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Machine Learning

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The pile gets soaked with data and starts to get mushy over time, so it's technically recurrent.
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alexanglin
8 days ago
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Ottawa, Ontario
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5 public comments
tante
7 days ago
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Actual illustration of how current machine learning (and AI systems) work
Oldenburg/Germany
growler
8 days ago
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Всё так
jimwise
8 days ago
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Lol
francisga
8 days ago
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This is actually exactly how Machine Learning works...
Lafayette, LA, USA
alt_text_bot
8 days ago
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The pile gets soaked with data and starts to get mushy over time, so it's technically recurrent.

Code Quality 3

4 Comments and 15 Shares
It's like a half-solved cryptogram where the solution is a piece of FORTH code written by someone who doesn't know FORTH.
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alexanglin
20 days ago
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Ottawa, Ontario
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4 public comments
majuje19
9 days ago
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Que lo mire alguien que no sea el Cuco
hansschmucker
19 days ago
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Why do I see many colleagues (past and present) in this picture 😆
alt_text_bot
20 days ago
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It's like a half-solved cryptogram where the solution is a piece of FORTH code written by someone who doesn't know FORTH.
rickhensley
20 days ago
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So it's securely obscured?
Ohio
Brstrk
20 days ago
It was until the new hire pushed the keys to their branch.

Here to Help

6 Comments and 20 Shares
"We TOLD you it was hard." "Yeah, but now that I'VE tried, we KNOW it's hard."
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alexanglin
24 days ago
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Ottawa, Ontario
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vl
24 days ago
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Instead of algorithms it should be machine learning of course.
Seattle, WA
eraycollins
24 days ago
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Suggest having this strip at hand when reading Cathy O'Neil's book, Weapons of Math Destruction.
Covarr
24 days ago
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For the last time, Joe, an algorithm can't explain why kids love the taste of Cinnamon Toast Crunch!
Moses Lake, WA
chusk3
24 days ago
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My life in a nutshell
Austin, Texas
alt_text_bot
24 days ago
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"We TOLD you it was hard." "Yeah, but now that I'VE tried, we KNOW it's hard."
JayM
24 days ago
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ROFL. :)
Atlanta, GA

Survivorship Bias

5 Comments and 24 Shares
They say you can't argue with results, but what kind of defeatist attitude is that? If you stick with it, you can argue with ANYTHING.
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alexanglin
34 days ago
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Ottawa, Ontario
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FarrelBuch
34 days ago
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Survivorship bias is just the beginning. We humans love stories, particularly about just one person. Alas, only systematic review of ALL the data is the only hope of seeing effects that are not random chance and seeing how big those effects are.
Pittsburgh, PA, USA
CallMeWilliam
34 days ago
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Applies to actively managed funds too, of course.
dukeofwulf
34 days ago
Basically just another lottery.
elwillow
34 days ago
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Applies to jobs search too.
Ottawa, Ontario
alt_text_bot
34 days ago
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They say you can't argue with results, but what kind of defeatest attitude is that? If you stick with it, you can argue with ANYTHING.
Covarr
34 days ago
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I'd rather see speeches about how it's okay not to be a massive success. "Statistically, kids, some of you won't ever make it significantly above the poverty line. But you'll keep going, and raise a family, and when you look back on your life in the end, you'll realize that at least you were a more likable person than Justin Bieber.
Moses Lake, WA
sfrazer
34 days ago
Such a low bar, and most of reddit will still fail to meet it.

Watch: Adorable baby goat pajama party

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The folks at Sunflower Farm Creamery in Cumberland, ME just posted this super cute pajama party of 10 baby Nigerian Dwarf Goats. Soon they will have 50 kids romping around, so keep your eyes out. No matter how your day is going, this video will make it more delightful.

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alexanglin
36 days ago
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Ottawa, Ontario
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Drawing lessons from the “ Bezos Way”

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Jeff Bezos (right) putting the customer (left) at the center, in a new Amazon pop-up store

by Frederic Filloux

Amazon’s CEO annual letter to his shareholders is a must-read. Customer focus, decision-making or the importance of writing down important things… Here are my takeaways from Jeff's latest.

Whatever we think of its founder and CEO, Amazon remains a remarkable example of great management. Since its 1994 start, the company enjoyed steady growth, relentlessly conquering new markets and sectors, coupled to exceptional resilience shown when the company weathered two market crashes (2000 and 2008). In addition, Bezos has demonstrated a consistent ability to convince his board and shareholders to let expansion take precedence over profits and dividends. (No one can complain: thousand dollars invested in Amazon's 1997 IPO are now worth more than half a million, a 500x multiple).

This didn't happen without damage. By some measures, Amazon isn't an enviable place to work and the pressure it applies to its suppliers rivals the iron fist of Walmart’s purchasing department. All things considered, Amazon's level of corporate toxicity remains reasonable compared to Uber, as an example.

Jeff Bezos is also able to project an ultra-long term vision with his space exploration project for which he personally invests about a billion dollars per year.

Closer to our concerns, he has boosted a respected but doomed news institution — The Washington Post — thanks to a combined investment in journalistic excellence and in technology, two areas left fallow by most publishers.

That is why I thought Bezos' written addresses to his shareholders (here) are worth some exegesis.

Let start with last week's letter. (Emphasis mine, and while quotes are lifted from the original documents, some paragraphs have been rearranged for clarity and brevity).

Bezos starts his 2016 missive with a question asked by staffers at all-hands meetings:

“Jeff, what does Day 2 look like? (…) [Bezos reply:] Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always Day 1.”

Then he enumerates the three obsessions that make Amazon what it is today:

True Customer Obsession
There are many advantages to a customer-centric approach, but here’s the big one: customers are always beautifully, wonderfully dissatisfied, even when they report being happy and business is great. Even when they don’t yet know it, customers want something better, and your desire to delight customers will drive you to invent on their behalf. (…)
[Y]ou, the product or service owner, must understand the customer, have a vision, and love the offering. Then, beta testing and research can help you find your blind spots. A remarkable customer experience starts with heart, intuition, curiosity, play, guts, taste. You won’t find any of it in a survey. (…)
Good inventors and designers deeply understand their customer. They spend tremendous energy developing that intuition. They study and understand many anecdotes rather than only the averages you’ll find on surveys. They live with the design.

A few things here. Legacy media are plagued by a poor customer consideration. After several decades in this business, I see little or no improvement in that area. This is deeply rooted in the persistent superiority complex of large newsrooms — which sounds weird for a population so hardly hit by an economic downturn. In response to that decline, owners have turned to business people who, unfortunately, focused their energy on the short term. The journalistic profession's failure to properly manage the business side has led to a backlash, to MBAs taking over. And, as it happens in ailing sectors than cannot pay much, you rarely get the best people.

This shift also left by the wayside the notion of product creation and management. Such notion fell through the cracks left open by journalists convinced (until recently) that their “mission” had nothing to do with any form of customer-oriented marketing, and by spreadsheet jockeys who felt that Malthusianism was the only way— “doing more with less”, and cut everything to the bone and beyond. The product was the main casualty of this process.

Hence the implicit first lesson from Jeff Bezos: put the product and those who will use it at the center of your operations. Hire, train and transform the mentalities toward that goal. Media needs product people. They should be the stars of any company as much as great bylines.

Next, stay ahead of customer demands:

No customer ever asked Amazon to create the Prime membership program, but it sure turns out they wanted it, and I could give you many such examples.

This tune is a variation of Steve Jobs who said once that no customer figured out they wanted the iPod. The inability to anticipate customers' unexpressed desires is the collateral damage of the management failure stated above. Don’t expect to innovate with inflated egos in the newsroom and with accountants in the C-suite.

About processes in large corporations:

Resist Proxies
As companies get larger and more complex, there’s a tendency to manage to proxies. (…) A common example is process as proxy. Good process serves you so you can serve customers. But if you’re not watchful, the process can become the thing. This can happen very easily in large organizations. The process becomes the proxy for the result you want. You stop looking at outcomes and just make sure you’re doing the process right. Gulp. It’s not that rare to hear a junior leader defend a bad outcome with something like, “Well, we followed the process.” A more experienced leader will use it as an opportunity to investigate and improve the process. The process is not the thing. It’s always worth asking, do we own the process or does the process own us?

On this, everyone will find tons of examples in their organization. Again, the reliance on proxies is a direct consequence of a poor distribution of responsibilities. In a previous job, I tried to foster the Directly Responsible Individual principle, borrowed from Steve Jobs’ doctrine of product development. I tried to apply it from the smallest project (a newfeature in a mobile app) to larger endeavors involving multiple high-ranked managers. I succeed for the former and failed for the latter (after a certain age and above a certain level, people tend to flee the risks associated with responsibilities).

On anticipation:

Embrace External Trends
The outside world can push you into Day 2 if you won’t or can’t embrace powerful trends quickly. If you fight them, you’re probably fighting the future. Embrace them and you have a tailwind.

… While reading this my thoughts wander to the people in the industry (managers, editors, owners) I saw fighting tooth and nail to preserve old models, losing years if not a decade, as opposed to a small group who decided early on to turn the ship around and take the tailwind… Today’s trends express by Jeff Bezos are of a different nature:

These big trends are not that hard to spot (they get talked and written about a lot), but they can be strangely hard for large organizations to embrace. We’re in the middle of an obvious one right now: machine learning and artificial intelligence. (…)
But much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type — quietly but meaningfully improving core operations.

On this one, the media industry — including, for a large part, newcomers — has my sympathy more than my criticism. Embracing machine learning and AI will require huge investments which, I’m afraid, only large tech companies will be able to pony up. I’m not talking about having a couple of geeks in residence tinkering some convolutional neural network, I’m referring to the journey towards deployment of systems critical to the news industry, to name but a few:

  • Real-time fact-checking based on large and complex datasets
  • Individualization of content matching user profiles and habits
  • Adjustment to users' consumption modes (home, office, commute)
  • Ability to predict which reader will drop her subscription or, to the contrary, which one is about to convert
  • Sophisticated recommendation engines (great Amazon and Netflix successes, and media industry abysmal failure)
  • Profile-based search engines and curation systems.

My guess is the future will be owned by the Facebooks and the Googles for the most part, and corporations like Cambridge Analytica for a lesser part. I’m referring to the East Coast consulting firm that played a crucial role in Donald Trump’s victory with its ability to fine-tune political messages thanks to about 5,000 data points for each US voter. By comparison, Facebook offers about one hundred data points (listed here) while most publishers only have few dozens on their own. Several people I talk to here at Stanford believe the next leap in political campaigning will be the ability to tailor one bespoke message to a single individual, and to do so in real time.

Can the media industry integrate such decisive trends? Realistically, not many players will be able to catch the wave beyond superficially gestures.

First, only a few insiders actually grasp the issue (sometimes, I feel I'm speaking Urdu when I venture to raise the subject with media execs…) Second, the manpower required to build such systems will remain scarce and therefore expensive for a while. Unless the industry decides to organize itself around full-fledged cooperation. (It could be a great endeavor for trade organizations such as Digital Content Next, or INMA…)

Lastly, Jeff Bezos on decision-making process:

High-Velocity Decision Making
Day 2 companies make high-quality decisions, but they make high-quality decisions slowly. To keep the energy and dynamism of Day 1, you have to somehow make high-quality, high-velocity decisions. Easy for start-ups and very challenging for large organizations. Speed matters in business — plus a high-velocity decision making environment is more fun too. We don’t know all the answers, but here are some thoughts.
First, never use a one-size-fits-all decision-making process. Many decisions are reversible, two-way doors. Those decisions can use a light-weight process. For those, so what if you’re wrong? I wrote about this in more detail in last year’s letter.

Here is the relevant excerpt from the 2015 letter:

Some decisions are consequential and irreversible or nearly irreversible — one-way doors — and these decisions must be made methodically, carefully, slowly, with great deliberation and consultation. If you walk through and don’t like what you see on the other side, you can’t get back to where you were before. We can call these Type 1 decisions. But most decisions aren’t like that — they are changeable, reversible — they’re two-way doors. If you’ve made a suboptimal Type 2 decision, you don’t have to live with the consequences for that long. You can reopen the door and go back through. Type 2 decisions can and should be made quickly by high judgment individuals or small groups. As organizations get larger, there seems to be a tendency to use the heavy-weight Type 1 decision-making process on most decisions, including many Type 2 decisions. The end result of this is slowness, unthoughtful risk aversion, failure to experiment sufficiently, and consequently diminished invention.

Media organizations often like to see every decision as Type 1. Again, aversion to risk, propensity to single out failure are the main culprits. By comparison, most newcomers have built their model on agility and systems that reward risk. On this, Bezos adds in 2015:

Most large organizations embrace the idea of invention, but are not willing to suffer the string of failed experiments necessary to get there. Outsized returns often come from betting against conventional wisdom, and conventional wisdom is usually right. Given a ten percent chance of a 100 times payoff, you should take that bet every time. But you’re still going to be wrong nine times out of ten.

This whole decision-making process is core to the “Bezos way”. Here is a summary of two other principles:

First, we usually want too much information to make up our mind on something:

[M]ost decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you’re probably being slow. (…)

Second element: knowing how to disagree but support a proposal; Bezos calls it “disagree and commit.” To illustrate it, he uses a telling example:

I disagree and commit all the time. We recently greenlit a particular Amazon Studios original. I told the team my view: debatable whether it would be interesting enough, complicated to produce, the business terms aren’t that good, and we have lots of other opportunities. They had a completely different opinion and wanted to go ahead. I wrote back right away with “I disagree and commit and hope it becomes the most watched thing we’ve ever made.” Consider how much slower this decision cycle would have been if the team had actually had to convince me rather than simply get my commitment. Bezos, who can’t be suspected of succumbing too easily to mellow compromise justifies his view by the core competencies of the Amazon Studios team: And given that this team has already brought home 11 Emmys, 6 Golden Globes, and 3 Oscars, I’m just glad they let me in the room at all!

To recall his vision, and its remarkable consistency, in each letter to his shareholders, Jeff Bezos likes to attached his original 1997 letter.

By all means, Amazon founder and CEO likes to show the importance of writing down key elements of strategic or components of a decision making process. He’s known to start meetings with his senior staff by requiring them to read in silence detailed single-spaced memos rather that enduring yet another slide presentation. Too many CEOs and execs tend to forget the important of genuine writing.

frederic.filloux@mondaynote.com


Drawing lessons from the “ Bezos Way” was originally published in Monday Note on Medium, where people are continuing the conversation by highlighting and responding to this story.

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alexanglin
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