More on Current Events
MartinEdic
3 years ago
Russia Through the Windows: It's Very Bad
And why we must keep arming Ukraine
Russian expatriates write about horrific news from home.
Read this from Nadin Brzezinski. She's not a native English speaker, so there are grammar errors, but her tale smells true.
Terrible truth.
There's much more that reveals Russia's grim reality.
Non-leadership. Millions of missing supplies are presumably sold for profit, leaving untrained troops without food or gear. Missile attacks pause because they run out. Fake schemes to hold talks as a way of stalling while they scramble for solutions.
Street men were mobilized. Millions will be ground up to please a crazed despot. Fear, wrath, and hunger pull apart civilization.
It's the most dystopian story, but Ukraine is worse. Destruction of a society, country, and civilization. Only the invaders' corruption and incompetence save the Ukrainians.
Rochester, NY. My suburb had many Soviet-era Ukrainian refugees. Their kids were my classmates. Fifty years later, many are still my friends. I loved their food and culture. My town has 20,000 Ukrainians.
Grieving but determined. They don't quit. They won't quit. Russians are eternal enemies.
It's the Russian people's willingness to tolerate corruption, abuse, and stupidity by their leaders. They are paying. 65000 dead. Ruined economy. No freedom to speak. Americans do not appreciate that freedom as we should.
It lets me write/publish.
Russian friends are shocked. Many are here because their parents escaped Russian anti-semitism and authoritarian oppression. A Russian cultural legacy says a strongman's methods are admirable.
A legacy of a slavery history disguised as serfdom. Peasants and Princes.
Read Tolstoy. Then Anna Karenina. The main characters are princes and counts, whose leaders are incompetent idiots with wealth and power.
Peasants who die in their wars due to incompetence are nameless ciphers.
Sound familiar?
Gill Pratt
3 years ago
War's Human Cost
War's Human Cost
I didn't start crying until I was outside a McDonald's in an Olempin, Poland rest area on highway S17.
Children pick toys at a refugee center, Olempin, Poland, March 4, 2022.
Refugee children, mostly alone with their mothers, but occasionally with a gray-haired grandfather or non-Ukrainian father, were coaxed into picking a toy from boxes provided by a kind-hearted company and volunteers.
I went to Warsaw to continue my research on my family's history during the Holocaust. In light of the ongoing Ukrainian conflict, I asked former colleagues in the US Department of Defense and Intelligence Community if it was safe to travel there. They said yes, as Poland was a NATO member.
I stayed in a hotel in the Warsaw Ghetto, where 90% of my mother's family was murdered in the Holocaust. Across the street was the first Warsaw Judenrat. It was two blocks away from the apartment building my mother's family had owned and lived in, now dilapidated and empty.
Building of my great-grandfather, December 2021.
A mass grave of thousands of rocks for those killed in the Warsaw Ghetto, I didn't cry when I touched its cold walls.
Warsaw Jewish Cemetery, 200,000–300,000 graves.
Mass grave, Warsaw Jewish Cemetery.
My mother's family had two homes, one in Warszawa and the rural one was a forest and sawmill complex in Western Ukraine. For the past half-year, a local Ukrainian historian had been helping me discover faint traces of her family’s life there — in fact, he had found some people still alive who remembered the sawmill and that it belonged to my mother’s grandfather. The historian was good at his job, and we had become close.
My historian friend, December 2021, talking to a Ukrainian.
With war raging, my second trip to Warsaw took on a different mission. To see his daughter and one-year-old grandson, I drove east instead of to Ukraine. They had crossed the border shortly after the war began, leaving men behind, and were now staying with a friend on Poland's eastern border.
I entered after walking up to the house and settling with the dog. The grandson greeted me with a huge smile and the Ukrainian word for “daddy,” “Tato!” But it was clear he was awaiting his real father's arrival, and any man he met would be so tentatively named.
After a few moments, the boy realized I was only a stranger. He had musical talent, like his mother and grandfather, both piano teachers, as he danced to YouTube videos of American children's songs dubbed in Ukrainian, picking the ones he liked and crying when he didn't.
Songs chosen by my historian friend's grandson, March 4, 2022
He had enough music and began crying regardless of the song. His mother picked him up and started nursing him, saying she was worried about him. She had no idea where she would live or how she would survive outside Ukraine. She showed me her father's family history of losses in the Holocaust, which matched my own research.
After an hour of drinking tea and trying to speak of hope, I left for the 3.5-hour drive west to Warsaw.
It was unlike my drive east. It was reminiscent of the household goods-filled carts pulled by horses and people fleeing war 80 years ago.
Jewish refugees relocating, USHMM Holocaust Encyclopaedia, 1939.
The carefully chosen trinkets by children to distract them from awareness of what is really happening and the anxiety of what lies ahead, made me cry despite all my research on the Holocaust. There is no way for them to communicate with their mothers, who are worried, absent, and without their fathers.
It's easy to see war as a contest of nations' armies, weapons, and land. The most costly aspect of war is its psychological toll. My father screamed in his sleep from nightmares of his own adolescent trauma in Warsaw 80 years ago.
Survivor father studying engineering, 1961.
In the airport, I waited to return home while Ukrainian public address systems announced refugee assistance. Like at McDonald's, many mothers were alone with their children, waiting for a flight to distant relatives.
That's when I had my worst trip experience.
A woman near me, clearly a refugee, answered her phone, cried out, and began wailing.
The human cost of war descended like a hammer, and I realized that while I was going home, she never would
Jess Rifkin
3 years ago
As the world watches the Russia-Ukraine border situation, This bill would bar aid to Ukraine until the Mexican border is secured.
Although Mexico and Ukraine are thousands of miles apart, this legislation would link their responses.
Context
Ukraine was a Soviet republic until 1991. A significant proportion of the population, particularly in the east, is ethnically Russian. In February, the Russian military invaded Ukraine, intent on overthrowing its democratically elected government.
This could be the biggest European land invasion since WWII. In response, President Joe Biden sent 3,000 troops to NATO countries bordering Ukraine to help with Ukrainian refugees, with more troops possible if the situation worsened.
In July 2021, the US Border Patrol reported its highest monthly encounter total since March 2000. Some Republicans compare Biden's response to the Mexican border situation to his response to the Ukrainian border situation, though the correlation is unclear.
What the bills do
Two new Republican bills seek to link the US response to Ukraine to the situation in Mexico.
The Secure America's Borders First Act would prohibit federal funding for Ukraine until the US-Mexico border is “operationally controlled,” including a wall as promised by former President Donald Trump. (The bill even mandates a 30-foot-high wall.)
The USB (Ukraine and Southern Border) Act, introduced on February 8 by Rep. Matt Rosendale (R-MT0), would allow the US to support Ukraine, but only if the number of Armed Forces deployed there is less than the number deployed to the Mexican border. Madison Cawthorne introduced H.R. 6665 on February 9th (R-NC11).
What backers say
Supporters argue that even if the US should militarily assist Ukraine, our own domestic border situation should take precedence.
After failing to secure our own border and protect our own territorial integrity, ‘America Last' politicians on both sides of the aisle now tell us that we must do so for Ukraine. “Before rushing America into another foreign conflict over an Eastern European nation's border thousands of miles from our shores, they should first secure our southern border.”
“If Joe Biden truly cared about Americans, he would prioritize national security over international affairs,” Rep. Cawthorn said in a separate press release. The least we can do to secure our own country is send the same number of troops to the US-Mexico border to assist our border patrol agents working diligently to secure America.
What opponents say
The president has defended his Ukraine and Mexico policies, stating that both seek peace and diplomacy.
Our nations [the US and Mexico] have a long and complicated history, and we haven't always been perfect neighbors, but we have seen the power and purpose of cooperation,” Biden said in 2021. “We're safer when we work together, whether it's to manage our shared border or stop the pandemic. [In both the Obama and Biden administration], we made a commitment that we look at Mexico as an equal, not as somebody who is south of our border.”
No mistake: If Russia goes ahead with its plans, it will be responsible for a catastrophic and unnecessary war of choice. To protect our collective security, the United States and our allies are ready to defend every inch of NATO territory. We won't send troops into Ukraine, but we will continue to support the Ukrainian people... But, I repeat, Russia can choose diplomacy. It is not too late to de-escalate and return to the negotiating table.”
Odds of passage
The Secure America's Borders First Act has nine Republican sponsors. Either the House Armed Services or Foreign Affairs Committees may vote on it.
Rep. Paul Gosar, a Republican, co-sponsored the USB Act (R-AZ4). The House Armed Services Committee may vote on it.
With Republicans in control, passage is unlikely.
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Marcus Lu
3 years ago
The Brand Structure of U.S. Electric Vehicle Production
Will Tesla be able to maintain its lead in the EV market for very long?
This is one of the most pressing issues in the American auto sector today. One positive aspect of Tesla is the company's devoted customer base and recognizable name recognition (similar to Apple). It also invests more in research and development per vehicle than its rivals and has a head start in EV production.
Conversely, established automakers like Volkswagen are actively plotting their strategy to surpass Tesla. As the current market leaders, they have decades of experience in the auto industry and are spending billions to catch up.
We've visualized data from the EPA's 2022 Automotive Trends Report to bring you up to speed on this developing story.
Info for the Model Year of 2021
The full production data used in this infographic is for the 2021 model year, but it comes from a report for 2022.
Combined EV and PHEV output is shown in the table below (plug-in hybrid electric vehicle).
It is important to note that Toyota and Stellantis, the two largest legacy automakers in this dataset, only produced PHEVs. Toyota's first electric vehicle, the bZ4X, won't hit the market until 2023.
Stellantis seems to be falling even further behind, despite having enormous unrealized potential in its Jeep and Ram brands. Stellantis CEO Carlos Tavares said in a recent interview that the firm has budgeted $36 billion for electrification and software.
Legacy Brands with the Most Momentum
In the race to develop electric vehicles, some long-standing manufacturers have gotten the jump on their rivals.
Volkswagen, one of these storied manufacturers, has made a significant investment in electric vehicles (EVs) in the wake of the Dieselgate scandal. The company plans to roll out multiple EV models, including the ID.3 hatchback, ID.4 SUV, and ID. Buzz, with the goal of producing 22 million EVs by 2028. (an electric revival of the classic Microbus).
Even Ford is keeping up, having just announced an EV investment of $22 billion between 2021 and 2025. In November of 2022, the company manufactured their 150,000th Mustang Mach-E, and by the end of 2023, they hoped to have 270,000 of them in circulation.
Additionally, over 200,000 F-150 Lightnings have been reserved since Ford announced the truck. The Lightning is scheduled to have a production run of 15,000 in 2022, 55,000 in 2023, and 80,000 in 2024. Ford's main competitor in the electric pickup truck segment, Rivian, is on track to sell 25,000 vehicles by 2022.
Caspar Mahoney
2 years ago
Changing Your Mindset From a Project to a Product
Product game mindsets? How do these vary from Project mindset?
1950s spawned the Iron Triangle. Project people everywhere know and live by it. In stakeholder meetings, it is used to stretch the timeframe, request additional money, or reduce scope.
Quality was added to this triangle as things matured.
Quality was intended to be transformative, but none of these principles addressed why we conduct projects.
Value and benefits are key.
Product value is quantified by ROI, revenue, profit, savings, or other metrics. For me, every project or product delivery is about value.
Most project managers, especially those schooled 5-10 years or more ago (thousands working in huge corporations worldwide), understand the world in terms of the iron triangle. What does that imply? They worry about:
a) enough time to get the thing done.
b) have enough resources (budget) to get the thing done.
c) have enough scope to fit within (a) and (b) >> note, they never have too little scope, not that I have ever seen! although, theoretically, this could happen.
Boom—iron triangle.
To make the triangle function, project managers will utilize formal governance (Steering) to move those things. Increase money, scope, or both if time is short. Lacking funds? Increase time, scope, or both.
In current product development, shifting each item considerably may not yield value/benefit.
Even terrible. This approach will fail because it deprioritizes Value/Benefit by focusing the major stakeholders (Steering participants) and delivery team(s) on Time, Scope, and Budget restrictions.
Pre-agile, this problem was terrible. IT projects failed wildly. History is here.
Value, or benefit, is central to the product method. Product managers spend most of their time planning value-delivery paths.
Product people consider risk, schedules, scope, and budget, but value comes first. Let me illustrate.
Imagine managing internal products in an enterprise. Your core customer team needs a rapid text record of a chat to fix a problem. The consumer wants a feature/features added to a product you're producing because they think it's the greatest spot.
Project-minded, I may say;
Ok, I have budget as this is an existing project, due to run for a year. This is a new requirement to add to the features we’re already building. I think I can keep the deadline, and include this scope, as it sounds related to the feature set we’re building to give the desired result”.
This attitude repeats Scope, Time, and Budget.
Since it meets those standards, a project manager will likely approve it. If they have a backlog, they may add it and start specking it out assuming it will be built.
Instead, think like a product;
What problem does this feature idea solve? Is that problem relevant to the product I am building? Can that problem be solved quicker/better via another route ? Is it the most valuable problem to solve now? Is the problem space aligned to our current or future strategy? or do I need to alter/update the strategy?
A product mindset allows you to focus on timing, resource/cost, feasibility, feature detail, and so on after answering the aforementioned questions.
The above oversimplifies because
Leadership in discovery
Project managers are facilitators of ideas. This is as far as they normally go in the ‘idea’ space.
Business Requirements collection in classic project delivery requires extensive upfront documentation.
Agile project delivery analyzes requirements iteratively.
However, the project manager is a facilitator/planner first and foremost, therefore topic knowledge is not expected.
I mean business domain, not technical domain (to confuse matters, it is true that in some instances, it can be both technical and business domains that are important for a single individual to master).
Product managers are domain experts. They will become one if they are training/new.
They lead discovery.
Product Manager-led discovery is much more than requirements gathering.
Requirements gathering involves a Business Analyst interviewing people and documenting their requests.
The project manager calculates what fits and what doesn't using their Iron Triangle (presumably in their head) and reports back to Steering.
If this requirements-gathering exercise failed to identify requirements, what would a project manager do? or bewildered by project requirements and scope?
They would tell Steering they need a Business SME or Business Lead assigning or more of their time.
Product discovery requires the Product Manager's subject knowledge and a new mindset.
How should a Product Manager handle confusing requirements?
Product Managers handle these challenges with their talents and tools. They use their own knowledge to fill in ambiguity, but they have the discipline to validate those assumptions.
To define the problem, they may perform qualitative or quantitative primary research.
They might discuss with UX and Engineering on a whiteboard and test assumptions or hypotheses.
Do Product Managers escalate confusing requirements to Steering/Senior leaders? They would fix that themselves.
Product managers raise unclear strategy and outcomes to senior stakeholders. Open talks, soft skills, and data help them do this. They rarely raise requirements since they have their own means of handling them without top stakeholder participation.
Discovery is greenfield, exploratory, research-based, and needs higher-order stakeholder management, user research, and UX expertise.
Product Managers also aid discovery. They lead discovery. They will not leave customer/user engagement to a Business Analyst. Administratively, a business analyst could aid. In fact, many product organizations discourage business analysts (rely on PM, UX, and engineer involvement with end-users instead).
The Product Manager must drive user interaction, research, ideation, and problem analysis, therefore a Product professional must be skilled and confident.
Creating vs. receiving and having an entrepreneurial attitude
Product novices and project managers focus on details rather than the big picture. Project managers prefer spreadsheets to strategy whiteboards and vision statements.
These folks ask their manager or senior stakeholders, "What should we do?"
They then elaborate (in Jira, in XLS, in Confluence or whatever).
They want that plan populated fast because it reduces uncertainty about what's going on and who's supposed to do what.
Skilled Product Managers don't only ask folks Should we?
They're suggesting this, or worse, Senior stakeholders, here are some options. After asking and researching, they determine what value this product adds, what problems it solves, and what behavior it changes.
Therefore, to move into Product, you need to broaden your view and have courage in your ability to discover ideas, find insightful pieces of information, and collate them to form a valuable plan of action. You are constantly defining RoI and building Business Cases, so much so that you no longer create documents called Business Cases, it is simply ingrained in your work through metrics, intelligence, and insights.
Product Management is not a free lunch.
Plateless.
Plates and food must be prepared.
In conclusion, Product Managers must make at least three mentality shifts:
You put value first in all things. Time, money, and scope are not as important as knowing what is valuable.
You have faith in the field and have the ability to direct the search. YYou facilitate, but you don’t just facilitate. You wouldn't want to limit your domain expertise in that manner.
You develop concepts, strategies, and vision. You are not a waiter or an inbox where other people can post suggestions; you don't merely ask folks for opinion and record it. However, you excel at giving things that aren't clearly spoken or written down physical form.
Zuzanna Sieja
3 years ago
In 2022, each data scientist needs to read these 11 books.
Non-technical talents can benefit data scientists in addition to statistics and programming.
As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.
Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.
Ready? Let’s dive in.
Best books for data scientists
1. The Black Swan
Author: Nassim Taleb
First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.
Three characteristics define a black swan event:
It is erratic.
It has a significant impact.
Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.
People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.
Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.
Try multiple tactics and models because you may find the answer.
2. High Output Management
Author: Andrew Grove
Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.
That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.
Five lessons:
Every action is a procedure.
Meetings are a medium of work
Manage short-term goals in accordance with long-term strategies.
Mission-oriented teams accelerate while functional teams increase leverage.
Utilize performance evaluations to enhance output.
So — if the above captures your imagination, it’s well worth getting stuck in.
3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers
Author: Ben Horowitz
Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.
Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.
It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.
Find suggestions on:
create software
Run a business.
Promote a product
Obtain resources
Smart investment
oversee daily operations
This book will help you cope with tough times.
4. Obviously Awesome: How to Nail Product Positioning
Author: April Dunford
Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.
How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.
You'll learn:
Select the ideal market for your products.
Connect an audience to the value of your goods right away.
Take use of three positioning philosophies.
Utilize market trends to aid purchasers
5. The Mom test
Author: Rob Fitzpatrick
The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.
Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.
6. Introduction to Machine Learning with Python: A Guide for Data Scientists
Authors: Andreas C. Müller, Sarah Guido
Now, technical documents.
This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.
Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.
If you know machine learning or artificial neural networks, skip this.
7. Python Data Science Handbook: Essential Tools for Working with Data
Author: Jake VanderPlas
Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.
Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.
The only thing missing is a way to apply your learnings.
8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.
The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.
9. Data Science from Scratch
Author: Joel Grus
Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.
The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.
Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.
10. Machine Learning Yearning
Author: Andrew Ng
Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.
The book delivers knowledge and teaches how to apply it, so you'll know how to:
Determine the optimal course of action for your ML project.
Create software that is more effective than people.
Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.
Identifying machine learning system flaws
Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.
11. Deep Learning with PyTorch Step-by-Step
Author: Daniel Voigt Godoy
The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.
It comprises four parts:
Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.
Is every data scientist a humanist?
Even as a technological professional, you can't escape human interaction, especially with clients.
We hope these books will help you develop interpersonal skills.