Summary of “How I Rewired My Brain to Become Fluent in Math”

If there were a textbook example of the potential for adult neural plasticity, I’d be Exhibit A. Learning math and then science as an adult gave me passage into the empowering world of engineering.
My doctoral training in systems engineering-tying together the big picture of different STEM disciplines-and then my later research and writing focusing on how humans think have helped me make sense of recent advances in neuroscience and cognitive psychology related to learning.
In the years since I received my doctorate, thousands of students have swept through my classrooms-students who have been reared in elementary school and high school to believe that understanding math through active discussion is the talisman of learning.
In the United States, the emphasis on understanding sometimes seems to have replaced rather than complemented older teaching methods that scientists are-and have been-telling us work with the brain’s natural process to learn complex subjects like math and science.
There is an interesting connection between learning math and science, and learning a sport.
I learned these things about math and the process of learning not in the K-12 classroom but in the course of my life, as a kid who grew up reading Madeleine L’Engle and Dostoyevsky, who went on to study language at one of the world’s leading language institutes, and then to make the dramatic shift to become a professor of engineering.
In my case, from my experience becoming fluent in Russian as an adult, I suspected-or maybe I just hoped-that there might be aspects to language learning that I might apply to learning in math and science.
As I look today at the shortage of science and math majors in this country, and our current trend in how we teach people to learn, and as I reflect on my own pathway, knowing what I know now about the brain, it occurs to me that we can do better.

The orginal article.

Summary of “Learning Chess at Forty”

I would explain that I too was learning to play, and the resulting tone was cheerily patronizing: Good luck with that! Reading about an international tournament, I was struck by a suggestion that a grandmaster had passed his peak.
Magnus Carlsen, the world’s current top-ranked player, was the youngest player to reach number one, at age 19.
“If you’re talking about two novices,” Charness said, when I asked him about my daughter, me, and chess, “Your daughter would probably pick things up about twice as fast as you could.” My daughter is, in effect, learning chess like a first language, whereas I am learning it like a second language.
After what seemed a particularly disastrous move, I would try to play coach for a moment, and ask: Are you sure that’s what you want to do? She would shrug.
Even if I was only learning chess for the first time, I had a lifetime of play behind me.
Chess, especially played at the top levels, can encompass both fluid and crystallized intelligence-one needs the firepower to quickly think through a novel position, but it also helps to draw upon a deep reservoir of past games.
As Daniel King, a London-based retired professional chess player who now analyzes and commentates chess matches, tells me, “Children just kind of go for it-that kind of confidence can be very disconcerting for the opponent.” Lacking larger representational “Schema,” the psychologist Dianne Horgan has noted, children players rely more on simple heuristics and “Satisficing,” choosing the first good-looking move.
She played, in those games, as if I were just some lower-level chess engine making haplessly random moves.

The orginal article.

Summary of “The First Lesson of Marriage 101: There Are No Soul Mates”

Nowadays, when colleges and universities offer courses on the topic of marriage, rather than explicitly offering practical marriage advice, they often survey the institution of marriage from a historical point of view or look at larger sociological trends.
Today’s marriage education classes are most often aimed at high-school students, usually as part of a home economics or health class, where teens are taught how family structure affects child well-being, learn basic relationship and communication skills, or are required to carry around a sack filled with flour for a week so they can learn what is entailed in being responsible for a baby 24 hours a day.
Other courses are taught at specifically religious colleges, or are meant for engaged couples, like Pre-Cana, a marriage prep course required of all couples desiring to marry in a Catholic church.
Northwestern’s Marriage 101 is unique among liberal arts universities in offering a course that is comprehensively and directly focused on the experiential, on self-exploration: on walking students through the actual practice of learning to love well.
While popular culture often depicts love as a matter of luck and meeting the right person, after which everything effortlessly falls into place, learning how to love another person well, Solomon explains, is anything but intuitive.
“The foundation of our course is based on correcting a misconception: that to make a marriage work, you have to find the right person. The fact is, you have to be the right person,” Solomon declares.
To help students recognize what has shaped their views on love, she and her colleagues have students extensively interview their own parents about their own relationship.
Maddy Bloch, who took the course two years ago along with her boyfriend at the time, learned a lot when she interviewed her own parents about their own marriage, despite the fact that they are divorced.

The orginal article.

Summary of “Why Feedback Rarely Does What It’s Meant To”

The search for ways to give and receive better feedback assumes that feedback is always useful.
Feedback is about telling people what we think of their performance and how they should do it better-whether they’re giving an effective presentation, leading a team, or creating a strategy.
If you’re in sales, how can you possibly close deals if you don’t learn the competency of “Mirroring and matching” the prospect? If you’re a teacher, how can you improve if you don’t learn and practice the steps in the latest team-teaching technique or “Flipped classroom” format? The thought is that you can’t-and that you need feedback to develop the skills you’re missing.
Another of our collective theories is that feedback contains useful information, and that this information is the magic ingredient that will accelerate someone’s learning.
Watch an NBA game, and you may think to yourself, “Yes, most of them are tall and athletic, but boy, not only does each player have a different role on the team, but even the players in the same role on the same team seem to do it differently.” Examine something as specific and as limited as the free throws awarded after fouls, and you’ll learn that not only do the top two free-throw shooters in history have utterly different styles, but one of them, Rick Barry-the best ever on the day he retired-didn’t even throw overhand.
Since excellence is idiosyncratic and cannot be learned by studying failure, we can never help another person succeed by holding her performance up against a prefabricated model of excellence, giving her feedback on where she misses the model, and telling her to plug the gaps.
If we continue to spend our time identifying failure as we see it and giving people feedback about how to avoid it, we’ll languish in the business of adequacy.
CONCLUSION How to give people feedback is one of the hottest topics in business today.

The orginal article.

Summary of “The Biggest Wastes Of Time We Regret When We Get Older”

When I look back, my biggest time regrets aren’t spending too much time on Twitter or mismanaging my daily tasks.
Not only did I look like an arse, I could’ve also saved a fair amount of time that day by simply asking my boss what he meant.
Like a lot of people, I made some common bad decisions that wasted both my time and the time of the person I was with.
Every time the thought comes back, simply remind yourself that you have already been forgiven, so there’s no reason to feel bad anymore.
It’s easy to waste time worrying about other people, too.
Don’t get me wrong – your friends and loved ones mean a lot to you, and you want to spend time nurturing them.
Regret is another big waste of time, so there’s no point in beating yourself up over these.
The sooner you learn from them the sooner you can free up your time and energy to live the life you want.

The orginal article.

Summary of “Why are Machine Learning Projects so Hard to Manage?”

One constant is that machine learning teams have a hard time setting goals and setting expectations.
In the first week, the accuracy went from 35% to 65% percent but then over the next several months it never got above 68%. 68% accuracy was clearly the limit on the data with the best most up-to-date machine learning techniques.
My friend Pete Skomoroch was recently telling me how frustrating it was to do engineering standups as a data scientist working on machine learning.
Engineering projects generally move forward, but machine learning projects can completely stall.
Machine learning generally works well as long as you have lots of training data *and* the data you’re running on in production looks a lot like your training data.
Machine Learning requires lots and lots of relevant training data.
What’s Next?‍.The original goal of machine learning was mostly around smart decision making, but more and more we are trying to put machine learning into products we use.
As we start to rely more and more on machine learning algorithms, machine learning becomes an engineering discipline as much as a research topic.

The orginal article.

Summary of “Don’t Know What You Want? Improve These 7 Universal Skills”

What does success look like? What do you want from life? What career do you want?
We think it’s the worst thing in the world if you don’t know what you want to do in life.
One of the biggest thinking errors that I’ve made was that I thought I needed to know what I exactly wanted to do with my life.
The truth is that no one knows what they truly want.
So it’s not important to know exactly what you want to do with your life.
It’s not even realistic to boldly claim “I know what I want!”.
If you can’t decide what direction you want to go in life, that’s automatically your #1 goal in life – to figure out where you want to go.
Persuasion: Learn how to get what you want in an ethical way.

The orginal article.

Summary of “The Play Deficit”

In a book called The Play of Animals, Groos argued that play came about by natural selection as a means to ensure that animals would practise the skills they need in order to survive and reproduce.
It explains why young animals play more than older ones and why those animals that depend least on rigid instincts for survival, and most on learning, play the most.
Lion cubs and other young predators play at stalking and pouncing or chasing, while zebra colts and other prey species play at fleeing and dodging.
Groos followed The Play of Animals with a second book, The Play of Man, in which he extended his insights about animal play to humans.
In hunter-gatherer bands, at Sudbury Valley School, and everywhere that children have regular access to other children, most play is social play.
Preschoolers playing a game of ‘house’ spend more time figuring out how to play than actually playing.
Social play is by far the most effective venue for learning such lessons, and I suspect that children’s strong drive for such play came about, in evolution, primarily for that purpose.
We think of play as childish, but to the child, play is the experience of being like an adult: being self-controlled and responsible.

The orginal article.

Summary of “The state of AI in 2019”

Experts refer to this specific instance of AI as artificial general intelligence, and if we do ever create something like this, it’ll likely to be a long way in the future.
No one is helped by exaggerating the intelligence or capabilities of AI systems.
It’s better to talk about “Machine learning” rather than AI. This is a subfield of artificial intelligence, and one that encompasses pretty much all the methods having the biggest impact on the world right now.
How does machine learning work? Over the past few years, I’ve read and watched dozens of explanations, and the distinction I’ve found most useful is right there in the name: machine learning is all about enabling computers to learn on their own.
If you’re not explicitly teaching the computer, how do you know how it’s making its decisions? Machine learning systems can’t explain their thinking, and that means your algorithm could be performing well for the wrong reasons.
Teaching computers to learn for themselves is a brilliant shortcut – and like all shortcuts, it involves cutting corners Teaching computers to learn for themselves is a brilliant shortcut.
There’s intelligence in AI systems, if you want to call it that.
Kai-Fu Lee, a venture capitalist and former AI research, describes the current moment as the “Age of implementation” – one where the technology starts “Spilling out of the lab and into the world.” Benedict Evans, another VC strategist, compares machine learning to relational databases, a type of enterprise software that made fortunes in the ’90s and revolutionized whole industries, but that’s so mundane your eyes probably glazed over just reading those two words.

The orginal article.

Summary of “Scientific American Blog Network”

My first semester I took atomic physics with Professor Delroy Baugh, self-proclaimed “Laser Guy.” I’d never taken a physics course before in my life, and as a reward for my willingness to transcend my comfort zone I received a D. Somewhere between the two of us lies a sweet spot: if you only ever get 100 percent on your tests, they aren’t hard enough.
If you never get above 50 percent, you’re probably in the wrong major.
An article from a team led by University of Arizona cognitive scientist Robert Wilson provides an answer: 15 percent.
The researchers argue that a test is optimally difficult if the test-taker gets 85 percent of the questions right, with 15 percent incorrect.
Under loose assumptions, they show that the optimal error rate for training a broad class of deep learning algorithms is 15 percent.
If you’re a teacher, your tests should be difficult enough that the average score is 85 percent.
Outside the classroom, the implications of the 85 percent rule are similar.
If you are learning a new language, say on Duolingo, then you should be getting about 15 percent of the answers wrong.

The orginal article.