Summary of “Why Robot Brains Need Symbols”

We need to be able to extend it to do things like reasoning, learning causality, and exploring the world in order to learn and acquire information.
In a series of tweets he claimed that I hate deep learning, and that because I was not personally an algorithm developer, I had no right to speak critically; for good measure, he said that if I had finally seen the light of deep learning, it was only in the last few days, in the space of our Twitter discussion.
To take another example, consider a widely-read 2015 article in Nature on deep learning by LeCun, Bengio, and Geoffrey Hinton, the trio most associated with the invention of deep learning.
The paper’s conclusion furthers that impression by suggesting that deep learning’s historical antithesis-symbol-manipulation/classical AI-should be replaced: “New paradigms are needed to replace the rule-based manipulation of symbolic expressions on large vectors.” The traditional ending of many scientific papers-limits-is essentially missing, inviting the inference that the horizons for deep learning are limitless.
Advances in narrow AI with deep learning are often taken to mean that we don’t need symbol-manipulation anymore, and I think that it is a huge mistake.
Why continue to exclude them? In principle, symbols also offer a way of incorporating all the world’s textual knowledge, from Wikipedia to textbooks; deep learning has no obvious way of incorporating basic facts like “Dogs have noses,” nor does it have a way to accumulate that knowledge into more complex inferences.
Symbols won’t cut it on their own, and deep learning won’t either.
If we want to stop confusing snow plows with school buses, we may ultimately need to look in the same direction, because the underlying problem is the same: In virtually every facet of the mind, even vision, we occasionally face stimuli that are outside the domain of training; deep learning gets wobbly when that happens, and we need other tools to help.

The orginal article.

Summary of “Philosopher Erich Fromm on the Art of Loving and What Is Keeping Us from Mastering It”

“To love without knowing how to love wounds the person we love,” the great Zen teacher Thich Nhat Hanh admonished in his terrific treatise on how to love – a sentiment profoundly discomfiting in the context of our cultural mythology, which continually casts love as something that happens to us passively and by chance, something we fall into, something that strikes us arrow-like, rather than a skill attained through the same deliberate practice as any other pursuit of human excellence.
That’s what the great German social psychologist, psychoanalyst, and philosopher Erich Fromm examines in his 1956 masterwork The Art of Loving – a case for love as a skill to be honed the way artists apprentice themselves to the work on the way to mastery, demanding of its practitioner both knowledge and effort.
Most people see the problem of love primarily as that of being loved, rather than that of loving, of one’s capacity to love.
The only way to abate this track record of failure, Fromm argues, is to examine the underlying reasons for the disconnect between our beliefs about love and its actual machinery – which must include a recognition of love as an informed practice rather than an unmerited grace.
The first step to take is to become aware that love is an art, just as living is an art; if we want to learn how to love we must proceed in the same way we have to proceed if we want to learn any other art, say music, painting, carpentry, or the art of medicine or engineering.
Maybe, here lies the answer to the question of why people in our culture try so rarely to learn this art, in spite of their obvious failures: in spite of the deep-seated craving for love, almost everything else is considered to be more important than love: success, prestige, money, power – almost all our energy is used for the learning of how to achieve these aims, and almost none to learn the art of loving.
In the remainder of the enduringly excellent The Art of Loving, Fromm goes on to explore the misconceptions and cultural falsehoods keeping us from mastering this supreme human skill, outlining both its theory and its practice with extraordinary insight into the complexities of the human heart.
Complement it with French philosopher Alain Badiou on why we fall and stay in love and Mary Oliver on love’s necessary madnesses.

The orginal article.

Summary of “The Intelligence of Plants”

“Trees do not have will or intention. They solve problems, but it’s all under hormonal control, and it all evolved through natural selection.” These “Magical” notions of plant intelligence are worrisome, he says, because people “Immediately leap to faulty conclusions, namely that trees are sentient beings like us.”
Writing in The Power of Movement in Plants, he concluded that the root of a plant has “The power of directing the movements of the adjoining parts” and thus “Acts like the brain of one of the lower animals; the brain being seated within the anterior end of the body, receiving impressions from the sense organs and directing the several movements.” Darwin was talking about how plants react to shifts in vibrations, sounds, touch, humidity, and temperature-but these are just adaptive reactions.
Recently, more findings have seemed to support-or at least point toward-a more restrained version of plant intelligence.
Plants may not be capable of identifying murderers in a lineup, but trees share their nutrients and water via underground networks of fungus, through which they can send chemical signals to the other trees, alerting them of danger.
If the plants were just acting evolutionarily, it would follow that they would compete for resources; instead, they seem to be “Thinking” of the other plants and “Deciding” to help them.
If plants can “Learn” and “Remember,” as Gagliano believes, then humans may have been misunderstanding plants, and ourselves, for all of history.
If we respected nature more-the power it has to not only be destroyed by us but to destroy us in turn-would we see more clearly how imbricated we are? Would we be more hesitant about growing plants in monocultures, genetically manipulating them for our pleasure, destroying forests? Would we try harder to protect the environment, if we understood that by protecting plants and trees we are protecting ourselves?
Why not consider that plants have been doing the same for far longer than we have been around, with an intelligence that is radically different from ours?

The orginal article.

Summary of “Curiosity Depends on What You Already Know”

What’s curious about curiosity is that it doesn’t seem to be tied to any specific reward.
“The theoretical puzzle posed by curiosity is why people are so strongly attracted to information that, by the definition of curiosity, confers no extrinsic benefit,” Loewenstein once wrote.
The Sweet Spot of Surprise: Like choosing a good book, curiosity leads us toward information that’s new, but not so new that it’s indecipherable.
Curiosity is less about what you don’t know than about what you already do.
Scientists who study the mechanics of curiosity are finding that it is, at its core, a kind of probability algorithm-our brain’s continuous calculation of which path or action is likely to gain us the most knowledge in the least amount of time.
That’s the funny thing about curiosity: It’s less about what you don’t know than about what you already do.
The sweet spot for curiosity seemed to be a Goldilocksian level of information-not too much nor too little.
Engineering Curiosity: AI researcher Varun Kompella programmed this iCub robot to seek unknown rewards as quickly as possible, using a probability algorithm to mimic biological curiosity.

The orginal article.

Summary of “Accelerate Your Learning Curve With These 5 Practical Tips”

You can spend 10,000 hours doing something and learn nothing.
That’s why I’m sharing 5 things that have worked for me in the past to accelerate my learning curve and learn skills faster.
When you start learning a skill, it must come from a place of humility and admiration for the practice.
Have respect for the skill you’re learning.
So when you’re learning a skill, your progress does not grow linearly over time.
“The more time I invest in something, the better I should get, right?” Unfortunately, learning skills don’t work that way.
We hit learning plateaus-and all of a sudden, we don’t get better.
Remember: When you accelerate your learning curve, you will still hit plateaus.

The orginal article.

Summary of “Baby Talk Study Translates What Babies Are Really Saying”

When babies babble they might be telling their parents exactly how to.
While scientists have understood for some time that baby talk helps infants learn to speak, it seems the student may actually be the master.
New research reveals that w.hen babies babble they could be changing how their parents interact with them to maximize learning potential.
Suggests that as infants reach different stages in development and change how they babble, moms and dads change how they baby talk.
If a baby starts babbling at a toy cow, mom and dad are probably going to practice saying the word or cow noises or both.
To get a better idea of the purpose of babies babbling, Elmlinger and his team observed 30 infant-mother pairs in a play space for two 30 minute increments, two days in a row.
Researchers measured parents’ syntax and vocabulary, as well as changes in how babies babbled from the first to the second day.
ADVERTISEMENT. “Babbling is a social catalyst for babies to get information from the adults around them,” Elmlinger said.

The orginal article.

Summary of “What Happened When I Tried to Learn Something New Every Day for a Month”

In attempt to challenge myself and step out of my comfort zone, I’ve decided to learn a new skill every day for a month.
I already learn new things everyday-reporting inherently prompts you to learn something each time you work on a story, even when it’s about an industry or topic that you’ve covered for years.
Thanks to Fast Company’s extensive coverage of brain science and its effect on productivity, I knew it wouldn’t be as simple as hitting up a new website, and I wanted at least the majority of the things I learned to fall into the category of useful skills.
So I parsed out a projected four weeks of learning roughly along the lines of cognitive and physical skills that ran the gamut between picking up some basic words and phrases of a new language and reciting poetry, to the aforementioned knitting, and the knife skills used in cooking.
I scheduled the more challenging ones for the beginning of the week, and on the weekends I gave myself the opportunity to just learn some fun facts.
From Learning a Language to How to Make Radish Flowers I started the challenge by trying to learn a few simple phrases in Hebrew since I am going to be attending a tech conference in Israel in September.
Making the Knowledge Stick What I tried to do while taking on these new skills and knowledge was to be mindful of how I was learning.
I discovered this through a report in The New York Times in which three experts confirmed that although children naturally learn languages more easily, adults can too, but it helps if the one they are trying to learn is in the same family as their first.

The orginal article.

Summary of “Every Interview Question is Really This Question”

Underlying each interview question is really a broader picture the interviewer is trying to fill out.
Recruiters really want to know if you are going to use your knowledge effectively to enhance the mission of the organization.
First, are you curious? The knowledge you have when you walk in the door is wonderful, but you don’t know everything you need to know to succeed in the long term.
Find ways to engage the interviewer in a discussion that brings up aspects of what you know that are relevant to the job you have applied for, and the organization, more broadly.
Interviewers want to know that you can handle whatever comes at you by drawing on what you have learned.
Third, can you admit what you need to learn? Part of being a successful knowledge worker is knowing what you know, and knowing what you still need to learn.
There will be times in an interview in which you get a question that you simply can’t answer.
Instead of slinging BS, consider coming out and saying that the question touches on something you don’t know much about yet.

The orginal article.

Summary of “These Are the Best Books for Learning Modern Statistics-and They’re All Free”

We shouldn’t be learning this stuff anymore-or, at least, it shouldn’t be the first thing we learn.
As a former data scientist, there is no question I get asked more than, “What is the best way to learn statistics?” I always give the same answer: Read An Introduction to Statistical Learning.
These two books, written by statistics professors at Stanford University, the University of Washington, and the University Southern California, are the most intuitive and relevant books I’ve found on how to do statistics with modern technology.
Number Crunchers The books are based on the concept of “Statistical learning,” a mashup of stats and machine learning.
Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens.
A section of An Introduction to Statistical Learning is dedicated to explaining the use of “Bootstrapping”-a statistical technique only available in the age of computers.
“While knowledge of those topics is very valuable, we believe that they are not required in order to develop a solid conceptual understanding of how statistical learning methods work, and how they should be applied,” says Daniela Witten, a coauthor of An Introduction to Statistical Learning.
The statistical learning tools are wonderful in themselves, but I’ve found they work best for people who are motivated by a personal or professional project.

The orginal article.

Summary of “Going Broad-Not Narrow-is the Best Route to Lasting Success”

I get press releases about “Learning hacks” on a weekly basis, which tells me there’s obviously widespread hunger for learning how to learn.
There are a small number of learning techniques that have extremely robust evidence behind them, and that in large part apply to both physical and cognitive learning.
The people studying learning and the people training and teaching seem to be hermetically siloed from one another, so we haven’t implemented those techniques as we should.
There’s no room to go into them in detail here, but I’ll say that the single most surprising study in the book, to me, was conducted at the U.S. Air Force Academy: The Academy provided a unique environment for studying the impact of teaching quality on learning, because students have to take the same sequence of courses and the same tests, and they are randomized to professors, and then re-randomized for each subsequent course, so you can truly track the impact of teaching.
Second, one of my favorite phrases in the book is from Herminia Ibarra, who studied how people find careers that fit them: “We learn who we are in practice, not in theory.” What she means is that there is this cultural notion she calls the “True-self model,” this idea that we can simply introspect or take a personality quiz and learn who we are.
To better understand your strengths, weaknesses, and interests, you actually have to try stuff-in other words, learn who you are in practice.
We don’t take enough time to reflect on what we’ve just done, even though it is a staple habit of the best learners.
Kaggle is a really neat one, that looks for outside solvers for machine learning problems-truly cutting edge stuff where it’s fascinating to see how much outside solvers can add.

The orginal article.