This is Alan Turing. The first time I ever heard his name was in a computer science class, where we studied different kinds of basic machines and how they work.
This is Alan Turing. The first time I ever heard his name was in a computer science class, where we studied different kinds of basic machines and how they work.
After millions of years of evolutionary trial and error, or natural selection as Charles Darwin put it, the homo sapiens proved to be the dominant species. Was this the case because humans were expert risk takers or fear conquerors? Quite the opposite actually.
My least favorite moment in all of cinema is a relatively common one. You will recognize it, I’m sure, from dozens of movies and TV shows that prominently feature scientists. You may even have laughed at it once or twice. It usually gets a quick chortle. The moment goes something like this:
I’ve worked with deploy systems in the past that have a prominent “rollback” button, or a console incantation with the same effect. The presence of one of these is reassuring, in that you can imagine that if something goes wrong you can quickly get back to safety by undoing your last change.
Welcome to the second stepping stone of Supervised Machine Learning. Again, this chapter is divided into two parts. Part 1 (this one) discusses about theory, working and tuning parameters. Part 2 (coming soon) we take on small coding exercise challenge.
Google’s rollout of artificial intelligence has many in the search engine optimization (SEO) industry dumbfounded. Optimization tactics that have worked for years are quickly becoming obsolete or changing.
As a machine learning acolyte, I spent probably as much time trying to understand things like how and when to use machine learning as I did understanding the technical details of machine learning itself. Unfortunately, most of the discussion around machine learning is about the former.
A year and a half ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead.
Estimated reading time: 12 minutes. I’m an expert on how technology hijacks our psychological vulnerabilities. That’s why I spent the last three years as a Design Ethicist at Google caring about how to design things in a way that defends a billion people’s minds from getting hijacked.
TL;DR: Better Gaming is an online casino games technology and development company building both a platform and a suite of high quality leading casino games. Funded by angel tech investors — and now ready to ‘show n tell’.
Andy Grove was a Hungarian refugee who escaped communism, studied engineering, and ultimately led the personal computer revolution as the CEO of Intel. He died earlier this year in Silicon Valley after a long fight with Parkinson’s disease.
Disclaimer: I’m not an expert in neural networks or machine learning. Since originally writing this article, many people with far more expertise in these fields than myself have indicated that, while impressive, what Google have achieved is evolutionary, not revolutionary.
Seven months ago, I sat down at the small table in the kitchen of my 1960s apartment, nestled on the top floor of a building in a vibrant central neighbourhood of Tehran, and I did something I had done thousands of times previously. I opened my laptop and posted to my new blog.
I love to write about face recognition, image recognition and all the other cool things you can build with machine learning. Whenever possible, I try to include code examples or even write libraries/APIs to make it as easy as possible for a developer to play around with these fun technologies.
The computing industry progresses in two mostly independent cycles: financial and product cycles. There has been a lot of handwringing lately about where we are in the financial cycle. Financial markets get a lot of attention. They tend to fluctuate unpredictably and sometimes wildly.
At Airbnb, we are always searching for ways to improve our data science workflow. A fair amount of our data science projects involve machine learning, and many parts of this workflow are repetitive. These repetitive tasks include, but are not limited to:
I’ve been working in software development for twenty-eight years. My current position is Senior Development Director at a software consulting company in Austin, Texas, a position I’ve held for just over six years.
Containers are already adding value to our proven globally available cloud platform based on Amazon EC2 virtual machines. We’ve shared pieces of Netflix’s container story in the past (video, slides), but this blog post will discuss containers at Netflix in depth.
It’s early and dark. The alarm sounds, and you reach over to switch it off. After a short pause, you sit up. You swing your legs off the bed, touch the floor with your feet, and reach for your phone. You sit quietly while your phone’s screen illuminates the dark bedroom.
Kindness: If you are giving back you’ve already taken too much. Evolve and grow: Life’s about progress, we can either move forward and relentlessly improve or be consumed and surpassed by the horde which stands in wait behind us. Standing still is proportionate to regression.
For the next minute or so, I want you to forget about CSS. Forget about web development. Forget about digital user interfaces. And as you forget these things, I want you to allow your mind to wander. To wander back in time. Back to your youth. Back to your first day of school.
You know, thinking, worrying, stressing, freaking out — call it whatever you want. I call it a preoccupied mind. And with what? All my life I’ve been obsessed with practical things. Practical philosophy, practical knowledge, practical books, practical work, and practical advice.
To become more more successful at everything you do in life, you need to do three things: reduce the amount of time you waste, be more organized, and get rid of the “mental clutter” that distracts you, preoccupies you, and stresses you out.
Distilling a generally-accepted definition of what qualifies as artificial intelligence (AI) has become a revived topic of debate in recent times. Some have rebranded AI as “cognitive computing” or “machine intelligence”, while others incorrectly interchange AI with “machine learning”.
We all care about what others think of us and want to be liked (despite what rebellious 15-year-old you might have said). The basics of getting people to like you are obvious — be nice, be considerate, be a decent human being. Those things are all true.
Take a look at the image below. It’s a collection of bugs and creepy-crawlies of different shapes and sizes. Take a moment to categorize them by similarity into a number of groups. This isn’t a trick question. Start with grouping the spiders together.
Machine learning is on the edge of revolutionizing those 12 sectors. Most leaders in those industries look at Machine Learning and see a non-stable, none viable technology in the short term. They are wrong. This will allow technological Entrepreneurs to disrupt them.
A few months ago, my friend Tim took a new sales job at a Series C tech company that had raised over $60 million from A-list investors. He’s one of the best salespeople I know, but soon after starting, he emailed me to say he was struggling.
In his backpack, Wouter Slotboom, 34, carries around a small black device, slightly larger than a pack of cigarettes, with an antenna on it. I meet Wouter by chance at a random cafe in the center of Amsterdam. It is a sunny day and almost all the tables are occupied.
What can neuroscience teach us about the brains of software developers? A lot. Software development is among the fastest growing jobs in America — projected to grow 17% from 2014–2024 (much faster than the average job growth rate, a projected 7% change from 2014–2024).
One of the most challenging aspects of creative work is, well, sitting down to actually do it. There are so many different ways to cull out one’s creativity.
That quote kickstarted my own reading habits and helps me regularly read over 100 books a year. Charlie Munger is the billionaire business partner of Warren Buffett and the Vice Chairman at Berkshire Hathaway, one of the largest companies in the world.
On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago.
Out all the different ways to learn, books remain my favorite way to absorb knowledge. What I like about books is that I can read them by myself, in silence. It’s me and the author, one on one, having a conversation in my mind.
Three weeks ago, the CMO of a San Francisco startup backed by A-list investors emailed me about her new sales deck. “It lacks oomph,” she said. “The information is there. The slides look great. But we’re not telling a compelling story. Can you help?”
I have a friend who quit eating sugar two years ago. When we go out to eat, I almost always get dessert. “Will you have a bite?” I ask, testing her. “No thanks!” She always responds with ease and indifference. Must be so hard for her to turn down a beignet every single time — right?
If there is one technology that promises to change the world more than any other over the next several decades, it is arguably machine learning.
It’s the first week of 2017, and Science of Us is exploring the science that explains how people make meaningful changes in their lives. Handy information for resolution season. Being a human is hard.
Reading is a huge key to success and wealth, but how can you actually benefit from this habit as a busy adult? I’ve said it many times: reading books is a major key to success. The mega-rich and successful like Bill Gates and Elon Musk devote extraordinary amounts of their time to reading.
My framework for getting places, accomplishing things and living in a way that makes me happy. This isn’t a bullshit, head in the clouds, you can do it if you just *believe*post. There’s plenty of those out there. I’m not going to write another one.
I’ve seen a few CS students fearful about the industry they’ll enter into when they graduate. And with all the recent tech news, who can blame them? Why am I even still here? This is my career retrospective — what has been great, what has been horrible, why I’m still here & fighting.
Oliver Emberton said that. It’s profound and so true. Urgency wrecks productivity. Urgent but unimportant tasks are major distractions. Last-minute distractions are not necessarily priorities.
Last week, I visualized my new workout partner. Today, I’m benching 20 lbs more than I ever have before. A few months ago, I noticed a 50-year-old man at the gym lifting twice what I lift in every exercise. His energy and overall vibes inspired me.
Want to find out all the things Google knows about you? Here are 6 links that will show you some of the data Google has about you. In order to serve relevant ads, Google collects data about you and creates a profile. You can control and review the information Google has on you here:
Starting a design project is hard. It doesn’t matter if you’re a freelance designer, work for a hot product agency, or help support a large enterprise design team…it’s daunting.
I’d spent six years between 2004–2010 on getting two degrees. And after that, I immediately started a business. And during my first two years as an entrepreneur, I also learned a lot. But after a while, I thought: Who needs education? Just start a business or get a job and earn some money.
Sound familiar? Looking back, I realize I used my work to try and fill a void in myself. The problem was that this void was like a black hole. No matter how many hours I worked, it never seemed to fill it up. If anything, it made me feel worse.
For this tutorial in my Reinforcement Learning series, we are going to be exploring a family of RL algorithms called Q-Learning algorithms. These are a little different than the policy-based algorithms that will be looked at in the the following tutorials (Parts 1–3).
Update: This article is part of a series. Check out the full series: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7 and Part 8! You can also read this article in 普通话, Русский, 한국어, Português, Tiếng Việt or Italiano.
According to new research, practice doesn’t make actually make perfect. Whether you’re trying to be pro at Photoshop, or step up your tennis game, or master a dueling banjo song, you’re probably dutifully following the age-old advice that practice makes perfect.
It’s 3 AM on a warm Thursday night in December, a usually quiet street in the Gothic Quarter in Barcelona is bustling with activity, as a cohort of 200 artificial intelligence researchers leave in single-file out of a sprawling yellow mansion.
Lately, I needed to come up with some top level principles for the product I’m currently working on. I seek for some simple yet powerful concepts that will guide our team design decisions and break stalemates in discussions.
As a strategic messaging and positioning consultant, I preside over lots of contentious meetings. They go with the territory: Sometimes it’s just really hard to get leaders of high-profile startups to agree on a single version of their strategic story.
This past week, I was in a Lyft. My driver was telling me about all of her ideas for side projects. She had ideas for a children’s book, an app that helps people find parking, and a more efficient way to package gifts. The problem was she was frozen by indecision.
1. Have a firm handshake.2. Look people in the eye.3. Sing in the shower.4. Own a great stereo system.5. If in a fight, hit first and hit hard.6. Keep secrets.7. Never give up on anybody. Miracles happen everyday.8. Always accept an outstretched hand.9. Be brave. Even if you’re not, pretend to be.
Explained in 10 sketchesAssigning TasksDelivering NewsConducting 1:1sGiving FeedbackDealing with TurbulenceFor more detailed reads of the sketches above:Managing with Martians — or, why frameworks are better than answersSo, You Think You Want to Manage — what is management? and why woul
The 8 Ivy League schools are among the most prestigious colleges in the world. They include Brown, Harvard, Cornell, Princeton, Dartmouth, Yale, and Columbia universities, and the University of Pennsylvania. All eight schools place in the top fifteen of the U.S.
Potentially describing what general artificial intelligence will look like. Since scientists started building and training neural networks, Transfer Learning has been the main bottleneck.
I love podcasts, having been both an avid consumer and on-again-off-again producer of them for over ten years now. But what might surprise you, at least if you’re familiar with my work, is that the podcasts I love most are about history; I almost never listen to shows about tech.
Most self-improvement “strategies” and even psychological interventions seek incremental progress. And although this approach, especially over a long period of time, will yield results, there are better approaches to change.
This is what I wish: that my daughters don’t go to school. I offered my oldest the very prestigious “Altucher Fellowship”. Never awarded before. Only awarded to her.
Not long ago, a friend asked me to read his book. He’d written a rough draft and wasn’t sure what to do after that. After reading it, I explained how writing a book involves not one, but five, different drafts. He was surprised to hear that. Most people are.
When we created Snips a few years ago, we did so because we believed in using Artificial Intelligence to solve everyday problems. From predicting passenger flow in public transport to anticipating car accidents, we always tried to find a way to bring the power of machine learning to consumers.
It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years.
I am by no means the unicorn prophet, but here’s how I think about which companies have earned their unicorn status vs. which ones are playing a dangerous game of massive capital needs, sky high valuations, impossible expectations, and deferred judgement days.
I’ve gotten countless variations of this question over the past year. I can’t tell you everything I’ve learned at HBS, and no book would have prepared me for the experience. Instead, I’m going to give you a taste. Some of these literally blew my mind.