Summary of “A pioneering scientist explains ‘deep learning'”

Sejnowski, a pioneer in the study of learning algorithms, is the author of The Deep Learning Revolution.
Within machine learning are neural networks inspired by the brain, and then deep learning.
Deep learning algorithms have a particular architecture with many layers that flow through the network.
Deep learning is one part of machine learning and machine learning is one part of AI. What can deep learning do that other programs can’t?
There, Geoff Hinton and two of his graduate students showed you could take a very large dataset called ImageNet, with 10,000 categories and 10 million images, and reduce the classification error by 20 percent using deep learning.
The inspiration for deep learning really comes from neuroscience.
There’s an algorithm there called temporal differences, developed back in the ’80s by Richard Sutton, that, when coupled with deep learning, is capable of very sophisticated plays that no human has ever seen before.
As we learn more and more about how the brain works, that’s going to reflect back in AI. But at the same time, they’re actually creating a whole theory of learning that can be applied to understanding the brain and allowing us to analyze the thousands of neurons and how their activities are coming out.

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Summary of “Is Chronic Anxiety a Learning Disorder?”

Browning’s latest paper, Anxious individuals have difficulty learning the causal statistics of aversive environments, had been published in Nature Neuroscience just months before I arrived and I was excited to learn cutting-edge neuroscience in the city of dreaming spires.
Browning also noted that, reversing this logic, it also means that anxiety involves a learning process gone awry.
Learning theory describes how the brain builds models of the world, with the goal of understanding how to behave.
How much prediction errors sculpt your belief is called the learning rate.
Browning wanted a measure of how people learn; something tidy that he could discuss with a patient: “Mrs. Robinson, we’re concerned about your learning rate.”
Now consider your next step: are you going to ask her to sit in front of a computer and click on blue and green rectangles to win a pot of fake money? How much confidence would you have in such a clinical measure? Do you think you could persuade Barlow that her learning rate as measured by the box game has much bearing on her anxiety?
To see how volatility affected learning rate, they occasionally changed the likelihood of getting shocked.
Measurements of learning are still in the experimental stage, so it’s best to maintain a healthy skepticism, to have a healthy learning rate.

The orginal article.

Summary of “The five mentors every software developer needs and how to find them”

Mentor #1: The AnchorOne of the hardest parts of becoming a successful developer is keeping your motivation strong.
A great way to find this kind of mentor is in local events where other developers socialize.
As you make progress in your learning path, there will be days where you will wonder if you really have what it takes to be a successful developer.
Even though learning to code is a full-time job, I constantly remind the people that I personally mentor to make time to socialize with their families and friends.
Mentor #3: The MuseThis mentor is the one that will inspire you to achieve excellence at the craft of building software and will show you what’s possible once you unleash your full potential.
Mentor #4: The PartnerThis is not a typical role that you would expect from a mentor.
Mentor #5: YourselfFinally, you also need to become a mentor yourself.
Staying motivated and focused is the hardest part of learning to code, and mentors can play a big role in finding the support that you need.

The orginal article.

Summary of “If You Want To Help Your Child’s Brain Development, Start When They’re Born”

If You Want To Help Your Child’s Brain Development, Start When They’re Born According to a team of Harvard researchers, the key to addressing the achievement gap lies in connecting parents’ natural instincts with what we know about developmental science.
There’s a whole body of research on how caregivers can encourage brain development before a child starts any formal learning.
Research shows feeling safe can have a lasting influence on development.
“When you point at something, that helps the baby to start to associate words with objects,” Ferguson explains.
Babies love numbers and counting, and there’s research to show they’re actually born with math ability.
It’s never too early to start reading aloud – even with babies.
Back in Register’s class, she holds one of the babies and points to his head – and the developing brain inside.
It’s essentially the thesis behind all five of the Boston Basics: “Our babies are incredible,” she tells the new moms.

The orginal article.

Summary of “‘I want to learn Artificial Intelligence and Machine Learning. Where can I start?'”

I began looking into Machine Learning and Artificial Intelligence.
I’d built a little foundation with the Deep Learning Nanodegree, now it was time to figure out where I’d head next.
Some people learn better with books, others learn better through videos.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.How much math?The highest level of math education I’ve had was in high school.
There are many different opinions on how much math you need to know to get into machine learning and AI. I’ll share mine.
If you want to apply machine learning and AI techniques to a problem, you don’t necessarily need an in-depth understanding of the math to get a good result.
If you’re looking to get deep into machine learning and AI research, through means of a PhD program or something similar, having an in-depth knowledge of the math is paramount.
What does a machine learning engineer actually do?What a machine engineer does in practice might not be what you think.

The orginal article.

Summary of “Can Artificial Intelligence Be Smarter Than a Person?”

How is growing tall and falling on your face anything like walking? Well, both cover a horizontal distance pretty quickly.
“At the end of each season, these tall stalks of wheat fall over, and their seeds land just a little bit farther from where the wheat stalk heads started.”
From the perspective of the AI, it rapidly mutated in a simulated environment to discover something which had taken wheat stalks millions of years to learn: Why walk, when you can just fall? A relatable sentiment.
The stories in this paper are not just evidence of the dim-wittedness of artificial intelligence.
They are evidence of the opposite: A divergent intelligence that mimics biology.
A machine is more clever than its makers.
And machine learning in particular, does not think as a person does, perhaps it’s more accurate to say it evolves, as an organism can.
Thus, evolutionary biology displays a divergent and convergent intelligence that is a far better metaphor for to the process of machine learning, like generative design, than the tangle of human thought.

The orginal article.

Summary of “New AI Strategy Mimics How Brains Learn to Smell”

Deep neural networks were built to work in a similarly hierarchical way, leading to a revolution in machine learning and AI research.
As a car navigates a new environment in real time – an environment that’s constantly changing, that’s full of noise and ambiguity – deep learning techniques inspired by the visual system might fall short.
For a while, not much work was done to follow up on those findings – that is until very recently, when some scientists began revisiting the biological structure of olfaction for insights into how to improve more specific machine learning problems.
A fly might learn early on that it should approach the smell of a ripe banana and avoid the smell of vinegar, but its environment is complex and full of noise – it’s never going to experience the exact same odor again.
It could be used in tasks that involve navigation or memory, for instance – situations in which changing conditions might not leave the system with much time to learn or many examples to learn from.
Some researchers now hope to also use studies in olfaction to figure out how multiple forms of learning can be coordinated in deeper networks.
“I’m not quite sure how to improve deep learning systems at the moment.”
The olfactory circuit could act as a gateway to understanding the more complicated learning algorithms and computations used by the hippocampus and cerebellum – and to figuring out how to apply such insights to AI. Researchers have already begun turning to cognitive processes like attention and various forms of memory, in hopes that they might offer ways to improve current machine learning architectures and mechanisms.

The orginal article.

Summary of “Mr. Rogers vs. the Superheroes”

David Newell handled public relations for Fred Rogers and his production company, Family Communications, Inc., as well as playing Mr. McFeely, the “Speedy delivery” mailman character on Mister Rogers’ Neighborhood.
In a speech given at an academic conference at Yale University in 1972, Fred Rogers said, “The impact of television must be considered in the light of the possibility that children are exposed to experiences which may be far beyond what their egos can deal with effectively. Those of us who produce television must assume the responsibility for providing images of trustworthy available adults who will modulate these experiences and attempt to keep them within manageable limits.” Which is exactly what Rogers himself had tried to do with the production of Mister Rogers’ Neighborhood.
In a now-famous Rogers dictum, delivered in speeches and in his books, he advises adults: “Please, think of the children first. If you ever have anything to do with their entertainment, their food, their toys, their custody, their day care, their health, their education – please listen to the children, learn about them, learn from them.” When Fred Rogers and David Newell learned about the child who hurt himself trying to be a superhero, they came up with an idea: a special program to help kids grasp just what a fictional superhero is.
In the cab, they talked about whether one special program would be enough, or would they need a whole week on the topic? They started to plan a week of programs to explain superheroes to children, to help them separate fact from fantasy, just as Rogers had on the Neighborhood in the late 1960s and early 1970s.
Through the course of the week, Rogers used the Neighborhood of Make-Believe to explore the fantasies of children: the puppet Ana Platypus thinks she can fly, and has to be caught by Lady Aberlin; Prince Tuesday alternates between delusions of superpower and fears.
During the whole week, Mister Rogers manages to slow the world down and reduce some of the most confusing and troubling apprehensions of children to calm, thoughtful, and simple explication.
One of Rogers’s film editors, Pasquale Buba, explains that Rogers deliberately lengthened scenes as the theme week progressed, so that the children would get used to an environment that extended their attention spans as they became more and more familiar with the story line.
Early in the evolution of Mister Rogers’ Neighborhood, Rogers offered this definitive observation to a meeting of the American Academy of Child Psychiatry: “It’s easy to convince people that children need to learn the alphabet and numbers…. How do we help people to realize that what matters even more than the superimposition of adult symbols is how a person’s inner life finally puts together the alphabet and numbers of his outer life? What really matters is whether he uses the alphabet for the declaration of war or the description of a sunrise – his numbers for the final count at Buchenwald or the specifics of a brand-new bridge.”

The orginal article.

Summary of “Is the emperor wearing clothes?”

Machine learning uses patterns in data to label things.
AlgorithmBy picking a machine learning algorithm to use, we’re picking the type of recipe we’re going to get.
The purpose of a machine learning algorithm is to pick the most sensible place to put a fence in your data.
If you thought about drawing a line, congratulations! You just invented a machine learning algorithm whose name is perceptron.
Yeah, such a sci-fi name for such a simple thing! Please don’t be intimidated by jargon in machine learning, it usually doesn’t deserve the shock and awe the name inspires.
The purpose of a machine learning algorithm is to pick the most sensible place to put the fence, and it decides that based on where your datapoint have landed.
Jargon in machine learning usually doesn’t deserve the shock and awe the name inspires.
If you’re an applied machine learning enthusiast, it’s okay if you don’t memorize them - in practice you’ll just shove your data through as many algorithms as you can and iterate on what seems promising.

The orginal article.

Summary of “The Best Python Books”

Check out the Best Python Books for Kids for resources aimed at a younger audience.
The best intermediate and advanced Python books provide insight to help you level up your Python skills, enabling you to become an expert Pythonista.
This section focuses on the first of these two scenarios, with reviews of the books we consider to be the best Python programming books for readers who are new to both programming and Python.
What I like best about Real Python is that, in addition to covering the basics in a thorough and friendly way, the book explores some more advanced uses of Python that none of the other books hit on, like web-scraping.
Any of the books in this section will give you a deeper understanding of Python programming concepts and teach you how to write developer-style Python code.
To gain a solid foundation, you really can’t go wrong with any of the best books to learn Python.
If you want to learn Python with a child, or maybe teach a group of kids, check out the list of best Python books for kids.
After you’ve got your feet wet, check out some of the best intermediate and advanced Python books to dig in deeper to less obvious concepts that will improve the efficiency of your code.

The orginal article.