layer to solve problems not solved by genetics alone.
Intersectionality is where each group who has some claim of oppression or discrimination against them has a special flavor to it, and the more of those groups you fall into (black, trans, etc.) the higher rank you are in a new, hypothetical world where oppressed people are in charge over their previous oppressors. The basic idea is that each group has some sort of discrimination against it, and if you’re in more of those groups then they stack up. Sam compared this to being a D&D party where you botch your spellcasting and harm the party.
Birth control changes everything in gender roles and gender politics.
Men and women are different. The biggest thing (from Bret) was that women are invested in their children because they can’t keep making kids. Males can keep having kids, so they’re tuned to keep taking risks to have more kids.
We have to grapple with the fact that the academe is producing people who not only know nothing, but who believe things that make them unable to learn.
— Bret Weinstein (@BretWeinstein) December 21, 2017
And they are teaching others. https://t.co/ztCEGwoFAQ
It’s not true that every non 50/50% profession has some measure of discrimination.
Gender is the software of sex, and sex is the hardware.
As a biologist, gender is interesting, but as a human the answer is simple: we have to have compassion.
Metaphorical truth is a belief that is factually wrong but you come out ahead if you believe in it.
Religion was valid metaphorical truth because it helped us before (for thousands of years). We’re now at a point where it (often) hurts more than it helps, but that doesn’t change how useful it was in the past.
Sectarian differences in doctrine is basically the same as evolution with mutations, and it’s not that they’re all fighting to win, just as it’s not true that all species are fighting to win. It’s more like they will all be tuned for their times and their environments. So we will co-exist being the best adapted species for that time and place.
The reason genetics has moved to using culture and memes is because it moves so incredibly fast.
Bret thinks kids are just as smart at birth, but that when they’re adults there are cognitive differences. I think that’s a nice idea.
Bret thinks that we need to be extremely cautious because of four characteristics that create adaptive evolution (reproduction, variation, heritability, differential success) which is such a powerful force that we cannot keep unleashing it on ourselves. An example is an evolutionary upgrade of social media sites that optimize for addictiveness. We created this system that adapts to get better and better at holding our attention, and now we have a societal problem with social media addiction.
His point is that you can’t simply make these systems and release them into the world because they’re so incredibly powerful and we’re not smart and thoughtful enough to know what they’re going to optimize for. And because we didn’t know what they’re going to do we obviously couldn’t have a smart conversation about whether or not this is good for us.
What you are describing is like one unnecessary regulation away from cake-utopia. https://t.co/Cr0TeUD20n
— Bret Weinstein (@BretWeinstein) December 25, 2017
If you stop eating farm animals they go extinct.
Wisdom requires delayed gratification. You have to go carefully.
The same parameter that makes a mother love her children also makes them commit genocide.
We have to understand that we’re all running according to this code and find a way to navigate.
We’re going to need drugs to figure out how to get out of our current predicaments because the current contents of our consciousness don’t seem to be enough.
We have to give up on evolution’s purpose for us, because it’s identical for everyone and bad. If you have the same purpose as a liver fluke, and a malaria virus, and a fig tree, we have to recognize that we need to evaluate that purpose and reject us. Good people want to live in a safe, anti-fragile society where we’re free to live their lives in a meaningful way.
Bret is encouraged that the Libertarian Left (liberty, not economics) will start coming out to oppose the Authoritarian Left that’s basically hijacked the non-right part of the spectrum in recent years.
There are a lot of great ideas here, but my favorites are:
Human success is so magnified because we’ve offloaded work from our brain hardware to our culture software.
That culture is passed down from parents to children, and helps a given group succeed even more than their genes.
The biggest difference between men and women is that women biologically have to invest in the children they have (because they can’t make more after menopause) whereas men can just keep taking risk to keep having more and more kids. This probably explains a lot in our society.
Biology is the hardware of sex, and gender is the software.
Metaphorical truth is untrue but useful, but (like religion) it eventually becomes outdated and starts doing more harm than good.
We need to watch out for any system where we release adaptive evolution because it will optimize for SOMETHING very quickly, and we are usually in such a rush that we either don’t know what that something is and/or we won’t have time to discuss whether it’s a good idea.
Evolution’s purpose for us is bad. It’s basically to survive and reproduce at the expense of everyone else. And it’s the same for viruses and trees and maggots. This is not any way to live a life, so we have to find something to replace it with.
Such an unbelievably rich podcast in terms of ideas, good faith, and potential for a better world.
I highly recommend subscribing to Sam’s podcast. Episodes like this make it absolutely worth it. Now I’m off to listen to his brother’s podcast that aired a bit before this one.
Notes
I went to the live one with Eric and Sam in SF as well. Quite good.
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Stay curious,
Daniel
December 23, 2017
Two Kinds of Human Meaning and Enjoyment
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The idea is that there are two ways for humans to derive happiness:
This will be a stream of consciousness thought process that will likely lead to an essay later.
By following tradition and ritual
By being curious and evidence-based
Humans have long been sustained by number 1. It’s what family and religion are based around, and to follow such doctrines and drives tends to produce the most powerful types of pleasure and fulfillment in us.
It’s only been the last couple of thousand years that we’ve started to derive great pleasure from questioning things, learning things, and searching for truth using reason rather than dogma.
This break from tradition has brought us great gifts, such as large societies, science, a greatly improved lifespan, less war, etc., but it’s also injected something non-ideal into the human psyche.
Doubt.
When you know God loves you, that your kids just need to be Christians or Jews or Muslims who marry others of the same kind, go to the same churches, act the same, eat the same foods, etc.—and all will be well, that’s comforting.
Following that template while having kids and growing your family gives a feeling of safety, and security, and bedrock.
It also produces the worst types of hatred and war because different groups of people inevitably have different sets of dogma, and since they’re all convinced theirs is correct they’re destined to go to war about it.
And then there are the groups who have discarded supernatural belief and dogma. They are atheists and agnostics who realized that the old ways were factually incorrect and the cause of extraordinary hatred, suffering, and bloodshed.
Their problem is that it’s quite hard to replace religion and dogma and tradition.
Yes, you can receive great joy from discovery, and from knowing you live a truth and equality based life. But it’s also more empty. It’s less fulfilling in some fundamental way.
Humans love ritual. They seem to need it. Tradition, ritual, patterns, absolute beliefs—these all produce profound happiness in us, and discarding them can be detrimental to both individuals and societies.
For me the obvious answer is that we have to move away from the first and towards the second, but because of what I’ve written above I think it’s crucial that we understand that we lose something when we do. We must find a way to maintain some part of the ritual/tradition system in the new curiosity and exploration one.
We have to find a way to give structure, and confidence, and ritual to the fundamental values of truth and equality and reason.
To ignore this requirement is to repeat the same mistake over and over again—pulling people from the pit of old-style religion only to offer little in return. This produces dissatisfaction and resentment in some, and a feeling of resigned emptiness in others.
We can and must do better.
We have to merge the systems into something superior to both.
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I spend 5-20 hours a week collecting and curating content for the site. If you're the generous type and can afford fancy coffee whenever you want, please consider becoming a member at just $10/month.
Stay curious,
Daniel
December 22, 2017
A List of Machine Learning (ab)Use Cases
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I keep thinking of interesting use cases for Machine Learning at various stages of its development (near and more distant future), and I figured I’d capture them.
I’ll also include common ones and interesting ones I’ve heard elsewhere.
Extract faces from video, and tell you 1) if they’re known criminals, 2) if their agitated in some way that could indicate a future problem, 3) what their current mood is, 4) if they’re gay or straight, 5) how smart they’re likely to be. All these things will be compiled into various predictions of behavior.
In addition to facial recognition, analysis will be done on the entire presentation of a human as they move through public places. This means their clothing, their body language, their gait, their facial expressions, any voice data that can be consumed and parsed for emotion, etc. All these will further enhance the prediction algorithms.
Realtime translation of what’s being said by someone in another culture or language (or even your own), with the option to adjust how much it interprets other signals like body language, tone, facial expression, etc. to augment the translation (Saša Zdjelar).
Public conversations will be parsed in realtime in public places, and sensitive content will be extracted and tied to individuals for the purposes of preventing crime. This will start out as terrorism prevention only, but will eventually make it down to regular crime as well. Not only will this be simple parsing of conversations via NLP and voice recognition, but analysis of the voice and tone will be combined with the content (and the person) to identify possible threat actors.
Analyze a large population of people in a popular public place and determine what the current fashion is at any given moment, and then based on how it’s changing predict what it will be soon (Benedict Evans).
Many of these might be disturbing for whatever reason. I’m capturing what’s inevitably going to be possible and implemented, not the moral implications thereof.
Analyze an industrial process that involves humans (or not) and identify opportunities for improvement (Benedict Evans).
Listen to the sounds in a household and recommend actions based on hidden problems. If there is tension in the air between two people (they’re stressed because of work, or they’ve had a fight), recommend a de-escalation technique to relax both sides.
Notice when someone is getting tired while reading or listening to something (based on observing their attention) and make adjustments to the content they’re being shown, e.g., make the text larger, add color to key points, slow the audio down or make it louder, or add pitch variation or an additional narrator voice, etc.
Read everything produced on the internet and surface great content that would have gone unnoticed otherwise. Basically, Crawlers + ML -> AI Reddit. You train the algorithms with content that everyone loved that was surfaced organically, and then find content that has the same je ne sais quoi on random blogs and podcasts with no followers. The result is the meritocratization of content.
Listen to speech patterns and identify early signs of dementia and other cognitive diseases.
Observe students while they read, write, play, and take exams, and then build them a custom curriculum based on the best ways for them to learn.
Watch a farm’s crop from above using drone footage and custom-build fertilization and pesticide treatment plan.
Using 360 degree sensors mounted on every person, monitor one’s surroundings 24/7, and alert the wearer of dangers around them, e.g., someone following with the intent to mug or attack them.
Using 360 degree sensors mounted on every person, monitor one’s surroundings 24/7, and alert the wearer when there is an opportunity for serendipity nearby, e.g., meeting a new lover, or making a business contact, based on shared interests or mutual acquaintances.
Parse all your activity for a given year, including what you read, watched, enjoyed, etc., and then build a customized Serendipity Calendar that incorporates the types of events you’d love, restaurants you should try, people you should meet, shows to watch, etc. They’re all automatically placed on your calendar, and you just trust the system, do the events, and marvel at how much delight you get from it.
Using 360 degree sensors mounted on every person, monitor all interactions with people and tell the wearer when the people around them are being deceitful, shy, flirtatious, or careful. When combined with A/MR this will allow for skins showing these various attributes in the regular field of vision.
Play the perfect song at the perfect moment, based on understanding the history of everyone present, key pauses and moments where it would have the most impact.
Optimize recommended route choices based on whether you’re in a hurry or have time to enjoy yourself (Saša Zdjelar).
Monitor the world’s telescope arrays (including amateurs’) for asteroids that could pose a threat to Earth. It’ll be like SETI, but for asteroid detection.
Review accounting records and detect fraud and abuse.
Monitor web traffic logs and detect which connections are bots, which are users attempting to do something fraudulent or malicious, and which are legitimate users.
Watch a workplace and find the correlations between healthy, productive employees and their behaviors vs. lethargic, underperforming employees and their behaviors. Then recommend policy changes to make the company more productive.
Monitor a company’s constantly updated data lake and detect malicious insiders, malicious external attackers, insecure configurations, dangerous business workflows, improper security controls around sensitive data, etc., and then recommend the controls that (based on up-to-date data for your type of organization) would reduce the most risk. Essentially, CISO as an Algorithm.
I wrote about many of these in my book titled The Real Internet of Things.
The unifying concept is the ability to use trillions of eyes and ears to constantly observe our universe in a way that humans never can, and then to find the patterns in that data and use it to improve human lives.
The primary themes here are ultimately oriented around human flourishing, and they are:
Detecting threats before they carry out their attacks.
Finding opportunities to improve or optimize a process.
Identify what makes humans happy, and find ways to nudge us in that direction through recommendations.
As many of the examples show, however, the technology can and will be abused. The tech itself will help achieve goals, but it’s up to us to make sure we’re trying to achieve the right ones.
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I spend 5-20 hours a week collecting and curating content for the site. If you're the generous type and can afford fancy coffee whenever you want, please consider becoming a member at just $10/month.
Stay curious,
Daniel
A List of Machine Learning (Ab)use Cases
To benefit from the work I put into my typography, read natively at: A List of Machine Learning (Ab)use Cases.
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[image error]
I keep thinking of interesting use cases for Machine Learning at various stages of its development (near and more distant future), and I figured I’d capture them. Here they are, and the list will grow as I think of more.
I’ll also include common ones and interesting ones I’ve heard elsewhere.
Extract faces from video, and tell you 1) if they’re known criminals, 2) if their agitated in some way that could indicate a future problem, 3) what their current mood is, 4) if they’re gay or straight, 5) how smart they’re likely to be. All these things will be compiled into various predictions of behavior.
In addition to facial recognition, analysis will be done on the entire presentation of a human as they move through public places. This means their clothing, their body language, their gait, their facial expressions, any voice data that can be consumed and parsed for emotion, etc. All these will further enhance the prediction algorithms.
Public conversations will be parsed in realtime in public places, and sensitive content will be extracted and tied to individuals for the purposes of preventing crime. This will start out as terrorism prevention only, but will eventually make it down to regular crime as well. Not only will this be simple parsing of conversations via NLP and voice recognition, but analysis of the voice and tone will be combined with the content (and the person) to identify possible threat actors.
[image error]
Analyze a large population of people in a popular public place and determine what the style is at any given moment, and then predict what it will be soon (Benedict Evans).
Many of these might be disturbing for whatever reason. I’m capturing what’s inevitably going to be possible and implemented, not the moral implications thereof.
Analyze an industrial process that involves humans (or not) and identify opportunities for improvement (Benedict Evans).
Listen to the sounds in a household and recommend actions based on hidden problems. If there is tension in the air between two people (they’re stressed because of work, or they’ve had a fight), recommend a de-escalation technique to relax both sides.
Notice when someone is getting tired while reading or listening to something (based on observing their attention) and make adjustments to the content they’re being shown, e.g., make the text larger, add color to key points, slow the audio down or make it louder, or add pitch variation or an additional narrator voice, etc.
Read everything produced on the internet and surface great content that would have gone unnoticed otherwise. Basically, Crawlers + ML -> AI Reddit. You train the algorithms with content that everyone loved that was surfaced organically, and then find content that has the same je ne sais quoi on random blogs and podcasts with no followers. The result is the meritocratization of content.
Listen to speech patterns and identify early signs of dementia and other cognitive diseases.
Observe students while they read, write, play, and take exams, and then build them a custom curriculum based on the best ways for them to learn.
Watch a farm’s crop from above using drone footage and custom-build fertilization and pesticide treatment plan.
Using 360 degree sensors mounted on every person, monitor one’s surroundings 24/7, and alert the wearer of dangers around them, e.g., someone following with the intent to mug or attack them.
Using 360 degree sensors mounted on every person, monitor one’s surroundings 24/7, and alert the wearer when there is an opportunity for serendipity nearby, e.g., meeting a new lover, or making a business contact, based on shared interests or mutual acquaintances.
Using 360 degree sensors mounted on every person, monitor all interactions with people and tell the wearer when the people around them are being deceitful, shy, flirtatious, or careful. When combined with A/MR this will allow for skins showing these various attributes in the regular field of vision.
(…in progress)
—
I spend 5-20 hours a week collecting and curating content for the site. If you're the generous type and can afford fancy coffee whenever you want, please consider becoming a member at just $10/month.
Stay curious,
Daniel
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